diff --git a/book/_toc.yml b/book/_toc.yml
index 1f8e291..e4eeb8c 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -46,6 +46,7 @@ parts:
         - file: tutorials/IS2_cloud_Landsat_integration
         - file: tutorials/IS2_ATL15_surface_height_anomalies/IS2_ATL15_surface_height_anomalies
         - file: external/ICESAT2_ATL10-h5coro_large_scale_time_series
+        - file: tutorials/ARCOdata_writingZarrs
         - file: tutorials/GrIMP/GrIMP_tutorial_AGU23
         - file: tutorials/SlideRule_applications/SlideRule_applications
 - caption: Contributing
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diff --git a/book/tutorials/ARCOdata_writingZarrs.ipynb b/book/tutorials/ARCOdata_writingZarrs.ipynb
new file mode 100644
index 0000000..661c70a
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+++ b/book/tutorials/ARCOdata_writingZarrs.ipynb
@@ -0,0 +1,8927 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "# Analysis-ready, cloud-optimized data: writing zarr directories\n",
+    "\n",
+    "This tutorial will introduce analysis-ready, cloud-optimized (ARCO) data and describe one real-world example of restructuring some glaciological data and writing to an ARCO format, zarr. The data is from an ice-penetrating radar called the autonomous phase-sensitive radio-echo sounder (ApRES). \n",
+    "\n",
+    "A zarr store (or directory) is an ARCO data format that is well suited for storing high-dimensional, large-volume data in the cloud. A key characteristic of zarr stores is that they are 'chunked', meaning that the data is broken up into smaller pieces. This allows for parallel access to the data, which is very useful when you are trying to access subsets of large datasets and/or process large volumes of data in parallel. \n",
+    "\n",
+    "Depending on the configuration of the ApRES radar, and the survey conducted, it can produce high-dimensional, very large datasets, making these data suitable for storage with zarrs. \n",
+    "\n",
+    "Before we get to the ApRES data we should understand high-dimensional data and chunked data. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "## High-dimensional data: xarray\n",
+    "For our purposes, high-dimensional data is data that has more than 2 dimensions. For example, a typical satallite image is a two-dimensional dataset with two spatial dimensions, x and y (or latitude and longitude). If the satallite image has multiple bands it would be a three-dimensional dataset with two spatial dimensions and one band dimension, and if the satellite data consisted of multiple images quired at the same locatin  at different timethe dataset would be four-dimensional with two spatial dimensions, one band dimension, and one time dimension. \n",
+    "\n",
+    "[Xarray](http://xarray.pydata.org/en/stable/) is a python package designed to store and process high-dimensional data. It is built on top of [numpy](https://numpy.org/), which deals with arrays of data. Xarray adds very useful features including labelling of dimensions and broadcasting of operations across dimensions. Xarray also works very nicely with [dask](https://dask.org/), which is yet another python package, which allows you to 'chunk' your data and process it in parallel.\n",
+    "\n",
+    "Before we get onto dask, let's take a look at xarray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Let's load an example xarray dataset, supplied with the xarray package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
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+       "  color: var(--xr-font-color2);\n",
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+       "  color: var(--xr-disabled-color);\n",
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+       "\n",
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+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
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+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
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+       "\n",
+       ".xr-var-list,\n",
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+       "  display: contents;\n",
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+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
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+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
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+       ".xr-var-dims,\n",
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+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
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+       "  display: none;\n",
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+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
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+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
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+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
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+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f23b7d12-d73d-4615-96d4-49826b4c7780' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f23b7d12-d73d-4615-96d4-49826b4c7780' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d3d923dc-2be6-4910-90ab-54e05669734b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3d923dc-2be6-4910-90ab-54e05669734b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-2c138fdb-6388-4fd8-9df6-270db4b36868' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c138fdb-6388-4fd8-9df6-270db4b36868' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83b27f31-8f41-4541-978e-bd4c27e13a5a' class='xr-var-data-in' type='checkbox'><label for='data-83b27f31-8f41-4541-978e-bd4c27e13a5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-b27e6e3e-b459-4b41-874a-c0f7ba01c527' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b27e6e3e-b459-4b41-874a-c0f7ba01c527' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e3b44c5-f555-4e9e-aac8-a64f751e22a1' class='xr-var-data-in' type='checkbox'><label for='data-3e3b44c5-f555-4e9e-aac8-a64f751e22a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-2259ba22-549e-4969-9e99-1b884f9e3dbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2259ba22-549e-4969-9e99-1b884f9e3dbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b52d05e9-428f-4fee-9641-4e3db5553ac9' class='xr-var-data-in' type='checkbox'><label for='data-b52d05e9-428f-4fee-9641-4e3db5553ac9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1fdeeffb-6815-4e93-91d6-1511687023d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1fdeeffb-6815-4e93-91d6-1511687023d9' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>241.2 242.5 243.5 ... 296.2 295.7</div><input id='attrs-215497dd-393b-4843-a263-ee6c36ced247' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-215497dd-393b-4843-a263-ee6c36ced247' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e37b1c2a-7f90-484d-a3a0-688bdb209d8b' class='xr-var-data-in' type='checkbox'><label for='data-e37b1c2a-7f90-484d-a3a0-688bdb209d8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><div class='xr-var-data'><pre>array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec1c4755-936d-42bf-b2a8-6cae09bdaf73' class='xr-section-summary-in' type='checkbox'  ><label for='section-ec1c4755-936d-42bf-b2a8-6cae09bdaf73' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7c0357e9-5306-444c-a38c-5231893773ea' class='xr-index-data-in' type='checkbox'/><label for='index-7c0357e9-5306-444c-a38c-5231893773ea' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0,\n",
+       "              47.5, 45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5,\n",
+       "              20.0, 17.5, 15.0],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b0fb22cd-6a96-4057-b571-b857b19d4802' class='xr-index-data-in' type='checkbox'/><label for='index-b0fb22cd-6a96-4057-b571-b857b19d4802' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0,\n",
+       "              222.5, 225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5,\n",
+       "              245.0, 247.5, 250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0,\n",
+       "              267.5, 270.0, 272.5, 275.0, 277.5, 280.0, 282.5, 285.0, 287.5,\n",
+       "              290.0, 292.5, 295.0, 297.5, 300.0, 302.5, 305.0, 307.5, 310.0,\n",
+       "              312.5, 315.0, 317.5, 320.0, 322.5, 325.0, 327.5, 330.0],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6670c29c-440c-444a-bca4-5ee8b66baea0' class='xr-index-data-in' type='checkbox'/><label for='index-6670c29c-440c-444a-bca4-5ee8b66baea0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2013-01-01 00:00:00&#x27;, &#x27;2013-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2013-01-01 12:00:00&#x27;, &#x27;2013-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2013-01-02 00:00:00&#x27;, &#x27;2013-01-02 06:00:00&#x27;,\n",
+       "               &#x27;2013-01-02 12:00:00&#x27;, &#x27;2013-01-02 18:00:00&#x27;,\n",
+       "               &#x27;2013-01-03 00:00:00&#x27;, &#x27;2013-01-03 06:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2014-12-29 12:00:00&#x27;, &#x27;2014-12-29 18:00:00&#x27;,\n",
+       "               &#x27;2014-12-30 00:00:00&#x27;, &#x27;2014-12-30 06:00:00&#x27;,\n",
+       "               &#x27;2014-12-30 12:00:00&#x27;, &#x27;2014-12-30 18:00:00&#x27;,\n",
+       "               &#x27;2014-12-31 00:00:00&#x27;, &#x27;2014-12-31 06:00:00&#x27;,\n",
+       "               &#x27;2014-12-31 12:00:00&#x27;, &#x27;2014-12-31 18:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2920, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3336d9eb-69c5-4a2e-8985-d12407af2e39' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3336d9eb-69c5-4a2e-8985-d12407af2e39' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "This provides a convenient view the structure of the data. We see that there are three dimensions (`lat`, `lon` and `time`) and one variable (`air`). The variable `air` and the coorinates `lat`, `lon` and `time` are all stored as numpy arrays. You can access the numpy array underlying the variable in dataset `air` and verify its type as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[241.2    , 242.5    ],\n",
+       "        [243.79999, 244.5    ]],\n",
+       "\n",
+       "       [[242.09999, 242.7    ],\n",
+       "        [243.59999, 244.09999]]], dtype=float32)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.air.values[0:2, 0:2, 0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The variable air has type <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The variable air has type {type(ds.air.values)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "A great thing about xarray is that it allows to quickly look at, process, and plot this kind of data. For example, we can plot the mean of `air` over the `time` dimension as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds.air.mean(dim=\"time\").plot();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Xarray has many other useful capabilites for slicing, coarsening, and plottnig data, some of which we will use below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "## Chunking: dask\n",
+    "The xarray above (`ds`) contained numpy arrays. To process or plot any part of a variable in `ds`, we need to load all of it into memory. This is fine if the data are small; the dataset above was only"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15.499672 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{ds.nbytes/1e6} MB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "but this would be an issue if a variable was, say, 100GB. This would be too large to fit into memory on most computers and would complicate processing or plotting the data. This is where chunking and dask come in. Dask provides a new data structure, called a dask array, which is like a numpy array except that it is split up into smaller pieces called chunks. Let's load an example dask array (straight from the dask [documentation](https://examples.dask.org/array.html#Create-Random-array)): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 7.28 TiB </td>\n",
+       "                        <td> 190.73 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1000000, 1000000) </td>\n",
+       "                        <td> (5000, 5000) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 40000 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<random_sample, shape=(1000000, 1000000), dtype=float64, chunksize=(5000, 5000), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask.array as da\n",
+    "dask_array = da.random.random((1e6, 1e6), chunks=(5000, 5000))\n",
+    "dask_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "This created a dask array containing random values between -0.5 and 0.5. Calling `dask_array` displays a handy table containing information about this dask array. It's total size is 7TB! Clearly this is much too large to fit into memory. In fact, nothing has been loaded into memory except the structure of the dask array (i.e. the number and shape of the chunks) and the information needed to create it when we need it (i.e. the method `random`). \n",
+    "\n",
+    "The table also includes information on the chunks. There are 40,000 of them and each one is 190MB and 5000 by 5000 elements in size. Note that we chose this chunk size when we created the dask array. This is the key to dask arrays: we can choose the chunk size to suit our needs. You have to [make this choice carefully](https://blog.dask.org/2021/11/02/choosing-dask-chunk-sizes#rough-rules-of-thumb) to optimize your processing. And worse, if your chunk sizes end up being non-uniform within a dimension, it causes issues when writing to zarr, as we will see later in our real-world example.\n",
+    "\n",
+    "Two advantages of using dask arrays instead of an ordinary numpy arrays are 1) that we can view and plot a subset of the data without loading the whole thing into memory, and 2) we can very easily process the data in parallel. \n",
+    "\n",
+    "Let's first load a subset of the data into memory and plot it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "dask_array_sub = dask_array[0:50, 0:50]\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.imshow(dask_array_sub);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a very small subset of the data: 0.02 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"This is a very small subset of the data: {dask_array_sub.nbytes/1e6} MB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Next, let's start up a cluster, then load and process a much larger subset of the data in parallel:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-3bffcb05-94a5-11ee-8237-fedfc22e35c9</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Direct</td>\n",
+       "            <td style=\"text-align: left;\"></td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/8787/status\" target=\"_blank\">/user/jkingslake/proxy/8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/user/jkingslake/proxy/8787/status\" }'>\n",
+       "                Launch dashboard in JupyterLab\n",
+       "            </button>\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Scheduler Info</h3></summary>\n",
+       "            <div style=\"\">\n",
+       "    <div>\n",
+       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
+       "        <div style=\"margin-left: 48px;\">\n",
+       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-8742fd81-1126-4f00-bdde-c23d15dc212f</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:44687\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 10\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"/user/jkingslake/proxy/8787/status\" target=\"_blank\">/user/jkingslake/proxy/8787/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 10\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Started:</strong> 53 minutes ago\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total memory:</strong> 77.22 GiB\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "            </table>\n",
+       "        </div>\n",
+       "    </div>\n",
+       "\n",
+       "    <details style=\"margin-left: 48px;\">\n",
+       "        <summary style=\"margin-bottom: 20px;\">\n",
+       "            <h3 style=\"display: inline;\">Workers</h3>\n",
+       "        </summary>\n",
+       "\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:44825\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/44609/status\" target=\"_blank\">/user/jkingslake/proxy/44609/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37037\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-xrusrulx\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 2.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 257.64 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 23.84 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 23.85 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:34303\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/34319/status\" target=\"_blank\">/user/jkingslake/proxy/34319/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44875\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-noj_uxzl\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 2.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 282.04 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 26.31 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 26.32 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:33765\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/34349/status\" target=\"_blank\">/user/jkingslake/proxy/34349/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40715\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-ytc_vscl\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 2.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 257.27 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 26.31 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 26.32 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:33621\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/44353/status\" target=\"_blank\">/user/jkingslake/proxy/44353/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36015\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-26uxlkk4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 2.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 260.57 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 26.23 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 26.24 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:33549\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/43931/status\" target=\"_blank\">/user/jkingslake/proxy/43931/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36939\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-ont3pt84\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 2.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 256.00 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 26.25 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 26.25 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:39389\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/37989/status\" target=\"_blank\">/user/jkingslake/proxy/37989/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38275\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-u1xyu750\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 4.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 257.56 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 25.39 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 25.40 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:33209\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/33623/status\" target=\"_blank\">/user/jkingslake/proxy/33623/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:42719\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-he8ryllg\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 2.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 253.17 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 19.04 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 19.05 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45537\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/38631/status\" target=\"_blank\">/user/jkingslake/proxy/38631/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33827\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-2s1_7a7y\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 2.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 265.66 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 19.58 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 19.58 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 8</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:38387\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/36123/status\" target=\"_blank\">/user/jkingslake/proxy/36123/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:36867\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-uelpl1pb\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 4.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 252.79 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 26.26 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 26.27 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 9</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:38147\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 1\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"/user/jkingslake/proxy/34271/status\" target=\"_blank\">/user/jkingslake/proxy/34271/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.72 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:46463\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-worker-space/worker-wmkh3k_e\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks executing: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in memory: </strong> \n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks ready: </strong> \n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Tasks in flight: </strong>\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>CPU usage:</strong> 2.0%\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Last seen: </strong> Just now\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory usage: </strong> 256.22 MiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Spilled bytes: </strong> 0 B\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Read bytes: </strong> 19.54 kiB\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Write bytes: </strong> 19.55 kiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "\n",
+       "    </details>\n",
+       "</div>\n",
+       "            </details>\n",
+       "        \n",
+       "\n",
+       "    </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:44687' processes=10 threads=10, memory=77.22 GiB>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "\n",
+    "client = Client(\"tcp://127.0.0.1:44687\")\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This subset is a little larger: 16.0 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dask_array_larger_subset = dask_array[:20000,:100000]\n",
+    "print(f\"This subset is a little larger: {dask_array_larger_subset.nbytes/1e9} GB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 150 ms, sys: 54 ms, total: 204 ms\n",
+      "Wall time: 13.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.5000024450518983"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time   \n",
+    "# with 10 workers\n",
+    "dask_array_larger_subset.mean().compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "## Xarray + dask\n",
+    "Xarray and dask work very nicely together. The dataset we looked at above (`ds`) was made up of numpy arrays. We can instead tell xarray to load the data as dask arrays, therefore avoiding loading anything into memory until we need it. This is called lazily loading the data. We do this by defining the `chunks` argument when we load the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 dask.array&lt;chunksize=(2920, 5, 5), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e70ce4d4-9fbf-4321-9b0d-6a8ab373d41b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e70ce4d4-9fbf-4321-9b0d-6a8ab373d41b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e6070682-905e-4eab-9337-e815c83271aa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6070682-905e-4eab-9337-e815c83271aa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-e6cfb907-e9b0-4ab5-9f4c-c1eeccadb950' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6cfb907-e9b0-4ab5-9f4c-c1eeccadb950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8382b6c-54d9-40f1-8077-3d49ca3ac055' class='xr-var-data-in' type='checkbox'><label for='data-a8382b6c-54d9-40f1-8077-3d49ca3ac055' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-89218dd9-90fe-44e6-97f7-e8c1bc11f20b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89218dd9-90fe-44e6-97f7-e8c1bc11f20b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bdd2006-185b-4c88-bfa1-e5e87f7891f5' class='xr-var-data-in' type='checkbox'><label for='data-1bdd2006-185b-4c88-bfa1-e5e87f7891f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-6fb17a31-300f-490f-87e0-2c687e94a230' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fb17a31-300f-490f-87e0-2c687e94a230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06c3e045-045e-4dd0-bc4f-795e1c1ddaca' class='xr-var-data-in' type='checkbox'><label for='data-06c3e045-045e-4dd0-bc4f-795e1c1ddaca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddeb9a24-65c5-4ae7-b881-e84542afeea3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddeb9a24-65c5-4ae7-b881-e84542afeea3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2920, 5, 5), meta=np.ndarray&gt;</div><input id='attrs-4c62cfb2-ee8a-4cda-b37f-389a521429f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c62cfb2-ee8a-4cda-b37f-389a521429f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50c3905c-805b-484d-ba64-a88d539ebbac' class='xr-var-data-in' type='checkbox'><label for='data-50c3905c-805b-484d-ba64-a88d539ebbac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.76 MiB </td>\n",
+       "                        <td> 285.16 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2920, 25, 53) </td>\n",
+       "                        <td> (2920, 5, 5) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 55 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b63a7e5-a657-47cb-b1f6-ea4ffce0d286' class='xr-section-summary-in' type='checkbox'  ><label for='section-0b63a7e5-a657-47cb-b1f6-ea4ffce0d286' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2686494b-8aee-4896-af0b-a026ad43bb47' class='xr-index-data-in' type='checkbox'/><label for='index-2686494b-8aee-4896-af0b-a026ad43bb47' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0,\n",
+       "              47.5, 45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5,\n",
+       "              20.0, 17.5, 15.0],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f1e3f0d8-1306-4dce-ae1e-0993486547cf' class='xr-index-data-in' type='checkbox'/><label for='index-f1e3f0d8-1306-4dce-ae1e-0993486547cf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0,\n",
+       "              222.5, 225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5,\n",
+       "              245.0, 247.5, 250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0,\n",
+       "              267.5, 270.0, 272.5, 275.0, 277.5, 280.0, 282.5, 285.0, 287.5,\n",
+       "              290.0, 292.5, 295.0, 297.5, 300.0, 302.5, 305.0, 307.5, 310.0,\n",
+       "              312.5, 315.0, 317.5, 320.0, 322.5, 325.0, 327.5, 330.0],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-72386e37-934e-4842-8005-839d7acd9b7d' class='xr-index-data-in' type='checkbox'/><label for='index-72386e37-934e-4842-8005-839d7acd9b7d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2013-01-01 00:00:00&#x27;, &#x27;2013-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2013-01-01 12:00:00&#x27;, &#x27;2013-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2013-01-02 00:00:00&#x27;, &#x27;2013-01-02 06:00:00&#x27;,\n",
+       "               &#x27;2013-01-02 12:00:00&#x27;, &#x27;2013-01-02 18:00:00&#x27;,\n",
+       "               &#x27;2013-01-03 00:00:00&#x27;, &#x27;2013-01-03 06:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2014-12-29 12:00:00&#x27;, &#x27;2014-12-29 18:00:00&#x27;,\n",
+       "               &#x27;2014-12-30 00:00:00&#x27;, &#x27;2014-12-30 06:00:00&#x27;,\n",
+       "               &#x27;2014-12-30 12:00:00&#x27;, &#x27;2014-12-30 18:00:00&#x27;,\n",
+       "               &#x27;2014-12-31 00:00:00&#x27;, &#x27;2014-12-31 06:00:00&#x27;,\n",
+       "               &#x27;2014-12-31 12:00:00&#x27;, &#x27;2014-12-31 18:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2920, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-060928d3-b1d5-45e5-9dad-ce8387c7e19d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-060928d3-b1d5-45e5-9dad-ce8387c7e19d' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 dask.array<chunksize=(2920, 5, 5), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_dask = xr.tutorial.open_dataset('air_temperature',\n",
+    "                              chunks={'lat': 5, 'lon': 5, 'time': -1})\n",
+    "ds_dask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Now if we take a look at the variable `air` in `ds_dask` we can see that it is a dask array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 2920, lat: 25, lon: 53)&gt;\n",
+       "dask.array&lt;open_dataset-air, shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 5, 5), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7235923e-c0ef-4361-9a74-dc2c489b8ed2' class='xr-array-in' type='checkbox' checked><label for='section-7235923e-c0ef-4361-9a74-dc2c489b8ed2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(2920, 5, 5), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
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+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.76 MiB </td>\n",
+       "                        <td> 285.16 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "  <text x=\"129.077968\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,129.077968,83.294544)\">25</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">2920</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-ef5f8a64-01c6-4e4a-824a-a1d5ff52f5ad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef5f8a64-01c6-4e4a-824a-a1d5ff52f5ad' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-f67e7ad6-2581-46fc-89b4-ca1e028628c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f67e7ad6-2581-46fc-89b4-ca1e028628c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8807803f-527c-4f92-bab7-80fe8359b988' class='xr-var-data-in' type='checkbox'><label for='data-8807803f-527c-4f92-bab7-80fe8359b988' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-fde7f5cb-3681-4fce-bb40-b9c748421c0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fde7f5cb-3681-4fce-bb40-b9c748421c0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08605164-2438-4073-a8fd-098dad776d3c' class='xr-var-data-in' type='checkbox'><label for='data-08605164-2438-4073-a8fd-098dad776d3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-e4499222-8dd7-43a1-8897-d0631b8b8268' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4499222-8dd7-43a1-8897-d0631b8b8268' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1e6652c-fc8b-4da0-9de5-cd16d8451dd9' class='xr-var-data-in' type='checkbox'><label for='data-c1e6652c-fc8b-4da0-9de5-cd16d8451dd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-47511958-241e-4f82-89da-a14b028f6f58' class='xr-section-summary-in' type='checkbox'  ><label for='section-47511958-241e-4f82-89da-a14b028f6f58' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-608b064a-b98d-48ae-8968-5d92c34c8788' class='xr-index-data-in' type='checkbox'/><label for='index-608b064a-b98d-48ae-8968-5d92c34c8788' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0,\n",
+       "              47.5, 45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5,\n",
+       "              20.0, 17.5, 15.0],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c49c25a-9738-433d-b887-55072de07257' class='xr-index-data-in' type='checkbox'/><label for='index-2c49c25a-9738-433d-b887-55072de07257' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0,\n",
+       "              222.5, 225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5,\n",
+       "              245.0, 247.5, 250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0,\n",
+       "              267.5, 270.0, 272.5, 275.0, 277.5, 280.0, 282.5, 285.0, 287.5,\n",
+       "              290.0, 292.5, 295.0, 297.5, 300.0, 302.5, 305.0, 307.5, 310.0,\n",
+       "              312.5, 315.0, 317.5, 320.0, 322.5, 325.0, 327.5, 330.0],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83984bb2-79f9-46b9-9916-2294906a327f' class='xr-index-data-in' type='checkbox'/><label for='index-83984bb2-79f9-46b9-9916-2294906a327f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2013-01-01 00:00:00&#x27;, &#x27;2013-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2013-01-01 12:00:00&#x27;, &#x27;2013-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2013-01-02 00:00:00&#x27;, &#x27;2013-01-02 06:00:00&#x27;,\n",
+       "               &#x27;2013-01-02 12:00:00&#x27;, &#x27;2013-01-02 18:00:00&#x27;,\n",
+       "               &#x27;2013-01-03 00:00:00&#x27;, &#x27;2013-01-03 06:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2014-12-29 12:00:00&#x27;, &#x27;2014-12-29 18:00:00&#x27;,\n",
+       "               &#x27;2014-12-30 00:00:00&#x27;, &#x27;2014-12-30 06:00:00&#x27;,\n",
+       "               &#x27;2014-12-30 12:00:00&#x27;, &#x27;2014-12-30 18:00:00&#x27;,\n",
+       "               &#x27;2014-12-31 00:00:00&#x27;, &#x27;2014-12-31 06:00:00&#x27;,\n",
+       "               &#x27;2014-12-31 12:00:00&#x27;, &#x27;2014-12-31 18:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2920, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b88e7dfa-f636-4183-89de-496965bc2ba8' class='xr-section-summary-in' type='checkbox'  ><label for='section-b88e7dfa-f636-4183-89de-496965bc2ba8' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
+       "dask.array<open_dataset-air, shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 5, 5), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_dask.air"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "The convenient thing is that if an xarray contains dask arrays (instead of the usual numpy arrays), operations applied to the xarray will automatically be performed in parallel in the cluster. We will see this in action below using ApRES data. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "## ApRES data --> xarray --> zarr --> dask "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "We are now ready to take a look at some real-world data. The data we will look at is from an ApRES radar. We will \n",
+    "- discuss the structure of an ApRES survey,\n",
+    "- load some raw ApRES data,\n",
+    "- structure the data as an xarray, and \n",
+    "- write this xarray to a zarr directory. \n",
+    "\n",
+    "This will make use of a library we have developed called [xApRES](https://github.com/ldeo-glaciology/XApRES). The main developers of this library have so far been Jonny Kingslake, George Lu, and Elizabeth Case. We very much welcome collaboration from anyone interested in efficient ways to process, store, and analyze ApRES data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "### Structure of an ApRES survey\n",
+    "The structure of an ApRES survey can get quite complex. The figure below depicts one possible structure of a so-called unattended survey (the details do not matter here, but ApRES can be operated in attended and unattended modes and the structure of data varies between the modes). \n",
+    "\n",
+    "![ApRES data structure](../img/ApRES_data_diagram.png)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "\n",
+    "**Chirps**\n",
+    "\n",
+    "The radar emits individual 'chirps' which each generate a 40,001-element-long time series of data. The chirps are emitted at a rate of 1 per second. \n",
+    "\n",
+    "**Bursts**\n",
+    "\n",
+    "The chirps are grouped into 'bursts', which each contain a user-definable number of chirps, which in the data below is 20 chirps. The system is setup to perform bursts at regular intervals. The data below has a burst interval of 15 minutes.\n",
+    "\n",
+    "**Attenuator settings**\n",
+    "\n",
+    "The ApRES has user-definable attenuator settings which are chosen during installation to ensure the signal is not too strong or too weak. Typically we choose more than one attenuator setting and cycle through them during each burst. So for example, if we have 3 attneuator settings, and 20 chirps per burst, per settting, the sequence of chirps would be 20 chirps using attenuator setting 1, followed by 20 chirps using attenuator setting 2, followed by 20 chirps using attenuator setting 3, followed by 20 chirps using attenuator setting 1, and so on.\n",
+    "\n",
+    "This complexity leads to a four-dimensional dataset: 1) the time of each burst, 2) the chirp number within each burst, 3) the attenuator setting, and 4) the sample number in chirp. A typical workflow for processing datasets collected by such a survey is through nested for-loops and it is a major challenge keeping track of which chirp belongs where. \n",
+    "\n",
+    "This is where xarray can really help. Let's next load some raw ApRES data using some scripts fro a library we have been developing called xApRES.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "### Load raw ApRES data\n",
+    "Raw ApRES data are stored in files with an extension `.dat`. First we install and import the xApRES library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install xapres -q\n",
+    "import xapres as xa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [],
+    "user_expressions": []
+   },
+   "source": [
+    "#### The data\n",
+    "The ApRES data we will be using were collected in the ablation zone of the Greenland Ice Sheet by a team led by Meredith Nettles (Lamont-Doherty Earth Observatory, LDEO) and Laura Stevens (University of Oxford), including George Lu (LDEO), Stacy Larochelle (LDEO), Marianne Okal (Earthscope), Kristin Arnold (IRIS Alpine), and Josh Rines (Stanford University). The project was funded by the US National Science Foundation (project number: 2003464). Three ApRES units were positioned near several supraglacial lakes that periodically drain to the bed of the ice sheet. The units collected a burst every 15 minutes for up to 18 months. You can learn more about the science being done with these data (using the tools described here) in two oral presentation at AGU this week: [one](https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1321546) led by Stacy Larochelle and [one](https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1316057) led by George Lu.\n",
+    "\n",
+    "The map below shows the location of the three ApRES units over a ESA Sentinel-2 image from July 10, 2022 (created by George Lu).\n",
+    "We will be looking at data from A11.\n",
+    "\n",
+    "![Map of ApRES locations in greenland](../img/ApRES_map.png)\n",
+    "\n",
+    "Using xApRES we will create an instance of a `from_dats` object, then use two methods of these objects (`list_files` and `load_single`) to load 1 chirp from within 1 burst from within 1 `.dat` file.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fd = xa.load.from_dats()\n",
+    "dat_file_list = fd.list_files(directory=f'gs://ldeo-glaciology/GL_apres_2022/A101', \n",
+    "                remote_load = True)\n",
+    "fd.load_single(dat_file_list[30], remote_load = True, burst_number=0, chirp_num=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Let's take a look at the raw chirp data. It is 40001 elements long, as mentioned above, and we can plot it as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40001"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(fd.single_chirp.vdat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt_1 = plt.figure(figsize=(15, 3))\n",
+    "plt.plot(fd.single_chirp.t, fd.single_chirp.vdat)\n",
+    "plt.axis([0,fd.single_chirp.t[-1],-1.25,1.25])\n",
+    "plt.xlabel(\"Time (s)\")\n",
+    "plt.ylabel(\"Amplitude (V)\")\n",
+    "plt.grid(\"on\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "We wont get into the details of processing ApRES data., but to obtain a representation of the reflection power as a function of depth we apply a fast fourier transform to the chirp. This is done in xApRES as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OxezZs62eP/vgdoT5++KKFtAJVQFEZmYmyk9UFXHlnlcB8DXZJz//Mv7++28Yfwy7du2C79mdEmfx7I/r/V/Wm9R/Llu2HNsvqZAULCA+CHgpvSr9ocWncEULaPVAj7j61aArLS3N3UnwWMwbacwbacwbacwbSyUlJS49n2c/tR1w4sQJAMCsWbOwYMECJCcn4+2330bfvn1x5MgRREZGIjc3F3FxcSb7RUREwN/fH7m50hMfz5gxA9OmTTO8LioqQlJSksk2gwYOwNhb/eHro0LLmVUXdmpqKga0igEAlOw4h+9P7DfZJzwiHMOHdcOTW2q+CNPuGoxgjfjH8nj6SlvZ4FYJTVviu1XHDa+f2FLzPubfngJgHwCgWduOeHpp1d/+sY2Rf7UC8+9ob3UqH2+n1WqRlpaGwYMHQ6323PZ17sC8kca8kca8kca8kZaXl+fS83l8kDVr1iybpUgZGRnQ66uGEXjhhRdw++23AwC++OILNGrUCD/99BMefPBBAOI9/gRBsFp9pdFooNFoJNcDgI+vLwIDTLdR+/kaLnCN2jKrBajg718z8OjCsZ0R3iBQ8hwvjWiLV/86YDUd7pR28KLkuqeuBVUAUKKtKb36aksWAGBU50YY1DbOYr+6Rq1W86YngXkjjXkjjXkjjXljydX54fFB1pQpUzBmzBir2yQnJ6O4uBgA0LZtW8NyjUaDZs2aISur6kEeHx+PrVu3mux7+fJlaLVaixIuR+ltDBUlWkpj1lo+Ktjfchsj9/duiszT+Vi+V7rUzZ3srfgrExnoNL+k9j08iYiIPInHB1nR0dGIjo62uV3Xrl2h0Whw+PBh9O7dG0BVkempU6fQpEkTAECPHj0wZ84c5OTkoGHDhgCqGsNrNBp07dq1VumMC7Ne0iVagubEef5vbBeMzNuE/dmWje/dbe85+9Ik9r7rbkUhERHVV3VmCIfQ0FA89NBDePnll7Fy5UocPnwYDz/8MADgzjvvBAAMGTIEbdu2xfjx47Fz506sXr0aTz31FCZPnizZs9BeGj9fi2W2Bhp1ZhBPHx8VYkOsB3SeTux9mwehWp0eFZWmxYNlWh2KythbhoiIvIPHl2Q54q233oKfnx/Gjx+P0tJSdO/eHWvWrEFERAQAwNfXF8uWLcMjjzyCXr16ITAwEOPGjcP8+fMVT5vY/H6CU2VZzpWAeRKx920cYgmCgOte+BsAsHfWEIQEVNWhXz9nFYrLKvHi8DYI8vfDuO6NRY+/4chFfL3lNOaMSkFsaIDs6SciIrJHnQqy1Go15s+fbzVoaty4Mf766y/XJMhGHZitdlz2Wnzf9Zj4RYY8B3MBWyV4e84WGv7efuoy+reOBQAUl1WNKfbasoMAgCHt4hDdwLJU797PqwZGVQH45N5UGVJMRETkuDpTXeiJbLUzMo817C2hMg9S+raMsXNPz3Uotwj3fbEN+7MLDcEUAMnhLADg8lXrjeXPXC6VLX1ERESOYpDlRmJViHbtZ/ZapVKZBFpfTeqGhmHeVU22aONJrD18Ebd9sNmkJ6bat+pvvd7xvKrUyVRUSERE5AQGWTKY0r+FU/s5GWPZDM76tIzBH1N6O3dwF7CW/vJKPcoqa4Z40F/bVieyj624S2wfIiIiV2GQJYOnhrYSXW5rfj5nG763SwgTOZb5uZ06tEco19aUQFUHUjqRiErLkioiIvJgdarhu7dxogYMADB1YAuofVVolxCGLk3CAViWDvl4cJT17zHr0xqUG5VkVQdXzgZZtkbzJyIiUgpLshRkOiyB5XrzwCgiyPqI79WC/P0wfUgr3JQSj9gQ8bZXYgPMv3lHB7uOr7T0E7aCLOOSLOnqwkobUeqJi1dx/ZxVOJjjeQO3EhFR3ccgS0G2ClCqQ4SFYztjxs2t0TahdgOi2tIsOljR48vFOMiqjq10OvtKsorNBiu9dKUCz/y8R94EEhER2YHVhQpKDJee7BmAIcoa2TGh1ucyL+gRq17zllqzl36rmUzaUF0oVpJlFnhdKC5Dz7lrLLbTswE8ERG5AYMsBfzySE9cKi5Hs5gGVrdz/aPfS6IsIwvXHEXHRuGiQaN54NVtzmrRY1QHlyUVldh7thCpyZHiE3YTERHJiNWFCujSOAJD2sWbLBPrSejsOFlizI8fGeyP4R0amiwLUHvfx51x6jJm/LpHNMgyHjvL2phY1XNIPvBVJu76ZAs+3XhC/oQSERGZ8b6nbh3ibO9CMebxmkqlwv/GdUFTo3ZYUcEazLi5tXwndZFVBy+IB1lGi7QibbaqVZdkbTp2CQDwVfpp0e0OZBfhx+1nZA1+iYio/mJ1oRs5O06WmCB/X7u2e7Bvc2w6dgkbj16S7dxK81GJtzEzXlZhtSQL2HO2wOa2w97fCKCql+fgtnHOJZaIiOgalmS5iPgQDvId/+WR7dAqLsRimAbjUpnqEp3OjSPkO7EL+KpU2JddaLHcuEF7RaWVMbNUKtzyf//afb4j54tFl58rKMWFojKL5ReLy/H1ltMWPRuJiKh+Y0mWG8kZZCVFBmHFk30slhsXABmaentZddjVCh2mfLvTYrlxSZa1gUntaeJ+pbxmUmqNn+Vvj/yrFeg1bw2iG/gj44VBJgOc/ver7dh9pgDbTuZj4djOdpyNiIjqA5ZkuZEr2v7IWSXpaYxLssqtlGRJdSRcdeA8Jn6xDWfyS5Dy8grDcrWv5dfixMUrAKrG3TIfBHX3mQIAwJ+7s+1NOhER1QMsyXIjORu+SzGJ4+rYqAXGQdZP289Ibrcjq0B0+dy/D+L4xat4+ufdJsvFhncwXlZRqRcNxIiIiIzxSeEiom2yXFDK5GU1gw4xriH8YN1xh/c/fvEqAGDPWdP2Xodyi3A676rJMvMgi4iIyBYGWS4iFuu4IgAKNOp1GKJRS6bFG+llKgo0HxH+my1Z6PvWOsntrfVkJCIiqsYgy0XEpnZxRbDjb1StVR1w1ZXSLbGpdpxhz2GM22GxJIuIiOzhUJuswsJC/Prrr9i4cSNOnTqFkpISxMTEoHPnzhg6dCh69uypVDq9nlipiyuCnToST4mqDlytjfYupUyrc+xcevsa2RMREVWzqyQrJycHkydPRsOGDfHKK6/g6tWr6NSpEwYOHIhGjRph7dq1GDx4MNq2bYsffvhB6TR7JfFSF88JgaKC/ZEcFeTuZDjkhV/34UJRGd5OO+LQfoIgYNYf+w2vfSRmzi6pqBnW4c0Vhw1/O1KSte9cIcZ8ko5d13ogEhFR/WFXSVbHjh1x7733Ytu2bUhJSRHdprS0FL/99hsWLFiAM2fO4KmnnpI1od5OrPmQa3oX2neS0EA10p7sgxYv/C26fnRqI/y4/aycSZNFt9fFJ4W25tKVCnyfUdMbsVSiVKvtzBXYPXMIwoLU2HYy37D8xKUrCAtSIzE80Oa5xn6yBcXllbjn8+1483qHk0pERF7MriBr//79iImJsbpNYGAgxo4di7Fjx+LixYuyJK4uEa8udE9JllivRpUK8LMyLEHzmAZKJsljpZ/Iw00pppN9Vw+MeujVmxCgtj6dUfG1QU7NqxirP3uVRCkaERF5P7uqC20FWLXd3psNaB1r13b/6ZKI+NAAjE5tZFjmipIse93epZHtjSSIjZBeV4h1WKiWf7XC6eM+9t1ODHx7vUmVJBER1S0OPx3z8vIMf585cwYzZ87E008/jY0bN8qaMG/Q57oofDYh1a5tQwPU2PzcALx5R0fDMneVZIl5sE8zq+utpbRfq7obVJuP7m6spEKH0grLqsYv/j2J33aes3rcv/bk4MSlq/hnX26t01im1aGgxPmAj4iIlGF3kLV3714kJycjNjYWrVu3xq5du3D99dfjnXfewSeffIL+/fvjt99+UzCpnic0wM+h6h4fs5HEPSfEkq4qvKFZJP5vXGerPSFVdW0oeSM6vXQj99IKHT5Yd8xk2em8q5j95wE88cMuO49f+6ug57w16PRKGi7XomSNiIjkZ3eQ9cwzz6B9+/ZYv349+vXrhxEjRmDYsGEoLCzE5cuX8eCDD2LevHlKptXj1LY9jbsKshw57/cP9MCIDgno29K0tOr+3k0Nf9flZkU6PXAwp0h0XalWh8O5xSbLLpdoDX/bM1iqHJdAdbXlzjOXZTgaERHJxe4gKyMjA3PmzEHv3r0xf/58ZGdn45FHHoGPjw98fHzw2GOP4dChQ0qm1eNIdf23l/savjuubUIoPhnf1fD6pRFtDX/XNh882dXyStz8nnhVuE4vmEy3o1KZlkxZq2o0kPESqNR5UtkoERHZPRhpfn4+4uOrelk1aNAAwcHBiIyMNKyPiIhAcXGx1O51ksg8wg7xtkfikHbxWPlkH0QF+5uuqLsxFl42Gk/L3Km8q/jbqE2VIACfbTpheF1UphXbzaSES875K+WoeiQiIvk41PDdvHqsvnc/N29jZa/q3ngpCWFyJsclWsaFIKqBxmRZfb0KZvyy12LZ8r01QdfsPw9YrN9yIh9P/7zH8FrOwky7Ss6IyG4Hsosw9J0N+H2X9Y4sRFIcmlZn4sSJ0GiqHrBlZWV46KGHEBwcDAAoLy+XP3UeztmSrL8e642v0k/j0f4t5E2QE6YNboluTSNtb2iFM9WFUcH+yKvjDbX/3J1tsWz8F9tNXhuHRV9uPoVfd57D4vuuR3iQWWmhHSr1ehSUVDi1LxFZemvFIRw+X4zHv9+FWzslujs55IXsDrImTJhg8vqee+6x2Obee++tfYq8iLNtka6LC8Gro8RHzncF49KTqQOvq/XxnMkG39rWtdYRxp9FddXkh+uP49mhrfHnnmx0bBSO5Ohgu4715A+7AQAbn+mPpEjvmiKJyBOduVzq7iSQl7M7yPriiy+UTIdXqu/VpbWhtjK6fF0lVpsn1iartEKH33efMwRNp+YNd+g8y/bm4KG+zZ1KIxHVKCytaVcpCALv+eSw+vekkxELY5xXH0uyMi9ZvmepMca2nXR+OIZKnf0TWBORtOuTIwx/a9l7l5xgV0nWbbfdZvcBf/nlF6cT423q8tAFjnAmF+pj1h0vEn/Txy4Uo0lUTZXgX3tyajVlz/yVR3Bbl0ZIsGMCayKSFmnUk7qsUgf/OjyFGCnDrismLCzM8C80NBSrV6/G9u01DXgzMzOxevVqhIV5X2+52vDWQEHOYQOqjkf2ELtclu44i0ELNuDBrzMNy2oTYFX74t+Thr89afomIm9iXCh8OLcYWXkleOL7ndh9psBtaSLvYldJlnF7rGeffRajR4/GRx99BF9fXwCATqfDI488gtDQUGVS6aG8oSRL9PnqAc9cz885+W2+YPmbJvN0VbXgmkMXJPfbdPQSvt5yCq+OSkFsSIBD50w/noeHl2TilVtTcEvHBMnt9HrB6SFJiOoq4zHt7vwo3fD3pSsV+Oa/3d2RJPIyDpd9fv7553jqqacMARYA+Pr6Ytq0afj8889lTZyn84Ygy1Edk8Id3se56sK6l3dKueezrVix/zye/XkPtDo9sgvs7/H0yJJMFJRoMfW7nbhQVCa6zT/7cpEya4Usk1UTeTtBEDDjl734dOMJ6CVKgTcdu+TiVJG3cjjIqqysxMGDBy2WHzx4EHork+nWRXXxh/8vD/d0eB8PKBirF45dvIJ7Pt2KnvPWYEeW9YbxggCsP3LRZC7FN1ccFt32oW8yUVKhw0PfZIquJ6pP9mcX4bttWXht2UEO8Eu15tBgpABw3333YdKkSTh27BhuuOEGAMCWLVswb9483HfffbIn0JN5Q0lWu8RQHD5v/3RHvj4q+PmoFL+5eH7OeR69Hth6Mh8A8O3WLHRpHGG1vdXM3/eZvC6WmOaHiGpcLa80/F1eqXNjSqgucDjImj9/PuLj4/HOO+8gJycHANCwYUM888wzmD59uuwJ9GTeUJL18sh2iAnRYJTRaMXe/NssLlSDER0S8Nmmk7Y3rmOM5yasbitirU376bwSk9f1cdgMIkcZt00s19av2hmSn8PVhT4+PnjmmWdw7tw5FBQUoKCgAOfOncMzzzxj0k6rPvCGdkVhgWrMuLkN2jQMNVnmbsEah+N7AICvSoVos7kT64tcozZVfr5V155UjCW2XOMnz/fzlx1n8daKQ+y1SHWScQ1FeaV0kDXtx104cq2W4NedZ9H99VXYc7ZA6eSRl6nVoB+hoaH1rkehMW+oLhQzqVdTDGoTh7fu6CC63hWPzkC1cw/8mFDHetfVVX7XRsy/YlS1YUyspE8j0xg/037cjf+tPY5t16ouieoS49u6tQbuv+w4h3GLtgComtLqfFG5yVAsRICdQdZNN92EzZs329yuuLgYb7zxBv73v//VOmHewFtrXwL9ffHphFTcmZok+7HjA5UN0d67q5PXjk8mp4pKPUYs3IiOs1favY9cQVa1i1fq36TwVPc58uP50hXTMe2ulIn/6KH6y6677p133onRo0ejTZs2ePbZZ/HTTz/h33//RWZmJlatWoX3338fo0ePRsOGDbFz507ccsstSqdb1JEjR3DrrbciOjoaoaGh6NWrF9auXWuyTVZWFkaOHIng4GBER0dj6tSpqKhwbvBHb6gudLVhSXr89EA3fHpvKube1h5NoiQmKnYy6+ydLFkpn01Idev5q+3Muox954oc2kfuamIde15RHeTorel03lWb2xSXaU3G3KL6w66GMffffz/Gjx+Pn3/+GT/88AMWLVqEgoICAFWBRtu2bTF06FBkZmaiVatWSqbXquHDh6Nly5ZYs2YNAgMD8e6772LEiBE4fvw44uPjodPpMHz4cMTExGDTpk3Iy8vDhAkTIAgCFi5c6PD5NOq6GWQ509bmxwd7YOfpPMQVHECnpHCo1VUP9LHdGmP8Z1ux8ahpsbu35lyL2AbuTgIAID4sAMcv2r65G3O2HZyUShtzuQmCAJ1eMFRtEnkDR+9+fd9aZ/i7WKT6/kx+Cfq8tRY3tYvHh/d0rV3iyOvYfdf19/fHuHHjMG7cOABAYWEhSktLERUVZXigutOlS5dw7NgxfP755+jQoaqt0bx58/DBBx9g//79iI+Px8qVK3HgwAGcOXMGCQlVo1+//fbbmDhxIubMmSPZvqy8vBzl5TVVI0VFVSUIAb6AVlu3u8Xb8/70ej06NwpBSlwA0tIOWOwj/gvOuV91Wq0Wer37ulXrdJVonxiKvQ6WIsmtXOt4HqzYn4u/9mTjzq6JOHu5FI8PaGExF1vBlVK7g7FybaXV6+PJH/dg/dFLWP1kb0QE+UtuV32Muv5dcgbzRppSeVNRy+P9e/Q8uiVHGl5/k34KggD8vS/XZZ8jrxtprs4Tp3/aVs9l6CmioqLQpk0bfPXVV+jSpQs0Gg0+/vhjxMXFoWvXql8P6enpSElJMQRYADB06FCUl5cjMzMT/fv3Fz323LlzMXv2bIvlp44exvLSLGXekBsJgi+qy5qWL19uZcuqyyc7OxvLl581LE1LSzPZKu+SD8xrpvPy8iyW2WP58uU4dE4FwD09WdevW4fb44C95+QtFXLUxbzLcLQ8cEdWAQAYAsSLZ46jX0MBxreBTq+twZ1NdegdbxoEL8/yQZEWuKuZ3rD97j17EHx+t+T5/tpbtd2871ajb0PbQbX5dUM1mDfS5M6bk8VALR6NmP3zNjzatqpXoiAAH2+pOdb8JX9DANAuwjVVh7xuLJWUlNjeSEbufVLISKVSIS0tDbfeeitCQkLg4+ODuLg4/PPPPwgPDwcA5ObmIi4uzmS/iIgI+Pv7IzdXekqRGTNmYNq0aYbXRUVFSEpKQof2KRjWw33Vo0p5YstKQ0HTsGHDJLd7PL2q0XVCQgKGDesArVaLtLQ0DB482KR086eLmUBhnsm+0VFROFZkOmp5t+QIbDtlfSTzYcOG4dymk/gj66gjb0k2AwcMQHQDf8zascot5692+krtK1yDYpMxbFgbPLFlpcl4Wz+d9MXrk4aYbPv4S1WfdavmyQCqflh0aN8ew1IbSR6/+vpo07YthvVoIrmd1HVDzBtrlMqbzNOXgX0ZTu8fHhGFoTelwtdHVXWsLTXH+vhQ1Y/DHS/0R0iAeJqLyypx5nIJ2jZ0vuc+rxtpVT/wXcfjg6xZs2aJliIZy8jIQNeuXfHII48gNjYWGzduRGBgID799FOMGDECGRkZaNiwIQDxxuqCIFhtxK7RaKDRWI7NpPbzq/MXsD3vz8fHx2Q7tVpt8losb318LEuxXhrRDiP/b5PN9Pi5cTw2Pz8/BPhLV315E5WPCmq1GipYVt4af37GDdwXp9eU3C7ffx7/6ZqEIH/rtxEfH1+L60inF7DrzGWkJIYZ1plfN+ZsTWKt1wvIyi9Bk6igOtcpxVbe1Gdy542Pb+0ei9tOXcbjP+7Be2M64+edOaLbXNUCkSE1aS4oqcA3W05jVOdEPPh1JvZnF+Hr+7vhxutiapUWXjeWXJ0fHh9kTZkyBWPGjLG6TXJyMtasWYO//voLly9fNrSt+uCDD5CWloYvv/wSzz33HOLj47F161aTfS9fvgytVmtRwmWXunUfdymxZ6A3PBf9fFRWH/TepDp2UqlUVoeO1+rEB2T891geZv6+H92aRuJAdhFmjmhryJslW08bthPrSPH+6qN4b/VR3NopAfNvT7GZ1m+3ZmHu8oNYPOl6dG0SKbrNjF/24oftZ/DqqBSMv0G65MwbVFTq4VdHrjNvIzUptCNW7D+Ph77JxLrDF0XXV5h9p55buhf/7M/F11tO43xRVfvfnzPP1jrIksuFojLszy5Cv1Yxde4HjNI8PsiKjo5GdHS0ze2q61nNS0h8fHwME1f36NEDc+bMQU5OjqFka+XKldBoNIZ2W2R9qhY5PXNTK7z5T82kxcbTvgSqfVEq0bhb5cboti72lLOVm9bmsVy64yx+zqxqj9e3ZQw2HL0Ifz8ffLz+hGEbsevpkw1V63/flW1XkPX8r3sBAI8s2YGtzw8S3eaH7WcAAO+mHTEJsorLtJJVM56oTKtDtzmr0CgiCL8/coO7k1PvyBFkAZAMsADL71z6iaoqrOoAy9i2k/n439pjmH1LO7cNYXPjm2tRXqnHe2M64VajKdrINqeeGAUFBfj0008xY8YM5OdXjfq8Y8cOnDt3TtbEOaJHjx6IiIjAhAkTsHv3bhw5cgRPP/00Tp48ieHDhwMAhgwZgrZt22L8+PHYuXMnVq9ejaeeegqTJ092auR6RvT2kcqnR/q1QNqTfQyvvWEE/SD/ujN11JYTefhkw3Gb21VKlGQBpp/ZgZwifPHvKZMACwAEs8pIQRAkA2hbSips71eh02PS4gw8t3QPNhy5iPazVuL15QedOp85V0wltO9cIYrKKnEgx709WI2dKyjF68sP4uxl1zYaltueswWY+t1OnCsold7IBT8yze+Jal+xZixV/4/+OB3rj1zElO92KJ8wCdXTC60/Ih04kjiHg6w9e/agZcuWeOONNzB//nzDeFm//vorZsyYIXf67BYdHY1//vkHV65cwYABA5CamopNmzbh999/R8eOHQEAvr6+WLZsGQICAtCrVy+MHj0ao0aNwvz58506p+eHBJ6rujTK+F7j6bUjvz7SEwFOTgfkiU5cvIrXlx+yWlIFAFor42EZf2ZSg5PuPluIaT/uQk5h1YOtNjdqe2Kc4rJKrDl0Ad9nnMGcZVXBVXXJWW089t1ODHlnA8orlR1CxLhE11Pmh/zvl9vxyYYTmLTY+QbhStl47BIGvL0O20/Znubplv/7F3/szsbj3+2U3MYVY4Zafq62b345BWUWy45fvILxn23F1hPyNeYuKtPilT8PYPeZAtmOWZ85XF04bdo0TJw4EW+++SZCQkIMy2+++WbDGFrukpqaihUrVljdpnHjxvjrr79kOZ8XFLx4rJq8UxktM3q4WPk56a5879w4wj0ndrNKvXRJVtVnVvVZ+UpEycv2VDX+LdPqMP/Ojlhz6ILTaXE06LB2HTnqz93ZAICNRy4hMSIQLWIbQK1A9bFxPtZmVH1bHXoccfBaqdqR81dkOZ6cJn1ZVcJzx0fpOPTqTXb9EDp2Ufp9yFVdaI3lx2p5TvMleVcr8PH647i/d1NDs4Wnf9qNHVkF2Hj0Ek7NGy5L2t785xC+2ZKFz/89aXFMdzbVqI2KSr3FmICu4vBZMzIy8OCDD1osT0xMtDoMQl3knZeb64nlU9cmlgGLp5dk1VfWRnZ35DNbvjcXKS+vwFfpp21vLMHRx5/53HJyWLE/Fze/txEPfLVddP2FojKMW7QF32yxfJ8/Z57FDxnWx9YzDrJslTJKWZB2BL3fWIuLxfVrfsnqkktHGQ+Y7Iogy7wzidgp9XrB4vOb+/chtHjhbzz67Q4IgoCcQsvSrdo6aiWQ9raCBUEQ8GPGGbSZ+Q9+utZm09UcDrICAgIMI54bO3z4MGJiPKMnhKuwTZbjVk/vi5dGtMXD/ZoDMP3SSpWEVPvhATYCdpWP1le11Tp7uQTrDkuXPJVpax4W9pQy1bYqpqRCh+Iy+0dszr8qf5D107WG/mslGjaPWbQFm4/n4cXf9pm0/SnT6vDUT7vx7NK9SD8uXb1jXFrg7AP//dVHca6gFB+sO+bU/s56/PuduPfzbW6r5vx6y2lcFZnaRsrlqxX475cZ6DFvNQpKqq4VV6T85vc2osxGu8Rle3Nw/Rzx8fiW7cnB7rOFJsHZnGUHZEmbrfuwN3njn8N4Zuke6PQCnv55j1vS4HCQdeutt+KVV14xDE2vUqmQlZWF5557DrfffrvsCfRkdedSdJ3mMQ1wf++m0PhZFulba/j+f+M6o3uzKCWTRkbm/X0IhSVa9H5jLV76fb9d+1hpH+8UqQl1+761DheKynDTuxvwdfopeU9qJqewFIWlWslhLMScMJpT0viBb3yMsYu2SO5v/DWobVBa3WBZEARsOZGHywoEndUEQcDvu7Kx4chFHLvgvmrFp3+WnoWgmp+PD/R6AZ1fTcOqgxdwvqgc326rKmF0VYC41qja3Jkz5l0pNwnCF208iUO59nWW2Jl1GQ98tV10cmtrQZY3PPMEQcCb/xzCn7uzDT8W3cnhNlnz58/HsGHDEBsbi9LSUvTt2xe5ubmG4RHqFW+44jyAtQI/41XWthvRIUF6JSmi1xtrHNpeJ+PD6VxBKYa/vxGjU5Pw/LA2Juvyr1Zg/srDOJRbjJd+34/xPZLtOmalTm8xBIdWp8fC1UfRp2UMUo3mm3trRVWHgOqekmGBzg0BYfy8cqbk29YDv6CkAoIARAT7o1Knx7aT+eiYFG5YX90zdNneHEz5difiQwOw5fmBFse5Ul6JHzLO4OSlK3hqSCuEm8016e/rYzG2kznjgLA6uCss1eLvvTm4OaUhwoJcM4zG8r22m61culKOrq+ZTjnz5j+H0SkpHFaaIMoqt6imqs+ZwE4QLIPwm97diOgG/vjkns5W9739w83QC8DJS1eRNq2v3ef0hsqbjUcv4YN14sHVDxlZiNe4du5bh4Os0NBQbNq0CWvWrMGOHTug1+vRpUsXDBokPnZNXeatjQA9lXFJlrV7jq2HVYDax6QaCwBu79IIS3ecldij9myM5+mVrjhQ7QJIlzw5Y8mW0ygo0eKTDScsgiwAKNU6/iRcvPkU/ntjM5NlX6WfxvtrjuH9NccMjXxzCkvxv7WmN+nCUucmla0ONv7cnY3fd2XbtY89JVmlFToczC3Cg19norBEi9+n9MK/xy7htWUHMbx9w5pjXbtHrdh/HoDpg93YvZ9tNcxt6efjg1m3tDNZr1HbE2TVJLa6LdmzP+/BP/tzsfLAeXw+8Xqr+8tNEASUV+olG8JfLrH8TMct2oqXRrRVOmkWnPnmSO1z6UoFbvtoK97rIb1v9XV1VKTE0eq91wueeZeuSLdDfHbpXujLvWTuwgEDBmDAgAFypsXreENU7+mMAybz6sLh7Rti2V7xaSmk+PqosH/2TWj+vOnE1rd0SlA0yKLal2TNX3kUWj0wc2RbkyoLscbbznz1jpwvtlh2WKR6xdFY8Ux+Cc4XlaFMq0d+iWl13MiFm5A2rS8eszJkgDVSbbLu/nSLISgCqtr4VDP+zlSPzWw8DtN9X2zDyyNNB7Y0PpZYVZ/GzxfFsB50G6e1ulfkP/urSpWs9SjVXWvgHR8WYPX4h3OLEd3AH1ENLKc4E/PotzuwfG8u0mcMQMOwQLv2AYBX/5KnbZMtVTMeJCIy2Llpuqp6j0qv//CAD26+2fL6kRojbP2Ri3hu6R6TxvRlWp1JkCr3M08QBOw+W4hWcSEIrENjEBqzK8h6//337T7g1KlTnU6Mt2GMVaV5TANZjuPjA0wf3BJvpx3BvNvbWx0xWYoK4m0KlG5nITb/X31jbdBSa7afvowfTvhg8/mTAID7b2xqEnCLNf51pkG4cS/JWX/sR25hmcngsmVaHXxUKodL5G58c63kOr0APLrE+UEkpZJiHBRZV5WPxvm59vBFnM7PwJrp/UT3EBv2IkBdU816/+IMvPafFDQMC8T7q48iK78Eb9zewaQEROz7NuXbHZg5oi0ig/0x+88D6Nk8Cje3b4iXft+Hb7dm4dv/dkfPFuKzexy7UIyh724AALuHKqiuNvxp+1lMHXid4uObOaqgRIvHvtuBryZ1R4FIqZotesF6795DhT7Yl12Ek3llGN6hoSFY6j9/nej2Ez7fZrGszcx/cHsX6UngBUHAS7/vQ3JUsEUpsT1+yDiD537Zi57No/DtZHk6Nnla4YddQdY777xj8vrixYsoKSlBeHg4gKoR4IOCghAbG1u/giwP+zBdbenDPbDq4AU80Mf6l8vebPJRqfDYwOtwb89khAWqJYOslnHyBHVysjX/X32waONJp/Yb+2kGjPvg/LU7G++tPmp1H+Mg63xRGSZLDKdgTHstYinT6rB48ykAQOv4mrH+Br+zHoIAfCFztZa16gtbavvjoPrc5t/BExevorRChxvmrkaUWUmKWJsk49KM1YcuIHb1UdyZmoQFaUcAVJU632DUMUWnF/DSb/tMjvHXnhyUV+oxpG0cvt5yGl9vOY1T84bj261VDc7fW30UMSEaBGv8kBAeiIvF5fhlx1ncmZqEbScvO5sFhvd+x4fpTh9DKf8ey8OPtRhawFb13V2LtkGrE3AgpwiTb2yGmBANKirt/zEkCDBMmwVYPvN2ZBXgmy1Vn58zQdZ3GVXvfbOV3rbezq7ehSdPnjT8mzNnDjp16oSDBw8iPz8f+fn5OHjwILp06YJXX31V6fR6FG+on1ZS1yaRePam1jYH/7P3MVH9a7u6kbHU86W3xK9dwPHA95aObFDvaeb+fcjmNsaDdL7x9yHsOVtoc5/qkraBb683LDPu8XcmvxRnL5fKPvaQn4/0bfZicblFIGUc5Lz0+wHYMZOQpLQD5yXXvbv6CApLtThxybKHGVDVJi/9eB50egEas4Ecv9t2Brd9sNnwuqhMaxL4nrh0FV+LjBN28tJV5En0cLx0pRyD39mAnvPWoLBEixm/7MHcvw/hwa+3w09k2hlrxILTvedsXyPu4Py4cYLNceqqZ2v4bNNJ3DB3Ne6r9Yj9picsqXCs3aY5tZU3cOxCMQbMX4ffdrpvuj45ODyEw0svvYSFCxeiVatWhmWtWrXCO++8gxdffFHWxHm6+l6SJQfjG7O9w7OoVCr8p7Njk5RKBXqjU5Ps2v/f5+p3+0NPYxxkXbXzRq/VCSit0Jm0SRGrjpOzlyQAqP3EL+w/dmfj+jmr8IpZG6ACo3Zdqw5dxJrs2t9oxO5VB7LFu/tXx4QTPt+GsYu24NttWTZ/SOkFwbThu0TVsY8K8JP4oh83Gvrizz3ZWHWwqh1XxqnLeMZsjKPySh1Gf7IVv50Sf4Qt3eE9D2ZnP90vN592uMfqBg+be9DasD0zftmLE5eu4okfdrkuQQpwOMjKyckxjJFlTKfT4fx56V9NVH9Ze2YZ/+I0v2E48qh7tH/V4Kazb0lxJGmID7PdiLZ1fAgSw+1vOEvKMw6yqnvO2bLq4Hm0mfmP5HGq3feFvPPzqSVKsuZdm7T6i39PmSyfaFbakFdeuyBr9cHzdk2sXa36wZd5uqqK7ufMsxYlWebeSTtq+n2VeHiaT20iNf6Y1Qbd646j1Yv/YOeZQqzNEU/XyUs1jfc9/cewsxOBp5/Ic/l7Mz7f1fJKvGhWJeyINYfOY5uV+SYrrMw0Yc2yPZ4184zDQdbAgQMxefJkbN++3fCA3L59Ox588MF6OYwD2Wbtq2IcgJn/wDVvK2K6n+lRnx7aGttfHIRx3RsDAFY+2QeTb2xqMxEtYkPEVxhxxTQb1tx4nXT1aH3l5P3XQla+8t25paq6pOZSM28zU/1edXrBqfZd93+5HX/tseylK1UKYr5895kCm4OxZuWXYL1RG0qpzgP+ZuOUDVqwXnS7BCs/at74x3Z1svkQHHWVy4Mso78/XHccp/Oc//5MWmy9HaVUiactqw56VmGPw0HW559/jsTERHTr1g0BAQHQaDTo3r07GjZsiE8//VSJNHosa0WdVMNa413jNeY39ycHtcSgNnH44O4udp0n2qhrd8u4ELww3L7xbjoZDd4omkY7HuhKXgmL7k1FN6OBMkneMblqw9bUKAAkJ5G29x1UX3//+eBfpL62CvtkalskVXUk9mzLOGW74bnxMBVSQZm/n4/J90nqIa2TK4qu48qdGC/O3K87z9rdwWLJ1iycvVz1mYkNBfHvsUt4+qfdKJToLSkIAlbuz5UcRsIR/1t7DBO/2OZQQ353cHicrJiYGCxfvhxHjhzBoUOHIAgC2rRpg5YtWyqRPo/GEEte5vkZFqTGpxNSxbc1Csi+ub+7zWOLdUuvWWdrX9uUjrcZz5uqdNWw3DbYM/6V1DQl9pYC7MjzwUfrTxga98/+075pjpzlK8PFtl4igNPq7CuNc3ZibDF1eY7ZCzJMAP7kD7stBm+25j8fbEbGC4NES5ru/nQrgKr5PcWG2li+NxePfis+pMn9izPw8sh2aBwVBL1ewJFc03Htjp4vxpKtWfjvjU1x7nIp3lpxGADwVfop3NerKd5ccQi/emBbPKcHI23ZsmW9DKzIcdbbZNX87UjJoPEvr94KV6e5a7LbaioVgyxzzowrpARrvfeq2dPzUavTS5Z4AcDbq2omeranVKk2VlsZONReG49eEl2eefqyoa2XNdU9GstlKKV4a8VhbD4unh45PHNTK+w5U2gYeNUbfezAHH8Xi8vx4brjFlNU2cNaw/vVhy4gt6gMy6beiLfTDqPYbMaJ2z7cjOKySsPwK9VeW3YQQf5+himwPI3DQdakSZOsrv/888+dToy34XOv9oxLmJQMJGoTJ7l7+CsflUp0uJDE8EBZit290aFcy9Hbvdn7q49i+pBWtjesJyr1ekQ30Mh2ff97TLlxmNx9f5DDKQfbVr3xzyFM6NHEZJk9P0ZtDQi7P7sIYz/ZgvQTpp/XtpP5KC6T7kUsNtG1p3A4yLp82fRXiFarxb59+1BQUFDvptmpy8XQcrJaVecFN6i7b2hieyOFPHdza6h9fUQD0H6tYrDk2kCO5N3SDpxH1yYRuFBU++ofOdR2/KPa0ukFj6kSJnFfmo3vtUGi9NKYPdWS5gEWAIz+2PpAsqV2tI10F4eDrF9//dVimV6vxyOPPIJmzRwf8dWbMcayj72BlNtKsqys/OWRnujUKNzm8VUKTKzj7+eDh/pWDU0hlje8/uoOlUqFiTIPHVEb4xZtdev5y7R6j2/QXM3dzQk8hdi0PHvOFuDjDSfw3E2tkRQZpNjURs5O4O4Kjleqih3ExwdPPvmkxfQ7dR17F9qnfaMwyXXG9yd3jaBv7RbZpXEEfOzpSuxA0u2dEFZl8rflCXj91R1O9lZXzK4zBW49//O/7vXoB6cxQbBeWl9f/ffL7bjl//7Fsj05hsbuFU7Ob2qLp7TRFON0w3dzx48fR2Wle4uYXY3POPs82r8F/H19MKKD9SlslMjP5KggnMorwfVNPWcIBPtHthf/27BMnuSQB7gsMdVMfeYho3TAR2U9LR6STI9jPF7VyWtTNylVOlmnqgunTZtm8loQBOTk5GDZsmWYMGGCbAnzBixJsE9ogFqyUa9Jw3cHjmnvjW3VtL4or9QjWCN9qfdsHo09ZwsRoPZxqCuzMceuBMevG7H2f57yEKLay5Z5vkSSj49KZXVAYmu1haM6JeC3XdkKpMq7qFDVzk+O3qJiyutSkLVzp+m4MD4+PoiJicHbb79ts+chkTlnh3Cwl5+vj82uxk8Mug4J4QHo3yoWN765VvY0OMu4ilAsZ8Qm4CUieVXdl5z7RdMkKljexHiporJKpLy8QrEfhnWqJGvtWs95CLmbp7Wj8HaOxFhyZn2A2hf39kiW8Yi2SN9pHunXHB+sqxqzxlZ1oSvc37spPtt00j0nJ/IAtr57bI9lHyVL3o+cv2J7IzdxuOH7gAEDUFBQYLG8qKio3g3hYG8DZrKPI0NieNptzZEgSKp6oXFkEEIC1OLHdyJNcuhgpdOCo358sIdsxyJyFVsl7ILgHUPRkHs4HGStW7cOFRWWjTTLysqwceNGWRLlLWJCNLY3IqvccXNSolRIifdh0rvQTUVZ5hP61obURMlEnsy8xuKDu7ugc+Nww2trX/36Olgw1bC7unDPnj2Gvw8cOIDc3JopBHQ6Hf755x8kJibKmzoiBcg/opVl4NavVQzWHZaeQsKeYxgHVu4KT6xN9eIoOQM2IlcxL8lKjgrGrR0TsDOrwOa+P2eeVShV5C3sDrI6deoElUoFlUolWi0YGBiIhQsXypo4qvvc0Z7BWm+hvx7rjRELN9X6HP8b1wXtXl4hui45Ohh5Il32VQA6GlXPGd/ae7aIlmVOOUcFqH1lOxZLssgbWf74MfuRxrpCssLuIOvkyZMQBAHNmjXDtm3bEBMTY1jn7++P2NhY+PrKd0Om+sHTqgtTEsMQF6rBeQenNzF/H4FWgpP3xnTCG/8cxp+7Lbt292whPtn1hB5N8OpfBxxKkxwSIwJlO5afD0uyyPvYqqpniEXW2B1kNWlSNX+bnvNJkYxaxYeggcYPsaGOtW8b1TkRv+/KRovYBg6fU4kpcCzOYeW+3CgiCAvHdhYNskwPUvOnozPev3F7ezy7dK9D+5j74YEbENVAvs4drC4kb2TeJkulMv1RVTXiu7jE8ECEBPjVuQnNyX52BVl//PEHbr75ZqjVavzxxx9Wt73llltkSRjVDwFqX+x4aTB8HRwPo3+rWKx8sg+SIoIcPqc7x5D9YGwnyXXmv5hrk8w+LWNsb2RD92ZRKCqznK5C4+djGFTw4X7N8eG1ISds8WV1IXkhsZIsweRv6R9sUwa0wMiOCUiRaDpAdZ9dQdaoUaOQm5uL2NhYjBo1SnI7lUoFnc5zBwUjz+Tv51wJR8u4EKf2G9Q2Dsv25CA8SHy4BGdYa7RubHDbWKfPEaLxQ3G5fVNXyTUPpNhRnB001pczJJAXMr9qVVCZTAptrclDgNoHDTR+aBYdjBPXppah+sWuIMu4ipDVhXVfA40frtj5MPdGo1OTcHuXRLRPDBddnxAeaLNNVkiAH4rL5M2jKf1bmLw2D9TWPd0P32zJwjurjtg8llzxjMZPvnaWjLHIG4ldt8YdQqw1PKj+QdK1SQSDrHqKjSTIwneTb0BqkwgsfbhuDh7powIGtI6THOfs/TGdMaB1LH544AbJY2x4uj++uO96w+vaNuC/s2sj3N61kcky85t7VAMNujaJsOt4cgU0zpYyimGQRUr5Y0ovRAX7o3W8c6Xb1plV46uAO8y+q1Kqg6zHB13n8FkHtYlzeB/yPHaVZL3//vt2H3Dq1KlOJ4Y8Q/tGYfj54Z7uToZibFWlJUUG4fOJ11vdJiLYH12S7At47JEUaV/bMuOmax0bhWH32ULR7Wy9Rx9VzTQXwf6+uFphfzW/tclyrZ+TURYpIyUhDNtfHITvM85gxi/SHT5u65KIX3acc+jYFg3fYVaSZWXE9+qgz1pvY6rb7Aqy3nnnHbsOplKpGGRR/aFwzCB2eOMqxGduao27P90qvq+NtBmPFeZo8ONsoR1DLFKKz7VIyNYPgCFt45wIsixLsoyJNXz/+/EbkX+1Atddazeq9IwNvVpEQacXsOVEvqLnIcfZFWSdPMkJYomUZO8t2LgXpvFo7M8Pa43Xlx8yvLYVOPkYF2U5ev83675uL5ZkkdJsXY/OBDuWu1hEWRZaxoWYfFcd7DwNoKpn4qqD5+3aNipYgwvFZY6fhBRXqwYXgiCY9LIgIueI3fvFHgjGN2sfFfDvcwPw1aRueKBPcyy+73okhgdi7m3tbcZNxj39HI+xnPvOuyPGaqCxeyhAAjB1oONthzyJreeRM5egPT8O7kw1a09pcV7Hz9wh0f7J2Sv1esNvJvIsTgVZn332GVJSUhAQEICAgACkpKTg008/lTttRB5NzqBBLKCyVV2oUqmQGB5oGBOrX6tY/PvcAIzt1tiO6kKjv535mX2NIwGXOya57pgUhoSwAJeftzbaNAx127k1MnZ0cAdbV6Mc16BldSEwtF08BreNk9zGmejO0aSywMMzOfyNeumll/D4449j5MiR+Omnn/DTTz9h5MiRePLJJ/Hiiy8qkUaiOum/vZs6vI95SZYUWw8T48DK4ZIsJ+/l7ijJCgtU4/sH6l4v2VdHpbg7CR5Jr0BxjsUYeNf+D1BXPT5bxFTNOtEyroHRPqY7OfM7xtGAkCVZnsnhsvQPP/wQixYtwtixYw3LbrnlFnTo0AGPPfYYXnvtNVkTSCQ3uR72tT3MC8Pb4NNNjrV3NK66sFaNYes9GrcXsXUzjwhS43JJzcjvrupdOLxDQyzbk+PUuQAgJTEULw5vC52bnj4P9mmGjzecUOTYfrUofazLbH3UzpT2WDZ8r3r93eQbcDCn2FBVaO3QSpfiqiA96T25l8MlWTqdDqmpqRbLu3btisrKujuAJdUdjWSc9Lg2TKv+7NvH7iDLxnEcaZP152O98fyw1obXrupdWNvH0l+P3YiE8ECHp2ySS79Wzo3ub08goNQ78vYqJyVSL/U169w4AuO6NzZ8j09clB5s1BVXoLeWZL15Rwfc0CzS3clQjMNB1j333IMPP/zQYvknn3yCu+++W5ZEESnh90d74YuJ16NJVLAsx5Pj12l1e6Ehbe0beND4lNZObyttjqS9UUQQ/tu7meG1tz2H3dWr0dsDFm/UpXG47Me0KMmS2C7NSk9AxS9Blfdeb6NTk+Av48wSnsaprjefffYZVq5ciRtuqBoRe8uWLThz5gzuvfdeTJs2zbDdggUL5EklkQw6JoW7OwkW1jzVD/lXK5AQblm6JnZjtrcky1bhjXEDZ3seAJLbiNzXt8wYiAe/ycTuMwW2D2z1nM49md68owPaGjUe93FTW+7QQPnmxnQVL31OG3RuLN8AwdXkiI9cEeh7c3VhXa78dvj2s2/fPnTp0gUxMTE4fvw4jh8/jpiYGHTp0gX79u3Dzp07sXPnTuzatUvWhM6ZMwc9e/ZEUFAQwsPDRbfJysrCyJEjERwcjOjoaEydOhUVFRUm2+zduxd9+/ZFYGAgEhMT8corr3jtLwDyLm3CLef9DFD7igZYUowDBmvBg1SX8X6tqnoijuve2O5zAo4FPPESvfn8fF1zK+3fKhYpRt3f3TUxdaC/L5ZPvVGRYyv10PbWKidjH93TVd4Dmjd8l8j62j5Hohv4O92zVAWA0wp7JodLstauXatEOmyqqKjAnXfeiR49euCzzz6zWK/T6TB8+HDExMRg06ZNyMvLw4QJEyAIAhYuXAgAKCoqwuDBg9G/f39kZGTgyJEjmDhxIoKDgzF9+nRXvyXycvY+5t68owPKKrRQ50hP92HvGWrb8P2zCdfjSlkl9mUbT8fjmiDE0cmmnU2V+XuvTUByR9dG+DnzrNP7t01QaDgGO4boMA6YujQOx46sApuHLau0f3olT3VTSrysx7OsLpRjQFNL47o3wdXyShzMKXLi+CpF2qNR7XnNoCizZ8/Gk08+ifbt24uuX7lyJQ4cOIBvvvkGnTt3xqBBg/D2229j0aJFKCqqumiXLFmCsrIyLF68GCkpKbjtttvw/PPPY8GCBSzNIsVEBvlj7PVJCJJhXEz7h3AQX+7ro0JYkBo9m0fZ3NZbmT8UjYerCPJ3LNCbf2dHk9dNo+Vpz6c085LHh/u1sGu/cd0cK+H0Ns7c5c2/Z86UyNoVmAmCzepaa5NGKzF8havUtXuQMYdv+2VlZVi4cCHWrl2LCxcuQG9WRrljxw7ZEueI9PR0pKSkICEhwbBs6NChKC8vR2ZmJvr374/09HT07dsXGo3GZJsZM2bg1KlTaNpUfNyi8vJylJeXG15XB21arRZarVZ0n/qoOi/qS54Y96a9LrYB9mVXXRcvDGtlkgd6vc7JvBEsttcZlTToKnWSx6usFK87EN1e4s5uT1p1InUUWq1W9JiOXheCYL3+4+Z2cThzudSQ74Y0VVZCq1WZvK729JDrMPuvQ7CXeZpjGvjj5CXpXmSW6dDC10fl0DAS9jwsdTrHSpx0Ovt6fseHyNOO7L6eTfDF5tOyHMse9l5bOpGSurtSG+GH7dKllcbP/zu6JCIm2E/0fMafmsX3Vme7Lk+n10Ovr0mf2DnCAsV/JAh6veh30RtotVoIItd896YR2HryshtSJC+Hg6xJkyYhLS0Nd9xxB7p16+aWUZzF5ObmIi7ONMqPiIiAv78/cnNzDdskJyebbFO9T25urmSQNXfuXMyePdti+dq1axEUFCRD6uuWtLQ0dyfBJcp1QPVXaHBkPsJ0Pugeq0fs5f1Yvnw/EoN8kVMKXD6SgbTjVfvYlzdVxywvL8fy5ctN1lworVm/YcN6HJJozlV1T6/aLthPwNXKqu+p6fFqziNW/2R+brHbxYkTJ2BeIL58+XJcLfIRXe7ILSc7O9viGMaaC9k4VOwD87SvWpVmUmpo/Dnt27cfgP2lWeZpDtfmWaTJOH+NrV+/HgcDAUHva5FGa4qLi21uv3fPHlh7H1U/fmuOkZmZaXX7ao5+RlI6CcfRMdIHu/MtPz8VBAgyV1Hbc60CQOYOy3zoqT6FH6y85ytGn0cv/9NYvlwieBRqPmfz9FTFENbz9ejRY9euVR+jY5juc+bMWYh9J7Kzs1FcooI7mpAH+QkoEbn+7bV8+XJcuGh5v8jLs/yuVfP3EVCh94zYwxaHv03Lli3D8uXL0atXr1qffNasWaLBi7GMjAzRcbnEiAV8giBYTEVivl5q32ozZsww6TVZVFSEpKQk9O/fH1FRUZL71TdarRZpaWkYPHgw1Grv61nlqNIKHZ7ZthoAMHxQPzwSZRpwD71JQKVOD43a16G8eTx9JQBAo9Fg2LB+Juuy8kswZ9cmAED//v3QJFI8yNfpBUzbWhXQ+fv742pl1a/iYcOGWZwnJrwBikTG+DHe1nh7Y82aNcOa7FMW+7W+/iomf7MDWfmlJsvFjiElMSERmZekByO94Ybu2HzlGE4WF5gsHzpkCEICam5tZdqaz6ltu3ZYesr+kizzNC+4fzBSXlltso1a7Q9UWpY69O3bF02jgzF9a5pDVTkhISHIKb1idZuOHTvg2+P7JderVCqTwsRu16di0aGdFtvFNPDHxSs1nYMc/YykDBs2DMsKdwH5F2ymTQ72XKsA0LVLV3x2eJfFvtbec2hoKM6VFFdte/PNktNQPbFlpaE4yzw9er2AJ7dY/4HVokULXK2oxPrcLMl0NWrUCNsuZlvsm5iYgILsYqDUvlJWOWn8/VEicv3bY95/2mFYl0T8cmkHDhZcMlkXHRWFY0XiJVk+vr5e09Lf4SArMTERISEhspx8ypQpGDNmjNVtzEuepMTHx2Pr1q0myy5fvgytVmsorYqPjzeUalW7cKHqJmBeCmZMo9GYVDFWU6vV9SKYcFR9yZdKoeZXllrtZ/GexXLAsbxRWWzrb/RaY+VYvhIPdePtP703Fe+sOoJ37uqEIe9ssLqtFB+RLo5qtRqtEsKx4ZkBSH5umclyf18fVNhRdVJ1bOu/VP38/ER/HGn81VCra25tgqqm5EKlcqwZqnkeNAgS6TkpkUynvwd2/ED39XXs1t37OvH7m3key/W9VavVkj9cVSqV7GNF2JtuX5HOF2q1Gv83rjOmfGsZhAKAr1EbLH9/6fdlLT32tPn18fExuT7F3pPY961mX5un8DhBAf5Qq9Wi33Vr39XkqGAcyi1WMmmycbjh+9tvv41nn30Wp0/Xvr49OjoarVu3tvovIMC+yV179OiBffv2ISen5pfvypUrodFo0LVrV8M2GzZsMBnWYeXKlUhISLA7mCNyFdFxskymw3FsX3OD2sZh2dQb0TLO+R9NjnYY+X1KL4zsmIAFozva3NbmgKoSyy0avhu9rM2jfXiHhgCAxwdeh9gQjWHuuoGtawKYm0V6trnj4Wf+PgMlGvw701POloVjO1ssiwmp+ZHqibHAiA4JkuvkGC5DpVJh76whSLQyXIsjk61bHN/pPa1rZkdHjwYBMvToEWEtP27v0kiRcyrB4SArNTUVZWVlaNasGUJCQhAZGWnyTylZWVnYtWsXsrKyoNPpsGvXLuzatQtXrlQVqw8ZMgRt27bF+PHjsXPnTqxevRpPPfUUJk+ejNDQqm7U48aNg0ajwcSJE7Fv3z78+uuveP311zFt2jSPaVtG3sMdl4xp70JrQzjUrPOkPkdtGoZi4djOaBJluy2jreyVel/m2WI8rU5tehFXD3D65OCW2Pr8QGx4uj9eHZWC2be2c/qYADBrZFuH95HKmycHtcRfj/WuVXpqY+aIthjZsSpgMc7qQW2cm2LI3PgbmtRqf0c+/uogul/LGMMya88JW8cOCVAjzGiA2hVP9LE/MXaewxnfTu4uue76ZNvP9NqMS+bsLdRWKbcncTgEHTt2LM6dO4fXX38dcXFxLgtOZs6ciS+//NLwunPnql9La9euRb9+/eDr64tly5bhkUceQa9evRAYGIhx48Zh/vz5hn3CwsKQlpaGRx99FKmpqYiIiMC0adNM2lsReQqxb5a942TVVcH+vrhaUdMDSyxokprQt2p7x8/5f+M6Y+X+87i/d03HGJVKhdjQgFo/9MUkhAfiyHnrbbKkPvoezaOQkhjmESO3mybBvhJYW0Z1TsTXW1zTa3HzcwORlV+Cq+WVwJpjshzTOE9axZuWIAuC7R8B9pZ2/fbwDRj14RbD625NI7HtZL7d6XTkfO0SwmxuI6X6WhCLI1RQQePng3KRntJJIvPP7ps9FCkvr3A6LUpxOMjavHkz0tPT0bGj7eJ+OS1evBiLFy+2uk3jxo3x119/Wd2mffv22LDBsv0JkTdQmZRk2bePKx+45pMxT+yZjMWbT+Gu1CTJNM0e2QYv/3nQ4XNJVxdK7+NMlcyIDglWq5NM0uRgALH4vusthoSYd1sHvPzHPozqlIiHl4gPieNMoNIoIhBnL5dKrp8+uKXjB7VC6rqrqqL0gCgQQOt46aryyGB/RAb7499jlyS38RTmudnObBDcb//bHS1e+Ft0X2vDi7g7WP928g24/cPNJsueuakVBovM9Rrg53DFnEs4nKrWrVujtFT6i0pUHynRtkWMcSmNJ1ZxL5tqWlX14vA2WPpwD7w6KsVkufG9+65UifYVIm/PeL9gjXjD99pU6SiheUwDyXX9WllWo8WHBeDj8ano0Vzensu2qmjlv5zEM7t255H3A5x3ewfZjpXaJMLmNtZKqgTY8e4kNhAE69e9n6/0o95akOXuW0xXkTx9pF8Lu773I661oXQ3h4OsefPmYfr06Vi3bh3y8vJQVFRk8o+I5CF2g1PZWG/s1k4JuD45AimJCk3tAsugpXW86bn8fH3QtUkk/M1+ZRrvZ176Za92CaES1YXS+7hyUOzqZHw83rk2K0pN3yK9b+2fqMaBnOlHU/NCbeWB74yfHujm9L6hMjbaDg/yr9X+SRFBDv0ISDCaI1Tj5+N0e0N3TixdfY2LXXm1vRw95Ueow1f7TTfdhPT0dAwcOBCxsbGIiIhAREQEwsPDERFhO5InIvv4iXTXduTG8d6YzvjpoZ4e2XarQ6MwRDfQoHPjcMltxO79xsusDhEgdUwbZQX2NMi3RizNTaKC0UikDYlN1nqPOhOAKVza+uLwNhjQuqZkTiqnazNRuHn+zkmtRKekcKePJ+93w3awYi2eGZ3ayOb1abw2Ith6UDcsyb5ZASp1nlF1W1d5zQTRRPXFu3d1wuvLD+KDu7tY3U6JH6C3d2mEpTvOYt5t4nOEyiVA7YstMwbA10dlMj2RMSV+Yds6ZHQDDTo0Csefuy0HfLRHA434LdXaeSXbLtVyiI5qi+6tGsz5/t5NscmsfVH/1jH4btsZh48p5r83NrOytubg9/VsindWHandyQDc3iUBDdRZDu5lmtn2BFmuKOiZ0r+F1Sq9mrTUJOb9sZ0x8O31VcvhfMlNqdZ9k4JbK8V2NN/Nj+Qp8xE7HGT17dtXct2uXbtqkxYiQlUPqls7Jbi0uPt/47ogu6AUk/s0w8u3tEVogH2DO6YmR+LTTSedOqe1h0p8aIBo1V5txhICbN94QwL88PLIttBW6jG2u/2TJc+7rT1+23UOj/ZvgZ8ypefBc5XGkUHIyi+BjwqGRsL9W8di83MD0HPeGsN2Lw5vawiy7uyaJHosZ4n1AAOAKQNayBJkzR3VDn//7WiQZcrVhbxS1291Oqxdnnd3b1zV0/Eaa239HNG7RbTkOqVLP9XXSjWVCIc8JMaq/SRVhYWFWLJkCT799FPs3r3b4YlLiciSq9sTDDdqJGpvgAUAQ9vF4Y6ujfCzjIHFn1N6o0l0EBauPmpY1qtFFDo0CscX/5oGdPbeR6uDDlvj/swa2Q7RDTT4yMF2VGO6NcaYbo2Rf7VCdL214G5w2zi88tcBAFVta6rZewX0axWDdYcvmuzzxX3X481/DuGxAdeZbJtgNhhmsMYPJ+cOQ6VekL2t1PShrVCq1eHWTon4a0/NINHOtsEz5+h35IVhbSyWGY+3lPZkHwwWmfnAXvY81KW2kcqTjc/0R3ZBKVKTI+Hro8IT34uPSu9MQHF398bo0zIGUQ0sZzOppvRtKPhaya/Y96O2567tDzK5OB1krVmzBp9//jl++eUXNGnSBLfffjs+++wzOdNGRGaqB0gEpEfxNqfkLzqVSoWh7eJlDbLaN6oad2fqwOtwsbgct3RKwIBro6qbB1nJUcHYmVUAoCpvzBveV1s9vS/KtDqcK5DuGf3ZhFQk2zHCtTP6tY7Ft1uz0DAsADmFZSbrkiKDkPniIJRU6BBp1M7G3iDiv72bGYKsas1jGuDj8fbP+aquRTspKaEBarx5R9VQP8ZBljvsnTUEIQFq/LPPNB3Gsc11tZj5wF5SX8XqANd8fVJkEJIk5ic15+gn+PLIdhYdUlyte9OqHz3WblHdkiOx7ZTtMb7Mvy6eMrWhQ0HW2bNnsXjxYnz++ee4evUqRo8eDa1Wi6VLl6JtW8dHLSaqK1xV8BTk73dthGVBsv2PqynV9iEkQI13x1hO0WLspRFt4eejwp2pSeiYFAa1xNxual+faw8y1w0/Y3xNvDi8Ddo0DMXgNnG4Ye5qi22jGmjg7IANHtivweNoROYsBOyrDrO3RMSeraS+K/YGuVLnCA9SO1xu4wnXTfUPCWu3kI/Gd0X311dB62ADfU8pybI7jB02bBjatm2LAwcOYOHChcjOzsbChQuVTBuRR9P4+aBlXAMkRQZaVMMo6aaUeNyU4hljwADuHVYyMtgfb93ZEd2aRkLj5+ux020E+fth/A1NEB9m31ysYl6RmL5H6Xf8n86JmDnCtT+ipXpjOnutSbV5Cg9y70T21YOh3nzt+2zr94r5+vfGdELfljGYalYt7Ams9dRNDA/E/8bVdOwRe9vVn1lksD/G35Bs83zmJb9e1yZr5cqVmDp1Kh5++GFcd53nfaBErqZSqfDP430gQL52JmSdp9w4XcG81CPSRpd9JTzavzmeHtoaAAztxpzRtqFjVXFfTeqGAdd6zkU30ODSlXKnzw2I9yJc8t/uCFDbV+VuD3tKdM23+POx3rhSVmk0HINjF/itnRJxa6dEh/apZpwjs29ph5f/2O/UcaT4Wbkn/vpoT8SG1PzgENuytt91T7lV2F2StXHjRhQXFyM1NRXdu3fH//3f/+HixYu2dySqw3x8VPU+wPL1hHoHOykdpCk56pJx1Zbxr3Z72+Y5KjkqCE8NaSXLscZ2a4wXhrWxe/Jq4/f+9NDaT/cj9rn0stKrzlXUvj42x7syZu3ytWfEeWPG19CEnsl4fKBl4YmzX+3h7Rs6FOSEyDgobLW7r/UO7tFM3pkTHGV3kNWjRw8sWrQIOTk5ePDBB/H9998jMTERer0eaWlpKC4uVjKdROSh+rSMQcdGYYabmjMebqNDYngAvrm/u9XtPOXXaW1M6lU10fTEnslWt7MVEM64uTXGdW9cq8E4rWkZFyJbL1c/Xx9M7tMMKYmOTybszDAC5nPbueJ3gF3XpoPVgZbrpTd4YXgbTBvcEquniw+z9F+jCc4By8BTroFZ3xvTCW/e4dh0Rc58r60NZAxUTcmz7fmB+Oa/1u8pSnO4a0FQUBAmTZqETZs2Ye/evZg+fTrmzZuH2NhY3HLLLUqkkYg8yA3NqnoEdbjWC9Dfzwe/T+mNOf9xfgDT1uEC1k3vg97Xua504a7UJJOhK1zlheFVJTov2WrnJJi/rFmgAvBg3+Z4/T/tTQIhe56TH9zdBaEBfvhqkvXpaDymgNKJdHxwdxe8e1enmkNUN7B24vT2ln7aNYSDE+e3V0iAGlMHXic5ftaMYW1M5hBV6vO9tVOiYWgGSXZkhHH6xBqxV3UAsra/CrGhAW6vaahV/81WrVrhzTffxNmzZ/Hdd9/JlSYikpHcvWyaxzTAjpcG45eHe8p6XFd7ZVQ7kwex3KRKYHx9VEhJDLN58w8N9EMXG7/WnTGsfUPsfnkI+rSMkf3YnkLt64Mh7eJsb+hittpt2SzJqsW5fX1U6NQovBZHUE4Df8erC+NCne9E4kqyDJLh6+uLUaNG4Y8//pDjcETk4SKD/e2aBkR2MhcFGIc5crfXqm1wq1Kp8P1/r695LWOLL3uqAZUe7dsakzkqJZbbEuTvh90zh+DAK0NlS1dt1foSk/Eata8qWL5rwLgnp/kPjKeGWm/758x301MKYt07EhkReY2JPZOh8fPBQ32buy0NbROqBhvVyDCIogoq2UfWl7sKRnoSbMk9ZDy3bIeqldq0OQsLUiPIiVISZ8gR/9ieILqW00o5uH+trgGzU31wdxc82KcZ/tu7qcUo8zEhGvz+aC/JQ93fuyl8fVQYc71zUz+lzxjg1H5y8IzRDInI4826pR1eHN7GPSVY13x4TxcsXHPMZqNxKa4cAsKdJUFy8JQgq0lUMP5+/EZEBfvjVF5JrY6l5Odv1xAOLrz+UiIELD8DxIVKT5tjTOnBO2NDNJghMrVRtY5J4fj3uQHoZTS3ZrWkyCAcfOUmh0aoN75+G4YFIi5Ug/NFtRsKxBksySKq4+S8sbszwAKqbpav/6c9WsowBYpK5TlVCo5yZwCnRGeB7ybfgPYSPQ9VKqBNw1DEhgYgNsS+gEFOcoYeNkuqatn70FhiMLBmWm+se6q//TspyvY1m2hlUGd3TwHkLO9MNRGRDDyltMYTSQVy4YFqvHlHB1nzrkfzKEQ1EB8vyvg0ydHBePeuTvj6fus9Iz1VrQfYdHD/pIggk3HUrO3fIla8V6I1njxEoPn16ycx5ZbSGGQR1XEMJMSpYNrmqS6MwSWH6649bEd1Fh9JXKUCRqcm4ehrNzs9sfTLI52fpmdU50TceJ139ozs3LhqwFCp4ETpa7ChlWmdhrdviJkj2uKXR+zvNWzeZjAsUHqaIkfvQ/aU1n45qRviQwNsDkcCAP+7uwuiG/jjlZHSVZZKYJssojquPk1F426uqsaTemDJEVD/PqUXTueVGObVq3Zvjyb4PuMMHuxT1fHBz9fH6Wvrvl5NMftPa9P01M2L9tVb26FxZCD+07mR6HrbQzjULl9iQwPw7eTuopPLq1QqTDIbsDTcStBk7OeHeuCNfw7h5ZE182tazlggv74tY7Dl+YGi68y/C52SwpHxwiDk5+crkBJpDLKIqF4y/xXu6QV+1T0rlRbk74c2DS3P9cqtKZg5oq1i7fJMAwzP+TSslf4YsyfgDA/yN8wF6Qw5fjD1bG57wN8Fozviz93ZeLhfc3yw7rjkdtUlmanJkfjpIc8fN0/u3sT2YJBFRPWGtZIAuctO5Lqfv9ylEh2u74Wm0cHyHLAWXNXxoVl0MLo0DkdYoFrWczpTEtQyLgRv39nR5uCXcvTOsz2Eg2vc1qURbusiXtpWLTxIjUX3ptp9THcEOJ6AQRYR1UvecsuP1NRMYVTNU9Iu50O/eUwDrD9yEUDVxOtLH+7pMQ/m27taDzjkcmfXJPyy45xkT0tPqvrfMmMgAtTKTE5elzDIIqrjPOnGTCRl2pCWAGqGiHBHgJUYHohzBaUuP2+1Hs2jsPGZ/pKlZlMHtsCqg+edHpTTnVz9aXpIfM4gi4jqJ0Vuwh5yY/dGDTR+mFmLXodycLa3JCDfj5mkyCDJdR0ahePAK0MR6AElSLa+P+aDsxoPJeEKnjIYMIdwIKrjPOUXHSnP1R+1PaOce5P2tZhA2VVZEeTv5zHVqPZ65qZWDk/o7GVvURJLsojquDr2HJSN+YPKmwIG8wfQzSnxOFdQig61CBKoaoiFhPAA3CYxxALVcKSk6JF+LRRMiThPCdIYZBFRveFFcZQNpk+QD+/pCkEQvK6Ew9Vsff7hQf6YcbNzg1UqPfcfeSdWFxIR1QHuCLAYVtRfnhjPtzKa09RTkscgi4hIJp744CFyB3cE4P+7u4sbzmodgywiIqoXlHzw152qaPt4+u8JT6k6Z5BFRATvqvrykOdHvQssrKlvWeEpQYwxD0wSgywiIqLaigzyd3cSyAOxdyFRHcdeTzX8rAw26YE/gsmLzLqlHQpKKzCxZ7K7k+ISnvh9iTAKdD0lfQyyiKjeaBUXgiFt4xATorFYJ0co6qobu6c8QKhGfFgAvn+gh7uT4THcUZUcGeyPLyd1g8bPBz4+nvEtYZBFRPWGSqXCJ/emujsZRF5PqfZPIzo0xF97cvBw3+ZO7d+3ZYzMKaodtskiIlKAko1wPbHRsTfwplH9PcW3/+2O6Ab++GR8Vzw9tJXi51s4tjN2vDQYPVtEK34uV2CQRVTHRQVbVo0REdmjZ4toZLwwCEPaxePmlHjDcqUCfZVKhcjgutOJgNWFRHXcyyPboqhMi3tuaOLupBCRF6oOqJpGB+O2LokID7QdBD05+Do8+cNu3NG1fs8DySCLqI6LDQ3A1/d3d3cyPJ4cNUmuqsZjZSG5g0qlwoLRneza9j+dG+GGZlGIDw1QNlEejkEWERHVC0mRQe5OQr3SMCzQ3UlwOwZZREQyMW5YrWSpFtu9O6dL4wjMva09kqOC3Z0UqicYZBERUb0xtltjdyeB6hGv6V04Z84c9OzZE0FBQQgPD7dYv3v3bowdOxZJSUkIDAxEmzZt8N5771lst3fvXvTt2xeBgYFITEzEK6+8wm69RIQ2DUPcnYR6ZUSHhgCAQW3i3JwSIuV4TUlWRUUF7rzzTvTo0QOfffaZxfrMzEzExMTgm2++QVJSEjZv3owHHngAvr6+mDJlCgCgqKgIgwcPRv/+/ZGRkYEjR45g4sSJCA4OxvTp0139lojIA2x8pj/yr1agiRdVIQWqfd2dhFp7844OGNa+Ifp42OCRRHLymiBr9uzZAIDFixeLrp80aZLJ62bNmiE9PR2//PKLIchasmQJysrKsHjxYmg0GqSkpODIkSNYsGABpk2bxgH+iOqhpMggr2kQPX1wSxy5cAU3NItyd1JqLcjfD8PaN3R3MogU5TVBljMKCwsRGRlpeJ2eno6+fftCo6kZnHHo0KGYMWMGTp06haZNm4oep7y8HOXl5YbXRUVFAACtVgutVqtQ6r1PdV4wTywxb6TVpbzRaisNf1dqtdBqa3eLNc+bh/okAwB0ukrodLU6tOxc/fnVpetGbswbaa7OkzobZKWnp+PHH3/EsmXLDMtyc3ORnJxssl1cXJxhnVSQNXfuXENJmrG1a9ciKMg7fgG7UlpamruT4LGYN9LqQt6UVgLVt9W1a9ciQqbB9j01bxoF++LsVRUCfQUsX77cLWnw1LzxBMwbSyUlJS49n1uDrFmzZokGL8YyMjKQmurYhK779+/HrbfeipkzZ2Lw4MEm68yrBKsbvVurKpwxYwamTZtmeF1UVISkpCT0798fUVHeX2wvF61Wi7S0NAwePBhqtdrdyfEozBtpdSlvisu0eC5jLQBgwIABaBhWu4EYPT1vOvUsxccbT2LCDU3QLMa1bdo8PW/ciXkjLS8vz6Xnc2uQNWXKFIwZM8bqNuYlT7YcOHAAAwYMwOTJk/Hiiy+arIuPj0dubq7JsgsXLgCoKdESo9FoTKoYq6nVal7AIpgv0pg30upC3vgZVeH5+fnJ9n48NW+axKjx+m0d3ZoGT80bT8C8seTq/HBrkBUdHY3oaPlm2t6/fz8GDBiACRMmYM6cORbre/Togeeffx4VFRXw96+ae2nlypVISEhwOJgjIiIissZrxsnKysrCrl27kJWVBZ1Oh127dmHXrl24cuUKgKoAq3///hg8eDCmTZuG3Nxc5Obm4uLFi4ZjjBs3DhqNBhMnTsS+ffvw66+/4vXXX2fPQiIiIpKd1zR8nzlzJr788kvD686dOwOoalzar18//PTTT7h48SKWLFmCJUuWGLZr0qQJTp06BQAICwtDWloaHn30UaSmpiIiIgLTpk0zaW9FREREJAevCbIWL14sOUYWUNWIftasWTaP0759e2zYsEG+hBERXWM8eQQLx4nIa6oLiYi8CWfrIiIGWUREREQKYJBFRKQAVhcSEYMsIiIiIgUwyCIikgvbYRGREQZZRERERApgkEVEJBe2wyIiIwyyiIjkwupCIjLCIIuISAEqFmsR1XsMsoiIiIgUwCCLiEgmGnXNLTXQ39eNKSEiT+A1cxcSEXm6ALUvPh7fFTq9gLBAtbuTQ0RuxiCLiEhGQ9vFuzsJROQhWF1IREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpAAGWUREREQKYJBFREREpACvCbLmzJmDnj17IigoCOHh4Va3zcvLQ6NGjaBSqVBQUGCybu/evejbty8CAwORmJiIV155BYIgKJdwIiIiqpe8JsiqqKjAnXfeiYcfftjmtvfffz86dOhgsbyoqAiDBw9GQkICMjIysHDhQsyfPx8LFixQIslERERUj/m5OwH2mj17NgBg8eLFVrf78MMPUVBQgJkzZ+Lvv/82WbdkyRKUlZVh8eLF0Gg0SElJwZEjR7BgwQJMmzYNKpVKqeQTERFRPeM1QZY9Dhw4gFdeeQVbt27FiRMnLNanp6ejb9++0Gg0hmVDhw7FjBkzcOrUKTRt2lT0uOXl5SgvLze8LioqAgBotVpotVqZ34X3qs4L5okl5o005o005o005o005o00V+dJnQmyysvLMXbsWLz11lto3LixaJCVm5uL5ORkk2VxcXGGdVJB1ty5cw0lacbWrl2LoKCg2ie+jklLS3N3EjwW80Ya80Ya80Ya80Ya88ZSSUmJS8/n1iBr1qxZosGLsYyMDKSmpto81owZM9CmTRvcc889VrczrxKsbvRurapwxowZmDZtmuF1UVERkpKS0L9/f0RFRdlMW32h1WqRlpaGwYMHQ61Wuzs5HoV5I415I415I415I415Iy0vL8+l53NrkDVlyhSMGTPG6jbmJU9S1qxZg7179+Lnn38GUBM8RUdH44UXXsDs2bMRHx+P3Nxck/0uXLgAoKZES4xGozGpYqymVqt5AYtgvkhj3khj3khj3khj3khj3lhydX64NciKjo5GdHS0LMdaunQpSktLDa8zMjIwadIkbNy4Ec2bNwcA9OjRA88//zwqKirg7+8PAFi5ciUSEhLsDuaIiIiI7OE1bbKysrKQn5+PrKws6HQ67Nq1CwDQokULNGjQwBBIVbt06RIAoE2bNoZxtcaNG4fZs2dj4sSJeP7553H06FG8/vrrmDlzJnsWEhERkay8JsiaOXMmvvzyS8Przp07A6hqfN6vXz+7jhEWFoa0tDQ8+uijSE1NRUREBKZNm2bS3oqIiIhIDl4TZC1evNjmGFnG+vXrJzqSe/v27bFhwwYZU0ZERERkyWtGfCciIiLyJgyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTAIIuIiIhIAQyyiIiIiBTg5+4EeCNBEAAAxcXFUKvVbk6N59BqtSgpKUFRURHzxQzzRhrzRhrzRhrzRhrzRlpxcTGAmue40hhkOSEvLw8A0LRpUzenhIiIiByVl5eHsLAwxc/DIMsJkZGRAICsrCyXfEjeoqioCElJSThz5gxCQ0PdnRyPwryRxryRxryRxryRxryRVlhYiMaNGxue40pjkOUEH5+qpmxhYWG8gEWEhoYyXyQwb6Qxb6Qxb6Qxb6Qxb6RVP8cVP49LzkJERERUzzDIIiIiIlIAgywnaDQavPzyy9BoNO5Oikdhvkhj3khj3khj3khj3khj3khzdd6oBFf1YyQiIiKqR1iSRURERKQABllERERECmCQRURERKQABllERERECmCQ5aAPPvgATZs2RUBAALp27YqNGze6O0mKmjt3Lq6//nqEhIQgNjYWo0aNwuHDh022EQQBs2bNQkJCAgIDA9GvXz/s37/fZJvy8nI89thjiI6ORnBwMG655RacPXvWlW9FcXPnzoVKpcITTzxhWFaf8+bcuXO45557EBUVhaCgIHTq1AmZmZmG9fU1byorK/Hiiy+iadOmCAwMRLNmzfDKK69Ar9cbtqkvebNhwwaMHDkSCQkJUKlU+O2330zWy5UPly9fxvjx4xEWFoawsDCMHz8eBQUFCr8751nLF61Wi2effRbt27dHcHAwEhIScO+99yI7O9vkGHUxXwDb14yxBx98ECqVCu+++67JcpfmjUB2+/777wW1Wi0sWrRIOHDggPD4448LwcHBwunTp92dNMUMHTpU+OKLL4R9+/YJu3btEoYPHy40btxYuHLlimGbefPmCSEhIcLSpUuFvXv3CnfddZfQsGFDoaioyLDNQw89JCQmJgppaWnCjh07hP79+wsdO3YUKisr3fG2ZLdt2zYhOTlZ6NChg/D4448bltfXvMnPzxeaNGkiTJw4Udi6datw8uRJYdWqVcKxY8cM29TXvHnttdeEqKgo4a+//hJOnjwp/PTTT0KDBg2Ed99917BNfcmb5cuXCy+88IKwdOlSAYDw66+/mqyXKx9uuukmISUlRdi8ebOwefNmISUlRRgxYoSr3qbDrOVLQUGBMGjQIOGHH34QDh06JKSnpwvdu3cXunbtanKMupgvgmD7mqn266+/Ch07dhQSEhKEd955x2SdK/OGQZYDunXrJjz00EMmy1q3bi0899xzbkqR6124cEEAIKxfv14QBEHQ6/VCfHy8MG/ePMM2ZWVlQlhYmPDRRx8JglB1U1Cr1cL3339v2ObcuXOCj4+P8M8//7j2DSiguLhYuO6664S0tDShb9++hiCrPufNs88+K/Tu3VtyfX3Om+HDhwuTJk0yWXbbbbcJ99xzjyAI9TdvzB+YcuXDgQMHBADCli1bDNukp6cLAIRDhw4p/K5qz1ogUW3btm0CAMMP/vqQL4IgnTdnz54VEhMThX379glNmjQxCbJcnTesLrRTRUUFMjMzMWTIEJPlQ4YMwebNm92UKtcrLCwEUDNJ9smTJ5Gbm2uSLxqNBn379jXkS2ZmJrRarck2CQkJSElJqRN59+ijj2L48OEYNGiQyfL6nDd//PEHUlNTceeddyI2NhadO3fGokWLDOvrc9707t0bq1evxpEjRwAAu3fvxqZNmzBs2DAA9TtvjMmVD+np6QgLC0P37t0N29xwww0ICwurM3lVWFgIlUqF8PBwAPU7X/R6PcaPH4+nn34a7dq1s1jv6rzhBNF2unTpEnQ6HeLi4kyWx8XFITc3102pci1BEDBt2jT07t0bKSkpAGB472L5cvr0acM2/v7+iIiIsNjG2/Pu+++/x44dO5CRkWGxrj7nzYkTJ/Dhhx9i2rRpeP7557Ft2zZMnToVGo0G9957b73Om2effRaFhYVo3bo1fH19odPpMGfOHIwdOxZA/b5ujMmVD7m5uYiNjbU4fmxsbJ3Iq7KyMjz33HMYN26cYTLo+pwvb7zxBvz8/DB16lTR9a7OGwZZDlKpVCavBUGwWFZXTZkyBXv27MGmTZss1jm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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fd.single_chirp.FormProfile().PlotProfile(1400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "### Put these data into an xarray\n",
+    "The methods demonstrated above work well for loading and plotting single chirps, but as discussed above, when we have a large complex pRES dataset with multiple chirps and bursts and attenuator settings, we need a more convenient way of storing and accessing the data. When the data gets very large, we also need to be able to load and process subsets of the data without loading the whole thing into memory. Xarray can help.\n",
+    "\n",
+    "xApRES has a collection of tools to load multiple `.dat` files and put them in to a useful structure within an xarray. \n",
+    "\n",
+    "Next we will use one of the tools to load all the data from one dat file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:          (time: 94, chirp_time: 40001, chirp_num: 20,\n",
+       "                      attenuator_setting_pair: 2, profile_range: 7134)\n",
+       "Coordinates:\n",
+       "  * time             (time) datetime64[ns] 2022-06-23T01:36:56 ... 2022-06-24...\n",
+       "  * chirp_time       (chirp_time) float64 0.0 2.5e-05 5e-05 ... 1.0 1.0 1.0\n",
+       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.5e+03 1.5e+03\n",
+       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
+       "    filename         (time) &lt;U83 &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DI...\n",
+       "    burst_number     (time) int64 0 1 2 3 4 5 6 7 8 ... 86 87 88 89 90 91 92 93\n",
+       "    AFGain           (attenuator_setting_pair) int64 -4 -14\n",
+       "    attenuator       (attenuator_setting_pair) float64 5.0 5.0\n",
+       "    orientation      (time) &lt;U7 &#x27;unknown&#x27; &#x27;unknown&#x27; ... &#x27;unknown&#x27; &#x27;unknown&#x27;\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 ...\n",
+       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 ...\n",
+       "    latitude         (time) float64 68.71 68.71 68.71 ... 68.71 68.71 68.71\n",
+       "    longitude        (time) float64 -49.55 -49.55 -49.55 ... -49.55 -49.55\n",
+       "    battery_voltage  (time) float64 13.89 13.87 13.94 ... 13.85 13.84 13.84\n",
+       "    temperature_1    (time) float64 2.031 0.4609 508.9 ... 1.641 0.2656 510.9\n",
+       "    temperature_2    (time) float64 5.195 3.039 3.234 ... 4.805 2.25 0.6797</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c09ca525-16f8-4815-bdb0-37ee0658e699' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c09ca525-16f8-4815-bdb0-37ee0658e699' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 94</li><li><span class='xr-has-index'>chirp_time</span>: 40001</li><li><span class='xr-has-index'>chirp_num</span>: 20</li><li><span>attenuator_setting_pair</span>: 2</li><li><span class='xr-has-index'>profile_range</span>: 7134</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-149292d8-da75-4852-8a4a-346bf6e5c3ad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-149292d8-da75-4852-8a4a-346bf6e5c3ad' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-06-23T01:36:56 ... 2022-06-...</div><input id='attrs-3be7d34f-4dfa-4356-888f-b107f951b08c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3be7d34f-4dfa-4356-888f-b107f951b08c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63aee477-9a8a-42e8-a2d2-38275e4f0360' class='xr-var-data-in' type='checkbox'><label for='data-63aee477-9a8a-42e8-a2d2-38275e4f0360' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of burst</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-06-23T01:36:56.000000000&#x27;, &#x27;2022-06-23T01:51:57.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T02:06:53.000000000&#x27;, &#x27;2022-06-23T02:21:52.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T02:36:53.000000000&#x27;, &#x27;2022-06-23T02:51:52.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T03:06:53.000000000&#x27;, &#x27;2022-06-23T03:21:59.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T03:36:59.000000000&#x27;, &#x27;2022-06-23T03:51:53.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T04:06:52.000000000&#x27;, &#x27;2022-06-23T04:21:52.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T04:36:52.000000000&#x27;, &#x27;2022-06-23T04:51:52.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T05:06:52.000000000&#x27;, &#x27;2022-06-23T05:21:53.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T05:36:52.000000000&#x27;, &#x27;2022-06-23T05:51:52.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T06:06:59.000000000&#x27;, &#x27;2022-06-23T06:21:53.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T06:36:53.000000000&#x27;, &#x27;2022-06-23T06:52:02.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T07:06:52.000000000&#x27;, &#x27;2022-06-23T07:21:51.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T07:36:52.000000000&#x27;, &#x27;2022-06-23T07:51:52.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T08:06:52.000000000&#x27;, &#x27;2022-06-23T08:21:52.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T08:36:55.000000000&#x27;, &#x27;2022-06-23T08:52:03.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T09:06:40.000000000&#x27;, &#x27;2022-06-23T09:21:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T09:36:40.000000000&#x27;, &#x27;2022-06-23T09:51:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T10:06:40.000000000&#x27;, &#x27;2022-06-23T10:21:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T10:36:41.000000000&#x27;, &#x27;2022-06-23T10:51:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T11:06:40.000000000&#x27;, &#x27;2022-06-23T11:21:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T11:36:40.000000000&#x27;, &#x27;2022-06-23T11:51:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T12:06:40.000000000&#x27;, &#x27;2022-06-23T12:21:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T12:36:40.000000000&#x27;, &#x27;2022-06-23T12:51:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T13:06:46.000000000&#x27;, &#x27;2022-06-23T13:21:53.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T13:36:40.000000000&#x27;, &#x27;2022-06-23T13:51:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T14:06:41.000000000&#x27;, &#x27;2022-06-23T14:21:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T14:36:42.000000000&#x27;, &#x27;2022-06-23T14:51:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T15:06:41.000000000&#x27;, &#x27;2022-06-23T15:21:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T15:37:03.000000000&#x27;, &#x27;2022-06-23T15:51:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T16:06:42.000000000&#x27;, &#x27;2022-06-23T16:21:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T16:36:41.000000000&#x27;, &#x27;2022-06-23T16:51:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T17:06:41.000000000&#x27;, &#x27;2022-06-23T17:21:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T17:37:03.000000000&#x27;, &#x27;2022-06-23T17:51:39.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T18:06:41.000000000&#x27;, &#x27;2022-06-23T18:21:46.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T18:36:39.000000000&#x27;, &#x27;2022-06-23T18:51:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T19:06:40.000000000&#x27;, &#x27;2022-06-23T19:21:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T19:36:41.000000000&#x27;, &#x27;2022-06-23T19:51:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T20:06:42.000000000&#x27;, &#x27;2022-06-23T20:21:51.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T20:36:41.000000000&#x27;, &#x27;2022-06-23T20:51:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T21:06:41.000000000&#x27;, &#x27;2022-06-23T21:21:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T21:36:41.000000000&#x27;, &#x27;2022-06-23T21:51:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T22:07:03.000000000&#x27;, &#x27;2022-06-23T22:21:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T22:36:41.000000000&#x27;, &#x27;2022-06-23T22:51:39.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T23:06:40.000000000&#x27;, &#x27;2022-06-23T23:21:40.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T23:36:40.000000000&#x27;, &#x27;2022-06-23T23:51:41.000000000&#x27;,\n",
+       "       &#x27;2022-06-24T00:06:42.000000000&#x27;, &#x27;2022-06-24T00:21:42.000000000&#x27;,\n",
+       "       &#x27;2022-06-24T00:36:43.000000000&#x27;, &#x27;2022-06-24T00:51:51.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp_time</span></div><div class='xr-var-dims'>(chirp_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.5e-05 5e-05 ... 1.0 1.0 1.0</div><input id='attrs-e449f12e-e86e-4120-b540-f9511ba6c299' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e449f12e-e86e-4120-b540-f9511ba6c299' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c928c5e8-3fb7-40e5-b7a3-b40159d49aee' class='xr-var-data-in' type='checkbox'><label for='data-c928c5e8-3fb7-40e5-b7a3-b40159d49aee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of samples during chirps</dd><dt><span>name :</span></dt><dd>time of samples during chirps</dd><dt><span>units :</span></dt><dd>seconds</dd></dl></div><div class='xr-var-data'><pre>array([0.00000e+00, 2.50000e-05, 5.00000e-05, ..., 9.99950e-01, 9.99975e-01,\n",
+       "       1.00000e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>profile_range</span></div><div class='xr-var-dims'>(profile_range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.2103 ... 1.5e+03 1.5e+03</div><input id='attrs-b21df0f5-ee23-4fb9-8480-76a7c189fc3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b21df0f5-ee23-4fb9-8480-76a7c189fc3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8abfcef5-163f-49b5-b56e-077c52a335bc' class='xr-var-data-in' type='checkbox'><label for='data-8abfcef5-163f-49b5-b56e-077c52a335bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 2.102896e-01, 4.205791e-01, ..., 1.499575e+03,\n",
+       "       1.499785e+03, 1.499995e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp_num</span></div><div class='xr-var-dims'>(chirp_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 14 15 16 17 18 19</div><input id='attrs-f7ef7c55-0500-4658-b5a8-a8c8684616de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7ef7c55-0500-4658-b5a8-a8c8684616de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cd39bff-a51e-480f-b8a4-3aa6c2933fe1' class='xr-var-data-in' type='checkbox'><label for='data-5cd39bff-a51e-480f-b8a4-3aa6c2933fe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>chirp number</dd><dt><span>description :</span></dt><dd>the number of each chirp within each burst</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filename</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U83</div><div class='xr-var-preview xr-preview'>&#x27;ldeo-glaciology/GL_apres_2022/A...</div><input id='attrs-976c7549-6ee5-4406-8e3a-81fd918d5679' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-976c7549-6ee5-4406-8e3a-81fd918d5679' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43fe39f1-6025-4b41-9b81-385ce7b7edff' class='xr-var-data-in' type='checkbox'><label for='data-43fe39f1-6025-4b41-9b81-385ce7b7edff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the name of the file that contains each burst</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "...\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;,\n",
+       "       &#x27;ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-06-23-0136.DAT&#x27;],\n",
+       "      dtype=&#x27;&lt;U83&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>burst_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 88 89 90 91 92 93</div><input id='attrs-8589c11d-e61c-4443-9861-cd2082a1feec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8589c11d-e61c-4443-9861-cd2082a1feec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d3f8a43-6cec-4386-a917-43840353aee7' class='xr-var-data-in' type='checkbox'><label for='data-3d3f8a43-6cec-4386-a917-43840353aee7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the number of each burst within each file</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
+       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
+       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
+       "       85, 86, 87, 88, 89, 90, 91, 92, 93])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>AFGain</span></div><div class='xr-var-dims'>(attenuator_setting_pair)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4 -14</div><input id='attrs-da731857-4c73-44d0-92f1-82bad4a8158a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da731857-4c73-44d0-92f1-82bad4a8158a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92064e20-9048-4aa2-9b46-13cae83b5ba8' class='xr-var-data-in' type='checkbox'><label for='data-92064e20-9048-4aa2-9b46-13cae83b5ba8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>audio-frequency gain control setting</dd><dt><span>units :</span></dt><dd>decibels</dd></dl></div><div class='xr-var-data'><pre>array([ -4, -14])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuator</span></div><div class='xr-var-dims'>(attenuator_setting_pair)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.0</div><input id='attrs-6b12ed6c-1a5d-40f9-9b0a-9dab1b2ffbc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b12ed6c-1a5d-40f9-9b0a-9dab1b2ffbc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-572a0582-4565-4059-a2e0-d063f32544cb' class='xr-var-data-in' type='checkbox'><label for='data-572a0582-4565-4059-a2e0-d063f32544cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>radio-frequency attenuator setting</dd><dt><span>units :</span></dt><dd>decibels</dd></dl></div><div class='xr-var-data'><pre>array([5., 5.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;unknown&#x27; &#x27;unknown&#x27; ... &#x27;unknown&#x27;</div><input id='attrs-1b4d898d-3d92-4601-82b8-2969a842950d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b4d898d-3d92-4601-82b8-2969a842950d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c18da888-09c1-4ebd-ae16-9da7a53c49a7' class='xr-var-data-in' type='checkbox'><label for='data-c18da888-09c1-4ebd-ae16-9da7a53c49a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>HH, HV, VH, or VV antenna orientation as described in Ersahadi et al 2022 doi:10.5194/tc-16-1719-2022</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;,\n",
+       "       &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;, &#x27;unknown&#x27;], dtype=&#x27;&lt;U7&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a50e7fc1-96a1-44ab-afdb-735776001249' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a50e7fc1-96a1-44ab-afdb-735776001249' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>chirp</span></div><div class='xr-var-dims'>(time, chirp_time, chirp_num, attenuator_setting_pair)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06676 0.06779 ... -0.9668 -0.2927</div><input id='attrs-5457f150-727f-497d-b676-3111caa05648' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5457f150-727f-497d-b676-3111caa05648' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82a10995-39ee-4d9d-9052-1a32731c4029' class='xr-var-data-in' type='checkbox'><label for='data-82a10995-39ee-4d9d-9052-1a32731c4029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>de-ramped chirp</dd><dt><span>units :</span></dt><dd>volts</dd><dt><span>description :</span></dt><dd>voltage from the analog-to-digital converter after the received signal has been mixed with the transmitted signal and the result has been filtered to leave only the low frequency compponent corresponding to the differences in the frequencies of the Tx and Rx signals</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 6.67572021e-02,  6.77871704e-02],\n",
+       "         [-1.39236450e-02,  3.42559814e-02],\n",
+       "         [-4.12750244e-02, -1.98745728e-02],\n",
+       "         ...,\n",
+       "         [-3.52096558e-02,  2.09426880e-02],\n",
+       "         [ 1.45072937e-01,  9.53674316e-03],\n",
+       "         [-1.48773193e-02,  3.89099121e-03]],\n",
+       "\n",
+       "        [[ 3.59344482e-02,  6.72912598e-02],\n",
+       "         [-7.46917725e-02,  3.90625000e-02],\n",
+       "         [-3.49807739e-02, -1.41525269e-02],\n",
+       "         ...,\n",
+       "         [-9.62066650e-02,  2.28118896e-02],\n",
+       "         [ 2.07366943e-01,  1.38854980e-02],\n",
+       "         [ 1.09863281e-02,  1.64031982e-02]],\n",
+       "\n",
+       "        [[-2.79388428e-01, -1.75476074e-02],\n",
+       "         [-3.80516052e-01,  3.17382812e-02],\n",
+       "         [-1.31568909e-01, -1.84249878e-02],\n",
+       "         ...,\n",
+       "...\n",
+       "         ...,\n",
+       "         [ 2.20870972e-02,  2.81143188e-02],\n",
+       "         [ 2.76565552e-02,  2.72750854e-02],\n",
+       "         [ 3.61251831e-02,  2.74276733e-02]],\n",
+       "\n",
+       "        [[-7.59124756e-03,  1.38854980e-02],\n",
+       "         [-4.30679321e-02,  6.59942627e-03],\n",
+       "         [-3.63159180e-02,  6.40869141e-03],\n",
+       "         ...,\n",
+       "         [-1.18637085e-02,  1.67465210e-02],\n",
+       "         [-6.37054443e-03,  1.42288208e-02],\n",
+       "         [-3.89099121e-03,  1.47247314e-02]],\n",
+       "\n",
+       "        [[-9.69543457e-01, -2.94189453e-01],\n",
+       "         [-9.81063843e-01, -2.96287537e-01],\n",
+       "         [-9.77172852e-01, -2.95982361e-01],\n",
+       "         ...,\n",
+       "         [-9.71374512e-01, -2.92510986e-01],\n",
+       "         [-9.68055725e-01, -2.93273926e-01],\n",
+       "         [-9.66835022e-01, -2.92739868e-01]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>profile</span></div><div class='xr-var-dims'>(time, profile_range, chirp_num, attenuator_setting_pair)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.010510674085874113+0j) ... (-...</div><input id='attrs-a89774cd-4a72-472c-bb12-bf6d3ec293ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a89774cd-4a72-472c-bb12-bf6d3ec293ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-724b56c5-7896-4d4c-b6b9-6275dedc3cfd' class='xr-var-data-in' type='checkbox'><label for='data-724b56c5-7896-4d4c-b6b9-6275dedc3cfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>profile</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>description :</span></dt><dd>complex profile computed from the fourier transform of the de-ramped chirp</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 1.05106741e-02+0.00000000e+00j,\n",
+       "           1.08683756e-02+0.00000000e+00j],\n",
+       "         [ 1.05202487e-02+0.00000000e+00j,\n",
+       "           1.08686791e-02+0.00000000e+00j],\n",
+       "         [ 1.05214892e-02+0.00000000e+00j,\n",
+       "           1.08722432e-02+0.00000000e+00j],\n",
+       "         ...,\n",
+       "         [ 1.05526683e-02+0.00000000e+00j,\n",
+       "           1.09014942e-02+0.00000000e+00j],\n",
+       "         [ 1.05561980e-02+0.00000000e+00j,\n",
+       "           1.09005425e-02+0.00000000e+00j],\n",
+       "         [ 1.05555235e-02+0.00000000e+00j,\n",
+       "           1.09020877e-02+0.00000000e+00j]],\n",
+       "\n",
+       "        [[ 6.87654948e-06+9.26221299e-03j,\n",
+       "           2.62546845e-06+9.57720616e-03j],\n",
+       "         [ 8.40296395e-06+9.27103332e-03j,\n",
+       "           2.34363450e-06+9.57746871e-03j],\n",
+       "         [ 8.32230342e-06+9.27216972e-03j,\n",
+       "           2.33378445e-06+9.58072192e-03j],\n",
+       "...\n",
+       "         [ 5.52602264e-06+2.05003942e-05j,\n",
+       "           1.07498299e-05-1.40413823e-06j],\n",
+       "         [-1.40790484e-06+3.39613402e-05j,\n",
+       "           3.30262138e-06+1.04449625e-05j],\n",
+       "         [ 3.22189292e-06+4.63970563e-05j,\n",
+       "           2.38404620e-06+5.09878855e-06j]],\n",
+       "\n",
+       "        [[-6.10397341e-07+3.49851428e-05j,\n",
+       "          -2.08889185e-06+2.84959121e-06j],\n",
+       "         [-5.89037309e-06+2.29462790e-05j,\n",
+       "          -6.62506498e-08-3.23939442e-06j],\n",
+       "         [ 1.76619358e-05+2.38125711e-05j,\n",
+       "          -3.44848854e-06+7.34902270e-06j],\n",
+       "         ...,\n",
+       "         [-2.25171974e-05+4.73599853e-06j,\n",
+       "           3.22732252e-06+1.00639419e-05j],\n",
+       "         [-1.98250025e-05-3.84439780e-06j,\n",
+       "          -1.05106024e-05+1.34817615e-06j],\n",
+       "         [-3.41412406e-05+7.89723629e-06j,\n",
+       "          -6.32450099e-07+2.00325429e-06j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>68.71 68.71 68.71 ... 68.71 68.71</div><input id='attrs-1267f160-2c31-4503-8804-61aafe78ad29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1267f160-2c31-4503-8804-61aafe78ad29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-065d6b6a-af74-4a70-9d17-d84e75abd776' class='xr-var-data-in' type='checkbox'><label for='data-065d6b6a-af74-4a70-9d17-d84e75abd776' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>latitude of burst</dd></dl></div><div class='xr-var-data'><pre>array([68.7104, 68.7105, 68.7102, 68.7101, 68.7101, 68.7101, 68.7101,\n",
+       "       68.7101, 68.7102, 68.7102, 68.71  , 68.7101, 68.71  , 68.7099,\n",
+       "       68.7098, 68.7099, 68.71  , 68.7101, 68.7101, 68.7101, 68.7101,\n",
+       "       68.7102, 68.7101, 68.7101, 68.7101, 68.7101, 68.7101, 68.7101,\n",
+       "       68.7101, 68.7101, 68.7101, 68.7102, 68.7102, 68.7102, 68.7101,\n",
+       "       68.7101, 68.7102, 68.7101, 68.7099, 68.71  , 68.71  , 68.7101,\n",
+       "       68.71  , 68.71  , 68.71  , 68.7099, 68.7097, 68.7102, 68.7102,\n",
+       "       68.7102, 68.7101, 68.7102, 68.7102, 68.7103, 68.7102, 68.7102,\n",
+       "       68.7101, 68.7102, 68.7102, 68.7101, 68.7102, 68.7101, 68.7101,\n",
+       "       68.7102, 68.7101, 68.7102, 68.7101, 68.7101, 68.7102, 68.7102,\n",
+       "       68.7102, 68.7102, 68.7101, 68.7101, 68.7101, 68.7101, 68.7101,\n",
+       "       68.7104, 68.7104, 68.7103, 68.7104, 68.7104, 68.7101, 68.7101,\n",
+       "       68.7101, 68.7102, 68.7101, 68.7101, 68.7101, 68.71  , 68.7101,\n",
+       "       68.7102, 68.7101, 68.7101])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-49.55 -49.55 ... -49.55 -49.55</div><input id='attrs-4f496257-030e-45ed-95a4-1d1bef36c67e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f496257-030e-45ed-95a4-1d1bef36c67e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-400e47af-3751-47e0-aa95-d02997cb1d31' class='xr-var-data-in' type='checkbox'><label for='data-400e47af-3751-47e0-aa95-d02997cb1d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>longitude of burst</dd></dl></div><div class='xr-var-data'><pre>array([-49.5532, -49.5518, -49.5527, -49.5534, -49.5535, -49.553 ,\n",
+       "       -49.5536, -49.5536, -49.5536, -49.5536, -49.5534, -49.5533,\n",
+       "       -49.553 , -49.5533, -49.5538, -49.5536, -49.554 , -49.5529,\n",
+       "       -49.5534, -49.5534, -49.5533, -49.5535, -49.5535, -49.5536,\n",
+       "       -49.5536, -49.5536, -49.5536, -49.5536, -49.5536, -49.5536,\n",
+       "       -49.5536, -49.5536, -49.5536, -49.5537, -49.5537, -49.5537,\n",
+       "       -49.5538, -49.5536, -49.5535, -49.5535, -49.5535, -49.5535,\n",
+       "       -49.5534, -49.5534, -49.5534, -49.5533, -49.5528, -49.5537,\n",
+       "       -49.554 , -49.5543, -49.5541, -49.5541, -49.5542, -49.5541,\n",
+       "       -49.5541, -49.554 , -49.5536, -49.5538, -49.5538, -49.5536,\n",
+       "       -49.5537, -49.5537, -49.5536, -49.554 , -49.5536, -49.5537,\n",
+       "       -49.5536, -49.5536, -49.5535, -49.5536, -49.5537, -49.5538,\n",
+       "       -49.5535, -49.5537, -49.5539, -49.5537, -49.5538, -49.5534,\n",
+       "       -49.5533, -49.5533, -49.5542, -49.5538, -49.5536, -49.5537,\n",
+       "       -49.5536, -49.5536, -49.5537, -49.5536, -49.5535, -49.5535,\n",
+       "       -49.5534, -49.5536, -49.5534, -49.5536])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>battery_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.89 13.87 13.94 ... 13.84 13.84</div><input id='attrs-e5dd2718-1016-4d81-a2d7-858856bb0bae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5dd2718-1016-4d81-a2d7-858856bb0bae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d1544b8-8e27-4fcd-bc78-30a24917d6c8' class='xr-var-data-in' type='checkbox'><label for='data-0d1544b8-8e27-4fcd-bc78-30a24917d6c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>volts</dd><dt><span>long_name :</span></dt><dd>battery voltage</dd></dl></div><div class='xr-var-data'><pre>array([13.8896, 13.8655, 13.938 , 13.8856, 13.652 , 13.652 , 13.6077,\n",
+       "       13.5956, 13.5835, 13.6198, 13.7124, 13.8655, 14.0911, 14.0105,\n",
+       "       13.9823, 13.9501, 13.9541, 13.9622, 13.9501, 13.9541, 13.9702,\n",
+       "       13.9541, 13.9742, 13.9299, 13.9299, 13.9178, 13.9178, 13.9058,\n",
+       "       13.8776, 13.8373, 13.8735, 13.8735, 13.8373, 13.8292, 13.8252,\n",
+       "       13.797 , 13.7809, 13.8171, 13.7769, 13.7728, 13.7769, 13.7607,\n",
+       "       13.7527, 13.7446, 13.7567, 13.7648, 13.7406, 13.7446, 13.7446,\n",
+       "       13.7406, 13.7648, 13.7648, 13.7648, 13.7648, 13.7205, 13.7245,\n",
+       "       13.7164, 13.7487, 13.6963, 13.7084, 13.7043, 13.7164, 13.7205,\n",
+       "       13.7124, 13.7164, 13.7164, 13.7043, 13.7003, 13.7325, 13.6963,\n",
+       "       13.7043, 13.7205, 13.7124, 13.6923, 13.6963, 13.7084, 13.7205,\n",
+       "       13.7406, 13.7446, 13.7406, 13.7406, 13.7366, 13.7527, 13.7366,\n",
+       "       13.7487, 13.7809, 13.793 , 13.801 , 13.8212, 13.801 , 13.8252,\n",
+       "       13.8494, 13.8373, 13.8373])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.031 0.4609 508.9 ... 0.2656 510.9</div><input id='attrs-b571dc00-4840-454f-9ba4-948b59474ea1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b571dc00-4840-454f-9ba4-948b59474ea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a8470ca-b423-4029-bcb7-3dcc6040860e' class='xr-var-data-in' type='checkbox'><label for='data-8a8470ca-b423-4029-bcb7-3dcc6040860e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>celsius</dd><dt><span>long_name :</span></dt><dd>temperature measured inside the ApRES unit in one location</dd></dl></div><div class='xr-var-data'><pre>array([2.03125e+00, 4.60937e-01, 5.08938e+02, 5.09328e+02, 5.05602e+02,\n",
+       "       5.06781e+02, 5.04422e+02, 5.10109e+02, 5.10305e+02, 2.42187e+00,\n",
+       "       2.22656e+00, 2.81250e+00, 2.42187e+00, 2.81250e+00, 3.20312e+00,\n",
+       "       2.81250e+00, 3.39844e+00, 3.59375e+00, 3.99219e+00, 3.59375e+00,\n",
+       "       4.38281e+00, 4.38281e+00, 3.00781e+00, 4.60937e-01, 4.77344e+00,\n",
+       "       2.81250e+00, 4.57812e+00, 4.18750e+00, 3.59375e+00, 3.59375e+00,\n",
+       "       5.11875e+02, 2.81250e+00, 5.10500e+02, 5.10695e+02, 5.07953e+02,\n",
+       "       5.07563e+02, 5.06188e+02, 5.07367e+02, 5.07953e+02, 5.08938e+02,\n",
+       "       5.10891e+02, 5.11289e+02, 2.65625e-01, 4.60937e-01, 1.83594e+00,\n",
+       "       4.60937e-01, 2.22656e+00, 6.56250e-01, 1.64062e+00, 4.60937e-01,\n",
+       "       1.64062e+00, 1.64062e+00, 1.64062e+00, 1.83594e+00, 2.03125e+00,\n",
+       "       2.03125e+00, 1.83594e+00, 1.24219e+00, 1.24219e+00, 1.24219e+00,\n",
+       "       1.24219e+00, 1.64062e+00, 2.81250e+00, 1.64062e+00, 2.03125e+00,\n",
+       "       3.39844e+00, 3.20312e+00, 3.00781e+00, 2.61719e+00, 4.38281e+00,\n",
+       "       4.38281e+00, 4.38281e+00, 4.38281e+00, 4.38281e+00, 4.96875e+00,\n",
+       "       4.38281e+00, 4.38281e+00, 4.38281e+00, 4.38281e+00, 4.57812e+00,\n",
+       "       3.79687e+00, 2.81250e+00, 3.99219e+00, 3.99219e+00, 3.20312e+00,\n",
+       "       3.20312e+00, 3.20312e+00, 1.24219e+00, 1.83594e+00, 2.03125e+00,\n",
+       "       1.64062e+00, 1.64062e+00, 2.65625e-01, 5.10891e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.195 3.039 3.234 ... 2.25 0.6797</div><input id='attrs-9dcf2226-b151-46fe-9fb1-01a4c22afb18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dcf2226-b151-46fe-9fb1-01a4c22afb18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2293cd7-d3fa-44a1-9db2-435602f4ac8d' class='xr-var-data-in' type='checkbox'><label for='data-f2293cd7-d3fa-44a1-9db2-435602f4ac8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>celsius</dd><dt><span>long_name :</span></dt><dd>temperature measured inside the ApRES unit in a second location</dd></dl></div><div class='xr-var-data'><pre>array([5.19531e+00, 3.03906e+00, 3.23437e+00, 2.05469e+00, 2.84375e+00,\n",
+       "       3.82031e+00, 4.80469e+00, 3.82031e+00, 2.45312e+00, 3.23437e+00,\n",
+       "       3.23437e+00, 2.84375e+00, 2.64844e+00, 1.07813e+00, 1.46875e+00,\n",
+       "       3.03906e+00, 6.79687e-01, 2.45312e+00, 2.05469e+00, 1.66406e+00,\n",
+       "       1.07813e+00, 1.85937e+00, 2.05469e+00, 2.05469e+00, 8.82812e-01,\n",
+       "       1.27344e+00, 1.27344e+00, 4.84375e-01, 1.27344e+00, 2.25000e+00,\n",
+       "       3.03906e+00, 2.84375e+00, 2.84375e+00, 3.62500e+00, 3.62500e+00,\n",
+       "       4.02344e+00, 4.60937e+00, 3.42969e+00, 2.45312e+00, 1.85937e+00,\n",
+       "       2.25000e+00, 2.25000e+00, 2.64844e+00, 2.64844e+00, 3.82031e+00,\n",
+       "       5.19531e+00, 4.80469e+00, 3.82031e+00, 3.82031e+00, 5.11508e+02,\n",
+       "       3.03906e+00, 5.11508e+02, 3.03906e+00, 5.19531e+00, 5.19531e+00,\n",
+       "       2.45312e+00, 2.45312e+00, 4.02344e+00, 4.02344e+00, 4.60937e+00,\n",
+       "       4.02344e+00, 3.62500e+00, 3.62500e+00, 4.41406e+00, 3.62500e+00,\n",
+       "       5.00000e+00, 3.82031e+00, 4.21875e+00, 4.21875e+00, 7.16406e+00,\n",
+       "       6.96875e+00, 6.96875e+00, 5.78906e+00, 8.73437e+00, 6.76562e+00,\n",
+       "       7.35937e+00, 7.16406e+00, 6.96875e+00, 5.59375e+00, 5.59375e+00,\n",
+       "       3.62500e+00, 5.59375e+00, 3.82031e+00, 3.82031e+00, 4.41406e+00,\n",
+       "       4.02344e+00, 4.41406e+00, 4.02344e+00, 5.19531e+00, 5.19531e+00,\n",
+       "       3.82031e+00, 4.80469e+00, 2.25000e+00, 6.79687e-01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-02d73c6d-0b3e-47e5-9de2-23ce7de16cee' class='xr-section-summary-in' type='checkbox'  ><label for='section-02d73c6d-0b3e-47e5-9de2-23ce7de16cee' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8274d9b2-0057-4956-9050-8c05dab7e836' class='xr-index-data-in' type='checkbox'/><label for='index-8274d9b2-0057-4956-9050-8c05dab7e836' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-06-23 01:36:56&#x27;, &#x27;2022-06-23 01:51:57&#x27;,\n",
+       "               &#x27;2022-06-23 02:06:53&#x27;, &#x27;2022-06-23 02:21:52&#x27;,\n",
+       "               &#x27;2022-06-23 02:36:53&#x27;, &#x27;2022-06-23 02:51:52&#x27;,\n",
+       "               &#x27;2022-06-23 03:06:53&#x27;, &#x27;2022-06-23 03:21:59&#x27;,\n",
+       "               &#x27;2022-06-23 03:36:59&#x27;, &#x27;2022-06-23 03:51:53&#x27;,\n",
+       "               &#x27;2022-06-23 04:06:52&#x27;, &#x27;2022-06-23 04:21:52&#x27;,\n",
+       "               &#x27;2022-06-23 04:36:52&#x27;, &#x27;2022-06-23 04:51:52&#x27;,\n",
+       "               &#x27;2022-06-23 05:06:52&#x27;, &#x27;2022-06-23 05:21:53&#x27;,\n",
+       "               &#x27;2022-06-23 05:36:52&#x27;, &#x27;2022-06-23 05:51:52&#x27;,\n",
+       "               &#x27;2022-06-23 06:06:59&#x27;, &#x27;2022-06-23 06:21:53&#x27;,\n",
+       "               &#x27;2022-06-23 06:36:53&#x27;, &#x27;2022-06-23 06:52:02&#x27;,\n",
+       "               &#x27;2022-06-23 07:06:52&#x27;, &#x27;2022-06-23 07:21:51&#x27;,\n",
+       "               &#x27;2022-06-23 07:36:52&#x27;, &#x27;2022-06-23 07:51:52&#x27;,\n",
+       "               &#x27;2022-06-23 08:06:52&#x27;, &#x27;2022-06-23 08:21:52&#x27;,\n",
+       "               &#x27;2022-06-23 08:36:55&#x27;, &#x27;2022-06-23 08:52:03&#x27;,\n",
+       "               &#x27;2022-06-23 09:06:40&#x27;, &#x27;2022-06-23 09:21:40&#x27;,\n",
+       "               &#x27;2022-06-23 09:36:40&#x27;, &#x27;2022-06-23 09:51:40&#x27;,\n",
+       "               &#x27;2022-06-23 10:06:40&#x27;, &#x27;2022-06-23 10:21:41&#x27;,\n",
+       "               &#x27;2022-06-23 10:36:41&#x27;, &#x27;2022-06-23 10:51:40&#x27;,\n",
+       "               &#x27;2022-06-23 11:06:40&#x27;, &#x27;2022-06-23 11:21:40&#x27;,\n",
+       "               &#x27;2022-06-23 11:36:40&#x27;, &#x27;2022-06-23 11:51:40&#x27;,\n",
+       "               &#x27;2022-06-23 12:06:40&#x27;, &#x27;2022-06-23 12:21:40&#x27;,\n",
+       "               &#x27;2022-06-23 12:36:40&#x27;, &#x27;2022-06-23 12:51:41&#x27;,\n",
+       "               &#x27;2022-06-23 13:06:46&#x27;, &#x27;2022-06-23 13:21:53&#x27;,\n",
+       "               &#x27;2022-06-23 13:36:40&#x27;, &#x27;2022-06-23 13:51:41&#x27;,\n",
+       "               &#x27;2022-06-23 14:06:41&#x27;, &#x27;2022-06-23 14:21:41&#x27;,\n",
+       "               &#x27;2022-06-23 14:36:42&#x27;, &#x27;2022-06-23 14:51:41&#x27;,\n",
+       "               &#x27;2022-06-23 15:06:41&#x27;, &#x27;2022-06-23 15:21:41&#x27;,\n",
+       "               &#x27;2022-06-23 15:37:03&#x27;, &#x27;2022-06-23 15:51:40&#x27;,\n",
+       "               &#x27;2022-06-23 16:06:42&#x27;, &#x27;2022-06-23 16:21:40&#x27;,\n",
+       "               &#x27;2022-06-23 16:36:41&#x27;, &#x27;2022-06-23 16:51:41&#x27;,\n",
+       "               &#x27;2022-06-23 17:06:41&#x27;, &#x27;2022-06-23 17:21:41&#x27;,\n",
+       "               &#x27;2022-06-23 17:37:03&#x27;, &#x27;2022-06-23 17:51:39&#x27;,\n",
+       "               &#x27;2022-06-23 18:06:41&#x27;, &#x27;2022-06-23 18:21:46&#x27;,\n",
+       "               &#x27;2022-06-23 18:36:39&#x27;, &#x27;2022-06-23 18:51:40&#x27;,\n",
+       "               &#x27;2022-06-23 19:06:40&#x27;, &#x27;2022-06-23 19:21:40&#x27;,\n",
+       "               &#x27;2022-06-23 19:36:41&#x27;, &#x27;2022-06-23 19:51:41&#x27;,\n",
+       "               &#x27;2022-06-23 20:06:42&#x27;, &#x27;2022-06-23 20:21:51&#x27;,\n",
+       "               &#x27;2022-06-23 20:36:41&#x27;, &#x27;2022-06-23 20:51:41&#x27;,\n",
+       "               &#x27;2022-06-23 21:06:41&#x27;, &#x27;2022-06-23 21:21:41&#x27;,\n",
+       "               &#x27;2022-06-23 21:36:41&#x27;, &#x27;2022-06-23 21:51:41&#x27;,\n",
+       "               &#x27;2022-06-23 22:07:03&#x27;, &#x27;2022-06-23 22:21:40&#x27;,\n",
+       "               &#x27;2022-06-23 22:36:41&#x27;, &#x27;2022-06-23 22:51:39&#x27;,\n",
+       "               &#x27;2022-06-23 23:06:40&#x27;, &#x27;2022-06-23 23:21:40&#x27;,\n",
+       "               &#x27;2022-06-23 23:36:40&#x27;, &#x27;2022-06-23 23:51:41&#x27;,\n",
+       "               &#x27;2022-06-24 00:06:42&#x27;, &#x27;2022-06-24 00:21:42&#x27;,\n",
+       "               &#x27;2022-06-24 00:36:43&#x27;, &#x27;2022-06-24 00:51:51&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>chirp_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-996aa2e3-678f-4e8e-8105-13ef7ccad784' class='xr-index-data-in' type='checkbox'/><label for='index-996aa2e3-678f-4e8e-8105-13ef7ccad784' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([                   0.0,                2.5e-05,\n",
+       "                               5e-05,  7.500000000000001e-05,\n",
+       "                              0.0001,               0.000125,\n",
+       "              0.00015000000000000001,               0.000175,\n",
+       "                              0.0002, 0.00022500000000000002,\n",
+       "              ...\n",
+       "                  0.9997750000000001,                 0.9998,\n",
+       "                  0.9998250000000001,                0.99985,\n",
+       "                  0.9998750000000001,                 0.9999,\n",
+       "                  0.9999250000000001,                0.99995,\n",
+       "                  0.9999750000000001,                    1.0],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;chirp_time&#x27;, length=40001))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>profile_range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83f9a9c3-d421-45c6-9da5-77749a931fb9' class='xr-index-data-in' type='checkbox'/><label for='index-83f9a9c3-d421-45c6-9da5-77749a931fb9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([               0.0, 0.2102895577845166, 0.4205791155690332,\n",
+       "              0.6308686733535498, 0.8411582311380664,  1.051447788922583,\n",
+       "              1.2617373467070996, 1.4720269044916163, 1.6823164622761329,\n",
+       "              1.8926060200606494,\n",
+       "              ...\n",
+       "              1498.1028096568964, 1498.3130992146807, 1498.5233887724653,\n",
+       "              1498.7336783302499, 1498.9439678880344,  1499.154257445819,\n",
+       "              1499.3645470036033, 1499.5748365613879, 1499.7851261191724,\n",
+       "               1499.995415676957],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;profile_range&#x27;, length=7134))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>chirp_num</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51bfa0bc-fb6d-4d23-8b69-05f6d5a656d9' class='xr-index-data-in' type='checkbox'/><label for='index-51bfa0bc-fb6d-4d23-8b69-05f6d5a656d9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
+       "            19],\n",
+       "           dtype=&#x27;int64&#x27;, name=&#x27;chirp_num&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-749f95fc-ae11-4695-8900-57b542cbd6dd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-749f95fc-ae11-4695-8900-57b542cbd6dd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:          (time: 94, chirp_time: 40001, chirp_num: 20,\n",
+       "                      attenuator_setting_pair: 2, profile_range: 7134)\n",
+       "Coordinates:\n",
+       "  * time             (time) datetime64[ns] 2022-06-23T01:36:56 ... 2022-06-24...\n",
+       "  * chirp_time       (chirp_time) float64 0.0 2.5e-05 5e-05 ... 1.0 1.0 1.0\n",
+       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.5e+03 1.5e+03\n",
+       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
+       "    filename         (time) <U83 'ldeo-glaciology/GL_apres_2022/A101/CardA/DI...\n",
+       "    burst_number     (time) int64 0 1 2 3 4 5 6 7 8 ... 86 87 88 89 90 91 92 93\n",
+       "    AFGain           (attenuator_setting_pair) int64 -4 -14\n",
+       "    attenuator       (attenuator_setting_pair) float64 5.0 5.0\n",
+       "    orientation      (time) <U7 'unknown' 'unknown' ... 'unknown' 'unknown'\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 ...\n",
+       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 ...\n",
+       "    latitude         (time) float64 68.71 68.71 68.71 ... 68.71 68.71 68.71\n",
+       "    longitude        (time) float64 -49.55 -49.55 -49.55 ... -49.55 -49.55\n",
+       "    battery_voltage  (time) float64 13.89 13.87 13.94 ... 13.85 13.84 13.84\n",
+       "    temperature_1    (time) float64 2.031 0.4609 508.9 ... 1.641 0.2656 510.9\n",
+       "    temperature_2    (time) float64 5.195 3.039 3.234 ... 4.805 2.25 0.6797"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = xa.load.generate_xarray(directory='gs://ldeo-glaciology/GL_apres_2022/A101', \n",
+    "                             file_numbers_to_process=[30], \n",
+    "                             remote_load=True)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Here's a screenshot of the output of the cell above (in case this takes a long time to run):\n",
+    "\n",
+    "![Screenshot of output of cell above](../img/xapres_xarray_screenshot.png)\n",
+    "\n",
+    "The resulting xarray has five dimensions:\n",
+    "- `attenuator_setting_pair`: this is an index referring to the attenuator setting.\n",
+    "- `time`: the time of aquisistion of each burst. \n",
+    "- `chirp_time`: the time since the beginning of each chirp of each sample within a chirp. As each chirp lasts 1 second, chirp_time varies from 0 to 1 second. There are 40001 samples by default, so the length of `chirp_time` is 40001. \n",
+    "- `chirp_num`: this is number of chirps in each burst using each attenuator_setting_pair. So if the ApRES unit was configured to use 2 attenuator setting pairs and produce 20 chirps for each in each burst, the total number of chirps in a burst will be 2x20 = 40. \n",
+    "- `profile_range`: the range to each sample in the profiles computed from each chirp using the fft. \n",
+    "\n",
+    "The two most important data variables are:\n",
+    "- `chirp`: the voltage measured during each chirp after the received signal is mixed with the simultaneously transmitted signal and low-pass filtered. This is an array of real values. \n",
+    "- `profile`: the profiles resulting from an fft of the chirps. This is an array of complex values.\n",
+    "  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.632827896"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.nbytes/1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "We can now use xarray to slicing, simple processing and plotting of the data, which would be time consuming and error prine using the raw data. For example, in one line we can select one of the two attenuator setting, compute decibels from the profile data, take the mean of the chirps in each burst, and plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds.profile.isel(attenuator_setting_pair = 0).dB().mean(dim=['chirp_num']).plot(x='time', yincrease=False, size=(5),aspect=3);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "### Write to a zarr directory\n",
+    "Finally, we are on to the stage where we are going to write a zarr directory. The motivation for doing this the speed with which we will be able to access the data: it took 2 minutes to load and process the very small subset of the data above. If we want to do calculations using more of the dataset, it would be impractical to run `xa.load.generate_xarray` each time. \n",
+    "\n",
+    "For demonstration, we can save a subset of the dataset loaded above to a zarr store in the cryocloud s3 bucket as follows. Load two useful libraries for interacting with cloud storage:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import s3fs\n",
+    "import fsspec\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Next we find the url of the temporary directory assigned to us by cryocloud based on our github user names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'s3://nasa-cryo-scratch/jkingslake'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scratch = os.environ['SCRATCH_BUCKET']\n",
+    "scratch "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "[s3fs](https://s3fs.readthedocs.io/en/latest/api.html#) provides tools to list and delete files and other operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['nasa-cryo-scratch/jkingslake/temp1']"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s3 = s3fs.S3FileSystem()\n",
+    "s3.ls(scratch)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Let's create a subest of the dataset `ds` we created above from `dat` files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ds_small = ds.isel(time=slice(0,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Then we can write the dataset to a zarr store:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.backends.zarr.ZarrStore at 0x7f6495772ce0>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filename = f\"{scratch}/temp2\"\n",
+    "mapper = fsspec.get_mapper(filename)\n",
+    "ds_small.to_zarr(mapper, consolidated=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Finally, we reload the data and compare it to `ds` to ensure we accurately wrote it to and read it from the bucket: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:          (attenuator_setting_pair: 2, time: 4, chirp_time: 40001,\n",
+       "                      chirp_num: 20, profile_range: 7134)\n",
+       "Coordinates:\n",
+       "    AFGain           (attenuator_setting_pair) int64 dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;\n",
+       "    attenuator       (attenuator_setting_pair) float64 dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;\n",
+       "    burst_number     (time) int64 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
+       "  * chirp_time       (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:01\n",
+       "    filename         (time) &lt;U83 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "    orientation      (time) &lt;U7 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.5e+03 1.5e+03\n",
+       "  * time             (time) datetime64[ns] 2022-06-23T01:36:56 ... 2022-06-23...\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    battery_voltage  (time) float64 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 dask.array&lt;chunksize=(1, 10001, 10, 1), meta=np.ndarray&gt;\n",
+       "    latitude         (time) float64 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "    longitude        (time) float64 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 dask.array&lt;chunksize=(1, 3567, 10, 1), meta=np.ndarray&gt;\n",
+       "    temperature_1    (time) float64 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;\n",
+       "    temperature_2    (time) float64 dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f019f436-ded1-4273-afef-9da7ac3d69e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f019f436-ded1-4273-afef-9da7ac3d69e2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>attenuator_setting_pair</span>: 2</li><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>chirp_time</span>: 40001</li><li><span class='xr-has-index'>chirp_num</span>: 20</li><li><span class='xr-has-index'>profile_range</span>: 7134</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-633a1372-f0d3-467d-85bf-dd1a081db860' class='xr-section-summary-in' type='checkbox'  checked><label for='section-633a1372-f0d3-467d-85bf-dd1a081db860' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AFGain</span></div><div class='xr-var-dims'>(attenuator_setting_pair)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;</div><input id='attrs-0e6d2354-4fc3-4f0f-95a2-4a451d11e38e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e6d2354-4fc3-4f0f-95a2-4a451d11e38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c77e7e13-7c03-425e-b611-2c2e89d15d4d' class='xr-var-data-in' type='checkbox'><label for='data-c77e7e13-7c03-425e-b611-2c2e89d15d4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>audio-frequency gain control setting</dd><dt><span>units :</span></dt><dd>decibels</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuator</span></div><div class='xr-var-dims'>(attenuator_setting_pair)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;</div><input id='attrs-b8e46f2a-fed7-4928-b394-fe13235de8ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8e46f2a-fed7-4928-b394-fe13235de8ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c91ff94d-ab8f-4449-93da-a1a51be626b2' class='xr-var-data-in' type='checkbox'><label for='data-c91ff94d-ab8f-4449-93da-a1a51be626b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>radio-frequency attenuator setting</dd><dt><span>units :</span></dt><dd>decibels</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2,) </td>\n",
+       "                        <td> (2,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,30.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>burst_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-32642a33-4cf1-4853-a40a-12a76e2c1795' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32642a33-4cf1-4853-a40a-12a76e2c1795' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcc090b7-c560-4ecc-b912-bc9a4dfca1ea' class='xr-var-data-in' type='checkbox'><label for='data-fcc090b7-c560-4ecc-b912-bc9a4dfca1ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the number of each burst within each file</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 32 B </td>\n",
+       "                        <td> 32 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.89879552186203 0.0,42.89879552186203\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"140.000000\" y=\"21.449398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.449398)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp_num</span></div><div class='xr-var-dims'>(chirp_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 14 15 16 17 18 19</div><input id='attrs-db7e1072-40e5-44ec-a916-de917310fca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db7e1072-40e5-44ec-a916-de917310fca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c6db596-7026-4217-b7fb-62fabbaa2cbe' class='xr-var-data-in' type='checkbox'><label for='data-1c6db596-7026-4217-b7fb-62fabbaa2cbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the number of each chirp within each burst</dd><dt><span>long_name :</span></dt><dd>chirp number</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp_time</span></div><div class='xr-var-dims'>(chirp_time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 ... 00:00:01</div><input id='attrs-652deeda-be19-41e5-b699-56016fb33081' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-652deeda-be19-41e5-b699-56016fb33081' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dba1299-9530-47ec-bb04-10954fe83eae' class='xr-var-data-in' type='checkbox'><label for='data-6dba1299-9530-47ec-bb04-10954fe83eae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of samples during chirps</dd><dt><span>name :</span></dt><dd>time of samples during chirps</dd></dl></div><div class='xr-var-data'><pre>array([         0,      25000,      50000, ...,  999950000,  999975000,\n",
+       "       1000000000], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filename</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U83</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-cf2887a3-48bb-409b-8f00-089cbd772b85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf2887a3-48bb-409b-8f00-089cbd772b85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a09e2814-a8ed-48f2-9e0d-9ba8be7e7ee0' class='xr-var-data-in' type='checkbox'><label for='data-a09e2814-a8ed-48f2-9e0d-9ba8be7e7ee0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the name of the file that contains each burst</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.30 kiB </td>\n",
+       "                        <td> 1.30 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U83 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.89879552186203 0.0,42.89879552186203\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"140.000000\" y=\"21.449398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.449398)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-531aa947-2075-46b3-8519-d17feb9d74d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-531aa947-2075-46b3-8519-d17feb9d74d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a8393b3-4ac2-4adb-b7f2-b8082061140d' class='xr-var-data-in' type='checkbox'><label for='data-2a8393b3-4ac2-4adb-b7f2-b8082061140d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>HH, HV, VH, or VV antenna orientation as described in Ersahadi et al 2022 doi:10.5194/tc-16-1719-2022</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 112 B </td>\n",
+       "                        <td> 112 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U7 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.89879552186203 0.0,42.89879552186203\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"140.000000\" y=\"21.449398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.449398)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>profile_range</span></div><div class='xr-var-dims'>(profile_range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.2103 ... 1.5e+03 1.5e+03</div><input id='attrs-46b4f1f2-4d3e-4ef5-bbc2-d7e5e1464df8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46b4f1f2-4d3e-4ef5-bbc2-d7e5e1464df8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dc214d8-75de-484f-83d8-0161403bed65' class='xr-var-data-in' type='checkbox'><label for='data-4dc214d8-75de-484f-83d8-0161403bed65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 2.102896e-01, 4.205791e-01, ..., 1.499575e+03,\n",
+       "       1.499785e+03, 1.499995e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-06-23T01:36:56 ... 2022-06-...</div><input id='attrs-d8d43825-3752-47eb-8c84-d8a5aee678a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8d43825-3752-47eb-8c84-d8a5aee678a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57427435-b890-44da-b807-1179953d4cfe' class='xr-var-data-in' type='checkbox'><label for='data-57427435-b890-44da-b807-1179953d4cfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of burst</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-06-23T01:36:56.000000000&#x27;, &#x27;2022-06-23T01:51:57.000000000&#x27;,\n",
+       "       &#x27;2022-06-23T02:06:53.000000000&#x27;, &#x27;2022-06-23T02:21:52.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2124625d-71cb-41f7-bd5c-73ed71e22ba5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2124625d-71cb-41f7-bd5c-73ed71e22ba5' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>battery_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-7e3d719f-74d8-4374-addf-755e471402d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e3d719f-74d8-4374-addf-755e471402d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50d2d706-cd41-4ce7-998b-886481815ce5' class='xr-var-data-in' type='checkbox'><label for='data-50d2d706-cd41-4ce7-998b-886481815ce5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>battery voltage</dd><dt><span>units :</span></dt><dd>volts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                        <td> 32 B </td>\n",
+       "                        <td> 32 B </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chirp</span></div><div class='xr-var-dims'>(time, chirp_time, chirp_num, attenuator_setting_pair)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 10001, 10, 1), meta=np.ndarray&gt;</div><input id='attrs-eac430b5-ea08-4efc-acbd-69f78405b108' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eac430b5-ea08-4efc-acbd-69f78405b108' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b57dc55-7645-45d1-b053-7379f1caf39a' class='xr-var-data-in' type='checkbox'><label for='data-6b57dc55-7645-45d1-b053-7379f1caf39a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>voltage from the analog-to-digital converter after the received signal has been mixed with the transmitted signal and the result has been filtered to leave only the low frequency compponent corresponding to the differences in the frequencies of the Tx and Rx signals</dd><dt><span>long_name :</span></dt><dd>de-ramped chirp</dd><dt><span>units :</span></dt><dd>volts</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 48.83 MiB </td>\n",
+       "                        <td> 781.33 kiB </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-2d39e1d1-3220-4d6f-9db0-b2239f8cd626' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d39e1d1-3220-4d6f-9db0-b2239f8cd626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d10c83c8-246a-44c0-bc5b-4528d7a7def6' class='xr-var-data-in' type='checkbox'><label for='data-d10c83c8-246a-44c0-bc5b-4528d7a7def6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of burst</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 32 B </td>\n",
+       "                        <td> 32 B </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-cbbb648d-0bbe-4a84-ac38-3d57b5ffb7fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbbb648d-0bbe-4a84-ac38-3d57b5ffb7fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-772a1f88-4439-467b-99a2-cc7d12a26d5e' class='xr-var-data-in' type='checkbox'><label for='data-772a1f88-4439-467b-99a2-cc7d12a26d5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of burst</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 32 B </td>\n",
+       "                        <td> 32 B </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>profile</span></div><div class='xr-var-dims'>(time, profile_range, chirp_num, attenuator_setting_pair)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3567, 10, 1), meta=np.ndarray&gt;</div><input id='attrs-3bbe24ed-509e-4f61-800c-f88103b218c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bbe24ed-509e-4f61-800c-f88103b218c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2426a253-1a3d-4fde-8155-0b999f58145c' class='xr-var-data-in' type='checkbox'><label for='data-2426a253-1a3d-4fde-8155-0b999f58145c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>complex profile computed from the fourier transform of the de-ramped chirp</dd><dt><span>long_name :</span></dt><dd>profile</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 17.42 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "  <line x1=\"191\" y1=\"70\" x2=\"191\" y2=\"96\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"178.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
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+       "  <text x=\"120.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,80.706734)\">7134</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-5cd35cf5-297f-4c56-aa40-a76de13160eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cd35cf5-297f-4c56-aa40-a76de13160eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaa60710-391f-455d-a6d4-daa79e6e1873' class='xr-var-data-in' type='checkbox'><label for='data-eaa60710-391f-455d-a6d4-daa79e6e1873' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>temperature measured inside the ApRES unit in one location</dd><dt><span>units :</span></dt><dd>celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 32 B </td>\n",
+       "                        <td> 32 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.89879552186203 0.0,42.89879552186203\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4,), meta=np.ndarray&gt;</div><input id='attrs-2cba981e-e78b-452c-869f-b89790ab8b50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cba981e-e78b-452c-869f-b89790ab8b50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-725c0df1-d678-4ef3-b5fe-be60b06fbd40' class='xr-var-data-in' type='checkbox'><label for='data-725c0df1-d678-4ef3-b5fe-be60b06fbd40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>temperature measured inside the ApRES unit in a second location</dd><dt><span>units :</span></dt><dd>celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 32 B </td>\n",
+       "                        <td> 32 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4,) </td>\n",
+       "                        <td> (4,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"140.000000\" y=\"21.449398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.449398)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-407a7361-3319-40dd-a593-189fb81c00b9' class='xr-section-summary-in' type='checkbox'  ><label for='section-407a7361-3319-40dd-a593-189fb81c00b9' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chirp_num</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1b9fb8f5-771e-48e1-90ca-4c146283d945' class='xr-index-data-in' type='checkbox'/><label for='index-1b9fb8f5-771e-48e1-90ca-4c146283d945' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
+       "            19],\n",
+       "           dtype=&#x27;int64&#x27;, name=&#x27;chirp_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>chirp_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-92ed317c-6ba6-42ff-8a35-fe61ce656211' class='xr-index-data-in' type='checkbox'/><label for='index-92ed317c-6ba6-42ff-8a35-fe61ce656211' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(TimedeltaIndex([       &#x27;0 days 00:00:00&#x27;, &#x27;0 days 00:00:00.000025&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000050&#x27;, &#x27;0 days 00:00:00.000075&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000100&#x27;, &#x27;0 days 00:00:00.000125&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000150&#x27;, &#x27;0 days 00:00:00.000175&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000200&#x27;, &#x27;0 days 00:00:00.000225&#x27;,\n",
+       "                ...\n",
+       "                &#x27;0 days 00:00:00.999775&#x27;, &#x27;0 days 00:00:00.999800&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999825&#x27;, &#x27;0 days 00:00:00.999850&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999875&#x27;, &#x27;0 days 00:00:00.999900&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999925&#x27;, &#x27;0 days 00:00:00.999950&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999975&#x27;,        &#x27;0 days 00:00:01&#x27;],\n",
+       "               dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;chirp_time&#x27;, length=40001, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>profile_range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e5fd647a-6f24-4e10-8278-65404ff351f5' class='xr-index-data-in' type='checkbox'/><label for='index-e5fd647a-6f24-4e10-8278-65404ff351f5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([               0.0, 0.2102895577845166, 0.4205791155690332,\n",
+       "              0.6308686733535498, 0.8411582311380664,  1.051447788922583,\n",
+       "              1.2617373467070996, 1.4720269044916163, 1.6823164622761329,\n",
+       "              1.8926060200606494,\n",
+       "              ...\n",
+       "              1498.1028096568964, 1498.3130992146807, 1498.5233887724653,\n",
+       "              1498.7336783302499, 1498.9439678880344,  1499.154257445819,\n",
+       "              1499.3645470036033, 1499.5748365613879, 1499.7851261191724,\n",
+       "               1499.995415676957],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;profile_range&#x27;, length=7134))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b49ccb06-7f74-432c-9977-3ae21c98669a' class='xr-index-data-in' type='checkbox'/><label for='index-b49ccb06-7f74-432c-9977-3ae21c98669a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-06-23 01:36:56&#x27;, &#x27;2022-06-23 01:51:57&#x27;,\n",
+       "               &#x27;2022-06-23 02:06:53&#x27;, &#x27;2022-06-23 02:21:52&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73c3dad2-f48c-4eef-8784-86f0fcea831a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-73c3dad2-f48c-4eef-8784-86f0fcea831a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:          (attenuator_setting_pair: 2, time: 4, chirp_time: 40001,\n",
+       "                      chirp_num: 20, profile_range: 7134)\n",
+       "Coordinates:\n",
+       "    AFGain           (attenuator_setting_pair) int64 dask.array<chunksize=(2,), meta=np.ndarray>\n",
+       "    attenuator       (attenuator_setting_pair) float64 dask.array<chunksize=(2,), meta=np.ndarray>\n",
+       "    burst_number     (time) int64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
+       "  * chirp_time       (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:01\n",
+       "    filename         (time) <U83 dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "    orientation      (time) <U7 dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.5e+03 1.5e+03\n",
+       "  * time             (time) datetime64[ns] 2022-06-23T01:36:56 ... 2022-06-23...\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    battery_voltage  (time) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 dask.array<chunksize=(1, 10001, 10, 1), meta=np.ndarray>\n",
+       "    latitude         (time) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "    longitude        (time) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 dask.array<chunksize=(1, 3567, 10, 1), meta=np.ndarray>\n",
+       "    temperature_1    (time) float64 dask.array<chunksize=(4,), meta=np.ndarray>\n",
+       "    temperature_2    (time) float64 dask.array<chunksize=(4,), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_reloaded = xr.open_dataset(filename, engine='zarr', consolidated=False, chunks={}) \n",
+    "ds_reloaded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_reloaded.profile.identical(ds_small.profile)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "If we want to delete the zarr store from the scratch bucket use:\n",
+    "```\n",
+    "s3.rm(filename, recursive=True)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "This worked fine, but when we attempt to apply this to a whole season of data from this ApRES unit, we run encouner an issue. The step above involved loading the data in the `dat` file into memory; we dont have a way of lazily loading from `dat` files. The issue is that we cannot whole season of data into memory. To estimate the total size of these data, we can check the length of the list of `dat` files we created earlier ('dat_file_list') and multiply by the size of the xarray dataset we generated from one `dat` file above (`ds`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "622.107428376 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{len(dat_file_list) * ds.nbytes/1e9} GB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "Our way around this issue was to first save the xarray loaded from each `dat` file to a single zarr store, then load all the individual zarr stores lazily into a single xarray dataset and save this to one big zarr store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "**Save each `dat` to a single zarr store**\n",
+    "\n",
+    "We used [this](https://github.com/ldeo-glaciology/xapres/blob/master/notebooks/to_zarr/to_individual_zarr.ipynb) notebook to save each `dat` file's data to an individual zarr store. The key cell is recreated below. This code will not work here, but we ran it on infrastructure very similar to cryocloud on a dask cluster. \n",
+    "\n",
+    "```\n",
+    "def xapres_to_zarr(site):  \n",
+    "    directory=f\"gs://ldeo-glaciology/GL_apres_2022/{site}\"\n",
+    "    \n",
+    "    fd = xa.load.from_dats(max_range=1400)\n",
+    "    dat_file_list = fd.list_files(directory=directory, \n",
+    "                remote_load = True)\n",
+    "\n",
+    "    with open('../../../secrets/ldeo-glaciology-bc97b12df06b.json') as token_file:\n",
+    "        token = json.load(token_file)\n",
+    "    \n",
+    "    num = 0\n",
+    "    for file in tqdm_slack(dat_file_list, token=slack_token, channel='python-timer'): \n",
+    "        \n",
+    "        fd = xa.load.from_dats(max_range=1400)\n",
+    "        fd.load_all(directory, \n",
+    "                remote_load = True,\n",
+    "                file_names_to_process=[file])\n",
+    "        \n",
+    "        filename = f'gs://ldeo-glaciology/apres/greenland/2022/{site}/individual_zarrs_2/dat_{num}' \n",
+    "        mapper = fsspec.get_mapper(filename, mode='w', token=token)\n",
+    "\n",
+    "        ds = fd.data\n",
+    "        #ds = fd.data.chunk({'time':1,'attenuator_setting_pair':-1, 'chirp_num':-1, 'profile_range': -1, 'chirp_time':-1  })\n",
+    "        \n",
+    "        ds.attrs = []\n",
+    "        ds.to_zarr(mapper, consolidated=False)\n",
+    "        \n",
+    "        del xa\n",
+    "        del ds\n",
+    "        num+=1\n",
+    "        \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "**Lazily load every all zarr store and write to one big zarr**\n",
+    "\n",
+    "The next step is to load each zarr file lazily into a single xarray dataset and save this to a single zarr store. We used [this](https://github.com/ldeo-glaciology/xapres/blob/master/notebooks/to_zarr/write_big_zarrs.ipynb) notebook to do this. The key code is as follows.\n",
+    "\n",
+    "```\n",
+    "def zarrs_to_onezarr(site):\n",
+    "    ds = xr.open_mfdataset(f'gs://ldeo-glaciology/apres/greenland/2022/{site}/individual_zarrs_2/dat_*',\n",
+    "                               chunks = {}, \n",
+    "                               engine = 'zarr', \n",
+    "                               consolidated = False, \n",
+    "                               parallel = True)\n",
+    "    ds['attenuator'] = ds.attenuator[500]\n",
+    "    ds['AFGain'] = ds.AFGain[500]\n",
+    "        \n",
+    "    for var in ds:\n",
+    "        del ds[var].encoding['chunks']\n",
+    "\n",
+    "    profile_stacked = ds.profile.mean(dim='chirp_num')\n",
+    "    ds_stacked = ds.assign({'profile_stacked':profile_stacked})\n",
+    "    ds_stacked_rechunked = ds_stacked.chunk({'time':20})\n",
+    "    \n",
+    "    #encoding = {i: {\"dtype\": \"float64\"} for i in ds_stacked_rechunked.data_vars}\n",
+    "    encoding = {\n",
+    "        'time': {\n",
+    "            'units': 'seconds since 1970-01-01'}}\n",
+    "\n",
+    "    filename = f'gs://ldeo-glaciology/apres/greenland/2022/single_zarrs_noencode/{site}' \n",
+    "    mapper = fsspec.get_mapper(filename, mode='w', token=token) \n",
+    "    with performance_report(f'ds_stacked_rechunked_{site}.html'):\n",
+    "        ds_stacked_rechunked.to_zarr(mapper, consolidated=True, safe_chunks=False, encoding=encoding)\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "The method described above works in principle, but in practice we found that the process continued for a very long time, gradually filling the notebook memory, until the kernal finally died. \n",
+    "\n",
+    "The issue was chunking!\n",
+    "\n",
+    "Let's take a look at the chunk sizes of the zarr stores we created above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chunk sizes along the time dimension = (1,)\n",
+      "Chunk sizes along the time dimension = (3, 3, 3, 3)\n",
+      "Chunk sizes along the time dimension = (1, 1, 1)\n",
+      "Chunk sizes along the time dimension = (10, 10, 10, 10)\n",
+      "Chunk sizes along the time dimension = (1,)\n",
+      "Chunk sizes along the time dimension = (12, 12, 12, 12, 12, 12, 12, 10)\n",
+      "Chunk sizes along the time dimension = (12, 12, 12, 12, 12, 12, 12, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "datasets = []\n",
+    "\n",
+    "names = [f'gs://ldeo-glaciology/apres/greenland/2022/A103/individual_zarrs_2/dat_{num}' for num in range(0,127)]\n",
+    "\n",
+    "for name in names[0:7]:\n",
+    "    ds = xr.open_dataset(name,\n",
+    "                         chunks = {}, \n",
+    "                         engine = 'zarr', \n",
+    "                         consolidated = False)\n",
+    "    dim_to_print = 'time'\n",
+    "    dim_num = ds.profile.get_axis_num(dim_to_print)\n",
+    "    print(f\"Chunk sizes along the {dim_to_print} dimension = {ds.profile.chunks[dim_num]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "The `.chunks` attribute contains a list (actually a tuple) with each element corresponding to a different dimension and containing a list of the chunk sizes. In the cell above we extracted the list corresponding to the time dimension. \n",
+    "\n",
+    "The chunk sizes vary within each xarray and between xarrays. To see the implications of this, we will lazily load a few of the individual zarr stores into a single xarray dataset, then take a look at the resulting chunk sizes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       ".xr-var-attrs,\n",
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+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
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+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:          (time: 245, attenuator_setting_pair: 2, chirp_time: 40001,\n",
+       "                      chirp_num: 20, profile_range: 6658)\n",
+       "Coordinates:\n",
+       "    AFGain           (time, attenuator_setting_pair) int64 dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
+       "    attenuator       (time, attenuator_setting_pair) float64 dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
+       "    burst_number     (time) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
+       "  * chirp_time       (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:01\n",
+       "    filename         (time) &lt;U83 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.4e+03 1.4e+03\n",
+       "  * time             (time) datetime64[ns] 2022-02-17T16:37:49 ... 2022-05-28...\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    battery_voltage  (time) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 dask.array&lt;chunksize=(1, 5001, 5, 1), meta=np.ndarray&gt;\n",
+       "    latitude         (time) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    longitude        (time) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 dask.array&lt;chunksize=(1, 1665, 5, 1), meta=np.ndarray&gt;\n",
+       "    temperature_1    (time) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    temperature_2    (time) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d9ddfd55-0ef0-4851-b35d-655901754ad4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d9ddfd55-0ef0-4851-b35d-655901754ad4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 245</li><li><span>attenuator_setting_pair</span>: 2</li><li><span class='xr-has-index'>chirp_time</span>: 40001</li><li><span class='xr-has-index'>chirp_num</span>: 20</li><li><span class='xr-has-index'>profile_range</span>: 6658</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-93bf7a36-2c84-40c6-8d68-04f628580781' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93bf7a36-2c84-40c6-8d68-04f628580781' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AFGain</span></div><div class='xr-var-dims'>(time, attenuator_setting_pair)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-e066ebf4-da71-4824-a7ae-cca92d85c9cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e066ebf4-da71-4824-a7ae-cca92d85c9cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d918a496-b5f6-4781-9c10-611168041979' class='xr-var-data-in' type='checkbox'><label for='data-d918a496-b5f6-4781-9c10-611168041979' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>audio-frequency gain control setting</dd><dt><span>units :</span></dt><dd>decibels</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.83 kiB </td>\n",
+       "                        <td> 1.47 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (245, 2) </td>\n",
+       "                        <td> (94, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 14 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"25\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"25\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"25\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"73\" x2=\"25\" y2=\"73\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">245</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuator</span></div><div class='xr-var-dims'>(time, attenuator_setting_pair)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-7720c3be-2273-4cc2-877d-235084cdcd0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7720c3be-2273-4cc2-877d-235084cdcd0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afa17b11-d5ea-4181-a026-a1c6e42596a6' class='xr-var-data-in' type='checkbox'><label for='data-afa17b11-d5ea-4181-a026-a1c6e42596a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>radio-frequency attenuator setting</dd><dt><span>units :</span></dt><dd>decibels</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.83 kiB </td>\n",
+       "                        <td> 1.47 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (245, 2) </td>\n",
+       "                        <td> (94, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 14 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"25\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"25\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"25\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"73\" x2=\"25\" y2=\"73\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">245</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>burst_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-52e72af7-37e1-4ea0-9bbc-220257d4904c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52e72af7-37e1-4ea0-9bbc-220257d4904c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6ca6112-67fc-4819-b61d-ce5a84a9cd1b' class='xr-var-data-in' type='checkbox'><label for='data-b6ca6112-67fc-4819-b61d-ce5a84a9cd1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the number of each burst within each file</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.91 kiB </td>\n",
+       "                        <td> 752 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (245,) </td>\n",
+       "                        <td> (94,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 15 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" />\n",
+       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
+       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
+       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n",
+       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"25\" />\n",
+       "  <line x1=\"73\" y1=\"0\" x2=\"73\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >245</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp_num</span></div><div class='xr-var-dims'>(chirp_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 14 15 16 17 18 19</div><input id='attrs-b24837e1-f7b3-4f65-a408-ee830352d7ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b24837e1-f7b3-4f65-a408-ee830352d7ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d609da4c-5f76-48a4-a48a-c44be9c5909c' class='xr-var-data-in' type='checkbox'><label for='data-d609da4c-5f76-48a4-a48a-c44be9c5909c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the number of each chirp within each burst</dd><dt><span>long_name :</span></dt><dd>chirp number</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp_time</span></div><div class='xr-var-dims'>(chirp_time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 ... 00:00:01</div><input id='attrs-4eae8c4b-862f-444a-afd3-cfd8c4817e3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eae8c4b-862f-444a-afd3-cfd8c4817e3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77309f49-def5-4bd6-a33d-2b41a14a8d5d' class='xr-var-data-in' type='checkbox'><label for='data-77309f49-def5-4bd6-a33d-2b41a14a8d5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of samples during chirps</dd><dt><span>name :</span></dt><dd>time of samples during chirps</dd></dl></div><div class='xr-var-data'><pre>array([         0,      25000,      50000, ...,  999950000,  999975000,\n",
+       "       1000000000], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filename</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U83</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-410c2cd8-c001-441d-a671-f386653ebbb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-410c2cd8-c001-441d-a671-f386653ebbb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-130d1ec3-c790-4080-881b-757fbb97c4bd' class='xr-var-data-in' type='checkbox'><label for='data-130d1ec3-c790-4080-881b-757fbb97c4bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the name of the file that contains each burst</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 79.43 kiB </td>\n",
+       "                        <td> 30.48 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (245,) </td>\n",
+       "                        <td> (94,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 15 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U83 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>profile_range</span></div><div class='xr-var-dims'>(profile_range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.2103 ... 1.4e+03 1.4e+03</div><input id='attrs-3f945878-ee80-4528-8b6d-dcbb286e03ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f945878-ee80-4528-8b6d-dcbb286e03ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69f48577-c76c-4179-b76f-6bb799c86c55' class='xr-var-data-in' type='checkbox'><label for='data-69f48577-c76c-4179-b76f-6bb799c86c55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 2.102896e-01, 4.205791e-01, ..., 1.399477e+03,\n",
+       "       1.399687e+03, 1.399898e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-02-17T16:37:49 ... 2022-05-...</div><input id='attrs-b9dd500e-ef11-4725-b1b7-0da9c6d30010' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9dd500e-ef11-4725-b1b7-0da9c6d30010' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd3e768d-e37b-42e7-8d97-4b5c1d05eb51' class='xr-var-data-in' type='checkbox'><label for='data-cd3e768d-e37b-42e7-8d97-4b5c1d05eb51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of burst</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-17T16:37:49.000000000&#x27;, &#x27;2022-02-17T16:40:10.000000000&#x27;,\n",
+       "       &#x27;2022-02-17T16:55:04.000000000&#x27;, ..., &#x27;2022-05-28T11:43:06.000000000&#x27;,\n",
+       "       &#x27;2022-05-28T11:58:08.000000000&#x27;, &#x27;2022-05-28T12:13:08.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1bdb05a6-33d1-4c80-bcf2-09fa00d841bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1bdb05a6-33d1-4c80-bcf2-09fa00d841bd' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>battery_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-2f2e5d12-e868-4344-97b9-59a5723cdc73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f2e5d12-e868-4344-97b9-59a5723cdc73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af2f22f3-080f-42b1-a6c6-c602461b4e99' class='xr-var-data-in' type='checkbox'><label for='data-af2f22f3-080f-42b1-a6c6-c602461b4e99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>battery voltage</dd><dt><span>units :</span></dt><dd>volts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.91 kiB </td>\n",
+       "                        <td> 752 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (245,) </td>\n",
+       "                        <td> (94,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 15 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chirp</span></div><div class='xr-var-dims'>(time, chirp_time, chirp_num, attenuator_setting_pair)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5001, 5, 1), meta=np.ndarray&gt;</div><input id='attrs-e88d03b8-75f9-4cbc-8075-c5b5a134d5e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e88d03b8-75f9-4cbc-8075-c5b5a134d5e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11fdb022-ad9d-4d7e-a2ee-a251f83085ae' class='xr-var-data-in' type='checkbox'><label for='data-11fdb022-ad9d-4d7e-a2ee-a251f83085ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>voltage from the analog-to-digital converter after the received signal has been mixed with the transmitted signal and the result has been filtered to leave only the low frequency compponent corresponding to the differences in the frequencies of the Tx and Rx signals</dd><dt><span>long_name :</span></dt><dd>de-ramped chirp</dd><dt><span>units :</span></dt><dd>volts</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.92 GiB </td>\n",
+       "                        <td> 2.29 MiB </td>\n",
+       "                    </tr>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2904 chunks in 22 graph layers </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-066214d6-f73d-4834-9c85-ac53a0e7c427' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-066214d6-f73d-4834-9c85-ac53a0e7c427' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beeb05a0-d06c-4033-a9e6-ef2befd5b7fc' class='xr-var-data-in' type='checkbox'><label for='data-beeb05a0-d06c-4033-a9e6-ef2befd5b7fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of burst</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.91 kiB </td>\n",
+       "                        <td> 752 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (245,) </td>\n",
+       "                        <td> (94,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 15 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-b5c67d31-ca29-4eab-a7e1-1b0642c04b22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5c67d31-ca29-4eab-a7e1-1b0642c04b22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acad2289-2237-43d9-b47d-c44c4cb4e0c3' class='xr-var-data-in' type='checkbox'><label for='data-acad2289-2237-43d9-b47d-c44c4cb4e0c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of burst</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.91 kiB </td>\n",
+       "                        <td> 752 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (245,) </td>\n",
+       "                        <td> (94,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 15 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>profile</span></div><div class='xr-var-dims'>(time, profile_range, chirp_num, attenuator_setting_pair)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1665, 5, 1), meta=np.ndarray&gt;</div><input id='attrs-e629c8a4-cfc6-4898-b17a-aef086682082' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e629c8a4-cfc6-4898-b17a-aef086682082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b57d59bf-8d58-4dc8-9a11-257564f306c4' class='xr-var-data-in' type='checkbox'><label for='data-b57d59bf-8d58-4dc8-9a11-257564f306c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>complex profile computed from the fourier transform of the de-ramped chirp</dd><dt><span>long_name :</span></dt><dd>profile</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 0.97 GiB </td>\n",
+       "                        <td> 1.52 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1160 chunks in 22 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> complex128 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-7f121134-e2de-4b5c-918f-ddb1cb8fb8b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f121134-e2de-4b5c-918f-ddb1cb8fb8b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8006831-1da4-43a6-8163-a70d9de842cb' class='xr-var-data-in' type='checkbox'><label for='data-c8006831-1da4-43a6-8163-a70d9de842cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>temperature measured inside the ApRES unit in one location</dd><dt><span>units :</span></dt><dd>celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
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+       "                        <td> 1.91 kiB </td>\n",
+       "                        <td> 752 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (94,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 15 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >245</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-4b6ab78e-997c-4192-bbea-40126204e7f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b6ab78e-997c-4192-bbea-40126204e7f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-376e52cd-6836-40bc-8d1e-28d1248fdd16' class='xr-var-data-in' type='checkbox'><label for='data-376e52cd-6836-40bc-8d1e-28d1248fdd16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>temperature measured inside the ApRES unit in a second location</dd><dt><span>units :</span></dt><dd>celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.91 kiB </td>\n",
+       "                        <td> 752 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (245,) </td>\n",
+       "                        <td> (94,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 7 chunks in 15 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >245</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1663d3b4-c3bc-4188-8ac4-6c0c3f9a57fb' class='xr-section-summary-in' type='checkbox'  ><label for='section-1663d3b4-c3bc-4188-8ac4-6c0c3f9a57fb' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chirp_num</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-09951b16-29ae-443b-83d8-651ad839b503' class='xr-index-data-in' type='checkbox'/><label for='index-09951b16-29ae-443b-83d8-651ad839b503' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
+       "            19],\n",
+       "           dtype=&#x27;int64&#x27;, name=&#x27;chirp_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>chirp_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-14d8c3d4-ea2e-4b20-b378-974fcee29400' class='xr-index-data-in' type='checkbox'/><label for='index-14d8c3d4-ea2e-4b20-b378-974fcee29400' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(TimedeltaIndex([       &#x27;0 days 00:00:00&#x27;, &#x27;0 days 00:00:00.000025&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000050&#x27;, &#x27;0 days 00:00:00.000075&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000100&#x27;, &#x27;0 days 00:00:00.000125&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000150&#x27;, &#x27;0 days 00:00:00.000175&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000200&#x27;, &#x27;0 days 00:00:00.000225&#x27;,\n",
+       "                ...\n",
+       "                &#x27;0 days 00:00:00.999775&#x27;, &#x27;0 days 00:00:00.999800&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999825&#x27;, &#x27;0 days 00:00:00.999850&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999875&#x27;, &#x27;0 days 00:00:00.999900&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999925&#x27;, &#x27;0 days 00:00:00.999950&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999975&#x27;,        &#x27;0 days 00:00:01&#x27;],\n",
+       "               dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;chirp_time&#x27;, length=40001, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>profile_range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1519af3e-e22a-4428-a54b-d99104810d25' class='xr-index-data-in' type='checkbox'/><label for='index-1519af3e-e22a-4428-a54b-d99104810d25' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([               0.0, 0.2102895577845166, 0.4205791155690332,\n",
+       "              0.6308686733535498, 0.8411582311380664,  1.051447788922583,\n",
+       "              1.2617373467070996, 1.4720269044916163, 1.6823164622761329,\n",
+       "              1.8926060200606494,\n",
+       "              ...\n",
+       "              1398.0049801514665, 1398.2152697092508, 1398.4255592670354,\n",
+       "                1398.63584882482, 1398.8461383826045,  1399.056427940389,\n",
+       "              1399.2667174981734,  1399.477007055958, 1399.6872966137425,\n",
+       "               1399.897586171527],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;profile_range&#x27;, length=6658))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0d40fa04-e20d-4478-bbfa-72d03bcef810' class='xr-index-data-in' type='checkbox'/><label for='index-0d40fa04-e20d-4478-bbfa-72d03bcef810' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-02-17 16:37:49&#x27;, &#x27;2022-02-17 16:40:10&#x27;,\n",
+       "               &#x27;2022-02-17 16:55:04&#x27;, &#x27;2022-02-17 17:10:04&#x27;,\n",
+       "               &#x27;2022-02-17 17:25:04&#x27;, &#x27;2022-02-17 17:40:04&#x27;,\n",
+       "               &#x27;2022-02-17 17:55:04&#x27;, &#x27;2022-02-17 18:10:04&#x27;,\n",
+       "               &#x27;2022-02-17 18:25:04&#x27;, &#x27;2022-02-17 18:40:04&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-05-28 09:58:06&#x27;, &#x27;2022-05-28 10:13:06&#x27;,\n",
+       "               &#x27;2022-05-28 10:28:05&#x27;, &#x27;2022-05-28 10:43:05&#x27;,\n",
+       "               &#x27;2022-05-28 10:58:07&#x27;, &#x27;2022-05-28 11:13:06&#x27;,\n",
+       "               &#x27;2022-05-28 11:28:07&#x27;, &#x27;2022-05-28 11:43:06&#x27;,\n",
+       "               &#x27;2022-05-28 11:58:08&#x27;, &#x27;2022-05-28 12:13:08&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=245, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f04fc99f-8bf8-47c2-8f45-33b4a5a08c2b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f04fc99f-8bf8-47c2-8f45-33b4a5a08c2b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:          (time: 245, attenuator_setting_pair: 2, chirp_time: 40001,\n",
+       "                      chirp_num: 20, profile_range: 6658)\n",
+       "Coordinates:\n",
+       "    AFGain           (time, attenuator_setting_pair) int64 dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
+       "    attenuator       (time, attenuator_setting_pair) float64 dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
+       "    burst_number     (time) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
+       "  * chirp_time       (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:01\n",
+       "    filename         (time) <U83 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.4e+03 1.4e+03\n",
+       "  * time             (time) datetime64[ns] 2022-02-17T16:37:49 ... 2022-05-28...\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    battery_voltage  (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 dask.array<chunksize=(1, 5001, 5, 1), meta=np.ndarray>\n",
+       "    latitude         (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    longitude        (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 dask.array<chunksize=(1, 1665, 5, 1), meta=np.ndarray>\n",
+       "    temperature_1    (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    temperature_2    (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_some_zarrs = xr.open_mfdataset(names[0:7],\n",
+    "                           chunks = {}, \n",
+    "                           engine = 'zarr', \n",
+    "                           consolidated = False, \n",
+    "                           parallel = True)\n",
+    "ds_some_zarrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Chunk size')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 3))\n",
+    "plt.plot(ds_some_zarrs.profile.chunks[dim_num]);\n",
+    "plt.xlabel(\"Chunk number\")\n",
+    "plt.ylabel(\"Chunk size\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "This presents a problem because [zarr stores require uniform chunk sizes](https://zarr.readthedocs.io/en/stable/spec/v2.html#chunks) within each dimension (except for the last chunk). When it comes to writing the whole lazily loaded xarray, this means that you must first 'rechunk' the data. This is a very costly operation, which is why this stage of the procedure was failing. In this toy scenario illustrated in the figure below, one chunk in the original dataset is potentially accessed by multiple workers simultaneously working on writing multiple chunks. At the same time, each new chunk in the rechunked dataset must be built using data from multiple chunks in the original dataset. In large datasets, this gets very computationally intensive. \n",
+    "\n",
+    "![chunking](../img/chunking.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "The solution was to rechunk the data loaded from each `dat` file prior to the frst write to zarr so that the length of the chunks in the time dimension is 1, using the following line:\n",
+    "```\n",
+    "ds = fd.data.chunk({'time':1, 'attenuator_setting_pair':-1, 'chirp_num':-1, 'profile_range': -1, 'chirp_time':-1  })\n",
+    "```\n",
+    "\n",
+    "Finally, when each individual zarr store was lazily loaded, we rechunked to 20 in the time dimension before concatenating, so that the resulting xarray had reasonable chunk sizes. \n",
+    "\n",
+    "The full procedure is summarized in the following diagram: \n",
+    "\n",
+    "\n",
+    "![apres_workflow](../img/apres_workflow.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "The result is a zarr store containing a whole season of data from ApRES site A103:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:          (attenuator_setting_pair: 2, time: 11763,\n",
+       "                      chirp_time: 40001, chirp_num: 20, profile_range: 6658)\n",
+       "Coordinates:\n",
+       "    AFGain           (attenuator_setting_pair) int64 dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;\n",
+       "    attenuator       (attenuator_setting_pair) float64 dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;\n",
+       "    burst_number     (time) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
+       "  * chirp_time       (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:01\n",
+       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.4e+03 1.4e+03\n",
+       "  * time             (time) datetime64[ns] 2022-02-17T16:37:49 ... 2022-09-25...\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    battery_voltage  (time) float64 dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;\n",
+       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 dask.array&lt;chunksize=(20, 40001, 20, 2), meta=np.ndarray&gt;\n",
+       "    latitude         (time) float64 dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;\n",
+       "    longitude        (time) float64 dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;\n",
+       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 dask.array&lt;chunksize=(20, 6658, 20, 2), meta=np.ndarray&gt;\n",
+       "    profile_stacked  (time, profile_range, attenuator_setting_pair) complex128 dask.array&lt;chunksize=(20, 6658, 2), meta=np.ndarray&gt;\n",
+       "    temperature_1    (time) float64 dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;\n",
+       "    temperature_2    (time) float64 dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-771b02df-2859-487f-b01c-871c7412d3e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-771b02df-2859-487f-b01c-871c7412d3e4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>attenuator_setting_pair</span>: 2</li><li><span class='xr-has-index'>time</span>: 11763</li><li><span class='xr-has-index'>chirp_time</span>: 40001</li><li><span class='xr-has-index'>chirp_num</span>: 20</li><li><span class='xr-has-index'>profile_range</span>: 6658</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-57cda239-8dc7-4297-8e76-0d47bf5bf3fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57cda239-8dc7-4297-8e76-0d47bf5bf3fe' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AFGain</span></div><div class='xr-var-dims'>(attenuator_setting_pair)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;</div><input id='attrs-c231a3c6-2b60-414d-ae95-59bd3cfcafd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c231a3c6-2b60-414d-ae95-59bd3cfcafd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dfcf98a-606f-4ac4-9b72-72dd9445420f' class='xr-var-data-in' type='checkbox'><label for='data-9dfcf98a-606f-4ac4-9b72-72dd9445420f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>audio-frequency gain control setting</dd><dt><span>units :</span></dt><dd>decibels</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2,) </td>\n",
+       "                        <td> (2,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,30.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuator</span></div><div class='xr-var-dims'>(attenuator_setting_pair)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;</div><input id='attrs-3dc3e202-25ae-4c9e-9b3e-3b2b80efae19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3dc3e202-25ae-4c9e-9b3e-3b2b80efae19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7125d56c-56b0-41dc-9f87-161686367f60' class='xr-var-data-in' type='checkbox'><label for='data-7125d56c-56b0-41dc-9f87-161686367f60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>radio-frequency attenuator setting</dd><dt><span>units :</span></dt><dd>decibels</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2,) </td>\n",
+       "                        <td> (2,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,30.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>burst_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-e14a24d5-fa20-43ff-ae1d-ea17ed30e9e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e14a24d5-fa20-43ff-ae1d-ea17ed30e9e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66b0a4ad-9f49-4c86-b245-49c90a715b8f' class='xr-var-data-in' type='checkbox'><label for='data-66b0a4ad-9f49-4c86-b245-49c90a715b8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the number of each burst within each file</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 91.90 kiB </td>\n",
+       "                        <td> 8 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (11763,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11763 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp_num</span></div><div class='xr-var-dims'>(chirp_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 14 15 16 17 18 19</div><input id='attrs-ec9fb1e8-eb37-4181-b124-6094ef96b8ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec9fb1e8-eb37-4181-b124-6094ef96b8ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52e10bec-6040-4c7e-9948-24e15eaa1739' class='xr-var-data-in' type='checkbox'><label for='data-52e10bec-6040-4c7e-9948-24e15eaa1739' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>the number of each chirp within each burst</dd><dt><span>long_name :</span></dt><dd>chirp number</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp_time</span></div><div class='xr-var-dims'>(chirp_time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 ... 00:00:01</div><input id='attrs-74b5a5ea-4284-43fe-9eb4-cfc5de9c74f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74b5a5ea-4284-43fe-9eb4-cfc5de9c74f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e61bdb6-d2cf-4885-acf6-5721e2ae3356' class='xr-var-data-in' type='checkbox'><label for='data-3e61bdb6-d2cf-4885-acf6-5721e2ae3356' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of samples during chirps</dd><dt><span>name :</span></dt><dd>time of samples during chirps</dd></dl></div><div class='xr-var-data'><pre>array([         0,      25000,      50000, ...,  999950000,  999975000,\n",
+       "       1000000000], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>profile_range</span></div><div class='xr-var-dims'>(profile_range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.2103 ... 1.4e+03 1.4e+03</div><input id='attrs-22f8c7a8-58f6-475c-b12b-f7962c2c0a7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22f8c7a8-58f6-475c-b12b-f7962c2c0a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d487945a-c0b0-434e-bf22-d64ada723502' class='xr-var-data-in' type='checkbox'><label for='data-d487945a-c0b0-434e-bf22-d64ada723502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 2.102896e-01, 4.205791e-01, ..., 1.399477e+03,\n",
+       "       1.399687e+03, 1.399898e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-02-17T16:37:49 ... 2022-09-...</div><input id='attrs-f4e1e471-48d4-4aff-a5bb-076f4c21598a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4e1e471-48d4-4aff-a5bb-076f4c21598a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2923c0cd-3055-4d19-8411-167f840d6b82' class='xr-var-data-in' type='checkbox'><label for='data-2923c0cd-3055-4d19-8411-167f840d6b82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time of burst</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-17T16:37:49.000000000&#x27;, &#x27;2022-02-17T16:37:49.000000000&#x27;,\n",
+       "       &#x27;2022-02-17T16:37:49.000000000&#x27;, ..., &#x27;2022-09-25T16:37:49.000000000&#x27;,\n",
+       "       &#x27;2022-09-25T16:37:49.000000000&#x27;, &#x27;2022-09-25T16:37:49.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9a25f74-c13c-4a75-9efc-fafa2abda3f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9a25f74-c13c-4a75-9efc-fafa2abda3f3' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>battery_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;</div><input id='attrs-00dfb21f-7744-4e34-b7c7-60a0240bd512' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00dfb21f-7744-4e34-b7c7-60a0240bd512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d4632f7-697a-4af0-b499-e341fb4f1596' class='xr-var-data-in' type='checkbox'><label for='data-5d4632f7-697a-4af0-b499-e341fb4f1596' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>battery voltage</dd><dt><span>units :</span></dt><dd>volts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <thead>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 91.90 kiB </td>\n",
+       "                        <td> 160 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (11763,) </td>\n",
+       "                        <td> (20,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 589 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chirp</span></div><div class='xr-var-dims'>(time, chirp_time, chirp_num, attenuator_setting_pair)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 40001, 20, 2), meta=np.ndarray&gt;</div><input id='attrs-88a11cb7-d1ab-4ff8-a31e-59b9a88a39c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88a11cb7-d1ab-4ff8-a31e-59b9a88a39c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d11fd49d-fe15-4ac4-ba6b-1f7e87b6fef0' class='xr-var-data-in' type='checkbox'><label for='data-d11fd49d-fe15-4ac4-ba6b-1f7e87b6fef0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>voltage from the analog-to-digital converter after the received signal has been mixed with the transmitted signal and the result has been filtered to leave only the low frequency compponent corresponding to the differences in the frequencies of the Tx and Rx signals</dd><dt><span>long_name :</span></dt><dd>de-ramped chirp</dd><dt><span>units :</span></dt><dd>volts</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 140.23 GiB </td>\n",
+       "                        <td> 244.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (11763, 40001, 20, 2) </td>\n",
+       "                        <td> (20, 40001, 20, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "                        <td colspan=\"2\"> 589 chunks in 2 graph layers </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;</div><input id='attrs-98c313c3-1a49-43ca-b577-2fba730dc524' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98c313c3-1a49-43ca-b577-2fba730dc524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a3e05a6-1ac2-480b-83ff-c9ccfb2124cc' class='xr-var-data-in' type='checkbox'><label for='data-9a3e05a6-1ac2-480b-83ff-c9ccfb2124cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of burst</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 91.90 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (11763,) </td>\n",
+       "                        <td> (20,) </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;</div><input id='attrs-d16bde99-4822-4756-a449-d3671cd7f332' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d16bde99-4822-4756-a449-d3671cd7f332' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-788e5f7f-cd62-442b-8326-cd34cbc8b8a5' class='xr-var-data-in' type='checkbox'><label for='data-788e5f7f-cd62-442b-8326-cd34cbc8b8a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of burst</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 91.90 kiB </td>\n",
+       "                        <td> 160 B </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (11763,) </td>\n",
+       "                        <td> (20,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 589 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>profile</span></div><div class='xr-var-dims'>(time, profile_range, chirp_num, attenuator_setting_pair)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 6658, 20, 2), meta=np.ndarray&gt;</div><input id='attrs-7a86e3b8-8593-4809-88fb-2e71f2fd12f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a86e3b8-8593-4809-88fb-2e71f2fd12f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b228213c-250a-4e48-ac4f-90b5b91d7a9b' class='xr-var-data-in' type='checkbox'><label for='data-b228213c-250a-4e48-ac4f-90b5b91d7a9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>complex profile computed from the fourier transform of the de-ramped chirp</dd><dt><span>long_name :</span></dt><dd>profile</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 46.68 GiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <th> Data type </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>profile_stacked</span></div><div class='xr-var-dims'>(time, profile_range, attenuator_setting_pair)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 6658, 2), meta=np.ndarray&gt;</div><input id='attrs-9527f8e7-8d4a-47aa-9e32-309603a32879' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9527f8e7-8d4a-47aa-9e32-309603a32879' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84a4fb23-e044-4c21-9a5a-8e73c6a220e8' class='xr-var-data-in' type='checkbox'><label for='data-84a4fb23-e044-4c21-9a5a-8e73c6a220e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.33 GiB </td>\n",
+       "                        <td> 4.06 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (11763, 6658, 2) </td>\n",
+       "                        <td> (20, 6658, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 589 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> complex128 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;</div><input id='attrs-32955e64-42bb-41f2-9d87-9f30331bd4ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32955e64-42bb-41f2-9d87-9f30331bd4ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61fb3102-ea06-451b-a9fc-09e761df50d8' class='xr-var-data-in' type='checkbox'><label for='data-61fb3102-ea06-451b-a9fc-09e761df50d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>temperature measured inside the ApRES unit in one location</dd><dt><span>units :</span></dt><dd>celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 91.90 kiB </td>\n",
+       "                        <td> 160 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (11763,) </td>\n",
+       "                        <td> (20,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 589 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20,), meta=np.ndarray&gt;</div><input id='attrs-40dbe035-6b0f-4d73-a9dd-2d7c800e036b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40dbe035-6b0f-4d73-a9dd-2d7c800e036b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c953507e-6de3-401a-b48a-939bd54b6044' class='xr-var-data-in' type='checkbox'><label for='data-c953507e-6de3-401a-b48a-939bd54b6044' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>temperature measured inside the ApRES unit in a second location</dd><dt><span>units :</span></dt><dd>celsius</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 91.90 kiB </td>\n",
+       "                        <td> 160 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (11763,) </td>\n",
+       "                        <td> (20,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 589 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-907910f1-e83a-4ac2-8cc6-c8ac04169c19' class='xr-section-summary-in' type='checkbox'  ><label for='section-907910f1-e83a-4ac2-8cc6-c8ac04169c19' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chirp_num</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-93ce1da6-97f3-4737-8ad7-1e6034135889' class='xr-index-data-in' type='checkbox'/><label for='index-93ce1da6-97f3-4737-8ad7-1e6034135889' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
+       "            19],\n",
+       "           dtype=&#x27;int64&#x27;, name=&#x27;chirp_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>chirp_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a633efe-bb37-4eb0-8652-ef9faa67c3b2' class='xr-index-data-in' type='checkbox'/><label for='index-8a633efe-bb37-4eb0-8652-ef9faa67c3b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(TimedeltaIndex([       &#x27;0 days 00:00:00&#x27;, &#x27;0 days 00:00:00.000025&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000050&#x27;, &#x27;0 days 00:00:00.000075&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000100&#x27;, &#x27;0 days 00:00:00.000125&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000150&#x27;, &#x27;0 days 00:00:00.000175&#x27;,\n",
+       "                &#x27;0 days 00:00:00.000200&#x27;, &#x27;0 days 00:00:00.000225&#x27;,\n",
+       "                ...\n",
+       "                &#x27;0 days 00:00:00.999775&#x27;, &#x27;0 days 00:00:00.999800&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999825&#x27;, &#x27;0 days 00:00:00.999850&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999875&#x27;, &#x27;0 days 00:00:00.999900&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999925&#x27;, &#x27;0 days 00:00:00.999950&#x27;,\n",
+       "                &#x27;0 days 00:00:00.999975&#x27;,        &#x27;0 days 00:00:01&#x27;],\n",
+       "               dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;chirp_time&#x27;, length=40001, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>profile_range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-daf8ad2c-11e8-4132-9de8-f1d697f8bd6c' class='xr-index-data-in' type='checkbox'/><label for='index-daf8ad2c-11e8-4132-9de8-f1d697f8bd6c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([               0.0, 0.2102895577845166, 0.4205791155690332,\n",
+       "              0.6308686733535498, 0.8411582311380664,  1.051447788922583,\n",
+       "              1.2617373467070996, 1.4720269044916163, 1.6823164622761329,\n",
+       "              1.8926060200606494,\n",
+       "              ...\n",
+       "              1398.0049801514665, 1398.2152697092508, 1398.4255592670354,\n",
+       "                1398.63584882482, 1398.8461383826045,  1399.056427940389,\n",
+       "              1399.2667174981734,  1399.477007055958, 1399.6872966137425,\n",
+       "               1399.897586171527],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;profile_range&#x27;, length=6658))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7d292ed9-7546-4659-bbae-d0a95f24b9e0' class='xr-index-data-in' type='checkbox'/><label for='index-7d292ed9-7546-4659-bbae-d0a95f24b9e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-02-17 16:37:49&#x27;, &#x27;2022-02-17 16:37:49&#x27;,\n",
+       "               &#x27;2022-02-17 16:37:49&#x27;, &#x27;2022-02-17 16:37:49&#x27;,\n",
+       "               &#x27;2022-02-17 16:37:49&#x27;, &#x27;2022-02-17 16:37:49&#x27;,\n",
+       "               &#x27;2022-02-17 16:37:49&#x27;, &#x27;2022-02-17 16:37:49&#x27;,\n",
+       "               &#x27;2022-02-17 16:37:49&#x27;, &#x27;2022-02-17 16:37:49&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-09-25 16:37:49&#x27;, &#x27;2022-09-25 16:37:49&#x27;,\n",
+       "               &#x27;2022-09-25 16:37:49&#x27;, &#x27;2022-09-25 16:37:49&#x27;,\n",
+       "               &#x27;2022-09-25 16:37:49&#x27;, &#x27;2022-09-25 16:37:49&#x27;,\n",
+       "               &#x27;2022-09-25 16:37:49&#x27;, &#x27;2022-09-25 16:37:49&#x27;,\n",
+       "               &#x27;2022-09-25 16:37:49&#x27;, &#x27;2022-09-25 16:37:49&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=11763, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bca10900-efc4-412a-8ba1-7a1530eaad4e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bca10900-efc4-412a-8ba1-7a1530eaad4e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:          (attenuator_setting_pair: 2, time: 11763,\n",
+       "                      chirp_time: 40001, chirp_num: 20, profile_range: 6658)\n",
+       "Coordinates:\n",
+       "    AFGain           (attenuator_setting_pair) int64 dask.array<chunksize=(2,), meta=np.ndarray>\n",
+       "    attenuator       (attenuator_setting_pair) float64 dask.array<chunksize=(2,), meta=np.ndarray>\n",
+       "    burst_number     (time) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "  * chirp_num        (chirp_num) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n",
+       "  * chirp_time       (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:01\n",
+       "  * profile_range    (profile_range) float64 0.0 0.2103 ... 1.4e+03 1.4e+03\n",
+       "  * time             (time) datetime64[ns] 2022-02-17T16:37:49 ... 2022-09-25...\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    battery_voltage  (time) float64 dask.array<chunksize=(20,), meta=np.ndarray>\n",
+       "    chirp            (time, chirp_time, chirp_num, attenuator_setting_pair) float64 dask.array<chunksize=(20, 40001, 20, 2), meta=np.ndarray>\n",
+       "    latitude         (time) float64 dask.array<chunksize=(20,), meta=np.ndarray>\n",
+       "    longitude        (time) float64 dask.array<chunksize=(20,), meta=np.ndarray>\n",
+       "    profile          (time, profile_range, chirp_num, attenuator_setting_pair) complex128 dask.array<chunksize=(20, 6658, 20, 2), meta=np.ndarray>\n",
+       "    profile_stacked  (time, profile_range, attenuator_setting_pair) complex128 dask.array<chunksize=(20, 6658, 2), meta=np.ndarray>\n",
+       "    temperature_1    (time) float64 dask.array<chunksize=(20,), meta=np.ndarray>\n",
+       "    temperature_2    (time) float64 dask.array<chunksize=(20,), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = xa.load.load_zarr(\"A103\")\n",
+    "del ds['filename']\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "To demonstrate the utility of having the data in this format we can do something simple that requires a lot of data, like taking the mean of the chirp voltages. The chirp data is ~150GB,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150.57016416"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.chirp.nbytes/1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "so let's time how long it takes with 10 workers to compute the mean of those data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 332 ms, sys: 12.3 ms, total: 345 ms\n",
+      "Wall time: 3min 56s\n"
+     ]
+    },
+    {
+     "data": {
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+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;chirp&#x27; ()&gt;\n",
+       "array(0.02804514)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'chirp'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d3199dec-87f1-408d-8393-5bc5473e4645' class='xr-array-in' type='checkbox' checked><label for='section-d3199dec-87f1-408d-8393-5bc5473e4645' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.02805</span></div><div class='xr-array-data'><pre>array(0.02804514)</pre></div></div></li><li class='xr-section-item'><input id='section-efaa5fe0-51ee-461e-9801-c128474c589f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-efaa5fe0-51ee-461e-9801-c128474c589f' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-39566a89-beb6-4720-8b38-899829df3475' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-39566a89-beb6-4720-8b38-899829df3475' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7b157ac2-bb16-4203-a163-04cd39f702ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b157ac2-bb16-4203-a163-04cd39f702ad' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'chirp' ()>\n",
+       "array(0.02804514)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time \n",
+    "# with 10 workers\n",
+    "ds.chirp.mean().compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "It took around 4 minutes to get through 150 GB. \n",
+    "\n",
+    "Finally, let's make a depth-time plot.\n",
+    "This requires:\n",
+    "- subsetting with `.isel(attenuator_setting_pair = 0, time = slice(200,11763))`\n",
+    "- computing decibels from the complex profiles with `.db`, which `xa.load.load_zarr` adds to the xarray \n",
+    "- compute the mean of the profiles from each burst with `.mean(dim=['chirp_num'])`\n",
+    "- coarsen in time, because plotting the full resolution would require a lot of data to be in the machine running the notebook with `.coarsen(time=10, boundary='trim').mean()` and\n",
+    "- plot a color map with `.plot(x='time', yincrease=False, size=(5),aspect=3)`:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.15 s, sys: 264 ms, total: 7.41 s\n",
+      "Wall time: 3min 43s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f646e9d3f10>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9y3zwgx80vj/zmc/E6OgoPvCBD+BNb3pTz5g3RXGIDjVGkV5rL37xi43rz3/+8/Gyl73MuPaNb3wDp512GjZs2GDMkzPPPBNvectbcP311+O4447DN7/5TdTrdbzyla88pDadffbZxvts2bIFT3ziE9O2llRSSSWVVNIfCpXAT0kllVTSIdAdd9yB733ve3juc58LKSVmZ2cBJJm7rrzySnziE5/ApZdeatyzZs0aPOYxj3HWt3nzZgCJQMrpuuuuQxiG+OlPf2qkU47jGGeccQZ2796N8847Dw972MPQ39+POI5x0kknFbr+aBofH7eu1Wq1Fd175JFHrshtxPOKvYrf+c53GmDLli1bsGPHDvz4xz/G5Zdfji996UtotVpp0OE4jhGGIWZnZ9FoNFCr1Xq2gdPU1JRT8OfXtJuWjjvDSb+bjrGzadOmVbcFcGdL0m2ZmppK2zI7O4tqteqsg8d1ovSSl7wEYRjiYx/7GJ773OcijmM89rGPxcUXX4ynPOUph9RmF/34xz/GGWecgVNPPRUf+9jH0vgqX/nKV3DJJZesaF7lkR6Lt7zlLXjLW97iLMP7gM9vPVcOpR3vf//78eY3vxmvec1r8K53vQsTExPwfR/nnXcefv3rX/e8f2ZmBlJKZ0a3DRs2AMjGWtNqsr+tNAPaobpLnnPOOfjABz6AH/3oR4XAz/j4OFqtFpaXl9HX12f8Nj09jRNPPPGQnq/7hq/RIAiscd63bx++/vWv57rG6Xly4MABbNiwoecelUd5e8gvfvGLQ6qvpJJKKqmkkh4oKoGfkkoqqaRDoE984hOQUuKLX/wivvjFL1q/f/KTn8TFF1+8YiFrw4YNOP744/Htb38brVbLsBrRAt3i4qJxz69+9Sv84he/wFVXXWVovO+4445DeKPV0+mnn47rr7++Z7mXvexlzvgrmv7yL/8SZ511VvpdC+e33HILpJR49rOfbd2za9cujI6O4h//8R+N4MkrpfHxcezdu9e6zq9pq5gvfvGLhengdUDfe+65Z9VtAeCMA6TbooVaHaBYx5DhNDg4WPiMV7ziFXjFK16BpaUlfO9738MFF1yAs846C7/5zW/ut1T3n/3sZ1GpVPCNb3zDmMP3JaCvJj0Wb3vb2/Cc5zzHWeaYY45Zdb21Ws0ZNJmDMP/yL/+CU089FR/60IeM6yuxegOA0dFReJ6HPXv2WL/pgNPcCms11jFF8X8oXXnllc5Axb1Ig7y9QBId2+e///u/jQyHe/fuxcGDB3HCCSes+tlAtg727t2LjRs3ptfDMLTGamJiAg9/+MNxySWXOOvSQNuaNWvwgx/8AHEcHxL4k7eHuAD1kkoqqaSSSvp9Ugn8lFRSSSWtkqIowic/+Uls27YNH//4x63fv/GNb+B973sfvvnNbxqARi96+9vfjrPPPhtvetOb8MEPfrCn0Kd/5xYvH/nIR1b8zPtC95er14YNG1JBjNLTnvY0p8vEC1/4Qhx++OG49NJLceSRR668wYROO+00fO1rX8O+fftSa5soiqwAu0996lMRBAHuvPPOQrebo48+Gtu2bcMnPvEJvOlNb1q1FdLCwgK+9rWvGa5J//qv/wrP8/DHf/zHAICzzjoLn/3sZxFFkSFQr5b6+/tx5plnotPp4FnPehZuvvnm+w34EUIgCAID8Gw2m/j0pz9tlc2zLMuzyjnmmGNw1FFH4Re/+EWaJvz+oK1bt+KXv/ylce273/2uBbQKIaxx/eUvf4kbbrghtdgran9/fz8e//jH40tf+hIuu+wyNBoNAIkF27/8y79g06ZNOProow/5Pe5PVy8XfepTnwLQO8360572NNTrdVx11VXGPNWZyp71rGcd0vNPPfVUAMBnPvMZw2ro85//vJWp66yzzsK//du/Ydu2bRgdHc2t88wzz8TVV1+Nq6666pDcva6++mq86U1vSvfiu+++Gz/84Q/x0pe+dNV1lVRSSSWVVNIDSSXwU1JJJZW0SvrmN7+J3bt34x/+4R9SYYTSCSecgA984AO44oorVgX8vOhFL8LNN9+MSy65BL/4xS/w8pe/HEcddRTiOMauXbtS4Vlbdmzfvh3btm3DW9/6VkgpMTY2hq9//ev49re/fb+8Zy86FOuK1dDk5KTTlUKnXHb1/UrpHe94B772ta/hyU9+Ms4//3z09fXhgx/8oJXlauvWrXjnO9+Jt7/97fjtb3+Lpz3taRgdHcW+ffvw4x//GP39/amb2gc/+EE84xnPwEknnYS/+Zu/wWGHHYadO3fiP/7jP/CZz3ymsD3j4+N47Wtfi507d+Loo4/Gv/3bv+FjH/sYXvva1+Kwww4DkABen/nMZ/Cnf/qn+F//63/hcY97HCqVCu655x5ce+21eOYzn+m0jgKSbEiNRgNPetKTsH79euzduxeXXnophoeHc93YKF133XU47bTTcMEFFzhTn2t6+tOfjve///04++yz8Zd/+ZeYmprCZZdd5gTCHvawh+Gzn/0sPve5z+GII45AvV7Hwx72MGzbtg2NRgOf+cxncOyxx2JgYCAFBz/ykY/gzDPPxFOf+lS8/OUvx8aNGzE9PY1f//rX+K//+q/cjHpF9JKXvATnnXcezj//fJxyyim45ZZb8IEPfADDw8NGubPOOgvvete7cMEFF+CUU07Bbbfdhne+8504/PDDDeBhcHAQW7ZswVe/+lWcfvrpGBsbw8TEBLZu3YpLL70UT3nKU3DaaafhLW95C6rVKi6//HL86le/wtVXX33I8W8A5LqRrpb+9V//FV/60pfw9Kc/HVu2bMHs7Cy+8IUv4LOf/Sxe/vKX4xGPeERa9vrrr8fpp5+O888/P43DNTY2hne84x0477zzMDY2hjPOOAM33XQTLrzwQrzqVa/Ccccdd0jtOvbYY3HOOefg//yf/4NKpYI/+ZM/wa9+9StcdtllGBoaMsq+853vxLe//W088YlPxBve8AYcc8wxaLVa2LFjB/7t3/4NH/7wh7Fp0ya86EUvwpVXXonXvOY1uO2223DaaachjmPceOONOPbYY9P4bXm0f/9+PPvZz8a5556Lubk5XHDBBajX63jb296WlvnUpz6FV77ylfjEJz6RAkJ33303tm3bhpe97GW44oor0rIayP5dWW2WVFJJJZX0EKLfY2DpkkoqqaT/kfSsZz1LVqtVuX///twyL3zhC2UQBGnWKADy9a9//Yrq/973vidf8IIXyE2bNslKpSL7+vrkcccdJ1/72tfKn/zkJ0bZW265RT7lKU+Rg4ODcnR0VP75n/+53Llzp5VVKC+b1PHHH289/2Uve5ncsmXLitr6+6D7I6uXlFL+53/+pzzppJNkrVaTk5OT8m//9m/lRz/6UaufpJTyK1/5ijzttNPk0NCQrNVqcsuWLfJ5z3ue/M53vmOUu+GGG+SZZ54ph4eHZa1Wk9u2bTOyEBWNw3XXXScf85jHyFqtJtevXy///u//3spe1e125WWXXSYf8YhHyHq9LgcGBuT27dvlq1/9ann77bcbddKMWZ/85CflaaedJtetWyer1arcsGGDfP7zny9/+ctfWv3iyur19a9/3chgV0Sf+MQn5DHHHCNrtZo84ogj5KWXXiqvuOIK67137NghzzjjDDk4OCgBGHPu6quvltu3b5eVSsUaw1/84hfy+c9/vly7dq2sVCpycnJSPvnJTzbapvv5pptucr7btddem15rt9vy7/7u7+TmzZtlo9GQp5xyivz5z39uZXJqt9vyLW95i9y4caOs1+vy0Y9+tPzKV77iXC/f+c535KMe9ShZq9WsjFPf//735ZOf/GTZ398vG42GPOmkk+TXv/514/689v8u6IYbbpCnn366nJycTPefxz72sfLyyy9Ps6Zp0v3pWmP/9E//JI8++mhZrVblYYcdJi+44ALZ6XR6Pj8vq5eUyRi8+c1vlmvXrpX1el2edNJJ8oYbbnBmYDtw4IB8wxveIA8//HBZqVTk2NiYPPHEE+Xb3/52ubi4mJZrNpvy/PPPl0cddZSsVqtyfHxcPvnJT5Y//OEP0zJ5Wb0+/elPyze84Q1yzZo1slaryZNPPtnao/VY0mxqd911lzPj4ZYtW/6g996SSiqppJL+55KQcgWROUsqqaSSSirpfyAJIVJrDt/375NFxUOBwjDE9ddfjz/5kz8xssf93d/9Ha6++mrcfvvth5S1rKSSVkoXXnghLrroInS7XSPDX0kllVRSSSWVdOh0aGkMSiqppJJKKul/CL3rXe9CpVLB+973vt93U/6g6ec//3nqQsPp2muvxXnnnVeCPiX9zqhSqVjudiWVVFJJJZVU0qFRafFTUkkllVTSg5Z+8pOfpJ83b97sTJteUkLNZhM333xz+r1XYNySSnogaPfu3WmWM9/3C1PHl1RSSSWVVFJJK6MS+CmppJJKKqmkkkoqqaSSSiqppJJKepDSQ8rV6/LLL8fhhx+Oer2OE088Ed///vd/300qqaSSSiqppJJKKqmkkkoqqaSSSnrA6CED/Hzuc5/DG9/4Rrz97W/Hz372M5x88sk488wzsXPnzt9300oqqaSSSiqppJJKKqmkkkoqqaSSHhB6yLh6Pf7xj8ejH/1ofOhDH0qvHXvssXjWs56FSy+99PfYspJKKqmkkkoqqaSSSiqppJJKKqmkB4aC33cDfhfU6XTw05/+FG9961uN62eccQZ++MMfOu9pt9tot9vp9ziOMT09jfHx8TIdcEkllVRSSSWVVFJJJZVUUkkPOEkpsbCwgA0bNsDzHtwOO61WC51O536pq1qtltlICT0kgJ+DBw8iiiIrm8u6deuwd+9e5z2XXnopLrroot9F80oqqaSSSiqppJJKKqmkkkoqKZd27dqFTZs2/b6b8YBRq9XC4VsGsHd/dL/UNzk5ibvuuqsEfxQ9JIAfTdxSR0qZa73ztre9DW9605vS73NzczjssMOw8eK3w8uZPCJO6vK6rE7iTHf8Y+8CAMy2zDom+xeS53Sy66O1plFmz9IgRustbOmbTq914wz1/bcbs5SnZ530X8a9+9uDOKp/v3GtG/vG99mwDw2/a78YgKWwkn6eqC5ZvzfjKiJpvrcvshcfCZJ7proD1r0/3He48X1N36Lx3TVCU8v96eeFVjX93KiFRrm5O0YR9ZubR9CfvaOMk/99AyayPFhvJc8WpidkRPq7HiT1DNfaRhlP3dOJsv6d79SMMvvmBnHixnuMa/csDqefNw3MAQBum15rlFlYTOrZMDZnXJ8m/RHFAo1aFzXf7IulTtJPgR8jj6YPDmDT+mnjWrtbMb431fd6xZwrrW4FDXaNjg0AtObr8Otmu2gfjw4mcz6UpjZjfqEO784+DJpdhvpUNrb9r70XALDUNZ8p1bx81sZfptdmwj6jzNU/fRwAYOuWbI2sI/Nw99JQ+pn3a3+li7tmxoxrj5/ckX5ux8k2e9uMCTwvq/F42NrdxvVbp7NyrU6AwXob+w8MG2X8qWQMHnfSrem1O2bWGGU6sY/JgXnj2p6FIeP7I9YkfXb3gtn+6eV+/PHGO4xrP5syGY1WWMFIfdm4NlprpZ/1nsH3seWwit0LQzh58k7j+ly3kX4eriT39Pnmuhzyk/p/vpC1Zce82fYDs8ke88rjb0iv/WR2q9UuAOgP7P1uQ2PW+B6RfXIwSNq1pX7QKPPbVrJOY7YHemRu+4jRleaeCwCtKJkHR/Rlc4+Xi6WHmte1rlHa1U7SrvvC9t6+c3HC+B7G5r2H95trHgCEyPaJZdVGvscPV1o42BlAn2eOk57zALC/NZDbLgAYIfNjwG8Zvy1GyXlYEdmaq5B2TVSzNRqxkIU+7H2Ol+FtBYBfTm8AAPzxWnP+S3YSBSJCxMZAlxHk0A8dY/7LuY3G92ZotmH70AEAQFfap1/VM8+zncvm/B+stKy2jgZZH/vq/v3tQaPMxvos2nGA0Yq5pilpnoH34/52MsYNsjeOVUw+YbqbnFFLYbY/b6zPpp+rXnZv3TP3WE/E2BjM5Lbr5mbSn2uqC8b1YT95l/+399HG9bFa9o5znQaOGDiIDpsHvhrDmTDbl+i4AoAHiWbEzrjIrEfzaI3AfKdYCtw9O2pci9i6bC5W0/MrJfI9qCV7gvDstbVuZMGqj+5P+jfOB+rgD/Qs523wvWRttcm7drrZ53aH1MmnsASiLtvfwqydfUNtox2aQlVGxsUW93HoIQ7ZmlN7z5nH3pJeqrJzfGv9AOqC7bGs8Xe3kn30jiWTLwOAJ43ebrZXmvMggsCu5rhxja7vk4fM+7P7PMdOBsRkDfJ9iJMv8nm+S/7zLACZ/JJUTj7XC4RvAfDGySi7119UfcDmp/7Wv5PNT9JlUQN42XO+Yz1Sr8sP/fSUrL7QHKfnPfqn+M69x+S3G8DyctV5PY4F1o6a+0gYmfNp/eC8cbZz+vW+ZH5UK2bf1YMQtYrNm/eTtfbb6WSODDVMvmmxXUO03Matr/i/GBw09+4HG3U6HezdH+Hun27F0OB9s2yaX4ix5cQd6HQ6JfCj6CEB/ExMTMD3fcu6Z//+/ZYVkKZarYZarWZd33j8Ygoc6MOP0+xyw/g+0pct4FM3JMGkZ7r9Rpm21MKIudiN9xiJMOC3sdYBvADAKSfflX7eOsgEslBibbXYbG4oFKh7buCHtstjh8hyVMOE10TEDkmfMEme+qlP2u93bGAKp1ro06QFQrrR9g9lAsLNe0bSzw323o1HLWDvDvOwjWU2ro/cnozHbiYQt6JkYz1ybMpqr6ZA9cNgxd1n/X42Tly4WFjXxGBg9qNoZOMzWEk6bGuVC83JgXHPjCnIbZqcTT/HUuDuA2OYXTABG/hJ/zUGMqCKM4bBoI+waoJzHZj1aIZmmR18oiYRVsyyvme+97b18xajScf1wHwyDg+f3GO2y5vF9LY+C9Sh7a/Wkt+G2KE621bC40DWtgqr5+jjZwEAdT+rry2yw/WE4QxoG6qYwmk7CnDUWpNR2FibTT//5/SRyTv0m/tJN+p3Xj+M1L8cVtAXdFFj87q5OXmXydFs7sUMhGlFAdY1zLbGdRPw2jzeVm0w710XRhhhGO3WwKxruDJjzetWlPXx5lrSJ7Ndcz/s90I8bsPtGGRC/o5WVu7xg8n4c+G9o4TodcO/Ta/NRea+rkGCLdUMnJkcdu99FWEztbOR2Ue0Dbc3k/Piv0Jz/e1UoMu7jviKcZ0z5Muyirow56du71SUdXhLmutoOa7hiIoJ3C9Ik4kZ7iTrSIMlmrrSx38tmwLmCGMqR0diTFTMOUzBnLkoGZtdLRNk+NHeY3HR9q9hITbHmAJXG5TAXiR8aOJ9Pxkk624pztbIgSjbq8f9rM0cjOjKwACM7Gf1W20FgD9Zn8wt/k5U/PKFxO7uCAY9c93o85O+RyzNOdaSAU4aNOdsKzbHezhI6h32zXHq90wlAwD8WXCr8d1HbM0xl+g45NkKCx/SOsf7yRpZUOu9ztZNnxLsdofZ3BtkYOAPm0cAANYE2ZhN+tm++t/tDMzdUDFBnn7RwdfnHmVcWyZ7zfhgctbe2d5ivlOcvPfjNpprZ4rwX9UwwinjOy2+pitt9tg1n/jYtdna/fbU8QCAe5fMNXhgfgB/eswtxjUOjj66f4f1PDpfH1a7x/odSNbC3nDImtt0jUyHA9Y1INvv7ulka73PN+fKcpSsR8ov0r1UA278uqardz/W+D5QyeaKBjbrTAk5pvje4cBcExQQ9yHx66VJS4GpQb1tfeYeR+nnC8fg9tk1ub8DwHgjacMfMaVIRUTYUDH7qBWb86klK2gyxSQdn4GBZN3NsT3w7vYEprr9CC2+Kav/iMZB9Uw2F9V7b2+YyiVKj3nUPgAJr6Fpc/9s+nkbVUiw8z6GwJ1LZp8FBJje3xxUbTXndV3xaHfOmrz5MlEUTgwsY2wotngAfY48+/FE6bVgtqFZG8Kp2+41rvH1Pa/2qjVVU9EcwyvsLyDZ313KGU0PW5coUnYwoK/qhRipNDHdMeW/6XY25iceftC6BgCNMEK4lPTtQyXcyMCgwMDgfXtXDt6W9BABfqrVKk488UR8+9vfxrOf/ez0+re//W0885nPXFVde/aNwmto5oYJvor5mVw7hzy6Zs8JAICppX7n7/Mz2WIfmzA3pMVmDTIWWDeaASUVssnedU+2+e3ebAIZM80+bB6aNa5x7fRCp4b+isms6UVDBe717PAMpYdYCuPgAEyLl039SZ/sWhwxygghce8NpiVBZzNjSBVAUiWWIs3ZjMGcWJ/f34v/v7XA4Sbj4fdl9dy8ezJ5h6bZ9mp/0g/cUqdJ3rGjtLRVpsnT2rEO0TpxsKPT9dGomcwJPfSG+xKheHrB3PxDpSmbGDXnxu23b8ie70uMT84jbJhj2WozIAhAJTD7Jo4EDh82tf/c+kYDKVXfZuiazDoorJv3zjT7MFI3GTdq8VOtJH358z0bjDKNWhedro/mkjkeY2NZPwxUk36fapl95ql1OkesfOZDU6jrU5Yf6xrZ3B4hAORokIF4c+zeNdUFLMcmkESBXd2e/dOmlmZ0JKmTr8MOmSt6XVWYll9Uk3eiWvvpttmupU7NYrg40PfT6c0AgEU2z6PYs+b+nbtN5kr4EoMDJngzQKzffrIzqTuo8DnmITzMx2Jo1n/HVAam7J4YSepjIJtm0v5j1/b02mDd3C8aaiw3EeaVavN/tieztljeb+7DY5vmMNowhfm79mbtOnmbaaWkaUKBbh/ce7pxnVoxtKIK9iwN4TETu4wyG6uJkNsigqbPzpYIwgA/ABvg2VRN1q0GaSj96Yabje/rKuaeefW9j8VhA6awTef/LXPJPvmIEZOZPvvwm7C7O2oxwVR4GfMX1Tu5gZ9+QQBvzxzvOzuJ5lQDQIAJ9vztr56Xfj5m/IBxb+BFGK2aY7mWWITMdJM2HtXYZ5TRIMnu7ohxfUNlNv1cF23s7Yzg5o6519TUeD+JaO9jJlgfCAdxRP2AdY3SZ+95TNK2YbPcutoCjmVCCQcortj7REtoPm00E5L0fri5Yio1xv0ltOBhxMtXEM2rOdhhFjlTyhrj560MeBn3zTPqR/PbAACH1bPzZaqafb7mwMPTz0cNmEDNbNfsZwDoD7J1r4W4Lrd8UN8XI3PtbKhl82nG78OHd/4xJhlfs7mRrIfP/+fj02uVNeb8XDuygJPXmXsCB7QfOZwol04eNa9XRGSAK4ANjl6x60nWulxXy9r571MJP7m2Zra94Xdxb3PEsiygfMhhjaTv+drVQP3mevbcxdDca/R5RM9COkZLxArKg20ZccLIHuuaJg1+BQww0vsHBzdoG5pRBbOdPuxnFhrakv7O+UwQ379grrlXHH0Dtg+YgCy3ZPxtMzkD71w2LX52LY3g7A0/Nq5x8L4rfQww5QkFaDUQx+fEvvYQNtRnrX6skDWowS9uFarpstuekn5+JLMw3rOYyAqHD2f7wUI3G++pbtZP811zHtA1qGmRlDltzW0AgDYbMz2GfJ+i9W2ozWEm7LesQGNtQUfuPXrI3C9umZvEgSVTc9VlVmAnb0rW7Tyb282oivnuEcY1ruD6/h3bULvVPGeb67K1e+Kjkro5bxdUYvxyZgPGmBX0EQOZkkqvo6Gq+d5D1Ra68v6JefM/hSIZI7qP6aci2Vvh9FCjh0xWr8997nN4yUtegg9/+MN4whOegI9+9KP42Mc+hptvvhlbtmzpef/8/DyGh4ex6Z8vSoEfEbCuUwdW7W7bUkhTe6syY+2aDMrAeMKgRhGxYKiYh3YUe+irdlAj16lg1yHm4lx4WepWLVCH01K3mgpNmrRLQJFZYzfyMVhtI2AWUHTTm1NgwWDVPCgCL8Zds+ZBxwEF3Qa6cdO6F6cyhkNUzDbU+juWaxO11Kqrvmx1meWCAkm4CTU1edbj4OVYfrU72WHHD4Co62PNaL72SbuRtZnZOBfcNU3dnAmn0ZouhkaXDNNr2obBvuxA4YdhpxukJtV51Kgr8/IeoAJgzmcAaC3U0Decb9WmgR1x0FxDffcILB4VIhgx50+4nPXx4Fgy5/l46HHYNpEdrgtds37tBkfn3t79mXvVSUdm1nQBY85bUcUCx/qCbK1pV6SDC8zK72DCOIxvnjWuLyxljEgcC3ietM39FY0MZOt8sWW+UywFhpjFT5u5lGjQZIHdG0Ye1g+blni7pkyNdXu+Zq234VFi5ab6PWam+Z3lCtZPzmKA7QW757L+3qAsrNY2TOFRWxTtW86YOg6KLbaTd9k6nAkutxMLOdoePv8DP7b6bLGdCTFHjCbC0v5lk6nU7kp8HtB9c6lbxUkTO3D7oik0/HpfYkV05hGZ9n9b3WRk56I+HGRusnxPOdhJfl9fN8fNQ5wCfJq4kLt7acja4/vIWbBVuYId0TCBCAD4zoHtFuNN26ZdYbiQoLWeh/dl6/LmeRPw3c4sYwBgx3ImvFFBjWt0KyKyXDcoSHJvJ5nP48zSSQM8CwxYoxTBw0zYbwljWnDf38mEpd8smOPtQeKeedN1c8OQOWYDav8YZhafkRS4fc4EYO/5rfn92Y/7qWVlR0m7ynGQ5M75CWwemMUgE06HyHctANUY8KOFJwoWLjFg1yUk7mll/bBMXMAONM29shVW0M/2C3re6HXH97Enb0oAuG/cfoJxvVrN2j/S18QRQ1OGpQIAzDv6sMreuxMHlsKL0xa1dnh/dOIA9y6ZCjp+htb8EMM1czzoOr11fzK3tMWTpvH6MkaqTWtvpK6aWnDmFjl6jOm4c3BoQFkA0XspuEaFae7q2I19XH/r0cY1LGdljj0uAcZ3z5t94ytebMuwCYRR18dYCky3G5hpmkBhW/FCh41m93Lr4d1T5poEgGifOQdOeOQOAImSlL/TPTtN647htebZtdysIt5ttotuWUc/5m7rfQCkfDV3z6V7rAYJ+H6k5/R/7syAjC3jpmJPh00YJgq5wwZm08//fXB99hw2F0frTYPXSdqfrZEB5arN9xp9Jnz/zm3GdWrIUqmGGOlv4uCcuRdI1Q8b12Rjyc/CfTND2D5pAvqcbt2bnLuUhwKAahBhzy2mJ0iwyfay4ErTgPCO2jWMK6UGay00wwrmm+bZUiPK4wX1G+evpRSIllu44yWXYm5uDkND5vp4MJGWt/fftuV+cfVae8zdD/o+Ww09ZIAfALj88svx3ve+F3v27MEJJ5yAf/zHf8Qf//Efr+hePREf8cU3w+9LNn2+yepNi4MIHgEPtCDMhVN9qLU6gXUtq1+qv+xa3ujxe6NYgFsH8nuFyK+vRja5vDJcqKTvXVcbW4uV8b0YrU7FuuYiyhTRNoREePPYe8dSWAINBdT05sp9eHX9zV2mVkiMZxv5hLI2yQPFogJf9Eh6hcAJF5Y5hQxQaRNroWiuCn+4A8H6UfdNo54PAEopMD9lHrQWmNZwAz/0GVl9vN0+N5RDt53NCU+5o4H3TTOAjIURnwkw/cmHFKDEmV1ttdZPXB01OJC2Qc0hui4bZJ7sO5AdGPV+s/8CP8YAO+DpWpieSgTyvkGTadL+3/PzJnNZq2fvKIREq1m1AEhfgZmd6YyBqIyabahUQstCqm/ALLM0kzw76DP71Q9idJgVnB53TWHo2bEfCMjabSV94FXseej7sTXHKUgYqTnRYH2mQdw22ScH+80yTQXa0jVRI3OervX2nNk/IogxNGoygnRdbhqZBWBbSGmBs8as/yhIP7XQj4F6G9vHTJBEg1nU7J2eAwCwPNtA/6gJAvB9cvNw0rZpZvEmpcD+KXMfG2cWg/3VDioMcKcCyMNGEi3xXUvMxW1hBGP1ZWzuN4UxqiHV73fvkilYaUUEBVL7mOJBa4Sppn7fTLYWTz/itvQzj3nUH7TxtdseZlyL92frxVubzJtHbjatmG7em1g3HT9pgk4DBLjwRIzfzK5NY/Np0u9CLRbp/gYAx2zch7V1s/8PthjQoQB/DnwudmuYYZZ99+41AdnGQBsbR0yLLmqpulkJdfMdU/iYbTdQ8SLLNJ4KxI/fkginv9xnAnT6PKoRQIUrcLaNuF0YNNFzdIQBXnuXB7FtyLRQoiCGBpwWmPb+1tm1qn3m+b73DjKPqzEeddwOKzaPFibp/LStmgMLrBlkwvFEPREauXVDUp+5j/A2zLXr1p5C956Jhq7bjok03e5Ln509L9sTtUU2tyxYVEoRannK16WmGeKeS/kBOpbcuqHqh5blNz2ztSXOEczNfkC5Us2y+W8oP2MfA5UOdsyZa0KvUwr2zLfNuTLeZwv3/L014M/HvK/Swb3T5jtxOm79XhxkgGad1K+f5VJ8Hmz2W/wWfW8ti/AzQZehY8ABcm0NS+c5jTdJYyFx5S5gKyeHyXzSc4jPQ30m/OTWrVZ9mtaun0U1iCywSb8rbQsHxQ4sD1h8OQeOaZxQSmv6F605y9f9dKvP6mtqrafbxgGpkVoLU017/6MKIx1bkt8LAN2lDq556scf9CCGlrf33nbY/QL8TB6z80HfZ6uhhxTwc19IT8TNH7kgs/jhgq9ap0GVCZ5EwKkoYa/LGHtNHhGcXCMTR54J/ORYsfUKgJeUYb7d1QhR173IfPJOrro9PzYC9AEwgvppwTVqMz/lSmzXx7tVgQFUyKcCp6R1crDMtztRkD7W78WDDaZtWmZg1lAmQOqxynO3NQRbtolLaY51cs0uz8Eb/Sw+N7oLGUMjKjGwUAGqbHIoAdynwfp4nB4hrb7wWB/GoW6b3bd8LK2xjQXEgtmnkljOiaGE+ZHWXJLAUgDJ34lU79WS9/IC96KoEde65qLJcFf77LhdywsZY9h3W1aeeQ2gOyARbHbH3QKQASi8u/R4s36k80J4as1Yc0wDZNkVn+07gA3E8TELVRBOvmcB9pqoVE3hIpbC2gNpMzWQ45oncSQsbaoczOofVm6u3GqttZTM8wpx++T1x+q5VKtP1yIFigOmtasEEeYOmJY11cFszXcUUFTbzyzx6kkbqoebQECTuJKJgS4effguLDJNs6a9xPWgzcBwISTWDZl1c4Z77vpES7m8mblIjLatcaqxWBP9tY4VuHnqnpH08+HbEhCEC0vLrSqaizUErL6QgB3aCoxrPzUA8RsSlLyfxaHbeW8C+AyPZetrcjDrh10zWRtdgTQbBVauOq7IviUTFNMCQ5edjxrABYDGrXU87Om3GZZnALCsxnaKaKnXjDCQZ67f6B8AWLvGtPjRggxXHiwt19DHXHj7a+b3etC1YktRQVW7JjUjc47tWBqHJ2Tq+qFp776R9POJRyrg514T+OkuJXVVSZKE4X5TeBrrSwDVe2az+raOZtYHdxzMwJjWjDnPvFpk8SvUc3BkTdLH3I14+0ii9f/xPtOqe24+E76ijo++IdsKVYNFVLPfapl9FrV9C9iOGM/QUC5e3EJACGm5X3MgPQo9qz5BukGoOS+mmLXnWAeNgTaaB5mFSYPsm+pcjxnPJ9WZ4JE9lvd9oJQTdI9dns32c8pj9DNg3vMk5mbc4B+QKQooSAtkSjfJ37VB+Lm+Lqr10DpqBxrJvXQNxx3zfKuPmO0E7GO3c0+yruMhO9ZTlSUI8Rl/N9LfxP5pc23FxLLZX5vMQb43h5GH9lwdYPVR3la7vPP5oy2dlvaQfarBzvplsx+AjA8DgCpR+FTZHG53AnTYWqXBoDUvVmHv1D6gxr+PAZbkHb0gRnigAY8ptPQ7Uh6gs2ieqd5cgNGjTMsmrqifuj05W+IKU1ZWYwytMfk5DsLwfQBgwKfiSSrsTGo1q4haPvyG+d5ULtKKJ5diNVpu4+YX/O8HPYih5e3dt226X4CfDcfc86Dvs9VQCfyskPREPOZf35pZ/ORYenBrDLqAu2ojzgONikiIxOWD3ivY75r4RiVgy538ka6NxlWfq6lxLNDhUfLJOVVRlhraCiArI+DXilP2xQrwocAPbQR1m7OETF86Bc/0Xg0wcIBC3eNxEM8AmXQlrNK0qoKlJWzgxwDKNMDDMybE+aBLeq8H+EFkM5Gqn+h1lzkpbzavRx/mLsDLsiJzWK1xgJCCQ5yJ1hSHHmRsAw3OeWBNbHXPUjb3JDvstbJGtIlVWd3dFtFigGlLWJn8OhPkYFfP4oe9r5iCGnPpdI1Jnjsh12Sb97mtBo12qv2IM6mA7abH7w1D35rDKwJ+ZAK4cBdMOs+0BRaf5bqvKCBUZ7GyNMhDBWIab4yDSZSiWGCUCax075tdSoSbkLmIrR1JwIjpRVOgWTOUCf1zzTo2Dc0ZAi6QMdNLBIys8Mx3ADaPmVY1AwzU0O4x3OLNExK7Z01rG+4acu+v7SQH3pqM4R5U2fYWmQVZtRpiebZhgskAAiIQthVY12DWZroPo3tIIORRcyyHxpN2UgE5JIBknYAgXce4jrKg6HR/0lZ5XCCf+2UyPn3Hmf29nrhjxVLg9rvWo3/MrF8ncqBrhc8JwF6XnGcYUXOQx0zbODiHAwxs4tkVtw0dxCyLPUStTbSlD7ecABKrNL7mqMXSgIrpxQELvcZj8h4dZk03Mpn03+Ji9ty1Y1mfbh/NXDO4JcytU2tw1mFmnCo6z2+YOjx5BrNe+a1yI9cZFDVRwAkA7jw4gSYXYNW6rxFAwLIQ9IDGSH7MOsCeX5ri2MPCnDkGFkDb17X2/piAMC3lLtxYY87DwI+xtFizzlqfKE703s33/tQqnVyP2fzUe6xPlCwGn0vmDOcThGcrCSkPoPc+Dsrr/SJkgA0FQPwgRhR6Jq+IjGehoApfc7VKmAIlaX2s7/m+r6k9X8P6jeZ+wXlw34uxl2XnpDQ66lYeaUsSvmfQ+nVoBy6T6DJ37CHuppYSINkbKC8Rk/7jyiNKosANHQA6ChC2wNGmchkdYvE8ybOEkIhizzqr9FlOwWzOqywv1yzAkbdzSZ1l/f1mG4SQFm/CXcI9L85V+FLi+2QcC8MqUhOdi9p63FV/tNTGfz3v/Q96EKMEfh5YekgEd74/KYoEoBZpHlxhgS4UqNGAAhfu9L1RwSQXyR89JI1ayE7BtfUuIIC7WnA3MqNsUbuQHOZ1xthT0nFjuFWC78eI2KbKwQNtcUK3YoNBosyHJWQK66CjjI7fl3zmB4dukyXYkv4JHBu48Wjav5z5ERIRY37ooafHSnCAQtgMtvVdSHTbgdUXmvnhFjy8/l5jXeSGZs0fbulE2pFeIwd4HvgohERQj2ywjJZp9KiDZILjDJx+J/ru1JLMmydbJXv/cCAGBthcIGU0sGnPJbUXePkACKAsa3L2FMqYWgyYyI8/pYnHEaN1eQ4NNSUXQ0jbE6kMaS7XzU43AH8yZWxTNwtu9q7Gh8ahWpamUKfBobnlTJCjfUMFMf4O9UqEmSVTGKs71jlfW9rVizOMu/Zk8cu8Soy7Qh/teRZIe1/S/gECNCzeMWKUice7GFhnAj3cvH1KxZBqLjCTtMhDH3Nf47FQZEVa1nRUsz6r1kJjyGSm2+0KRCVGfIC5TpLMiRhRbr5L5jhJdUZteljmUrXrbhMU0wz3wzZkwWBpTDgaEJ+7/M3N9xmAH2Ceu1tGkv62zPg3JfUcO2G65FEXiVh6eOwxd1luURp820vc0bp7TRAmWNeEz8BKPm+WVFwpLnDcsms95CxTsLC5uH/dINr3MjeGsWzuaOXFcUeaLm537p/AYH/LEoiHSUy4haaKwcaeqcvQBBLBJvfeMzyZASUH2iRWF+lfHvvG96QVoJkyYHPK5YjHdjmwU80Vply6tZUBndFcFcM3B4hZQqe4pt5xX9ZGb4itkVqM5gEG7LHU0sGS2rNGGMco7bJxjcWPW6hYSopgPpsTclgB4cyCvB0JYH89zaaqKSJrXAdNjZtuS+2wSiwY2ryd6reOmxeIhsmeafE+Dkt1UqazmKzHDj9fdF81OP9AnuslZ3XEzuNYKXSodVPM9rtw2UM8yYAIzivdk5wpHhs3saGN6Z+asby4JUmwbdHSZFBAZPpgToruGEDHs/qRHvf7AzegpBWbhuU747+W55L9xLC8puUZGGPUHwsDTASYVb8qHi2542B1ltl1yn9FAl49xJ695sLUeG88kpeBOKF5Bn5bCkHVNwvMCwEe4DFLHc67cc8LTnmeGHHkodsKLF6N7qcdBRbFkV0mXn5oieyRlIjuo23Kfb3/wUgPrVl0P5BLCONUJNCngru1i9v12PM1scbIm8f0OTE/mDxhbTZcKyICmVs33cgE4xOkBGTkWYKcGYso+RIxbY0MRE/3IA0UGK5epP9c7ly0Lr5B0/SlqaaSPTPtP3Yz7Z9eAAklwQZYSuG4n2rPVDneN+q9LdCO+3b7MjfGT5EhkoSwgR0LUJCqjb03VF5CxvY8NKr2dDm7b8O2D+ExKxSq7dOud9b8FFZ7eRvSrG7E4oda/wz+NvscMMXcwlYfnT7G1Btgr7aQclhXAVaMKwv4iYTFgGoBloLMfK7EsbC0udYeIN3Xgd7xmnrdUwwQSkjJtWg2QM5Ja/nosyO213VEUi81S28RDTQF/SyQ2ZP2XkDmogbKqiytc1MBEDx+1tBIBrhU/Qj77x1F37gJwjTWKmshwvjLYWYCPx/g5j2TxrWBPlNI2aDiurQH7YDwe+4wAZXGehYMdssMZudNRjlyuEwuz5igWNAXQoY2ExwTobGmFAJhh7crqf/gfCZY11nmo+ZcImjdLLN3b02RNhCBvsPcp+LQw+yUaR1DNyS9Hqf2mkKTtuK5a85MOrBEgny32wG2rTuIu6fMMi3lckAFOjnMYmN1fCwcNK1L/FFz3uiYVDyWiLevBmw0LUzkbnNMWnv6MXz4rHFtA7NWAuwYF2tHFlDxIjSZK+K+32Rz59EnJm5iHCy762DiNvHLhcwFLGSunLFyMekn85/G4aLCFc1cCgDTBwbxs5qZ/ZO6bmiAeG7ZfGZlLJlP3EqE7ovVNcuY296P6rRZpjqj9ue12XMqc+Z4dEYBDJrjy/mRUCR9Jbn1ZSwsACGWvA0eKuy8CekyVfFZJHOfjusxqksCLJkSvA45xxaTZzMDQbTHZPrs9JkNs+36N1p/1EcUalRRws9jT6I2yyx+yGvrrZXHkdeJMttj7NygxrXzgN8G2mPMIk2VIUn9EAe29c7ikNmPbHjQWFJ8LMMgvZkqoho7TNi9rb19EFxpRKyK03nDgDiv7UFEBGzT5Sl/PexWQ3sqPpS/O9tv6DgBwOh/J88L2ln9YZ0oSpaJJVe/2f7qfIwOs8igIYRaOjRbDtAnPe4ynX2WPtAd9gDmVqerGvjvbBC63JBD2mPE2dXBHcn/+W3sh1jAZx7CPFmgJwGWyNVY37qXBJOx6nNA1LBZakrdEaWgdYCqwvZGfFBTDIm4SGBZYR0lmVQCP6ukwb42fHXwutJUArD86ylpbVqe0EyFjDxtvRHfhnym5blQ4Ao0bDHrjjIuodliRlXQ6eWmuRPS+nRcgm7FtrToJZxqRlXmxG6hcYkss+IgLrRUyN7PvE8z7vxen5qs5mze+p7COEvCTnOdV85FFd5n7Pc4ygcajOuO+n3Wzy4tT27brGnN7uUqSJjCt44ZYLmpxR6irsO1iIyllwNIaeCF1sln0qCyiKAadhqHa264wJ0QQIMFfKaWNDpeC2+XLtNXZYGVyTr2lLlz3n5BzbNdwR/zAl1r0muZW+W4gqK7iKcfpgEXu6pteW0vqj9d8wXWk3mkA8kHhoVGVg8NoMrbEMaeFVzTCB6q3omXwQosiAMRw98ssWVoxrjep4IGXzt1THqtf8wU7jsDgaUdXWLzcHYuOZhi5k4rJYC6Pb6Uotiz9uE6ASya2lqH7w1eDNmtwG8xgYbEkdCuWTx+XHWnCiK7Lauz0Weuo7a6p03evT6e9Q1do1ypACExOGKCbB2yvnX2PJ8HNldz2Mr0SOI61GpdNMOK5cYjVUZP74QMuKAWM0CS4KDTb7a1tdPU9o8dN5vUx8apc2wT0/MmYFPdYsYQ8rzYyhJGx3uPypZkndehh8GhJub2MGsb4vL3810bAZjx0oDMNY3GRYoYDzJQU3FWiOsbDbq+PJ9JfXul2QavGmGWgTr0HJ/VCQm4e6qyMOZB7akbaDv0EWxcgLfZ7VI1SPa0mPE+NdguQ3xtNdgcpORKsGHQVvueGtmP9P5L07Qn7RTARgf/UuC2k7ZX8y8FLuEu3jcv6YjFWwlpWRxS4q7P6XX1v7/AilVKlTjDsmZP/lOe1Hf0TR8Dp7klXmsk2QP4uh+shmgwl0veZ4vtWuF5rHkP/kwd4oHPKyPBSY7Lty7jb8/2IC4LRFuTNtHLRkw8oz62zzvOY1rGd8wlSvyd6uxcDULfWnO6X6MnZu/EJa5qJbTu49Q+Nnn2kJU92aEkdySxKOJf9J5g87ECPuw1QS2bR9TcctUfLrWxI/epJZW0MiqBn1XS9NQAvOWc4M6KbIGfWqfY1itAJkya9+ZI/fRcoBsL1TS5EHausXcBEzl7mWFFwdQg0pMQldhKUU9pQfn0Oq1HOsUbdEpRTn/QNruq4swOZVA0htHNqZvXZzwrr7PU/5yYRGlVBZZKlnoivUk4n21lbIsdz9S30DFYgSBtt63gN14dLxsL65G0OWGghCsecNKTgBS2dZdrzltzQK0tYoknuKZVWTtQhpFqfai8y5SyiKsSzT5TiFqmARR1XCZmEt1U7Vq0AlaTd5ICUtpgU2o5yINgM+LWT7zz05hRfP5IWGPJ+961/9F64rRuG4DUzCwll8WPZfGmGVkyINxyrhc4RF1LXe53HACn1laaQcuLt8AFOeqCIaVANFvDXmFaidQnEqGZWgfxWBNR17f6vz3F4oOo2FOSuUIIaVsbtJn7UTcUljUDXYLxZiU4cyF3zkNl3kd3nMUkopYFv0me5THrg+6waifZJy3lQ1Vb8bktyeq/yPqgPWLeGg5FWFhk7hPEnaFPB/DkmnkF1PKAoYPEXa4b+ti5awKCAWHeUSrGz28y4GJ/3WxDY69A9Y/MGDP1I00wUFuuLLGYSgMDLYt36N5jWjUNHDODW35jWscMkdTSOibQ+IRpSrJ7dhiLiw14A6YAS2Nl6HnZuc20ktqnrAji/mzfm1g/Z5TRcXZoEO6lZRLcllhvbZ4w+yOSXpoFTtP+pey99fyoMLBJB1cNf2G2d//a7FnBWAtRx7cAU0+tp5iApv4isxxqCssSg7tFzfep3x3bPNcRMkNC+B1hXaMWLq21an2w82zkVoHFwwCfWQgEBINqj+qHsjaorgn7iYKMl3FgL4ZLGg1H4EqwYWmqsvJt1X+8X6krUi6pIpUF5rqvLBCjoQJlm4CdEINRZXcyh0PWlOlNHfgsgLEFREthzQF6vtXvTfad+TWsjZ5MeG6O7ZA90Vfuf9wtMNZ9lh8KMOV9DYsVal1P63Twd1bCjZWQixeFySPJrkj4MR4DVFsLFcgbzUqcunKl9eWArEuc9xew3Pm5fCD7I3sS0yLqfkvOk+qVC+5Nz3kHnx03H1omPzEkotLi536nEvhZJfXfXINfS5gVC8RW3+tmFkpDUGwqN2BugptmhZklIBHb74Jmch9lFuhBTg/jJovVWZsiBz3cbajO2Wa/+h2p2SQ/9GUA1A8Cy+vZddJOr+N+pvSAuskDgymvUFHvSPvDqJvwqR47kbv9NtBA6wm13MD3fnWGc5PPyhJhhlQ78+ZBdZ4IpxWmMehINMfyl19HyQoBC5uk+77L3Jd9Ui6uJhbgfP7osaX38vdDDES8bqaw1O9tMW/ScZ4xprU+K60xou3UDMfiJnMSNA7EmD/cs5lgOvaqnvqUWX/QUkwfkeO4eXJHyWm0H2n5wXuzsaws2ozOwmZzLDsDRPOt5tjALs6UCePZmuiYiDgRaPOMCGl/VxbYbzHQMj18rHq6KmaFBXwKWFYcUb9tmccBhZVZgQGyKq3rdLpKbXHH31sJOCEVAnjbNehG66eMlWHxZz4gqEYWwx6T8l2ZjHOdWabo+DVceBTEXNtf9jB23DSm7zWF0M6uRIBtEXcfztSKWBgZeQBboBLqfsHiu8GXqOxj2YjqtoDQXss2DApeqtgClVmz7qguEQ5FluAZLGTl2oep9+JzRccrIJrZ5l7TmsVX8VGwmWRwIUkEwvVEUBhjm9lCxWgHwITmAeWWxeM37E42wDXHHDSuU0sbGQnUhtpoMMsXHfw6pAImBwOHfSzuNcEgDgqjpazlWCD5+ZlqGiw+u9f8PndgMLV00UStexZU9iWx1rQK2jAyh/l2HWMNc8OfJnFzdGrxuSOZIKRj+JENicZfAoDmvqT/BjdkG9X4UAY+UUuEe0m2NgDo7G9g7eEmo7BAYnhNrk1ApmXmNju3M1lrYixfOI32NSCHwyQbpvFOSXv8JQIUs7Ai4UBsufLxzKU6m6Yzeynfdtn3KBQ2cELryVE+TT9awFvy0Rll70TmmbW3atJZVMmathQuqdUvNe3Jqc+hgJJtjnhlHyPmnZkWUU239j3+Hl2BDgeJXMI077qOB9T4PHGfbzGvv+0hZIHYnUoqtjdTRVN7wnFuAQkw3utclaQsId01km5HrO167/bIvZTHj2lxl1cAO39poguuJEvrV+2JBpjFJJlzXtOH1xZo7GNKECWLdOkWynmJ/hiNPexsZ4n7NH8nGRsuRcazpcRB22nPAV5mH/U+UTGNMVFdlAjrAh2TBTD4stTdzbG0o9YhgGz/g6l09XpgqAR+VkntCQmvXjyRmgwAMbJbKb/qPB9P7V8NuIU9EbPrVKAhTK1kAEtn2A42x3eucNCzBTlFVItlbZS+RHtEWM+k9WvtGY+PEtVtQIpT6GIEqHKJtEf6ZsdW5+zDh37XGjEOksRq8+W+w801RDut+docK5e4UqBmcTB9RjZ3dThzH2H9LI8BIMY7yuR36721sRC5N2QgDzxYh1zEfJlTBsyyJkFPWt4gHOrD7KMGLzgY0pxIYh1YftX0HRVoQ8cIAKJa8p36j4csgUus1rQgPLxeqwAwfxixYGA++GGfA1Ql2sq+e5J65g9nVkbqXax0opQxE+o7ZzzUvKCMY2sNq8eDYeHgIu0+6bL+i0e4dRBrQ9uzhNA8raFF9cieB/S1dRB8Jozp6xF1e2JCs7b4CUimKepmVGQlFYWeZV1luNaqd7ICFStLmArLUkLTHXc6AWIpUB0ztXbRoAokvz9D1zlDHMz6iJm1Ac26BQD13ySTmq/XcEDaDDgfmv4IwT7zRorPamDQZ8xz2C/Rv8NHcyMDT8i8qO5K6uVCqDeWtD+mwYr7Wea76aRPYnpWUQGFCh3TZvurcx7aWxgYRIAUHfciHDDbVd2UHFIH9pncuSBxVOL+CLVGF7O7zDL+SMd6DzFnHozd8RC1EXPsOvuZ9ZZ6Rz7GlWqI1j6WonucpTteqKI6YV6jgUIn1yUgyf5pE3zatzQKUYuwPGD249hABgTt+/k6o32aog3J8/qHs7nNrbc04EPdO+ab2Zyn66wzZW7QwaKHqTkTFIxms3ubymWMu3cMbU7ArYV5JsmRIKnekgc5VTGslQBkaanJnAlZcGeXhUiFja2OL2S5bggg5iAtIxnb1p5GsF1tcca30oqAN9JGzNNOk/WiU8HnueF4BAThVpTayjIv2ULPJCUsCyB9AZ3tMk9esyxFKcAiJGRkx0/0HPH/rPPMlz3d3L1j2u7roW9la+JuTADQqZvMMz0flpSrI89+mFih5rt7A0Cz7kYL9NypEndWy23J4QpPazFYS1dcv5xENUDGV+TNMW45S13Og4kY7U6A5Q0MLNN8rJ8/P2XoI5xkiSHYs3VCFz7mQLEbun6eFVKAvKJ22+OWYcuucAsw17grGYauXyznJ9ApqaSVUgn8rJKiyTZkQ2s73GWKYrt0VKBAbgKYIun0EHPtPZaGKCtkHDQ56cnNhrKvnnRqZwAWsNrhShPJHK2Ebo62VHEElO7yCHo5JGgbcjRN/HzprLXfydCqae0WczdLwQ2uHaH35gVL1mWMrF4OzREfo1Vpz3pYWsTF45HRStDwfAHdKpbXFyt8XHej7lO78e3Q4erlekfrQFZjTIUCzvQpoU6QGERG4Eoih1hApTDdAQDz+8J2BWIwZlcHLec+/QaYK2QSoJy7eul2EQbXyRgW7BdAtq6LMr2l5Ko+p11Ath/lusRyBt6xl/FA+XpvrQ7lM0C6ng6xvqHp0YsyoXmeLGT6dIwYvsfrlPJF8Zq6dR/NTsVyIdq0MTERvTfKJlaw13TxCdd0DVdFABBMyK0+LrGI4MJvEAssH2BZnrj5PATCUeauRQReqQJstpjnlIwEmo9oI55naBOxTuquUcEquXucFohHCDjDur67JQESJAmGXF2boU8dMm8ld98cjCz3C58ENvUmknstfYUe/yVmvUUAFhH6EEKib9JU6S7P6tSC5D42V4KDAaIhc4wak6ZGpK2CzfMkDX4jRmXCBA67zMqsOtFEZ4FluiPrZa8GtFqmQLXusBnMLPRZ49Ql8UPCQbVnMfDSU3OTxqpYarPsdWqvogHQKSAzNExiNzHwpLo2xPKCCQYJ4pKmBa0mA2R1iuyYWeH4BLyPqxKNvR7azNQ4UnyekWmLb9WLPmTNHKMOX6fKusXaVVZwPosYNr9F3Y4rOQBLKBBPV+303uRckjrTEt/vKrbFD+eDtNsMBdFz4/q49lNuFUUVedzaQhfRvGOO8RGgrGxiYVmBxhq+oK5EDgusiL8n41crA9xEWtflWVnIOPgkJRCytUrL1O9I1kvzCJt39/zYZsNI+6t97nZp4oH1jd9cZjkrBN0gHcCko425wA+3siOKW70H5rmYh3QO8WaFngXs5cUL7LCU6xCOd+Jsp+/IgEzjoOpEA7xrJCz3YADwyLyTAT+bs3ZxfujBTn/IWb127NiBd73rXfjud7+LvXv3YsOGDTjnnHPw9re/HdVqdg7t3LkTr3/96/Hd734XjUYDZ599Ni677DKjzO+aSuBnleQdqMGruwPTpfFiiu7X/rRsM9MWAkWWNZDJHzXDpJ+pBYbld94RRqYVZ9vadst1/YalheXqJSEkDLeGpEJyi2ImqvO2iS937eKuZOl+mtN8ow/4GeZgnGg/pVZSeQCFtblnH7Xf+IpNnFn9Vlvp+wnHNfozv077TCR/eX753DqI18utn6yU7AU+yBbx89O3rxnP18/OO99csasUpa5JeeAlWRM8m0phvCWYfWL1vQf4LD4KyHdtxRRXuLuNvs6aS4VZkdTPx6RHYsG0XTzTiO2mmfzusizjWSUiHjsmErbVF2l7btwsPTeLtGo6Zg4zvdeWg9SUnz9Hm65Tiy3DjYmCHqyJfi1C1CrQwqv3DeomwDO/J0FElpnASoXobjtIXIQGzTL7VOpv6t7FXbH8qQqiCeZnsmy2c95zB3f2F33LMovHVZP1yFIO0FhBgrtm6OsAombgdPtMP84nE9xngEWawW9fJtCLdWaZeF4Lp9m1kFhJeLPZu1anWPyhCtDZYs6fiAQQ1o3u22CCN81dCt2yLGSzj7IrEIa+HZOombTNABbWmePdXaig9hvTaqc5aQJUNRXAujVjgh1LSwMIWOYzsBS/0YEqqltMIMkIGD+THOaD22aMMq1ugLDjoztj8jdLxPdGZx+zsnOqGH6dRjZHQwbC9LIwoRnYGoPmOy7PNhCwINyjg5kl0tRscm/AAM2pXSMAAK+PuUkKktmvFmN5o70X6T3ROBtcbWdumcECO1v0PFqBq5e1BgVsK2yqg1PzzJLHA4loMITgcVDod50MgcdIc4QRsV7bZY1O21mY2ELCy9lPgAxwsvgXXX+R8qftQYQCFc5nKvIoxsz466gu0dhrPpQrebxI7UesCVEd6LJsWdztym8LDJreo4b19bwKct93u7luompSzuIByBLUPATvs9RtnGbL4ix45L7ueo4rM53PdDCG2xLPdMbrZjIJxTX67vGwvDFG4157XwcAOUz4JH5kBxKCATpWHEM9/txqT8JyXeb9Ggcyn0cFUpd0HjoDUiCu2iBeRPo1ktoHjd0rgLi5MiX5g4Vi2Cz/odTxQNCtt96KOI7xkY98BEceeSR+9atf4dxzz8XS0hIuu+wyAEAURXj605+ONWvW4Ac/+AGmpqbwspe9DFJK/PM///MD1LLeVAI/q6R4TRtorG7xGdmEXL7RhCIj8K6zNvO6UQ35gZ9BHixfXAslH5CO01Zrfzx+KbtPWQpZwgEtp5ifNjOnFh3PDg6XB3bQU4HyYkXuLEJa77kSq530fbn1CA1a2Cu4cy+gOaevjTpySPB3lo7PKwWzrMp7/F5UTwEwk/wuijOZaOsXF4Mdi8JA3ToInGUVpEgSYSV0BN1LKsmuU+CjMlvAqHq2G0tM4qPozER8nut2WgFiyffE4seDzLFi6hXc2ZFEzSQdgNmh4YqYG4LH4obISFjWAYZwnBPcGUL1d5FskBPcOa4p82yaLjtnPrWJRpAGjhVebJXNvksIxsDTMjrzVY1Z9syrvuL9Qb97QYxYetY7VVRWkahKwAI2rJHnw2NZACWzFPOUlZBkAFE8GsObY5pmBuJ5TT9/PwNSQY0rD7yugNcRVuyT7jDZJ9W8935jWh3p4LEx7e95hoLqn/qyd49pFkfST61NTLj3pJ04gKzLoJ81Wt83klyvNszfB0mQ46VaDa19fRAslTcG1FgSHqGy0wRvwoEY7XX51lUA0L07ATIEsyT0XGAqt5jdaFv8YDCTeDW41mQxeDxPJuuOgyR0H9D8wJwJDmm3vRaJvyTYfK0oKzDaNo+sS28qG3tvmPkUekCDga00+5PeBzuzDGzS4A1LP0+FtuqBABDAwC6ziE4fTl3JuatxsAi01rCqmU+JjiPILb/cMfHMMl4It/JEf1bvwQXM2Ac6o8KK00bPNN1OLrjTOtLnOOKgAKw/KPuSo5DUFDBwyXBz99xCs24PV5TQPUnIRAHkMQMY3c/cDdYoEwq0x81rllJSY3g8RmdNWsCzNbYxsHQY4xMIH1edSR7WHnGclw63abpfc2vN7JnJfT6NdWZZr6jLdAzI+xl9ycYyrsaImcWbGetGPZ/NMX1eRHXzXgpuLR4ZQkhgeSNrr3onA2BlTQj7c/ZK2oYUN2auZKEd+5Kzo91B+5lGGc+tUBMxgEVbseSKX+pyz46LlFIl/U7paU97Gp72tKel34844gjcdttt+NCHPpQCP9/61rdwyy23YNeuXdiwYQMA4H3vex9e/vKX45JLLsHQ0JCz7geaSuBnlTQw1IKvsjSsJMXySkmb5NIAh3mmibk+2fQg4D7OK8zelJcCnLqRufAEAdPthJMWmHgZVx/mpn7Mqd8wMS4QvFxltGk6H7PUBaZAGCoycU0aRgvbP8c8EJ+rPodgCtgCsbuBKyjCAQWsAK8qcOGx2sV5gqK4LwBqyjWDzxP9JO7/7Eohm5eylq4J7n/fVaa51LSbxl8wLNO5VrYirdgulRVkgtL9V2fpRPlajWKRe2+nYK/xYJsj87p1+mWe2tZZH3cbcKR8p3Mibx3zckXP4uOU7ZP5Lm6umAV5zwtc6WkKSKffbbIgslsmE3etUBbvu7EUacYmTTq1NHXR6m+YFhvdUd+aJzMLptWIf0RiuSKtdO4CctzkZOsM1KgEkZXml5LubwvHlSKJi8SEfEHaUFNudp1B1i4dp4pcqzGrDp2uPSRuVzUCYkQETPWZiXxnoWYFOaakz6Tl/Syg9FDShgp7p6kDmZ+bN1NFZcMy+hpuqXmOBPDmMYTiRmxnifHtPQXILNw0iRgIefybZTYqOxsQPHg37Vc1J2lgZQDYPzOIaKliBDMGAJB4QdFUAvj4LK5QqKyOKOhe4WO5R2VOJEHMqYsgxTGjn2X9BwBYH2F0oxl02uBJVBwlHu9I7+Fins090mVBM8mONX2C+Ui9dKklNAc+5XppBkEHjFhQACAH3UI5PFjuiZwsix1O2n2YZz+MAVGJEdb5/CH8Zd6jXXsl50VTq1/pLtODNwoLMrnqtVAU28hoCw3WD7VX5enkmFKFU9xmIhGPg6nfm/d3JNDqY+/EFD3B/ooVgJ6ezykmN8D4AT+GFA6LFVp9y+1apMGhaKLYFSxpjPsyVzpZtxVYS0dqjw9z4vlZY0zXZRAjagWWwkxX1Z0oPrtjh0uVWY/aYx0ZOrlShXdNJScOjyYtT3B+N5bCGVOIzsWqOsdcMaKih1iMn+h+yOp1X+9fDc3NzWFsbCz9fsMNN+CEE05IQR8AeOpTn4p2u42f/vSnOO20035nbaNUAj+rpGazAk8cum9eV8V34Fp+v6qDrGbXnKmYi6xEelBRMDLA1pxToqALD+inBawiMELf79LO9/LFzXy7804m8plryl0+8uSGjnCb7upnFeIUvfq+B/BjVbcKi5/Ce5H0dy/hOvc5vV6rCPhZkQ9SPoWK8XLWHdkMHZ1zOitLXuC83CwmSNxVABgMHdXWmNoj9o4dIOyyaNFEU9/RJvX8wFfVLPG9wMgdr/6tAAwVjBnxPDtleh65AF8rTTqrP+rYKcbp3qDdQVzMnWvduwJvxjwlrtZwE6Gc72OBCphJ/eEpc0fdczgT1gsg1xYNPgMFpuPEQqPeMBlsCjZ2WwHirm9YHwEZ2N+az6wo4l+zYDoAWExxdCdNwbqjXqXvbh61xszKCAADvzUvzB1lW+3QQM6xag4vgwCodGCtS2rh31yn1iXf57Vmk8S+ajVNliRQWcRqRPg2MvZQkGKZddBwBNlk5v5D2Qu0Z5M1K3iActXO5nL+OR/1R4gXq2iy+ZJmHKPxUfgaaYQYXGdatCyy+DVySltvMSAjFqgyS6Uui9AvByLrnI2JlU1tOKlzz+2mqUp13TKirrAyFhmyvdrXeOYql/WUz/iESPUJFYb61mVudpWN2TzodBlruq8fnTCfXRXK4ihgQJ8OrioYUEuDj3eGJfwlD30zZp06I09tLnuP5gRz62oCs0eb13zmMt+4Q2X848n2OiSrqH4my6C5tBG26zWhzoiyjnG4u4rYXq/UiiDUCky+D+vnkbODWzFp92WaVbY2m/Vpayy7l2dT8roScZXxeBQAydlrdJHWmHmdWh3JQCJYcgjMelmSbHvW0RgJZ5p641nKUpKf5SKQEAyAlczNKT6sabGM1GK3/+5kcJY3MbCgFkP0hTZbRtlFl1siyNlbwJunVneGApVWkgPuQfHzBTyZTnqwEiDOeq4U8OuhzZsoJYTpbsobISzAissn6XcebyoWEHw/47KSSwlt9FPWDvNG063LVX+7AOCMlx8ox6U/TIpk8ndf6wCA+fl543qtVkOt5g7bcih055134p//+Z/xvve9L722d+9erFtnptgeHR1FtVrF3r1777dnr5ZK4GeVFC5X4WkfzDwhgWszqEWiYqp4oM5QaXYM00CX9YQnTdNuWqZqC4y5dQG2r7SrjC5CBaccsKAwEHKeZiuQdjrmnEMqN2YIbQbv+thhakw1RKrPLFNarWlbSdDbHPcmbrZtleFVU+sV/XmFwJ5RSgpAyFyfbUNzxs8l0aPd5B6rZULaSBn/2hWFoFYaD4HFl5EenBlUjHfRzA/zb0pdHGlZK5i3ArOoKTyZb5V5qvU1mxD2S0tLZ/SDBkZzLNZivgyJlldKkdTFLX58xz7iSuPWa/7qdq4gMDSEbV3EwRuX6xkPkJlkKbEDVtMZldaTo/GmGjku5La7aoAWs+ONuuR4hpuY2bZICktLR8towKfGggZ3lWVBm2mMaUDxoBoBtQidJRbrSbl4DU9kQrBg7iPtToAOC9BZYf2ng+a2RmzgJ2QA2sHNZr/WHEFLaZBaz6XhRzJFWovVVHGhifZhoPqMx4XR/mwcRDNIgRSGhRdN+TtGAECmAa4I20qUZnfzlUWKkSEO2T21frNPusTfxauHwF398BjIFij3tXBtNt8sa5OlAAuSZfE6aDKg6T7ILR8jge5uE+ixYvOt7dp9TeawtqbU7lmaOssVoBGhwoQeGkRVB4nma7e9X7WJ7DfVCRPFqG9NGG86j8Mwq7vVzAfaGuuWsHc/swKiIL3qYwuXnExQB77HSpI1z1uooDsYo8tdbDTITBBXyQRVKWBlootYUPDOphyLH/YOgEP5JmG7iFFS686y1gsF0BUpUKfJyZNxt/G8pBWEeDhHi3pYJAvu7kKPyzzXfd/dLh6DKeoXNk+mixTFa6rEdtIV1pbUGo6heF5XoMtcbAWrP+74qO0057gG0ABgYXtyv8/2CxF7wGLNAdBl9Yf9io+FmzgYaTYs2cNo3EMjT0yPOD1Wu3i8SRdJ3S7mbkq23LAPqC44+HdFHRrjh5WpLAp0B4sV3Xqe+1yjIiSiDk9la38t0qn1iktaRHksvxSAbP7urFcebLR582bj+wUXXIALL7zQKnfhhRfioosuKqzrpptuwmMe85j0++7du/G0pz0Nf/7nf45XvepVRllhTTxASum8/ruiEvhZLXU8wO9hfsuJrlUVnMuyPkgPW2Ff43XRe3u5G5Fy1oHKD2df2ruOtu7oBTRHAgE/zKlM3nBXwGMbOMu4sjlQRoEGlO0VYwYwGUYt3HDfeh2EewUAe9FGfV8pj+kriscBwB14zmW+5DJC6TVPcuW0AuBQf61IB1NGxqPj1lxBwAYIAQjyTC8PJNCpwSm3yqrSv1Erh+HfZm1oj5Bb2fv7bWB50hTcODgEZBpWTVFDgTdFMao86YxtlHZZERjqwQHeOPoVsARMV7useR45QDxaKG/uyuS5ljLMdS93gdGxf4hliBWIUVt+kbgwtHwUZROQm4NXaqEVlJYKA/pZXdY3nnJnkjxGDbEsiLpV9O320dnEXEMU6LL8q0yVbQV3bgkr2HB3zBQow70JmMDdXYQv4d3DLEpY/e1YpIGJ0/tIv2ogRzAZVlaA+rRAu8DsPu0+Nn8CZaET07awMl0loFNAlgqz/h4agIWBeHVpZdszEhDofmJrS2eAs7LykPrDqTr8LU20DzDARusLDmbzwMJjpYBgqefBglrX7kjGssViYAQDXciGOQj+nSaI1N1fB4ZZGWIJE82pZ3NXlL4EMOJZwvx9WVu7teSzFXRd9asggayXp812+Tpz4mT2rhGZl/7mDCjiAaaXlyqWdWGVBEnvqLHhWZd0PKEaC4oqSVDw2AeGb/MwsNtcl0uTycB1s5jTqM2Y9fhdiZntbL/gFl4zbkBLxPZZYsW1CcwYQ5z0GcIVV347sZipzZiTr0swwzReDWteGgeHVsmPDc2uUr0D+Wy8Fz8GpB1DhZJ+XyuWj+6HIgyjJuG1bMsd/V33l4tEV1ggh2T5uGOtNOCgzkCExi6zwd1+dt4PxmhvMffmNnkZPYbxsA0UWoAU2Hkc5pyX6pyKSBwbi8Wv6DK0seQzPXccij0Ohho/58UgVFWGLDYiXeMyFgiHPMN6GoAzEy9/J5590tk2VU84bFv3yB4uli6Z3QBt8+73JKQrIxg9g3iWV9IuLK/AZe9BRPdncOddu3YZMXXyrH3+6q/+Ci984QsL69y6dWv6effu3TjttNPwhCc8AR/96EeNcpOTk7jxxhuNazMzM+h2u5Yl0O+SSuBnlSSDuHCjA+A86NKf6jkHj76HVu0yq+RljDryLTkQC4eFCRPsYoc0ptMxGu4nXGqWgACier5mK2s716hEDlNLdq8j85DRBL/gvV1E31unLvXZQa6tRgqEcukC6/Ke45oTRW572kokD1Uq0jYIh+ZQP7PHvfp+4zZW2X0BqntmVszxodf3FrknRj2y1kmPMApsjg3dkVRMzcuba4igR2QYzojLQKD/HvNaSMKGaMHBuk8zMBxg4eu7KF1qrWAvEr3HKu0/DrRJ2L77fD/yZD6AjYx5slzcpHACqkbA8rxprxkq6i7FlXPaeotoTmmcDepqZc1HkVmC0Pam9yrrFCsOkwIJfOZ+Rf30u80A4domKqwf28ripHIkyS7FLDa6kWel3wYHzVW1YjdzO4QNOnLXkDj0rEx3MQnEn4JAjuxv7QmHUELWb6zdmLlLlQJ1aGaumAf/j3V7SXwuokSgQqvgNuFCom+n2WfNSXKvAgUsN9ldiXTMLWnkqJkNbmCgiTlmOVhVc0fHs3GRv+AhHDHfky+19rgCvNiYy74QEcvixZOPyXoMsEQLkgROhrZ2YQ8NdQYxXt+GDKiRGjTi00DFLqFWDnWWfUwqtyQap215JHuXgABtFhgugY2bpoxLsyRWlk6l3Zk3NwMteHKBnwY9loHE7HExZlmMH2iAmABki9xSVAJVBga1maVBqifkfeb1AP2hzghexEgA4uZB9PnTWltQt96r+R6+Qj6hsAzZS1y6xBz9onmDFdw2h9+iz4qBcDjOtVo2zlZubVV1KEfZ1/qBpFHcRU+2bQuTaNBc496Sj5hFyQ7ms5fUILXHLFBkRc2BArkiBazYewuFE/gE+LSs4pua1yTXKOtaFO9AAl4333pL8zx58zxgmSlDcnQN3APMb5MQS24QpTZLzhguQtTsa3zO+Wp7crk6RuwI5eB9XJE22y/szxzYTV3yOOtPxSsdbNuVza/Vi4F+cFEMgWi1cS8cdQDA0NDQioIpT0xMYGJiYkV133vvvTjttNNw4okn4sorr4TnmXP1CU94Ai655BLs2bMH69evB5AEfK7VajjxxBNX+Sb3H5XAzypJVKQVE2BVpLWqvAq9yMkctzcW6T4109/JZy44e9IKFOgyW7YOZFUmF7kHoNPMWwcKPUg0I8qbHnuOHZmVydks07qpiepKLKAc0rAVfV8/s8BkWeSYHmdZvYR9jX7nljPCVT7n+UXvGQu3wK9vKUq7Kuy6rap6BGguIhEjfyCBlMl2FREOlyfzsLXXkEGE+eBBNRe2Jv/zMpzUD9iP0RTVgKVN5jUK8mihgwu1eq7y/cSIpSOSOejKjJVUSjcMu91eTlyhlRCPf8NTV0uHu5YR3FlZK6wo1hTM6a+fxdefpwTrosDNup4uaVuFWEnQ2CM8gGLgR4UBq9MgxzlxpDweZ4l+qSdxfgaGTIF4QVlYULBAMi2k7HqozJjHNbf4qe9LNs7WEVwrKO2U3wwwFNXIWhPOwLJ9nJOVkIuBBRrxxwOwsz4q97uYBJ7mLtCBemfvsMwaRLapsETc0ViMH6/pYXkzW3MOAYQHgJYKUOvyTDmGi6zE3NSAUxMPmO4DXLDoDkUQLM4T9jOLLG3pxNzggrsa8BjXxgFl0fKsrG3BYVkg5zSrFj/z+rsQnkSNBf6mbnaRCszM431p0EjMZmPT4dnxlIVVu5VpWqnFb0Qyb3E3GdQj3HuvGdzFSO+u5lYwbM597fLmMQsiunyDBQER+ZY1W202+T9Isn0FLfOdOgMeDj7cvI8H29YxZzh/JD37PflrBy2X8Jh91rG7eNYtr5u4zRQJmDo0FLcycrnW2MoOuyxtFw2C7TrLg4K01ClIzY88bXXI1hONP6TbaZXRU4XUyd87bABx4OCHaD1qetVY3KKwT6A7xMB1BmoEiwJdP/+9dT/VZth1T0D6dpwlqiBd3uQ+Y3WfubK5Ze1Sz6GJv8jnKgmNwvs1DmD1EQVctNJLsn7Va61ixpc33qm5JgGs6ibem855Oif5/I9qEtXZYmYnD0eM6kDFDAcDnxsFRcLKEEct1LRFmys+V9hnx6+iMfiGdiSd0BkyF2K3AURFLnsl/U5p9+7dOPXUU3HYYYfhsssuw4EDmZAwOTkJADjjjDNw3HHH4SUveQn+9//+35iensZb3vIWnHvuub+3jF5ACfysmrzpCry6WuE5iC9PJWnI/5rZ4pHk1SbiE6bBTufnQJkdz09utm61TGcFd+8QjkM/BQtYZbSIEBChQHUuv3HaH5cfHJUFge4AE6IscEgdXnkuRrS8QyvC35sy/zrII3epSplARz+m9RQc4oC56bu0A3nBTosrtesGTPALAogDaR2G+h7jOSs4R6x25t3jeCdOflMUvmekNJWubDZRQ9ouYNTNTwmNTjcxAPX9JL03SyfbHlPgDGFQqDl6l5oOc0YnsC0q6NpND3nmCpmOR44lUHq/S7gUxr/kM2cMJfK1ufqrdk1xMBRRrXhdCld9Lv7TeU3YIC2Vq/WaZ/NJu0AZ84y7emnffdLdkZ9xaR1i/cOtkeK6QMxiqLhSrXa466r62uHAAgH1ZAwID1i417RD1wGHA6LZ7A4wEGPZt1L2Wubt2xRq6dps2JzimZviamyDOgSo0aCN5FnkPMBveYj6GeBFhV/dB3xy6POnRUESs+2RAg/ivZmKXadbBwB/kVqJsOr74tTtT1NtX3YYdAeVhn3QPLDDfhUDh/cR6Z+gL0J8oGZkqAKA+K5+9R60PrMN1RkfIbeuYn0fzCXPlh7LNlmViNaazwz2sNTqHQHJztQOCRyephpm2af8tU2EzQq6fE2Qvo9Hdap7ZpmlXb3IY4ePmDXKLCwk9dAU7nSvo1nAuiwOllgI0NhjronlTcR9Ra0hnqFHZxXi2bjoeRlOxBAdAcFiw+k9f88fZdc8Zonn3DdzrLot4MeH7cLLyoRdUWgdo+eZxSt6MrECKbi50K2a/ndRkWKJ/+7oDisFOC2u9wvrfFD3FPEYKmakBURrK9CCcEtC2m5vvG/aij/wHcAVV+zw/aOzuYPqveYD2mvJWaT2qiWG7HodkfCsRdh6OmacD1EKCTJv+Tt2RlkdrBo6t1xuh9w6hpLmfSzASG1Hbcab00Dgy2tj+E2B1rj7xQ3whFtmHRQmzwb7WNTKvO6wXXdrbbGyqlcYh3RfcJTzOqb7KGD2676T1K08MJmQiFs9GOwHGcXSjs92KHU8EPStb30Ld9xxB+644w5s2mRqfrWXhO/7uOaaa/C6170OT3rSk9BoNHD22Wen6d5/X1QCP6skf0nA72HxwP2X6SbRUJtN2DDr6Iwk/+sHs2ssWQekL5IAXzmCs8tckP7GEepcrQgto84yisyHfWbbwz6gPlV8qGrXAm4iCwB9e4u1Xo2pZBHR96YCenUhdl4HgIF7OgiWmBawkZ02M9uTU6s6b3ZGfVqlVGTZJzpEIOv2u62YrA3bQV4IhCx+g8W8FdDQ3eZg+eRA6IwE8Jsxwn4ONCTtqk1n/eEvm4PWGa9j+ljzNK7Om+8z/rO55B2abMK0OpCzc+Yz14wb32dPXIOowkA2ymQoc42QMRP9+0LMbw3seC5kB+soAJ3PQ23F0yEHvAZ6NKUxKugaIvONxv6JuJm3Z7vOSOoVouoM2L6gAWIusFouVb69VvU7GgATe28RwWFZaH7Ve4LWbqfXK0A4YLaDW0N5HXuvo3tImp2FaxiXEzCW74G0Lr332e5xyT2UQfRZJpzqYlKmSdwc6JxoVghYwOZZHHqo7zfbVSca2NmjldDMxrs6qyyBGHbSIQFj474IqEQQIQMUlAVLdzLrAC4gRHWJ6gEWNJYFo01TmzP3INEVqM6YA8/7tbG3agPk5BU1c8/HMg6S8YyYdpvO6frB5AsNZAoA80dpa9LsQTwmj6+seMJh4nZGAAtqXcHb73UFqjvM924RC+7UQPBuBvRpFzSeUYtYTUVTNaAqrbWqhX4qGPrcEqkLI6sUAASz5oDo7EPc7aOyKCBiE+jh6eJFbLrPATBiCOkU6sGiWXc3qMNreqjt43Mv+9zyk07uLLL6h5KJEYrs+uLNpoWOd3jCRESzWYVUCdPdn22s1TmzbZ2xyHDTAwBvhKSF1y5de8zN2dMAMotGbABBkYCsSoDH51dnv17bgFtZU581rzXZ+ZZaZXIrwQi2a44Dsy0MIhtn5Yx2SQFI28qbngfa3U0egu+204hzJVlXFRUFDNZnaZ5yi4N4tH4ZqO881o3+TruDWzvHttKW85Paeou3TfpAMM0AG34WDAurL/wFAl4qUxmLlxBAMG/3LW1DU2fEs5Smqi1kbvM5rM9Sk792P8fF43MlmsHHqmZblmzqzNbWLZraxMol7PfQfw+wvN5+JgD0kaRIfDyimplxDrDln0C7erF6Y9/eL7jM5XXtG2m/6j3TVrYD1Tm7P2i5qOmWKyAEIm41+yCn6H5w9bqv9+fRy1/+crz85S/vWe6www7DN77xjQekDYdKJfCzSgqHYsuMmpMDP0lJ+1znWXt0B4lGxnIFUubBDg20/j396GhioU9qHqkybdoWa0MCFvt6WLCoerj5ZdhnpxrmTIVOWZpnTbJI/SrZO80dWYMULAAnNWlVt3rMxUDEnlWWlk++uNurGSl62K4o0DMpkz43hz+aP4Kb85jtEtzBmbRHehlz7LLm4e/cXGs2fv7wEfuZaQUb3Q3WPztADEqpRQdbRAuHB4lfdcG5p83HLbBAu+EQJpvPQ8380G6jQEeNgF+8/qgqrGsayAXI/Gb91RlVVkbcddQVGypv7RrrknMixVpCACmD7IrTAr7PcabDNTcd68MZm8Czx9KYF3mxi9RYdolAw8HARfVOAcm2E9E04cTVK2AuN74fY/koc/JR/lEHceZuMu0BZaHB+sSwbmj58GeqlsZaxzaiwnrIA4MORIgY4G4FhtGWHAP2CdTtmnsgjXECAEvHdOzsa1Ro1G12WBZEy76lGacWLFFfclNz0rw1neYElPBYdqtIZ8qh8d2IRUWbpI22XFRDYazD5GL2UYMOMY9Np1OWswDMDWIx2FqbvF+VuUWF6h6aNpq3KxwUqMyxGE6jXJLU95qX2+OxlamJC5xCZtZMKc1l76IFS26ZUp3yIT2guZ6NZaWY3wEAbzaRXPwN2WrhmeRqv0qsobrbMunaI3G4vDXZPOhyRLYWozFpSnJDjWyD3ntHguhFA+5+lL7ZFuqKF42ECajKhTHVtA4B1qxkCzHQHTb7y9qG8xQ6rsybbEkHS14uDwBkAIqLv6jv9S3rDnpOpeeOa3+22sqKOOanJOaZhuu8dXZJM8h6zrMsF0Y/513J3hMHEl7bs2P85MRZoiQ9e1/kQJC27OWWXsES0GGBikPHGdZh65yCwnGepZgPhMIRsJpUVdvvnmR6frUmHDyFomjJYZ1CixckXezFX2hAw3KnUizogUcyhQQBBEduE+j2w5qP+p3mj6SIHyvjuxXZlFpqLVvynBS2VRmf/0Gx94WeRxZ/XTPjP9L66OMBt7Fe3HxoWfyU9MBQCfyskiobluArpjZv3fPt20gZnCcM6Z97pcEU0qiPChrUzJnH4yh45IqIpiW2fpOA8GRh5kIdE6O1jqXB9KUzhalZv9JM5aTDNjZpp4CcY8oMwFdMbchNUbVWviA+iZ2OWl1X94RxAfMDe6ydsVByBs16NvvKY5cAGUaQ1+6kDbbwalHRHO5xb6KJzP9dZ5/h8y2WdswZTp4S6Hvw0gCS9NiUwkClnCZzzEirHuaDixBAhQmArrHkbgh1xSjXq8yFh5trSzvGj6feKoYwynHyGecRs8bre1ztDdl6C1i2iSj2rHg49HldrcF01O2L2GqLR0Yqku5YOjreCK2Tv6OmdpiNcYVYjNCYJb5nv9NwPwvgQGhJpZ+N2fwcbCyo9prlO8Q1quJFWGjW0VdnrjqqDfMDmbDbYWnhK36McNoWhg1Sc7QyaFqqCE+iu44J8wyoqVciY+4DZhpvTwfY5753AKKGb+0pAcWMFPjFgSW9n0uSTt0bZ5KWsrKhgb99Eq8pIqCEa914B03JNx7J7k3bwy0x9Fe2nozYScs+RH8XnUUmWevYTDRt/Dw78zoC4WFMTc5iMGlrBA66yFoMwQIMhyzWUzDrWxm7PAIG6n2twrKDdQ7UAWlbh3WIlZTWhHNhKVJ7WEz6ozrEMhjpzG8UxCPzJiTuXYK7zcwGaM2bMRGizSQYunL14rH69Bjz64ZlWSjQGYsssEB/Lwxu6yVgg/FMHj8rnWfmrVLABitZmW4ttl2xaHnNQziKtAPPvtcR48oCnxzp3K0g/s7GEP6zqM8ErDlstEfN3ZUaIlF+RfgScSPOzcZqJSxg1A2KRSLdnxGzxu8OCtT3MQs65q4bDkZWJHYKdOi4biG30ooF4h58sqdih+a6wlNek41Nx5WRkVZTNA4O6yrjWb1wCq5LIjze7NEB4j47mU66nnrIStFA8VjrcAI22yQhGzlAvG6DLwu1udpi3do7dCBVfpmGLFBr1BU/TvpFKNyDj/6QLX7+J1MJ/KyWfj0I1JNV3Us+TonMO+0yteZnJoM7dXzCMI39JmPG5rawYJ4DCfpumA/SPZYGF3NY33DthctCJ88MlBI3JukOAY39SNB5Wo7yxLqdrNMaB4DWaHG7dHA5anJOtUEVwv/5HfMBrVFhxQlxHUZWX6h3tALqkeq121puOncq/DgUMi63CU2B4pktvE19524TtP1BS6I5ISzXnEBlBGgPZ5OG9h0AeJFEk/lUB0xGCZragoa1IbKthfh49+/tojplTqpwIJsoBx6tYmSwuqsLEkvrhdVeaonTVNr/voNMkz2fHJYLm7MO5ubJs9uVYEuYfxrA0Qgg6cggEVVZTArSBK39qU6zts8qgJBZclBrJ+kn86LIDcfVRiCZf8s94sdpzRs3iwZsdzhXXINCylnzOhZUcZri2KxD36qDrhOBydLuaiaKuOrEJNsPjfviMYufoBZZ7iKGoLomqTNmmQhbU+oe/q5UAPckEAu09plSgz+W1BlNZ9Yu3D3IXxYIJxjTx9yrdF+FC2b9XtNDwOMUM21oezw2YgwBQJ2496b7oaUBTyxwaIYVAGiuc1hJMCHXd6VzZ++t29SdIIDNvuwgoIATz8IV1xxZeojVWuZSwsACNW/iOotjQxa1N9SBnK5Z7+SpmD+SuELx7D5RPQYWzQXtM7erNGbSIBu4tm/NjYiBMOHari1YUEuqmtuiSaxpQy4HaG/IFy485WYXswxG6TiQ/ujsZ+tIvwvdvKg7LXXh22hGfe1WIoyOmdcoMN2ZS84NyQAjnVmsXeGpe8n+MVeBHOlaglbsSmjhOOyt+IAMdNOArAUwCjiEai64FwMgek66QH9ZkXZ7qf5CMUb3l1hE2+D1sBIrArPShAR5RXiDef/7sQ0KpGdRMYrBXU0tqwttVcnBPgE0Aw7wchdPDzHLGGnEINN7Bps/wgX68JbrpBg58ZoMwN6VlZeUVYXsSvhl/ZPrWl75HteNBA/jXchQ2GBd4GgvH6dIWGNkldFB/R0KZcEFKM5iVIsBmHTvcOlyXYpXuiWqZ/M1LYRELNgB/CCnWAqnEnu1dZRkUgn8rJL8ViZX5MXN46Z8Roro9cmivmeUaQoV7Rknps88pkY3EdSoUEzbQJlyFgYAXht2sF+ewcixiWu/WBofxRIw/cQPNy8jEpAJy1yA7Q7YweY4LSpgiArrtA/aBDiysoG4iLxjaorKY5bqvnRaEK2MCk1ke5AOuMcBED3ey+vdBzwAQCYMgGUIofkpylhw9w7pYB5WjHD2LnvQqwCo5BfQjXZoO2ozQGuNea05SbXHyX/LbUkhDHT+cwG2flBY12nMGeofbim4WoDot/sxa5hq6zrzPp0JjAunBqghkTCxvF8L0t4bz+2h4dTEY1kBttk7jyWSNLDg8UUCS+wQuEk/VFRcDe6OMnRXcs/c0dk1K+aDdu07QJBiUrfh+cmnSuRZ7iIGsKQEdgsortrAIQBU78kmTtQnITYtI+QAiGL0/GkagJNZOtVgzYEKsyTRbeLuBHHFtmbgazxY8tJAypq6o9Iqz4OuJ+l8BZYZWECFHg3IRP3ucaJulzzuWTrncjI3UqFb8ngKEtYkrN+btau1Vgm+XNmxM5k3nfXMim82e0D1oIfOMU0ETMCNdIB5avXBt9NQWDEkrNTPqp9jjx9MsJIotNjalYuBZXVSIcBSpPoyGmKHS8uH37TnAU05nWZWYtnG5LzqG9JcC5DVwA4JiO1NEfdGmuJ+l8lEyarEHBfkdpNYQbr/mHVA+6DWziCX5HA3aRtXWrj2Tlc9vF0sxpZeO8JC41ytcTygIEBz+ouV7VL23PtTC+qcjJGGBR3HURwZNMUqGIUVlcx7Ji9G3WT0WckPH91cLtDTejxpJRywrLKL2spARw4iebXQ4k8rNWK92HBbQcmcLK0UDNJAg5WRsgfIoOsHzP41vAp6WO1wYIY+M89STLed9y/97ldioCZzMycWAkzVYsA0eZb6wLMbOyysV0v6fm4ZnGctT63Bs3bbQPEhhOMqqSSLSuBnldQZl/CUMJIr2FiCc1ZQM/QxR4xdMtUK0mbnZnbnjP5o743sUDOGAUroLnIh0mbTbKOfH48tzYh9c1JvlJcJIkc5ATgsUGC2M7XK4cK8DtC4Er+hlWimXE0v8iMWxWWKNGZpszjQp+qipun8IJGuurnm4b64Gbs0RC7icW8AtMfR23QYyGV46ZrgAZo1Q0bjTXUpqEoDHDrS67ricWnKYvnkNN7xrppiofqbj5P+TjYAK/4EYDGynNK4CS7tdg93QhELa65QJjCPYROeRBw7NHmE2soKijOuMxr4I5fz3GMFDYxLYq5QVyG+gfpBbGecIaCf7iePpwDX7qgslkh7kk4iCSxWUR8xzc3ai8mkk5MZ0MAtkWTkWRY+eTHV1t1gjuXyWmFZY3KLn+6QHUCU1q9B/eG7zIfObfUQV4DaQRug0DR4d9IHy+vMdi1tTq6HBhJvtqE2pQHA7Brdjig46zPr1KAlDIUFr0efr+GwCYB4KtWMP2NuoPS5YT8g9tYRsrWr10NAMvhx68zWujh160jbMmeiVtoFRLC4WsGyZylPOIkYdkahddnzqiqzWcT6RvhJ3Aq+LikQFGnLqy6zbtCBd6lV1Fpznkc6pTq5l7qpUfcfC8wV0lqrcjKr39f72D7THTKth+1F1J2tMy6w7kceuizYvF4z9Bzl+3xtTloBgJdYmDu9drilqJD2OuQ8mN9xWAuTKaGVg1wZ53eB5XWJJTal+kw2lgubvLQdlLQlZlGyiTRtOt2HqWFIwb1Coji2kQae8pKFFNxbWUjWJn++7kPLgpuQ37Ktm7ncrjNFuZSV/J144oPWWg/Vqfx9Ultlcit76ScxhCz+nNy7pNK5W3yI6pvKUnYzVyinSmK6tdPYTT34WCtGJ6lHj2GenFKU1rw2DVSWgWUWG063h5a157C0+SF+1iuLTzuBhMwyy/GHpl+L+SoN3HMWygtt5TdgKr1CvvcRildrdf0/nEpXrweGSuBnlRTVY8gcN4SUrHWbXfCXcoIG6wCfRBNoWQK4UH962FKGgG+EPYLqppQHrhdYikpPMX58ozWCO+v34/cKdxpqQoES4OgGbWRconXy/TpwME4O2NzqmzxAjcooaZriFWwsK9Ag5KXTdJXhmR8MLaAn3YpDra11+PlnNwP8ZrtvNGfmqKeH3zW84qB4el3YgQxFwvxYhzR5tPpsZclKK8k+VqfNRujDmAI8lEkMiJeBHTzafhQ93CtKQy+ZKbi2Zomsl3V85HKQ5yjMaSVzLifOCaSw4zAw1xeJHI2ifnxR03wJyWNqUGZLf2RghwaJ47GMY+RxZ1I3g75sAGkzqaY06BE3ipOXF68mZ1JTCxnRFYirEu0WCxChzhPq6oJ5NskCiQGWoWp5AxPQlRB9sGLeW5sGusw1p7JottfrAmGD1UfSkGtGePkw9p5RBK/r2VlVCFjQHlXgDU8BroEX0h1cMOs0tKKEAAqkT0MSx4Fb0cBzgK20C/UxzvasUAVGtph/Mp+irpeAMgwk8VWsmdDLJMO4Zo6b9CXAXAW5m1qQY/FWWQTaE+zaAqtfAN0hbm2Q9X13k5pvPP5RX4y4PzIBUwB1Ejg2VIG6OW+ireR8AnhxEEbvB3nAFXV344FugwUfnSqTtsmirhzQYBabY10NyDKAmoIFsz6WJ4C+A+ydFBAkiXDK294aFTbIwJZudSb57zsACyuAMStTm5XocmtS8oqp8MvuiypQVpXsOrEOayiX6PYIWzfaeIuAIFbGPPIO6TUy5Sgw4uKthu7m5032fXmt53ymftfA9PgzMsV2h5I03ZVlNpbqvSkAzkEHvwuM3moiP+GAOUCzKqmGFe5g0Habt4L4iixsgSadiRQAuiNJR/H17LUTwJrPFdrHgdrP+f6p+e3Be7L+6AzmzKccBaoOEWCVQcL7cIVsbS4b8IG7ExSrvcZEhzqDKuD/mHkvBea7g8k7D/3WfKi2Cm1ME3fiyCyzNGkHNue8Ut9+FVvvML4wBXye1YvrghYEvK75zOoi7SeVlTE0y9T3t7FwRB8CFqS5OZZNqM6wVpKzVsV4CGb18hBZSO9q6yiJUwn8rJb6IkAzsCsFEsna19a1FiOigwxTYYgD1mnQvRyEgFy2xBnpwqO48GK1PKu6oF1SJId0nn8xkDH5POCa6HgImYms5W42pCrKaUPETcp7kHQAUnxvSdM15+MrGdPP+81lrcOb6LJ8WU0AxSJrMImkoT2sPZzt8qSVYrbnPfR6kRUTYJv/M3KmXVUUNYQds8NhNWIBMarPBLFqiXlKeW3uTI1BaJppmg3FAT5xNyjXHLOCfuoy1rrJvgshE1PvHOuYXDNofX/hr6SEA6yWbBC4UCLgABgN39OCR8Z2fWYmEff6i1UfUtCIZ82JtUUB7Rti4k/jT/CAxpWKzSa4LIoks3jQri88EKUkzL9sekmck3lTotEm58GmTKLp8EDOFYn545hQy5hAoYRunio5rghEbH6GLBW8SylAARb9s+WeJ9Sa5S4MM1n/aPDCY+3VmXEoqMMtPTzlbuiRMYiJq2p1NnsOBzsg7fZ6JP17Gi9tPyuj9gAapwgAJAGoRMtDY4+fuotpimYSaaN2gLjtMQHWX/bttnLBXQktVrbAUZkCyWlZBtj5TWFrwElWu8au5HOHxfDq2+mjM+zbgjV573CNkpYZOKQBHyObGaPGveq5dF8leyIF7Pk+H1cl/H5TUqfB98NRtymHp8rw6U0VUGFDKvdgZhWkLLW0Sy5gx4YToa3Eq7FYboP3KpdCJpx6oUTEQEFLgF3vO86z7OPwXQpwY0Ko9IADIwGCtv1MTdriJ4/HMRSJTGGn14jLKhBIXPhd7U0qBmaOdjFE6mffupQ8Uw1/yHBzukb8NtAaB1osTqGeW4b1HZuq3X5g7+MYWMmXaY4Fi4iBxa1sv3CknedgRHc4u6d2MOlw/n5CAYQcxKPrtKsAJG59o583ty2fEwgW7Wt0Trc8ctby945M4A0AmmuyibOwWZvNsfvUaxvzBKa1U3dQwm8LzB/uVkS2JrLnuDKEWsAbD71VV1Y5bG4P74iw77HsfGftb48DRcKBHitb9xYk7eBMi4O9s6zTRALollTSfaUS+FklDY0twVeavzyNNs+oRf1FQ5XlhWeTydMYu+rJK0ufEzEbVU9IK8gV9weOpXBeA+x3cj2722Wm8Q7NuhVoS8LJGJj1qDZQIY36I5O2uSwQrIxIhg+ytOsm370ePuErJstiQ9oC5Wrwq1U8uqh+ly/zSuais3rR+50Sf/X8xut4J66AbAJ2e4UBkiT/XVmkADOTV958pvMgoi4J1PKExwHwJQKWQpvOsTSGi8/XfPK/ysAGjwE/YeTBt7K/KGuhgmx7vC56nyYdINW1l/E+4lnJXET3NVcGrrx2ADCyetFsWJRcWch4trGKZ88h2oZewf78AUeka9auZtvkwHRqaVdmKUqtMEB3iMUAUbe0mplUIPpNrs+vRAhbjOvjjOGwCiy8YJZrbe3YACy7FZ609jOPxFyJmyq2EXNxE0gEcD6aERHKtbUJD4gdOSwHufVNxNOSq7Zq6lClwQq2rYgHxEY+CCx5avCmeW9zc2hbxal7u0PkfGIghr9kr9lwhLmbLbmz9HgdD62NzE2MWywNCSsWEwXidNt43e3xBGCJN5u+IDQwtAYXOYidCoqGaZ1RBM3NKvMW7Q/Sp7Q91Wmzr+OKhNxvCuXGo1RAbu6yGumYUazLOyQFu4gEOhMh2izznVYUGBl3HFugtZ2wQosn5OybAuilj5YdaT+UPHDuEVpR4nhGO8TyFvZOdMwF+589wPFDvlLPRb1ipPC9wLhXAfO5/FXBs4WQyRmeoxBp+/nzExAOhJB9z2MHpYA3Z/K/3oIN0C4fyVAGsn80t6hswVzJ42lgPU/DiDRoPXft1soVqiCyhiYNlkz7xt1/PTO+wmxmz3AV/Dm0DaHA0tGxdebo+o1zjrfLh3uxEuqMudu2cBQga8W54N1W8+Sant9c0RmLRNHHxtjM6pWqWawnxM2HVnBneT8Edz5UeebBTCXws0pavm0EXr1eWIZrXOnmpjWYlkauqrWiRPPh0vQ4+IC0PN08eIwfz2EG6sgqlRc3gqPixn2VBKn3mTaDtl8fGLU53mg7NWYvqxF+zdCE8PYH9v08VgNgx4dIZXFubkn6PjVLzWEGDE0vj/HmALwMQ67VxtHh54vDtS+Nm0zmJz+YXcGdLbcyRU4Mr0e7ZVUWYlb6dkuDEyZZyooyQSHnvNRtqhJtX4Nl/lrY4gCaqPUPnWO8rytAHOSrYipKA1xlmrX2iPrPthNj3Xg5PuG6DDVqYb77IgbCvvy9CMgyAvFAzoBjH+OAtkPbbWSnUObSFjOqLDG4dQftYz0vuTl++j70vVnb9fymrkrU2iQaIC5gLJaOX40Q7TNVmEb9qv/67zXbdXCD+pm75xJ3odgHUI1R3WdO4s7apD3BXNaZPINaZcHWFvP4NbW7lNUF209l4FtxHRoHzO9CAq0x8xrVmGojG27N0B1SLg5s3ZuZJ5MvVvsVAFEjbpfcjUlH3KVWLtQdk2q4+/babVjclL+X6bHi+1ikrFYq+8yFR92u4v4IqMQQHGCZ125t+WCWiGFlUAPL6pWmjWYBseORburWpMla4wBiFkMIJDC1rrs6bT4z7EtiW8gW22RJZrFAPVt2GHipqqruz54THWYeqrHOZEbTVVMXcvKc7igDlpYDOxYasTqq35lsoq21TEhU+xsXyqkrYu2gj1A4hFSttXfFP9MkJMD3MR7cmaeGTn9AvvbQKFcgHGvgIA8c4pdp/JXcR6t9mwrwrLD+La+OQpFbSPudjJvV8/l4OAJKJ21wgFnWM+2GWcCSkJCuoIe0SOrebz8iGmCKkrrJYFdqEbpL7DAnAKpuj8fTlwvADxyWqLQPdQzzHMUDVeLYyjNd1nqE8XteGavLKH7US+gumtsgQJ6D/PGW83peuzjlKf+EkPD5GLAyDvbdrLt/BeuaPVNTkGb14mWAqFKQQedBSGWMnweGSuBnlSQ6Ap42fcyZTy4hKr0/J7icBmoMMCZvI84RriUNMugALLgQZZmgR/YztWBH0z07s3AJoMpAHZoJSTPo3BR1eEeMmaOKXUrSjAw5Cgmf+uDz0BhNl0aO3KvODiurV9d+JsCE8h5MBhVQXHFg+FxwBZG0YkGpuivMHJ/6nCfZbJA7P6VPT3L+G6wTU7B5o9/L2a8FSikg8Y22Yy5lH7XZsOV/30mC/OVlOaNUNN7pc1j2IF/Pbwa6pL9THoPV53XMsQbMWAIaILR0SJqH7KU9lY44VZ4ttLqCn9ugEWMM1Ttaa1rCCAIOwA4c6RW3nbugmM8t1mTrTc5yLXIEwuaBulMNJmGqI+LeRWMJWRlJBBCz9NiSAnOqzjmWoS3doxjD6JE+9IWEd1cfQp7dSjGgNKtRXDUHvDsIxBMMfV9gwqyKScL7rDonjNgJADB3FNcq22BTdTb7vLRZWVGxOZbsT8JSDFBQSsdg6zI3Xx1naHlT9t4eU1hoRUhrfVaGWq/QfXD+CDaWMTCw06yvNZF9bo+pseJaWSWoi4hZsHJAfKZiZe7pKMCij2QP40Bc2G+nmecxfnSMCd7fwZ6qDcgy8lsC/oy5oEPiWhYokInPw3hdG1is2EGtyT6o13Rjj3mgaZcN+l4xc4dMgWP6SqS8R8ApnlnPb3mImSBNgaD2eGw+Q1evgCgKAgNm8O3qHBDVAmsP12CpEYPKYQjAt7Ghu8wLS5MKLONnuQf7bHLtpwVnqj4P+f4dVRPg3gopQPaGdM5xC1Zhl+Wk33k1cQmzgrbLnBGmQGdazXm8FRCb8k0xgNjhJqPaRpUjLsVnr8DpWnnE+bmwngV+Tutje3gs7MRxEdk/goXk1759rOka27PiNWWfdd9T5RYA9O1Pbpo7nAZDZI3w7Ou0b3r1SdHvWm7wOQ+pXBOXJ9m+R+ZFVEvGK2AGuDp+U0iCsXMXKL9trwk+nzSvaa1L354bfLyDpkMOI/M/DSbOE4HECb/I+8zIOKtlDx5Ifj5G2C19vUq671QCP6ukkTsl/Lyo6+pyZSnf7OHgI5SrF7Mw0Yu9Qja5yqL5nKCZZJCg9dPPLRIgjAu2jakIc4ebw80tBGqzdgpwvfk0SYpsnq0grgCIgfo0F+Syj50htwZ/ecLD4K5iRratgp1VFwiTSDZm4wBwCLD8tDXAG72Pcl9u9d5WNkZq0aA36JwDgPaTZcHiAiwc1s9889fPt4It8iRxASyGUfcZBcf4ASdiWEKipTlxAHFp2fwpAADo9qFQVaLflwN4ficJ2sj7mvZDbU5pzHIyRUjiq86tG1Kwjtxbm81a33cg62ARcsY+MNYHYLYzrZN1Rn0mudDmVlfks9dBTn8psICAHq55aKVpZ2W6KtAjzzwE2IwrD5rtte1rBvOjA2Y7mHAhHQwj7TPV3Xw963ZSaxS+l/L5y9venswaFPOUqZ5EZdp8KboGdSwny2JDP4drP2kGvTgBDyrMBSBUmwN9Du+b6pyAmGYBMofNtut3bOwx6+8O24LWwC6zTHOdDS4212f111X8CZfywG/Dmtt078sUAuYz9bqk1lj1g+4yET3P6EfSZ1zhIqIsc1h2jQi+y+qd2Pzsvy156MKRDAAkQIPX9DCwU2DhCPOZcV/SoO4gAX6Yu5rXEYitmF3uTdFnsXREBFRn2Pzh2mUJy8KUgkWRdlXv2vuOv+RZAKAOOgsAYixhGpaZea8Ouk7HI9hrltH9R0EuGhuNxvvxmgUHr6KhyWzTWtihkEYOVquvHFije+XyBolgSVhntJ4XA7uyazQYL5CsOa6MaI7nP8u47sgG6VJAWXsstZLSyRA4aBUCcc221qVjm61lBrZqPoEqk/Isn1drmaweZykRHGQpy3Ksz2m5uJoA1nkJHuj+ZdUTANV5c/50Bxxry0Fhnx2I3QKWqoC/bNYnvazxOvAz75u+fTE6A54NMO7MCu57TDJYfH4uTyowiVhDcrlCyxCUjzaCnxMZo9vHQkiEQHuI7YHEdVbv3zwOkI7byS1IjbH09TuYZYJlx17J+qY9DlSYIpr3n54LVpazFcTaDOsoBG0rOQBh7CfXOH9LweX07Gf1L272ELVW0LgHEUXSQ+TSmq+qjvupMQ8iKoGfVdKBP+7CaxTkqgTsw0E6OFXOyGitS0EsHSkTITzfJDM7CJxmstI8iSyNjsun2vUsl3mmAOaLYoDo2Bxcgx/EdjtcPkSA4b5mppik9svsuR7sHZ8avCgG3GqDflYBSGEFGmZU5GIsRT5DWPTcPC2YbZlUYApTEIdhtRpHi3qk0HSlHDcepceDTwFlxWT7v5PPeRmqdB3Uz521w1faNjrHKNMX1rOt0sriUgHaHEgifay15NZ80WW4FQszbZMxbCs4bfFD563rtYu7O+3PdtdxuPZKBR8Kq35j3vWYrNY70Xoix1wF0FrBfpTOEWrJQQEYElPJ4/FXghidtVwiIPUoxosH89aWH9Y+QgIRy0go9zs3CuzReDF8O+pzCBLc8kPdEzHQvzIHNCfNsl0mIEAgy1Ko20v6pjugXJiYcJNnXUjnQVvFUuCAQirIEeG0w4JOp2uH3NolQAoFALmQKwUQM8sRCiikIDrbbJLAnXb/GgGrGxKLh8dpQFZNftsO0Frfz0EMoDPMBSg2h/VYs70mHJTW5sjPkagq7blB49ppxQQDL4MDdbQnIkQs4Kqxr0wlklx1nsXEU9tjdyKTrCtTLEV91bEP0lche3uFpb3ujEcQLNPdPImcnrfN6bligYI0Y2NXoDprHxvrbkzMyfY+IXtO336z0ODOLua2mQBXe9ispzbrblscsIxJAGLfbGf/vhBRlc0VIslMH5P0EwdkvU4SPJ1bS1BQYc3PEsS8xTIu6bGkbeOylwYA8lyv6R7ksvixWEgyh7X1mEspBTjCDtC9QX3nigzdZ/S5PLZXVAUGd5oI+exRLK5UV7fRnnHVWfMaD2vQGbKVEnRe6jb372PWac3YCJisaYlkCdX7sgUk6pTlZDk117C1oAbXUGqSoOD0en3KHJS45qHbZ67VynLW/sb+pMNqu83OiPuTObf/ceZi6TuQ3Tu9xoNYshXNmv8auJfwWGwsMeNQpLL1rUEprgAPIqDDrLe4YrUyAyurF61fZ+KzLPMDJS7lhGEAsrXmCs0RPbRC/CCGQHwfs3px5V5JJfCzavKnK/DqxeZ2ha4bmrmxABC1ca8gQ1Wuq5fhmsJQ+JrsqWWRnmNDUu2RBdqfuCrhdYSNZZGNSzPwtobLTgVsxRJJgR93uw2TTrbG45q0rhlxehSDbGVv6LiBKtrW1AQ3Z18xwSlXAfa999CnxOOj2Gnspd121X9GtgleJnYId5YVWB4qVQx2Ae61Qe/RwidfB0Ii0S5yGYoe7rqdvIwSenwqGLJ2BktqvHPMndujtDGs/bGZjSJpF32WYr7yBOsebtteEVhGMU9HbKxeyhINtPkOoZkDVRZ+Gtpacpc1hnPMYwdjRsvlxGuyMgfCFvi1+Tyd5zRFNQUlYtZpfgUAi9FB14hmAAMm+KYZkngA/WUSULwRA8JmIr2WHcg3GjQLSd9DbO1F5ned0pdru7uDsKyMeL/Wph2CEJEHdZ21KbNMOJBYXHF3O0PAUntsXqww6hJopTVX7ab7HR0yvW4Bh1DVBOQis3Ilcl1rnXZfY3NABRymMZcAMy4MIgG/5VnPzOYBuejYmy0LUEtAT96LzxU4GP/KvPk93CBRnWWuWEQrrwE4foa0JyIEi36hUNJZk2yK8TIDfhxW0JabkapH0FTJND4rAZ9ptiMgAal4ivf+W7KDX1unWeBfzv5HgbG44gax9/xRAvgM7cja0maA3fRxVdstme3n676zGwAQTZjSZNhfRbBgDqh/0BzMvWduLHRd1uvSyjYWA7Jb7FI1cyz3kVV1qjJ+h6xL9o4aFKJCORWEmxUSr4xZyIrITrduZsFUIDOz9kwtkRj7behJPADSEefMd+89lIIl4MCjzD7h8yfKscaIA/s8W9rI31tYPADltRZU9qrlDdzKz4Pfsvd1V4ZZ3gbd3nZOIGMAmBmxlZxUMUatkpzE+QJyLs0cU1N1rHXeykM/LG0i57QAuv3SWtN6vGePom1kFYt8eYET3+/8jrLoKaC4Als7SSjN/uay2PdgWcrRccuy2kmrTOzUfJVU0uqoBH5WSVEjhmzkIS/qf8EZo7WGVspdbelArVp4oFAv2QsMntIAFwgzwzZUSDvF60qAUC0kUfNKzuxKXwKxfTAZbfA08OMCvDhixA5MHe/ACBhKihOmhB98LhNuWqaq38sCC9R/S/DMPqfMbh6A06N/XTFTsspZXbxogek6RKbpMUi3hzBrLgucynwPJkn93AvEcZHXdWsANWnh1WLuNIhQ4FqUxxhqF6+i2BjaFNpIYUvGp3GQuFSOmpMlqikXNvpM0k7N3FnrWQtGOfF7ANJXOR1bZEElheM+Lv+rZ4WOeiyrK86IxMLmfWjbc6zmhFDlrP2Icp5uICXz3cguRQ53Lav9dft3AJYFme/FECxDmyFoqeKdUQbw6KyFzHKKZ6QIGl2ES8wFZixZkGKaXOfjIWHFkwlmzYmj47gYgjUSAKxHDE2010o71g0lL2evq0iItgdZNdtmpJpX1UZ8/TnOvLCPWegs26AYXUchtVxi1XeHALD6jMxceutnbkVLR6iNhL0TSEYwMdZBtxkYcaSAJAYPYIJEXAkjY2EBeyGzEtFWYbJulgvmAiuYc4cHNO54aG0w57BPALDuOvUbdxvoixAGMo1xlBKNJaWCoUebmOZfpYsXJJZWPMmyg+ksPKNZ28SsW4nmsTkc1+39YunIrA15cWm0O5vlVkraCSnQHIptAVatwYXtFFmI7DLckpOto7mHrUnK8iUtAWNjAiAE97PuoeJP9zKORMC2+gZMK0sHiK5a5qzSVcSsnH7pIXUXBMwWlY77+XnNFWaZ5Vg4zo1elaygboDEqXLUs8QDTLKzZbCLcNGciHTvi1WMN1mxNbSuHFNG4GStPLB4q6QNTWpJ2RsDM7vJeFfXzQUazDyr9pWYrnsJD2GtGzWvLetvWiaGnXkrh9+wPChiAcH3/lVmtUvnieOZUsIOLE75Tkcg76SdAvFyGdz5UOooyaQS+LkvVCDA5lF3OEcNrnhKQ3PmENoK0ylSXptrvgJp37uSFPI6/iVhNq3MX75EXHdYaZBy2iTYsiYJbS0If20dY9NA8MmjwoLsWU4rEeqGowX2HK2FJaznAG09yQWScBBsNenhLYGbfXfgaek79npOjqud9ewVRVbm9xY/O8+KSopEQLMsPlbi6qU1mERLzcHApgrWS+cYNSmnptYWYCcdFg80+7sWPthuqwWLmC9MLjNL5GuXaLYOrql38L8W5VkaujKv8OC2oW3lZ6TmLnq4y01S5nzuRRY4K43/gLnWaCpqjw1mLAVks+BYVO32Zs0ycZ8bdKHAXNwXoRtWDSEcACJlFi6IQsHaC4RnWaDw9w4WkonTGWNgwbxvrXkOHHfGI3tfoOOdBzCGIpk7bEHR+vMwPK0YMMDLtlmPdp+gsao6xBqkOpWV524t3UGgcbvZZ8vryfMVkFadMxsWhio7Gs/SQ13xpquQg5E1T/V5QWNAueK4xO18wRfI+oxnnokasdkOwGm1WdttCpg0BXxFBTymwcQBJOMfC/j7TGCyTqy8lrYm/30GOkqVCQ1d4hLLgEoNsBmuS3Qf6CdATmi2Ie6LLEEuqGfvFM4kgIkVt0h997g7HLEACeZ8xA17L0vPIqNCe68WbCwtEDQHUF1ZQi9RfF5rAMVK4y0B4XgGab/k84iVMaw+coDjQ+KHpCgMHM0tG636iwAhKSB86QS9AMZzufgah9UsJX9RxwVjwGRFptkAU2Jlgt80EK3n/mukOfrZ8w4w1DVUFCNp2TG2AMJjOJIiaOIWj/S+5DN95gr4BEL8nDHug8u6nDw3SqwT7UDIMv09r1mygp4WP3pduiylXRaMRpHI9dDsY14ClFwLbKow89ULcy8ECYiHmMXP/RPj56HVZyuhEvhZJcnAzshhUcEBKKIcydaluclzo1iB5sPpgmHtwT0QcSDduXqmF5f2RmtY3+gzmx2GfkvY6dwtzVvxow1T3x4Ht1VeYwUODTvgALPovYF9zajT8Ryjrpxn0nvzzlRrfB19lGfh5Qoibd7I687rA0fjeu2xPbjdPMWc0PPXOklJQS2o5vWZI2uapu6glrSya5S5c7n4pd99B2NF16IDiKBlrExV1CJFKO1V3r0U0Oiphc3vfsuSSji0UlxwjxwWP1Sw0Jo5ay+Sbmsh2h49lIegrNF7gMHkE2FMECnQ81kAXylSqwbSYPsZa03NW/raLD6KYQHkAZ4XI2aCYaWWbJzdhUzY5cIkfNuFhDPEndGk3Y09LCPYgL1saJYnIAkibFlZkHnpt93gZVxL3HytuUEzzigAVTJmWmeXoq5elTkmRKtXoXF9KsTNjrpj8nMiWLZjb9E+1JaAfD8c3JFcnzvGfCnq5hb2iyR2D1si2sWECjQR62uvYwdq5a6DkVK0eMtm4/ymsJ6pswGlbRiU1jvRtZC63jGLn1gGEF2BGotVsrSZxMRqasEXbqK3cqtTDcCTjFoGaNUik4Zb4i0ECDaZATOiHVnsHZ1Mz7IU1V3P+qx2gCTC6JPov9tzzO3kP1UAuDJz2VlO3YJKL6s7Vxk330baqIM7W4ordWbx49I4l8gzaBlX83msPq0Y89xl6F7iqq9xIL9dnRHWPlamKHaLDBLXoLw4QMGifU1TVHPwcnwbzskCCylQuZtlw2PzaX5bjIHbzYs0C9fCYW7GTMTJeiuylNaKWavPHAHVOQV6j6FFVqqEEaKQP8837El+4C7IdA3KwL3PSMWPFmVarSyYmSVdlM4RPg+qgMfjBnKdCHPtt+rR/ItLHpGOcSJAtHZxdLrcFmSMLqmklVIJ/KyS+nYG8GtJt/VK511EViR5xWQEhLexLGuU/zJch3fBZwBJSm5+wPMsPR3HO6mDT2ccAGzf66im7uWME/muBQceqC2q2toTTlnGM8JM03gU9GNs7tCtUVsooRdSpo1v7OpQsdIx0rhFOtXuSoC4FWTsigMCNOg6c6xtrMCHDJgI++179UFHrVOs9LQxskxn7L60/iIlTi9jIr+4TMgDiyry27Y/eO4zcto3cE/2mQsIOnME7VfKJDYOEkCDMYPVRYn5LUxgJW3QghR/ZiqUcqadMrKew02BkAEGcCbDwXDzzE16XnJrCVd8IC4YuVIbG9ZKek5yZtpPYiJZGfgIaXdS/u4uxpu3XbezPZY92G9ljacWMdyqI6iHqN9lTjS6trr9KCY292jsJ+klc4oHVpZe8ryAuELxzFx+F1Y/zrOsUxoAsC3LgL69Zn1hHwfg7XWjM74B2bk0sMdsxNI6geqi2meNh2YfdYDLzpBZprFfXR907MeKNPNeI5Y9tAxlwO04W/aeERBhQWfi64y4DlqgOm0ugPY4VelKdIckfAaeaAHLJ4JWwMAbEWWgkybuPq0F55gpmfr22FluLBBpQVhurY1dJNaUWlPWe/eH8PZVjXWT1EeCyCrLNt5eDeLR9WqnElfXyeQw8jIYbu5m/ZBIXOsI0Z7XlmG87boejwlMdO+qLAoELaB/j3kgL2yy15Mle4d2/1sWCppN4HGdHOelC7y09n+D39HlzIrqMxGmtwdoHGDXZ0m8oqGkQXmptpvjxGqNbQ4ddU4b70TndA9lXV5QaACozai6+T0V3RbzOl33UcXF75EMgpRvcgFvjr3T+F29b2XRvO7K8sQVocGSZ6V8X4kxQxwkFijW/KHeir59DQCQusgT/oW3M7WUXl27knvsTqT3pmuaK5FUI7jbqhWqYdkO1aD7NVXWOagzhN5yWA44IyJ7/+QUuvgAkd/HjiKsLcIq5KojbvbSwD+4KAnufN/Arvt6/4ORSuBnlTR0V4xA+4fnzKdOP9+pso9jtyRSSmfYPM1ntyXfaUT/LmPO44qAiKQRhNMAV4iAztMneiHgMzNBytgDKhhmTupzQ8hzCHJeDMQMyPBpatem/s9u7heWJpv3a31axRWoE0aEADwDJBND1DCn9MSNcxAds2HLRxI1sKqmNc5P/ORfZdHcaFOwB0BHZbrhYFNqkUzBGJ6mNbQFX57RIynHTWGT/zwbCNUYiEgiqgmENcbsqrqo0Mf7XkTSqpuDTJphXJG7N6PGdISIpxinmivVZppaFEjAFS+U1vzRYwAAvspEwbMa6QN0aUN2nYNZ+js9mClD0CaCLX/FsM/WwhsMpLq1y0AtPT9caWV5OUvzpTOckHXpEjjseEfsu5qXLkaGA5M8YGawjEJtcZ62HQBaDUe2Dmoco1OtsjK6TRQ8sTKqSV2HW5Ckrl7coiiOPAssoJrFWLljccsUvZa5ixZl9GUg0R6DFXg3Bd8Ws8a01rI12BGGUiApb9aj+7PbzwT3ZWFlKeHrvjVh97XBlKuOmjqBn0kS0YywBTkqA2qggaWf1+NGAzTnAWc02xedl9Ga7HNlia17D1af0TWorXKsGDw6QxUTSoxMfX0xgjnfCjzanVAL1bAWNMvUDnroDrn39bQNqUBv3tsad7lt8PZLO7U0zZymAjRXpsxBC0UF0UBsxfjxpm3kORw2ywTzySSOhrKNyl8w69fv0h4jL0uqptmquJVpXItRv91EmygAqPcLIwA3MsGTxzxsk6qCRYG5oyTmjuLrUrlv9rDctYLL14rH1vyx4DfAcl2z7tFz0nL38yEDieYGjkrRTTZfwFSFCxoG+1669x3Ke/WgPDDCAEBUUgurz7XlOjkvXdY9lUV+IJhfdRMsRZ5nKwUGdjMlZFugOsfbRapQbeMBuaWfzLEiMEaf35xP8Jcd19l7VxWIRflY2n8Gv2YBlcLiC8zfs3KU9FnTGebnCbk3Ss6p+jQ7UxX/SnlxPt7tMUdb+Xbezp5jXI8dWdtcAC3fhsliSIM75wC7hUq1nAUpfSDq4SL8YKMYHqIyq9f9TiXws0ra90QJr9FjIlmpODKaOlFxILwKtVPMHUc3WcdzBPLPY1e8k/Q+aVsAOFO+5xzI1P3EEZtGxML2ZafZc3I0El7bDrpru04l9TotDAAcfDjh4rhgK8btG0iZNH04B280Y8L8VAxT6WAV6PtKQJLVxAyyilqnkKtQ8kuvNN1cA2KNR857i9xHskL5pNsm+NyXat4V+P5kh2muWiVrBWNEtNDtSisLmKlZrRhUVdtSic6T+cOT/zwrTarZKTrXZPGyLIpZ4LyP8zPaX97llmHdy0C3qg14uSx+8rJ68bhIhsm+dgvKyapH3YYslxUlGNNU3obLTMEU9PzYTtVOGV/Vns4od5XSAqbZIRRUEJEAKnZcA30vBcICBmKE/cWafyCzUOOgKWCnNw54CtumzQBTqxQNhjWYVU17TKA6rzSshKhFa2Mqed9ldj6kyglyOWCadC1g0X6kgjV1FeBCVXfAAbY6tOT+Mu9rNX9Y7Au61oQEajMCzXXMjUu5MdF2WVkwA0dqe0b1g+odWL/GgQ0G8fq7VVhue1SYbOxM2L72GvZ+MSCaHhp3MVdB0gY9JwKWol4Lnv5cxlJSEAgAhI7/QyyBaNyqmMS44me9iD3rWnMziQnk2XUDSF0muasLt9ioTQtUmOWg5S4Ex95fEWhNmNca+9mzlLKCp3+OqgJtJvhy0L82I3NdyZM63EqYsC8BIjgYaljI5cSJ1UIrzWwk2bmr107sUHAAOQoAQpXFfOVSqvDkS0TdQq2WANMyKa4AY7/uGEpCWtfS2nzzLRFJLGxlz2TV1PepsjxmVAWQTJE3ezQDBKsS1RnzmnZrAzKgna9vQJ2VBeymtp7kCl8NjtJwCvnJToTjGjsXHIoeC7R2nfecN1HtdFm4aQr7kjNxcaMbNDKeydaNy4I1z+PBtrwv6iP13cUfOcgGIJO9zarfEYbA5SVQEBqrpJJWTCXws0oSoQPgsAqx7y45NEdTXhT0Lr0vT64tTNcu7GwvVlR8+9nphk0DAfID00OioeObO+knXx/s/JCo2Eyq5aKUWtC4+8bI6tVjswfMTTcvzaeuM8/PHCC43ErwGtdjVhCPKK9uO34PAwC8fNPgnmnm+bPyDvZVtDetS/IGsNvTweY3qgxtbDycmpI8wII8lx/2OuYAtYTwSDpbK2Ws0WZbG2dl1YMNNqUaaw78OOI3WT2Wqh5J/S6c2IUHUtJpoy3gx44Twpl/ryPsPYS2R9VpxQpSz7MsF2j/OCywVCOM3wFbQ6rdbNpEA0uZRSN4KHdr9CQkz5hH46Noi4IcYYwG9QVsi6xKy3ax0VpmOic7o2b9wRIsNwGu3dVzTmeo09SaKIjJoiiq2lZfVEjU7p+8Gq8NILaBJfou2oVVcGFetZeuOasNDq0sfRftFgIA1QWmGR4Qxu+AOZ98de5w0EqDNl22EGmMn+6Ah/aYLZTrsaTjzjXiftvW6XAwUGuz+b7idey9xWdWTZVFYQmAy4dlnaazwfFtuO9elZ6egwgDRIGj4lNJz9wc9JholysAaHGtuf5KX5XsDQY4w4H5lj03KGik50nEQOD07GJ7ED3342oyRh3umqMEcDp+3K3UCvQPWOBBxt/0BtetpAMTohio1vPMwcqFDXtuG/Nfg4858QKNuZ1nPeEAUgEGsDoUG0VWv3mxCHU9rXGzQrr/V5aA/SdWc5Uphouiw3qlKEEVALSH3dchbFdTDlRGE7DARbqW9TkTsPMs9pOzwwIASf8sbcpRIql2NoirL1/33G0TMJ/l+j2rrFDPTWJo2vcBDgslGmOxo6yoOJugkwIUyTuudvEpF5v/NYUVWwHhXIMFazfNCszDX/gqLAYHdUg/ZLKgXX/hOz8IqQzu/MBQCfyskoJlD54yl801juELlmpoGlr7w8so5pgyPC4zwQJQqWcwYb6BMtCjqP4i1D+uAoiFHXeGbKhaQOcuBlHdYcnDDzjtp5/jbmYI/z3ikiQNo4ysusQDU2thj4NQ9J3y9iNtVlzQZ07/ckd9eUZAViY0YrEkvaTP8gxzioIcA7CCDfMMGasxTLLqFkAROqSzKViBr2OgOyQd2Y5o5Y5ryNYCZdo54Ke1m0ZsHjJXB+7NJl+wYE7Y5Q11tEbNk5y68aX9ncN4WONOX1HfwwCWtJ1FVn4SNlPPH6XN/dk6kcJxL0/nXnHsM5T5KZgo0mGBaATN18uPC2zaIoy8a8QsMFOmiqbKpeNBhU3Wxij0rYweZvys5DefpQBPYywwpt1YX0EipNSZtURrrXIfIwF++ZzoDkkr/g0HwrUrVXvELBfXpeVeFnLGM3YwwMRaRo+zx+ZJVE8YcW6NRL9rAIkzu9qyhgrW3GIq2JO0m1r50DLUbbUzwhQbwgZdaB+G+izmipycfUSPEwDIqkQw6xtgEAC0JpKb++/NrvM2RHX7HLRI3c7Pr3Aktucemythn7SAk/rurCIN5FRnzBuXNscqMK55LxUaw75kEK34QCpjIhXCrbWrxp+6WsZEoUT7kgubcQBE/cxCaV32sNZ0Ig3yNaHBvTyBGEjmSXXWtkDU4Bm913JrkfbeaVkQ6Dnn4Mkinj2IlbEAeUZdbkWatkHAafJJXXhyBPrc+IKUXL8Ze3/BvSiO15drUJxzXtJ9qj2WgAmcF9XtMYO728/Iu0+THkuX9SV33a8yMFxWgMXN5rXAEaOMWynGPtwKX9JPdW1lxtur5iYFTS3FVQ9Qg85nl3ucpb90gX45IAlfcx2WEMbrOgIpqzYUAVLSW7lcYcUuDGElnLGVffnP1nW4ngnYChwARv+ksawc8aV4jMYHO8XwEJeuXvc7lcDPKimqScgegb9yAQFkzFDkErQA28XFKKTLOG7kP6wA+XFmmchLUegSsPVXX0LEQMx5GIpiV7WAaVs9WFkxcqwU4iCHmygIquZ0d6HjkweO6DKceaW3alNRq46kVFFQXogCS5pDINdBlGutRFNHctcpCWd64F7PygoXNrMn6ZTrrrTtwZywgqI6M6PkmOnS/uUMXn1K1UcmMS2zsCkbzDjgEjgw/muzwvZw1vlL65UFAWPotFafx3HiJH1AFGTlyBrm0ijnmKboW+oaSLFvtdrB+j5YdqRapSEkQvuaboKI7flp1K6aHeRksaCBHa01rwR5PZcAOGMuAUiyeBHy/MjeEjx7X+UCpraE6A7nj6WIBarzwnY/6mVBisRqoDprXuPafL2P9t3L2hYIi9l0ZT/iQqhPAAANvMguf28J6cg0ZQAbWvtpZZrSlWR1Bgx00My9EXuLfKZ9wDXuEPZ70nmTxoVh53kaWLyPxboh7oIyBMKBGCEbbz3nmmuza1ag0Njed3nf6DligR2hsIBJHuNKBtKygusOZ5/TrHKMUUkybMaoTZuNWd5AYmKpR1dYFjJ9FlJwzGua9WhXrmCGBJomfdMepW1kAOCiZyk7vN2Z1FhR+wTPuKot/ajVEmDOm7geozsk7BggOsA2ea5LIWEB2NzdLI/HEC5Ais9Fe29wWeLY9cTKgiUfWOrl8l0ob/XasowFy36TALoFledZ/eaRoz/4OKXbPB0bS6lgV50fjJd/F1bhLosyICKBDgMr2hWytnKUPCuiHLBOZ8AqzI7ZS5PXizEt4DFyxZnc64y/c4RTSLOFkjF28v05AaVpGSfFwsgCmlu4qFtU26z3z3smFePSd7YXTtzspTEoqaTeVAI/qyQZSDv9slWo4Le8zUJv9isIeme66uRofFgjHOdSjtB8CIeOSO7jAoyRvlc12tJad22NWX7/5fzgMLFMKXacW46DlQu1uZH1KeOk61nhIdbzZ6e0XViFk3JjwqyEkRJ2uVVlr+vR3sJAlyCCgGMehv22gATHWlhJRoWYMYZzR6sPjgB9ALMWctS/sNWcxFRYkIq5W2k/8jUoYuGYK7qf6ENz6i8iHavBlXWOA35MqIqronCuFDLNLg0mvVc9u800b7rOOMetICmjLBpIJigaz4QykzydexyLYos37erCgLg0CD63oKNMsC+xeGQIEbJNSvv0t0iKaRY812sLxIF5Hw9kq/ej1oTthhOygM/cwlGEwgKzjODOut+ZQB5XkmQDfKxDhzbWAkDUTYK64bHzQbpc1Eg76bq0Mk01gfao+Uwae0eDQrLujuXjszTpNO5SVEtSRvNYTzpWGAUaeHyZoGlnpOFpjfV8itmZ6jcdYCsHGjrCch2rHczmTtTwVV1mPWEf4LU9I5A2J+2+GQ6y2EbqHY04PcwySaespxZ6tG/o/OBWR3FVov9uBiTRflDFeVB0vf74GNC9LNbP4yyAWoIBCajqTPfNcT2u8NHApwOXt2RTrtjosaenCkPrbBHWGgfY+dmL+1+JW0kuT1EslBcFd7b4wV5NcOg7OS+ahmqk1/l2FNvzzrL40euKj4sHa3Lw+S8r0gIv/aXsZXWssXDIAXY4uosCH5X55DO3OtJn2aqVdbSZhyIT9CINSFm+w6QJAvC6gR0XVAH29Ezj/eN1bIDclb3LdV16AI/tyckVf8fgfSL9TEfnuuQusofnzn8BoHUoTN7/XIqkQHRfNOKqjpJMKoGfVVLcF6VZL/JQdMnXJo0toha4VUYXpUxDzoTNE6h6CV02+uwGO8xrumyP+4StDTYOFC1gsY1Q+I44ITnUM/6Ro2nwpH2wCfY7YLvJ5IHulPRY5m0s3ASKE//Zc/y2EnCI16XL5Gli7utBrsfQOcl6jFEvRlYxKs45XhWFWro0lE+OO5igWkY2Ntr1Ky82AdXQW0yUJy2h1kjDrJm9Cuc81D8GPngsO4qMhXkN2d5jrGnX8l2hlexK3KA5NiodrqG0nSvSNlKilnuaKVtBW/Lq75CMT7RdQSWTaFy3euOmuQTtm6irUjz3mRyrrjOOuPrfbKcv7f1CqPfu0oyEPFPQQGwzt0yYjcZVG5g2PWpIe4/l1pN9oW0cENE5r9Ylf70Y6NRiO54YAdekBrrYotaBd8HdXQjF1gcYe0xrY+S8DgDdGAADK7s+DQSrhTgGMqix5Vmp2hPZs2Qttn4HMosvai0kWNyZcFDaLskVNh6ucxcJAMJTk3OrIK/l2UAcAVC14MRBtqg/RmXOs1IsUzdBfW97wg1cxdV8XwTttkRdK6grE91DLcBD2PuuAVCparglkg7UzgPAG1WHAn5bmHs2sufR/YVbh0nPBt6cadtzqCdLtlI5z6E88BzSPgW8tFCa6z7t4il4O/MUjy6Bl9zrFwA/cQ4/mFnF57QF2Z5lxTTTikcK8nKWzxHUl1Map4yPUwjrZg48AHYmRgoEabDYBYq5+ouCJoN3J/fywP46lhINIs0ptVzPmadFfHevIMfOuJJAuv9ZQdyN2HAJSG6HxbDb5QJvvHk3mJ73PW1a5NiPGbkU6Qbp9+aeHQootpTwBJDMBX4kEOVYQD9YKbofsnpFpauXRSXws0oa/u8K/KrbhydVwvOo82St+h33Bq3jMhRZFuj4MrkBvgrmd2L6yIrzwJFdx2amcRHujkUoqifR93ngQ9p+7WPcv9fcoZfWeXZKcQvhV43I2fP6d2cnIE/L3jgYIWyYL9oZJHEF5pOH0bTgQIboc0sImuHHpdUGyDwoCDrtRbBAEFfsnRW7g5HrXijhdwAR8dNFMQEk6KZOgZ7W72VZSDTVp0wuSsd54NrJxHS9APzT5aygjVmh2lzy4ovrzXGsLsRoj3iozpsd0r87W2zaTWt50gwg0O1P6m9OZM/hczVljChfSLqBBonl6y+qCzstqcMiJS+uR5Gro4gTBiiPQaIuPK4Avpb22RIIkwsVnmVI2hYULs3biuLuuQApae8p1FomPzhnUhkFPSxffsecoxYQrdGso7waS0tdjRDda5oZ0WdV1LO4W5EewyLBLQ4Sob/O4sIsb0kq7f9tNlhWavUBoDLPrvE4BAvJ/TzQMjyBgXvNwWtOmB3sdTw7yxoh7RXUd8AczPnDRbKW2Bgbc129bsQDaes5TONPsL0hdccibaNrq3939h6NKfMd57d4Virg1mjW923lisHndeUe7W5qXu8M0kXtI65I1KbMftQuVn4r27+omxUAeG07tgWfw6mLEg9ovGzvP95us6GdYduCiGaeSoPHsjHzQg/dAXt+dhwxKfp3sDN1NPlPA3jz/tOgSdxPXoAAcRTQ4vdWFgWWtpmdVJnKJlS2H5qDqUErO0g2OXMOCsggCwqvSbvbGf3tyLjEXdCtdNx6KrishXoodryuwxqTlNF7ghVEtprMM5/tI1SgzfaPHAG5APixyrIyLosIox0Fv/s6QH4e2MSAKgokCqnGK0epWZkj1luOmJS9guCn/DCLUeS5LNfZC3SGZZpEIms7AT5z4k2K2H0e0ms8g1h6r5q7g3fb1zTpPSZPcWyEbHBZBnM3cYfyzOobVWbkTnNNt8aygosbBSpL9rmhleMD91CLHzaHAxjJOZKGmV+13LC0gVlrB7Y7Np9OfjtfVgII38XXcwcY2B3ZLpzkXi2f8EzDABDxdyqppEOgEvhZJc0dH8JrFJ8OhUiwDmboMW5Ta2KoKepKrT1cD3bF+3Bo0XrSSjROXlLO0vKQ5mit2Pw2pu2WtiY7b0M16ieFpo/PT9EJEVgVGkFnnaYTRJtgWceQwyZP6HWNv4OJKYqd40hEVfjDygIuKwslPvesYvyhK98merbD+TvpU82YSh4hU8AL7UxlEBl6kMcYpvWT+cPbWZ3Wh212jQpjNCaEK7MYj6FiuBJoc2tH3CIAduBmZvGD2KFe0q9S8E5AAVCYtiG5KVpBnBlrbUXCvuYV7EFpPTLR4Bese91XljWPKhQbqDJj+hwumNRSxSMWP37FnGfCk4iHzf09kubvANDpcqDYfmZyA/koADQ9NI9kz1RzrjlJrGSY+xFCge6g+UxufaDd0JqW64+HxS2cMWZtiGwXNzogQp1bC0fb89BreXYgXxoXJXRbFqTaeSKEWxrSHu60M6PkM/tNxDEWjmHvSdzp9HO5C2NbB9ueNhtD12+wLOCFwrKO6SjXMiP9PHe7mvIs1zsrzfOwfqb5TtK3XbRc2vzl9Wb9tI910GIONrXHYvjLnh1cllguaBcvrkyxAmQDqDKNuxbQ4xoRvoklA7V2DhhwFfZJI8YSwAAWVZxn1dPnRZFyKa4B9YNAbd7ss8X1tvWI5fpTscF1DuqlwIRrOyzYL4AEYCjaw7sD7oXhhSJJmX0IBgJ5ljVFZZMb6A/F93FAyqgzh69K28MOBSP4cJQpS4w61XcDsGHr0mXFkdv33Korstc5t8j0mwJLG80ydJ/oDseqLg7E2dkDeePyQjToulJXdhSfu78zUs9c2sz2WGplHUi028J6Nx17rj1eMLklimNUAZg9OicIp7TPBItcCk3HNVffzm9zBZMi9TjiGmmKm7+Pwfr9USw9xPcxq1dcZvWy6A8a+Ln00kvxpS99CbfeeisajQae+MQn4h/+4R9wzDHHpGWklLjooovw0Y9+FDMzM3j84x+PD37wgzj++OPTMu12G295y1tw9dVXo9ls4vTTT8fll1+OTZs2rbpN/XcG8GvaNtJdxpWGT1NjSgkuvjmZtcVPnXCvXKsgfSCNj+GgorgXUtiHLdfoe6G9eVWWdH3Z4uGpOLsDQH1KImhxATYr1xmyBWsgOaCLNAYAEKjNjh7mNN5F//6sgg4TjkQMK3sZZaYjBTTkg03sMqkr1Zjl3UsFWb53OcbRGWfFvgQAqM7n97UUihnlB7zqJsPCxAUGsvepLJsX8lINA7bGzIoV7rvALPP35INZxO8AnQFA5AUfLyA9Do2DVEPEyyS/NaayuUSzdzXXZotRv39arhlblk7ayggAqio16MJmlvlLVWlp0ZjVUVwp2FMosMA1hbLYUg/ItH1WYFzpYJ5dwXmLxtKlOVZlRAwr3oERX6nrfqYuQoVwi1HWLpiEr6P9F46Q65YLXQyxyGLkuOYYdw9S72LF+KHm6FKBICx2jBZK6btyIyZXzAJLyM0xA4+rdlYvaz759lyh8Vq0ptyKIRFI1GZEmvpaU4sENq0fVM9ke00alJi8VsBSk0fKMpYK+RSErU+7rwNAdd4GKIwMYjrT5CIT0FQmseYGs8E+sYrrjMSoznrW/NTBnft303bZShifWdhxqz8dyJtf7w4CDLNDY7/5vbkG1ni4glpX5xjYNCIQtGwwgZaLK8kLayFVk1Dxj6jrVrffvTkHC9mYGIJeQZyLYFmgxeJe6XECCODGLRe1/oYBPzSNdtgAmuuA5lrz3tRyjly2LEKEbT3Kn5Vn8dMrzpluW5FlAU+WkV4XQGXRBveMbJOVrKyrDbmgDm1DHoPSAwSyrKJclkM5/JEzzpIuEyT7KR8T/TJmOnf2gB5WNUB2xlp8bMUEcZJnMSAoBMDXPQHvG8p6MWTAa3q+ccydtEF7EFj6IVWmMpdd42vLmR2L1tML0MubG7RIzhy2LKdYBjGvY4PKekwMi1fHe/dy18oNuu7BDv3AXsCZLZhQmtjC0QTPocgzEo/kuEFKD0Cn+J0ebFS6ej0w9AcN/Fx//fV4/etfj8c+9rEIwxBvf/vbccYZZ+CWW25Bf38isbz3ve/F+9//flx11VU4+uijcfHFF+MpT3kKbrvtNgwOJmr4N77xjfj617+Oz372sxgfH8eb3/xmnHXWWfjpT38K319dfryhnTECHayV36oFfgdTrWlxfTKJfW7arQ8Uol7u28eR7hjdAR+VJRJAsZ19pm5Ihlk6gPpshIWN5nB7zCWg72BkbaAa6GiOZ4uPAzwiEogrArGDKfr/s/fvsb4kV304uqq7v4/9Ovs855wZe8YzBtuYGC7E/OTYoBB+dnCu4EYoihxklOAIIUc2CAscuBAp2LpgrsEJKJBElygKiIQ4V8qLQJDsiBsQ1zx8HUJC+IVfEj/HnpkzM+ecvc/e+/vq7rp/VK3uVZ+1qvu7PR6wx7uko7N37+rq6up6rMdnfRaXvSfCoExO08E5enRKhx8HixT0YXEj9Hvnjv3e60vCiwsZkuqZM5Qx8SjOGgPfY3oSuQHgnaSyyhu0JpeNbUuBBJXmVh+Gsg6PexfmNlKkYc61ROuJ0+EDcWzkgagUfjJS1kKZnEbFeksPR9L2jhs21jiuB5dbotmxIasZ3tgyM2ZyfHH97X9qlTyfiKjY9B9WQnMRetzMCrryh6mkubjVS1PVWRPbS7W4bn6gNxqELldrgwLPOZmYx4KpqzAoKLw3lSAoO6/nNSrWxUbvdXL8WDhS64+IyJPOvJIogeF/NDRUcayk8U0J+dEIJ0OeGEFBRNTsCp6XKl2YriAdviPWyPJm+Djzp6DvcU2tgBhXCq3eBaUE08+zB1sSAiMXhC+Iqk1+vIh6xQMRCBzKIguur9BXuO/T/QMY0abCFaNXG8OB5L7SPQsVudhPGcK2vJHWmUaFJUnDLowY5RPS0pjeW+9qAV3OFe4X1pnHOksUwMHDzsYfWTr01gPyYtpMWxqhgtAHM+UvBY4SRH61oDy5xlCOhVFlEtGNZw+llSb3XeA5AYOsRACxQQnnJ4emra6JMxrWfXWsBXhpzJThacrpVVDPCcX9kpm5MplWeU1sYO+Se9vkNPKJgNGR96CVuLcCg0XgdkmvKUBtHBszDHREaXat7cTDPqLRv1qFNYmhgonBJZ4N26iSanQt40yGPsdCNivZQTwghwbqzioMgxV7mXdhbeUye8p78Z3aie6reXZZfTOMeDvPpr+fvLjPHsqlFnIFG8LnEA5GRVjTKtzIGCdc93x+JrIo3IOGYyxD4W+jXDfQDy4sN+A7oAxQz/Ph6jL83qKtwD0ES45OwXKaYijZYMZe0aZp8HJkC89Y8N6CqNkmu+tFuSgjxXn/hYODevrpp+mBBx6gX//1X6c/+2f/LHnv6aGHHqJ3vOMd9AM/8ANEFNA9N2/epPe+97301re+lY6OjujGjRv0C7/wC/RX/spfISKiz3zmM/Twww/Tv//3/57e+MY3bvXs4+NjOjw8pId/8v9BxU6G3IXLNjshhkGwW7nNC7Kj1+XmZyA5FEmb5TnZgjnaFCpbN0xEGG9XdbyGcmaHT/Zf/ihjiq19UYUHaeQHKqF8KAyhDDs4aM5rl/OakH1gniuj2mfBVN8ZpMbItJXwmfcq5p6Rq7N1OBKORRtCK1RIiQyPa/W1pEmhyKGgvPeZaGgTgggbuIiINoIXyeLKQsVNKmMs9KBnjZWVoXci50NWLxwP6x2tzzrmLOEPYhE44ndH+LOF+Cng72TUIeoFoFxhYmg0qkZDikSjqL2OlaEkjET0XRAzFwCrriYNrY5R6xzoZ1fH7m8Silc78rNWtzcLN80/3kvHyPHTTkmT7gJig+eZQo/cJTUPUAG5/xJrbxY/xvvRYFHvBOMMGiqkUM7fGY1DbIxKCD2h7ztPhf9PHhHGHsm5JDzZGimq15yFgMNnsnKcDdmh4M0vly59T+q5dKaiXyoUaKYRDwqdyYqDwfeBoV2ouJ3dMsZCGrzimDS7uFkHxMzsTnp5KYxYHToNz7M4NjhHZZk/rZELct7I/lSAwirW2oghjT2ccU3xr8Vvi9wqKDNUC8OLH7sgr1scJ9aakEV9ay7FMNcNP3sI8dMp5fA9mklwbuBZlbxjDrVjXUeZwNvX1d+tOt7gtTMcBtsgSbBeuQhtKwN1vFdeR4NGufJ6HSKfVnSe1cDRsjrUmQ1nR+mF40cd7T6V1tkIJ9vsXuyqEd5pnZeVCPs5ebF9mPI8Z9QlUR6tIueKdIwlMpZlCBs8x+06PI46i13/Tm3lyDXakcdjn6K3oEtGuB8Wngsb2FdmR6Q5fuDeZiSMskOGocMuGuUtJHz3M1NcGfO9WS3pv/+9H6KjoyO6dOmSrvACKaxv/7/+06tpZ/+54VMWJzW99U9/5AU/Zucpn9eIHyxHR0Gauno1mMY/9rGP0ZNPPknf+I3f2NWZzWb09V//9fShD32I3vrWt9JHPvIR2mw2SZ2HHnqIXvWqV9GHPvShrOFntVrRatWfEsfHwS1ZrB0VcbPKpo3GdL2i3vReVFwQbhmFHhkXrA8AP6yEDGzQviCd4tLimMF3irtPwsOAim8VhGDNv6K7aIesDBukSstjJp4lPcYoUFSnhsItkFHIl9DVicK68hqIPjDqYDBttXGf+XeC7z0i5EjFgsgW8LZJi2plW5hA20qonNvP5BCeofYLw0MqCwvJykPbEO0+pcOsavF9OhQMhqzEcZBhIQpVdjNtgygN0WChDOsQBeG9xvPE4GtCj7LjTDi5jD5E2awZlgJioXtU6mdEH0SPO8LUifRaRa+6lQLZepZlEDb3GqMoDyyT3m5Kda37PfY74WgSCuZGGHuQP7EpPE2eBWSkMCAxfF+m9ibqxxkzA1UA7a/3nCaufSo8b/lAP+AI9/aFDmNBBZP7prMf6YFeXgO0x6lTc8MSrFeAVvJVMHLiM+U67ULvEPUYm0qy2EAfTl5iWYr7HxeCywbnv+WVTepwqm+4b3OJnTB4s7i1DBxLJ49q4wlRKgPgunGNUynT8Xuz4qAy2lS6XzKsjvugsk8ZWQlLCAdb3appclx1RM1Yn6g3Gik+qFgSLqH9dANZXQkNeYHySVJeW9x7sayvtQkxb2ior9PxqGGmsjhfN2eAzBKPWh8G+QX3GtPIZThw0CCF6wiRkrIPQ2HPRGSH04rSzXFUfJuYGDBnsCF7XyYadnYNlW0ST3BBg2lyb456JWMkS8J49yMhNsg+/K5nD2WeSUS+NJyXyqmnjZdEYcyQFwZDB+v9lppZOrhTIbvee0W438yiVXgDMabnPxb+3jL0PFty+kwzXMekMYDn5yBjQ4k+XBvkk1xYvpRZtPw77q3hcUZH6PKGngfm3M7oSrJtdUuGqDt51kCIfJszIr9AS0sFtc8x1Ou53v9CLF8whh/vPX3v934vfd3XfR296lWvIiKiJ598koiIbt68mdS9efMmfeITn+jqTKdTunLliqrD91vlx37sx+jd7363ul7dK6ics0nWvjd36BCNh9Hk2PSJiGgTlIaEzC6DdrHSoWrvefp7O7EQG6y99deUMFo7CqEb0L7B7q/eqaJB4YT7pe4VbQ9lo5EhHv0DxN8v2/fxOyqjjkH+m0U6oQIv/2QIfVunbiUjVMPyIBheEKLU661gvO0wLH6sjB1qYxDZLjuSwe1y+pBtbOLCBoCcgLi82v+MHsFpFEQltFvO8ySkDENsWqIpZlIShY2H+O78rso4o2DqhsHXMKq0akKNC0AsrFqZxTxydED7vtAe0qTvQ48ekUNZGEOFmPfPhDwbjIG9oUFCVsSjxfVioid3Demxk7Tm8blogMwJuGvB8+DaYORAAw4P6/ROP3ERReALw+AI58yEURLYh8v9/O7fI+1DuTTWpuHxL5F0dyeG1MH3nJz0P68MwxNRf47J/RszkvE4y77JOTZ7Nj+RfKHPBom+6OY8jNf+J0Kbpy9Kr1eyb1GprgEVx/uKRM2gAjg90vMnl81sG0cGIko2+xoNJnlnmEME50Q7qUxngUQbMtrKIXGtwVPlAM7CTpZGZDSVDoylMICjjDF/slCotZU0lMZ07RMglJ5F0myV6S6RlRxN7xFN76cTYT9mjJw+1U9mPwWj8NEZPfnnH0ybBoP59NTeCL0LIfiyYIYw1+g9VmYr4pD+6f20nXq3oKPHSmUkrMQ8uPJ/rmJ7aSUOM5e0A5JTkYho+mQYUDke9ZV+Ytbz/ttbnDzz2+kG1+z2C3yzF9osN2m/+DvgNzh5rLfqV6eNmQRiEvn6lg/0fURagGbiVLalBpwyHeIHsvKuD5wyVuIcPrtZKOSZlMX2Ph3uf+A/pWPjS0fV8YqKozSmuT3slYmP/8Ug6CqOnzgtXvQf+3snz4AH6n743V/uYS6b6/3P0/8lYEorEJyKgqhNv1P9pf3GefZgGO9mnn4QPvP2H0/bW1/u58HxS0rN2UXUrQeJeMKza7PnTBoDWZi7EsWmem5kzFQGKtLyjQmB15fKlXFdiirxWYqqYUPUDJCiX5SLsm35gjH8fNd3fRf9l//yX+g3f/M31d8cpvLzXl3DMlbnB3/wB+l7v/d7u9+Pj4/p4YcfNrkvsAxF7JSZmGH2+ifea8O7ZMXv5+onfzIszS2mazbiiDvIuWHEkb+XKz0u8lBjwRChzZP7eUUd701i2yW3ieTsMQws+Ez5PBZGFYnfSNgQt02kBT2rmGkwkQhZrsYRY5J6JnhZ2orUfOjiqpf6WlJvxEDYvYs130YcLc4P1+HxRoWnWGt+BaLUAMbGGfW9Y5szoWSgoHDtvy7icwR31LUeTpQYVUFgbKeaR0oK8MvLoQNaqLT7koQWVMELlptjcl6ZfD6GMUiWejdcYDJZWU+lHIVSLnV7snTvYRh2rfj7xIg1tRvuv23/dzTi8b3yGXL81gJx0GLGkFlDs2eB90fMfw7hq06SKp2BQaFOMNVwTWrM+B6LeJjL9FgjO1CZ5ftVaImnhOg6PCD9de8pTyuDhLj7kdcWtOOaMAcVyk4Sa7PBGSPoeC8zhN7udyPsQwr0Q2dHtSA11jLkacnfDOYnc20ozhfxTeo9T7M7TnnamQNHvj/OicmpDiVQCE4W/g3nDIbp6bTpeg5YDhCVUr4JeyU+cyLCrtjohIgNTk4h90EkI+ePIa9LTifJ8YPb1uZAv/fuJ/sX5/dd3AJ0Vau/h3wPohDq4QttjNvshQm7/tP9xJ0gF1pjeZbS0pGIr/W5cfoAEMnDPr/eHw6hn98Nfzx7IN0w6nk4M/aeyt989+XhvbZBNyjD5Mv0Bp7s33K8jWlw/+G8B4jvVfLqV9qkLXKvrJaFaSRnKNju0/0fMGnF7O6GltfTjWz+TLp4T17EnUubd63BuQN9mN3T81Du+SyfnDycCubVWUveOaofAmiZKBYROVE/hqur/XttDtJO+OpK9w5d1yUv6Z97RLXHxbWah1ImQMmFMvI8v/vy9F2TPlRERZlJckOUTFTUiYrNOD/mOobvY716h2jnmfTj6bDAVhn6kgQ4rM/Bet7sFeG9MIRXGFkPP/hHRETkH04NylQVVDdfXJCfxhfUPMesXs/1/hdi+YIw/Hz3d383/dIv/RL9xm/8RpKJ69atW0QUUD0PPtgvktu3b3cooFu3btF6vaa7d+8mqJ/bt2/T6173uuwzZ7MZzWb6oKn3PLXsxc0pPQP7DXuPNaFYhHn6vPAzlgXivIgfPJgsbx87NBOUEXpw4uas46H7nxkijhve+tKAIYvv3dF9kOW82bMsEllF0FjruliGYnFVm5YzYATpMVTG4petZ1q8RVbf8bBWBIdD83tEiBz71kPQ9WbHQsPoZ6mU0PEemS0DlbHbrw6TLEdkOCR8+1ITPlvcQ4ozhMMJMVMScjCNrPv+RuPaKAIrejB3tdJvkh7KpldOac3Je3NoqdrH8h5wLLn5IvuCoXKsWCYpxeW7iEaR48c5r1Jty/AVNjLlkCQqjEsYZxjlh4g6fhdpwJKcDNyuDGkl0sacHKHk/GlbMJZlve+UYpqEs8UfL3803QxOb5XUTIkcGEmu/FH/gKf+t0lsL63D81UajRQHT6yThBuJsZfIGsXnM0uRR9g+o7cQvcLfA0nDZVhpO/G0PnCdl54LG3ASPiOY1+tDvbbQMMN7rhWqqe6FsDFX63BCyb0zu2PLIK4JBnYMnZRhrkcvD+0sUpB1F3Yh+4LeegsVlKS9F9/GyvRpondjYQUaU2j3ii/cAMbG9aFXhu510/+dC65dbMsqbCxGgliyUAO4DQ8Q6xIRnb44vKDKwFh4mpw6Wl0FInAT0QUXeE9PBEob/Zlwzw37CZJ6GCpr9tFwfJrXRR98GekYMjLL8oZ8WXzxSo13kUmLaSXrwP0N50p1ppEk8ndej0tIU+6aMrxYRp4jEt8wM6z3vrT/8OelhxyjIMBMq0PTBssQArytyCTC58J8akSG3FkSrUacXqz/IPrVtaQI9FXfpsN/7xzKhhPS5rns+3D3Fa+0G3VEzXJK9HuDj35BlZYctVtvLPk2LkpaPq8NP957+u7v/m761//6X9N//I//kR577LHk74899hjdunWLPvjBD9JXf/VXExHRer2mX//1X6f3vve9RET06le/miaTCX3wgx+kN73pTURE9MQTT9Af/MEf0I//+I+fu0/lmaOys4bYddRhLerxoaYzQbH1eeDhbMQQ9yZInIF07vLZVr+IiJyVJSAjgGKdoiadMlV6feNmp72JBuO/sYmHH/J96O7N0wCY7Xef0hCC5f9W6ULQPhvDj3UPoHbMJmM7g94lR6bXq+PAqfS1pJ8DIXtENJiqEgt60107fF/OwFJsyMyckRBd8kFuy6jp+MJ3nd8Jf5SkhtITtPepXvqo99PF0M4KOr2ZnvCS/JHJA7Ok08h7hUTj3jB4GW1Zc3WMd8sxEXImJj2pi4Q4RIQE0I62UAS8swVIKcA7/IF/j3UF6iq3dkl+ksz74VgTGZ5mYUDqCGjhPhbyUWlG0uNy6UzCXqLUiGEadvHbYSbCqHgozpFSKyVo5NnsOyLgebL27+WVdHDqOdHu055Ob6X3ri73g7//eLhZhcfF77QQAjyef1xHKlHynJN9RERgM9XjL/nLGGGC82cWyZLRWCBDo2Z3Cqp3NQqL+VykEc9CYOE1zMzFiDKFVNnRBqk5hLudvkifHzu3tYEFUUcUkXjrQzB8irXThV5mzidpeGl2LRJBSrgG80T8cKuhAJ4+0m945WlsG9ZlN+8RQXev/3l92CO1ZGGjocxiqrhSvIGCg7Hheb9NuDfaIiyEYPJ3ls+UbOeoXAR0WdIXYRytwVAqbu76xwWNVh3vYgbdMYQQHw3zHXFWaLJr0YdpnDuWXEOp/KnkTNJ7CCIZmYwZkfvFRht6lPF1xwhhFKFfnYyMBv6CMslA+p/ZKKozuZIqGp2s62R5faxzG89fwxmdIyjHT52gt85C29Z5RqQdIbKUq3FaAS4Y4rnZc6NUCp08mildlmYkGafYr4FvyXPQ0oMao72LclHOWz6vDT9vf/vb6Rd/8Rfp3/7bf0sHBwcdJ8/h4SHt7OyQc47e8Y530Hve8x562cteRi972cvoPe95D+3u7tKb3/zmru53fMd30Pd93/fRtWvX6OrVq/TOd76TvuIrvoLe8IY3nL9TjkYPLvQGywXcRG90AUTLPReHtBIZz4b25P4xZmlXRiWoY3H8dJunbDuXwhnGRQpquU14s6fvywqVI6mpzXstr5p1G27sPMbbGIszXjsLjZJUs+XipM3c4aKEI+OQyPETJWEI26QrzcyJrbJ6bdG8LBkHGxUTImqHPUiWgYeo/4S5NLGykhReZXz/yUv6RY0Ik82uS7JzyGcS9cI3IpGakRBHbqg1+Jq6MmDMMveqjABmIr+QH2gbrqdt05Q68BZjlThWKumk0TzW8cb8lLw+boD3yBljlqy1TIa7nozXNlQRhTW5OfA0v53WWd4IlSbCC449nJxpIwaGB7GhQhmWrDBT+JbTI2+EDPX9YWEU574vAr8FKuVLgTTY/0yYmCcPpg/gbFHSgKUcA2DkIEp52ZJQR6jbzLTBS3KcNJv+HWRhoR8NILvi+6yukb0fsYIp3gkVl+k9bVRS4YpsCDLWHJIFKyWFtAFSKvzsjUYD0tnDLZWnhSIkl3sivxeGXTF/mpyTiwxPhnxuLuUy7pXthOj0errBsbGHqCdRx/Y4fAYNbXI85s+EDEyT03Sw19Hrn2Q7wnTunghEOTMckoiUlmt5/71lxB5SMDeinijNLHPeibamJ8bfqf/eci0q3kqWTzJOyGR/MeTYIf4VdrhoZ4c9EJKrx5dhr0JETp+sYwBpNNHGUMWjFvcTK3vc3meG7z17UDtXJWqFv5cyfrfhfYZkHyYxx/fjb5iEx+IQWEMiz60xJPqAjJGVLzu0Tf7ezU7sKxpDuU3pDDDW4BgFA89R5GtqZ+NO8lHjZCZVvJnRDkqHTDfOli+ySK+LUK/nqXxeG37+4T/8h0RE9Of+3J9Lrv+Tf/JP6C1veQsREX3/938/LRYLetvb3kZ3796l17zmNfSBD3yADg56Kfknf/InqaoqetOb3kSLxYJe//rX08/93M9RWW6jeaWl2fHk58PqLHrPpXDKniXFTcDCgtwoLPTKgOEpmd9G9qwcj41sX72ZQcqMfW8n4e8ePEiWQGrCHEc+Q0cCKuvlDFG4QVuKnPy9MK4Z7Vj3cr+zBzKgcFT7QzDvXJs5kmyrHurMFurKes7zuU+OWIKGuJVM5IvhZVRx5dHAINNAYzv3X6I92VIAm9+xPZtEQXgZSv1sGQLlswbRPPGjmeSC2JxhXMVMI1h6NAtuDsOGGSJbKZH3jKHWhhQabgc9+N29Cbw/Y4wR+3DCxZIw6MM9zlO7g5un0T4KlfH7NAdoLNOGoLNH4FL8Rvcf678Bft+idspgtLiZPou5WNDQUO9phQbDoixDsRdhcNO7oUOLG2mdduqpuec0J1eSUa3snpH0Id4zEcoQGkT4nhy5s9z7FYKlJbXfSIJ3bkeF50WFeAJE1qsr/c/lMiBc64yiKPu72QfDZOF0aE5mLZjnJdxbg7GjnXhloJPnRXM5/K8dQYGzSKVYFnsbI5HObuG9sW3RFwz1Yl6g5fX+pRL+IBnqBbJJuSLC7jJxM1Ee8cDcQ8rwIwnFj4mOvtRRChHsv6UVtttfoNHSh+HojzlmJ7fDQvqf2YFonQdWSGHSTuZsOG8okFnG3msIYcrnpZKZcgeK+LkYUfh95meicKbAQxU1waHdFdcQLWBNqExbziBmF2tkEw3NiOTcxnnJayHHF7i6ck4X3HmqD8mLmfk3KhtQmNtmVlNjjx1qZ6xYobKj69rpbGOydOsyV2dAbhrSh7bJVvZCKg0V1DxHheS53v9CLM4rl+pFscrx8TEdHh7Sy7/vPVTO4u6dMSigEGxl3soha2S4VO7QTzwrGcs8evysTdLcfDMbdaJgo15UhT5lSYCJuoMED9KiHrZ/EPXvlQgxGT2uHTAMWA/owrUQuru2+yKfy4LqVmlRLUMGjlfG2DHaNun+m6mMDYJWM9U2vDR6o/OCGZ1PYID2ZN+04jsOSWYveY74OlkfKFwZaKHcfLMMsrjmUx4L/Xz5TKWESw6qnKHXmBiWMD2eZS0iENf6Xs3lBQJSo1OAJ7F9WbgaRQP2FvAtZwhl1KcuJzL6HvsgFXeJ3miu9gvPTdLJUkxaap8GwkmZ1Su2XZ2mA8thLQ764mA4/EQb1LhOdTz8sZSykdl3JJ8LUVAucA2Xhscdw43knO84VJC8ehrmt1qv4vzp+gmfiQ1UErKPWRa5nxLtJPuQIHqMPRH7myBCfP8OsrBXX6EPpdHfhTWO5wtzM0nuDkXkfI+oATQM7tcckob16h1NxK4QS3ua20j2oejQaXBffBYSl0tDU9cOfEtG8ci+qAxtO7ov0iAjZQMrZHKQozD2B/m5mM8IkV9yHkzOiKbHXodx8f4seaGQ26WxiF8zsFfjXB0z6NcGwX4yxjFj2OwoHZzpUU1P/19mKnRpdtz3bRN53TDsM+eQMIt0wCXIdieu6/Zz6B2i/l3UWGecQtOT/sFnN0qa320VioNlgtMHC3VNtq/QktB1dmZa8pc0DMv+cinWpOaAnDu8R1t7VrkkIy6q/5ENnGo+MXprAO1s6hkG+jxU1nVxjsl7mfswZ8xAVJp0qs2OQyitduTFewdQY+1kiwiB+C4q7HYyjsoZC8UuMmhSbj+XcTfXHlE4c5rlkv7Xj/0QHR0d0aVLFuHYC6Owvv2+/9/X0c7+c8OnLE5qeufX/ObzMmZ/8S/+RfrP//k/0+3bt+nKlSv0hje8gd773vfSQw891NX55Cc/SW9/+9vp137t12hnZ4fe/OY30/ve9z6aTkcm2fNYPq8RP5+P5ZF/c5uqMkoNJ6k04ePvq9e8Irk+Oe6l7ideFyTYCgRxFi7kRjg7SneHatFSPXe083R/WpSr/vRaPNBLMwj5nN2pqd4DjxYaB3x4hiwMu733JX3lnWfTOs3U0frQqQ10eiKEjHgQF036TvWOowqyK6jMFgfh3olIiyo3yo045NEg3sxIb7LSUMSZldDrGw8VFOLkc+uZ/U6dQjGRCqPRhxyXEVG36StkT2xnkNzZE9WU8tUQ9Yf/RiACVCiF17H/2M4gyfaIFa+tdB35PfjZCNkvNiG8YIgToYOIAx+BdYgrAmuOyZaGn0wKd82dpD3sVratXFajIYGuaIJHX2WBaNkA0l/D0A0z3BOE/OXV0M7ubT3P15DlCdE35coPe5QZtWPMl2qpFS35jj3vS1qHx9AJBBbzM3HhdqURQZLwHotUwx4QOkXR0OF/TT/IpU/2C+DJ14QNA8Os9kIUMp28CN5JrJ3Jqaezm04pJfzeUlFWHCeVRtvkhGZEqtQ7KUEvkVYEJmeeVofpfdLYMmf+Ifjem0vByICCvSR3Pn5JGDNFFm5wzWCK8e4dE9Jw8R61UVcUJASW3+0sEhQrTpbYDhpkp8JA5erg4Ueeic4ZIPqF67LZ1UYl7HuHSIG+Vaf63LZ4kSzDORe+HxG5rg1KEIYUyudxP/Fc4vMoyZYFSCdG97QJ6bo4I2VotKEwofFG9qs3dMHcj8YsNd5CVjl9EVFb6nBFNohsBPG9VaeBDIS4jnKcHW2l31MbNbwmEjacE8i9tbw6JefTLFZE6fk5iT5fRY5vIUdzZ2rGKJBmZ9IGtd3bab+kPMPvkjNi4PXTW/27T4893X9xqWRKlocT2caQVVTo0TkQXsinZK0vRGOSmIe8D1rIYNcaz5ZyLPc7w3FZyv0T9zv+lpnvN1YQ7bxVYRkZZSJpNNqJ+4mBriIi8vkkZ6YzLueUtMJNx/iBxpKrOG4zI+sq6sLESZxH4mEW0hd6ab2j9jlCEJ/r/UPlG77hG+iHfuiH6MEHH6RPf/rT9M53vpP+8l/+y/ShD32IiIiapqFv+qZvohs3btBv/uZv0rPPPkvf/u3fTt57+umf/unnrV9j5QLxs2VhC+TL/mYe8cMlp6wTCURPxvsprdg5j0uycWcQP9Y9uVTtSVHw19gvYajCTa+dhvdSqBND4UYhtVrYJJNWyRkcpGFCpUG+r5XyxhBkFcE0b9AofIimmgzCZAye3dVD5JSBqsptWejRTbIclcFgkOUaGOqX0wZHvI8VaxPBMmb4KfU1WWb3QgOrK7rS5L5XBhBpPMgZ8TpuiQEPkZU+Wb737tONuJ6+pC8dnd7KG1U7YTODELSy5HU/8xhjHb6eCXvp+jEioLCiNjQ2XV0rnGdIgOkuGtcs+LpsK+Ml5/1TCtA5zjEFtY+lvtJ/5GIGiJ+qJf+ZVJKVvD3tjBE/oAgx/BxSWMusRd6Fcc6GPAnFAdfukHeTCxs50MjTToh2bqfXcO+vFl59382+Vsr3P53eeHqzCGg86O9UIAv4WWhI5LW2xv1f9n3K/euvyYxZTBxtlXpXp8KW78hzCM9rPuswc9Xh/+iftTp01M50lh5uayqMeEq5N1B8uWyIaHxyjd77sa3VFVJrS3LyMCE1GpuW1z3tf8Kp9iSBdZeBDr38rJQPoFx5Dsu1K+UK+R0USuo4DREjSte9DBmTpeM4UeTm/c9tRCzjWciOK2W4EKVa+sQwRERq7CUiRRazXQsRAiK6nCurQ1vo4+80vwtnlajecflkELJWuHJXxenr0jjkRJ9bCMUtGi2TmUYMLPGWITm3ncS5mVlfs3v94GmyZDdAeB3bj+9SQgrw1aFL9jwizR2zPhxGoXAf0QnDqC8LOcXl9EVcJ63CBn55tuTk6JxzaxD55Qw9wnCsKmcZJwgxuLy6nwub443J1pdX5HyDLjjtLMtxVeK5Nzmz5eLk3hE5tuuPZfhhxDO2l3kWF18QNasl/fe/98WD+Pl/fvjraf4cET/Lk5r+7//br/+xjNkv/dIv0bd8y7fQarWiyWRCv/qrv0rf/M3fTJ/61Kc6FND73/9+estb3kK3b9/+E/uGF4ifc5YE3p2xwPsByHy3saH3x7qe23xysq6EGiKiaG7A9NGzYGw43jhsddYbImqJ3ABvQbfBwzPWlQHpxfcbIVqWES7Yt8U1ndpT9otRAXgYdoLtgGJrer0oY9W35spA29nwsXgPKhzqUDNWNo+DPOisrA+YTtQiGzSLZfjBKiOeJH62EmA2AVk2dCB3hMAZr1dO4SAi2nnaD/dv4L3KZUvVWfpQqTSzIo4eQDYcqjCxbUhALaOR0cfsWuK/x98tzqixVKzFikzCUuzPWDtYP9yUeWYUZIeyOfG7tMITL+d8sy+4dAAi6CYNVaBESqNLHQ2NSG7L6w2F/eweLgojSZLUvlsQdausXhHNgvtpdZJy21j9qBYuS4hK1Ht115DmtpkHpJI2ZvUP4PdCgwUbqpKzBdrhd5L9l/PpTGQTs4wnQ1luurBS5MiJcwVRUScPi2c1oV69k86f2b1QRxoucD1Xp0b69gw6E+9tZtpLjkZBX2qU0f1H+34yiTjuR9WZo+V1vR/J9tsYSoV92HkyvrcwytUzQNMZGcGkoi33ZBWGtqeNO7nsbrLkQrllWE6xCe+MbSxiWGni9FJ7tVPGXqUoZzzOZlYvs2pew+zeyzognKf7L8ne2pfPEQWGz3GnGQcAIk9lGXNWDO6pfOZkjRtD2jrpM1SFdOX67ekUr+B7l56KZdqgNKAyx8/pI/iMYT4ZImFoMzjlwt/z9+acS/ZzBu43G8/cxzLywL3FxtHpzA/oO0MvRaPv03FeQnj26rofzwg2BiJhuSrTh7FsfqES1vHULs8BxbooSTk+TuG5s9mMZrNMdoHPoty5c4f+2T/7Z/S6172OJpMwgX7rt36LXvWqVyWhX2984xtptVrRRz7yEfqGb/iGz9nzz1MuDD/nLLtPEpV8+Gc2LTzU5GHDLP7ogeoMI+JWRj9wca1WAnPGAnV4eqIG4sW1UOy1UafSQiKiA5pp5B6AmH4pJE2i9wwP3ulJ24VM5crqcvi7TLsoBXiJ+tD9H1E8WYFGQ0Od/t8/QNThjBoZj8dYSlML+t3VNxAosmwQ5iplrpaIpnllQj5HeaBaLTSpsBPD22f1wyyWh0iULk2sYShrp7pv8ndOnZn7HtIAiOPK3qNcZhImqCXSyqlFnJ6QFMbxGzPwZMuAQS1Rmi2I/ghxOpMFDmU76eqCIlcWbhARN2bwGVJ6OkMUjtFc92UDHmWLtDRZW4K02MHAeu9oA+Ff0ojBPEdnkL6c5+EQZwEVoR+KUyD2jclvw4PSOkWjU4ejDsN9wDA01+p+qcxQu8basvY6A81a1DpDnURhTmLKYgspSgQGrMw6knuQ9BIzpw6R3hPbqZ6DyLlEpL8HP6uCfQTJh33VG3q6NmP/pWEQQ/SItFEJUVp83lrpuJHjxyIc1uFgqdGKSM+Jsxe1NL1X6HNDcioZac+JSCCUfLZuR6Yt1mdCAC1+RMNVOyG9F4hvycZTlS0tyio4b+Uamd4PBih0EHU8fxK9Aigd15IK9cI9sTtrDNTmWCp4Vw+fE8zZgzJUtfK0uOYGecE6PsSBcc1d2yobaPdQfQm5GGXpUB45IwyuS0koHtN4b3O2Ygg783Yl/YQ9i42AKqvX3FhzMOcWDzia3aFs4b1ErZvSTpSSpD5fcBtwnsX3kai0rYwRhjFNXY/3qPTwI7yaRP08xDFKMuitQvhxLo293Let7zaG+OGCYzs500hRLHY4pP2zLN3lgXMpZzTyzlGz/hxZab9ASusLap9jVi6+/+GHH06u//AP/zC9613vek5tExH9wA/8AP3Mz/wMnZ2d0Z/5M3+GfvmXf7n725NPPkk3b6bQ4StXrtB0Ou2ylP9JlAvDzznLZOGpbNiIYdeZ30l3QikYbPbDDoWHTmNsZgr224Q9QW5KhYCcJuR6k7Rz07M24dwh0oealR1nehwaPbs5QIrnQ2pQycFDRNQmnrLQNhrFNjt6Y1e8Bkt9XR4uuSwvRER+ShrlINup9DXZjnmIGe1Y1y3Onv7Bw8JJ9y0zz1CEy4D88IU22PA90gCB7biWaIXhBRn4sCoW4gSrjO3h/CxMH9sSFUtjGGXGvEw4DCuKQ9k+dp/Wnd9KEIp/2/tMOtj1bt+xxfXwMyrbHeJgQDjvno3fwPCYqblfkPEBoOkhj5wyLOj9IRdmZT0D+zYoXBV9PbMt8XH0d417jRT8xJxv5XsY4aiYjSgxMkdEQ2lwY1n9RWPW9MQIgY11JErEQrzhWjWRl6SF4OVVnWoYIfTTI081hKzI/YOVq4PH08E+erSkcq15liaCY2N2Jwzg8lq6qNvj8LyFQBjmxi8BE4j9J3kOhFqcPVBoAV2enRmFnI0E6yv2dSKinaeJlsbc2YlGLqmMoiGuMTi7kFenM9IjeutUG7iwrSFeDKKeS0pnGCrI1XqeyXrMaYQhGkxCXYjMicq51b2LrYQmIdi47tv070SCd4r6LGMKubuy+1sJJ9XqcjCwqTD0GFIiCZYnwB0TnHFgDDL4s4h0/4m0M05976U3OXK6H+P7zo7TDaNatLQ6rPQ8ENX4Uco5wMCsAV/A2DmeOADUH4nmaKCQ3C5RVhxF/vC9knx8GoyAGF7H/Vle7R+kvoeBqsSw98V17SDiYjlbZSlW+j753h0fFfaroK3CQ4mIzm7axtbBMubz8ZmfuXtbGFhycqAyfErD5CToBorjKtZR5PvwQIu7x+wndG51eTgkD/s51LbleDMpF0YceERhDjQo97/AS0OOmtEJOt4GEdGnPvWpJLQqh/Z517veRe9+97sH2/zwhz9MX/M1X0NERH/zb/5N+o7v+A76xCc+Qe9+97vpr/21v0a//Mu/TC6Gvjqn+++9N6//cZULw885y72XOir5sM58t3tfmg6r5TlVKXeZa0MI5GiIMRU5sk9Y9HCdVsVoOnfTyBAnpzRIcQYBLu00wtf3084l3lXeABGKugXEsTtYhGKSeAZkvP7AQWLemwnp6lKD4woR9bpn5az3GWSWqsd1jCxoWR6UEUiyRVRYGx4zS/jJecBVH9D7YyjzgyR2Vp3MmvJFCC8YgshumGcCDZqxjnwPDIVE8kosiXBjrJN7L0ulEFmfPUooqLABYCyzy1iMffejYYQZMlzKZ1vIHZV5EMOiWh0elBAT4loX/SIPdfmh2D+sY4SMKoLIDn0g+yV+ltw/0HxZtiorVivGlRE/mhg/g4QQmdBcG/Z0DHmyhEw0rha1zhqD2Zd4HHB/LpcpLw6R/rZnt5wSlGWIDyNP7r00/eD1LpE/JZ0RSbZ9I0x0nJ9damRJop5JrS4NAfLslJ5sVKLL5Qhik7PoQL8UAWssEtG6vB7mgES4EPXGUbmHKgXEG6gQRB+U3F56fXOg92c0eG/2NVJIzh12pCCHUbEOz8Uxa6UyFvcsRXwdjVtyj1vcTMdm/oyL99qIH2lwUtxJDdHmMD1c1tf6dvY/FgYMDWjdshxCiSzDu+M7scFHIo1RxiGnkyGocHa8R1Yb2QJ95fIyABE1HVcOokQKqhaWc0/3I2tc38Lwkzu7k/3RaGco5Kr7NrkqcF2eQc6H77h4AA/R+HeJ6FUJE/T4LCDsvXvvLc5olIVmd/W5kcxXRsEYhuiJ4dSScm+OqJ7fUYZDq+lkjE3CNzlk5PD5MEuolj4z9wfZhyrsi7i/sbObk75YxdVG2xl5u4D91Cq4LtvpsFzW7WWW460w1rk0fGbG0/m8s/mijJdLly5txanzXd/1XfSt3/qtg3UeffTR7ufr16/T9evX6eUvfzm98pWvpIcffph++7d/m1772tfSrVu36Hd+53eSe+/evUubzUYhgf44y4Xh55yl3GhvFJYhZEEuBbjpebfCWgpo3zKuGKVYDHsb+H4rxSURhAcZ8EzrXisNqjIqrMaNNR0JYW3XUWFPsi0/fEh3fUYuhda+bo23QloY31IRa3tSSA8zTAYPDvbGDSn0Loy9hRogSoVexd/jjUMpQ5ZsPnqLLBhDnsQuDabBN1ItjHPUGDP0uJox7PBd2Ssrv6VUCGW2OxRai02rsr+d3uoXTEcIqfgCyC7G+OQg0UmKeiMscCgjDFFPuKsIY0l/S4UIWZMRmiO8+aN8SXkjMCNqCpVJLPzPHv6uH7L52I6VvY+IaCnfA7lpmkIRNydhRoYhnEjsBWhIkuTORRQY4Zl1nCoSGYLkts4wFqDBK4fE8xWFNH/ymaistnoOyP2bQ5QtAv+a9D5y9oCN6kjujd+kGDjD+Nyx0pETpXsZvtPQuSCfVSOxf5y3ii/uRlonoCrTuWIR/uO3LNfGmQffh5UdtRc7vc4xVMGX2nAi59Yqyr4KEboM9yIyUSJ3GAWl0qPHsV9d6T8mcvJwnakIj0vGRi5LMMwXG6LpXeBRu9Q/K3cmdeGEGMYl2m/mQTHGTFO7T4ZKJy/uJ9kOoLmLtaeTh1IxGs+HG78d4hHrK+lHqXcrmt5JX7TZTyfGZr/K8rsRES2vhBdXYZQTR21FdPjR9CMnMklEElUndjy5F0gjRJBzO7kQ4cTZafD5nD2AkF7x49i5AY+Uxt/uHLYUbko52LbhUcPCe+IQspMLGl9PHzLkX7Fn9wTUui3bEdP/vBujRmb308HjsWG6hHCjbpsIjD3yOfJdrXFFXQb/PlCU0Ui8UzslmrR9NASXnuxaPMaQ08dQOV0YsbGfWnKxLOXKkGO3AXA4CnQKA1X4HDbl8C8yxM/nMtRr28KGnM+mcK6s1Sp8qNe+9rX0oz/6o/TEE0/Qgw8+SEREH/jAB2g2m9GrX/3qz+oZn4tyYfg5Z2mmRDQCIRzcEOLPKLQ3kSBwKINEjinerGsZeTKHobwnu0YGdqo2egkHuWEsLzxlvKsZeHKOV2/ICt4a7518Hz540DDDWbUGECydspcROseQLIOGuJFvnPXSib4pw5IBr7XaGUWGPYfiGhqcS02GoNm1UWkeMEp1CluGd0YqKyqDURSQpLFHzmdfFOJn6POkyiL4QkPhv5z3dciwdi5IvWF0GyOFbOeMYDHQNuXwJCzqYbLtbPFk8vAkRtXIZ6OzgYQHtAI1gCTH3fiJ/qfrVtwL/bUQP5aAqRQetl6AoaqQup4P/ywhkihNP48KQrnUeyfOjU1U2GdAPF2udBYa3F+WV5wSgBPUSlwvGKK82Y+KCrQnDTKcWQjnyvKqU++Ba6RgvgMZoiWUJYnuw3DN1aHTBMbCe8zvpIyqsZ+7T6TXE+PTJrwjGlg41EvusRavhyJQxvCqjIPIQnFgmF295xRHThoGHu97Jq2zvEbm+q3hvbkfSX/j95b8JIh04j5JYutcSAUaMduKFJJThpWxsQqNl132IESSCRSP23W0uqyJy08fDJNHeuCLWhtAkK8Gh/Dx/2tQIkznT4FWR6P/A/ILh2ziHC42Ic383S8DzdpEdaPhSlfRyE7dl60jMpzes0fbFtd152QdF5DHGW7EIXSK8xpFqRKPjCQakWUBumOxNpwzYp7z31aHaZWO1zMjFxP1a+oM5iejNgcdhZbTGZA3Y/fmypgRL+cAJwp93+zpvZK/rdwzTQ7NER4qHhPcp3xBHZegbM+6N70oqo/Nk4xsTjQ83jk00Au1NESfg1Cv56f87u/+Lv3u7/4ufd3XfR1duXKFPvrRj9Lf/tt/m77kS76EXvva1xIR0Td+4zfSl3/5l9Nf/at/lX7iJ36C7ty5Q+985zvpO7/zO/9Es7JdGH7OWabHIm48Mx+3YdBX3vToiUgEKguq6NJ7s0TP2/B9QBmDdOZKM7M9Esnhm3m2L/RhqDwbjPjZ2HVSBV23tU24kSLma/QzseSEc+sgVQeFcZAnG751GFP/LurANIiic7xFiSBrGLYGv+NQcbofOPYmWbVUyuO32YBCVUTuiSGvV2fYynAWSF4T5KPi2H2fQdNZhpGkf0jWKj3KuXnCf7cED/7ZxXmRgSmPIX6ygmssm/iBmPejv5FUqKlKMb40jFZbhDSGfgwbjbp1B/fyONcitEjtdXFOSMVQGuZWMzEIYGhvmoIm9wFlJAwgrGAqAuUY4pMNi6QwV2dHBhKFPa7y/Y2wKMVhBWPD31AZIfcMZAqG6tzTxlC5H3VCNhp4IrF/Ll0vUR8uoYivDekDM7RxnSTjkwwNk0YjDI0u9TPlfOUxrmH97X0mvCTyZWDo7mZfG2x435JhYRsk5d5o4d4jv0xGxqj3NNpGpjUmCmOPIU9p0oj+HdIboycbz1AZthrfTyHSDANdjsQc+9+1LeatUvYM6T0hQzfaIOoVeXS0rcSYeRfGSzmuWMEU76FIdp2BONxSLjQdbZZMMCB7cZ+xHZYnUVEc5B0U/SIacSoYYUnbEvtbJMqWHKueGdeIQqoliMz4jTIyn9xDTXT7iHOUjaxqvHedurb3ZHrznS93NLmd1pHfh41huGe5xiasluN9+X9EpMFh5syWbW5hjEiqbMwqXUEZMmkno3Vn+TElkn83oKY8GLE7cmfJIYrzvLBTwVtlDXvZ9GyY4oHI5hcyjaCWw9cw5poRFYZxyOf4NS/KH3vZ2dmhf/Wv/hX98A//MJ2entKDDz5If+Ev/AV6//vf3/EHlWVJv/Irv0Jve9vb6Gu/9mtpZ2eH3vzmN9P73ve+P9G+Xxh+zlmaKZmEoES01YbaZfjJZDhpDZRM14yl9GfCV0yP2YgS6FpDaDGeacW7Tk7SWHii9PeTF4XOTYEsb7PntJcL+lUy+kYeIhkhQ9nKHOnvYqB2clkqlFIufu5SxWa8FmPp3IeyIXTCF74QK4lo3GjTKvU8L8g2QnhtDUirMtZsGerlDcMPFstzlbSRIf3zRQyRGOrLiLFMClkeeBd4XibIgiS0sG8UUXltRQreL4U0zmiHKJEunXvGm9o/m7JjlvRxC8OPiq7KCNOWcI4CqWX4sYx4WWOgQeDNBZEUXdlN2ybKk3ovH+gHDQmbRW8ynROXksxcoX4NnDasHKq05lJR9AE9ocKlWCEWgioia4p1Xtnufs/sYdNjGvQwEoU9HPcjeQsL2ZggYLPvyDWGsiLmIs9RPPNYMJdGXvzundItHiuV7x2BWpFrNDxXc1DJec7t4NxnJBIax3aeSvtZndnGE6J0Hljp1rGocybjcGgrbdzAdd9OdeYvibJhvidltDrwVGy0QbY0QqzRMMNzdyj7ZxdSkpE6JVIN33F2RHRymH7fHbGmed6okLlMaAQapfae9IoQ+CyS8hdNXsBoJ07xZ+XQwyZ6M3O+c6nO9DVZ2IicyxqK+4g0ALCRFY1DvE4TAyuSYy/SuqoPss+GA9MiyebCfGFjaFUuSf+LYHQt13b7MiRbG1g8rS+lA7n7VDqhOKwP90rXElXwTgWgLF3j1FhL4wTP3Wv/LZ2c3jlqJ46anXTjkHvbyUOluibLpU/1A1ydpp1fH+qbLLJtItt4ie+UGH8z+1gZ05LjeKcJXIJugHOPv5t03qFs5ctxo2oXWgdOnHpuJE/B/mdkjq7kUGsRiTSG2CcyDH0FUfNFZvj5kwj12rZ8xVd8Bf3ar/3aaL1HHnkkyfT1+VAuDD/nLLMjkc4dS1zkygMkRpk3q8P/kbq9jl4WTvCDjy/EfemEXdyYUrVsqZn216WCLLNPqI2lyfOEyP4rY0EUejgzCxGpA2hxraC9z9S0vJ5OJxl7zd5oPDCn932XPUP2VZad22EXrvf69pu5hEXo/nJpKzeolHf31el9fNghd4tULjZ7RbyWtsX9l33RyBdPLcTNJ0iPOMTVwtb4V4cAOUcS0H2nBA+ee4vr/b1TyIRTLT0tLwNcGIUo/hUPUm/MO0vpHzD0sRCBZLHTE0/1jjO4H/pOTGN8ewXfbLMbOiUVrvmz6WI4vRUeLMdbfj9p7EEFc3Lqu6x1XGR63at/ECZ/cZIKdIuXXA5tT9NBq3f635tpTJmeMwBKY68R8qQUX5gnjMbYeRYtA2EOJZfgu03O/GAsfBdKM5IhwypM3LhNauTZUTrJWIF75lX9fpHw54i142fpvUXZqhAYmcnq7peGDqFSy+FMqMDLeVyubO4tVt4k2gXX82xBVMM45r6tUi5OvSCBjX1BjiIDWSDDZjoFDyzRxTqsSRXGKMYhhxhkg0/C2ZP53pKcOMftsAZvd7UgahEBIt6beYsU2XZ8Jmew4nL2YNqHYp0PZ5LXlf5qID1yKAb0LFdnem4gSmoxG96HcyGmu592VDSaCFyue0bZKCMkoxWkFx4VNjaOZpwD0tiD83N9qPt7+iLdL1QSO6MiKlGi3vpSMNxv9tIH8DtKtKfay7w2glmyl1n8uGF+s2cohdIOlZHgXRO5SiyeyFhyYU9jBjqinifqsy1Fkz9bsuEsXGcLNBEma+B3XV5Wq7G/t9RjvbiKUPDYnpWtCTjwzm7pZ21g3OQ34Ln1ma9LLc6dw3dgHnBB+YjPqbuvkKHqIDsaCK8sQn9LYxyXbv6pvoYBHEIMl0vbGc17opRPTI6fsfGKt6vwYgsxic7ukaQZJrJd9mHAFtHt+cZYN1sQUb+QSuMLap6j4ea53v9CLBeGn3OWepYKtVZxAygRFsCXr0klJw4hWB32Lk+FeiAi54v85jvElWAoXyYyQiFTwoWTW/ncie2MaHVpojYza3Ms6vQBRZPCrsMz01851j6HOhqK27ZC3BK+j8y9bLwbQrB0z82EXeXC8MIFw/MvYb8Z40p3+0jMdjslcsA3wvwo0pONmRFc61KUAukwEXzf5P4BoYz7Nii4ZZQSRhYMZbU7uxWFiRzCS1w/eiydz+zBkfNTeiWHEG/l2nUkyVyk4vf4/x4Wdzu1peWtwqW2CFQey2BHpL8PzwUkbg6V019xna3WGq2XjA0/2+qXQQZvcaHlMvxJZMjZGg2o0SAr2pdKe7vTd8jBgHifEkcTEa2u9h+IxwARCeuoUChDGGSsWl410IXsjRYOA0RPbvZ0KEEu7BHX7/2HHe0BX83lj4En+1ZFDow60gi6isZg5EHxVTBiO1Dk5Ct2pPK4r1hz2kBGyjaGfkYD0/owDbkiSudNR6KpwjbC/2h82BFhGg2TdKOiHhWGoTPJJAaFPnSGLkS/rlMuKCKiFa6RtVYw5VpgIyiiqzZXgmEJkRCSpJaRQyqTWHzvBFEF7XeonFyYh/h+aEwhSvmDiIjWV/uXmh6FNao89fGWEsM+RB+q0/AtcyG3kgjaEo9GlWH+u4UMHkHIIuoMC89RK9x8ahhkE4ROht+w+/vAOX0e3j+L2wjDK2UVDMvrG4r/oREPkNKbUoeKO+tefL+i5xg0O0b9/FbhxSMOLSKiOXCvEaVcgF2IsmFc9JU9jlx47uIcttZrlprAuKaK0YfBNPYj8qByNIu9J5eO3Zx7BspOhWPhPsnf0ghjzM5BbqocXvedYjMQnwABAABJREFU0dRwjlJL2hA9oLuZ7V6Ui/IcysU0OmdZXyYqRw7joTLG8zFodR8TMqRBw/I6ndNan5SR8JxiPqBwDxSzX7hZGkTLWT4jVEQtY6/0LmX4V7bxbHShXhnPwhD3kPlMg+NH35RpT9R3PhjdLFguEQh/xg4waogY6tvYQW8ZfqQRL3dgtmSGoSXd4n5njBFSkVPZNfhbiHdNsoUIgQQP5npXjxmmmCUyxjHnPUSOn0J/S2scthGIVJjmzMfrKDlpARRTVxNpY8QgxF/+qaCeWJ1vlYbSjJeTx77eEZB9o+9E4N2e5rRN6Jd35FXdvhKPAfIA8V6ACr8UPp2Piinyu0UlRRpnLESIDNchMsJ547OR2Ldc6rADNOAUjVchjDJsh8dy59m08+XKUVs5paDKvnGmmQ0Y/fnbcmgV91UWi5tDPkuuRTSWtROnFMC9J/v+33+E8fhpHTYSoPFEcsqw8opoITYOyf0A94vNgc74g3tAh2CB+bm+RDQDRJpKRz/PGxeJBEII2l7e8FQujdAliaiNewLyVHWOEmG8QcQU76HLq6KOGD9pGFQhW2dEZy9KP9T8qX7Q+PlqHrKhGEPH7olfXOBu0Z7/MEAJUT+s3aLR31eRt2fCM9piXEQqVzQoH3XrAupwuAqAO5L9iX/+bLgqRxOMjKBHsogIIuoA2luiTjzMz6LW88dK/LCNowTfk/dE5bQotbES94bldU30noTT5b5DSyFTqWVE4NLaz2TnlcxmtlUqeim7Do2ZgVpL6pwTOZRkVV3bzgGe89lwcIo8agOGMqLe+DgxiOpxf7bkryEgCfcRkyq0VfgWQ0lKcmPjS6LmiyyrlydH7XMkd/bP8f4XYrkw/JyzNHuevFKA0jK44WSQHKyAD1l7WbHOHcgJOSgI2e3UE2acMQWK3BoZRGl4ahsdhpPUyfF9lPrgUM9iA8uAgae71bo+YJBigVuNKRtYPlur/hb9GszqNVaGjGUufBN8aMc1IDMq4ZwgShkbichhqNzAmGyF+Bn6s5XtKT7UtaQyTSUGwFZfS/om3hUNXqwYyDmWyySkvDWGtxj/bvWr5zNCTaH/PfAmOfLF2MCSQniFZ4zsVXG8kciZyBB2UImY+ec0F57Lva2cJ6Dd8DvnspK5qv+wRZUuwqLwtNlJr7UibI/bXGfIvofWtKtD2Cl+EzY0Hb+0rzy/DQiaXT1eSvCNzeIZsr6sw6A8GHmKDam1KecEK1IrCAPd7NuKrURJrK7yRMdK8TnSEA+KO8+DxCAk+snhWkREy2vasIRIj/sP9/1n44Waa8ZeENrvf2aFHA19/E2kscCaE5gy3crKaF23FGZMY+8rUpmm5Pgxoo1TQHPZe9zR+lCnk5bv2DmoMCSPicUFkDlnEEn4Q+S+KY8lGNflDf3M/U9JRFpE3MFcZI6fFaDNZOhgO6GA2MwgNsa4+sbQL4jQSm4fc5YNyHtElEUMOR/mby7DJdcZ6pNliNcPsn8ec1A2A0p77vzOkj7Lfu5HQ0lmXAZlLK/Xl+KbjPuJyjBXEzmQf3FdWt9DrpE2FxrdROPWgMzX7ZsqxNOpujmkbW6e5wyXfE/OwZj8nJEl1HiL8Pr1JWd+R94bEpoCow9Dcz/UcbGd9CGb3UJRP6hMrnOnjYuJ89Wb901OdXg8FibotnQlGvgWL8RyEer1/JQLw885y5X/1nP8ZIkdi/ypt/94WPUoeB89Gj7F/mf6XXmznz6grWgw5bp8LsIci40bTSFLZBzY8Vmze/0u1ACfyWbP0f4TDa2xv5JMN96i+HyMjV1BZO+EMZNZW9pZ/ywmYSTSB8lmzzg8rAMTDg72iiLHDvK7EGmiQj40Jkf9Lt3M9VKrztLOrg/7Aat3wztN7qd1+HA5eikSC6S/bvacOrD5XVhIJjLS3zbp34kMUt/c4WMJknhtjODSGV5WCnO1nhM5BdEQP7JgmBFEEmJxRL7E9SiREbKdyUleum9mruN76tqTntWSnwE3Oq4LSrk0wjhe9/aeYhm+ur85Eu5TcU32k9N0Y+y403VR2HW17QmTbYSLRsdp3LBEpN+JFTsZg6+JqcODZaYyqXxthNFIpWJ2ROUZoGFEqCMjh8oljGvOsG0YyxwY5S3Ok+UD6e/FRmdeU9wHzJljrU/8lijIToe/R/ej4YEtNsa3M75lLqyWLEUhFg6PkEaXnNEf39s12gsv28/xzvG7KEOtzHK2CF5h5LpZRaPR3uP9NUSbTE600U6l42YEC57jtTaKoKFmdYWyihYR0e5nwv+SI4cofMdyqbNuLW72P7MnW4VoRFlConY4tT2XjohYEl/L7IfiXgyLaWZEGwhLXog50eZkMlYwYW5Izi/nAwH/7lPpRrLZDx2VBLQTIDevli2d3oJ06DA/958MHxd5tsi5RNEl0gb4atkOOhJP4rNxTjgfshXhvJLIOUQBdtf5niHDPKOwE6emcFiI90Al2gpzl4XDz3NnC76TRLNUi8AZqc5UNmYJ+U2FNJ40SibD73HvS+exD2nnAv9geg3l5MWuQXAv5K/OyA3teOdosvAKFSjH5358XxWCFu+R76q4+o70PJD75JAz2jKwWIYfnWgkPPP0QXTa9J1jmQQzsHKbcnzVOTsZN8j26GFwsszHU4hbWb1kqZa28abecSFDG57H8veMfteWRM1wty7KRdmqXBh+zlnuvjKENQ0VpYiIhXzyMONr0zrs+VreyJO3jHpiBohe28ro1whBmSxnN6WGqdtePFDahKFcp8uepRV3REvoNOSz+L+4LaPwmqiKzwLx01nzlRFPKIGVfn5SU5yYNuInPT2sDFXkMifMGJqitDxjTj0Hw0dUP2hYSDtvGT2MM/Bz19r9SOYBe5QznwwJo63nSq4SqZRUB/2gWAYRTPObxGxnuBRyJJom3Fwp7uFlEy/TAGFmrjDiRxmWjGeqUAivDSdDhqikGIYls1rGkCJRUhimxmMivcpJqJe4t6jSSVWUrSIY9olCY7Qn+lWsoS8wZyfHTin91Um4R2aosuYTokRQAeK9bIJptPdTBA5RzPQl23d63knSYDbEqhA041whSqHyuXs7Y5kYIyQq5rZlFhX53nKNYqhXvauzekkj/f1HorEXwyPi72jUqUS9Yg3GqFh4vkpjD35vN9FGMNxz+fujsW99uMV+7HR7K2EobTPkofVuULYkGoYIDEEsg8Dc47UmvzHOYV6XhfiWchxSlFh67+S+HouzB/tv2fH/oAMnIjSUcU8m3Dj2tLrsaHUZeN9W/H/f6PKa5hNTaxXG9e5+Xswek8FcWwyiVJTBXpTpfa2gJqGPcd/MhusaTpWuinXWin043bf1uTSUapvJnbNcUEo+7H9e7jjyzuWNRkOasyuomQyQcxHRNO4B67k+c84eGHaYUas5xzbGPoFnCBHR0josxTte+8OwIFeQoYu5BzcyjBKaQcMHEcjUqR9KdWFoDuf3Ktu4l3D8zMP8xiQTXbip2J9V8hQjc5YlUxJpnst2ovc3LBYHWfL3DKm1L6ijLUiKGMOOZwrrOBo0xr4QS+sdtedlFDfauChpuTD8PJeSmU/ZcBXqrdTqYIr3DE3SjkAu1/zA/G4NIsqtEHDxWYluhdb1Mq3b3SoPBEY34DMtBBO00xmN5GYs+pM4nVBAqfMKB1FeuGDBcGjPGDX8jIWm4fewDD/bFjkePNaZdNlj768IauH9uu9tGVjGDFJjc47nG16Piuk2RMi6X3Ft1fmPafFIyTJIuDfWL/4eRop6Ir1fIGrGymzRhclI79oW4WC5OdcYTJ5oSFI8OZbxRvITtX09VayuWsZREIL5OyTjmwn/GyNGDX2E9lun2zMk31zWJGWMwVAgI6tXvR9h4XekRTqtMznTCE2F2Izdxowks7tG+mTkhdnRiFD5jmzIQY6f45cUSiEnomQeMMoAeWG4/9L4jOPXKd9iT5LtSAVSEZS32nkqvcRsTELjEHPcDPGQnN0K+6QkfCbqMyBK3iKLVBRRXvgt+Vvk0qYn14Cjorb2YUOJx+x17SSMBSqnsg+d4RXGjENg5FzANcLf2cnrEn0rvqtFnosGFvl9eFyU5z/D4Te51/9czx3NjjztPpkefOUq3HT8WO+Zmt+B0NDaK7QnZqW68f8JECt/kL5AczBXvFrtTGvRzSx/aDJqpzpN++7Lgu5+2bzLNsjl4BOS5Tc8u7qbTqrN9fAhinX/rjKjKhHR+pKNNOrqi/BYy1BTg+FE7v1swM3JXojmkX2YnKSonq4OI3qb/BnZTpzOyIvISJ4KKBNVGuGGXFmuNUinxRxm5J5C9rCMjOPo9c+KE4cNdAOypelQy8j75rE9hHLNfEPur8qIK+UZzo6L36TUdZWTqhwOUZP34N67ccY44nuMcO2wnlPA+xX1uLHXROzGPvjPIkPqF3JpqKBmKAXalm1clLRcGH7OWcozRyUrC7lNbYAo2Ny0RGOJ0r2lx8N6joqdrSwEiNW43TaSBye3TEK/K/CcJqFeBuSU+4me1Rz5b857kBCjGQoZ6rTbGFi6Zw05iPjeXBsDSCQro8e5kDUZ9AhRQGKYBhFDCNgqOwcqLrnv4PJzc6g9s4oh/LiahtExHUoKWwsXEr4P/B6G4Sfh+BHr0ppjOVJa2S9lHOrQI+ARNQSnbYjTtyKrhNKFLhleRoXugDqWgGLOrW33GVkvp2DGZ3oRfqUQJ0bbjTBayf1A2a0KT36ZvnhCEh3JsMsFuhhj2yPZ9lyTkgQT9RnBEgMIjO3qiibLxe/D+ygaB9aXtdfUJJbHfd3Yv1aHoOTukEnQL1GcFrm6bF9ex/HrlKfM+SOVK9w/y5U2giUcXiPn6FA41uQk9BUzwHEIoEQutWgMNPjAEIjAoUhoLGunGhVlzTk0Bg0lfuBydsvT/GmnDSxizLoxgbFj5UkiC3D8urAWea/sl5jz2H/n9XvKLF/8fOx7ZxDC80R+kzIY7HA+rDCVN/XGoL79Ngk1J9Jn1L0/E1MKokOrcsqhYJ17OaQCUW9orHbSjzo5bahYe5qcpv1dPNBPvjre074UyWhiX6TCnyHQz5XB0BVPNL2f9kuO/WaHkUi4Z9kLNhmPjTeNJBxaJOVPHjsuzVSjP5TxlzNBqW+p+7X3VPqO9x8utPFenF05xCb5YLDC8ZBj/OwrQwdUaHTs/9X/s3+Rep5+kLob7/5aFvFjce4YfEdcOkc3fDt+7w2gjRIk3kn4RgqpHX+V5xzuaeVKh/thYeoDlGdd40b5gQoakXXZEQSGxpyML/tqIWH5etYodFEuyjnKheHnnGV2lIfXbqPz8oaHi5oFvCEIrIn4MZAeRMYGbeyBahNxeR6SIYNUvath9kTpO/JGaoWbYZrW3EDKuOocakWjewyF1jDEKWLHJv3fKl2GBzTgsDdDCg2G8INx4tIb3b1T5jsOEWL7IsJVVVx12m8i2yuiiDsHQhdHy5DRzSrx/RCiXq4pS2Sp2s4YxaTClMv6IVNay8NYwv1VqFfhTE88F0ae5FBBTcaj1XdOt9l9A2nwM7xBg2TxRNREI7bqv9fPVd9kqeskygJ7vQYMhdl72VCshLLY5oCS2IeDib4KZE+z6R/kJ0Dk3BSJME4EPCSx00ohiNMjZ9wgCnt/MzEQFfEb7T7VzzFErzQTPQ2szF/YX6KA1sFwWtyvl1edoZiKvtfpM7o+RCQpXpfIjxz/ipVRcXaU1uG9Cg0gXCRXC6JjmqnOoiOftboc+4Upr+O8yXmaiajLGqPWKr+jGEv1vSd54mMuuYw1vjIUAujD5sAwCgoDmInQIiLXBj48hfwSc93F74xoOmvMcueGdPTItSu/g4UkxD1K3svfEtdlN+8H9ppmFvh3ltfSj7ITOX/qeb+RIC/O5HhF09PUQITG6uqsNe+tFg1tIAwMOWVmd9ZU3UMrWF/n3qsuhz4i2o3C+XUG1AFTwZey//gq9ivdA7kPCf8QeM/q/Ym+V/LnSK4ceCfvHLkWnikI+puILkL+I0Ygra6lE3QtwrDnz2yonWoBhd9JGq41ItqrvRPPrlUMnbJCyXSCEG3UwGfKs6ZzauBcLYnqqSUE9D/yHlMBspPbP34kTzsw5qwbkoGd4WRMPlvnuILzLO4fSgeRjjcfjLGHH00rbQ7Cu5zezAuizcyNGic5DA77UO+OO2EbK4uxKLwPOfUx7fGWe0ZWZnNffFm9LkK9np9yYfg5Z/FOLHglicf/BpTyXIhxtxFKYwlWKuIBMwBx7C5bhJWoyKFDC41KosjNCDeuZhqgu1MgPkx4TuLBi8R1k9OWTm+lA5bj25Gw6IR4FMkD5b0WtFcagzLfshPS4LokmLaEfKJ+fkiOEWvvKTDMRK7GzhABNzH6CY0gMCeamaGcGgqxlYpeGXa2yMaTbQ+bKkbPY/u+MjxWCWbCa8fvmzM0sABORFQtQPBkA6eYS9IIVAmibj9N5+tmv6IGvGjLq32dWVTom1n65uwNQhJIRAtZxJFcMFQgfYCRbQyaYbkdOV9Cf6E5JEg9NgythmEPvxkrzIqQ1EAW4L1dXL7gu0GC8i7kSdSRHDFryR8Ca9C3WsGU7a/YIIXzMP6O0G65RqozovZSms5atiW5FjAkoFzrFLOKOybOf/xu60P9Totret6otLYyxIdDAjGDVBnWpkpZLvrGBgB8YsfnJebMMhOemt4onjOQRYrIMBhJo38mk2aXmhz2EWkQaavo8Diy68h+KUdRYaRDxxC3eA9+y/JMG2BxHZVLbbyR78LfEN9vehwQXBZJdves2B9FKB3ntOQ2yo3NENEslxpQwN7p9iQPEKONVbal+Cwco2R8fDB8lqu0M4tr4WXlXEYyWivWCc/ARTQoqVf12pqrjvtHd4gcwJhE6eYWtLO4FjgXKzg32HBBRLS8EiaCCo8zeA+ziNNtHY9QcC+TpXOU5GTbjLxFRLS4PiXyeQO83HtRKd8mbLzbG9BIXuq1imiWtiKarrGO+Hu83wpzHMsU22UEA2QnG4RSbidoZ8SR95w4HkfaVs4qafiJac9Xl4ALsyNlFteM54z1O+uENNCFqsrIe3XGcste54zr4vvmwieJiJovsqillgpqn2Oo1nO9/4VYLgw/n8vSKY/5Kh0vzBabqdrs2/CIbVJVWpwBo+EiBoHuNqnJOQZZZdQSXqhOcUBBdqWJ+LB03kR5SMhDb6Kf01WzSN4kkVqGSLBghTpjeCESAlKG7yP5TgOhV3if/JsScvjZ+EwpdPkooORsAiMCnVKgsJ/jzWYrD60NIiH0GWNaz3W2r23MSNxWtew/vAO3bAevlY5KMd+KTS98WLB3RfApldkroT4qY2y0UeFrsnnHgh96Tfu/d1WtPWVkeFiJVF5+QzhRyIJ9o86WxsBRJ0zGwMnvKA0QisuHDfBiXBNFe0ioczo8KHlW3L9QOV5fibdjdibJTXMQ7lMGhdhPabBEJJ5rQpae5D5U+NljbPCcqfAdELg3+zr7VFJyDoFpOGtySFEioiIqYRZ3hew3kZ4XvAa9ERJBlI4ZhkC5VvdrJjLYnN4KD6vgrNx5NtQ5fimgLyDTVDsx0s+zIVWgalQGGCM0FEuH3jJQRyqcCQx2i+s6K1ZC6B8Nojg2bRX254PH0499/Eg/Djx3cY1wGKIMV1Qh3NyGRMGJsS+EMUDxQRlhvnINdmGzE7uOSqMt0YCrQKia4+3avW2jQLld04EGdYhsR97Y/tlMhp0pOb49XwTD5ApJ+60QZJRNDFklNza5FOC5UKGu37gmpEwyhgoekMlcS9FZYtcZzMZkOD6VgYflRYPXBt9ThVt6TVqeGMvis1oD7TcUuk/UG/ZxXM9ilGESqo5z5TlqgUNn+Wjo/9C3bMJc+GwAG8+JcsCNP9Mas4Ru4ZzGsq3e0ZGZAfSiXJTzlgvDzzlLURN1qNTcYh2w5k7jYaDSKrPwLwW9jFCQ9aZIuCAKI61BNGZ4FnIZHhK+Ezz0IpEaZjWQShBvxBivu7qsw2QU4ofDKDLpRwcVh7XXQp9ASXReGTy0lxwTnj9B1pHUERFHfdiQfCbcbEA+GyPON4dmUugROdatD39HYxbXEdcVQSxpwQtT2mcRb2QYH6w1MnAoN9HrimiGcq37kfSFhKEmE14m5/beE6mGvroSKklOBIkmKpf9izHUuOvz1CkEUS04F3idrQ+AH4WNLgCFT8aM0VuZ2O5EgTGUSRQE0fC2uB7+R0UR2ybSc7haGAqBRDcaHn4ulqCc3MvG3sx8kgZA3D+67y2NtMLgvDmkbHGFV+msZfus8M4hhIj7g4YAmamoaIIyrMKuotKcGDTAMFM0+tu1aEAyPPVEYV5gaNcGQkP2nvJKKZJpdJeXw4feAXLbk4eKMA/gW86O+3orvvc4vXdxgyGIoq/wjvwt5feWRpWER85YRoicskJ/seRCAKSwX52GcVaGH+7/kBHNUCxyGTfRkNiWep2r0CJjXUpDjVJIZT/WRPcfhnPcp38n0nPYdGgZ4c1EwM0hHiXbxHVvZRtDUl8iLRvlvOdyfDZ7YZzR2MTvLXlfTI6TMcPPORTjnFPtvMW7sO8qDqGMoSatBP8b/cwij2NJzp1tlFXZF34WOuxycgfIWUWd71fC4WeN/cj2YCXI4IJzFg2fvjDkLSm/xHVpOXDGskxxO4oqIvY3MYRh3y3ZQn77EV60QYdyzriY+5bSYMh7XWb+D85lK6QqY8RT6Mh2nEtnCJVD1H+vsT50l40wd8uY+8UW6tV414XXP5c2LkpaLgw/5yyHH91QNQkrU5GHRSTA3kePksvr671E8cxXBukGDwD2OibpaU/S02v+bE3rw4om9/tTYHLcS4zlaX/z2aOXknt3P35Mpy9NNR7cjKf3aipq+8Q8EgSAVkjXZs8l3lSiNKadD0WV6WO/oCv/zYgzEeXeK8O7zI76E0iOweKGQGMgGeyq1SSBMoVsNKxgyAzG41uFjUI6M0G8njk8Q/vDoS48Tmjs4jo1IgvkvS4oA9gv7q/kEcAQIud1xg2FAlM/DBQD1TSUxa3LjgSK0PS+p/W+M3lsuDAiYhvU2vGjNi9DQl4rxq/CGBQoZzdtBBFRr6jlhJ9BL2S8bxsFxvRCwXrAVOVcrPZRobSePSTYmF7hgefJ8WaFGqHNzthHch7LrNfPDVTyToc+yPZj9VW6vfZ1MXObeNZmGpRelbqbU2FLYmwju5UyAmTCV5BTppn2qDN8Zt+WJpo9u9Hfw/sLprNudqIRBPaaWmQ64vFc76f38j2tMJ7g2mUjhTQKSI5xaaTDd/JOo4CS0JTM/OY5l+OOIoqGlFbvU9z/nWeE0ewKygmk9s6cwRzRJNVSKyVKcfFav5Dj1HGeZQxAaMySpPgWtxhRr3DKcwjRDd0+KI098llifNH4Wu8SNbvpW1WnemPBvXQW29HE0KJfa0+TU6/kLZ6vDpRRWbzT5yXKGxhuxaWtUmePVaqlHzR05L5HWzoqaq/3MgMdnTXAy25n5msug1PCo2go7mpdSuPNWKrsAaWbw6JyhhK5XhXKu/baIIt7bpdVEr55pY3rGE67OdAE/XLPvfzROt6nqQ/aynDkCRqDji8QwsZ5LA8e7ycx8kiNkZNKYmbLoIbP3AZtw+Os5HWxt7VlyEZZnaabYxOdaqtL/cTCflVLr5yjWCZxvSNh++pyqb+v4qcbafvM1qOc104XorT/3G8rHT3yQb7QywXHz/NTLgw/5yx3XjmhcjZs7r37sqv5P2Ys4J3wnwiXaaWTB6fhcPXyZBQ/F72BCQ+su19ydXxDfokhCXPIkdh714egRMyC4aqGmOZy2T+Qvah4OPrC0ZOvSw1SuX5yCtFwoyCrG1ACXV0M85Bk9gQXIdJDe4ZFTkpEfViOJaB17ev3lIp232amAwPhV57COyry15jOXApWbiDFOZdGCbbx/3OiebYpjNpBj/Ji4oicFeolSq5f7J2s1aWuHEahSyJ3JEmkTG1rvSMSg0oBm1FuiD7ohMiBMJkO5bfFuCr+FUdZr3t3Txxnk0wW6wJ8vl0OePKo/5uZbczyrhrCvyLhZoVYzo+Mh93iNSEiCHfQWo8KHZP7C4NUMGyP+4soSoMnR6ESoxIkeS9wXCdnWpBF3aYTmhGROdWcGojCmpx4hW5cCCMPnycV9GtFAbGk0GBCsWOkHirlrIBIpIUilo/PlcasxKgq3mP/CUAUXS1okkMrUq+w54w3Kk26RIT42I8MSbx8fythQ438WdhO7DaOmWv1GOGca2Z63cj3nkXlcwkIGh8zfyIhtsxcxn9D4xN/Q7kuEJk0vRfrSv4jqegJsQr3o92niHxln0NE/fjhPOdvmEPzyLbQMMl7uET8IOq0qHWoYDNJ67BzBffBZtoji3NlvYcbbFo6ZJ2xr2/2Hc2OgMtO9JXP0hwKTvIjKiMJrxkLqQPFzNCZcZYRETUxY6NyljEnHhjLpHOsmQTqAGXAMVA06NyzjN+o4LMRD78x1Y7WwK/DIaOyfZXVS/zOBh+Lt8+maugvcIilMpzHvUfK5hiqbiJMEs7IARnR0+A5npVb4nXMeiWNIsXG08lDBSkuhVgmwgGNY7bZd9qYDn1ZXQovXi30uh/l+LGGJDFoa6Mx/27JPomTjbtjoOz8c+FbuigXJZYLw885y/oyUTGkfJKh6IhF3glMsPBZkUkyW2Sgrtk0tEb8ttWH7LUBSO6Q4unLgEBBZSIhY44bGxqNXGMovmjUMFJt5w8paGokXj23wZuCDZSs54mNZdLwY7RjCZ/YRq6M8iKVuk7nYZcGpi2gu9nQQuudBshtu+cNzM01G28Mg0WxoTz8O9Of5DFG6CGXo8fCJJQKWJKhRnrXDI4fNFRJTz2HGSlkz5ABTbRN3hhXC2puZZrKGEVUH6x1MPItrZJk22MhxZhjjobnf0cIjIKTZRjEfsaxl4q7MuZ0DYJhoHVaCZfktqVum4i6lN05omCiMMbtVCshVmhoA+2sDp1O1ZuZE8jVU2xIfwOlKDo1V5K1kAk5da2j3adbWl5JJ+T+pyVBFivQaZ3Tm1rBU2l5ozFKKi5yHc2E8oapmMmRUsbSkFh9jag3WCAJtzQQNfOgtKGhgcORVuKcU8TXe0b4I3qUM1yAq0tEUyQzh+JqjSiWoR5syKmgnXoajHj4zCRDGxNyo4Ml3jOURZDDzeRalHuyJJhXRkJnoKLkesmEWnJ/FOehTFZROzP5BRsGd57pFxoaX9tKO7wQKanSUXcPzpyPopj7sqjDIcVW2FIzI2ogw1Waajs+E8/3Tn5x6prqV0bJH1OcNSJHyIo5h4GzkRDJvWVoe5ustg6J/d142F7XB8O5pIzWmC5+7pTBFDNIEtnnojU/k8QBcR/CJA1sUNsc5L+lKd9l5EsL4TsW9jRUynV+7bSVo2aalz8T5IwhF47xqHZRCJh1rtBzzKTmGJCHuj7jNzOu6ZvtZ5KDjGlfBMX7gtrPJtYV2rgoabkw/JyztBNPhNZ+Lh2yQAugXHJzsJ2yV0ha5tM63YGYM/yI+ubBN7ZpGId196wB4dwX4TxE5Xcbstl2oq8Nka6NXbc4R6z+jrU5SjA4VLjNAag0WQaQc5ShcKnueYYwqPpiKeUoGAwZqMb6pSrQ+MFn9Mv5oGwMIUy6W40Dkyh1HOH6WN6I12U8uySOPdOCqfxdoWEsA252riEKLu27iZix2rQEpJGx9lX0YM51RU36mfaz3tXGgq05ncb6lfF68VxMCNqRWDveK7N6JQZ1kcK9gHdyjmhzANck2Ish9YrUN7xQmzsbiIgcUXVijFlEN0iDzc7TaRWLE0mHtYb/UfEsl7ru7BjQPdedCl2WHD/cJgrJRR3T6kLf7j/SawMWN1doP7YteXowNDF63OV1WV8q4hNIZTw98QpWL8N5fBk+JqJBuH00zkjjyfR+WONoHOJ3kshWZXwyDHEKocDPNtCqOblA1lHcXvIsjoYtRRxdBIN1CfvI/G7/MxsLslyAQqFXxlHDmCXXpUT5qCx0NwxC8iv9IFYnoQOIMmJDx9C5VK18MN7AN+H5JPfyEhA6pbU/IzomGk3RwOhLA91xjrNW9s1yDEzva45CFeZjlI50XYSoSUQOkciQKTJfJiHkSxs5y2X+GYhHFe0sHwoWwnKdvlSxChOnhPT2zZV+4hTLDS0f2KVq0Zj33vsyC9oa+1nqUHJFKF7b6K16rg3zp7fS97YToPQ/T+IZgiiY0CfLcqXbQaN/uQiVLn28fzD2c3WohV0p88lvqdA/3quMphJJxWcKzh+e5xgmORXjX8+Jio1T48FrJjlXoFvVwmtHAJT53fBemPl3falUGdly5Oa5kqO2IB/WpOJ1FPX4rFX7RTHOPfRCKw05arZSGIbbuChpuTD8nLMcfMJRmYvvNJSSXB08YDgUavdpAVu10jx7yiJJ5HMtIUMphoYxIkc2mJLigQK442hy6pWALz1dCiURy2ThdfhORtlL+GhEe1bMbNdUqduT790J81CHofrKMyXu7YQubN/w4NsIDQ0F7m+IbWS4H9Qck9+2CIcm3suCvhQ2EKZOpDNiqJTazwFu6loaVPo7pAzUKdfRwDiEQrEMbuK6M7z9XC7/z/DBJ3d7oVLO2WLZn7j11VS6amclLa+lmpZUSFmwUIiN+P2H0rkXdfjWyMXE7y2FLvQw+kqHCqAAtrwa/j6/i+07I3U3GAZO/CBZbpeNzyJ3rmjQ8JlL586K19mt/rmYWrpDJcjlJH6+//J+THwFoRDe0/yZ9J0kx0mnUON+EfuJYQhp7H4kXMXQB+NMmN1L+9XMnIazg5DPv+88DfvzrlNGERR2y4WRXTHZv8Kz9x9PD66jl05Mgvjdp/vFtolzEDlUOLRBzisMBVYCMhHtPCN+flYYcjAM7bBQZ5IM5+FUyrkwNWxPZnfyhaP1oZ7b7PyQbWL7kzMd6pXN1AmvP7+rzyRc0/WuVmDlGcuE4+oMsRAP8TqXHGF7ZxAVZ4fK/BURRphRq3uMRKPA3jM7IlpDe6zU8t+JjGxOxnOIUoNIW4X1MIc1N7vDsEPRR1g3rm7p9MXpQ3E/79YeyE3kHGGaHkQDTo7W+txY9QO1vBU3SjQW1J6OHpsqfqHJcf8AVtCr03RCldFI4tYS8pcuhs1lnuiiijC2NPN+clmOGMl7SZTOYTba5EKb1rfSiSANS+W0oGrRJM+X/Tn4RD9xLT7LxQPpwsS1O43j18zQqFOoPXtxHfqw0LKTlNN3nm2SZ/RtE7WzQqNOJqIPHJabOVMTvk0IcSv29b1jho2+olOyiUTrdfucMhjF/5T84JMfp2ct1ZjIJPY/DV9LW2mmNChfEPVnwQYM89PjNht6y2VTai5AWXKOK6Jwpik9S453nHOKL7Agarf9LhflogyUC8PPOcvZDaJyLNRrILMRM/crWHE8v0/lRmh49oayQCTkoMg1UeprlqCf28ySjRB22XbGHg+4npAo9+8gy/qSjqvOKSBF4/BSqC+rYv9bGg4P4mtI2mhAmbF0SqARxyv/V53s6sF4Sfg535vxAI5lA7EUayuccDOSlpKIEjJsIiIPSou879zpOzN/V8S1ky3Ihkf+nigr0I/bXx0WdSGYZqVSJ7+lypRSGNeSvmjUguyDH4BKc6a97PeWKCaLr0ldSi+woqSI6o2C36Q2woNMfiLLI27AqRNOnh19jagXFK301PhcWSfP6wWGGkP5dWK+8xpiJZZLF1JipN/mMr8bxnuKSAhOESze9ewBMNg1ehwxzIj7tgIy4fmzXpHEoxFp76maNvvpBF1eFobPuH+fvAhI0afBc4ohMOtLejFudoFLIRp1Eng/GHrYKyvJoqViIY3n2gvvVZiNVO452xh+SzaaoPc8IcZ14SxXKJTIXzOEKnU1UYFrPmeshndqS0rIsImIJkhy7PVes/dU/wD2suPa2twKBpIW9veUEy78jxm2GKEjx1KFVzr4n9J+Jrw+iJrZaGeW5H3qOIaQDDuuNRWCKfbKeie0ff/F6aCdRcU9Qc0oBdNpYxn0/exG5mDa5rwcEdGzBsM4/xLjABE5EZLZ9zt9AXPfxn3eCGVJwnwH3mssDKeTT7ZcE2lK9EnnILXuzcmNXHJGeS4TTv6A82BCtLya3qyy7017Lp6ujjgv7r4i3F+ssRPRODlgmO/QnjBdeO3Wu/03zoV759oeRIhbg+j1z1npQsk14sxpw1mmvnf8X4bJqcy9jdYrcgXH9exmocVU3GItOlRTxte/W+T7CeIn12+XT87xQi2tp88BufPnqDMvoHJh+Dlnmd8lKlmBzsxHK30xFxZQFBqDs3rJrAPG4egLLWzKv3ExYYJwDePoi5qyO7SsaxmQXKMVEVk67z+mKd54FSNvkSAS5cdVhphZkGePm2/SAW4E6rDxZiyNJumDo7t3IDyOKMPJgm3mBBhoT/J9+HhA5LxLzcA8IRrmaSDKC22ODEEUlZ4RtBA/W3lcWiJnhLuk3lj7mV3bA6TePC/luEqBPskCYwieat4ZHv/BtKe5fnK93OElkXhDIWFjxaiH/VDGUcNgZWVz2bpfVmpu/E5xXtRiP8IQU74n4WSRc1qGd6Hg6W1+ke7n2A4iC9oMX4bcP+rd0NexrEx4H7er9u9MamD8TsurLlF0Q6X019XlSo+FNK6xIQ6NK7tEi+vF4NnC+zbu88xHkeVfot44I4t81pFISGAZ3bC/EgHHZ6763syVBN9Y7l3lKuwP+N4cFoUZLmWp506vZ+Q2igYLDNVZX3LqPVUoHWYRoxDKx4X3tfkzaZ16j2h6pPd/GXa4eCD8f/i/0meePRDal98S1+X86fT5/MzuPQbIzVvDkSHD7NjgpBGC4f8c2pKIyJcu7HXQPo+9RATg2i02XmeogpILx7LSjptcIlCSM4oNuHjOciQW7tni3hIM0N29XtfNhuPndLIRZWsoZLsda3PoHPXRsGrIv0SprGbJIoiGQUQKGoO5tIUObbTOJYXEE3V4blkccOWa1JhaadZ3b9uWQORgw34RQfvn0LWH9HL+DjlDIO6xKMcWtWFA4zVhyX587zYGEuu9+blI+A91xmTkLEdhG/e/AQNaTo71RAk5+xdDaT8HHD/P9f4XYrkw/JyzzJ9tqZrYmysXJYCKBbzLnjeAPnIGlUsf729W3r5ZQW3lFNy4+7v04hoHNW5eCEkuV606dLjNJCwJY2Ivl1QtPE3vpi8u4bZthMYWkDrRtUQ1eIKVIYUPY+ENToxcok28d3VYGTHT0vsZGkKlhOHZkxMNu+Wy2Svt/sbnpUYEbdyy4sTxPXIhPipm2qVjs7xSqIwhnL5SQpAx60ex8SqdKLbToQcMg9cQioPIRiIlaBojkwpRCPGxUmjKb8vrLkeELA0ZqFDtfWYdn9NLjMWiP/3daS99+Glq8WgOdxSPgRee1jJC189uYXyHS/rXPVfMz3LZUrNT0M4TqfRTnoX+rh7oJdHJcSoVuFVD9WH6TBnKRkR0/2XBBbn3yZRUo1htFBwfQ73mn7hLfp6OhVv0e8DqJQEWMLmTPtOXJS0e0jEZkktgcSO0i2MzOwpjefyS/uia3wGoeXxlGQLKGYWIiDaHfaPe2MuRX2cq+HA4RS9mbOH9ArkBsM79hwuVbr1TSoSyIjMfhT/q95QEwkS94oDKbzM3FEpUSqwjRdzTcRaggumDEDyEeOuymSHaM0ZsSCUN2+8RaaJOxjirkBeGvJc4CZiQGBEu8XccR+QZKmptHG0ZtXLdRihxHxTKFRWE2LfV5XRAqoV+z9NbMGiFVpZkmBS/1+IG3LahLrOXLNIwyQYWaUgi6ueHPGrRsMBGIYnQSwidZTgaKF7tRBtIkzlW6GtERKuIsOBQMC7yrC3XNscP857N77TmfURE5LTsgAhgPi91SmgrFTzeS3ptSrt1juMnGrKGDEnMeZd1SAwYbyzy2iHHIxYM6ZTP4n07j/xG40zar6IWHERdf+L+LAy/lnMJkYvIFdQguk70GY29uD97p422su98pszupc90LdH0nvZ6SqPUnS/fic/E0O7w/83/773+2jp9cbdmr5fo/6SfKM1Bf1b7abrAfOkM7jkZSh31B+BrmtwOG8npy1PooNQzlpdL2r29ofVB+szJaWirFfLgeZOIhJvCfxsjc57FFZr+Xb+39SwTBWRkeUwTW2QWnnNU5/52US7KOcqF4eecZX1YqEwJWIbgohaygKgX9I8f66Wb3MYyBu/N3aMVTLw/34AMFcB+bfYcTe97Wh2mrttEuIoGjLZKp5xrvU61mfG8SWVd9qGZ5qexa70Ka5CHO3OGqAwPndc3z5GSQzGxgUrWtSz/KuY/kaKisJgRysZCc7q0kaKw4p49WGKd3adR8EjrI3wcnztYasNoJcokfuMGnjE58zS/2ygPXPItV7aAzQYWKYhhhqHVK+eq/3K+DaXd9YVTnB0oQIbGc/dDNTmGviRfEi2AV8j5oGGmCoyGjmjC9dTgwiEbZzeBQMNrBRMV2OoVNweFq86goVyfUcEc4M9ihR/X1upKWOuS3wbDgxg1mfPOJmTauEYaZ/CECLREfBXmZOPCqIAc50voc8gYpowkbMxNhD/dBVQSca11Y4LGkznRzjNg/DYQOjjH58/2lU4fDJMBuYLuv6igg8dbRYIqFS/eb04ewhi68F+SYh24V/eeDPeePmAvnv3H+/4ozrTKqe+0+1S/tx09FtP5giDOvytiV7EfrA5Dhh5EK/G4piTA2nXroG3M7sPfFjN4WVlu8NsurhupjKVPKPZxfietcnYrzN8GPpOci/zsFYRXcv+lcQbXAvcJkRFWQQNOdUa0whA3YUDlta6yJkUjF85taawp6hAOieuSCdDlXj49wXXkR9clv7eVGQ+NGA7qoDONiBKOxfWhARckouqspvsv2aHpfSA5Fr9y267JHNqib+08A6GQDidJ7nzWTxpfpfe2laN2ikKy6PvJxuxXMw8fCO+VstDszoo2l6ZK5uNvkoSJGqTBY+nc+ayx9ho8N/BeM4OqeJVV3KtQnnWNp+XVDHlVLB2HF8o+8Zn3vuJQXcOSoqG2MzC4dth4mZUHH4kHYwapRhTOyc1eqQ3RnXMwD+tpJqQ4uzRHUriAe8Nm1ykUpUIgTg1xTo5ffHS1BAPkqqXJ/Q3V++kmy05kot7grOUjPyi7vxBLS47a50jO/FzvfyGWC8PPOcvhR9dUVWEXQAWW41PbGe4Sfb3TW2HIdRrHcK9EYGAGiaL25EuXWM/lBi1RB0g+NzlpVAwyW867+yuXTdsrUTm4IRUNUXXm6eCjqSt7daOX4tk6j4LM6uqU9v7XPWgw7fvZS4KUKREBxab/eX05H7/sS42QkoIsv1eW2wi9S7J9FnoUDtQw2ijvlVe3mYJh5nsowUZ6AV24D1EtHcmwT4XepE7rlQdkAt6zbjwND8pY9oN27oia/Ea8iYceGk6cJ5rfaZRBSPI+WenqiXpFe++JXur1SJQb54E8WKWRKfcdiIKBpESSQvFBWFlGL2QunC9FNBTUVlo46QyTQuiyMpLUEOKjjQVR+DEyL61hjDCkYXLqB8PXWPC15piF3koI5EfCaWVdhVKM24FEEyREyNI4BXOxmHodyirh57nwtTh86MneiPbbCRE1ecPPwaf7RpGbZnLi6ewmcOQoRTX8b4Uo4XmDROBE2ht9dlPsq/HRE1AkJ4uCfKX3I7lX17thQc7vpvd23mmhMCoSzaigYwhA9xyBiMTMdL7Q81oa8tmoguPITo4VIDmksaDcRKUH9jtWwKSpE+fT7MwrHiY0qjIqTJGWHuiUzUp5MrYqaVBj44gitT4LY5bluxNFhULEd5QoKcW3cy/8L9Fssn6yT8D6qpZEDYaSizp7j4f/l9fTKoxQGkoAQRQJsWFN8PjMn+lftt4zjBgTPe+Svsc56P1wPSKtty9uzbNIBSLIuCTK6uqU6h1HDj6erL96NGwUuT1c9k8hKizETyIbzezrFObnUBr7Zmobs3L8f/Le9ksi2XzuXJJOWDjTi1ojoFGWYEJglB+m971Ce+49mS78o8dK1XeJ/mSDq87S56iu8mchEdHlj4bFg/vw8kp4gTTRSnqvReieZBeWY2DsLYO8SMa4J3WG7BiOaHW5zCc4GXB0lyuie1e2I8Sx+BMVIhaKGeoljesd3QN+TCJy08EMndkwNUfULL+4wpYa76h5jhw/z/X+F2K5MPycs9x5xZTKWeZgimUIztmRsGEmjigwSgZ7mYGEiPrMXM7eGRJLO2yWi+s6K8D6krEpZTboJEUjsOCzovHMnz5MrqfomHBPCWFE9dzR2QMgrWUQPzLfvNwc5XibG+qAwIUKVtffuDLW+3nPVKcIQPumZ8cSnPAALuHv4n/r+aq97mFB0Eey3y6rl1CCLX6lDXjdy1V+DAb7kSsDhz170DVE3dGdV8y0UCR2MFZo0FPP91Rn/XsovoU4v0vZvvBmytSyGPLkGp+kvk4eKt4FDa9cVEpf2YwjO/yiS68t3sHgjMoRSvedD/8pQ4w3eBKMtgZ0kh79oIwMFInAcczEvetoKAZDCrcljSCIIOiy1QhjmRdwGieNPaXuQw41QNS/E6JEeK9DQ7E0YM7vhRSzaIxYXg11JKGzDC8hCntKhZm5MuTwSFbsGm2gw3Vfz51SjiVaicMYViBMb3aI5s+S4ntiYw9Rb5BRijWnah84t9hYdCa8/DJUSM7JCWQNO7tRKk1a1jm7ET2rMAV4LWBoUWJsXId1iRnlELFEpLOvdZwyAyWXXKBaGIZD3BOdwa/zrKwQ+woGwt3bnhY33GDKaTba4Ht3hqsBzjEON5P3ymxjyZkOhqVmSrS6lg7G7I6Yr5lQry7THYRmJmPmg2FVI97CRtBWfeWdp9L4v/L+ko5fcTltG/pw6X+exOtgZF5sqAaUtHJSnW2SjJJERO1uPzGXD4UPUp6lC2f2zJo2+/uJ0QoLh7Fj2vRyESaAJATma1yanTAJpYxXrPs+tDPBvXWGVkKXRSoR9Q6XXAg9Zx3jstnvF8T0aEPrK1NzTRARze72fSlP03Gt96eJE5FI77GMIlH76a5TRiPcY31JVAzwAO09Ee5HVIdrPZHTWaRkH44fjd8DnbLxda//5/5QcJDNbH0lop3FeSznvPz2HsnCa6+QWRK1Vt4PL+yW6RxoD8OmcvpIGtMsjXGL6yXtPKMd1jw+l/5Hbwlu9gBBc6mi3f95L7mGZ8Hmenj24oF0Lha1p71PpIcoRkOsbu5oJ6fo++zZ4CXzZVqpnZXUzEpttBKOQ86yh5mQ24mjul7T/6SLclGeW7kw/Jyz7DzrqZoMw+0UTFJ6wdkAAkLw9CQifo4FqsVAGWAqwMQrIzYPRdy8IaWlKbhq6ZTQwpts4tUC3hnywai091R66sgsLjmUyM4zjRKIMASKUTlSYZJjXIuU1qr/y4wxiP/O3BUYBhdfBZUVOfYdAsLjQZ22QWQZfjwhzxN+M6I8ofXQOxERlSudDYEFW0n0iigO13qa3gekxwIgs5xZybBXjGUpGfOUzKKid/pgWrHzmmbCuIYK3yPTpOKc43eR7zo5EQLPQLY9V7e0AUG2FQYFFo4VKWvGW5sYYXzoa7nCbxD+l5mYLO8vIqewDxz+tvcExP03XhkxNuDtnt1Zd/B7LjJFLofEynEMfSqpnRYGYrL/uUObZDzD0riBacLZeHB6S/A5SLlTPhaZRJ02eE3FnswoFRX21u0FcK8IDdnsBeE9y80j1qIiFZ0TbYD3yuJBsfo2ve9U6mJEKZQbbbxMOVRC/YOPp5pLM9ulcuMHvfDTyFmBaAk26iUGJxg/dk4wVwtRSIduFT3XvUpnnYR+sdET+s5JCtAAmJzjs2CkVsZRriqGG5MeWMYhFe6cqVuujDBA7OaI8Z3TrE+P8FkhRGx2D4yXAknF37mGsKs+tEG0B8/lscI0712RyA04z5p9UnNDKs1sxJLhX0TC6H4Z57v4ZR6MfGxs5rK4HjM9CkUc0Si+2B10RhARnd28FOsSVBxGLXAdLLKXfeIADKGfkfNE91+E1kvxYy7RgonSwJjhzL1G8U6zt1uhrF39jFG0C4kdkDF8MTH5hbrzfdmPE4Y9E9nIV1l430QZf7LQSUosh0euX0Q9ATPyOjIhMBoCEkLsOGZqOsVxPnlEoO8neP5o2VyivBc3xbdH1ab1WpZKvl9ElcF5xme/Gm9py63Dt57et/majr6sNxqhLDo583T8qqvwzLQO34MOj9mxp6NXpJuU6cyFd5J9WF3es+/zWvYkSuXi5hYTd+n+NuuRzf0FVi7InZ+fcmH4OWe5+3JHJRL8YRmAlnXeY1T649l4+qC0StjN5g6+odhaK9X2ENwWi/QkqHjXCVG1dHT6EDJWijp8MKlsC3pRak8PV7b7JoUIE+EwYiQJ/YBncj/xsJXCKWfyUVYYo7/WlMDvYSB+ts2aoRAhxspmg4+ZNl52FOcF5JA8D3JSzamxc+slmdC7+FD17WRf4rNyxHyu7l9chUXFdp3QOJzQCCXqQ0OxNVx6G+JRnvvqunwnF/+pl9J18TtZbSt0wyxcOH5M471z2fW4lOtKj4W5B2W8uwPzqNsvlHLiYl+EV3EFyMgiqRraq4Q3c0eEylZ6Qp6+OP397CFhWO4MbtitqBAgV5gUZJtAeItjxsq95FxR3uJKGyjAmdgZjnAuri7rfRaVvvWBVo4lfw2HF529JoWvhExlpTIESEJsJvnXZPbhf4meUkTLEZ0lyXnlPJRZvyxE0RJCqqTCz3sAGr99bu7JvX8SFTJcXxzyJIwFG0DIFmsiD+OVO38QWUbOOr808guJqZPnZ3gGFzeIdm7rkDlpYGGjRgHt87ku01UrYn82DglDmJwT8jmIWJrdI5ocp/1aCaAwvxOiq9johmTXEu00PfXklwbyMn5CaSxXfHelU4YeAoMhr28lvhRkKndJlZEsmDxPlP/DEVFrGAK88fOWMoZqf+DesdTu6jbpzMw8d8ypRBTmwVDqc4les+rgWs0ax+BDLa9qXq3JmT4LcH7KvYfX9fKavs+XNHhecncwip732M1BXvlFA0Yowmk7IrMNyYPZb+b5vMQbRBUiWlzPq6hKThJl2Rrky1vOq9WVUp21Wxk6Zd9ZHsDMuk3sN6576cDnV8b+OiOT5wu8tOSeezr3C44fVS4MP+cs+58mKqPgmTMkDhEr8saPBpA6LmjpHTQFTwftywNzIBbXeUMJRMHMMA6xZyMRBlF5nIe2pLCP/WGhSqWxX2tSxRz0Xnre0pjw/ML2haGoG99NI52yTfb38CacMVRZ3CBdHT9irPF9Pato0t7093rHGOv4TgnBoREepLz3yNGSExi3EMxcMyzAdXHuSKwaEWvqVmn4iOOZOyfQ8y6LRdQtlTfphUMFwZeaYDjJNsRe2Qy3y5jhxxd5o22CXjPmrFJKlLAbGsLwIKtfOF/LBQ0rLjznDcHRl3nhnKgfvywhvTCw5JTcJAxw09dvDvJ7GZFWEuXvbAxBtETuLEjCV1Z2XxnBIv+G4ZYWEqmC+czva+1l6FlFb3S1dMqwtxYfl5FLNaBq2jKsaVxblXzP+KG1EuWSfodfoIZBypp4pgVKSZHs1kT7T0JYg+Av60LG0IC2SLrddw3Di502xnEP5Fq0kEMtnr0Z1AgalsqVJpSeIeHwgQ4LkUTnXUis0XZbDZ+Fk2gAtFJOE6UIJfzevKZlCJ2c83IOWencc6TJRGLfgW/Giq9SwGAtlStt1GGDz9B5WdSeCgzzhVItMgeSG3eihKxeIFcZyO6cswxlo3QO2yjaLtRKjAeiTbq/Gcpu6Le415C/BsnHWX4acVRa7Rc1Z82zKydrxziX0AisZKFMqm3XGI4RQO6UG6f2fynz7cW96uyGdvKMoasZEamI6hmgOeCEtDh+tjWSEI1w/OQKI3dV+LKo4gJCM3e2SuSnJV+PhdNyPy0aBkWsbNy7jT0i66S3DLX8txyPoNMG94tyUT6bcmH4OWdZXiUqR4i/hhTgTpjALEBRYBoyFlgQ3CTsSyJGDKOORf6KRSOFotHFErJiaafhgEHCykFy4+6BGmGS4xlyGVJgKYzhZl80pNuXSIBSX5O/D23uLPSrzd0YV+vwMlOd8986xJHdr6wnKpaiJjWOpsFLdcog41WY/cy9tL2wliv8bBWB00bFZABZ03Uos27kvZrjJ/wvBVYrmw2RVpp96ahFQl1RpRvPjLKnkUDwu9OKljfmp2koHkG58nspw49hmERUR7Uc/t6s4CnFl8I75lBXRL1AjH1gQVGmk1Z8Oz5tgyjdb1dXZSdhz2pdkvo9PLP/mcljUUFgozcidSRvSD1zgQgcFIJOkRPvevA4GCz2nQr1UmdIJg05FYYCA++9uK65XawFrsOPHc3uteq9JXx+5xkO30wnurWH4RzmsZJhXHI/ShIUwMJxnmg14OnmccJxZCQVZq6SxqzJ/RDaoQyTUdGS76+4rFptKJ4A90cO8dbM9LpRKcZJc+S0CWol/I/ZwO7vhLMYjXhyHPhvuBd0/RRNIvcWj5Vcl/K8Xl7rf1YE1l4/0+LeUsbRJfclfVc5X6ulp2ZiIDbi3HJGAoHu90aHuqCxpVszYDzxhQEkgKlSbLzh+e8vYBgMl5DRrlDh9zLMh9eURnwYC9+nz+lQY+JbyrUwOxJ8Pwql62j3U5iurn/m6lYmHWOmJBledwo6+ORaGcuYf+XsoX4SbZNcA3k2eX5ZBMy47+LaLdakhlaODRuO8HwomjjvBvpbnWk5XbafJE7I9SEnqw0YjdTfCQxxY2h9NUZ9xWbm6ODxRmVgZflL7nu4n7rWkzORTLJSvm9DTimizNoVhecJhgQ6H9bmkDN6MDvziNz/Qiv+c5DVy18gflS5MPycs8zv9IifLNHuAESc49vxkLBSnarMGd6TLyDzlvSSV6kwI0szBS8s6XAgK+V7F142kEVns+OoWvksAS1Rf0ip8JGZBeOAPmTCxLq/y00TjVKG5ychseMDAz0GDNlHjh/RfyaG1lmA9Aupg8nrQ6EFbpfkfyhotFMGu8LwTjIcXnhlkfPF8pQonqGc581AjJ23WMoBEXVk2ENrq/PgZA7VZN6i4cdIX5qEa4mJpeeYujUpWS8he8kRCZR0LHyPnBE4uW6sD3McRWkyHnELjoxkt6anULbBxhvDUD6K+GFPNhqfosImCX6xXzwmpdhLk9AsEfal1n3haX0lvSapINhIgIicjjtGzStpZAqhTEi6zu+4vNFfO7ulFQ6FMrKM3GQI/07zGEjOHKKgIKssTMK41REh4x4/C1xKKjOW2DPOHggdVaTEcSyrAc8thl4Qpf08ecgm/CcKipaaGxJZwO8C85C5h9CYJT365TRwqKGBhUOISsEXs4awwNLg28MQEN67a6RoaYgwohM5edpKGzHkd2MZA0NKmnkIqVNhDuKb8HdV5w+HpuXWHPVGoxSJJyoMIN6qhQ4D3Pu0RmEqTqTO0QZrToxPM3WmIZqNBxJVps/swnBSpHXYgKS4UIo8mqHvv1cGCYvXUWWyjO+EGTqlTNCH0KOnDf5OxvnT/UG0vdbfw+pb0RCtrqOnZFw563h6zoAzTqSad42ntioUpxnP4YVMLmIgX3CPUtQEPDbKCKPHCA2VFnJXPu/0wRxsNDP+Ysy6tWYgkYgMQ2ravL5VqhhyzzS9fZkGZZXMWa9lJyFnFUS+KI32w4VELlXvPb62OoS4gSq2+DaTLljOXemEY8MPJn7JcIommVy5bcPp+0UX6uU/B6FeF1m9VLkw/JyzLK4LxE/O8JNBSBBRNpUqQ7ET4Rva7+NGnVknzY4CNxe6X1bq7VwWoCGFnokRh2KBu76jgaLU1/QDwn+5lL45sutwwUCdGJ4ANRad0KO9dF23GPGD/ffa+2IifkABlN8+F9tvGeLCH2TDUYhBL388zKQyZIUC4TUVVpaByMq/mf2y/g5VcuEq3XWDX6dru9bXiKhff8fGtVjWE/3NpHFWeowtmC5myJFj2OTmiVGXCAxOhgGGSIyDtCtYpJYjwg/vQ/iNh+pycTVpHjJTQIU68Z3UVNliPlljaK15IoD1y7YHNjPfaoL7hOOkMNomsReg0UUqRpvIDYNKGt87wKNmcTzkQvFw3Zcrg4wXhqDesYxspIoKwWF+q4G+bdiTjSiUWEcq6wqtZCgIifc6MWhoI72H9qTzY8HIL7X3+9geaob9j5sDMsM7dp6OxoIkEYFhvAKHhEK2RWMDojHW+5rcGg1URa2/HaJviLSB5+ATntaHTqH/5HdbRqOoTENN1GfskigiJJG1nBnZBAhwa7nSSKSzB/tKbOTFfnUoigGOn6IJBkbcf1aHleqjFZauKP6Q3Jmz9Vn7OF7IyiGZwg4A40yilmh12Xgo9iEnx2aU/3MXwzBg8dFxaUccqyqFoJSBHZFrqix6cVDGavW+qAwAmfG2nKZYmh0dqpOEN7LvwHBkWOee7Ov1/xJeBg2cnIwgkflwXDlDaPJQ+2dzHgzMjQ5Uk9kK1AIAtJCzwtdzMjA8YBR9Hv+Oc6Wd6rNK9d/T4Ht3iLjMeCnkoPzdd9V0+SIz/FyU56dcGH7OWWZHAvEDZZsNiZUI5TGIC1p6P9Xh5chEcuDziTRSpZk6nWXAUMq1QKoFUPR61buOik3KwUOUerisdrgt9cyMVySXJQEPO7xX/VUe+HzYotXf4OnB3y2FTdZJPGZGHHSOAyEpGR0VhQN1UDt9b+etNTy4Xb9aIyNcRoj6rNA9xoFscbsoz1uTDxniUmaMqjz2MkU2KiU7z4aP4TYiq56Yz5Pj/sT1VTpZvHO0up5aTiQ8mZ+F/eJ5r/oL8HlfkPqWlpCpwsGgLauw7VGRyBrPUJxRSxo2/GTmCSPDBsMoo6FCGWEYoSQRiDlEWs4gLBVxYy5qHrL+Z0ZmmBxUNL6my2U+U5+81zJirCHUSykf/EycGxOdzhpRBKvDokO6cJHGIisUkojo5MGCLn+06RQMLntP9JIzZ57D/m5iGIWc/4hMmkc0y0pk45JjOruXX9OWwiTXNKNycqgyhXwU+2AZwxxxrzy7Ge49/JiEIqZ1rIQD1jwkMjK0nehxRHTv6tDR/G56jRFboX7fliyLG8F5g/uRTFPdzMI3w/nJY78Qz0HjKM/7hMxboqgkvxpm5yoNBIUYB1agl1fTOrtPhX5qA3J/c1ET7TzdEqJOOEui3Mvnd2Bd+hCGmfQL1l9nbFTImnEH1BiadCihQVEb6K3ESOLUNastq06HXJDXRwwf/XOHHVfNCL/KYNvsSMT1FPsp917FU9X0cmr+AfblcmMY62HMFjecMkzKculj61gv/Wi+HHeK8PrD0DQ+P3ef6gc8l8E2T8cgfgTZ3xfOmBs+/TvpceXvz1kbucg9t1yHcC40YrMDePfp/Dv50qksp5pT057/0+OmyyTctQd1mrme7LIPVcyKpjJ/VeNG1C5U3ciW1myRKe6FVC6yej0/5cLwc86y2RGQ45ynZMQSTUSK76NLxSszW2AWzCgwIgFa/2DRT2N3QcVBKYqmZ2HcxN9M44GKsEZpJKk0oiI8VB8c2fAmsREmRi4hMCpYsQHBTVA7uT0h821l+51QhkKXcZ/1SgUeRFZWr0y/hrIhMBngEDlp7tpY9gj5rM9qP/V5+SJpE4VRF9bIEB9OpyRl+rW41v8Bx2ZxNXp3JZpAzNWi7hem8v4U9tj2Fexn5qDGElngWiKqjDAErip2cBU+6rXgr8KA4t+Rs4a84d1GDozFsBDDbVuGDhNhZEHEUTjnqtIzqEI04vWcAiFjM/CblF6RJlvC+vzZ9HdWjlHwlor75MzHMBwwykfDteR24DBSLuVaG5HQ4NWFekFfA5kqKOrISzIj2gACUa5BFr6xX74MijHuu6e3+s4yF8P0JN200MhDpD2tPH/lu8tvv7za/2IZhjH0RzoJ+HzFeT6/F9u+Avcm3zKMiULfcBYd8W4K+WLs7ThXuZ+KB6swHATGukcUULL3ZIxKXea0nIJE/TxAhYb7mZBaYwILVuCkTSyREcTPGOq1SkMhidK9oXPCwHpmxMvu02D4TJCdcV0qR0m4wE6B0EdYlxtPuOKQc4nXNSq/3nnltNJ7lrHBGo4Sk3S3MhRPgwfSQq+MFTYcpwZ1uw2zPcPp2P9iK+TdrQNnjvdhH8g5peQei0ZsXzhlnEs4xIhodcikOWm7AfkIRuu7wNN2oDsuz+d6N7Stws6bsK60I6avd/IiC7YjOImMFOJd302Hoz2AFm+OolMww/Zs3UAalcP1/rnr/YL2bjc0ezbd2F0T7rn3ij4+FudEuTH23YyegTLG6YOVcoLg2kUnd6gj/h7bxDBQT8ForkP3xXnBju6V8Q2M576Qy0Wo1/NTLgw/5yxDiB8uivzVCvNRB18UjkVmLIWOiZsxZljgIg8MxeUxcepAsbJkqEPA23Vl2ey5LutLruQY9quVp82QkCr6MJbalMj2ZihF3TCwYLYaFAqs9juhLaPQJ1kxDKSTRjb1dfjebcncTK91RpCthVKCh0vw+KZ9RY/yYLaEESPeGNKDyXtX4Akqam8q8mlGj3CvFkTifwOhdzwn5HjI501O+puV12tSGAq/PsiVstTweqZsYRSeJWjIfhMZxhsyUoBjuE0UjiRyQj43aQv2vfmddlAgWl8KnZsep5PYV47qeaE942Kend6KKcAz/FkyfAbRLDyech+WQvfZi0RdnAetU2ExkuODDR84rqzoKwFUKOXr/UCg3GL2KQ7RkusfZf+pRmWh8aweMLQh1wfu87N7XhkLpAEgx+fUVnFMoL+JlzY2e3oz7QMjIRJkkeLUiOOdyawkjT0WqnJo7+++PfT99Fb0UiPxqFg7s6OW1pcKZZjhuSi/wQqNnEaGqlwmLdx767lT2TOHsl1Z7fP4Ia+QL8N8XV9Or08/JerE8UReoW5diG+J74QGVaIB5wEqucbeLzm8+J1w7fJ9lpGRy+qwoPmdlqolKupF7GP+3towkON8Wl6xX9K1frBtIlvBTMJ7455hhehXC08FdKYVsmN3/qAcEo0EucQhRETNgCGBaATV4IjKpX2eEQ1wnHSNDzTdBFQFOla5rdWhlLG0nIBhmbgndsYElGsaPUYnD6IMY6AIxT7B5Pfm+rUQvpvhvxNRx/uXyNjb6ME+U/+cdofuDDYMZUREmxvpgKQOXU/+lKidwtlVsUAlugj9qhatIoVWJaPb+EJ/d3yBsYxhfLYpw6sLWUWHQtj5rLPCN5v1Nh/volyU4XJh+DlnOX2QqEQkjir5xZkzoDDkeX04tEHbh07/Z3Goo4JXERXo0bUQIZm2B1NwT4lW1oYk3zUjZDuDUyMHe88ZQcbSpg+mc3fwP//KYzWwz3apVDPfVBK7Ka+ad8qzmhqkMg/myyMHcDvRdZhoNlGiNsZzcmPR3RS7YN06hETaovBBqb2QQSHPhXFxHbwm25I8DyoEhMn4BKIhSeMtDIPWfEK4v4U6QmGBvXAm6kr23en54I1vYCJccMnD70xcvNm1JMv0V+z/4ppBvCh+79KSNjYsemhP6cZAfafwAEkciyiUHkkljG+Sp6eSG4Z+tiSOJrJRIjjWrFAMZU4sV0HZRmQW11kLYwkqsEWrDT05FBh+k3pOWeM0F4sLxCJCNhUMbxhdjD1xohBKjIQQ45vh1JgJfq7UCCScHQot5GgGRpIkZJkNbtB39vYqwuqE66Yw547ppQVHTXXm03Oe9LrsjNhIuL3yieFe9rdvTH8nydvDRrm9p9L7Tm86ane1kWUp5gaTxCqDWvxdcpYgmpbHTxoFc+hlRfK9ZyAQpbLEiGk0TsexUZkq5b2Z8HuTy24LxVdlYsvcExAmcBH356lel0P1ZQk8f/kKPiO3dOtSIngwJNwgqU04JkfGSRrTw83977Njp66F9sN1hXxMCKs9tROn3rs3rutrsv+4ftH5wQgVdHjUc6eUfIgIp8U1p3iq5DgdfJpTmmrD7hho4d6XTmL/ALEUHWn7H+thq8VZ+jHbvbgA2n5AknAtSdmw1EKGn6YL0zXCSbbid0oH289Df9cPHCTXOZSUiKjeK6mZFnT/kfTg4+99+f/oN7VilW7+6+t7dPB/HCfX0GG+uRmEiPVhOqGKZ1va/Z8A6a3TRd1eOyAH4+g2/di0hwBZ7Oo0RN6TL3BzFLrbk3dCf6+kY0POUf1Fxu7cfg6yej3X+1+I5cLwc86y98Q44mfo0GMLuCISjKnQZQx5zmBhEYERocd4HMmREzaT+yzhB0q942h635veya5vHFIC3ADNzMhAggaLjRZEpKIhQwhwzMplq2NlDc86Kr48xiqjhmiKvei5+OXkPssghV4QIax074zyUayC45goQI2nxfVK9YOFGqkoKwGMDGVDIX4GDJuZ/nb9bHT2NyngsdC8vIKpnz3N7jYaJSXGrDpt1DWiXnA5e6Df7qwwx/Ac+13StNFaIC02A4iKjGcphypDhb6ZEs3uph3mObe62r/TBEJpyOnQIxYEuRw/EsZ570kdD49KFKJG5nfb4VCvqLRgrD05F0NWUDgXCJ3Yb+xvtQhtMSKIKCjSsrCCKVE+cv3JdO7o7S5Kr4RzibBYxExI+4/bWpNEhhEF2DiX+b2WlpcLpSB0e6N47uQMBOXSJTw3VmGBGEMMfGEjR2Qx0Z5O70d7j6cWqbuv3KXZvVaFEuzc7oXi1dXQMCJheY1vBsid+ftLxIZUbqbHMqYE+u9JeX3lHLMyhhH1e2kDnmbZz2rpybVOZa5aXS7ie/TPwflZ1NooUmV4n1Dx3+zrMbIy0KCRJOHVid8ZDabNPGSOwzUn93s2FLO8woWN5EkoNCroHRLW7mcOyUUU9uKdp9Nrks+n+xsi5aLRNpeggCgYuuodomaWnjk935yQFwzUtEJXw/fAtdffrC/hforfIlff4kVspo7mcG6k1AD2/LeMu5p7KBq6pdyR6arlhNQIQsPwjMpx3KyVcVW8RzNzVC01OpjfW/alXKUfrq0c1TsaaZw8KsMBFNK52yi4rk5F2iEk+rm8HOaf2ptbH+SVAeM9y8MqRDju22cPw0YlSpfyXa6JRMeQf9AOKIVaE/Ut9DtRL3+hfJLIcwtPmz2NjuH1tXiRbVzhduu9K+lFlEVX9hnuWk9nr7ie9kslZCgG0aQ5+b1c+4RHsmtPGLzWXxY9TyCctJWjZr0k+kOz2y/IchHq9fyULyjDz4/92I/RD/3QD9H3fM/30E/91E8REZH3nt797nfTz/7sz9Ldu3fpNa95Df39v//36U/9qT/V3bdareid73wn/fN//s9psVjQ61//evoH/+Af0Itf/OLzdyJ64LufjZIDayQ3jSjIRIaC7UI9l0MwijYVZ4BB7owHVb3j8kS+bX7zbyfhgMQDKyGFY48xvFNbOipzLjEsGY+gFI5xHNf71SCZMN+b8+4qr7pon+8pVNaWaNwTB6LpER+YA929KGhw6AMIss1cf5NcGnZ5uPoKO6EFDyxZpAGROqyUQFo6NX+lJ4ZTm2PoXbUiWl0plRAtx6ye2/EojKiQ4Yize+lLnN0M0oSE1kuhr1jLAG5QmApDsBEoKTbSoUKQg/kjmWE9c9nwtXTe4sBqYy+mSc6lVS1qw9sM31LDodPv0Ye46Q3LNTrOXc6DbjwN4Qf7i+3wOEuFO4H4iyZxa2gbndVo93avPHGmH3x3JjOWAhxRqrix0QeN8rymk9A0DHXcaIOpJqsM/y+up/eWS69RUWj4NKDrcu7wXDh7II0PWl8i2uyV6hssr/SwWG5bGSOiDanIGPSJ+n1AcvVIg836oL/BygiGJPiJAB+bwXW5jkZcRH5JxI9rHTVzA0EX25Lpk1XWLSPMF/n8eP/A/W5dOYWCU4b5qaODx9Nrkty5IyKHs37n6Ri6BwYxaZA0nU/Uj7NEqmHoaRfCIJEhBvKGKA3jIgr7kUrxboS2aqcRXwfZRxK27wRS61xmQzm+2lmjv6+VHtrqm3ekt2xsvzLuy4wf1mkrR80UHCiG4WIs3IxIj1/fX9kZ+Yfh9pTTwzA6aORu3PvRsSPPnImjZpp3YKbJOcDQZxDvaqMeW9qwb9qwreaFNcziGssbiFIksvfnhINwo8+Q3LOVPBzHNYe4S8bbaF6fLWKvySRJYTkLDfPyPZuIWMsZTs9EmJiV2RPnvi4ZvqZJOYrEM0O95Lfs9AdLHi6Vbmdx2GEqeF9chHpdlM9N+YIx/Hz4wx+mn/3Zn6Wv/MqvTK7/+I//OP3dv/t36ed+7ufo5S9/Of3Ij/wI/fk//+fpj/7oj+jgIEDl3vGOd9C/+3f/jt7//vfTtWvX6Pu+7/vom7/5m+kjH/kIleXY5pCW3adqqiYcF2Jv7MpL4HUdB3UWm/Apdp/spUuTkK1wCXZY8vYo5TDpXIp0sfowu2ugMfidhOKLwsPixpRmRzUVK4BDyoO10O0QEa2uTGh6PJzGwXXQYyGASWjkun+uJjPUaeYTwSmOweYwlf75WZP7YD0RAtDmUpAUizp9QLEM/SkW0r0EY7+uyc8we4NQaC6HtudPgBbKPE8Ir133Y+hWNS0fvUIFeBaqoyApT2/0EvTsmVTCduuaVg+mENPJEWhU3CzOT++VR5RwbFYbojVYjmrx/afhO5y+MvW4TI42VKxbPYdF8+X98H44Ns1uRByc9c+t99PvPb0fvtlUGITkvCqOey3DAezXzypqLqXWmvWVvn1WXg8+nmoq5WmYW/VhqvVhTPvq8kTtKSx4SMFJZZfzW6Rp55AxJPstjJAFTAlupIiWikzOA+xdUE7VXiPu7TMraaU5XO+vWdmc5P9EpMPnMsUVvktN3bcn4wNi/yDc5vjR8L1xb5TGM9cG4+PySkboEx2zsjIqLhH0YDKqHo14xjmC86lYa94iK2OeQi52yMj0uhRkWUFBjqJemZQee2iHubfE0pHcNNK4h4pWtfAqu5VEDnF4W5YIFrYpub6WV4LhAUPy2AAilSFlzPLai41d4O9jrRFEgSiOvJZobaB5+s7H/2GsFw+EkDA0CiaG6PhoDClZRzTaQhAw437Ehhr5LZN1KcfJMIgoZ5Z8p8w37A1/MB7i3V0djFK41/A3qmWYqCJl1eOlEC6xD8oh6CzjBt47rOh19Q0bvT2vh4312ZJBB2X7NUKvop1lRn8yVcY48YgMmS9bV14w6kCYey6Jg/M9p113DbNDbkJyGFnkPtkZ+3NGM9u/mLSDRhjeEy1UYNeM9U5b+mLH+tWNgapjf8TEdrjnIneSfb4nzh/jLKz3tpyzhmF1LFRxbH7331XvzW2hLmed2vjM5gtGY//clAvEz/NTviCm0cnJCX3bt30b/aN/9I/oR37kR7rr3nv6qZ/6Kfpbf+tv0V/6S3+JiIh+/ud/nm7evEm/+Iu/SG9961vp6OiI/vE//sf0C7/wC/SGN7yBiIj+6T/9p/Twww/Tf/gP/4He+MY3nqsvR49VVM5Ghm1g02CuCRSKWIg5e6DfoZXQ11IQNGT78mdp1Ueoa2kIKEaGH+UJ5jblZoRekBlRUZfDm6XhceF+jeYsZaORFKjkoVfpuklfhzh+MqWPYc+fmPzcXGrS5BBDT5IxVlYqbPryq7oi5QUQfrDF11REFtVUKUMYim67qHUd1V/KHJYD3sqhohBSTUVFYyytZJCjQSvzjEJqMCoMLjYnwsEqgYjzTmibhrcvZ3wg6ufHyYtStERbht8xNThm6utS1Bol5fgxhHo1FiAYxi4hwspSaDBD0uqS02Nh8GdlieG3mBtaeA6NbgQaowFhmtuVSvn6UPxZotyw/41T5La1+GysMOKWxcpdsYJ5ABw/y2tOoRl4758JI4UFEZeoJSIjHCaO1fyODvVCpwC2X8+d2qN2nu3bYbJ1y6tdLtO1wtf7DoT/dJbKUEeeQ3huscHJyphClCoFKhxl4miNyrwwTPP8wPv4e6uzUNSrlmEuqNC0SKUhtxpc30Q61EtxRkWjlIViWkPInzpbaivBRF+JDXBI7rz3RGgLOYPuP9Lfy0gmyftD1H8nRMulfQj/18KwKsMq5fmOY18t0zVMRFSIsfGZs5iNNhWMI7YfkCLpNX4nOZaNgY5R8xoKn2Xq7Cr0RZ25abjtHPrcF2EeWBkou8dnlHIL1bXVuZ2cxSNVB96rWy+ZNoYQ3Jz1NodcHzKAEOn1hnssOwXULPAGohEMgNNjrzJ7yXfpnmUYUixqACs0S5Ecd0biAUMby1oZvSLJ0GbJrQP9yhrxMvpAMi8KImr1XsZnmcskHOjaMbIdblN8qZE6dsX8n3htWTYHK3uyFf6qnNgFUZMJCb4of7JltVrRa17zGvr93/99+r3f+z36qq/6qu5vn/zkJ+ntb387/dqv/Rrt7OzQm9/8Znrf+95H0+nIZvQ8li8Iw8/b3/52+qZv+iZ6wxvekBh+Pvaxj9GTTz5J3/iN39hdm81m9PVf//X0oQ99iN761rfSRz7yEdpsNkmdhx56iF71qlfRhz70oazhZ7Va0WrV7xzHx0HiKRptMMEydKhZgixRf7BaqYOxTj7def+zSe480u9ySfkNeiAjErkgTCo+HAkX5c1akbXqlKZ6U9Tez4T9X27w6DGbuMENuoOfZ7Kq4DhKyDOHRKn+bjNm3pgDVfr3cxWo38yMsWZyzYSrQjeFwpEag5wiv20ZeDceAxV6t6YYTw71DQNLLuX7YIgaGykykHbpZVcE00a/5ERkQ1uOYFQlIEHDj8Ej0c1/Iz1vX2ncUcuKNqIbiDScuYS5Uq7JNIL1jcf/MpxXVgY+Ll3a6IyBJTFy5zK4XBGXWv139TMRucIIqTLSFGO/ik5JTK97eW8TxhDr8HvLOYZZ7cq1V1wxiDryUclt5sBr05DOumgYljDEc3Uo0GQG4StR2OssbriJeB7vh4go6gwY4rvjPGShH88ILtKIjUgYXziFwGqNsGDFRbOxr8uQJ+fDWGBYJt8jufpwXKcn+szDws/SBnA7E6MszdSpcC1pbOzqQxcW14PhZg2bxuxe/zOvKURvdeF8EokHiCK+R7aX1BdragrG0c0eKe4tOQo539HOMxEhOMBvWDSBE0adhWxAFqEmVamVUJVJLIOattOaDx/05dqoI75Pl4nMCOFaH5ZqrlgcP5Zsogruk5wRTBhWk28wdPD44WxmWbQb3z7g9OLsWtn2JZ8gnseN7heG0B88Hm7CMVtd0RMQ0c+bPZddN0Q9cfQUuPp84agZIOkm6vnsECHGDilpxB/iaOz6nhh+pCUHK9pyB5cyIvwVp2LswhB5eTe30IHZMMG2qItG/9J1KO5c6Zx1qDNY42PIOWN8luE++B6Vbgvv7fchWLslUW3wA72QyxcK4uf7v//76aGHHqLf//3fT643TUPf9E3fRDdu3KDf/M3fpGeffZa+/du/nbz39NM//dPPe79y5fPe8PP+97+f/tN/+k/04Q9/WP3tySefJCKimzdvJtdv3rxJn/jEJ7o60+mUrly5ourw/Vb5sR/7MXr3u9+trk+PPZVGDDFRvxEMQlEZGq+8ieGmhGw3B0XNwZqTgzf9m5kRCQ65cu115gzjnVA495UzSXulpb5DxxgeLlRKVBx6/FVa4aXAIZ+jCddcNnV3eL5L+odtFmvsW//zJhLTWYSPWCyPO94nlYGujS29Xlrh1nDnzisvvjumVXVee72U8LgFJ0C2n4aX04K5okBQrnz4JgOHNBPiajLv8H+S/hTRDc8Ebbw6lm5kMX8z/EHdK8CB3Oz19ZncVhEZxn6VOJ5qDut52Lch31/XQSOeRqeF+zE9c4gVSC9hSuTpiebpScY+/pgzCKvwILEuy6WNeJhGku2Th/oHT4/SOmy8k99bomVW14TyDyEHrSto/kzanhyb0wdD/V3IiDQ7amJ/84aGzW5B64PCzAhDlGaG27kD4YSFo7MHgPcnkyXLElyR+FspwlWveHTXRBVG8CDfwma/pPndVilIifAfx6SAZ/IZKNFmOZ6MBKkgvqs0MOFcrBatCv+S5yuHRXiYhxzGhAirREm719LiWqGMBR1RtxgnVIyaKRnKTPp7l4UItp1ypdGFFnk0kvZ7gTRgYxWGwS0phKDlkgLEniX942JxXqChuM0Y9bv6Yj6r7Fx3PS2uwloVhiXeAxQSr3M4pe+KZNrT44amR+lEaObhpVaX+5eb3ks/VLVsaHMAocXwfabHTHSSPjNkbBw5S72Wq+Ta5f3FGWeya4gqJImX4ZvRaGRSCmBdCNFn4vOc8p2E4qv939H0bjo5pCzEodka8RB+LzaQ6EC076uQXQspBcqYgen00R5uhmuunheKnH8KfDvrSyzIpvea8x/aX13WWb3k+uYEIzps1nXZPZMiusZ7GZ6Xu8+ETkjZEs8oywgt38cNzVGnDbxJ23Mb7sZy4VCmvrYKe73idYzzIEnegCKUJ2rmaeM5/ibLcYGyjiqOVJYwq+RoP6xU7VxYDrAMyM16S8j8C6R8IRh+fvVXf5U+8IEP0L/8l/+SfvVXfzX52wc+8AH6wz/8Q/rUpz5FDz30EBER/Z2/83foLW95C/3oj/4oXbp0yWryeS+f14afT33qU/Q93/M99IEPfIDm8zyeFjcm7/3wZrVFnR/8wR+k7/3e7+1+Pz4+pocffph2n9pQVTEpmO1F2vl0CtvxgutmE/k8ykW6u+8cTNT1yTNwSqw31Fw/CCkBY0lSM4qUhvVDqaGrPF7Q4iWHyTU8HKd3llTvTcw6m0v9dRQCNgcVudoreGQpOH/4AKhOUqvR6tqM9v7gqeSa30tPks31/Xhv/65OvOvmag9hwXeqDyaK6yb5+15YAiiscfag6T3MvSw8uXuR16NBISN+Q4l4gvDAYlErjhx5UNU7ZXw+pIyMaTGXD6Su5slJP2+KdUsnD8/VAT+JWa+W1/rnYLaoYt12hgouyMHEY6z4dhqvDkNE0PjSUbkAwU2M3+JWeC9tZGiJPFG1yHtxeMwUTNcwmKKSePRYmHPlpp97cvxkBhBl+HJaeJLziZXZ2XE6T6qz8C7ItZUSqQchOyf8r4VSgkI+EVEN2erQ0MdjNruHMBeicpFeO7uZzuHZvSaLdiPqBVlFOOk9NTP9TgmH1zEL+cARFb/tRCBiMAMWK5BSeZwIT6tMH65skIVXoS5s1CEi2uyHMUCBdeepoMjc+fI0fka2td4vQjgthuSxY1Omn0fyx6kzwqBA4Y/dnNR6HiCZKs89LvVeqQ3uMjwoEpTv3E73w8W1XSpqTx4U6d0n+3rMWbW6ku4rjg0WA3Ek/FyJYJHKjTSQaTSgTlaQogWisoSUY3Etq8yJot56vyDyOoMaG2Wk4RoRP9twavTGS1R+taEHS1tp5TFBA8dvpULGSiK3NlBd4hU5dCqHXhzywjOaWfKcyLGXvDsoq9c7jqYQRnYKXFxEGonXhVnBni3nOnN6qGx78RvuPtFPuPogncOryxMjiyRBHVvMNkNK4PysThul8Etex6YMg4z8Qb501MxcYrQiSvcM/lsOCWuFKHJRocF47wjX2/JaPsuUxdFGNOBQFdcDAstrTrwqyIhyLSrZxDlqbg0r1rwnoJzDqDFZFKm7EXqXZBmL32ZxDQiPeX4ODDl/qzWoScw7JM+/HE9bvvGRvw8YpPIcP/b1xMBfE633taGb75XvZKJoRpBsvREGvtPG4EYc6KdVeExrlIdbez0kxNo5RJsjas5HSXtRROGoHS6z2YxmszESzOHy1FNP0Xd+53fSv/k3/4Z2d3WWud/6rd+iV73qVZ3Rh4jojW98I61WK/rIRz5C3/AN3/Ccnv/Zls9rw89HPvIRun37Nr361a/urjVNQ7/xG79BP/MzP0N/9Ed/REQB1fPggw92dW7fvt2hgG7dukXr9Zru3r2boH5u375Nr3vd67LPzk2K+y+ZUhlj83JW25MXp7ws1qIuNmDciOfg9FgIFi8DHHm0MqNi2LUtBXVU8Ga7RmrKdBc5fXCS3RyHBLrNHh+26fVi3U8vFtzLNQj/jaenv+EhGip9H/oxSzwDCYIH7m1thQCLhvuHRt2LUJIU97C3TQnBoZL0YJr8JTDWiZcz/m3xgO210KmwRYYD5wLvEnI6RW+BFEjWkKLbtToMYXWIrK/4JuL+ISQSReFn4NDsED+gLG12A1IiIdolMtFCuXD2xBAG/eSwmUaG5ohxKgeyKfhSI5QsBdPoERHpuYfD6yutjHFJ0yHjRDeECFSomONnR0sUbZVew7aWVyr9alII570O+RbY0GF46bj0ghegXDhcUWyNmAGr46gQYyZ5gNpKGPGM/e740bRjp7eEQTae7ZP7aZ0nXrcf207bW8rU8WUYixwnmE8UYlBCDZ4n3AP4DEGup9lRqzOf7cG3LVyCOAr91V7704dSzaKeB4JxDF9b3Oz3aiYgR0MKK2Fyv9EKW/jbBEPVYjl5sB9wRFC2E6fWlkTC8H6L3vqdGKaF31J6mGdHnupdp+Yeh7NJFKtC2lZ6LSF/FhsnFcn3zCmeEIX0c5qDJwlTi/Nog2deHYxCNSiPUjGyjKpE/Vkj31Uhg439WYb/JfsEcvXM9ThKPiHM3tb1Ie75uD9LhbuZhvNlA0ZylqHOHhTOAEQAGwp/g4T0PKdReWw12kbNi71Sb1KiIEKl75ij9aVS86NIA2BtP9M6a3JoCZPPhUD2wOnZ+sE09fyt9D7p1TPxue3E0exureqwTNYhUIioXILxe6dUBM24N8zv2uitttIcaZgavi2IJsi9JdbWPKLJZseGQ9kQIeR+d/xIWBxlhrtw96n+XTVHkh5XM8MsGf3wGj0qy4SdR0YoIpHBjShDB0tHk8mA4edUkg/BeTmxHWGysNEV27ecUrgGLfS5HKduzVvRC6WeK2kWtfCLmUHwiyzUyxNRO8BPtW0bREQPP/xwcv2Hf/iH6V3vetdn36739Ja3vIX+xt/4G/Q1X/M19PGPf1zVefLJJ1VE0pUrV2g6nQ5GHD3f5fPa8PP617+e/ut//a/Jtb/+1/86fdmXfRn9wA/8AL30pS+lW7du0Qc/+EH66q/+aiIiWq/X9Ou//uv03ve+l4iIXv3qV9NkMqEPfvCD9KY3vYmIiJ544gn6gz/4A/rxH//xc/epmRDRCCfTkGLLApNT6bjD/zK+3oydLShJl5j8WQpUGDvriDwoBHioWaRj3d9kXdjL2klAOKgYeaE450hRXaOV/BzhYUL+mJEbTAFlYJPNkf113oCBTYeFVqfgqlGhEQI0jrVl+bcy4WzL9YOpH9uJ4f0sWNGS17BfWlnKwdG3QVAq4xbR4Dt1XBFGhqGiHj4wx2C01YDXixXCJEWq+BnRU0kfnOb6GIvjlsVaq8l9BjF7l9lCjJNFBDtIAi7aQVJW543089DW5FRzIkijNL+XxZPlC6fngeX1yoQryrAPhM9be5iMDliKzEOW4Uqm4ibqCXuJiBZxvHafthUqVGikkbXYRBJlYy8Idftr1lxB44BOBRXbg324njna7A5PhNm9thM4rb5z2E4D4U/NPNRDY0GyZ8R+Li+DkYSJdxfyGhgsFlpxl2TbU5FdCs+R6sxTi1x3SYhw7B70nUO8cN6mHFSOijXR6nJaZ3Yntmlwj3V9vudpcT1viAjP8t1zZHFNniNK/o6hXiuRSY7H20L2tNPh8ItcmGa3b4s9A7lvunTs0kAg2pH7DYaJzY48nd7CuaHvVUaYjBEhMZg3QflDZFYzY3RHfw3rhMbTX1WoDhtUM3JNrl9ERJXz1FZ5gZLDzXPcK6jUJnJIxgA/5kBJnieXuZRdpaKs3rvQKGxrjFHxzXHTgJGimU2ySKrVJdlHrf7kQqq5nN7MoLeKvEwqi0L/ieaOHgu/DJHKJ0U0tfeZMJ64B/L7Lq5Lx2DajGk0zbyLaXQzjEF9/Uznc2BPacSrgkyTI/Pe7OfTuZdLjZzC0q1To/2xbKi+pO3Wh1FMnQTem8gY64KoWV2Een02bRCFCCIZWpVD+7zrXe8yaV5k+fCHP0wf+tCH6Pj4mH7wB39wsK4VWbRNVNLzWT6vDT8HBwf0qle9Krm2t7dH165d666/4x3voPe85z30spe9jF72spfRe97zHtrd3aU3v/nNRER0eHhI3/Ed30Hf933fR9euXaOrV6/SO9/5TvqKr/iKLsvXZ10y320bjp+cgUUpb9iuz99LA8KmRaaqyH+bfN8TDw62HT3ZyogiHQVMZIvKpAHHzzHeF5gqvGtE/IxZXFqfjWEn6oVoxTtgIJ1UmWTq8L3ScGDUGWo7SxDOr6I8WunvbZWvkxg00CvrjMwWKOSzgmkN64jw49rUOBCe2TfEZLgWJNo1hACQdI7xe6GOGx8nhS5EFrDCmRh+RB2Lu6ZrvhhG/OTgzvyOgyl9XTT85FAzCYE66TIglA3V82QoUdBPSxBPyHM5zEI5vYIBb2g/YoMWroNNfGbC04UGzvg8iXrMxeN7I1RN84T1P7OCjqGhFgE99qHYBEVepeOO/AwS1WKtSwd8E1lybOj/5NSr0C4MmVxendAceIWk8rQ+CINw9b8cJXXufOVh8ILD+M5v95q7j4rrGpCDjASSSAtUvNjgurxif7/p/X7Aq9vAQzLXnlv5be+/OLwTGlKY1wORNXIesGKARsJ1lCv3RPQy7nfNxI2mAOe/W2ch7l3KGOGt1Pb9zzkkXlGHrEW7txE5pZ+lEBXxc8vxleTqRESzu2Te23U78Xqnf2srp7L3WSFKuUx3ljG9bzx48HXiifC/NPorp4g3UAuoWA9J2SP7M6KQsAw5Ga1xlnMxNzamXLKFrJtLjKDb93ocx55PvQw1dG44g/g3do6I0uQpKE82UzdKnO4jubfiH3JW9imC3wstS4nfOWxUo7k9WZx7sjBiGfvLfGWVCNtWfTfmp5XQwCzeq70/zTTK5yKODRv3Bu5tPFWnDXlwSBBz2c3663byFPiWuLYi2h0RS+XSq7mhSL+tUEcx/7v6hsxnJdlJEVb673y9GTFMXpR8uXTp0lacOt/1Xd9F3/qt3zpY59FHH6Uf+ZEfod/+7d9WBqSv+ZqvoW/7tm+jn//5n6dbt27R7/zO7yR/v3v3Lm02G4UE+uMsn9eGn23K93//99NisaC3ve1tdPfuXXrNa15DH/jAB+jgoA8A/8mf/Emqqore9KY30WKxoNe//vX0cz/3c1SW5w+Y3HmmpWrC1hu7Tg4WS9RvqLjZLq+E1Z5wbaj40OAp3ybVoEkwnMlewsUXOubc2oTQkLKuHc2O2uRwISKaHvUPWN6YqWtcVpch/CsDC03S+CYCh32diALvDGzsUmHle5VnlWl6DOK3rh0+oKEK82nI90Al2RLKkvjz+K7K2GV42ojQWOCoOdVzgIUaVriINELBNTrUawjtkt5sXDM8kdpg4JO/E5EKO6mWXoUvEsGhzV6kzLKW8xPRDcydJOf/7E6vRRSL/mckeq73JjqUZi6FEhevZebh4LcMAmmOt0gaIKzxwW+J7bDBSoUwtJYnOL0wWXgTNYh9tBQ0y0OaIBYjqhHXH6dllsIxzs8uhEEKkVugBbu/D5DbVplMILmMiZJTaX1QRIOBLexKPqCzmzA/jcxcNSqcUfDE9bV8saPZEdSFkMnZvTbxChPZGVdOb11O+7BLVO9UypBx/8U9LKczOGe4ROScxDVyGtE30hgtFaqzm32f2wlaJmlQWcoRKLORCa/LecOGlDVQlTD6TIZaoSE9hIbm+xWf1tVNrjptDFXhQTtEO8/mjeuOUTtwtiyuBxQTnoV1EibJ90K/mF9KGJgmiPjhKSEeKxVhOU6WIwNJ189u9T/vRtQ8ohd6Alf7OlE0qhooKX4nOd+Uwa72au+0EkxYxRfDYTLhXq8dOOL3DnWc2UvVviTqsZxiGTGIYO/dRtbNGNdNtHPGuUfUI8NySA81vuL7+Gkwqqrw4lgkkqPxuCc6NfexDIVco8EGUWv1jjYyy1DHhg0Z6nzSKFHr+USG4ScmJlheyyN+tpLbuKp1feg8HR7OQV0pJLUo9T7MdaToh+dxbRhp8dmZ60XrVAbKQRnNqHTuzLfyXfhTmYifkQF9gZU/CXLn69ev0/Xr10fr/b2/9/eSLOOf+cxn6I1vfCP9i3/xL+g1r3kNERG99rWvpR/90R+lJ554oqOj+cAHPkCz2SyhsPnjLs77kXySF4WIAjHU4eEhveJ73kPlLE80TWQIa1LQyQi/m2inmgiHqpWWekhpSYwh5/DayHtyvBtDG1kzjZBMAwnU/Rz/Lwwkkmo7IxTkDDwJl44Rrz7kwc8RCeYUuTScTvcr+V32F71UnsyNvft7brxZ7xwQ6Ihs+C4bs+SBmHtPWSwDIZF9+I1x/IzNSxZgrIM8lxZcta0MFvo6vhMr93JcK6nwi3hxlRGpdIqMWsWlk4YPWymQsZ+MjMl9p+RbrvXfzXknCiMUMDMW9oNIezDLtSE8ief53Fw16oYbxLNyIZicCUqEPclQLCLb8CfDLhc3hfdzB4jGJy3t/vf0Q0mFlL8hPnMbJJ6vAlcJpvTlOpIDR3t9yeSwSX+P7YAxt506tadugwSTih8rhzif1pdCymwdYiOajoov9r/jhRHfCxUv9ljL6zKsTCrruEZaI62zJLnmNjFMk1OA45pOHDgGFxpRb+iTiC1cN/XcCKeF8Z8dsxEvvb7Zc8oAiHvZ4rrrxq17pqHQ4n66vBIQEaj07D/ZVzx5kAnb0zqs5C5EGCXuNbzHyLUo31uGiqsMbWeeFjdwYxfPyhl44pzA/iZGch/2/2qVDsgkOqmS9wBx2dWe1pfTzUoiEYiIJscZKwRRHiXDbU10FkC5jrpEC0jvVha0vD6l+bPpi8uslUliEKMkxsIW0HQHYdEUK2lR7+u3O/0kckg27z35AgUu8eMitOkrNH571S8iIi+SYhQnYRK1kCCEE6I0+/31AhKsNLtVkpAkXASj3jQsyAY48VaH1WDYMxHR8SOlCkuW5fDjMbPoadqvdlKQL5yaK3IenN2axWembfIedvAp8d2B26i6Fw8kkWSjvdRviu20f1eF1i6ccqIlY+jZuAjfMl6v99PNphFttdHhpTLKxTrzp3vLMialWV+eqKQkOG84kQ72oa0cTY/StYFzsVg3ai/wpew7E6enfS9PVuQnpUr2knAbxWdZDut6s6QP/YcfpqOjoz+xjFB/HIX17T/7795G1d5I3N1IqU9X9Bv/t3/wvI/Zxz/+cXrsscfo937v9+irvuqriChwEn/VV30V3bx5k37iJ36C7ty5Q295y1voW77lWy7SuX8hlZ1nRTr3jCFFKTqGQQbrLCP/DgudRMZhzopUIoiIHxNGfNyUnBIiLW+Q2tjjhiSVEvRWbvYKmpy2yvMuFWdOZa28843XcGY0FsT9O83EITZKuW8jyZsR8pT8Pd6LXnL24mHIhSysgGRD08S5s00mBQsSrqDBsZs5nhAum12NEmHhV4YwKMRPm8bBE2mDQg4FZRmzzluHv4eFOnKtvncsu4KsI4V/VLx2n6pjnX4gu+xsFLLidQUFz0lJy1tpNieJ+GFD0TABtO5v+CUo+Go9x6ZkaJWFzBrLbMENIULAtTpzi8XxM2To4/oWfL6ZOnWvnK+MgMytGxm6gkquxR9QCyV+JfiBGrDhO+dpfie9tvNMPyeOXxKRmRB2xSFHk/vppiqNhOvDctDLL8dXK/eeVkAkjHOC5zfuBZMjvTdjxrbFjSpkzhMlWYNRyJ1BOuv7L55QufaKuLla9vf6+KFze1ZC7tzgXNGohMIIjyDShrBio+enXIMd2g0QOTxWHILXP7dvbOfZJnByQPvMGy7HewME+rtPt50BxXqnpC9odF/ofSQhOSWK5P6IZtA/q7XVxLBAuL467C+wcQazA3Y8agunrnHhd1wJQ5s0oEqDHvIDhXWTXpN1mKMMDcYcPjlkaPNFmIMOUmP5K6ExaQBEAxLfL4sO8ZmY111Dg5mziPKOQi650DuisBefvBgMIEmCDN4o7bYTpBIquVb6+AxSW7VbOBOd2v89/K9C6Hich5yfxR651jhz4q9yXShDeqX7hXssy7HWeKNcPL+LB9ywY2x5NUwGfwMMERk5Qcrv03juaJRuuPnopXZilFB0NqJtMqj1laHKgMMT7xlCfpUbH9B4ygMe/jt9UKT1M/YaJOrG0kxikh7DObC8PpIK3lpvxrsqHjW/Y0dRSOfnhK/p+d+st/DUXpTPm1KWJf3Kr/wKve1tb6Ov/dqvpZ2dHXrzm99M73vf+/5E+3Vh+DlnWVx3VPLhsYXXFEunRMO9jPiRBgidjUorznIDSjM2GIfmFv1FkuC+8TxcqJ0FksEhVFDbwcvxgcbBZsuUKc9JDr2BKIURErY+3Wt6vanZS52/t4NZq0aN5+DBYdTbihcm0xcrQ1gBJOAddN3gbEiegYKrQl8MDWj+T9uUTilBJXEnkIdrz6ao0+prsk1pwMLx4sxNUtEql/1AFbd6rcQKgUIlUQqtFhGz7Kd6J9L1FKG7YQAs0KvvM/NOFN53zHWPcwqMJPWO0/NT/s7bpLF3mNPE6TFTvBvx+2xEiA0qwp2QL+atHHvmaSCiLuyl/90rwt5aGPFymaDOHogZ2h7MC5u+ooC8QWe3cSb4AhSXqTb+6dBB+7nVwuAkAmPEZt91PD5dv+S+Gquf3Ujvq3eCp3MNjrQ021z8H0NiosBdJUYS6CefYxllgsc99BcPDj3vpIGF5xDyZzE6xkopzuXo0YrqWR962LepkVHYL3xHrE8kvheeZ5M0JJBI7+HlhrKoWaIeEba6rPtRzx3N76U3Hz/c15vdi/1Dw3z8tnKPqADdwOMtjT0Wf1ZoP73XCpeS78TPVRyCcQ4o8myxvnwRlL1cWvZkfzH2O4XIgbFnR4u5xWKYleLtGj5Q+bxq0KjkwzdBh1ZqfNJzlaiXQVNH27BzjggU1RGHzBCvHX8HNV7b8tq2eaeKLIpc1Rm8XvB92MCN37ieB7oDWaTBlIhGM5ry/oLru4ioeEsf4HLvpVF+gf1o/zMR1SIyjOFa6oxkFkcN2UaOXD/w926cMm2oBCcybC86q3ME0NJpie9Uz1OnAJGeT/woy8A49s54nmLpws0N5/02SHnrXnI0KHq/EMufRKjXZ1seffRRsgKoHnnkEfrlX/7lP5Y+bFsuDD/nLA//vz9BVRGlkhKltbDRNA9eSy5LOOzdr7wSbl3ZwqAUuiYnmB4ixAEnxJxioiVGIITJek+bg/RzFwBtRkglUQ/TXV4XKU2Xab3NfklF7Wl6hGyRok5MHzy9l0pg7bSkcpnep9ArB0Frq077e6W3ZnU9DwUMWQcGjFbR+669iW3yv1XWlzgLg0YxERE5GSoBXn5nCHQJLwx7UDP8OlIZJQIB0XtaXimVt5jfRWbXQWHF+ZQDiIhoeoxaRNrH7rLBqaFCtgxDmhQwGf2Fyvz0pKVm5gwS1P5efr/CmMdERI3IbIR1+PfqpLcONTv9pCgFLBxhv+20VHB2CT2uInII4eG8RusBAk/vwrzI8Swl6C3gqPClRu1orovw952nYR60OtTFn6S/49whSvcgVkgnkFbVFy5wIsCakGgPFpoRWdDNH5kGFzyrPCeWV+1xXYlwFMtJjaFYEg3F/C84j6fPxr1/AGnRTgKXDn5v3vslv5skLSYK8+D+i2H/xvUdlW/8xuXaawJL5HTadTSBsZaGpElUeHAenjxU0uy4VQbyxkCiaeM0I4EG5rBh4JSoC3leKkRdoQ33EiHHBggM9cqFjMq+rS65QBRrORooVd42wFfWTJ02/iGhfpzDKsx2rdclosPaCSkE1kYYrdiYtv8EoL6uFVRserQdl72nhIwRbzm9iQpx+H/3yfx7F5wZThiHpDFefgfMEOpqfS0J8e6UwbQOG8AUWatCL7ZKNmlmodLe4/11VDDbWUn1DnopoO3YJ4vnTxlk0XbZ+kG0ActilmGsmRVKVkvDqm3Zp+zgGLJNjUAkyij5Y8V0OIo/FxmEIPNjDhh1ig3LfPYzpayB6b79xCkSfDSgbqLMt40uOUEE5bTURO8JAin8r41PMax6ADW+H7N64b3s0JJ7BMqrnUFbvFTOoXzukhsnb/9dnl1tGb41njlsqEmca8ZejUhL1TV2FCqjuzGXcZ8f4ebKcrj5iGRCY6uxLs2sp19khh/vHfnnaLh5rve/EMuF4eec5fZfeAmVU5vjJ8tpYew/GHPPJJEyTvTsRmrQCLBHIvIy77f4+0Col5WBx9zcrM2G0sPCAyl2vRMg1euDdLeThwcLnqtDLSS15TALW58JLTNdB9a1L4xD2thkUZgoNhHSm8skRiITFFaxOImMkJshb0eX1WUAGiyL8kDtGmiJaHyT/Cgs3Mp2EJlSz4YF27SDUNXo/5CA2KFgkLB0t6Ry6cmpNO99Z9hYoFFBoU6S5alIH3DwyVXsn1BYz4ShUXIZTNN5WNYtOeRLEO/NBqTNvg0fHiIDzfEpdWFU4rHVUhsLcDBQiGKUh4LeewOpsA9trdtBoyorajpMzVO5Ngw/ol6fCj69l/u5FqlcUcnt+icURcmP4CQaEtdgU2jjoiRpj31GHh0ee0UaLj5cM3W0vlQohAYLu/K6RygSGQIwGpMzBot6R2erwW/iWq2oSCNJH26bts33SY8yEWYz4w0RlGZWPOWahu8xj8bFsxvCaJtwD4m+qCw9RZd5kcvsqD+cN7tVbC+tw+0Xa/AiizN7chYQIoi04Dkvxw7XVj0jtaBxv2sypFHe6TWhvu3GqXGUxkze/1eA4th5tqWTW4UKnZRjzEYn5Crhs3Ajo0ZgHjIaQfYlpyBNkMh8x6nwL4nM243GKVR82Whbg8iWhK7PHDXTwgiLCv/7G5W6xsWXbjTcOEfATGQhAsCgtvTa0CHlikP74UHu0bwwVjs51NGw/MKCobiWsRNZ/R8K9epksozcNpjJjJ1PGRlDopkdWjRaoqJOrw1xhOFz0SCrszemIbBEqa7Ac9JCHTVTnXVOdv/sgaLrhyxM7ix5u9wgCowriR9lm9sgU2Qzme/gMn+XbZVrHxMi2GMm9z1lvJm50fCsnGxVbvLIwb4PlvVStDGUndlwSMjm+/UH+7DX2SAvykX5bMqF4eecZf5MQ9Uks/p49Q4cth2qA+qw918eFhYqBNMNW6EuVvGFV4IYQtxDSAlc42wB4npbYb8KqlYWAqSvN7tvj4kvtFKiSmxXhgrIA3kqeDUUKaoxhhJ9w5kUVPYj9p6jp1BUY4+CMrAYh8IY8R8RKHLxzwrqnTEuJgeRc7RunM5O1CEC8koJOacEAVQIc6kqrWLxSGCxUCIKgbXyVK58NrsVEY1myZoJRFoBJI4995bMYyoVXzEG8BJ+6giJMeShPX06EFBMn4UwmUtB0UBiwkSJdkHpUUIL/y/etZ7hYGsDcy6cwQr5w7mB3ulmWgx6X3vPlRZkWyNjlJzD/Kycl1PODwyd6bJkCe6Ts+t9Q+1Efktov9CKVmOlqM/UQc+7RFpUKx/6mjH4zu/0/dIGFq+MtFg6zhwMfWu94u+pTmD+N16FYtVOhlGF+xEVUVzfiYIsGCNEW4ymKwFhunmIORbERTU2Lt7b/yExwJwKQy2038w0AfPysiAq5bTAMPcZCYDIF4niqeeOirU3yKj1IYyK3PS+V9ewdOMH1Zq53tcZOcXFl5obaCkoyNjwgnvs2QMFFY1xFhoh1lmlSjqf0BYe/7aRdGgZZQnRQq4xjERiHFaHthOGryNyUL5jWwYj9s5TmAs+Gp9XtbrGxRdOkTtrgncf/4dqpVPrxnRaDISD4Zrt6riAJM0ZKoiIHP+ttNtPeBQz/FwJd1TCCSSv44NJjVHadlyXmXAanLcKdeTzyRCsVOPy3jGHKb+LrYCDkcjgGbKSVnRtx/dCg5EvgpF5CPHD3KDokMC2iWxDh6ovx2mASoHI+L5GOxYnFJFG1OMcOviU1bnwjmc3RSg+InDvtwq5rkK9oqHNOsvV+YsGeMvYZcz5MXnXuDVf/LYVXzilJUftc4Q5Pdf7X4jlwvBzzlLUnoou1RVOqLiRGOiO/ufwC5IEWlZspfT7SEAnD3MZhy0MBxa0H8ODlLB+0qqMFG7Jm2Pq7UwrBePNYLp4r9shCsI6KvM5Qs7qtDHroLdZltb6m7iXhUnMZMJCq4JKGwUPnS6MaEBwIudUqF0j+5oz/PCfVfyvPDBbKtel+r4cciPRLupbklcHJpYuJNBAe4wRPFoCgDyQGZKM8fFB8HdZASLUyRsXifrQPKK8Z69Yi2wkor3yUu8NRu8PFTq0Uho2mgnEknQPsI0bKMi2JZHLCLuSCwPnobcEbFSs+XXxnbxPQgJDX1BQ9tlUxUT9GrXqVAuNErH6hQaijsw0b4cjpu+QQnuSKroeEAQM4TxpOwrqa0A/8TpVmZ7Er+sDR6VpICd1ryKRdYYigcbf2M7sGAyThVPnwebAkNYHPMHMMeUxvNmFeYfGJqmk83ivL6ebbJftS3xLVOrYqCcNJYkhQhhYdfirNgbJ0NnllcjLBLoFo+DQMSJTtLs2cKjhNzi9Gfpw8Hj/B0Rvhaw3uLbSdqzMkEThXFIKq7HtDSVy6M9bNArb5MWJQTYaZHKkwwk6CHifpvd0/ZxSif3fvevp+CUgO0i+/cyxk0POSsNk1QQk8vJyuk/zmEmUGM6xdqKN5lmEsdVHW4Ts+7kx9gtRh+eClZK9XHtaq9DTLZQg9p+IOal57fL3YX0LaYEhnWlGR2c+s2svs+9l+0Vkyp8aMekVYhPbYwOi4kEsnQrPxTnsnYH+k46BGG6N56Lz4Z2H5GQ+q/GZO9GZUFfp3pU2RKpgWvWhss156RpbjsZ3RadpM7fkd9ah+rrqfNs1kHgw5SyuNaL4vrhWoWpbuhHHZ3SywtUiconhmEkjZ69H6TWNVI8v9PKFxPHzhVQuDD/nLc5RDtnTKTp4CIvDhgUdleZvE40bIu5Yp/OLylbGy5KGiGhBXMVyI3R0VmS9CglXBaazLojIewVvTATGaFBS3BKN1wo7Cj9LQ7jJCHrK0zMQqkXUQ3eVUSyT8UIetlmElZnxAtvxSqlNCPU4dSl+j8wckwema8MhoXrR3ZvpN4X3VqEEKHB0iscW7gdUXLzX60aMDfM9bXZTbP7krCW3aZUiVwrep/IouLI9pMps50ELXV/NZ7ZYH0ZyRBEeNDnu2+YUs0RExTLVjtr5lFaQ1cuJMZtF3gDkFWIEx1DKde+IXFPSFJR5Tpkq+SXmT5wkdfy0otX1VJmZPZ2SYPgiaGd7j6cEGtXphtZX02+Ahp/J3aUm/UzeI3q6MQxuUobMQxAaWt7v67mXhX7lst7IsxyzGjEq5fRF/feW3B+DyEjvaOfZtMKlj/Y3P/2nQ0wuCvCXPha0UFen32l1LR3DxY1Ke6z5PBHGt8JIgdxMQSlRJObhfqxXrTT0HJXVyWmrFRVxnnA65xbatriaiNI9g/e5cgFzuONqEM/EUBcjjC/JzCUzgmFK+ZnTBinRXocuVEYSRmik96Y8GaF9DItiA5XkcUKOq8W1UmkEuQye6I1vK41IU3Jtq4m/5T7MGe40kXCoh5xHpQx/jfNIIUDip50K4mZ0pnR1pZIv2xH9mR6n96wPtJGtEWFlRXxuLnREoRXF784HItg1cjHFe6VsgOE8gUQZ+gXvrb4F983ZRhFZ2gmNKJi2kcR5omY90n5WXo1tDCn/hu1wCLmHXR7iSOmzGun7rOvqvGwNObDVe402Fmq0M459FRGGKgyncIrGAM8uX5CaY7I/Zw+EG6zwx0DxgIaq/mcOZdRyf/g9ceLkjHjylc6hK2+T8SpX1FkMjRX7pArLIcrJgu2MvENn2Dfm2XPiNaL+7GgQTcTIrYG+8bOttbtNFtuLclHGyoXh55xlebWgMgq/yvMfi4qrloJOxV7OdAWvu8yE+U/Ce6I81Hzm4LUONSwo/DufF0AtuDeXzZ6jtiwGhYwuGw8SxhY6M4oqcTyldzDJBiL6VkNmyuqsVUKGxV2h0ErRS91ctpEhRAPp3A0vo8nxg4a3hOuC5wncxzqi8noJ5FfhqJnqg7GZRo4f8d1b9OCTMXfAY52F7zoatQUVjSarlKVLfasyYJXUVqUh2PfKfRHTxuSyQCRKZMajkxBsF70EVuxPxPV0kvnSKc+VHPtqEcMJVRamKLQj1YD43ReOfEW0AvSN7BuXs1tX0jpOw+VPXpS64buMMwUYefxcGQARhVWupoPeQ153Vip6Xxj8E77XOHn8cvwrTLJMRDQ5S/vFArQscl/wk7zlxxWeTm+l7S2v9NLnptun03F95ivDA9CTLdfa5CwYVVFRXBuolhWkorWI0zXppd239QFRhYo6oipnhQpbkt+fw1o5dIZLPQ9KhUpHLvYM9qwipwY7AawwQy6La3p/kka3jTCwYCYoK4uOXA9cP5fVqP/Wsb+Sn8OxIG44QQg4NcCAZIU5Kk6z+C1UdqtCozRxPrUTotkxrl1pxAv/I4/O+pIjmvS8IH37EiUVXhAV6w6hJ98bkXic3U1c3wgjU4ISaa13TMdtA2e9Vbg/MxgzObdDGu+Wdj+dCiJ13PMlCnp2D0Ika98R/sprssw/EwiR/ASy5q1rcqfAWD0Bp8XORCUOSLIvzUIf3Qbhnp5OvuSQdj+TGvoLYYRvd6exv2n7zGfnK8FtpPZqr54r3685EFkwEfHpfZI4AUtHH4ROtzgnkExbtl8u23CGZ5xLQ2FyReOp3k3btgjjQ4Oq1wZ/XrqxrC45jfQXQ89nG5KY58YjyeIbZeOdO+m35D5IEnxFMs5rTWbVPIfh5zycj/hMnBsKZea0w5rfNeVsxDrDaOSki8a9yjAJv44ZhrLcRmxsHehaHk3pyBmIzBdyuSB3fn7KheHnnMU1QrHP5B0fIuDi1NioVBWtoehbzcMcToxALVZO+z0G2XStYcRgTg15L/arse+VAiNnF8IsVa7RG7sVC08Ewod87wEr+GavIBWXL41lmfTMfECr0CVJEmt9s6Ruvl/mPRJN5NP/sY4m5DOUC+Qe4kwc8l1xT/Q6E9R54opVc3ChGYGac98UDLvxRAOGSfnwXDr3oXnSp5zuB01m1StPRbavPSAxr1siSq9JHiVWmlEQ4b5nESCinnWNiICsklTR4ZcwTyr+3zAMIz8QIgZXeR4Gov47eFzPLnq9BghueZ/E/pdrNlTJ9tI6nDUoyRAiOQ4GPNO+1V5fy9CO/erW5QBPfTMl8mWhwyaqtG3sOxF1KD5Z9Brh6/peRfhshGYNhYHw/qjmcBnmiULbyOUQb8H5tLrEXgXZYNoOG9Kkw0CmIZ8IkBsqVb4wDIfi3GWyYPTCc/uYjjzp18LT6tAp7zwjgFJEiTY6qFBslbHK3nRXVxzVLU6g9Nei0QgXifjKkaFXZ2HdobJbi7Fn1NPyalqHx1Ly+qzBcMap4HP7sFxnOFcmp5qDqhHGX+Y4qZFgN84fXBMyVNNXRL4o6f6LU9Qmn59SwWymuLj0usTz596XXCazWMsN5TY0qGL1DIqXkS/3Hz7QN408s3sfeT3jKDmHaJAUK6Swe5SxH8pnapSIrBPDNDMyeC4/SNe+IduaVfHccKRQfPjtyo1tnOaCRvWkfQMlkoQ7x3ZWV8AoFu8pV0OWBuNvOV3CmgeGDIk/6/EaV8YZuaX2i2gsHka/bNd+6Itx78jtvhwx3vB3NcbLvE/qJPG9LEd6M+Ykf4GVi1Cv56dcGH7OWabHLVXsMc5seEOEeu1xVFyUEB827J3b/UmARod2WlA7AeIxKVSKzVRz/LQqBADbL2qf9QYMx1GXIb45J0AQ0W70lHkDcj0/GrCUiWfLkDApTFdHvdTczlLNq51qw48sxTo8ewkZ1Jh0rlzm+7Y+nKh+EfWemySdu5VVDeOZhbeJxxhJWLmUK3S5pmNz9tCO4ifiVKWrq/0Yze5ivmDSyIJFKsHU+7Z2O5pFhIjcplU8Ick98cfFjVSjmpw0NL23phYIbhvhje2UuowHf3K/f1eM5T95pMNK9xeL/j0xxCXpw36V9IMo/d6zY+ZWSu8bI9ckCmuomRVKEWRFPAkpOUULi1agMESJ+Z527qTzqdjoEMzltfS4mB43g5B9FlzQyO1LR8W6HTQa8V5VQnjc9DjMxXtf2nuUZRp0+VxphNmIDE2nSVYvrUTPjtJLMvTr5MHQr5076TtN4jodSpu+2SuomWmkDqMo5iJsSKUaLp0yKiHPUJc2HZGElaPpMfBPwNhvdp2aP0moV5weGILWTAvaf6LR6FExnSZM4AnzaR1DI+SYIcdD2/EAif1NtC35VxCFstkp9JkkM+FEwm8UsKfH40ikzmgFhQ0uu0/3e+YKUKOzo5ZOHhzOlGiRmHPf1BpW565TKDtpcGfDzgbCKMp1mEPIfYFp7IlIZdiyZGv0XDeGwpaQ2w44puodR4vr0F+hBPG3wj2WDYa4f+48m5L4txO9B3bynAgvRmSnL12WAFjWscpYqnaiMIZDXCWMjMxy7qAhQ/zezZEcKkMOx5Byz5dyDiVDgc0ZH0M7toycr58+1zUGyjTOf7mHoiHR+k5oBGWSfIUSrJyaY3j2nt4s9LoRfb/yR2HBbPaNheAM417CVxT+n9+FUONDNoQNjHccm2yIlnR2qm+pCcrlvGFdKGekUbQR4tXrWXAoKD0o3iM5lbaRr02eHuv6RL+T6veA7ELU79+Kl6nxWoeD0q1pw/DZZBwCF+WinKdcGH7OWZbXSipjyEzuYLKyOnHpQ73S6+wdO31w5JO4dGPNxcujcLg+KI2MAih1GGgby8sCt9W7jopGe7KT51+KEHFMlez7v/UP1XVCf0UVeYvg8jCzDgygRCxi1fCsqERhwL6oxsgYKwOPKoZgr7KziMOkm0NXMwrCgEAXjAWOHAQYF3WYW9IriplriPShhoqq6RHkSyNc2BZyIXk2p3KF114fODp9SK+NhBeJ4cMG0SURUSG+JfZz9+mwqCphHJI8XDKzS4vp3M+INpdSQxWjfIj6TEK5bCQ5JFDoaBiLbNpTqQQgB0yhCQ7XoOyx8res9TzAccSUyPXc+h79zyxkqzAgIvLOFm7xXoKQ2HIdninRBGh04/1Rhrsk3tRCukq1cIgKseQx4m+ISI/1zcgRhagEoThMzrxG08l7EiN+Wqc6bWh5HeZdRgi0uNa0cSNVEJpppc4MafRmwuMKOLbWB0XkBYNniqbKaHDG79RlGRTCMYZHMMJEcoPKMZX3IhFoyIQGiptQFiZxH1TkvLEZdOAkiKwm7lWZo75NEi2kf9vsFR2ireurQu1EpIphoFPrCe+tdXsSacCGnMkJrO+9YDSa301vlpnp+HsgEoLPFPmcBriCqnvh/zaHUhTdmWIWUB/QTrLM74hn8Xii02Jl91fOi3ruaPfpJkF4hnvCvezgke1xqc42tNmHUC8Vwm0r1q72ypCKYWKbg2rQocZZ5JRjovFU7xaDWZOyYSeGjJFT3NNwdrEu13lBwJdOcfVJmakL5crICZoYt++cazy5Vqc+95HxX8q8yDnW7BgJMU7sBa5kpNZw7GCo40YbAhrJyxn3DAwJ91EGGGqf95v7L7bn4lSGfiJ6mNGcycWkFcoWR0RDfE25RArxrFOo1cQwGfYT5BHkuaL0Bvnc0tZvkl/ZmYGO2403ubxkQUSlKt0a2tJ6Kap1sq+BFmq2DF97oZSLUK/np1wYfs5Z9p6sqZrY+NtOwRxA/LAw3cxTxecsKg4J4sfwlCAiQ3o2pGCNyuTsXqPIoi2h1RJOQn/F4aoMGyVVZ+2goJwrvnQ0u4sxSXiyh3YTBIvY8+UBbsWTqxCGxPsZ+VcgbpzRMUjCLdvf7JWxjrbqE6UCoEoz3xJhHHo6xmlbfQfiK2Q840ThG9Y7hfpOjCJYXemXPSqA2I9wH2QJ6gw/WkDBfulsIG6wTpeBC4gSp8cNNfNCoZiszCMlfDPOuDW912vEbp2uYQ7fkiFdydiLb6X6Pylp/vhRcm1y2BNQVE8H5lEPRCvtpWhJwfZEhjA/KWizV9HsmTQWxEdhV6YSrk4xPsjR6kpquMRvefpg+PvuU+m9ReNpeRUQPveTX2l2t1ZrKxHgI4Jtcpo+s62Cxwv3I6ksnD0Q+oUGQDYWlALBg0oiZ/+Tc2jn2f7vZw/JCQf9d552b6ft7T7VWzXufFkY7znsWV12HZzvYl2ysChDlYh6AVQabDAkoK1KlZIakRlll0EFDDhrLaguL6ff1heaf229r8UD9EbXO4GPCBVi+S0X8Vsq5BevdcFthGTGrNBL40zi7BAKrzbgOJo/C2jFBCFnG4rZAKYyt4lxnZz4gH5R35tUQQLo5eVCh5BkFAuN0DMMCxmiZVmQNyRXJmde81eIYWADKrbHoSYTYYx0mRQ00vAjR1gatHAuTs48lSldDa0u9z/z3pTjA8Exk4bKigL3FnLdFFFWm4nvnqR2JyKqWyXLIQJo95Nx/zeQx5goAEt1PE0SCxCl58PmWghPQ0OLa1pqdmZqvy8EUrhYZchEuCn5HkbWRyLS/ENcXZ6vigjZ4BUUpPjTKRt+YJ+Mz2r30s0x4TfynnxRULtjo5KbPcFbZGRMVWc77A283yjZnLz+vpb8PuQcjUZvlchh2dJmv1QyZMJtaYsS3byf30nHSBbmsMplYhtMhuDIdGp2fzccVLL9HL8aUTCAlcdEvrL1qclJHvETohfsZ3a/14ZeQWHtTA4gA6WB8NIhh1JGjP/DuizWbTBsKuOldBTYhmJfOKo3Ax/jBVj85yDU68Lwo8uF4eec5eTBikr2+Gc8JSh4JfOumJh1Vofh/7aU7jBsJ3jwk8NJ/phkYcHN0hA2ofhCbzZWjC5ueJtdR94VgyFuLJRbPAwqNkd1LPwn3zvxvm5ESA4CdAxPTHKYxx8R2FOy0D/gbeu8huqwjd6lER6iIV6MbowzQ5r7TuGPEWaMnEtdBrP+JaTSxQUF13KTQWQZYzOa0WHk73zooUd8vR/mlwcvj/T+s1FAkyWHOhOBmMgR407OeqGSs0MREU0/fa/72YGA1x7M6eTlV5JrCQn4Y3Y6947PCBWtxC4RwudOH0phKBYyqlwhpMgyOILivst90fciQSyHOXHZ7E7MParrY4f4QaJi2wvfCmLTbg3BGuFwpDPxLXFtrSLZ6u4z/QPYsE4EgmSBizcljg7t6/0FjQKscE7BQ8yhaURE7UFJq8tFNpXx0NqxMpgoxOCU+6sNS2hQmUHo19mNklr4HjIcowtbyDgLlMHD6T02V+Q+mUVLZEKN5XdAI0lbeTq7lc5rqfCvY9gSopVyqMONND41RL7SBjomRpZnMYZ8lBuvuGhyY4TGrKLWxkVEfq0OdbrglNcu/pLhSsG+SYNjUdr3MndfkjgAtpRJ5EySx0vC6yXqI2rNF9rYlKCLMtxaPPa45qSc0ExdQESDIbq+GZ1CMnMcyjjeljlk2exH4Q6/sXGvKs4pRd0KXbfkC9cSrR+ZZevx3roNmsdqm2jAuTe05r0+f1OHgTZEyP6gYTtBGtUR7ZORyeR5qOT00uJWAyN6NBLgN673SrVfINqqMFC1cu9h2VghgCcFVStPLRiqkn0zGlkLMLay7JOE62Pmr+joSziDpF4hqxtzWCGwZHhcZg7xnFPZ1+SxURNVyzaLHtscaHmh60Ol141Cgsd+qrDYeTWO6CG9biSJP1NIoCzd7pbUzAv1TLt96K/bzpl+US7KWLkw/JyzzO4NcPxwGVrT8R4tQIRTY++JfoNWCB1nCfU++Xv3I24shVOwSrWhFk67DPjgELD/tkp3vOJKSbM7NVUAnZV9Yzg0cnZQ66mdomk+/bUz/AhrtxPY/2RMMM35Wmf1khwzzKuDGz16yqxn9QemfSAnY4bGlHWreGYsHhnlyeNUllBXHiTeBc88GuL4nZYCBTK9D9+s9SokQ6Y1J+rHz0IxKaFMeWLaQWI/HgMZKhX63tLsmSU1u5D1RMwd5gpSIU9GeCWuD/6WEj0mv2tztSf+xHes9ybKayTHZrfj+IF5yEauIdJ156iZOBViwwKAfNfJQq8tRGigknh6M3L8PKPnO85PRC6g8UD2i6iHrCvumCK805B3tQ/dsOeKVAIV+qDRSI6EqFdItQqV4APJrSySH4dDnlDoY84QnO9y3RdNQIqoFONRN5MhNkiCX658ksEqNJj+anp3KQjBs7uAcIM1funjjVL0GNFIRDRfMudZ+i2PJ1OaP7NRe5cM5ZhF1E0Na7dDWQreC9xTODQrySgnquzelqke0/63E6eMjpVAnzUzPpPS+9iApAw2Yp7VO47KlUaT1t2cF+ggNPSRRmvpLI3xHQyicTMNe3KzEQYo12VG4alLR5s9l3BayfpE/Vhhv5j7JwnVyAFKhFFfjmmWgJ34bIGG5Dx4OvT5FIzTFPs7FH5fbgJHVwV8fsU6nnP/f/b+P+iWrCoPx9fe3X3OeX/c33fuDAyDgN/hE4HEQk2MkCprPorGaJmYilZqVKA0IGUsFUTRxCigkCAqIJVgCAZNEJJotMrE0qClYokaDOLPfMoYomhwhmFm7u/3vOd0997fP3qv7rWftXa/9x1mEubO3VUz9z3ndO/evXv33ms/61nPEu8wHkOk1xsdcpj+NdpwlJaIBb5YDFnUTuRECMi2zMZr4dKjvSSrLAi/YwbKqeF23WN1yJKOxofSxl3VLWwfX9BoSeuItAUdgP4uEDVX85cpwhrI8ybO8/UmaNvc6t+Zx81zB+ofdUtHsdL3lIdVs50OF0hri9SwLIb2iYL2ZKm4MJ9WfXzOyN7iDG0z2nAuxOF5on4iY8+N/A7szHZel3E4idug2a+4Rh8nRT2RCFkuOErQ5st0HQv2YHSO+u0R93STlUga934kddwqebkF/Byz9DuOKE0KpQlxjho5CY/axj+q8stiZV0pIfNm1gHVTtiIeldMye4F6wMXve2eI9fXKlxKFqZu46JmpZBXZWT8iDaIvphbmI6qm58D3lMpnbSchUrAD9PeO2EAKq+gIVonF9BR12gLN5cWOO0Jzuva7muvQpPaIzdRlrdShZCAOGkprEW2u1hukPGDnvR+5enw/P6NiRQi2y31mTwXvXGLy8MupbomNH7WIjRMUNoxLW9NOiRQCmCP6aEBhGFgpKT9w/cUau0tnPSBpu82rTaQ0IjA5zNq/sR69jiiPCU6EVG/0JRlWfi+MGwjOje0Cx+T+NwlkhRuyrsUVsEMSSILgBw+S9JAKxL25BsnRAscbc7ANYXo+jZdF+cLGT6ZtUXobMUqaUYggY5PlZtzMKbrg6C85PieMuB8HVguw3nzzzcY4tHZhp9DZgCQbfeGcEFMMS775/odieVa0ELLGRX5MQcXkraQWDckSM+himYxmEiHp6fj+bftyfyY5mpaE3DtkD6WpFWG65mVmccCeY5iifAcixuQzWmnrol6U77VwLl1LXSI1OvhntbnyueWwuN4nZRtwXYxoCa1hTpBhszZD3iPDqeorM/GNQ3nFM50pxwvgmGyHFgKUWU81PN2v4MvL6kyJ+ac1V/pe1I2XoxF8Hu28KYWwWI5hnmTjZc0zDgrZBvPzefVctNcNIBJ8XG0yQpafUcVFzSR08rsaWVOPDyfv5jIJBsBeQUye5XFCx23oSGdVVPUX6dU7Io5H4e1dA68LI45dkIKB6ISI+c+kWxdQwuKyLKtNHtLjomK7Sxs3mirYXtzEK+52uuEIWPCGTmX5xdornXUN/PG/8gCQ+B24XKHglH6hdd9bty3koqo3WBPqHlKzEc8x6CUgo9PuFCvQI7cDWRoO6qOWyUvt4CfY5YhY4D9240sTOMkWqDvZoavWpAHDyNmIxh/l7HzRsYCtQlUOgNRgxiWYYMex+U82EUkY+0NLw52hSWOTJQfV8o0gJ6F3jJaRLsYpMFQrw3/XvayjFo+6K2N+e9ERkRJMPoi48Cn81TY4PCDAtAkmyUMrAJsO3+WC7VatGmi5I+fIcMQGg5ZOwoG13i9wzAbG81gWguDtV4PHrW51Ll8fzo9eqpjWxgzNLHYvDAWQjWpGVcPT52CugZus6YoGEFEwPY6OQwuZO2w+KDymGXG9JAFwsqMRZQbphjqYtWtRFk5FMIYTyjmXB+AcXWgvZyy8HPAMCOKQ0avOeYCb3jxWY7UdQEI4Rjmd1cCThg6MrVFtx9Ta0umE19/9bA9Rx1F4d6c1BsEFt6U56qsN85pAAFDOdPzx7GyPeGVbgSCZU0bi+A3kZjToO7NSU9793dqgymZcys21LFdCVCWzw/BiJ0Hh5vMnqWoR7JprNAQDd5Mx29ODz82oF3F2Z0wnE8+k2ob6NqTa6Uvtftx3hjKdkHDKsrSnhMZoae1HudEZGr8qGMqouqgPA4RTJoumtYpGJ9yXHK7SvORnFcbYM6xs0IyfuS7UBIK5uu1++XxPzmo8muyTTS3jteHcWBSwXNiZ4Bk/9ZXYSLpI7Vn81BeZNPt/uml4ZqYCj4EcqAZhDpwcVUbG0DJWB7qVKzjw54O79hRDJZMh4QdOOq9Z+3CAquaiKpDFhQT38vMe3KNxHcwkMpkmV1/Zd/TaE8hE0gw0H0KaVL6Ramd7elpUauv4YujQbb2VL4IloS6u10tDK3F7A3wRjj2Vg8VwAaXO6TGr/tpXF57xsmsfWNJl5NMzQZZ96zXJN+/wvOx2NqYYTYTtudEOFhfYuyrexaHbc4vqbmyyZ4vEY2aTt1eefsaak/Lh+eFzfhdw3rq65Hqg7xdmY4UEcVzO0Q3EK6FxbWBqrXOfpuNHWbxwaYhVNoOvFVulUdSbgE/xyw7D7ZU13ZWL544MeWn3LxzaEWFTA5e02bAm/owUt/kaSOLDAi1IYs61TCGpywMwbLRKyu0FA5woq+o2gRaXMoX0gzFTgwIFV5mrS+w4Ldp4yzbK+vO0qDDihxqp66RG4zsPbC9S8qgkgZ6sh7RQ+PGjGDCYEZhPmOjpUUXNDOF29Xt5QbijVBRx/aIy1qeZqSqqxBBNhgt7yQUBEntNJiyQWlzgF7HONy7BiYlSpKArUJIwBxbaH3b8OJ5IXorQ6cWXhsz4+fGq+chN1eWt5GorLFgxnaXAJbMy6qPOSospATYRq9BIgRhiIxxl40BZ3yXfukNwFQ8W9YqQJCEDeet3ARi9eknGYp2eHbqCHzX8VzlhcwM4uFfHJ88x0vBSaJ8HLhAtPBGCmfOUiKAhr7RfYYAWrtjv5fqvG1UTAV8pzennZHVa7oXXtOsYdjuae+nZEDxb0psM1Uv2YvIBGCwS7J85BoZRJtxrITG2LAaoDcCIXyvCJpK73y3Uw+C2ABG8BTeGAK2Yzt7/S7htbgfOmA+Vtuo2EKKUWe8uxi2RqQ3oaF25LporFWycr1JJCJKCSOztgUAjhdXSJ0r2ylBfMW0IKdCMCVTi7WV8D4ZmMPxiWtzvxh0CrP6edMv569zecPG7G6i4PNZ/9WUh95wQB3pMDM0nXKALP1rvJcuEB1CVlDMTGeey5+NqXwqi+J1sY2qRNtJwaUITN5gcVHbCaN2YJZRLh+g1r3gelkKw6FoAbL6HnGsyHLlacPLrsBqR+TCcnYcsF4ZroX8zvQrwY6ZcYTKa1rHW5EEmslp1FN63LeXGZvRE12/cNJ2FhOsd3BPviU6uB3Tc+r6S9d1Ku3ijZ2L17LeA99aNt/09zj/Gu3tt3O6ADdfuZXV67Ept4CfY5b1+YaqRaKuG5Mg0QxjhcpsoXGPlGnIgCdmNVyjF+uVvJaVplzWj0Yw0sHnw2hkO/LJZ5vq6ReQbcGoLnpMN6k3QuU2SLrNdNLsAtDFI2jHaWOBCHzhGcp44zFUx8rYBe1Smx4wiolyo6CU6nv6XQN741/OadE8WdeMQRcap8EVlI4pGC+DEDEci87u1siyJgobferdqhxVW80wyTLEsOBk4RipBYPGJW9uJCvHy78PRQgYeGnDqlFhXLLPSkDZOF8U5pHxb/6vcMx4TWvIHmHszG0ccAyhNmVoNAPLoovbc5H+3mK8IajKxrQVUsNl3DSKxh1ng6LFwcXxvX3elOku/172YaiGsYh6YnzfWaYwNOA3QWn8KDZgZRjENPSZx/TJkOq22hrPUh7Pz2ODRvjwjAI+S8moSGBY6b6zOqENzDY8OC8egnj9asFGVKFL17RemdQe2pwc6kTvK9fZobaEDH/ohzkDw5k45E3qWmFYbr3W2lvoyWXAB8G+zUkd6iVT1BMRbU5V6ppbsQdiBhsyhTYniepDpzSJJJgysidxHKTbWT00fY+JAxgQkX3WXJv+lt+jsHz0APZS/p7yeFOAV+pHZGbJc+t1HOYqeOem7JxibcW1pSIFbCNjkNtkbehvpKj3XL5baSzYbDe9lku2LoaZTfVrwL6YvdEdPcea1zAcO2Pd7AQt1Ddrz6UQTKX1OIanC7vRcDzMgStERDEUvnf6fVLtdkQVgvPi4xhGaQDkVkYwOdbZnsF3d3k5sWMk8F8w80shXUcBP7Yzb2o7UfmZldjyRMNaGb3hII36vcRSbQ2wG9fUBD4qHbVu/p6Ijh7rI+hXcErhM5bv17geWbZbIdrjZi0huty5+wjruFXycgv4OW4Rm7BQmsxgoMmPPCGgd9LtayNYpxBkb0ahbVL3xorTntEsIBpu65FkeBiu5xRfJdv8tqVJdn7DAVeZ/hLnZIAJzvWVtTmVv6c6bkDFX7XGMJBkMzPhQ1y8jFCvDMQY2SCFsaQ8EPmmuVuSyvDAetiZ5xrF9eKNe9yOMpK4LbKEhTaws9/Ts+ww/CkSOUOsLwNKR2oPHjP8uxWcc50RaThJ0oqzkDa3a39PRFQ5TZcXmxiO60dP8CjMOQM0kBv6BJlsVrFC3JTYMHT+KJRosXkUMKPrV8Bh1mfpOGtOicfbKHCxQhNLGyipE5W1wck5FkDOqOvLAAXP30ETkgF7cBt62HODrtoQEY4DYw5CIKBvnGJ24OaY22SNbQUs4LvkLCbndAxrFtTXcySiPqzJBaIKwh/r6zKGJ61tBXFzyTDsgG3IY6QyWCdEubAuMljaPa/AICnmPf5W2kgiiCc2bTsPBeqXjjYn8pPHjczM69ruewUYIRDHwAquEYurhigzbHBc0MBMpqOU2rg+D3PDgshf05pEUr+JwZxYEMRe3ybHcH4Mh1tmLB/xHhXfV7L1cLJNY9R1EE0aeZgSWrKpu11Pq4e6MbSLS5tSOsswRiVK3EedDUwxZqlYjhQEjlHftzhGCb6PB/G7g4u0+LOg8TOO3RkHDb/TZZ3LqI6d6o+GzIA8OdrNYl2YGaYoM1BKa1fGwjHm+SOzLfG9gM1nMbMC2FHVVgv7S5ugyMCi5ChRY2P6k99RZCC2KRQwcypgOw3bN2PFH7FOH8XuItLPbLr2fL3VJipgnp+RdFxrZ1/U72qBmaTD/vW8q2zdI1DbKRslzhfDWFR2/RGsq/HzLQzjVnkUyi3g55jFbyJVo7cDfuSvZyZC9lrVBcswmwjhEN/OswPkooXZs/qFP3Kjbi16owBaxkbR3hS/jcrwlhMte3uR4hudMb8V7jHzSMisAzNp7EOtWSgylGzMXIQgWMj/NcsnKDevw6xE1aMH09iYkjYqkI7vex22Ny6EYiCgce6iDi9Q47DEWkvXnisu2MYtF97IobFcbSO5PmrhQ1mNMU6IhC7Mpvww63SMzExXHUw3Wl0SqYcg5jx6r3UeMmFv7m9kECQPH7yY2XOLwxiwaONEOXBnPRcENpBBwOcob6XTYy9sYHN6qL1xXnR+WeNnCDu1Mvfg3zgO2NgrGZJE0wZTAlVZnwY5X+CmxKmIy2zMpWdV8u5W4HHNRKX9AAiqeZI3xGJeqiEM17VxyhTCdQMQNArCYza/dTgS2HZdVGKYcgzzswrLfHLol47q653SSZCfuZ4aMgxxOt5sHMAw5020DP2R68/uNZGlC5mXPdHOJdBQEddaJJFh1PIZQRJj/eVy7UkV+Z6oPsD1Jl072/jifGHNu/bmBkvVRnWfOw/m97g57XWorHSUpN9Qy61eD+1XbROPDdksXMZkCOK9KOm9lTZ78p1Ch8TiaqT1hfy7/Y9OF+A24hy4SO+j0lAS81ZzPZCVPoZ12hYPlbVCYuMzIX8iHaLfXOE83XCydypzIoJIrjN0FwXIiteeKhrCwTGc37IXVfpwbtLcUj4uaWIu7eTzKDswXRAaQcbxo8YPavmMSRpgbsRw/G1PDnRnXKIkbi9ME3J1HZi7C69tYmD3tiftcKxYuSwLItGQKU6WzalaazMKIHrvvm2qG227MCY1yL4X/b2+kHQEYT7itWvn42U6EvdnljBFZL7NnhXqTVmM9KifpW47L/DwjKXGYu1p+fEDcqDXxFpZ7o5cV1GW6jCQb2eMBJqcfLh2+W3INBqJDGCp8Uq4OSuF5XZwRDt1rvzMSXKUg9E56ro54/vmKzF+wtusT/j8m7HcAn6OWcLCiawDhYNuYMFE442NoTADYsTKUaiOACNS6TDDVjS81Ah29BqFOYqqSTR4lF2YBwRGEeUj6JdDO+CQdCtRekVLbzMY2b7X9N2cHcP12DN1KdQqr6fUFrmQ4rl6MflEYtvzzdMwTlTCImPzp7KKkDEuVKiX/f3Qhvl78G3U3ZVxidM/Vpc6VxRulu0pPe88q1c+yFjQUoI98tnHHREjD1m9qI/KyJB9XXHoHxisPAcoxk+WppRoQGHA4OJ+AlFvPAZZX1pYO32v5gKD9m4Auzr+Xv5te72iSyGYcyLhLAgMc8FoOJ8R82SBLt4brMfhANkY/RvOY/JzaYyxtxZBnVpowdbrYdOOITDcT5mGmQKfggrp0Vpcw7+4aV5uonq+akNYOb3xNUDW5kpuhK/PrgbDGZ6xZPwsLqVQL0jnXspclbUhOTDkRkQen6XaBj20brcatbu4rARIwv2E98nOCQTW5NrFIAW2fXVxGAh5WnP93hwl1D15jPPv+d3J26UXZzwvE8FOhyMYsj7naHGVaHHNGBvcrpGplLe/uTbUdf1JElDBRqlmgqD79LcKzTJCQ6WeFmd/w3eXHW0q/EqM180pT7vrQN0eAjjDs+zF9xUw3vxBS4TAD7DfqutpQ4/rRgjkOvyOdFHhudMXzeVhgsGEA7H21K92FENP2hyst2iBTfi9QyDSyjDbybjWcvuJiDwAP3KMcb/PrQ+ySM0918ehf9B5xeC62GArgd04PauxXZjBM4ksh2X+fXuy1sk0FDOJzPVm/DO1rd5owKJadxoQ2k7HcXZDfEd4vayvTC+A29qGuttKhFc8tK0YQ8fMMNcwULHA1IOpHgR19icNhPVTTlK/21APTjWev1Yfm0S/wjKf58OyoubBfAJCYWqXsrb6J53O2+acAvwwljksG/LYj2LtZ6BRCVP3kdy200Lvwn6sHxrODau8z1zXUdeXslTcnOWWxs9jU24BP8csfhvJ84JRYPwUBeBoWnhxUWCDJds4KLs7kieI2z4GmnlUrKRvtR6OBTKpcK2eGRl4PdFMx9fA+qNCtnHz1Be0K8b2zPW3ccuZUZ76vUjHnoWL7f60+szcKD4SJJqd0UdQRYfQO/QiT79xsfpO14W/D+cfX+zXyGAFhTdUiurfOKrXRkYwKftUiPkeNZfEBgxTeLIRKI2pTA/g+rTgRkO4tboGfX1qMlbY66Vo/QtbRNYLgJMp3hUy5Tirl7AN8BhyRO2MuC3RBAyhl9r3GqBTIt+GQHn+fJK3HTMMOkoG5Axo5Ox5krPUVNvpxpFRxPco55qs7Zb4pPgN57FMEHjLjB/w6idNlxbYIxKs6ZZDyvTl5XwQr8/plxDDRbqlm1JWp1KDDcj14nM6uK2iEwD0oNjzdt/PCq4y6BdP55vcfjmAzCpLj9R64o0ZMhATA0+K6qKunWfdCxnCIFkLklmgxmKk5SUIQROe3PpwaBdq3Y3AGdySfG7RD8AQMi/N8F/o1uZ6oM1JO6SNC7/XyBCItQaBEcQOTaXD/cQcw88ZBZSbg+m+smtKdkya5/T8zL/r76Z28h/iGPG3zLynwmKdOD+Vy8+Y3ht5XVlKNoOcixlkqg80A2Q4ePquVwybRos7g/h/9AVmQizr5XExWdgZo6Jg/ISBIcvhauO52bhgoxPaP7K3xHhXbDo9DoJ0jsyFiXkiB0yQ4ySnmBPqJueGDEzIYkoAjuwvy9nR7+b9peysxEhRNus2kptxShERuU5fUz7fEYhDgCJGcoedwYyXQMPwb4kZHKvpvvszOaAwApO1fBnF1XZlGj79XAOAYxKE9AlcyeqmCRjpIRuqtOFcGPQgca0apRSEYHUPQJyLkbYX9uE7aHgcwvc7dEi0UbGAtPPKkQfQVj43znRmgZfOYgqJw9BBwiVUjrrukOj/M3++VW6VGy63gJ9jFt/FLJwhK2wvzghw8cYBveddmrjkRsHO9pN74jPDN/s+PzfUjkZlOj4G2znnYckWV/yxouXFzqB2TwdynHx9oCe9o7SH2FuXeU5FG7IF1ognV0aINRmDYNO4IM8AP2xAl9KOZv2rNH60NgCyM4gKm2Y6yvghiq5SRiN7xDNDHp6HC9qLjWEnxTbcEOCl+9PSa/KoebEOVK2DHmOyD9kwRDYd7zsF2IMpSD1/Fp6dPDtF+d1wMVJY5gajpPAzlRpZFn2iW0UHxjuIlkbvdAaesY0CnDI2CTr8EgCerX2u76NKv+32ESzTnuCYAXE8UKBRbhivKsWyIe5stUvVqTS2+PPRnh5lnHvdrlzklNuVH8KAjxT1JaKMCbO4Eujypy5o9TCCEWkjLjfna/0yIcCl2G/cNjAs9+8Puh+vaSr74kph40s0vluYIvfw9D4113u1AZVtmLR0cBPFrBax+Wlx/k7/SNxHNF3OsZgxsj2hN8VSt6jEnGWgby48bnG5H/QzoA7OHrd/n9j8oCOjcUcOzRG0hHmn2ursnKqdwQDlJXiRpiocT93OAIIoZnAWGp2qK4jnyj7VIsdpfpYbJAnOiikUU8FXm0ibM3hN8fuWgQh7/GA/Zjo5e47q66TmkSqlBpdss4zVQkRu01N0OSMBbTsO9VJhXd5RjfMFFNcGxUyR7ax5PKuU747akwu1zsm28YY/LOxNuRzbDhgPnN46c4LIuV+wIRS7gTRDicTnyKBQKTMetEWyclzbD+APMJ34uVUCuJNOnvG7NTI98o/cNgxh6pd+HuyiwU6YzeaU9gAqzXwXhntUulcyLIr/te1CmY68XuceA8eJK7JnKeppJS3P6LMaJwx5bpu+gsan+uurgOYKOyqeHWhbaDexzVE/cGX8rtrJwaz2zA4tPvKgamtWEhjmbj+VN+2wVenbcW7ozuxqBrRkk6V+wnHu2n6wc2ayPjLbCN/LugvkbzF+HlEdt0pebgE/xyw7HzsknOfGwuMLPQnSQ5MmdzTOQz1QHFf3TfREnDTCoqbY+BwVlxOUDC2qcYWJY3wyl+bqEWkIiEaD34kJX3oPhi9WtLi8JafitqdztrcN6Ht9XV/TX4XJDDwIfC/ZPUkD8+Gr07FLpJSSWryl8RZODP2+BTopxzXXl3PrVRo0/eldsgofk/UTzvOBKOzkAykDQJLXsPiMUBPiQBzXR/JPPaU8mHwv7qknxu9WD+QLr7++oc2TTmbfYRvG53wjVGykU89kjiMi6veHMbo5lz/H5YObYdxj3H8tjD6miCvdhOFzRuPGzVh6NzJtAjEOY6bUDW2IpOjTXqTdrK7YGhF+nVLI7wMvXQINtaduty7GnMvNLYbhuEgUmrwfm6t5PeuU7hf1j3wbM1FTIs0oqg96Iw5d/Jk2pGi4cdtUxiXBXNicGgxA1NLZJrdqpjsCx4zij4I1IwHU2VBZl98Dt5ULb5pRnHcMDwL2iOwf30VaPRQMlohuhmJmdUEBrYdnQPMnXVsJQy8drdDTDCAJ6u9gu8ZsZj22Kw7Z/GA9k20dN5+wPoRqGPes9YPXHE4e6pGggBQqzbJNwnhywWBUiDHBQJsC7tmjDMuctNWv3Zk0NVA7Jt2KnMsxFGvIBJXXrZgAqcsQADw4X80ys4iIFteioR8nwZuhcRhl1BxE2px2tP/RfCxwGBWR0OgzxG2JchZRC0SXZp3mAzFNyfdIthnDIRtPmX4QUd5npRTIDKChvdVnToAEvK7Q88/ghniHMaMHhhSRfpZFQMA5FTKEbA9fh1nn0xh6aqzFsXbkDsrnjuExKow42VtinkJAltkWxXCsOD1Y6/5VCJqUACjMzyGBy7PZZ+uKYuXIrZC5YxyM9VROOXCUztAYNg7zna+0kwvm4egc+Zksawy0K82lpqHqYDvj4CDy/W5qFzp6hn/7E+J5VHlK2fHdkCLZBSaa6dxTDHFJ3U11Gg5ZIj0OZLhlrBzV11uKsG6wrbu560x2rCxh6Ymedhu0074nDNPv9xZqLsB+7Zd+FrwfATfFMrLry1j4aR3TkgVEHTpGbvJyK6vXY1NuAT/HLIe3LaluWOCtNDnOLNTsyQYmB2+yNucnAEItALUjivlkUtLesdJvoq5QeyIHO3yrtSDG3+RGAY7ZnqrIdwvyMFnmYnFpowrgU1h6cmfyhUjrhjjdBmnw7521G02Dl620eBNNC6/a5PIGYYbp0Z5McdVKqI3jrSXgp9kSHrxx3QlJleYD8x1mkW0j+zBG2pyp1IajSYaTDEdxHfT96eUoLscl1Pkz890i3QMMhKC9z1ovKM4abixiiBu29kRDzZW2COoQ0SjvhNTjUZNL9iW8H6yJ1e0I8Ucx3qr1dJ3MsKHhWSDVOIjnHS8M71npvtV7LrWF/NAX1dI+Jtdz0UKfqh/3EVQd/sWx4puoxj6G4XRbr47J8DHOWobzpBu+U8bUQjM0lN4Xj3+JAatsaZpd2IpwKhbVJjIMM9J9Jpk4/FdJB2txGWN45VxDtDldqZS7PC9L3ZduB+k8up1mulfSjL3mWqDtSexbfMeN+o1ycPuJ7PP2pCNyjVqH+kZ41tluL+0RM32y/Ld2d2intN3W54TGyXUBMMGaGiqnxmzGeBiPh+e9YLAp/34tw82iDsMiItp5kBGy/B2WpWqjyr5TCvtV72WvQR01XhtHDbLpxCFThk0AEk87Wl6JCryUzJrNqeGc3QcQSGFW1/SdZD0RDaLTw7HTdxK0lXMHgjy+M1jM4r2vU4a7DsLU+F3De5J9GGo3bPrhecdqeCmkuC2GCJOr1NqODpH2JKDE8nRcE3CqXAe1VsniDUfa8ANRu19TdYA0QXnj6RqPQP10dMZJNFRuXmGdxtLtl/tkZOIYLKahbmQX9vkx3mXMPiKxXsoMbRjO00U1BvDZMvtZhetYj0gJAhthyTL8PLVxfQcwyCKRC0tlv8t5hbXL0I7l+S8L+cO9QauZ4KWsXqZtbrDZpwP4xPwQHvcIfAawfQ6esqecFP2TB5BLgtk6A7KjUM+kDMsK6APVbnYPh+0ci5xjDQbk0NDBrrKjOVLd3Gxc3ytHXTuja3Gr3Co3WG7I3HvFK15x7Iq/8zu/k86eLW/IH68lNH5c1EpAIhoosnTJQHLgKeINXCcyA2ijIF2wMCmZkxGfSnoSUpuXaABJ7P2URqIxIYWln0exOYTHCvXBSRAX/Jo3kDZNP0obAgWTj8hmM26WVmjQDe3cniovHmyUY5jYGOpVyDw2nBOVESlTmI/3h+1KRVF6YaEOlVPeSBc1qIKGgu+1AHdNaExwfXn9R+kpDe2MepMjhxYzWTBNbTSeEdkex9J7sDBisLEei51CBIwqvN7uwgQ3sD3Ki+by38e2SIeZdym9KLAG2FkrbSysx1khH/oYvk72fWU/g/wYNyucPgqHI3jjB8NKgYTZZiydivpHLdRtFH7vMLX6eB3ZHpXVS7N5JJDCzwZ1UHiewRDF7B7jsIHdnLTvWwJCCGJYIZhYmLWFYEHVRlo9iHE3eoOjdGFEPfxejBmKUrn0/+zR6sFWzWWSQTSGzMBmcJxbRVO2EJ61emg4V4o0SzbUUmTtUjo5K08VeEjlGsVZ0nC+W1wZ7hXD4+S4qQ8HA/7wNL67Q/t3P96L84Ads43kUF8V906pGxBcqrxTx6K9MaQLzr+zWEIRdaoOBqFj7Mcs/DyB3iggznpTh+em75TjIenwSLBHXkumjUfGG8+DsliZ/fA7nj8wpFnOK9U2kuuiYsiOXnvB7kB9N9cFPa8jgfmKHZ4RnVMCsUoAmigLgyKiDFyLS5sFE2tva7CJk10hQxCDOlmdBbaJF+K8sm8sBqE816F4rgRKma0DIV1jz8zo/FEgciEYz2loT3vHxGa2QDHsx/YMOEcZlEKA+tRCOwmDYUuoNXT6e8GMXcVq9uTaQD04A+TawqxJ9Y6wtvKlmaxevJ+QmkHCPspC6Yy2YbEcvirUMoFNpcxtREPGuubKpsgAk3pbFtCnbDxsQyk7c4jKuYcMr36nVoCUvBfeI1qZv6JzhtNU9H36G9sQFp66DlOk3tzlVlavx6bcEPDz5je/mT7ncz6HFqjMXii/9mu/Rt/wDd9wUwI/y4st1Sm8RMdeD/9g7HDGSkiLOB7TJFaAVKr3V/IwnLhqqD+xys+Vi4sU1ASVexei8jxVEM9cHbTmRE5E5NdigYfYU9+vqL7aUnUNNhcyRWcSu8VYcXKOPJwXlzeA1Iu6/VUhCICxs3WVxygTjXHHRETx5MDw6E/amQOaj4NqpAwT43NUtoAU1yz1AGASp/UhxRN5qJhU8edFbXHflewYvn6A9mbARNvR4aeep+ZibnDy81k/9dT43erP8/rdlWu0/f/dkX1XQygeX0sZldueAoQt+Wtg9Lad9mBKo29vQJXq2/M4geXHrhP1Pbk1xr+LZ8sxmA0wz9KYDvs5KyorwaAey2Ok8Y3Cjtc32ftBRNQLQUQ27vHd6hL9ukcvcla5oy44FVLC7ZWbq8rQhVHeUdzMpo/VVr+XuPnCDbLro+GNln2WQG6sOkQ7O6GkrncM9uaHTLpa4jSVHS8/VrV9Dj+JTnllM02ZjuvOj+GNOvaZ3Mx2q4FVgJtwZrA0c2EZlWagYCgSpxNW2ZrOGkwqpWM0/m/6SlyuS8ybsMgZgt3SUbdniAnvTtdkZ4bSuFqwM0C0CzewXn8vQYdGbLrrA2BQ7lUqtFFmXDo8Y8Tp0ASGq3Er/m73BoAEkxVwe7sszBDqifmGj4gowhJVJyYAMoPwekTaCRAaHWYigUoeRwxwcVnf5mn1kB6DEjTBbGRYFmI5wWfJIY6dWLqkDtBW3GttaZVA02Q9nEEMN778GUFsGULX7Xrqdj1tT2IITNqAyZBHYxOKrDIFoj+5YDc7w9GGh/Sk5yuJx4zM4EIFTwJbKmNvpDoUeD/8K9/Log6jxLWNukttU6Llsn52Rsw4FbIiv47D+qLeE3ayiDnU9fn7P4AFM+0i/d5OdZF2aIHDbNDnyo+Q69Klu4cBjesP0dAXc/pApWzBvNZcv3NHHTvWw/dUWn6gf48qmf5dWtNLkQTKWShtQTeA92qtSodk7GU4pGo1cxHLGH2BIXneCBE2s6qioLQ8gO8bhd6djUSI+tHxJI/pjkhRf7OVAfiZX3NupI5bJS83HOr10z/903ThwoUbOvbEiRNHH/Q4LWHhJ3QZR1R6eRX1V04a4zn5grw5MzyK5tr01iMdNlZ+iF8Wqu9yUizF5Y5tR2MK48sTTTY/JyHXq/JQ6VeeqrWjfh/ChiQCPnqmtJEUK9DKQUYTt6nAdPIbESKFQoXrrQaDdqdFkIXt4hloQ2q7EiYUm/cODEUuleExw0wffm+pgBOZUWLM2nQqB3jG89forRJ909Tk2zDGxHNxMenYyE3zLoyx3bNUQd3+KgJztvhiNBg5YReAoI0vgotDJcmTiJohtSdvPcul3rwhcDh6MIV3FTV5OARR7lVkSk9XWe9w+q0LFDDbi3iPGOxFwcBFyhQWIW1nJjDdR2pPrxT4xv1weGEaH+jVc10gF/L+WVzOj+l299K5+TOvNj11cE/hKnjwr2zVs5RrNHsokSUS60EIE1kiWRaZKoUTYnr05EmuD2S4Tz5WFpeHe1nfJsC3DFyQF9VzSg0AzOrhaRxsU3jnAgCFJgkjKy0RMcdWa0fdbqU2isvLMbVbjDdjqruuQnBgHKZuRl2Y0FQKFEEPaWjcmORnOmZq50hdN4zw+kDrFsnjmOWJHlgO05KbMWRM8Voq56wSy217Susy8LrKRQrVj6ASgi4J6NBhYuK6bmiH0gFKx8hxgMyXbuUVM0Wlfk5taq7p/kb9mwqmhmqrQXnp7eY6ZcgcEdHicqTtSUeLK5ptI+sm0hnBujTNyI1tB8y25cUErmcIwfSnzNqlGEqOKMB99mL5LWXHmoDa8j0RDZtyTDwxJWmQ+oaaCYBruwpr4fTo6DAw2AA4J1aHfZFZPhxfmdeMfmBjqpAVmamSs0gVGKdyQ4xOTnZWyHfRlRjOBnClNH6MMKNSCLRi+UoFAGRHQcnmY0zwsO012xxYiiz+HOGZtyd0uCuCEfWhZtPJft3/6NAnyBCL3pNvddvkuZf/n2HPhWshs0B3PzI5MB0INCtnGhbJdqnR29ASrQBAEzYa25clm6968Gr2OQp7cXPH/uC8xO1Uem4rUaceKz4TfzZLchD2YMfHylN1TT8DWcJOMyu6PuqzouZS31PYMxwOmeE5tSOrvvZDVq9b5Vb5BMsNAT/vfOc76dSpU0cfmMq//Jf/km6//fZH3KhP5lKtO6pqmyI7HgNiZHLSwJhWLqz5IxcGVU9dDRRCMcFk8eeC5WOFXfUNGi1lo0AdY9E3+bqVo35VKxZTlg0kGQoePCoukgJ0dEhM+iyaL40mBJyyU5vKptzyuXuJ8YMgSWqTEvsTm/eJkgn9sTAEvFELpamUkSfj4nkcaKMyAUJKiDJfTNp9TUWdfpvq9C2ADlGnsqx3CgysG4DS0fDsTi3nQ8TS/akQu0jkF/UgcihLKxlYwzjQYVfJ07xnA6ZE03sk6d9ZaKEEbXC8Lmty0C6XCZGnEJzTELvfMcgFDEEBNgbnKFaewk7BDTSj/xAXeoyhFlEsGBlhkYstExH1kE48Vk5nZ5HsxjGjiGYTRe8M1sn0ecxAV8AIsw0HPI/xHg0P3FCnBPGg4jjvhR8ZHajZNTJiACwTc1n0lMSQodL0WYb5YqhEWHoF9CADiAErZJAtLxmbBhQZrT1V0BcSjGBPOM4pvo2DbhdGboi1hUNoSv0qgREErSwGjWS/xWxNMPoHl7hsM6vrIJoAHwxnysKS1pG6lVMsrTCGJWuGzdjOjqjDvAAAavDzwVBHFzXbrIE+Owx6rEh5M3ZCIxOpXzlqrkfF7JNzw9jHAPQxw0Lq7mBWL76XIDV+BNgjv4fIClpeDip0U7af2T+cmn2sJ9WJAqFygxwaIuqtjEupvSKbhwJSFrX+Dscczz1Ko8hrZxycG2qvASMx55bqdj5SqL0RWi/Gf2q3Bof44hLIAUAqAQpyfZF/Z0kSjPluzvnDYO9cVr2sOlFV11SDfiKG4bA9l/U3gDd7tQawcU5MWoDI/LLmNkvEH4FiOVbG52pIHcTOaba8ZMi2+JxTG9L9BmHDxQaccQmIkGu5XC+y62JoVrWnARA5Pg+Hl1SNQ2aun4Z07lKvyRGFnVrbySmpweqjAtgBO71feYolm2lse2kvplk16h69UyGYVmhkBDs9uGZw2s7ZGDwua73+PNF0im9l9Xpsyg0BPy960YuOVem99977iBrzeChrIe5c2sD6IldPZBIBQ5nTwLJw7lCAjeGHzVZu1NnXUgKQFekFBcXiXPme5kSOtyeGTGNxv0x95DZ3u8ZkW2BOjR/ZQDHSVQ+Vl9sWKg0EZJuFNMejZgcblduT5VeENwZIK+ZFLtMsQfwm6mckNxrsqZSbQVl0mvT8t+0JT74Denvq+43YwIc6Hz8uROWhrndKu290M2px8Ll2WoX7qYXU4X2zGMaQy0E+S3sK9V+4DSrVtjxmFPKcDAgZmiK1GFCHIVaONmfBa5S9W8NvCBbwBszS5hmLG7zrFv17qFN48k9h/EhUG8cNHLM+l36Peh5BY1ZpoVyrZ0ES9v77OzRw6IJhBBvnWp55IspSO/eNvSGU07Bse6xnAEufBItFiV5nncIQnHY/ZWhTrBlRdTu841rsN7VRvP/ymsO5gRbXIDTnnD3fYv3tfqXeVQQG+qWz9bjG9qQ27iFw6Kjdr7QTQTy39YlhvURGhSXUjWxZPqbK0rYLh4J8/417dBBe5o0NKW5KLO0YoiFbFpdqGylUjtbn8/bufixpIQngDt+baqvXm1IIBYbEhMYZbBtkSRFR7kjPAKrNybRxupj3zcFtnvqFFl2Xm0qeq1S7eIMp2gZ5A2gnhZEFweqS74ccBw3U3+556oH8uvrodAzPc0p7aG2DGyosqXLUABuSwQ45B1nitj3aNMrplgTK0aHVR12fciLljj4icBDN2T6NUwxruYFlW0xlgkofSywqInEvEoAXf6MNkdUftPZQJoJfuC4/s3nHUWrTnj03yvfODAdToF3+mUOpzH7HWzLCLTGUTK7PbGuGs5hZLNnmKPXU5fMRfjdcc3gO6zuml1H1/ScQC+MCmYyu6XdtCxNN/TqnwelCpG6v0hqIo/7fqek7w4ZaX8gBrpJgs3rGC1cE0sb6Da01Wfh5qHerjxRv04wfeY8j0xSb4Ii6I9p1s5VIxaXxWHXcKnk5dlavP//zPyfnHD3lKU8hIqIPfOAD9O53v5ue9axn0Utf+tJHvYGfbKVqI1VHDKU5RXhe1EpZYWZTBEYaUnVLQ0ysr1naUfB4xKWnCGENKoa6ckpIbaTDz4AYfC4uOrKwxxa1RCywSTEquA1Z+Iw4RoovwgLQHAYt/CY/pmpqlx8zxv/OZEdxBbHVyUCR3mVt6H8iQLROE5l7sucAllK8NRcUlq3Qkz4K9h09pZrtLKT2TGcM/zd0YVzU4J9cWEvvkGkwQrtWHx/c014wfiQlWopRug0aIl5l9e2Fh43nA4/Z0jAklL+XLBFH5BdOpVKf6PjTsWhUWo8Hjd360Jnn+i4qA1wBFpYQthRnTBtebPtwnPGMxZhlLRGrXcP34r5RQ2UUbRTzYSboPP2JIJCjqLJuLS8LdkBi4KE+CrcL+0yCjdENwAn6Bdg43/vY1Eg0PH0X6Opd+YnLS7ZXW+lU1I78pixESUTkl06xjLL5axS6zc9bn6/MUC+5+W1YHNhab6C9ih3DOitZtpfpbxlypzRljJS7rQGkI4jB9dS5xF72TLqVo3bPqbBAduBk2cbweVTuSMYA3yOCRt2OZhnh/Fxt9YZKCoPztRF88v3QFyiELJk2/I7WwFAaWUTie8zMNSaKEPvaLHxOPBoEs+rDSPX1/DupN8TPsMQSUVlOJQOwckQhKja2G+caMcY6eEf6SC6ggw4A8gcHASLFcnFOhRwjC8HFqBnLErzkdcbIsHn45D1aXEQxsqmumgHzklNPXgfbbgAfUgBXglMqpM3Kbll4v/ML8IYY3ldRf2wGIWQMq2YR7FpklcVwM3KO/CF8B23tTg8ACor9dru1Ws8d2k11Rc1B9hVFcQzPL6hLZokvY7n+lKFdmC2YQ1uby8KuuYGdcGZHzx1vgWXCfvco4s0ldR/KEchn36+qIaV7IWlMfV3UbUhUzIUTZt/D2O6XlQpP14x9r8aAHOcuMcgjRngktvOcI533PwgUR++o62yh+FvlVjlOOTbwc++999JLX/pS+uqv/mq6//776QUveAE9+9nPpne96110//3303d913c9Fu38pCn7/9+DVFdp8SgxVfClFjTZMSwJ1tH6+vD96r7r43f+EIVse+rP7GdGSyaMLDIs9BdOyjOpunJI2zty7SUUd/bXN0VhZSfagpokm3MrWlzaUHUFtGCk2HQKw1ELsvdUXcwFlMesDvyZ2yQ3+XJTckm4N1HzJcZMzHmoR1NX+6ec1+cRUXURVmphODFNVVGxD/Vip0Itth0RMBWkIHCfwpIWH4frQ/usdrku0PZJJxX4x3HLGyGcvPzY9ewY6nvqT+VxCBVq/HAIVI2GTk8BNZZQjG5eb280fDfnc3fx4tKWquub2QXTHaRFsfD+yZBAjHNvTxtaSlGwf9QuRrbZqfv0IkyMw9PqiwUDeIb2HpuKuv0FLR64Zv6+edL0ni8e0mNlcyGnUjeXIHa9Hn7f+4v8Gfv1VoWXZSlhiWj5AIyd1F4uDH7Vl1AjqhrC18AokvPR+vZkyILxzJvy6Ke2NNdAOynpFR0Kz1oU1q7UkelVVi+ngKTFlan+9fkUxgnDmhke6J2UmwG/jQkgQoNOgxuqWd7rNPBqczz8ewhMoOXFTmUsQoCqanWoi9xEdMlAXz4M46QdNlBhC89yY9DlwVDmZAOSlIChXjyHyfT2EkyTc5wKK931aiO0EGMlVsMYQs0c1oNBEEGCMN3K0eJaVI6BTepXyVzCsbLdcVokFpliC2YW5N/7Tj8nLNFrTZuMecbgDQBIDKSgGHjm9ElTQoD2MtNtK0wMLYScQP2C3pb0S6m02pFodTFvrxSDHl8pWBa5XZaA63jMNpLrow5tZaBehhaqkFinwsRRZ6Y9O6ynivETDKBKpTAnXcStVJyKG1nNaUO+OW9rBGaXtFJ0E2U2BmrdjM45cWrR4Wms2XMZl9i2K6Vkx/6Vcy6POQSb+LPMDBoaDHMnqhD8K8gQ9JA8JTZ6rlEZKa0sdOIUrttkeRsAS545dPgXw3UZfMIxKgs/77nw6bky58B0pety9TPnjjqXoB05ymLMMN9C5ZS9UhSYxnMbR9tTRyQyqpzW78n6L2kUKkelHc4ngVK2GfDc0DwRxZ1vhXo9FuXYwM8f/MEf0F/7a3+NiIj+w3/4D/Sc5zyH3v/+99N73/teetnLXnbTAz/t7Sco1pzP2h5QKquXpLGmhQU35Ty5yZjQCJ6euL8iqhwFyU4RYEE8PW3Y0UDpT+1kk8tQ9OPH87hIzRLUCQlLT/1Oozf5orBQbH0ZF1JP/Zl99V1WPwsJypSHYlJ0izPTwehU66MCSTLtlvTb9jTEPW/tCVrGQbd8Dhi4FWd9y9LAwlgxxOGk94MNoX7fzjyjCm56VhVVSgQxbbTEQtqdRDrs4L3KvtsCEMfjCBdS1CMiUgZjrAxvR9TPFRkE/U5NLkalKZNlrRqFBG1jOmPEtbaxnm1OpUSTDPXCbGbWsJfgJcd7Y0x5ukcp6o3FdZFi400BQqLcSKw2GvRUmaAgJSwzA/CZV1aqdvRALWrtSZfjkPcPOF8sUugRjJ9cV4DMYmaiKQhySnahzOojN90O0n3TwmBfZN7odA/oyEvjtkZGo/jcLyvanPSG0KyuE2XkhtTMNFs4Pfnycj4gN6d1KBaGN9XXeh1mIt7dUc8MxD1DM6xpCHjJEBMGR5RYpXfZ70RGCnAW2BXYvQwDba4KJp4VNgPWfpZtLAE5JbtQaf+IOak5GDL0IKB/6k8H1FDOsWbWPtyg4WdmWGH4Wk+znn9uG4Y8SdCKWTkBcnBUm+HdmGPu8nyCoadtqn8p1nfMAMZjLhsrcvMq9lo7D4F91LgsvHOoZ/qbQc9SSMlcKvhYp7YFfCeGm5TvATrLYu0VqIpzYs0OMcuWAAeEWhstdoy8Fq95hi3anVooMEsylpwBzhJNNuhcGBmzbKSthlp1Y32GKDFq4mWMH14n1TxcCHuXunIhpH61+62ZYSK5G9B2YXujwkySO5XS0ESm3+KqoSMlqmGHBbJkovfkN63Sm5SOE85gVQKFV38xOUcxVH1M3iHnZ8lsk2LNNQJjQQkfZ33G9hSOcz4H9jhBZLptz+9Qc2mjdC557EnHOCZh6fcWoxD3eB6KWvN8BBqW7rBTfYQFxb2xsLNU9U3qLzWupK5j2geoeeSAyD/B0rnfivV6bMqxgZ+2bWmZWBW/+Iu/SF/6pV9KRER/6S/9Jbrvvvse3dZ9EpbtqcXkKSgZjK6MNJdSNx+e4XgmkXEqaG9NqFDPRbAYpPcA22Z5DDAd4YyezVyc+/ZkRdE72pwtb2A51XR7Z84miW4SChy/Q8AifZQelQwEmvFOuC5qRN8QIewg3atnI98CklIZ9YpwA1vrDYVlRCkhRyN0LNT2WLI8weO13KDrEbboBUybfOmN3kMR5ajE9Bx4P3CTlx17RAiDb+O8sF3asOhNqKPN2aUWLTU2Kbio8n1bmiVj/QwuCl2kmIVHlD1A0XsFcGXeJhasPllOrZu1RYI1zg0x54V3U3oVo9chBxgqghoqvDnbnkSDrlLPADdyoVoZQtr59YmIPAp1j+fDqSIGhDf3aDxzGGKm1dBhu4a+klpWOXVdusz1+OnhPZR9M2oTgBdyfWHoe8UskBsaR7Tz8Y42Z/Nnyc9oIUhdSueidyp8B0My+dq4xlSbqLTVLHAL3105L/O8h7oJ3WpgPJhhw/C36hsOe5YETEwak4DNbJyLY2QmL/So90unBIYxhIhIz83bfXuCkiF/vh200DBs7+BCk46d0fg51ALKCEww0GWxMY7SUWt3dTiY1D1zpbAoR9TVjlYP5wNBvkclI5r7phPvJeoY8jws2yKTPcjxjc8JtbPwOwtEHK6ZjsV1SWpqLBx1Oxq8ZOBYvk89AOcuROX4wVICMsi5KVRrtgI0FMRPJbaod0N/z+gnjoA8ADNj4ogZR3lIdkOWubWfXkztZMxLjRtrca1x7Sw4iBC0zoAl086dHDuZZqOhJYlzwRwIimVOs47IBmXk3Ndy8ol9YBNVjqrDRjPLJduRWXwQfs025fpTTk31qblaZ2E+FuPHYpsdUc9kx6LDQLLrHG1P1trm4/DfZtof4T2FxusQTDgGQ2SnNs9rDw0HzQOjPrGNzUxvxmnZXmHGvn6iafzcKo9NOTbw8+xnP5t++Id/mL74i7+YfuEXfoG+53u+h4iI/uIv/oLOnTv3qDfwk62E2hV1ekpFvtS8t0GWgOV1xEnDBVIpZEtaLjqbg96w6wXU8LSnU7JUnQbo4LeB6jXe1PTn4blkEF9Ej5kBiMDixV7abBHJQC7bg0hEFAzR0qzu5GFW/c+eQpyEhaE1jgPoR9wUWvX4Pmp7TvTFWCeyLPB3/h4X35oo4obYEODUaZjzDDpD3Xk7ecxZY/aod6KvojbMRD0MTODm2/WeXCCKGC0lUlyXQDHu+yz2HIZqldJdZyncZXaKAxEiZRikmEFCGr7MkNN6R+kfzOwS8oN8pzf5ozCuOBWp5uSi8jghyMrMJwyxoUhK/N0CFNQDEX3Dm3UU9SU3vCOYSV0CA3yungOT8S/ThSM7Jj3bpUhLLUNQsjnCeO8RoMj6LAwNwnTurKFjZpbiv5eerj+pzgAEIqJF2hHKdxrHp++i2swjeMDXDogvdo4WV/NOqg4hrGVZqRAKLxgBXdroLR/OhW/avT2qr7azYXsjSwueJdcpxzCOFdaRkmBZL+ZSOW5xDrTE3GXYHs/TCBTvPDwcg+LZuQi1G/oZpwKft5uIlMi+ldkNn/eof4TssEoL46p5xWn7QmrvMFtncTXvn/X5IZwQQ1Tk3LO8xI4SfHGSU0GGw4HfqkmkAxk1K68ldYMU0OP0eiPnSdb4UbpFhY27/L4+GN5x1PUYwSTxHqiMPzHS9ixka8S1Oa0FCqSJschs5FJd2+jz5OaY1xkjRCx60mutFS4Pc3id5gGLRcTFG4CTzP6EuixZ25yj6nIhhJ2IqisMlKEdUgCTJOOn7QcWSYEVJ0GO6jrYoo3PsnoSGUDfyEj26jiPkwHqA620uHMmaJ4YZnhNauPAqkfHoGBHTolG4N1Na0Kmh6OYkUa/iq+sUDpZ5kTAx/PQ1uV1UQGT8DEY7z3bc/LdRvu6DUe+W2NWVQzZCpGohz5RmYznQcERcLYIOkcBP1wtAvyGc+amL49CqNcTLhXaDZRjAz9veMMb6Mu+7MvojW98I73oRS+iT//0Tyciop/5mZ8ZQ8Bu5rJ7/5rqJAiqU+RyHLHhFUplwZkicLJMeiI7908uShUy5hyFVZW9/M7QqyGiLLU70bCJxbhVBGqqw055asY2SE8RGNib80taXG6VhoPsn92/4HooP6ZyVIOOTqzzttcHw+dSKJmXRgRQUSlEoi3SisUxif7ZPflsfkxqe/2xy9Bg0cdJCwcpyH49WLCSOozUUHfYUtyD8B1hwHQpE05T0HZR+jqX8+Oqp1/IDFYiourysGmr11NoHdbv1hsKZ/PQO38FVE75uaLHI0SKOO7wma0PiRbgJpd032TcHT4tfx6Lhw6GdmCe32CMCayfNX5EKKQKGeN2y3EuQ48KIZBERNFXirJcCZ0nptS7DnNeJ3ADvZ8QYtad21XvCJ+7fsqk8bN8MKc3uL7XGj+QuaY5k7SkLucWiusDdRCChmLOi4cP1X3LTQ+HKbK2FJewqGnR9yqMsbo+ta1f7pvXbJIBK8FJ1PhhUceDJ03vVxZeJQV01KZZAyxMwSciuvZke8lcJO0k3PR1wKhbXPNas2AEbCSIabAg0UgGg4YBAQluEA2GOYYW48YlVo4iZACSYA5vElGMdroHAI0ksMSGesFJIXWQVGgKR9yKyy4F2FMK/yWa2SimUkq+2e4wEAc/SFBzkX5Xxvnwb3ZPM5l8xqoLoLBi95AGxxTrMerzpJ4PO46AmEy+GwAUzIoo28+bz9IGVs6ZCwjrZu2sPOxsOibLvIcYifUoxXfj/ZZs/CNYUhR1CPs4h0utQpirXQhGRrv8I79HOvOQU6CjGsMnl7MsWrY1rTbEymnx3MoI758L1y21a9SFkR64kp4L3GMkIto1DyWaQl20WLstk5CNjd1msFkLwFNWp8r26ujwtnlNpAqFqmXBa8Lz7pcaCJCgCbMpTUdJ42YdZtz1PYR4stMiZCF5UL/xbuXhmPK9RKDGmQ5kvH4xq54hyjz97ai51haZNRieLku9Dsr+VVkfC8/Sbzpy+7A4IKa3rGZZUaWMkRTI7O9MDqQoQu0ozo2/m7DEWMRwj1XHrZKXYwE/MUZ6+tOfTh/5yEeo73s6c+bM+NtLX/pS2t0tT+Y3S6k+foUqnzYyhQmpWgO/XBig3VNvIyIiBx72JhniknHgrxoZGc7tZ5tpvxYrycG0Qfengb4ZAlWo4wEixP5gSw5iZVVGCSJyYFD7TaDoScfUSkd5ojQjOETkyWEmBRC3C2xkFBbzuBAWLE6q3hMBGJEt0Gmh0Rk9UjsRSJLtHwEQuKaRfUK1PRopTaWYomVY0WQ8Ki/gKQHWdIHCwmvvZDpFhvs0aIzur5Tuj5okSkZVCCrWWpVmzxDFkx7Mob8xDCFWFXUXTmZZt4jyjShfG2O5+f2T740SGWcvm2yLGBNBhFEpj27lFJAkY+EZcAv7OdDHseuKFi+NgMZTt6OFkMffxbPsToCx4lMq76xh+W6PU0NvIRzLJy+jLBh+4bqlwVYSm8R0XzXqPHlH5EmLd0rm1xjyBwLoCTSRwq7IqGDhZzn/IDtmLKiiTHpjLj2wvFHFjWO3l8Qc0RjMjPPBeNfjZ/hX0vQR8PLbQJsz5fAIomnDgJ5hZIsRkQpPiU5vTJkFRyTGqNKcGTSo0DiXOGlJN27UvpLeZQCkcJNHlN/fotNzZtaObRkk4U0YAkB8DobHkZgylpcCbU57lVltm1KlZ+xNI8yoBPRgUWEZ3ghLRv2mjpS1m20s0tyqBL43kbpdp9g2MgxtPAfaNTKTxPf4zlngTQYUXZ3ajGPW9UYI5qnpb9YYUgAPD1vc64pnGVw6Dlkzo5NObJpBf8VtevJLfMB5I+org72oGDSVI7dBJACqsrQTRf11Wtus8JB4fpfqq5ggRDDkCmsKr2WZdk7BUSLtuayd0kZEGzKU12giIl8Cl1lA3rBJp0akPkXQN/V96KZ7xgxerq8mPSb+Dvp+TBCB7+V+o9dCJTCtmX1OOCR2HhjGiXSAjPe07XTGN9EP8Zmn0zVwoCdnwMPCcYT2V6G/pzrEsbh21X5+Z13IxDY6X9FxJPp78+RTVB20xcx3UiAcbSgX9F5DrUOcar7TbVDrOLS/irp+C0gyJR6CRjNk20Y9PZyPake+KycZuVVulRstxwZ+7r77bvrDP/xDuvvuu7Pfnva0pz2a7fqkLe2dZyZx58KEZ03cXEaRY6CZ8mY8Z+rkG0VyjvpVnSnzZFONiBfv93Jr1h9qdyuKplHf6OwN6WrS4FGeqyTYi4uknFA5DWYNIQeuj5r5YtVPuQhbBojIJiM92Mi45ITXgjNpYRgLU5X7U+ABkjHZZ2zhZc/ZjK5N40D1dSTt7ZB9zyANiPqOp2MITy/GHPcXsoz2Unul5xrACL/VrK+SeC6OFQ3q0Sg+OdZ/faPYSnKBG41NXHcbT6HxFAHciNXUfm63yvqR6pQbj5IXNUvJKQwOL8UowShwRNSfyUFvuYE9ePqZdM3CZmwmppsoeW6xz3z+Lx+XtSvG8jVl40nTln0XtPccmIxh4Y0YdrHB5LA9ow2xdsqIbIUGErMt0JjiVLfrcyJjFwptpuvKc6PTOj1mCU7prchwyBGQRfu6Y7YneNhl07yjbtep9OFM05fQG4ocW0ME28AbY2Sq9EunUmErw9bIUiLXtwksBo2fpaNQOQWwqPmONFjNTCc59lCDygL7szGXrcF6HCgA2ctnmdpRCLPCOUiCEdUmUKgqOrg9v8/dBzi7m2r21Mo+GmxhaAO/30oHS4NZGOLpeu1UkH3G4CUCKYfnHNVrms0exwDLAo5hHSDJaFAhGulZSeFpqcGU9RmCl22kHpbbWuxleTyXM/fkn+W1+H1XDOtxLZLgIszDu16z5xDgYdABtfO6SKSYBfo91U4FsUkcsxzm1UQ/vJcqbAivT6TmeWSLExG5yg7plQ4O+c7n761Oaa0yhcr7HkN5bIBnFgCpPVF/Y1qVmIGUnF7yMMvtmHGsMeZ5xcjRAPxcRj4UDp8qjxSXjQYCBHtrHMOYoT6tgVlfInDG40lmhpXhhKJXTGflbLKOVCfahkl0C9nO0nHoYiS37crjQGhKeQASY+PJI4hXAAz7E7mNH3Yaah64mh/UAQB4bl+JoytGNxG5NaYIbYbnicCksYdBJ2rsnLrPm718smf1etrTnkYf+chHsu9e9apX0T/7Z/9s/Pxnf/Zn9A//4T+kX/qlX6KdnR2699576fu///tpgZEJ/wfLsYAf7z3dfffd9NBDDyng54lS2r2aYjPfbdvTMw80jcEAD509hdWhYBYosYbhZLm5yFg1wshVNNkzC+2VRVr9SgscTkVkDwPPBguxdjvAMso29MPfyovqdRhCSShUesEzUUEJBqiVW39nzQPIjGAjrdqULXjenKmNQ+rnfilAFSNeHjdfMgSE29g3BeAHvUtRpq4esjyg57Y+TKK3JwWddoHg1VKNgeh34LPZpKEY4V9ZsajUMlyDxbaR9bVYUrWZBzK8IcYpi8y+YnquiMjJEC2DiWQW7wdAK2vw9CcbdCrT3qj9A+MANwi1RSs2Nqc4zkmDKyVatvVO4EZFhxpFfeIRINbY1KhZXXIDy2MMAZDR8Jy5jLkJLB2P33trE67BAhXStWOHc8hw2q7xtLrYZ+LqRNMGXL53GL7mu6D0XlBDha+982BubLLgcHasSi0dVTiHDFPjcYMs0Wq7oHqtM4JZouu+zc/l0GNkN+UN1V/JsD0J7vXwfrkQqbkOk7O4VL/S/UI0PVsU/5SAxeZUQ81BzFOK0wSA7N4/3evhudxeWF7ptRYNrtdpYFoi3FowHxwIC6dEXknaBulSzQGCdZ58G5WI+0pk2GLWH4JNvHeUfaQif9KjaK6JtVuMQwkw4ZxYX9EhaLXYm/EajKxEvkcN1AJTzDkNVjIoLucl1NxjEWV5Gs7no4hyXn1cODW+EeiO3lFA6QAJyHIGIJzH3ACut/szGSN5niyAjjmIhnpXenxm9cjNP77ejqhf5iHI2VrLrOYCo16FnUHWRQv8M7V5wHCLlTMAU3hPOUOhAjUNh6XFxIMiAUjOEsvZ5MZ63ADgWWNvakC6BmrMLdhJJ/YMG3zg6RwBqMmQrsz2MZzdCogTjlZ8Z7EEzGgK1W9v31fMKX6+8ntlD/eRwh5mrLUBVHzvfRsUOxtLv6rJKVmD6V64PdVS7+GGOTyfxOW4ZDtRZf6qPXXHEBu/KUp0tnF63Doew/La176WXvKSl4yf9/en6Iu+7+mLv/iL6bbbbqNf+7Vfo4ceeohe9KIXUYyR3vrWtz6m7Zorx9b4+b7v+z761m/9Vnrb295Gz3nOcx6LNn1Sl9UDB1RX7I6ep7CPH6Wnk+ceOLVvBsbA8mGh8QNGdmyqYZKSZBfJZpnZqEbvqIdQL4yB9dtepWvkTbmk32qP0JLq650CcHJPTCFUpXZUQTpUJaTGTA65AIhFtVlL5Uq476bSYT1m/HK+i+LY5OYi6NsIxL0+OZyjKJ/puUnGSADmi+uCMhSk5gkbi80VG1AoZvNIdftzq4wKSyQACD8taouL+SbRBU2bxc1eMZuBszYl2pBVpxnhcwg6NNc6ah5ea8ND6vAk2nxYASuoYW0AwVqD/utSpr4ojWwp7jyTRTM6R3EnNzJk/Rzqhe88s8owTWsDBkp7Zidjj8nizk3jdvFArgPkth3RnSez7xYP5cfUtw3xEs3V/Ab9tteMh0PYVD28NthgOrNLBeEG/E4yC3C8phyvlc2mq68Nz1CyFWrY3C+uDPeyvn2qQ4aRzCKXkai5lvf/6mPTnLw+uzselx1z//Xh6wYBS+GVbLyZpaRZW8Y0vCet1m/AwmDR9kQ+P68e1tR13Dz1q0qNz4xKv7CN5NA4M0ws0wdK73eobHA9C4eDW2wSU1WGudVCFyZrj2p/1CCwuG1mRiHQwZn9MLNLnkFvaOtsBixuB4Ak3crruR/2B3yOlf55AaLVluNEOXnE682gUw1L23CuG1lLXOTGnVk3ionEzBMxH6AOED9nOY6zjWqmr5af2u5qkES2oUpgEgJqPE/UB7iJygGqatOrdW46oAxiuL6ngML+sAldXEqpndF50AXNErhRQWMseJhz1J5aUY2h0dKxweshtovtpcxuReSMkb4CaCsFgQ1tRlx/XRaCll6GUgjRXGhRHxWjmGjqx16EsaM0AREpFjOycMZw6hlwqdROzMiLZVz/UBOpj9RcPtTfiz7bnD49tA8cJSxwn82TmM492U0Zy0cCRfL5KdaRwaiX+5CSHEJ6HvWDucak1MIMy33TxrBCEWMAZ8qqpgalMkpi5bimLSryxjPIPkeDkSbmXBQJH9vpkiwArlUGGwn3WT705LsnFuPn8VBOnDhBd9xxh/nbe9/7Xvrv//2/05//+Z/Tk5/8ZCIi+oEf+AF68YtfTK973evo5MmT5nmPdTk28PNVX/VVdHBwQJ/+6Z9Oi8WCdnbyDfPDDz/8qDXuk7H0ewtyNafqsymORQOCiLrk5UQUm73fUhgaF30XI/WrOgtliXLCcBo1Hn8ywnCwRG+EbnCce1c2QFwcPCF+Jl6d70t7jDWyTRgbbV20EN5lxrrPsBC4j5XXizNgrfAVEc9n1NtByvDwvcs2wSAc2W5ybSIoo5gqhjoUDC0ZM8/nqMUqneuFxwfp7W7TU38eqQTguUpAHWoEuE1vfpd9vkE7Fts+LqSGXgAXpuyGAr1dauDg+zAKR8qxJ43Xg/KC65pKhxLIkB+X+tMwmqySbXqcG8JfXA6EmPHkCPauarUJwU0KP1sUpCfnFADYg1HptxqckW3n0C2Vupc024coB/t4041ZyHjO7bLNI3rPhnuUgNDBHcIbJ1O4owEfB7acLNszgu2Ynhn2xfbcbvb72BZ5n3HQPUHP75ipbCYTTlj4YogYFwY5MBRofb6m1UP2NbnU1zsNRoh3mX/TIq/DZqkFhl7OtOFQS5sNhUCVLDwGZYauVmjcjLofpMGC2DjFXpT3XdZ/YSYEgCcCyGgO4qANBNfkjGDyOsisaXe9wdCANqQxgqRf30UjAQNuUgzQJfu9wPQ4GK6LGldynC0SwFLK5CM9/BtkDl1M41yydQWAKwW8a/B39EtHDeQ5kNmap9DQ/Bh8Z7ggmytWer4b1yDxsByK8ASn+kIJcvO8b4WaILCjNpy9BjrkOVy30vPz5GLMQ5QJ2Bh8bil8RLZXsXaYJVJ2Ao3XsdiyKivp0YbBuNFH8E2Op1U9CG6DLoyyMYm0YRk0IIW3zXYIOjKjd2o3hfNdaNxsJii2EevLEAa3qIf7mRkrPF+oscdznAxhh+ftOfmCSILiGjHxSLsFhcC7XgMqEijlfsK9AX+P+xRp36YwMhXqyPa11GzEYyqv2wXvn0/OLgWwbFoNVqIWkRUOKd5Bx9qr8MgckRnqm7U92WIeAa9tR9Q/sTR+Hg/izm94wxvoe77ne+iuu+6iL//yL6dv/dZvHcO4fuM3foOe85znjKAPEdEXfuEX0mazoQ9+8IN0zz33PLaNK5RjAz9vfvObH4NmPH5Kv6qmyQwKT/QlyvBwfmKvbAt0f2lsG4ZIrB0FCXR4saEpMTGIiHyl9CeOSu+eHVuXw9e6lSe/W+Mcnp9fSGndryqtvg9lpD7KkCDxd7ahh4U1NF4Z1dnvqQ9QMJO9yg5csRKQ4I0tXpNTnWbggkq3vtKsk5UeV27PBoeUGDWES21PVMqAZwHxVmy08Hn4bVCbuOiRMqu9u3xd1f4dNPCcBiGzrCkcNgibm1NLimdWN5RJoUgRj8bBqfBmOUvnfpToIR+33ioPkTRk+72hvzkcaDpx+EdtHKTt4wYPeWkjJzdlKoySNDgQQDuDGQr1GjMRVootgZvQ6JrZOWcSaLaPwfuWYNYUPop9U6lzVf08nWTZSGQlhb/H+uBzJtTL4H4+hjk0sbkGHmIZLrUJA0AONvCYut7p78ZzW91fWgtt+Bc3zYurfRbmONxIfu72VK3uSb4vE1iAO4th/lNp2OX1nD0O+H2Qm3DUxhnXVLEWyn5YPjjRVlDcvKtqWl5FAVcxf+8VNNoKm7PVJZEKvhno+vhOsNZGtRZzmhrncTYTDpHYsOHS6PUzUHN4HxU7TOpWcajV9gRc1BG5Vggl8/ECPOGxWyLNuU6AROj0Tj/Jd1oyIGSfYH/U60ibk7jeTH/z/ap+rfSxRHk47Ti2EdSsDbAT3puwqGfXpOH85ChBTRlDV0uxk5tKow9Z2kenvho+h4GtPbPBZDAEM325LoEbgqHnMHtm5DlLzI1GuBZRviEmomGzj7sxCciO14Sb6rz5vVxrq21n7vTG0KWZhBvROZWBUh3DIJaKX6uMTHHIMlPNyt4jdugiOzxWnpwhotwLzUu+NoZ4joC9BEmg/wIzzcPSPCYbt+h4tLTh5NzAh8M9jSDWDIObvKNuf1FMDiDZ8IqJuqhGO9xuGFFMzHC0w9vVSjPtscSo7Vgx5vvTydmHtxdjitqYue8SUtEsqC8x7G7WEkk75x5JHUR05cqV7OvlcknLpW0D3Gj5pm/6JvqMz/gMOnPmDH3gAx+g7/iO76A/+ZM/oXe84x1ERHT//ffT7bffnp1z5swZWiwWdP/9939C1/5EyrGBnxe96EWPRTseN6W+3lFdszsODTC9uAw/SO90bZ5br5Nn8+I04SjUufbU79R5OvcS9Q/ZBd4ZGj+Q1asN5U2zZBjAfNrtVeS3vWYhiAWBjSeMxa2vtTr7FBTePElKu2QXZVO+5TlCQyEzaIbfNsByYW+0omxaxg2uewnIqoTuC4bzuLZXnrAsFXYCgYrsMYw9F30TK0eLK52yTVjTqRIABIqMU4zjWOTSXC4sgoZReZTegZURLDMYxyxreRhQddjbIn/G5r5kwGfjs5RVIRMNF5sDkakvgjZIrCvdF+L5LO4fxChqCAfj9wi9h1lGoDjoitUgzs5jz7fTuc1Vg7p+BlKyA4DEqctrYDS5qJkEqLvUXO8N1qO8VmLtWMao0wBdvilPgBTo2iwvDu9jJ/rSypg3HCM3d3Yb51LkWiWMLD/0fPI1kUY/3XtYeqKoN7Xj76LOxVUMt4lGGvj8fGZHKaF6Q0MH59zFpajmb2lMj+/QBhmbg0sOBdWzcOSN7VkllzIDietWcE+8kY9+asvimuxTIXCKovS9Zsc4wfYaRY4BZGNGEYIIMvSr3RvYschcY0ZMFN9jyFi1DXrNwFc3/Y6gTqi1w0YLFzuq4ALynG1il6O4c70exGcrTAQl+ocfA7KYRoDZye/yehbJ7pbrkpxjiiFgqd7N2fwrGarWpAhWBY6mDlTp7cU70aX3Sq1VzKKYySLltx316BQriDvreSro9dBKpY72izTA+H6NNNsukmYZyXvkdmHWJEPUushWleuvbIJ0TsL7RX0kB3pfUvzXGdnUiKb+U2HuclmvHJHTmWKdBYBh31SVth9RNLgEpHk9xrBU26jWl8yWZyFmI4ubv75Rz0CyqavT83plcsiEJj/WHw4vfBZuJ+YLf00wkHAcOFfMPDu0MT1nBG+4Hty/yOP2F+S7SNVDudDymL1V2MYeULWwqBTbrchMQj2xtrfZebKqaqmlOOQ+j+vEZ+mHR6GiBCSJL52j5CE2PcV+RnPgVpktd911V/b5u7/7u+nVr361Ou7Vr341veY1r5mt67d+67fosz7rs+jlL3/5+N1f+St/hc6cOUN/7+/9PXrDG95A586dIyJ77okx2nPS/6FybOCHiOjDH/4wvfOd76QPf/jD9Ja3vIUuXLhAP//zP0933XUXPfvZz3602/hJVda3LaluCighr4W4mBux62hUHp5NG/2tACBwnnKDYVOki0rvi5VJB9c02EiYyCrfk7gm6q9s9/1wz8rgmf48PDssNsvL+WSJ2YqGdtgLSdVMkzGzKNSpuJk0DKlcrHX4GxkmfI+ucB2iaSOsngfz3d1k+SrtG+MZZl7IwBvJHJAajQscYlBfe6JSwA9nWdqelGFImsmF4rNK5Jf7dI5hxs3EIWGMMXkM95Nim9SLoz2r6X5LrAFvAJFcFpeZmiuu28oUNeLcjWZPzIk/b+8YRM+V4DpnG8ONHPRZWHozMxZRvnmrMLuM8c4jYMH2ErILIxn9iPZ744201HqeUF5Tb6SzpnzThptPLsyKy4CcAtgoN4+ZCGk/M26DU+wAJzehBaYDg1sIgLRi/qgOA3UrLdTNm/Kl2EzjBra+3usNf2HIYXhcu+MzIVwizRgkMsL9sj5jdgC+l45ciLPMr26/kOp+rLy8bvF7iiw4qyBztD/ZkIcEBtV6eqfX54c1Cd8RBpa2J4CRKdYI3xNRF5V+zzYBlsuLIqQm17ElZ+HoeE/pM4Izro9HbjDJa+BETvMMGK4ezvvm6lMqqg/198gSJBrW/Kz+wHVLYI3MYzImmfhbPgcEfPuFo/p6Xl+WZp6HswJy2YCB9VEwfKMf3kEcw6MTwul1mYt3ToVxY2nP7aXr6H5UgCmWGDW5RM6xPG8btle3qij6fPBlyRBYKwWdSC1/L6pTjCJjEIpqstAiA3xSYNbuMvudiHQ4WCG0XjoBI6XQIAzFGseBsP1Ac4wqp0Lm1NrMa7YB/KtQaBR3bnU/ysdmadfwQXHZ6HAmMYfzeuE7GAfGc2KgZ6weM08R5ZqGM2wU55wOg5L9WgoDYKBPZRgTmnhtIG+1LQGV2RjCvj7CmZy1AZlUJ1aK+aUzqmmdtqyfShnanBtAWWSTyznE23YT1Ub42k1eHs2sXn/+53+eaeqU2D7f8A3fQH//7//92TpLWcz/+l//60RE9D//5/+kc+fO0R133EH/9b/+1+yYixcvUtu2ign0f7IcG/h53/veR1/0RV9Ez3/+8+lXf/VX6XWvex1duHCBfu/3fo/e8Y530E/+5E8+qg386Ec/Sq961avo537u52i9XtMzn/lM+pEf+RH6zM/8TCIakLPXvOY19Pa3v50uXrxIn/3Zn03//J//8wyA2mw29MpXvpLe85730Hq9ps/7vM+jf/Ev/gU95SlPOXZ79v/0KtWFXckYe4qTmXhZ2yRoiht/nrhXD0xuLMuTHVZNnppVTjDiugFYCS5EaoEKX19HGnzQKcA73kQZseGp+PMraq53Oo29aP+KQyQw5IBIaQMpgdQVh1QJuqpE0uWmwDDCMKY5o0VzWNYZEJpNhguK6jrR1u60kaGKpkXHi02GYvyEoFPDir7vkhB3c8lm2yhvGKbQrHYztgHRlNUqil3A4lJ+f74N1EEmEMwyMbYBF/UYdbswI4MhbJctZmnMbM/lz6O5sh2otWiASqp06m+ldVNr41h5OZnOLI0r+XyOyOSHxpMXbagfupZfg0vBq5SJLFaOwv5KZQ1jI3AjxJubB3O1VhcCudv3s+8WH8/FnbvlAEotH4TUp22vGBooDt9c2mgDRvQfA14WbTo0lQKbJJskNsO7pbIHpTEsMyWplO+XmWEi2FAHhVgv5SmPam6WmbmYsan0atLch+wtee4AxFVq486sK8kSqa8ZDCwEi6HreRO4fDh/X0PtlNB7D8BPbLxmymWqy6kdqL/WxRSCnLdFroH1qKUAnuY0VrYn5bOEjcUYViq+E39nWaEgVLY6DAq07oWxN6ZMh/WMwRbMECXL6mJP/dLr7G4r3V4soXamUydrexr3CCy1e56CK4/PoTQKzFIbd9Ji4auLkWKlHUJ5CGK6AqRzZ8aSFLdWLJs0zl1hTySBIgyLqQ8jNQD8SFHuEXhCu4qlRBCEkk4ADokqhC7LeU8lOqi8Zgohmyu9MzqkVzvLcDOJ8x/WX8oARJGoaoNOPCGmhjEEqxS2J0/F8WQwdzOxXckWBOFd8k4xazK7oRDqMtp8cG63uyvOHVhYSh8z2YAhC/HTiUz6U7ndoXQSef7DOWOpO1EBfc6YF8TnUlYv1wUi57LkH0REXrCaxiyYMGctLw/3LW0PnIdrBpxuJCOnlZ0WASnxLEcnWRHgKYOm0ROR9wros9Km65ONtsK7wNmZ1Z7BsGMVM8/pMexQx4hICY3H2g9MpbnkLFvOAmsAS0eB/jdjeZTu+eTJkzckpnz+/Hk6f/78I7rGhz70ISIietKTnkRERJ/zOZ9Dr3vd6+i+++4bv3vve99Ly+VyxDD+b5RjAz/f/u3fTt/7vd9Lr3jFK+jEiSl99z333ENvectbHtXGXbx4kZ7//OfTPffcQz/3cz9HFy5coA9/+MN0OinYEw1Zxn7wB3+QfvRHf5Se+cxn0vd+7/fSC17wAvqjP/qjsX3f/M3fTP/pP/0n+nf/7t/RuXPn6Fu+5VvoS77kS+iDH/wgVYj6H1HaU0uKdSHVH2sZIOtELGRs5OKCzh6o7elpckeRToqDcZ9tDMW1pKGFBq/rojI80MNreWRH0WkhmKvSle548l1FHkKEZArZsW9Q/4gs6nrBGMpcUOK+JQiEYp7bzshwJgyUtFlCyj6zAtTkKxbQ0egqCGdnxgeSZupaL5je+LtgcClxTbkWcly3lX6bcmPGQwiR63uKvqznRETTIgiWPRso2XeGEaeNxulzn9J74vMIjaeqDwpUdc4w95AAAQAASURBVOIdCTsMENoaAFlIGYAwo/i4PE9uDiR9GetvqrHdY1vExrrmPlGMDYNSTxrUa/dqahbowUztFu+5P6k9GAEM0vZsDlYyiwFBYd8G5dVE7Se/bYjgvuW7y3NQBzpVsfHDZhvtMtFnPB8uUI+As0uJKnG+YNC0PTHVl4XzZDQDeCZBp/TNAAUmuyHDLvU9bgxl6JfvI9Vrrb8yNkuICePm291A5i8O19ucycd29E4/XxjDfeOphvCLXBNLb4K57n63ph4ZY6JtVQonxblgymKjBfO5cP9l6bdl+mYBPpvrLoIPwlFS77LuHG6Ok7MDmV9iPjg8U1G1jRTAy3/yTxkAFCDegbE+YLuA4cLzu2JMBq0RJYEzoiGEDUWNM5ZrlV+DS7d0FGtSWb0OT0/3OK3VyMhJ1xb4N4Z68XXlOtfJNPPSgY8aV51mOklwiH9TbwmbDjPOeXKG7hxN/bO4KMJ8jfUtNLZg/lSRZq3x5+jxndCbaJ1RSLKq+qyt00GOQu21o03q6TF7EpnBhnC3Eg1m9k1B4yezUbE+78jDeM/CjJhVo+zAArvpiPUzVTb8O0OwCguv9SYLDAt8Jr6NSu/LagKySeW4HMF5vO/Gk9t0iv0i7VrO6IjPkufPzIGIYGOyhbKxLU3tI1hpyqGVhe4z8mofMwcaUWLTeWSH8fVCGQCKtcHCNgAevk7+vVFf6Vz5ldhL+qSTRfDMnHNDnxRsc6IpxMsSPP9E2S+3yqNXfuM3foN+8zd/k+655x46deoU/dZv/Ra9/OUvpy/90i+lpz71qURE9AVf8AX0rGc9i776q7+a3vjGN9LDDz9Mr3zlK+klL3nJ/7WMXkSPAPj5/d//fXr3u9+tvr/tttvooYceMs545OUNb3gD3XXXXfTOd75z/E5SrGKM9OY3v5n+8T/+x/R3/+7fJSKiH/uxH6Pbb7+d3v3ud9PXfd3X0eXLl+lHfuRH6N/+239Ln//5n09ERO9617vorrvuol/8xV+kL/zCLzxWm5qHp3TuJW0JFbcqF+rzAxilXur0Pi8eniZovwY3lffU7y0z9g0yAbhgfOiwWQfdFMX46TX9VfzGBSf6elmZWjRyUmcWDBpYrgsKKY/gkYi9ngCzbFFyrTjEzUsk1894CAJ743JmBINg6hlL0ITbgDbXhhdhackCu+HgUKUdlx73kRaPi27PDCzoR0i56bdBe+cPOW3nvIWC6YGxT0dgCYFJp6nSFs17LjsFs8YwxM63A6g0B+KNjB+onxllmbYTbkJTX0mWVlaMlLRjXUTUPJgv8EEKx6Zn6DD7TmpXfyoHYyRzjtN/ekzLO3owJ/C9fhhjIYhCfSL7qoGMIdW5YSwgs8xvWvWeuxbSzt9/RVG5s3TOJxIY8hCkbN1dEnVB3bec10Ji/CCbgRmOzbWpLahPNAr+uskTLDN/uU6MIWMIN5AuW2786M5hnsB3ZPnA0PcB+mwp3odYeWr3K5Uunr3FjWT5oF0Zog47gNeIQbx6jeFmlc7ehw7MoDfW6v2m3MtMRFRtl+S6qNLRyvWA5wAV6phAw0wDCpkv6bftqalfFyJcWDKZEMzanmr0phm9/KTDqcaRVdjsEg3ARL2J6tz17Smk+WEtbp21A6dFeJYcrofH9Uuv2DCrB/P5eXNaC/tLdswIMuEYc0TVYVRZ7XZEynoGgbT4eAIGZUgZTpl8WRlpLH02MjQTuywSbVHcWfyNYanjMUZ4mWwv0dD3vgtKL7HikFAJaBgbPgUWoJbi1TQfWRu5wlo1/m5pqMjxlNZD8/ZPr2ZDXtyhzVyvPAMvMsQNEYX0ucBcyDR2kPnS9SoUKwtJKmj8sO0zx6inGIcMUmBDMtujEixWBFIwi9JsQcbPjg6tVw7SxmlGo7xtzhiGzsvWyJxFlDPdWYhcZfiLUx3jd5jJNQHzpexdwmaPwAJ2fVRMGjNLXClz3E6+J5E2WvSOnHfU79mOyEYy6lEvM+jxY2knZf+m4g87FQ6HdcW40CFuUhCbIwlwL+iH9zkqkqawTZlFvUAnS5kldLOWRzPU69Euy+WS/v2///f0mte8hjabDX3Kp3wKveQlL6Fv+7ZvG4+pqop+9md/lr7+67+env/859POzg7de++99P3f//2PSZtutBwb+Dl9+jTdd9999PSnPz37/kMf+hDdeeedj1rDiIh+5md+hr7wC7+QvvzLv5ze97730Z133klf//VfTy95yUuIiOhP/uRP6P7776cv+IIvGM9ZLpf0uZ/7ufTrv/7r9HVf93X0wQ9+kNq2zY558pOfTM95znPo13/914vAz2azoc1m2gyxIrjbdFMMa0mw7AZSp+OiYKU3LqUjlAu/3Ahni60Sm43KqFYUX2turvSCEg2ar28DVdcO1fdj05dDrLkCMmJUWR8UtZNXOGmAiUnWyXhcCMmJldOaLJmXePgbQyFGoWCkb2YhVewxLnvkxu8wvWgXyMN3wQujhMECWKy4HsWeloBF15M7vdQbuVZ7hVS896Yjj2GCuGiXbMk+Eu1AOIcFTBYot0Q0GpIVglYHLbmu089S9jUbk8oDNYzhIFkUUP8Y9iEXeAnyCTZExLAvp1kiGd2ZvVsoFJ7eUQU2ScC06ymuavIH0I8Ge0uBGCHosQnv3zhGEIvz3ggnhGN2tPGT/c4G/ArZUA25RdQbAqMuJaZphRhg2lw2TjMtr1Ij9VfIQMw12tJYQq0kHnMlVhcNrLVu5cgBWDA6owV7ywoJUNo9oLPCYsW4AVldbGfTCA9tcJolKvqa9agaeHe7lSPfBhU6hqAzkbGh6RncECyEJbZBhyFIjbIsLELVr8P25D0ywIGgwfhsceiJ/cfOgz11uz4TmiYiancTS+TqzLMk413CPe7SZqJS1ABdt5/PR+2uzgIoPzMbCDPmhWYI2UJgSY4DznaptIcCX0c0tZDeWgpZS5axFMFeXMc1lRRbIgtbYhMB57GCzS/b77sEqqohG1Odei6f6tfMRdWGkaEA1XttFyj2RNtr1rHcAJY21MxyLW12ZT1FZvCMTooh7pzVI20sZElZuw7JEuHjsV18TTQhJRu3p8EOQPa6BZxgqE7b6w0+HlPKuBRIoW9WqJdi1WcZTRO7UQkC++E5zmaC4n8NNJdyh4QL9jyfsbfkWixBOgzR86TAP3do9BE6lHsdgkZEeRiXI6IQM102omnPk70XKquqdjRYtsNwTbAFVzVF7EfMtld7HbIq0Jwx9N/KAN0HIkx0gFINREQebbWe/JwT+2YskUwb7dh1PAblMz7jM+g3f/M3jzzuqU99Kv3n//yfH5tGPMJybODn3nvvpVe96lX0Ez/xE+ScoxACvf/976dXvvKV9MIXvvBRbdz/+l//i972trfRK17xCvpH/+gf0Qc+8AH6xm/8Rloul/TCF75wTIeGIkm33347feQjHyGiIZ3aYrGgM2fOqGPm0qn903/6T01l7+78PlEp1IsHmPA0Z98T0fb0YD3ixpaN0IXQV1FhUTEOISRy4pa09xNTu5BZE32lDAjU8CilqSci6gWDCKm70dOgDQQefDmZ9RzygAvfUfHeNGlXZBkQJFgkwAYlJFs5csCsyQwRTue+hwvQUI9i5Yh7GtMHo7eWWQoyxbphXCmhVCnWl+5ZxXVv63QsCrEKQ6v2w0YNKdzpGcrNRICUxm7ZKI0frVmQFm00SAOpzReyOlwX9LgT7wI/e6XXsbeg6BdKh0Qu2hyGUtLPkkCAU+9CAvHEuRKYQ/AiKzFSfyK/zyDa79NzxnAwHs9q0yzfkRip36mp2gFAM41hGc5ElLN79O9Eoc7FPrf7Qz3NaRhnfVThOyheuTC8vdn7kfoA/XXc17ghCM00fynh+emqw7Fi4xZhc7w9NRwjwyaiCFGKtdygqBtQG+L25HQHriBs3qdwNmRRhizETIf0EAkAQmZzU2xSIwSjwGLYwjNv1s7U5snb6dV38nlz2HFnjWEjta3cOPD7jm1gPbk5seIRPMzCYAXzRr4XOGS8U0Z9LTYRI7PF5Scy8yRCuJTUtfFdpC7mIs5ERD2DwuIdxgQG9WFQmb4wqxeHAGFGvLBwVMEYqjDjn68VsyAIkPDw9HDt1UWou+b26U3UeEyaN1HjZwRY5HCHe+J6GRwjIqrEMa0k3QJ5ERlGRES7DwT1ewfL4k4Sqtb9LZ1IQ6gjnYK1nkWOxTyoNOuifr5aMyfVa2RaVSFmhs6QBkzz9cGum4i8o/bMDnwvN/dRfZcdKzM74ebYAGGye3H5vJcVQ9tInjuGehXDrMrOhpLQPM81cg6tD8H+qr3WVIRnGbxtI7sYKRyR7KI50PctQWeey9C+9tue+p2lsn2kvYjzxHRMctxKIAedzokdlYXCy4VxLsNVNO5J7kOY+anAy4XZlsyOdY5i42m7n++n+BnuCFapclIFY/wgyBQ1sEuUAEB858BQiN5p/R6hnekatie1ozs2Ontc1lZmq+E84hyFJ1o691vlMSnHBn5e97rX0Ytf/GK68847KcZIz3rWs6jve7r33nvpO7/zOx/VxoUQ6LM+67Po9a9/PRERPfe5z6U//MM/pLe97W0ZyIRp0W4kVdpRx3zHd3wHveIVrxg/X7lyhe666y7ym26K3ywUFRojXuBlYRJdJk9kLUIxlHBqVRHRIs+YIMO+xPHhBAIGQWVyUfR/g1I6po0W9xSQ1dFF8tueqsu5uGxGEa0LE6En8lfy8xDwcjxhGyLARET+umAa4ULS1JqBJSmZi5QqHvuGRa1RVFcwPxacdhG0kvgcCU6h6Ku7fkDxVB5exqAO0QQSVNA37ElwO2CkXhPHxUh1c17p9zDlvBbPr7qUi/3S4YYaWPQqDCHiPkbqaQgDC0S2Cxk6m42mVMuNV1r0egA6qssHw/VKHjcSIV2wIFfMkpIpwOE95FAv+c5lAqBHLLgVjBMWASaiKR1qIQS0xv6Q49U7Cqf3yD94OT8mpWR1d02A9uJPPq7a5Z+ai9RhOFi3N5y/BNFnf22TGXBEOoypfvBq0VtMRFSfSWFRF41Qrxg16CjmMtYmwFAvDqvw20Z8B2FXKdTr8MJkMGZsGUlFMByki6t5fc3l6dk2SVS6gvCy+tLwjKOaYwUAwlnOcAo02DjI7nG91mhDsIA1mDBzYrtX0QJBFwD1FpdbNV9I8NIScyVKoP+yUu8HZ/Ia7iXNkzu2uSEZVg4Bi9RnEsiQGkkSIFSgv9Oiq07EGWEmx/GaqZ9Rs0OmD+esVpyNk8sYriSbYrG3MF15oShnTeUogn+l2kC4UGuwqzJdmFQ3+g5aolhpZo0EEjlTkUohz9r4YspAQWXuB3nvcuNbif61QrdwvF978tSIvfvS+AP9LE7VroAw8S75LlLdBi2qy/ORHFcq3NKTmxFqJaKRCa10QrwvhltNDdXXlO/auGk3wp/a0ytqHs7n9ew9ZTYd2iZbw7617Co8pBCKYoZm4Voo2mCBSkQ09gPKEWT1899wDG+sO5HEA0P0jkw8QRMoo3SRGn3vWjuJlD5XnuEuMa3QudXHIQwMk6oIW+Xa0wenj0p0cD0Bn7tlxxWHT2dXlQwU+e6ojLvVfJ/xmC/Ybcj4yVlHkarrW6ofLCQXEXY3jr3QeKU/iPsdBmzR8VZtesXCVkwwSwhbZszjZ2NFbQRS3o4szD3dFzqGY+WyDINPjOJIL6CPpI5bRZZjAz9N09CP//iP0/d8z/fQb//2b1MIgZ773OfS3Xff/ag37klPehI961nPyr77tE/7NPqP//E/EhHRHXfcQUQDq4cVs4mIHnjggZEFdMcdd9B2u6WLFy9mrJ8HHniAnve85xWvvVwuzXRvft2OHvxSlh4HIpnZy58c7mqiwpTZeB4RUd9rzyqCQ/w90kUDqU2Oys7VBbW5U/UY30VHicEC7RUhWxjONf3gdMgcLvhjCF3BWM4YPMizNkJKhNfGHQzWpt/aGbrmrmWKCBKNRkeUXko0inZ3iiBb9jcaMJz2VHkzBOASo5liljfL0khBPSXyXi84yC7i54ORjlvSngwE8WhphPgITxOfj4+xTuFO+Hx7w+hDdg73lTwVNgL8rDIwOIgxKw1VNGQXjfouGuNELT9cpzKixTvddcMYRkOZ55hwevoO6dRdp0VNVWp1bBT/YLxragPrdF8Ew0jELBz9YthcIPAjQbfCq84Gme8LrEt5Xel0Dvbf+lyjT+ReKTGNlGAp142hmVlcZUP9UofhcJ2rh6QuDhr/QYn9K3Hn5PVFEe7lRW04o0ZRe8rWUeDCRnIN4Yq+i0TByOZnAKWYItd1w3vanhAgXgH4kc9kcWnq41qI16Jjo7nSqrkGwS2sm2gCEheQGlmGB8XKDe8WzMNVYgnJVM6YmcsZa7FKE55uCzdy0fvyO8t1WZEWUts1XatFZtueo+XlqNg1EoRhNkWFIaSpmRIca4AxxfVIRh1n+pJ1EGkw1Lek5h+5iWYGoNZESo4rGFfymQzZ2TqdsjmtI1m2QQP4weyEigHkhrlKbR4dkSuAodO5Ts/hxvNVIW3eEUWi9mzOlpBr+8h4ULIA6ViDHTRdL/0mTzVADKyHaAA25phOfLwKqzMEpbEwQ6okiN1JJi4yy2tHVWsDRliPGivOYk7pd1qxKsV4GhMB7OkxEWqnzpXzwM4Dwwvensxtn8MzSXPsUpm9tTlv2L6F52eGqs2EoFlZGYmmMawz3U1/h8ZRe7LWIt/po9T2U+HeC0d+C/YQtIHXU3xfu92a6Azs/eDcWDkF0GXHnE3n4zgsMNIkuDv2iQpfc9TdoMPgpimRtHPukdRxq2Tl2MDPa1/7WnrlK19Jz3jGM+gZz3jG+P16vaY3vvGN9F3f9V2PWuOe//zn0x/90R9l3/2P//E/6FM+5VOIiOjpT3863XHHHfQLv/AL9NznPpeIiLbbLb3vfe+jN7zhDURE9Jmf+ZnUNA39wi/8An3FV3wFERHdd9999Ad/8Af0fd/3fcdu0/qpp6hOoV43qhmVJZEpbBi2J4bJp7ptWqiVAGQyNqVR6YTBnnlOYbAr3Qoi8gA2dfuN3tyxA3PG2Gz3K3JhSRHCRfKsMPwltKt2VBfCWMbPqe1ShydL7ysz1iDttDbSrYrCaD96f5k6u5ih727TIuthcvfMqJGLJwAWVgY1Gd4Ux6wuu+q4oT4N2o2/OUfbUzX5Ll/4GAySGhkuQv1Bhwc1kMlnNKoMtoQCF3Hj1fZFajnR1AfdHgJXOxTdrrER0mCZ0lhIxzTXBQCBWbI4Q5sM0RCAV7bRQTCuchQAbMpSACfDAjem4zNGo0U+28oNRt8deYgWb2hl+tZu97b8mMqp9K7diTPZ58MzySPegbh5t6f0RXqYL5rTC/28xTnjJhEMy1i5gcmEdlmYjtuc4owz+UH12aGf14JpscKwq1V+L0RDFqapXfJgtOgcbU8Cg+LJU7u6xCho9/N29ct9XTcB4FSYhlhzRbJXmqvaeYBiuRjWUpqr1xcWtHs/gi4IVNKsLoZnPR4EdNyg8RPgfZLe1REMw7mZQ/6yTHAIpCS2XtOI86YbDGKOw/U4LLSotWQSLq8Mvx3clred5x7sRw6LJBo2cc1BHMcDFxZRXlyZvsNNKK/1WYGuZ3CkcsbmFPV7IMxj0KyB+uV6WRiHJ/53T92OU/e0vDKdwO1S2YmMjF3rs3k9i6sxtVewdWU9omHKflloACQLGdvaG8yRuWuEgMtjho2cZiMPdQq7CoBd37XUA4Dtwd4YWbtKH8UXhXbHY+qqmJ2T65BtHUsgai/sU3Mp1110a4NhVKp/JvR/dLhIu8YC/a36rbql82fuukT6XuV6HMIQTg6s9PEdFKFvqB3j+mi3VZQSq6k7sVAMOnyn+4YyPTMsy4eGZ6XYJn6wYc303qms7xzsA8wWzOxPTj5ApG3HxeXECpNjUfap/NvqnzmxbXb6INDH4wa/F87i7o7T5DetTtjC15O22m7+DsZVTdXHLud1Q/ZGZk1HOJd6Q0wbw+P2lnmCGaJMn8gxAzwgs60e+hP1s+S4Yi1XZM/3kbreTuZzq9wqxynHBn5e85rX0Mte9jLa3c03jAcHB/Sa17zmUQV+Xv7yl9Pznvc8ev3rX09f8RVfQR/4wAfo7W9/O7397W8nosE7/83f/M30+te/nu6++266++676fWvfz3t7u7SvffeS0REp06doq/92q+lb/mWb6Fz587R2bNn6ZWvfCX95b/8l8csX8cpq/uvU50C04sxyAVPRWr08J2K8RwWpDEjDRH5A52+MewusutKumdGATbiWVsAZpDaTL3huU2TkAz1UpRlWlF90GWeMSxjvWiA1V6FiEUInYpWvKzoY39tZjKs/GyWNZ5w2ztOZoewoVBdBKq0YHP5dWIdoFcohRxZKUrHY7qg+rESekIMHDQfhzCr8eL5x4xRFQL5J58es0SMx6Tx5PoJRFh87GpeUYhUn81BBhUONlOO9PxvWpuVxSWxXzZPOZUdsvjYtSH2GuOxJS2dvXFLYC8wICTZTdCuMRW8eJ9wszAdjHomRBWKJguAaszqourh9sI0LK/rByMDxyH34ebOadwuIFzLbTva3pHr/tQX843Adn84f3Vf3ka/aak7lbNqEIiurh4ajB/x58nhfAwVjMuaXBeoPQ/j7Lqc71KYGGZBSX0j2ReLK/m801wc5oO1AGyWl+XcIY1aBDtifiwR7X506tfNqaE/K8ictfPR4T3qTpeZg6H2VDVaCHxMES8F9BHY3YYxxf3YVMySlQCRGoCB6KMW9QaDu9oEJXAvQRLeUCvGAzv+FUNJ3ou9kWNPq3SGoGh9TEqwEgjIjhHNsby+2F6ph8MaPxiSxMypw9OolSTuyQ0gCGaAw4xYRFoIuV5HxbbBMCYe98jyqjdTmNlY/xI/O2quwfOQh6Sf9u7PL7o+XxM5otWl/JqSATTisAAOjYwfMfYamPY4HEx6+SVQ5DLmJz63QNeeBM9DTCtjMoaC42GO+ehisuUKzq1Mr61Fm69TzizlsEvrjmIIO0fuGiR42Nfhr1HRDbT9gusZVQYTgyhj4JbCNy3dIMV8CcYxwjGQ2SOK0R1vKMHDLKAgC2yiY+WKmUiz4xSIE8bQufGSOMdyuK6yYzUDRM2VldPgg3i2/Y4djuW6QLTtNYsps4f3xnZkx7AY+76OXhhLqkbb9akO+RzwGXRBhTeb2eDwtvkQfMQS1Gw8tTs7KmMwM9YlA8oKteufejb7jOvbeB1kuHv9Dqu6dypyu+U1zydZAcV470Oaa5z6fqybHSKGs7jrGqL/Md+2m6rcYvw8JuXYwE9JG+d3f/d36ezZs8YZj7z81b/6V+mnf/qn6Tu+4zvota99LT396U+nN7/5zfSVX/mV4zHf9m3fRuv1mr7+67+eLl68SJ/92Z9N733ve+nEiWnD86Y3vYnquqav+IqvoPV6TZ/3eZ9HP/qjP0pVwSCdK4e3746MHxWqkz4qzQZx2LhwwDHska6vi83WGQhnCMP5mW6EmHTlpG8JaaoMIRj360lP0KlUwguDYWndXkXVNlCsYNEUbeBQAvbgjsdEou5cDiKqxZYn+tZegOSCpeLVu17dkxLNJkGzTYUN74htE8+NabU6VWxaZKUhC5O4b3v1XbdrCRto9oJV5JiIPqUyhmwv9fWhvZtzQqi7yQEW18eMDk1EVMPCPva9YYfNClGSAQQRGPkci499cds+1de1t7JfTeABUu5V/ZK0g8YE36NoSsZakLpQyPhZVtQrsFKwElb2PGOxjIbvxXvcR+pXlRLE5mMkS6w7icbdkloYAxRhTkmX6lDYtNNG6OYcpHNfGt5omaUnAQcoTB1qT74LWtxZgCZjphF4pCycLhkgyNaLfrhHCaQcnhfPYIapGXunAJbNuanPSszHNs0T+O5IrZ5+6alfOjUPM4NDMvEkY4Qo6fRcyQ1XZB3xAFesh21UjBzZf8MxvdLVktoHbDRvwXkQmkFMWwmSCsYbP0tN609sDKn5An3D9cpzM9BPivQi8ON1WIPMgMXzAmaxYiFgpWMjBk63crS4FhSTtj5I3toZho3fBpVZBwuPCSsUAhk/lkYUgtat6DNmHRyehfeyIaKogao+OzdvH36/FcBXD9PR6lJMx4pnJh6PZPBsAbvfOSRa5FJhuXh0wbi3mLVE+Thjxg/O4WMdEnTEjZx3GbuTSINWfswS59RxAeddBK6c/V3p+OwY5yg2KIx7tEaIFZ6jrmNcN9u0Z6FbcKp31KDTUf5+hLjzHNvZtBVFe6Vd4VWInm4X2rpj/8HyGCpHHp+Taqdm7UjAd0xi0sO8vqwpnlzaujJceO8B2nDMIq2FGDsyj8NCh01nwP1syF95DBJRMZX2mDwF7TzJ4llUtLh4qEOak0OzOzVNMArw6qOR6KUw9pGht+6nxC3jMdD+I7JW8pygwKY4wLgoBB7EvmW06QywNTzRUrpHpw2aR1LHrZKVGwZ+zpw5Q845cs7RM5/5zAz86fuerl27Ri972cse9QZ+yZd8CX3Jl3xJ8XfnHL361a+mV7/61cVjVqsVvfWtb6W3vvWtn3B7lg8fjixV5cFhbzQYAnIR6lL2F1yYqhSPmnligRbqt30mTkmU01Ul0o4LVn3QDbGroqDX12/0DD4i6WJCRp0EF5pBgA425llKazbyt3rCNMX/jM+4IR7/nhHedW2vKNYy7f24oVZx7snjCkCVNEbGTZ3aaKWwCCluDcwCv+mUESnXrtGAKXgplIddaj05R26/0ToJqV0yNKBS4rkdRZdb7AiSjBu5BkG2oEAOBfQZmlGyjIDGMjdYm8uHFFbNAOSJUl+V4hW2gc1FCs6iUcD9nHmd5XiTY8gAt7J2EFEQNN3RG61i9NN8AX2WtcENabZVPDkT6MSmrDYMGyUOC8Yuh4Ipj66LKrRLec2NTVUGvrFBhWOVRSzRiJSb+8RiQBFcHtNH6ZwM15GVlw6Cj5VOly3nUmY/oKE5AsdwbifBmUhUrwPFTX4uh/6sLpbfjeowUHvC2KiLguyosc0rr95DtTEiPQayPjZAGiIi16d5EFgzchPI87NHrYY0VmQ0nxI/DXquqQXbSj4bpZOz1SHOUpS7Tpv/7oz2+A71UbEw+6fbzesfmTYyTTOCYpUO28My3QuOM4u5kH+uNlHdt/W+1KDB0y8cVa2+b7k5LY2xsc5SGJcoMvxUpm2XujvYhnbP0xZ8IJKpVbW6rUQ0hqTpsPnp71A5qkKYZa1N5+nNmDoO+4+zg4G96NtADkJNTZBnJjR6jrWD9h4ez4AJgkH87kr7Aec7thsyXUJxSCXXK4N5NJeZi9jhUnDYFfUiiajZDlpNaFOybRzrybmAjPewqLTtoLS3mB4DbVt6vZbg+miwF+R7yXUrBgr3/Qw7hsd3aY8r132lc8bnyEcmxc/FO2+Nf1XfTJp1LNU1O7ySaFgfYu21zcjRjZLxozrHqTBfLKNzCe9p21F1DToSs9Wuai07IH1LnCzEsHcHOxbGlWKCGUCVLwPZt8qtcpxyw8DPm9/8Zoox0td8zdfQa17zGjp1amIKLBYLetrTnkaf8zmf85g08pOpdHsLoprTc9rHaBqosSFDqh9jCHLyxTWo8cMkJeYISQ/NPGmG9wG982rjZW2mxk2aNLb1RtV1QU9yR0z6w8mkKbdo6PB+vmArZGwIvO+dSot3GpueUkEvuJywS+wtNvS9AO6U93hR6bT14jM/e9yo82LX76BXSOr2DAu36n6mvUuqPS5oO7XS+HGwwxzHDYY0NrUa+wx0jk3YVkWdq6E9w/1iOu/21GoYZ7hIi8Vx8hRCpU7fd+8L3l2Zzl0Ciq3N/hnOCRR2cw+R5Z3DZ2axGVKN2adQO6KCZ0zeq8pgZAzzotfQ6eN02AQ0wRCkz96PXQ6lQT0ZN4Ql3cD8gM9yEviU7YJNbmo46sWMbZTNwXsMWnuovi7nNc64AZt5Ds1BoFgab55oe6I2NgjpX9EWBUx2gbZwHm7cu9Tf+3+Wh/Ntzi6oOoB2wRzeLyvlnfRGaK1vUW9hQa7XGj8cbkck+goZXmkM95lOXXYINVf1Rkv2fcYCRPCy9gYYNN3j9q7EQgWwicO3lO6bcIysLrajcKosDOLJzGr1IcyxS69ZWTAO+V2uMU2z8xnwRWTo13j93UJE9DJ4hOwqFp1WIInc0CdwBtOjj2G2mVht4R2RDC85DYsmoyB2cy1QD6zHTvgGGOBBMJTBzBqjbeW7FuIgOGwwp7AooCVGBQ5g2C+HwEcY3NG7LPSQSNtW9fV2dp7kBCPKnmwD1TRv67CzCMObrIxEHL4+HWSwcrz+3SwhzGa+HUOycQ5k3ZQZrRtyjty207Zun8Lv28mppfSa+qg0l7CM7HLVrY1eh1RmPf1eZo4bzuiLfcfjCe1FqUFYJSYsgovJ2YfZ3bJ2HWzs646Vy3jMG2BPyL7HRDdjnakfMUxPOMyquiK/brVeU3puMkMpjuHYVFRdghBzTKLBzwdkMfq9pdJZclucYzWwZDo1cTw5R1Xba9tZtKFJ4xwTr7i2J/8E0/iJ8ca2kUfVcavk5YaBnxe96EVENAgqP//5z6e6vuFTb6ritz15FuxC+t9o/CC3VQAyDBYoxfbhXzlxoyEeKzcshuJUubCXvC/D9bT3E7V0fBtMMV6iBHgZ1xna7KjbaxTTSS6OvCFCICVWLsvMQmTE6xaABi4Zs8ZYvG5IELiwnmnKsAAG0qKq+sxgNWmaaZjPsJQWWIXw82fFosjbaGb+uIFNs2/jGK4wfoesIwaQULLASDlttUN5iEQ9gTFV3O+EODDeZrKBUHpWKK7J9+ivievCMX0a39mCL+9vhmYdF7XKrufEQs7pfMMKwCFD42Q4OR+fHem05vyOSE0NBFgGgEGnKs3a4AsbhxDtTIOi+G3QVHixIeSNlAX0uT6OQMXUtulemFmjQn98MoZuwP6UG/cMUM+YLHCSMV4zEJIJHQV2gALTpe0WhrkSN/O8yZX0cQX8HPbUwIYftV6a5D1d354z9pqrvQ5vxbnU63ldMjhZvBwp7r6LA3NBMRXEOEhrCOpMjKHRYr7EcK3xfS/MWZIJpDNjBRXCK+ePsd9RNHuHQZeyo6RfVeQi0fJSfjKH68k2IkgyZACi2TICHwYDBNdx3OyFxqljpE4PAzD4brX7jhZX4iyzj8OxcJ5fXNNANIs5j/XwR+m4QgAplQgbrXbfK1CnFntZrhsZP2N4JAJtMkSwiwP4o7QZk60mQ34NIeSjvPDj2oBgBJEWrkVtxr6fZTTzLx4zrVYV+crpdUnWxUy8YvXS0CzYtPI9l3WHnIGcN8Ip5q7S8yHSWSpL74zUUlzU5EPQgMMogi2dcbrCFjTarOyKw7/g7LNYuei4rQzWozRfzg7XVnNsG8gf6tA4mSVxezJp4hXa0O+LcGVkb7EGVWX3DY4tVWYY+/6Abc7Ce36yrInn+kAUo9YAZTmA0xPyi2H+0TuKda5vqJn5HDaO48xR2INQLyihdtqhL9S9x6QEWE+kJNQNTjq5h0ntUQzcnZq6GwCnb6oSybTJjl3HrZKVY6M3n/u5n0sf/vCH6Z3vfCd9+MMfpre85S104cIF+vmf/3m666676NnPfvZj0c5PmtLtN0R1mihxImHjHxcFGfJkeMaGH9I/pcwIfJDB5LGuo70NOr5YGTFOnxcNCxV1E6Jj8WUwnCSzhNuGYEGMpjBbfsG0eBQ235nhjo5Irw37LJ05hxsAW2IMKZkB8UrMFXPDbLBQUFvCynalvEicThr7TI65mDJBYV2VNtqx76N3qi8CpjjlNmksS3+HC1XjZ4EOpqejzoRfpvMUZV8YngXqLg/hRtJ3jVh1IlioxTFVJlZrABlFq1Qs5CqT2FBPrzSUxJ/dAPQqwyk1R/ZThZtmAygtajworR59iGlU4iWMMawAzsaZOidyPuUNHJ7L4JB8p7FdVTKc68Opfs62RASgInaH02wDGRrZpPTT9bV8Ulk8kCgVsFkJAlyPlScXasXMmrJxifcS2Uq+nk19O5w//IvA7eH5mnY+BqAIpp03QkYyJiW/C4ZgaawN9pZclxp7vbQ0uXCjxWGQWfpt+YrOhHpFrzNBybmG+0lqKxFNYMHmDIBGIr17d9J+dxl8s7JojtfdBNoiqxLJsul0nCvrQyPdOo6VQHq9yRguw7+o09O44TesfyXALc6mhyGYk8j39B0yfjgMThJfpGB2J3FBZIeZbGRxOLPHCpkIEWyV75LzRKHz5Df2M8vm7Q6B80q9h4qlu7DN7Og9xZMzwv5ERJWj2NggMxFRd5L1zDRzKHpHrqTPRyKEGzMWpfe+GEYmz5HYkHSaSN1FDAnfdiqrJnmB6pXSzCfHiTsErb/MBkgZmZDdwXXNgHSx8dRcAUYFgoGjswaBdGO9Vc4rPQdKwJwdbOhsdF1I7zSCTdM91oc69I6IqB81jwR4eQBzv5HpLRvzsjk4r/UWYCqOWQ9OL7dAACSt41dspyIRUX9qJ2PCYJHjLWJKtT5SdRFEwQqMJqf0E6ssQxcR6Wd5YqkTJmR9vEmXxLC9ilwIRpSDqCe9y8perGuKfVkb61a5uUvXdfQrv/Ir9OEPf5juvfdeOnHiBP3FX/wFnTx5kvb3b0wLlsuxgZ/3ve999EVf9EX0/Oc/n371V3+VXve619GFCxfo937v9+gd73gH/eRP/uRxq3xcleVHr1JdpYkSY6P5xUdWgpgY+5QxCRfVNqWQra9MNH0rdW5Y1dkk5CUFV3o+kB7ZBZWlx6Puw6YtTrRyUkOku1l4aq5uFdUxm9zSnxbSPVJ4x/pBy2KHdZGMFMFkL1zTdZ0y1rLpNLXR3557BxjEwoxMWYz8ZpHaC/fEC7h4NuqYw1ala6wOZcrioQ9qzObE10djSo6DPhDFU2ojNNYVp0mieSinw7q2p/50rq/jr0FKWAZY0FMSoxI+xgXUXd9oo0x45+qUGre6LZ/I6suHKssdnsubbmWUjcaa2CTiWD0z3HOWmU4aNMKr5dQONqqNinx3+VreAX04GS4Venmll9YT9Sd3qH4QjBh+j+46NX61eAjHSk+hPpl9tYCsXvWFYcw1kF3GH7bkW0hRDwzB+uKhDf7x8UkgcaSwpxKbauizCFpSgvnHWgw4T3L7tycmUW8EnOvLwzgpiVI6ibgaABduwuW8g6yDsb3pGaqsMFJ4dDWE6WAYowUkqrCrTU+HZzHkEtow1gN9drlXYL1Kc17p8KM8iUOqE/rayu5DlPe9T2KcKoIhbVpnMFNq0rkHIkNbI0C37LlimOlOpVKdy3Yx+IL3zc8H9Yay7FY1kRUWxXsz2S4EEoeQj/w8pcdW2HS3S2cwMjULC4sc8jx/WZvQ6DXTSWryMKCEoBGmgCcilVra0k4qMW3R31QfBCKHtsNUPwM5+Dz61C7MMJYBV3MAB1HGTkGRfYvlqhjGvP4YG08FFqjQ64VpB06VM3sLw3zjABpCB+fZ9tgos4GlLOPaEWwWIqIgkoVkiRGURotXALkZMlZgGfUngJWjMhb2xaxes0LEtSeHOpU4rzHQjOF9m37WgcPXxnczZIwp7neDIVV7xUxBkWarvWMYsXwGaswZ41I8P5V1VBY/gJ/FstKJTuQ15wAQco4o9KZUxtBG8YxzE4PisqK4wLW4wDJDDVCLfWbZOQUt0uxvJQuQAB28hDyX+8awJSxn3k1dbok7ExHRRz7yEfqbf/Nv0p/92Z/RZrOhF7zgBXTixAn6vu/7Pjo8PKQf/uEfPlZ9xwZ+vv3bv52+93u/l17xildkmbPuueceestb3nLc6h5/pXLT4qw8JQmguJSnyI7700I1iszhfMfRY5K6jjTdPpJrqnxyL9GAEYV3RhphNZkFg4lkTGDGhBe91hDKrp8mM9xwxNobnhGYLK20pHIRlFat1QYDdBkPNzJ8De3i+4bvJR22lP6SjTExcSuxwWWtzzc1WeAYFvyfi0V3bjAWCgZjHuqmPYLF7AfczKVOfT58bimeykGjCFOMq1pFw447AgDgNqIB0/ZDSBVQnhE8IyKKEFLFRo8ENS3QKvtXnEeUG5fKE+lJjbssw0nqY38VrBPLwCAitxHGZ1UN1G88djsc4zcTQIbPI1aGwCHUMzJoMF593SpvlWonXI8oH+ec5lUBs9EWuJfnjsCrCl/TBjwa02M9OQnOLjj8vQ6TzHWfhn9VCNqC044DY2VferKJNqcrlQJ8VHWfsU/63Zoa2PCzngwXZlSgNk1zXWsKIIgXGq/aHrL4uDROMMyocmMGIVnkJwbu1byeHCTSc41sSZ6H5YZVZjOrRPibR6Ec0s9JgkaLxM7BfhzZI8DMysOYhrBoZLUwAFIJXR9kLw7rJbTT6Fer+Daqe0Ig0QUNgsknwswaxSb1AyCCQJWVfh3vaWIvSlAC2j6yaEXbZRSlBGox3bd3GdNraMN0MoNVCEixj04zv0Q9q4qaa70ZLkJEROJ7S5QYHSwIwlR8jiXSrEALmMuc02FckrXDU7ACICOFeqkTcWTrEiNxyBhjdp+8j/l7JKI8k6WwqzBUPCwrbadJAIT16wohjQhiu14+n0h905DfsRleUmqghjb4bT+f9pwEeGmw+rVNj+uSvidrXVIgXuWGc5EtL8bi4W1Ls12LS5wBayXOg/tONpXlqMWCNmX0Xr834h6LGlMl2QYxVvqdmqoYTbuYiIjEfIAO67CodCbdDdhHo+C3XnPwvbGOUXOssCmrtNYpR7pzg4N0BmzqOYmDER7XP8FCvVycB2tvtI7He/mmb/om+qzP+iz63d/9XTp37tz4/Zd92ZfRP/gH/+DY9R0b+Pn93/99eve7362+v+222+ihhx46dgMedyVtqolm0OrV/OJBZMRk84IiJ1YrXXyMGZBRAlt0TO3Rox+ZNrIen8VRa8+b6w1xZ+nZa+xJdtCj0YsoHkM047XIMnwZ1FH8rp3AA1dzNjXbEFGAjQBK+Fkpr1BqfwYc4Ca57XR/G0awAnj4Wvjc5T1yHDqCBUd4NsdLoHGLLJ4S6GSBTXhs1xEB7Td7ZsGwhGgAalwXtPEs3sEJFAKggZskWXAlY0J6RGdoxscp/Bx6AMVYRDMg42eVM1L6vYb8NmfrjXpNwrhBtgkR6XAaDKFIl8JsbC5ozS4UHq12FybAPLUxhVGegHSx3pugl5wbOHzFgbUbuqENchOoNggGCJNtXuXhxlBW6YdlaKjBWiCiiY2G865kJawqWlwNatM8buQzoCo/pr7WUrufPw/c5HI/NBCGFmpHjQLcwePeRCUALcuobYHMoX5F1WGngCQ5hxQz+aXxlDEmrEw42BbRhEpq3MHcUB0GHZac7WUTQI+vebqmymAlPrZ7nlwfx9AuLtuTwwUkYws3d+2JSod2AQC5uDwcIEEuogSegPHfXM8rOzxbaWNXtIFBErwmxeG4Q8hytvuxaVywwDKyXCquU5zaQsYzfm6SSSWBM9nf7V7ehp2Her1ZMoBdnfVsOEimsyYiigI8qw8DVZteaau4cQ2eAf0dkVNrlh0Cot4D55RoMnr5q4OtZg3I43ntMELEXB8M7R/R37xe4pqQ3kvcKMtSJ4aofCaZXVBaX4mour7Rz9KwKbAvxrUG7xWYvFUIisXh+R7FHFEdQN/XXjtASmFMeH8rY76AEiqiGp0U0gmX1hnMokohtQNtq0zk3gYLuM76kmDV4xx+HRxSRPmYkwxtA4xQTjrZZ4XMnmP9yM7OGFAL8tgX4nqSTa6AuMZTdQnY+gUtIhWK2PYqWYdDjcWdZhJBHxsm9yFpvWwx3aUb7HMApaTtxu1W9mflyPfld/JWuXnLr/3ar9H73/9+WsDe6VM+5VPoox/96LHrO/bO5vTp03TffffR05/+9Oz7D33oQ3TnnXceuwGPtxKWNYUqeTALG2CcSLIFjNcNNdkwR1y6ovRiHpsq35zK42WdOEFbejQWcwEKGw7ynjTl1A9eTGRRGNRHS3NBZUQqUDtzlo9kBwjDbGUYCwjQCQooT664yR3TlaqQqmniDXvsOoXnxM9Shnns5WCgv3yEYB7vBY1FaWgghjMBYGh5FRLAkqdaBtbOpqOIG3U0WBl0wM1ejNoIICg7q3KmByKKO8O1Lf2ksLvI06pTfi8cRonvDRs/mXgu3FNImYVcL1haEgCTz8FioGFmCEHD9gyYwGn9YhgT2guYG1rdfk1EtgiiTBfuIoBDzimwILq8Ht4ooeh6aCrFJMDPRDBOKN/48X1VW2zD4C1DdofcLLPX00GaU06FLlkPigGxN1xvK9KfSx2UbMOr9olae0v2YYk51CVR0DlNmbAYvMIqFCcBOLKNGG7Tr2qdJQlDaZgNqLI30tEsvsqRA2BPems5e6RibHpHYVlrlpRcoxrWgLPZDVnaaFySOC1u9Oq84XixkTVE9VHcWWZR6gvi5czwQoAuS1WemDGLq8iyG/pJvv8RxufiSk8trDcqffk+ojKpzUuv5hHM8jWysGS7vDw+9TsA/OuzFVXbCCF++XvPYWaoA8RjT4aFIXNorE/0a5aSWXzfAFDju0idnm7GsryU2G4QcjaKPSvtE/GubZL+IQCHY3ZOYRgpVsehFk7XSRsKTi9Hqq/RBguLhXLEZCFuBcbyqAE3l5V019ZmnATVxSYcASR2bhnvORZso5VmW2Y8G+vG+YJZFGB3ZEyx2g/rC86BBvhsthdYgz2s676gvzL8CJ+NMFC1PojLsX2i+uuwH8Yn2nyybr4W6gCNCT3EhSFELFRp7SqAeKXnSpSeEQKHVXncTAelf2eWJX6uyqZMbZP7KuU86yNF2FcEh+un7XiLJ1YKAMSdclh4io2tW0Q02YMlYXa8J7n/8F2SkEDWZ4zUoxP7Zi+RTOfPset4nJcQAvUG3vC///f/ziKvbrQcG/i599576VWvehX9xE/8BDnnKIRA73//++mVr3wlvfCFLzx2Ax5vxa+35DleuOBFdVvQ8rg4hX6Fu25LX+b1jhtduWBZk+JMOJVKuSl/23ZEJ+wQmPFzF7RGRapTouXYLNctyLVG9gmJsKeJ0KItO0ydCPWPE3gpNEZMhsrRGYLSXJJpHX0SCnRoVY4i3OiK1UwjZFX47YDyZ+MAF0/UuSHKO5ZxQMPjMfyAYJM4LsRhMVbsGHshxaJEwJH1xAYChsE5p70qVvgaxl7LtrMeDmziXBfIHR5qRtdm8qj4NjFqcICmsItOZOzAQ6xNY6bnJPSFLA9XRGNf0vH5XCX+aMeYy3EeFzX5baO1nrjP7jozftV8PNcBGt7HU9l3zcO5F2y5PywazeXcM+XXWu+r28ufW3Nxrd9J8bE/NfS3ysqxqAdtADSqxTzAG2d8LnUS36wPRNpXCAdj5kqdjWMBkM3ZTi4qbZflw1PfrG+zhU/5+fQAmmbMl76izamVkX0qbcTFRlll2Fr3IwuEC4IDzdXhdwl4EaWQgzz6WHlgh/AgfJYCIE6/WZs218f5LFUFz+qRov40hX5lGyYJYlwXYbu4uV/VKl22LHUaNz2w3RgkQPBNvr+La5G2Jzwd3J6vqcuLw9iTTKN2H1gc687QZ4Im8LVhqu5WjhZX8eT8o4tRZ+kT3cChbQhARk9EztHyCsz30hfFoWzANmMgiMXPh+sA4HWVQTxxnui+TLAd2BPbfU+o3yqzevGzV0y8dI+QSV3pJIXKFbNZz43t2Hit8YOaTgxYOF0RrpehtoGGrEg7qwCqEhG5RmeakoCIZ+dWSfD/BhjCVvY+otxussJm6kv5epbZncyqRPDGSDNPRMop4w+7XPuS5D1ODjgLDK8gHFs5iNCxyMcF450z5tgS45aIRvtMzQ2NH0AhtMvEM2eAXIWJ8TQix5kSNUuMerGxlA5BZQNnjdNjRL7HVjj40GCWvCi/XC5EE3RiJ0Q2htD+WtVKBBydhlN7QU/vylbZJhEsRt8GzRCUzui13jcNX7hBdmOvrG84AlKYdfmwI+pn9ExvxnJL44eIiF7wghfQm9/8Znr7299ORIPD4Nq1a/Td3/3d9Lf+1t86dn3HBn5e97rX0Ytf/GK68847KcZIz3rWs6jve7r33nvpO7/zO4/dgMdbGQS20suMLzWLnG3g5Wz0YqEmDZ64JBtDLbxRe0GyEB/xN7KOKh2TahkLir7I8508F9sVhwlaTfIWTmNooeBmHsXkptApuw1KzFee21Q6oZYEGgrnjoYkMmKkxkxKkamQec7iJL/rNeClitVfhbA3JRYtgaSuH4wOpClbnhsUWVzpcK2ilpExVhTLCNP1bjt1T+wlJ5oMLZ1tzJOrDWCvmsCcKWMeZmlImxUhZl7yYGbjvxSHjt/3QT3fDODkv9HuSH3fg2cKjVaTTcdMNalzoUK92qPFQ9OGDY3g6L2Oa7cy3KkwQGHAcIiDRZfvdTY/1wlW4ej9B9aRkWa1ZHDnWlaFxR8B+N4pbYasnTxNF+qrUBw0Szc+gA0ONrWaSaXDXb3vDYZU/p52u8PviysGQFTwmk6f9cbFS1FbNoRRF2NMDV5mPUyaHdD+BGpL4AXxbH5n++X03IOVLdIooXbkO7gnySorCAKPdcMjloLAfhOITmhB7O2p4V5WD07H1hCKtTldq3GnBMUZDFThI14x3CqIJqi2etMp+4GZOCqk0TvqF5EqiPyQGkjTubgJHf7t9qfvV5cA1Ejt3ooMe5gdbDwWQ8k2kRZX8mMzdo+RXpxo0rtiQG6sX+g3+W0g30Yt1J36MHMsgb3g2jCFEfF3aKOlNQWPi5VTCQtK61J2jHh3/ZrbaoTONl5n75PrG4fMlEJWZhg/o86fnEfk/UkCMnqqjbUla38az8XMSiVAaKxfM6xNLUzFjNRAg9Zisd8dcvqdQKafxTzwoukcWoeMH94rFJ2A6frm1wZ4h31vpmvPmJQzDG3nyHXlcMVSptXRji4k4hjaFYbnVuEc3uo6jWUA9SBVGNyurYtE3qs9joex2CPbhyjfuxXmo1g5ctue/BombdlMBuIsHconmrjzrUJERG9605vonnvuoWc961l0eHhI9957L/3xH/8xnT9/nt7znvccu75jAz9N09CP//iP02tf+1r60Ic+RCEEeu5zn0t33333sS/+eCyxqSYEXIEryfuyl4dTyIlwZPaAEj5/L5HmoNKghIFWL4y6sDstcJlwn5VxCRYHRJRdr1O+jxOXaItOl+0GcVA0/gVtdrw/dG3GqIEe7NdR0LXAhqoMAzBrH4Z6GWLQJa0k1OER4Raj/hFm8eA+W5Vfr0GvBr407qPkEUEvTJCCxn7wlKMQqz/krFfljZPrDGOzkL0AtZ4YAMwKGhWN3oTmooLs7oOqlxVR5zQ7ptdeRmVojumkxX0jNTgZnnlGLXEtCU4iSLIw7knqDpSyUiTR43omVpxCHEStC8aX3PyjeLQFiOpwG/7X8BjjpgFd7imkMLumFNDmPoHQPtfVg2cWvdmibZyNCYVBK063e3tZR218Lw3wYbgPeTCc7LQhKMGcqk3pk1G0Mp2Dc2o2DvpAoXaWTIJqFzKdYqXFQ1FbhLV9lKDx9d5g8+QffReKtHSiySNZou7rLGTimXNIF2xOWUhVghtqbRmzv0zf3ahYo++iCueS7AMGPZXYKb9Tu2VQaXO6Tpmm8vZu9/X8pcJWnQabNAgz/Iup2kPlqIHQLhwrfaMNO1l/P+pn5cdUbSSKGoiU5zIgozKVGRviw9N5PYurwzG1YAtJwEuyH6wsZ+0+PEuxrxuZDTj1X2cmhGbQcelXfgDLCvoumS7OBh1BWwoVsIVxDM+Eyqj1FwX6/aKoTTL7Ha+BBZZ0fhF4lhzeL9qmWK68EZbVi3Ge6a9YzPgSEC/OdagxVBsONYL3l9lV6AMZtXkkeHt8vRR+PsgeibUnTwU7cDyZFANLgkM8zjwCFpRs8xktHf5Nsc/4o6W7w2UcK6JuOW7CjPabd3rsyTE8ygvABJfmdYyOkNQ8t6gHeYtCeH+2XnkAmVeGrYil9G54rx2+ODesag2IybXx0GbmOCKilQEayaYke8kdoOdB21s3fYlkAqbHruNxXp785CfT7/zO79B73vMe+u3f/m0KIdDXfu3X0ld+5VfSzo4tAzFXHrF66ad+6qfSp37qpz7S0x+/xfuJpliw4OcW8zFFshJFTceKyQO9QbFJGX6kEYxeES642eyjCv1RSHcXiADomFLUC2MNEQs3UJLNdNtjSei6tYFVmSxgc1qlTXlB+Hou1bzrjFAv+aGwNoyoOy4OBqqvUtQb7VIe91anqVRpPMnoL2YiocdeGgt1Ra5b6axJFnXbMgJmMm6Mx4i2ZG3F+1TeOMODCenLiYxQr7Ynf22jF2vBrmPAVYE6zCISbBEEHQIDsUW+v+gTYKg551S4X/bsWehPiW2zgY0ePgCf+qgFyvm+o4jvxXF49Tq583v5d/A8eIOFG3q/aXXYpzFfmOwnPp43PCVG3gwL5Sh2rtwYKnbDeC8ivXBJeFOTKnUzJbvQEHzNro0btyA2T7UnF7Rxzhvt+kCCmPkx9UFL1WHej9uToAWxYfYWvoPBAOjQs+2U5ou1OVPhWnUCtDBsy5onDRYqUQ6MlLLAyPTn8t3NN+Uwhp0GN6TmyeLScPz6Qj5fcL/iXkVm+druO6oP/Zg1iguHh2VAI2z26nVQqZ897iGSuLEOXdJhS5h9zASWxDms8dODHk6oiJZXog4BEyFK3Cd4DGvmSEAIdYAYiG2lxo9stpGeffwtaGaTDBNjbSHsRwa5GgiPy8CtpDmG4z2k+85E4hFIpIVmQCs2SBrnNwDYKEFjbzif5OkM0uC7WtVDwo0ZtkZJM2bcaMu2lbAr+ZwKjpWwk4P0drp1cX2+X2xfMOxAAnCdksZP6b73BXPQcP7M1S0/mywYFDDGzI/We5kBxMlxheC69xRJg8xRosvcTMSaeH2fAfXHrJuyv6UtKtcOywmIGlTSVuFEKp0N/MhEK+paMZLbdjpEi/tH9rfSg4raVjRscCL9zkXndFIATIbgvQbiMme08Q5x6QORA5vP6uMC2/8JVW4BP2PZ2dmhr/mar6Gv+Zqv+YTrOjbwE2Okn/zJn6Rf/uVfpgceeIACvFw/9VM/9Qk36pO5uOtrcglddrgw8Ys5I8DlEgUVJ40xZn5Ow+dgM2ymsixWUlRD/G0tmrgwKe9/T2QI/A/XkSEwKAIaB8NJAQSCjVDSWgikQr3UhG1kK8qK2Cg7HNKbVgNeW2Gtp9nan7BRU0UXlRuaRbouMp3YYyXPRU2fw22eZYpyT0+sE1CG42FdoMjKPlwfkj+zq8ANXuCr9WSI+WtgTW+3VGE6dMz6wM8V19W2s7+TpW116CMu/kRE5wCwOOwGoATZPLJ/Djf2NZmyv1MeR6ZhlDHMZrxHXa/HvuXlxWOYoYTPWF6r7QbA6NDIDoEF73u11PRzxehL/1q3vyxZ++lUQz/L9Cxiuxa1aTgd6aErFHWPBsMKN5JjsfZi8DiybGO1/m6uSCM+LGsKtaMK2TyewQJxHmoWVF5tOlGDhr3HzRUAn2qXpckdrqnbX/Ks5gch04OI+khRif1WcBBpgN9ITatC3NIclmf+kpsSHYY3XYCoR0eAkRYeWSLLy0P/HVzI5wvZhr37O2r3Ky32yynLM1FvBPEClXfS6Zg1U1jy76PT77CVNhq/k4BISLeFOj1EjkKj2TFLoS21Pj+0G4XGWb/n8Oy0zmH6dRZ9zTa/cr8jpWtQg6d21M+YAVOmMhyH6fxleRyQi0QhKrBg3HBK7Q58b2KkMCOgTEQTM9Zw7MRmnpETa6/XJoPZbYFKYVERubIi9jg3uYIdK/sIp4vUJOnMyPR+RJuVw8DShRHsG8cob4kaiedLezTGYewj0F3rZ6BCkL2niOMk4DGJWbPCucHr7LQIwEdlOmdrzQieB3C4xEjRVYo5mK0XHA6mQrmTk1rYIwrM2jPGiLwXAZCpUMRFbUheSIpgo78jmkA8sIvl+xubity61c7tZOvKRC4KML0RJyeH1OMa4b3OqIWZ9SC7cmr89GeaH1V/eW+GZWbHpD5Bx5vfdtobcavctOVnfuZnbvjYL/3SLz1W3ccGfr7pm76J3v72t9M999xDt99+u85KcJOX/uw+uTpNlPj+8juJ67SkzLKuAExM7OFbSoNxFymBCwqrJvM2o1L9+D0sTK7rFSsh7OLE4nWWiNRM34rNjxLpdNTv1Do2WbSTBdmcyp1rFFyYUruDF7HDciMjwt3UArNs1KRtidWpdNYcurEL3ipRF/exyTIiysA3lZ1rUau29uJ58AbIGgPDwWDsZwKHS+r2G/JbyGiTxor0elf7EDITllk7hu8AFCtpYqxq1bfYBgqNXoBFqB97+1BwtTqxJLe70AuwQfMuGYyWvkHx80w9Wjup1gLY8pj0bNS7ynYevqvC2IzeUb+qqfbn8vrT+JDPsj+fq/u7Lqh+9JBSnjUw2rP5M3b9Um1UkKXgzu3P2iEhHV8pI9kNegrKmzqNxVHUF5lUqW9k2AfqhHRJTFcCJV0WoikvCo12RNsT0Gfnp77h6avbgyxud50ks0gvuE96LyXwbWYpDY3PMhER6U3EIonybk+DmPBhUM8XUwZXh516vrLvR006zAhTO+r3m9ksJZbm0vADMyHENQHcQhCCaBqzRESrg3JCA4p6nZWe3FAIeSpl+1qI1O2b0xX51mC18KY/w6Y0w+uoUK86hShZ2Q1VBjUVamwAneIcZikpfaJ9R/WalLiz7MPl5dQuvKfUlxJMakCPfqpQNEsMJ9nmzcm8Q5aXQyYcXSodLGcNa+DgRlDiiCmjl2akJcZPL8ACrKePmXYckbYHWLRegcUx2ixc2cxNq9chgx2gAKm+p7jYnWUjcXsUCGzNQ9hMi50s097PsUT6KDWW07kSIE+2Yil9uQJ1pHB/IOrjkZtrs3innZCKJZLqRcC3cpoNprK7ke5H8bm+uknHGQB85ci1OP+L9e1EWvPUmpDGpnQWxxndJ65bjlW5hmF/GAllZJidCtfjYowfohyEj85R3GlUZjUeF/765AxTIZKWkw/HOY8by87DsWe9u0rTVEckqHc+DLIMCnSEuok0aPSELE9gxs/f+Tt/54aOc86ZGb/myrGBn3e96130Uz/1U49ISfpmKNWDV6nyhZCmkDofxZ0FVbe/c9jA4aKGWSaIiPyVPBsOxUjk9rIJwV0Tx0hV/hP5Bo+cm9JKc/1gNLu+J19gHGVhXDCpur4ZQr2uAytB3hIbKDiZxUj+Gmb9gfqrpTo3V9AXbUMmTIiKJaI0YIjIo2hs6gdsmywMpCjKMMeBSy/WFhaAwy3RyfwZVTJLScqgpMLnEntC9dGBaKfzVF9t1Rirrg7HSJALdWHocENUn8m+8pfBgi9leWg7zc5Ctkffa8aPMLZcM/xdbXIvlL+6GYwyfHbyXVsla9IaA0QUJchVEj2W46q08Cq9g17r6WQx20M9StBvzAI4IyC+PiR/+xnyf/EgnMteoTumW7jv4fyYpqEKMnHVD1/PPyetnMVD+TN2m1YJZHoAcKrLB5oRJ0o4PbC2/McuZt/H0/uDuPMJqP/q1D+cQQw3VfXlYbwurgrWGogcc+ayeG56v5ZXpt9dkLsfbfSxVg6X1f1Tn23OnhqOuZLfd3P/cIGAQKoosalofceOYlpYmz8dvhZUu5DFwOFssQIgbMfTzpV8HkE2YLfXZBmyiMBoLYS+VpslVddaZeBWMgse/wb32Z3SfYUhnk1q9+aMODYT5i2Hx3W7NdUHhY0HTSLYm9MAaPLebgZT8t0AJmLIEztwOMPaUB/ctyHmjUBQv8OgAzK/nDJkHYK7va4vqzuBVcigcT2RpXElr9eygPhVGD87DCKI7wB8Ym0f+cpJJlIl+kmDnE6FekkfEgOECA7xu6Y0fkRIWtUFcn1UrDiOFJ3bgLmuo7ACxsQNMFyIaNiIWmxXWQKRC/n1JTAQdwomfNIDUaw9MVeOdlAJWJIb5RvYWBQ3+VY4sFovBTvcYFoR0cSaRuY6Jh3YGmAZr7XCzlPrbtAsoBJzKBqMPcVgRILvVo+DjLDIwCOOnxCSvAIyJqf+qQ7S/QH4NM6JEsdR2jT6uTmJIMlMUpZ+IrJArwtblPc+MIZHjdOIz1iEZ2/aIcRfMUVTe4EdJEv0Xo87vE8Gn5DdYzHSDvLJx9VeOyGtDHxbsN+bZmCkzySkYX0gB8z7ISnGEwwMegJn9cJoqkezHBv4OXXqFD3jGc94LNryuCjxxC7Fyg7X4qJSSYrj+iT+iR7XYFAyA6QGpjCEC2RTu0XTJMOLQ6S0aJQIYa09t2wcBAH2KMaMczZqLidmBn4M70/cRfdPKSyjMNHLDakRfudaXAxlDtkE3hRotHEHACHRhu7kMjt2rL9PDA8xcavUpItaAUbZg2VKMeqssOGB91lNm1zXBer3aqquwxhL7KGwLI8xX1eauYVjzDIMiQbmDj47bP+20+FGRopU9HTH3cUgGgguLenpDIV3gceh6m9ZONV9FqIi3cLiIeN83FTaQBR9PG6WcSEf6ecwPmUYxc7A8vMnczbPOD4kI2YXROVrr1l+AOaM1HUEc5tKPad2P++/6lpF4VSZYs1sJHc2Z8OERZ1STuN9C8Ybb9SA1lKl8SQz+vgtbPhP6nHQCpbInFBwjAabQbDueEOKIUk8TyghVxgr3Y5XFx2fgTDaESwIzquQIWSJcFYvBSx10fA0I2tNsz0zZmkCF6o1tKshpRdElI+nEuOBBaGzMQr9xW2SIqgZOCTXBJWZzunsUxLESCBNUf9pprhAtLrYq80dAyMSPPFoq28DdTvQ12pqsoGM4DTDDVPWh6rRjB/Jskl/40Z0YOLptZCF1ommkCrUFZpElAXLFcY5r5Py6yxKR1Qp2VVEA3Ohnwn5nkDPvO0MDtWHeX1yGWFdWMXaTJv7bC5HEMhygqDpxWPUsmtK+iVcak+xFIpFZKbiJqIhVMUQ3zfbpYAZ3ZfKbrOSaAjmbrYJRruz1skQqDf6BjOOpr5yJcoxt8sKGUawiEivu8bGGjOEjsLCZhKR428w5dzA55tZtkLQIHCt1wt1jOVwxe6zwISiDY6LTq/BG9kP/LeHCY6viU5ACK+PjV5Tx/c0S1GPzDZHtDjCCcngixVqb2SikyUsG6qwr+W2gkE5S58L34dCsRL0FJ/LrXKrHKMcG/h59atfTa95zWvoX//rf/2I1KQf78UdHI7ZqrToGCPRQNUUL2tV8ECycZWJO68NGiVmipKx1DLeGkOEDAQcJxZ/faPP4yZkmyU06NLnEhNEtE3F4DcN1ZdytoECBozwsCyGPDMyjIUPxXjbfHEh0hvAUdwZKZ9St4gNUWRKMxV1LTV7YEwctso+zJ4HL+TolSqlGL8q6UKRXLuvn1MCG6VnSokstp0KHVBxyqPBeAMGAxox21ZrY0lDgd+hsJ9Xu+2GdszoQflCJoVRkHGGVj8a9zIsU3SDF1lgFOAbgr5v+ZnHED6Pjt8J+F5lkAhFAeiMuaGed6uMPO3lLIyntldGL6YPpxDGNM5jkamGU5hfxsijAVRybU+EgIFMIZveUdzUjoa3FPvFjXBirnTnJwColp5uscM0nUH4KC39J2R+c9sRBBagRqwcLa70in3An92V8oYmOqdBKjiG1xAE+4Z01fMaP66PWs+ikhsLu13MilG6M+K5jTokuCnn5y8xVeib+jrPWXXxmPGa8N40xti2NmeqXal+BAvkccuHWzp4ks7OsnrQYHDAvNMcBiXMjTpDfG1kEXjvFDCDzztWToEYWdpoBnxgzmJh7W4mmxkvxfjOMRC03S9vTFYX0z2JTb7c+EpNIBxv7X5F2xMIPkx/ot7VVI8NCEkwi+cA5SxjXF6u4yocjGw9keyg9DsyBLojxJeJiCxQUs7zpYyRm5Zo1ej6S84Mecio8VNeWywGd2bjZMkQcBFy+tryWiubNanCsKaLiTZUw9pY0JSZKy4E7VBT87zNXhzOtwH9sQmVm7VBRqDdsvG8V0BqFr5VAH7MOhUwmb6QBk9mv8gMjdCPi0aj1lbiA5yL+ZjKGBtcUpZYjzYqjz3J9kSAJUSdWQt1NrkN6BBs+6KdPTYhhHlQlceqNQ5D1M/A6jMLkD1qrrnJiot6b/ZI6ng8lh/6oR+il770pbRareiHfuiHZo/9xm/8xmPVfWzg58u//MvpPe95D124cIGe9rSnUQOI7W//9m8ft8rHVYlNQ7EqeJ7G0A0w/gyEV22m2ZjLPJi4waThxZeLmKxaTIQ6Fr3Xeisq/ttrb4Bh/CjdmZg2R2oxFwtyotiq8LIq6M08elmMdJP59aUHDIyrEPKFC49PC4YWUCxcy0LrcdPPdcn7UrG+xs1IMIttczyO63QzSpd9IBeikY6bjQMZzoTCed1spqasTYbQZUS9GpWuM2pdU2PRU16vzhgnqb3TScyaw9A7FgMU4xZBEgb6jJj14Tri3UKabquBHytTWYTVnvvG4SVlf3hHLkbdjynELTM2Tcr2UZuSdBz2d9sroUi3NJYLC2zislcw4I3sb2YbiOb7dfwSPt+YQ204VTF+5j1qo+4oGtjcLgSB5YbGezq4Y6VCtryR3VU5CKIGPKLPxyEzhhCgi94V06RP7YyaHSPH4QhsoaFO5Ded0n3LL8bvFswjPH8IphO2PfCYK3nTpdg+jvU2jHpPXGQ4G7NKUE+muTb0PTLcZP3bUzX5bVQix93e0A8LIbCNTK2q1cCGCu1LrKYemEGh0owflTXsMOr65d5tWR7jLhBVAKLIutrd4dzFNTgxncIp24m0ADSNAItoi8QKZJgYnOs7NwFWqdQb+XtqH5KHU9/Xl4AVZWRcUlkqmflSAjQovfcQuqd1PTTgxce5NbyruHbFqNc9uf7wO2SFKIWogIxsPuJLKw+U4fS6gYxCruSEOC4Iw2swbtS5IBtD3ONouxTam+mJGenRlfZKwGMKTpw2HJkMIdT6vc/Yp6XMW94Pjhg1xwr7mt8tZK5zVxWy4GalBKxJlo+Hfu06zdqRaws/+x5AGGTjjNdy+d9Oa2/xWlOLpCPIePOt1+FlGMo5hqEZ/Y42qQHalhx5WbFYRn1PBOPKZKmZcgRPMMZPpPJe7Dh1PA7Lm970JvrKr/xKWq1W9KY3val4nHPusQd+XvziF9MHP/hB+qqv+qonpLhz3JkBfjht7UwIT58MUdfnx3AoQiNitj165LYdhVVFvrUncRkypVgJlVOLAnrtYuV0StM63au0AVQa8mTEzIgvcoibR00Q5zKK8NBWmOgTmJAxUeRELM/HlJdVdUNCf6XU6irUSgANHP6Am0DWLnGtEC2GMeF7S9w2ZwcQkc5WkOqZG2OuCxSaSiX04IVpLtTLdQu9iYNQPGbWqLaFoL+zKOIz7Bget9iGuKptj99SjHkeB1B/WLEejqCiQ13MPvAFA8mRGLdGJis02KXBHdNmED1XHHqmU97Lv4cQHI+09BN76TpS7wGysfVRh8zhpoT380tjfKrU3dAGZu7I7wTwx32qzNXaD/2j3lXR92kjjIKZ3P4sGwreY+orqb/SidCU3PgnVVC3JUszz9XgPovfR5xqZLhr7anaBsWoYGBA3r8URyYagKBuF0Mf4FKMx4JOTmi8HnvAHO1XNdWWRsF4reRlxXHoU0gfPkuRvnd6ZkdbYGqNWhvMF2OcmMU7xQKSoZHbJBCOgFeX5nUMZ8ozgnmqDuOUfWs8NwEN18sOnO3Jo80uPscS9NabR4PFhHvthT4G1/9u5ajaRmqu5322OSNskvSTYkOlNmxPztiDTHwR9ySZOhIEQgZUcz3QwW0wHwmS66rVTECiKTtahH7MWL8ujSMcw8wsmDMfKh0Sq3VhOD05HNdHHUqO5/ZEDp1Ncv0phQs7N8ztOGfLNoxMpLx67kQ5/xdFqEuaIyKWTjmX6kpviOV6uRz6RIXe8TzS2fM9ERH5NEcVMmXK+cLK6oVFhdA1bPPBgd5pIABJUiFn3hER9ZLkndZgxRxybthnYfNEn/Fai+vlqJGHgEp2gdTPCOBwyUK3DGbN3B5wk9BZcFLwuIkQnp5FLVSOwqLSe0z+KG0uK2xvAe8W2o8ljSuK2tGA7bfCKGU93DYEvLitcxo/HP+K47ELTzjGzxO5/M7v/A6dOnWKiIj+5E/+5FGt+9jAz8/+7M/Sf/kv/4X+xt/4G49qQx4vxa3bcSHGzeMYjlEI/SEi8mmhd7B55BS/0sBFrSCiZIRYKZMJvO0IpsRKG4gIiAS9CeUzskW4w4nYmZ54eQ7rOagYZApKFDCi145TVRY8Fdn5KiTGCA+yyinQumFDcoaKPW4qMMSZn5t8Nooh0M6CNyM1Fj0NpT6QItRVRb7tdcgQi05L/QkUj+56IhCoVeOQ24aMIiPkyRQSVPpE4sZHw0p7WGJtgHiSiVPyZCUxXxlipnS4GCyQ7c3o6pK9VQ5RGr+SIU9Xt9k1xu9LWcjkJr1tyTeVEglkT7E7P6W9dyiuXnny4OWywkeJDCYesuSIqAKr1R9s9bsl7rEqZFxyfSR3uKGAqV2MDZaV8psoN44VUMPzrexW+S7O2E6uCROd32rXyMRDVoS9O5TP3HWBqsNAHjeAqcj7qNbw3DadcvbhhpTDV7an8nmlXodMp4hIM+pcrz2YEiwa9bHwmG4IEdIMIv1e4iaKmaPSSVESY5YslXotNweif2HOiF6PDRlywPUgEMfnSB0poikbFtGko6Qy93DKcokrWENDbTrza/WJRYLsnhA0qKPTSBvrrKh+ldgvhyBq3a0c1YeRtqcAYJT7vtQeBKRMQWy18x3+kWCPZBfJPmDNIFkaYBn1YsnmPlHaWGySYVPkO8ibVyUmbNtzWWmDDqdVoq8pIQOG6XtSLFAMV45NpZmimUhSArYs9kob9JookxeMocZwrsFcUDYas9sLtqgZuiJ/QzaPXMcL6+LIwCqE5I3HBKN+qyjJgqDvXa3tdghP9FRmJaZSbaN6d+X8xPMXiow7SuuxxQrhkoTyVWh0x8CnANYw61xrgEPSoQTAGhaHCVAkgMSAicromEAuTHghjnN9JL/t9TPhz5K5jjaqldUL7bbaoNpSek/BUaL2es5p7o015q0xEaMGjULhPcrqoscte+VWOX45e/Ys3XfffXThwgX6f//f/5d+6qd+ik6fPv2o1H1s4Oeuu+6ikydPPioXf9wXhQoP/yjviPFS42RmhvZYbAkURZVsiRnmy1A3GqkomtaXF5cwTYTRG4Js3ut4aJKLR/rXikmHCVnF61pNKmye1EbUufnMGQsG4tCFb1870xZiw8nKKEAAehh6OCUxyfxLpNamehA0qvNnH73WeRjPnfOS9+FIDyaPe7N+RXM3PG8z4najkYzHxDh4DlV/GCnaVdatoNuLxzAdXzyz7LnKdx3ve9GoUCxp5I/joZDVSxUrxAZDvRpjI47zkWGI4AaBDU+lB7XtFUBnMjYM9pM+RgOQsfZ6g2SAJ75Af8/AS9SkSteT33sjLfZwzfxz6JwOk7GEPHGzlDZYKgxWhrtWjrrdqshSkRtsTK3utr0KEeshxIQZKsuL+VjZnmqoWsP9o16T1Z6MWWOHOESXQNk5wcoxEw4UZlSIOtsTEJqVgCC515VMnEruOYA+P7QJxoYw17lOZH0xEISgkfwcXWKuAE7VpOyccuxVmBjSOWr3kGEHjiDW+MHHtPJq82glmdC6RdPf3ZLn7/z+FteGz8hiCkKDjMfc4mp+4we3640WsiIY2JFaPhngJZ8xgm6Hkdpc9i0D4kZACsOu0mvkMKOSXO8LtoTFctGhU9FgScOzLW3kgr42zh+u7Wd1HVlEXevweYqLSpN5sjTraa1Fhm+idWV25g4MOp7vBMO3CAIZzIi5TGnjdfG5lLJ9xXyOdd4bYVHTtdV1xvqN41UHlsEdtOmRtey7/D0kytmq43hDlpZzw5qmwmyncxnw0TpqaS4T77RaZ48Ij8vmCWR4OTcf6lVybo0hYDNAWwhUXd5oe5RTpS8kmGVsZdU1bYax+U5DQY3FWHvFbpeO3NHBZq2LfbCFm8eTh3ox4QYRUSg4wm7W4mjeUXejdTwey/7+Pj300EN04cIF+pVf+RVqj8oAeYxybODnB37gB+jbvu3b6Id/+IfpaU972qPWkMdNEeFQarLhCQljS+WkcdQgliFV1ubUGRt6uD6RNZkdzXpxMVLEunmClp5r3LDFoa1Iq5cT7WgIG0aA9jghA8jwvMm+kf2LC4l3WllfLmT8zLANXA12tah/TO2Jm2m+R1knGhnO1jnJ2k2G55ZDaY7wTMXGa8PGAEaUyGtTl2ndfE6BXk7eGyJ70ISu189ILo68GbQMgspP8f9jYwxWzqrgycnGsM0skOMgO36GKh2d0zHrGdOj0C727iIAJEUW2TukdBISYCOfFQIKXWdoiQDYmG5FiY+TfidUcU6DTUdpCvGpbW+GCk4Ny9uXXZMIQr2g7RzSKIWVJfAjm6jqP6LdDNB32I826BhFqu3o3bChxy4zQCkL8MLjStmXOgwT24SJ8l8oLhptNzZP2iM+jBMEI1Ckk0ivQTFypjAZrmx7+SvB8pGbezm+cWyHplHha1mo14nEWkB9HWZ7AiFNZsHqlo58G8fMYFx2H2C2p6gP09jv+KOFYBt7PLkQqS+lbB7rMgAjUfoR+Mm/3+57Wl4OCvCSbWOdnQ2wylYPDePr+pOECDe0a+ehxPwSAJ0cwzLTVgvjoF5HxfiR7eT+UqAYJ+7BpUriEsHwwNM0JzoROqnCgywHm/Lmq0OGUrkjQzdiVZXPJxKbYBSujWZodJYJKkkGqLmMw8mNd3g8d7bVcPwN6JdES/OlxKabu25B82h0/kjHHdrEPc2LYRNNumrIfGyDBuvBhukXOtQr8/UVABgXhrlYPSfxLMc1EMN/OZJgLgPW2MDe/lvuJQxdR+VstRj2aG9xP2K6cwzPIguIieXrjG3wU5gZF2SRs4i4JYkwB0gRmeywjHXEbbNYQHWlGHSZDbwetIvUkrrtKPZwT7fKTVs+//M/n+655x76tE/7NCIi+rIv+zJaGO8HEdEv/dIvHavuYwM/X/VVX0UHBwf0qZ/6qbS7u6vEnR9++OHjVvm4Km7TTSEjCJLwBg43P+Kl5lASk/lC+WJkZnRC0V6p7C8ZBScg45r3mo4fjE00xrLy5CYpwijTEwZRPKXVYxXLUIKsPyrunZ0GcgGQ6KdkQ6AB0fXlOHSiMdYcQ0pGIWRkWmTCyKletFmCNjIU48dYfOXiXAJfOJwn7sCuRC6gIZLfBg0QsgE/lxWk7YawEvguryexpDAcqOsNhpRBNZ+heU/i1YZ513YaaJDPjXcySiw8GfDSM1hgaUngKjt+pywMTVWlwt6k0cyeKbVpTsCHSlcqP4QwGFs7dqr6LJ079s2i0RshRVFnEBu+X1TKyAuQPagiUs8yuxfuA0zLu6iJ+l7PgQvJkkp/KIp4Yg1InRDQFhvbkDm35Qd5cF49Bad2FlbaXDU+G3tNyFIxO0ftiUoxdxhMkgABAgFxUR2p0TZdEz5XTvd1xA2N3oRm6wzvp9R7k45FsED2GYdoFLzwEqhC9tZUyfRnJgAt31EAUP2219nGxCaew7VQSJjbqzJXiTHnIhFFDeocnhvuZedj07qhgBpvAJolMVrEeyungJkKwhLqTaPq43slmjafmM59iPWeGEFWGbNkFYCq+lB8V2vAi2gCY7DI7z1GF9caXJTA1ei9h2OaJOA9F/pGkShWXoEno8bPXIhN7fV7oxIHJDYG1G9l9VIAfNtr8EGMaX8tdbjRxlh7nbVTsvjaQrvSBvZGPOWZQ6TkkLTYPZhpVTzM0Y5E8yW1y9KvnOpNzlFDSzL7F/9On1Vf4LgZ5/n81Fh5XR+GYDaunBGXprVZO3Zj9vtYvF6XAtj4I+NHjit09o12iTBke+N5EBmhg4ZTSs4N/JzRuZXaiXastBdjVVFsjHBF/l3oCaq1JYSizTRdzLZPwqI+Mnwz1hX1GCIs1zwGgQz28KClitkBRZ8lpg867+PekrpuXkD8pivRGYvlI6jjcVje9a530Y/92I/Rhz/8YXrf+95Hz372s2l3d/dRqfvYwM+b3/zmR+XCj9cSdxYUqwKjwKCuY2GhYgSH2PMVxGSG4s7B+8HDam2uyPD6yFIZGxqcLJ2xQbC8/kgn5c9z6bK5bwwjRgE9eAwza4JkUYgDBNijmDtUzQqpjWLCS71pINIgVCamPZfJBtuCmcoMplMWh80bcrxGs5N+x8V2mhDcphtCEQCIGw09uYFXXv5an7drZ2eKAPoOAoSwyYFNhDtstbdPggdp/JuCpcuFBuLks0+ikIoqzeMn83biRnV+cciMD/TWz2yUslLKFIHPGBhQYVFpFvqYLlRcG0HPXofgqM3NuHFHw0bPJQqMqBwR6uGIZ9snoXp3qI0cJY5ptGE4GD6yh1u0BTcCowEtgRQMVRiPhS98VMazfEd4U1x8d5XAp2hDIFo93NL2RN63FuMHQTbXhywFNRFRuweAYTqlPtCsHC04a3inVRZJvUmyGD8UiAjvQRpbzIBFIHEUYxffYSp61oYTYEfG+BHvVFjAHN7qsFUpfjpm8iuEJCkdJ0li2A7C6Qj8tLsaZEaRaG9lUDO8/ERENWg9ocA3kQYLQkU2cM6/s+7oUp+383BPHYRLSaCt3U8ZdTAbFa9X4rLtXt6GxZXU3xKwEZeS73EDY9iFSAG1WsXAYTYRvud8LzVmlJNhe7Ujdz0q+8Un5qVsl7LtjExQqvDvCnxymkkAm9ywWpRDc2bOi85RXDbk6sKaQ2KeVBlmDUavFeID9WXZtST7wWLIziR4mFidBSDJEMlWBZ1LrIU0g+Fxn2UF3lOVnCSV4CvDrsG1Uc81QdoOfF9425Ub+hOBANH3/C7jfDTOcdJuVdkWLLabeJZyzrec2nN6TiXbm+vBLpLHVzqjF9G01s7pb8amorAPiUvw+NR/VpIV9U5AQ6PXfZ3Xna5pCZQbwtCZ44TPwW1R25Pv55m7N11JDpZPuI7HYdnZ2aGXvexlRET03/7bf6M3vOEN//c0fl70ohc9Khd+vBa37TKR2Ow3ngBLTCAicivbm8FGvWRboGCZI6JQrbJJwwkxQCnmq8CULuoXwPDwWtRlLBZrICwq8kF7tLiwmKjFdFJeFTDgx3pKE60AA9SzqfwUUiO/4+PHNqBxlb7HhV6e29kLxxjaIM6N6HyIUW+2m5j9TkRqLI3PGNOmS2Hf0A8sLBX6w0wk4zpcT9ebwsp5PQUvjlGUt6brVdxzBuZw34U9wuI67f3ktOZEk2cVQ/v4jHBa1FlYeLOsErUeJ8P3mKEiljWnSIxvQ7douPYMrdjTIG5+o+xCWULQm1cMtxk95bA57ToVQqX6PpBO2SWOYfAURcbjstEC3wSg1OihhWOMrF5K9DhqozLbaMvwIOw+4zHmWjf8JZy2tfUysmdbe1qfb2gJqaVbZptlITX58/bbnrZn0BLMP3LGJZXt67A3PLX5R1rojUvOpkkbi0NESdJvKr1x7rklsjfNRJQLkCO4mAAlCVJkIK0cb8AWCgudcUmGvI2gJ+5lEkiAAHieDS5Su+MpotRaAiwkywffwWodiGYElIlEqNessnA6AsZK9HoMZHrAqWpmw3Bp9z2tz1a0+zHMPqhfilI6enmd1cMI3gz/SqFuyUSSbQZTghZXAvntjAMnPWfcjHGoC6aCzwSrN4EoxlzYn8SYl2PZWOvRM6/To6ebsZhDAWwpB3XVngier6ynaK9xFqiStopop1pBCraHWY9kKUvm9ozQsbMye2Zi8PaaxGwQVTOKSltgxAjmFptFru/1GCj1r7He4zyCuJXvjfdSgqBzjmPntIPDWkNx/hwN3BnwkhndBoNoqESyZfG5GbaJjBoogUJ8r5ut+f1w3eWwByqwb3JGvWZm+YeuQrsK7wLaPkbSCoxm8IeVGitZG7lPkdnW+SH6YlGey8Z9Az7fGG8oQ/GtcvOVX/7lXx7/jrxvuIG9eqncEPBz5cqVYwk6X716lU6cOPGIG/XJXKIxAXMZ027OKLaP2RBgQhwNvUyzA+pPeh85Ai8OkJs6yzOFG3rVTiM8iI+VkxySTboBZNCIuviTU1obmcpUaM4RTBpu61hkKlAEjawUxdIbxMZEIdRLh50JYCC1U4dlGGwJBCO6rTZs5OaMhd0U6zfm/3LJdKQ8RW+MUzZa5dqKbeh7DewVWGDqnradZiLhRtgCKkTbrZSqw4naSzK0RRxfiH/ldy5L5w7jArMcDceI8ZyF+OlUtBFDJCXowNoEkK2jqC8lwUbvbUMwhfZlxqaKtTfEsFUISDKi8L3ve3Jb8HYiSyEEnXFG9I1j7ykCfe0gVOmAtiyBbr9NLDPclCeA03cTkloCOPO2CrBcHo5d22kWh3xubKira47ixzB+ZV21p8XlXodsdUzTFyAGAl7eK3CgAtYF14MCty4ShS0AxeiJ9MZmXvYjj1VkKPRkZvPLnBP8biktpjTHrsobWX4WWUjEDVK3Y+XU85U6HJwxC7N6dXs8r+fnyv7Znqqo2kRqgTUzOnBkCPaBXuvVBlSxe4d/MawrVHqDidoiQ3ry/Cs5tjjES2XDC0RVINqcyeuTYs9jliwAI5h9lgE2qEG10awLmdq9xAQiGvp7VnuI3yEYGxxehuNdtj8sPMVDg2Fqsfgs59MR5soICiOLsnLkDkCzA9+vWoOX2XvJtob1/gVS830GzqR1sajlloVxwU9dCruSDJkslFqstRg6boFCC6MexNg43Tmu1ZL1vmqGe0TGT1q/JbsSGfXDAVA32nIldpdzmsUH9rZvDeereJlHG0SBK3HQ+FGZpuTnZfoO3+mCvShLZYxzeS/ZnsQAIRWbVDKrOa7UCMU6ogyZXGvy1w/zHwyHI44xt6j1s8QsXtzPlo2k1jk0RLR9nT2POR1WIywws/lKG3rWe3wilScw4wfLv/k3/4be+MY30h//8R8TEdEzn/lM+tZv/Vb66q/+6mPXdUPAz5kzZ8a0YjdS7rzzTvqd3/kdesYznnHsBn2yF9e25PoCwFPatEqNH56g0BDhOWqOiloZE5XcWF+X3kw0/qPOvGJQiJXHmje/8lQFFkRzsswUFOdsdZUdCgCqJm1Oi9kxxKZ8YxyDWjQH0sNsW22j9wX0h7INDRsFKKTN6YvFuXwPY1kfkkOKKdKMyQAZGO1FY0pq/LhBB0KlbE6LizR6MX0s9b0OO7Diuons0BzM4ISGrQX8ZAwspi0Yi3Fd6exWN1DYU5hpFxkeIiIavY3DCeL5ZMYGjJm2I4JMd84CcHGccz/ie5llKvPDeerdYgB5ZlWzvG1zRkXWNjJDQfNjYp5amHKDf6zTyrRnbTakJ5vBMkzRzWCrJH0UMl3k4qTizxwjPVbRuiipDek5BENkdbpYJBcpE4slIqLEDpEhNfhu+bZXrAUEeCiBDotL+Tvd79RakB9ByGV9dMgK6TnYhUiujypUNvNG8TqgNgjpGUvBVWVwJ1al2IjIkDdzDhovoFlAcjy1cXhnEVDg8KpDYFVGuW9wA7iD53Io1OphwUKN+NxqvSEP2AZGGPNbcpUOEUYhcBdIgYT4+9BWDVq5QLQ9gew/sV6wZimCi6n9UptHCdlykftLEXLbiUxeFa7jzlF/GoFnea3heBTbXlw1WK4EoFFqQ5G5K0NG0T5qdbYlxcTgMa2YQJHCqVyzQWn+LGpy63zNs+wEK/NRWFVajFreIw+xkmB1duy8jUZEFE4IMF5mx7QY3GrNsxx0BrOWCs+A/94MSTNUfyTQLZsP8Lk11cDIkN8ZdjSRBqhN+xdeXgtcN99TQzMq+ooiZHmynGQKJFwbc7qhV8PXGUsrn58hvM3FGza3rD69C4rhEtjesu3b4UdHfrMtOhVMhrD8jN+hkPSSnUvaFlT2tVFmRdy5z6z764PaDmVAaSGD5gCyHdNgeZwXF7Wj45HU8XgvP/iDP0j/5J/8E/qGb/gGev7zn08xRnr/+99PL3vZy+jBBx+kl7/85ceq74aAnxgjveMd76D9/f2jDyZ6VNOOfdIVibpaCvfyXy5ST4TzOaq4Z/5dfGnEMrs+llk+AsTQHl2NzJuTJW4kLVE8XPS9AQ5AGTN+YZ91wRCHs+vIYu2zH/LFIiuV10aGNMxYeBG8k6OhiOfKjehomyOdM/0g70uJwc4spkTTPZU8B9guCWC1rbk+sMcs+81aVHGcFFJ2xxrADutcrL9rdZY1SfctGRmcaWFOQLEveMxq7dFSYXYWdb20qVQMtdoEUcd6Ss+QNx8Ihsl7YlFwpETfCOV30WgWBxg0lpYSEZlZK4psLHmeNBhXBW90U5Fbb/UGRnqM+VJKg0qzMdS7y7H7JU/2cdnS4lr9gsMQ7HYpMVXZTqpoc6ai5cNwjAHeKSCljyrkphTO0+7je2kAPdg3Mer0w4YeCFVgJHtHoamMUFkD+FTxcZrxpp5ZQukkwFJKG6027s7oo06PMdTd4DAtDOOqDqdz63Wgds8rrRtmD8kwSdRwMjOoqXgzgwFMqR8K2Y7G+gMpwEgCKQywoI4OxSGN+s7DoDMj2r+4ktZLXH5S9ZtT0w89aJ/tfjxM9wBtIQJQFZlfXRzTzVvnspaPEjZnX1khA551L1gy7b1CUo78BFw/2ZbIv3YxqlAXBTRsO71+CIB4nHdwDglE3nslF5DV3dnz5BTOKhHymTWKz8MwUC5WOu65jEy8XuM7siiwxuVxiYmhNu4815wpPz+36TUgi3YtOyQQbK292mHiWOwXjupDew7MCm4rCs4fC6hXsgk1z7HlvYG7dn34Q9plkuE896yIDDkCI3T/OjwPtl/RKSuTa7D0REFSQDpWIzK8mkrb2HgfbC+iZmuMOpzfeu9xrMjP3AeojcWs9LlzS5IhBYmRW+XmL29961vpbW97G73whS8cv/vbf/tv07Of/Wx69atf/dgAP0996lPpX/2rf3XDld5xxx0q29fNUuJCiDsXNhZKGEx6sht7URsNNWkEg6XgfJzEwfg7OXGKdNHKQAnWNWFhrariJjXzYlnizgZoJOvqktBrdQC6RTFqEKMg1pqzjsThc6FeXdBAgzSuqoKRwelhsR+lQb+bxjiuvwwIyWcJbfAxaqFdS8huCeLSI+gCBqJY+OLOcgj1wmMS+UZu8AJsvr3lucK+WRXAyy5oAULMmFNXxqbHEI82wIJIpESZM8Ar9YHqx/S8M80OuOTocc/eVfGeSeYXeogqZ2SNEc+e+7igWRBRr0mCCi5lGtmDLH2pyE0ZxqG7PqowkEoJqU/XydpveJURjIhLg7kgdlE8Z6ARFWtP8cTK8MoKZmSByeRTqJcLU3+oDEPJM5qFcxTCR3VITNShjpln0axmOmdmYxgbT6sHO+pRgNYQ5vQI1BhtRRHnEciwpnBkF6oU4I4c4bg2+syo27e9ElaWIRI+MRYUG2oEt8V3hTU1Cw8S60U9B65bQ0iyRFIXt01+HjNAkHWS6eTUKbQDiXgszyCen5pjN+FItIEziCmsNZJKk47FBc1istg0W2D8uDAAWmrDKvrHF8YYn7O8PP2AzKFJVNqJ70TdwpRpIXnJ8nLQ2lWGPlCFov1jmC2E24nruhCJvFPvPa8XXuhCKSAgRvV8i+nRjYx4iuGmQKP+aOYl2e9qrD2FPTsxA9GMs84I6ywxh+S9yrU+Ww+VlmE1OUC5yNeY24WAQvo37Noh3eNxXdB9xnYTrK3ZIUtPMYD9WACClF3odagXgo3R51galnEOjboNru31/CmqH7N3lUS4pS2Kk8rusJZmtr/F3CUy2btxH2yTqJ+92lewvYXh+RLIr9yQFAXbyxo/8joocL+sdVKS0t7GkkRAW804VzsajPfF0hw1mNhZXaOTxRjDt0K9Hlkdj/Ny33330fOe9zz1/fOe9zy67777jl3fDQE/f/qnf3rsim/W4uKUxUG9gxY7hsgWglPgkHEMauZ4ougWykAf65gTmI1lsV/ZTuVx4glPGt4qM5BLNFlY4AVwNab0VRowzk73Ce0igv6QlxJ9pwWTK71XkxMvh3ThRtOgeeO5Y38W0rnm5xop0j1kHZBtT0AZ0p3HTA4zKTRdCKYhMoagzSwesfZaRHCJBofxPIiGLFLYXgyDO9hQ3AOla4NlYzEqXNtrkGRTNuSmdhmb6BbHCXv5CxtJEOnNK3PKoMvi3LmfSlmTsL/luXzd0r3NjH2yBL6hlNK5m9e0xs1M24ulj4OW1P+fvX8Pti8py8Pxp3utvfc553ObgRmGi4N4QSKDJmRMoWJiGRW0MMRLRXEU5RIl5ZUARo0SwYCWDIxYlJdSKSQi3pJoefmVQYPRKCgywailhfkaVDRMUJiZz+2cs/daq39/rH7Xfvt53177fMaZOA6nq2Y+Z++9Vq9evXp1v/28z/u8HHOvxrSwLhoyuLyNDG9UBWhIF9SxNcCm4ffeb+9UzeQNp3c+A/CpKcdnATamUT+G9VEmEFJX6YAYPBdxuM2kL2PEYD2gh8N5HHHbQuzTrgPjD3C1dDy2HNcv81B3ZttnRqNC1o0aK0j/Tc8krq0YfJFtS8AVCqGbQAHGmBV4MCwDQioBFQBY3T3eYwG48js4j9sUx5g5fEgG3OD6+4VlGWlQRUCh2NuNKWBDwHT/yLvWEJAiukjrszM3Jz/p/ZpiQGgsgEG35jgZZtb6nAKzcp/0i8qcRf3YKHZKv9cgHPaWCSGbVm2f8HvkOMvM+jqBZfRO9Fas1diFTZhlU3sA/fa6wYSJlVqN0rCKA7O4EL2X2Q9SrFVqIo4q2YTVMuzmE0OIfV0DvEzmKtU/fRrrr92Tfld4btg4ySNqoXIOA8qstfSexh52fVH1TKx4A5JgZNVzW5yJxGqw+TZqcQyziYFyTmkqNhEw3jdr8HgsbAYxKok4zDPpBsO8nNYEvQ/xgCUT3sz2am7DygKJO8OeJfpCF+895XoOj8a+4EPjtg3CmnaBpb+BoO/fyXIK/AAAPvqjPxo/9VM/hX/7b/9t8f1P/uRP4vGPf/w113fNWb0+5EsR6lXzxNRjCSadHbbNxeAttBHo5KYpgCcABZOkWNRroWT6O6b2r7sRiHGOiZe3kzuL4sXONz6KbDgZMLFZhZoipACAYccET/RO6wdpIIfTFG/6+XSTkyeo/NpLNz22zaE/MwMqh7hob54JvTtYGeBt0DHcOzbsBszQ99g5/YWtkagXLH4eiNEaqDUGlvF6RUeomMFLmyrTLR4OsekAZq5rD1SsGMGyZ9WCzvw8Jm+b6hutHaDfOaZ5O+yt4lz5u5JpxDX0pr/FcKuE0uhxwmnmPeOa77s2ZfUJaW+3p3lOY2UCQDnOfdGM84oxIjWoKv8y8GrfV2YHTeEPRWYSdYAeW87EaEOo9PuS74nt/sy2NIa3FiVuIuJ6KFKKF+3Xpzp6OLN6Tth6mHsystvLnQn1CrH8PDATDDQ3yH3xMJxC8uh7Pfdn4GBgjQpZC9WmvNZ/msGSav3AAsnLxowfw0wC0BErsc1CvyZES4XahQR4uh6i8SMAEAA09Cy7fee9pHqarEPHY3tYRptu/cgyd9jYTQUOlQEv2pj2y4B+L2Lvr8tJVo9XubYJOxQMW/tUuA35ObcFU0fbCNtjWR+o2484vsCgC0xhJtLysj8/9TrV9jDadMYpJc1U65wZnymNoTgz7Qo1waXBgiKJxMLj0brKXhnryPdnmAUBoR+c0B+rJWIYD1MIpHpONb2dZYUZoutktmFsZx1q7NzZtt2yjMx1AaSFo1cm9qdmKJkQtGanbSIp5j12tulrSududKtAjF3pJwJkQxwZPyDgvGCIZvF+k9kw16/nIPu8HWOr5kT2QsPn7Gt5NwyLvPI9sf/TorHvZX7JNahp7K+ULNhlJB/yZ+f9Mn1k7GRnLtDzxJQyj9qwakcwayYJyjQOeD3bazFwaNxp+Vsvv/iLv4hv//Zvx+/93u/hzJkz+Cf/5J/gP//n/zz9/ud//uf46q/+arz1rW/F/v4+brvtNrz61a/GspKMxisvf/nL8UVf9EX49V//dTz1qU9FCAG/8Ru/gf/6X/8rfuqnfuqa23wK/FxjSW1U4E1lw0YbnUkMGkA4I6BRpX5tMHPYTAhmUiqonbrOHZsEwPFAwZnMPNDFC1NLySDe2pCTjQUvrB4ww0wSoVIHhxkCoJjQDfDghEygRmktTqwwIfS952pMlp7c/jmvQThcA8R8KdPGineL+iuDF0YsrzC0mpHtwQuy6BnVMjYACOs1ErMqWGh2yujBuYyDeXYmPnwYDPmpyJSWvS/MWgjHm/G+WIy6OCgzkSqhljrcJx5XjEf1dWHc6bHHBt6yNc9JGyLTO8UGbUUTrHgvU87ix+CJCBWG69w2AhjDrthOM2KV8n15WOg6DIFCVMioHIE+ZjNsLyib7GDSYofR+OF1z3lf+N3yrmMYavmdLdLR16rhS/Y245KuXwxpw9CUTdQeh/WoMJFuQLe/Kja+Y/25iiKrF3tuB8Pwaa+WY042AtwfwzIimcxPlu0xt8Gc5lsOH+3H94sBliKdex5zkUBgAcjjWo8Z3kRH831tH22MdVmX9FeqndJ/rEUjzKmq9gmA9kqPsIoIV8pLrs9J2qvtdxyaFZIdPyzQLMULu2qPaD7muXKw47oIqcp9Ino90zHXNWMGOHoGOmRLABnuG0mbfnydCvFj0ezM7ukVaKQzhmm2DoOvQxusWLROhJN/Y7F5uVZ7hdZRPcU2Y1h0SIw25YMKe4Hekc5uEk2aZ/md57cmjKmjVeEN7LC3dJlC0/EZnLdsz/z7CRgCNr23ZRUzc0j6IVxUbA+98Z1jfg4O6NI6ABL3a2YRmU2zrqsdGVImjN5j47N/ywF9zLxZe0+bYJKnGNZytMBkkW2yktUrhTCOgRmNl6k/GdNZV8A9XTwWubY3lISEq81k7luznfO4qQE8M/pBY9KYAVj6bQ8eADq108lQzPawtIGfe7Q2nmnn/sru4dQ8PDm7alnQaI4t7GaRACBnTDzcIPYfWsDPg13c+T/9p/+Er/iKr8B3fMd34J/+03+KlBJ+//d/f/q973s84xnPwI033ojf+I3fwAc+8AF8+Zd/OVJKeN3rXnfi63zBF3wB3vGOd+COO+7Az/7szyKlhCc+8Yl4xzvegSc/+cnX3O5T4OcaS+iGCfU3rAyZ6Jk6WYRj+FodU4YfPQkxSILRONee2GLBV6Jq6RzrffR2w2o2gTajkxgZxXXIWxy6kfY4JwI3KxBXo9NO7XLaoIuq24az9TBpHfUx+VnF60hUQO6b6aEaAPC0hwBjzAGVTZUBDjVDKP975AsZmr7QwEPXjQs+b3ryvegNfOAsWYdHiJsz5XlXD8tjsgESSNgUjn6Pucej4zrYBky047Ap35Ow6UdjJPECr+5bfuPwsmg9hQaAzIaiNhZ0qvJiHHB68uONue/iHRVWWWX8GkbMQO/0mT07hmUczDHDrh4idPQs2WvYOfMORsPHhP7we3n5sBp/DwBxGif0PuznZ0EMkKJtFdBU+rUwmFkAN79/8Yw2nFQ79eHMSmiTCf0p2FsyTzNYnY8xKYJ589I7wEX+XAgYGyBlKARpAaAn5kjKhicDQl4xzzsGy87zvL4mgxEQjzdG50S/g2Ks1kKRdV+6ukJAMWdr7a0CuOYwn8EKVutQMgHSGGSQdg4GK9vWv76uxeCwsKb3SX0/0HOK6wGBaCF1oW4HsKNxMDibbDN+NDsgM32OHkYgQwssribDKNKbVdHpYfZCn5kqi6vb7zfEpJLwsKCGmQ7vmjPQ26PB3OfykmbTjf9GXi4nNh2BoVq2a0jjPMBje08Ysg7zbTooGZvJhDeLLoyn4ce6LQze7xLUn5xPTvhxjIjEECjeQdmo1pxbc+Eujs6KPrqY87050YBgqg0yH5gstPkcNh/0O9gA2HTW6eUktgj83u+1U5jwVJgpWQtDg7MWn0ALUIOZ28yi9pqpaezcyDYBYNYzT+PH2I5S77HKwKqjGQ6P7LHTcdE6uosQeUGKqWGiAcv2XOFcyinq2Q6Rvte2n5OsAEeUUZYfuHzNz62xcgeeFpf3nEwxYb5pzOpFfaadqJOOpSPF8aGn8RPqnp5rqeMBKF3X4eu//utx++234/nPf/70/ROe8ITp77e85S34wz/8Q7z3ve/Fox/9aADAa17zGjznOc/BK1/5Spw/f37ndTabDb7yK78SL33pS/GmN73pfmn7KfBzrUXFWU5Za/RvMKB76f0XuiqzEsQjVwgPOwO2CQVAUNShxMy8hYmp6w3jE55XILdXi9553pzURgwspqarlnTHjhCiofbWjP/apDfnzWgb21/akMueJhbBNToZXhuEkcm6QjVKsi6DQ0VVng0xUIymTcWrXyxMbTuGhlDotRhSxTgwIMyeY5SxqG9mb5lsIxuks6RbxLz31sm2oNuer208/21EGBqflixF6mUx4Vx/nAmF3IYQObomVKdH9zdFM1Iq1F0xaGoaSgCAM3sY9hcIHQGTU1vU3zxWFq31QtJGY8pSxR7SPpk5pKf5Lpw7cEQbNXsrizt3pecqtS3SyvGQas/iJDBK1ef5Q4/hgTb8ogOkvy/SPBc7lLL+MbNR2a7uOtKkguPNzf3XH/jvKzAa/u2V3rRXrrdQoYjGVokO0MP7KfFH8LsTgIaYKaYkO461hoS8F0aTZMjPxHiaVXY30WgzGSPHurpz23WDNX5kPihBr+j+yZu9CFjGp9b4WfpzSXt1bPvmLK9n23O7vYDF1WQ0ZdoJ3FAgs2P77yrCQlpcLtvfnWlK4AswKZtTY8ennlOEGcZZvYYmIHbJZCorwCdPJBfbjFybM4rNQxmMZK7RjB8NdmqgbbnmewpoaB+3UXpCi/fndYNxlDxPcFib/izv41ALMSqygFqGo2GKVNjWbuIM3jySgywtHe3EaMe/aUMIwAKWMaw/VtYllwHCRebnisbWXArw0PUmC1PhcJH1kJkv4kjgcBe9Tm360aapiDIXoYiOfWZYfrUMekbjzWZdtKw7mDFc+CBqepPI4CPjc/rZ5lPMey9O0zmnigA7WutGZ2bWISlzgN3UMA3My1iidskzZIBOjZuJ6cSsdHtFG6qekoPsMZU3723Y/oJn0zs2ZQXgBbDtF0cLyitFCKZQGXmf9aEG+tzP5eLFi8Xn1WqF1aq+Z91V/sf/+B/4y7/8S8QY8eQnPxl33XUX/sE/+Ad49atfjVtuuQUA8Pa3vx1PetKTJtAHAJ7+9Kfj+PgYd955Jz7t0z5t53UWiwV+5md+Bi996Uvvc1u5nAI/11q6HlOK5RrgYEK91CSSvdwmtlvWE60L46T2RJ/KTaU+XrFDOEwMcAxQjlt1VOMnEEMbLT1NSHGc9EyaTVVXd/3IPDDHAAhXytWQwQMBblzRW6BE/tnzfNxV2RIApgXO0uXFlcasLs3hz/9yV0vfay0dQ8XufS8FFcM4yu0NM9kQRlFfxxg8SekcYUQ20oTRxsadx+QxbJZkabPaKBDjjsfquh8NUu4jXVfwF1vJeFZ4/2sCnF0FHFL34QI9c6k2J9Yc9YXUw0yaS1fVh4gmBIR7aecuHtfrt4AQv0cYesR1CRhxel/Z5DHbIxyui6x0ABA5POXK0SzoGrMCaLhMjLGz+wj3HgMPO1teUxmnscvePgova4TNs9nOEQ2lEZ4yf53bviPlZnZOWDQYZs3i7m37wyNzKCKLEOd+bdlgJM22q4/aMyE2k9GuNCE8dibPUVUxf8NiCmbTMNC7OiwiAu2aC6ZTX1nzUp4r2aYu9N1qxq59Xy1TxYZd6eczp3uU2uBs5Lbnru4dx9v6fHnf6+sEXCzr07Jy+3/dYXOmMXouAl4UGfAcnR5zn+zczYwak+V9fd+Mf329SdSaxvD6XMDiEEb0eXFp+14OokHD02cem1EJQ3PiJgnrqiU0033A99kvgxG1LsK1gv0O2Io+m+x9OlPZZhTm5Wcim2gNYtrEFo4mHo85SVjgMQ6MDeAB9czEs0kzTBFbjjfETtKBagYs3bbgaKhQWwq20gxmlNrGDwGXuuU4T7cFsHOQtnMigPVgNuqTntFcRjwJqy5O9IEgBt1ZzwdwQBjYsa9B0GmcsEM5pZFdzc9Sg7nCNKwxgPWegQELL2OoPl6vFQZ8Shb80fsd6QO2V2vaVLq/mwB0llE3PW8PdJquGx2wieqRMeLIW8zqak3XoEPUOdNezNN1XLTWSacdJfk3bkPohvrk+VAtCWatuU91ALj55puLr7/t274NL3vZy+5ztf/7f/9vAMDLXvYy3HHHHXjc4x6H17zmNfjUT/1U/PEf/zEe9rCH4a677sJNN91UnHf99ddjuVzirrvuOvG1Pu/zPg8/+7M/ixe96EX3ub263Cfg55577sE73vEOvP/978dAi57OM/+QLP0W+KmGL3kUzFxCnzdi1G8SXjAX6jVNItqbqOvpHEN9ukC0RgIvJMNgQJ2Q7L24nqvgGNlF6EKF/RKCReIrTJiib4oFQrXZAxVYm4TYMV6ZgDleXBR9NKRxc8prwjSJaz0aFtrtnVCsE2QPmtrHhs26TDduqM5yTZBxwMDM0bGlmNbGOYeJdR3A2kMOjdY8b21Uyrj1PCUpOdTZE9A458QHuR6dzl2zf5Tx44lhhvUMuCiFGS61sD09JnOmtMBGTCPUde1xteN8VzYn+d1kY2sbu5lnkc5FW0/Tiq3hHvgda5pRR2pGZFL6puHsa06/smDvsGdF8wqtoLkhE5IxGPV9i6Fu2JOSut5ktSsBkDPvPcT64Sv3GB3K0hqdENtUEx5UMfrbw97J1sib6GSehxd2ZdYVYNTscDY+25LXy13rD2BDswR/L5g9qo0z73/ytBq0M2I/MyAJoFjdk7V/mM2jwMNhGTG0wbBmhFm2vFeJO9ONblatZbI5ERRjG4mJ6mz0beYvmPlzUHNPm8EQZp416zFUqiOB4YN7tvP81QySMQgjLDMNlu3dTfd9YBk/mhWkwZmj68r73vtAh+E61hLR77TP+Kmx/AvQK+S6zJojTCCdPag8JJwg3frElvPWNPPO2TnRJLtwhHoTzXkjQyQi7dfZtUYrh+vX65/nJKT6qoXfc4e1XjCE5Lq1JBNGL4aatrQh51Mf1gSokefuYUe4q8d0wjhUmMHaENuwWSezidXArvT7YObqvJYaRmY05xrwUhj7u0IG1bFAudyUewxGfIONANAASM12FHuK7U/tBDjejPM3s+Dk3DkBcW9c7lvmLgCz30jL1oZcOgk9vIQYU5kBpdykG7rvZZ/jOJFOotn1UCr3p8bPe9/73iK0qsb2ednLXoaXv/zls3X+zu/8zoR9fMu3fAu+4Au+AADwhje8AR/2YR+Gn/7pn8YLXvCC8frOM0spud/Xykd/9Efj3//7f4+3ve1tuPXWW3HmTCnh8HVf93Unrgu4D8DPz//8z+NLvuRLcOXKFZw7d67MXBTCQx74SXsrpCZ7o3lSk5f9kGJstKenGq6U/52bn5s4qslrb6JOZ62ReU8olevmRa2DEbNzBe/I0Bna6HqXisXG2VhP7ayAL8UxoIWpry/gptRixYHtZpnX90G8k7QIq2c5bZqNCHcWd9bhQSTUFobBGh7eBEGsGqnTGJHFGOuRGmv0xavECAHs89jfr8f80zlMkQ3HFiywDKyNBcE844Cv2Taj45NBVW38R3+MeRm7qmPG8Tzyuea9bxu7mOtrSX8yrT8zp9KKjfYSUHO9lBnMKjYw7FWLwXqz2HgUppO3od9Be0eEY/xYUV+3eP3vtMEK7I4XLMRqa9pJur0nNB7CEKzmi3cftfAIo0qrNlptxOGj9gyjaJsiux7q1az7nffAYJSUuHGAd56rV8FS6neEMI7H5LoJAy6YnbJZYmabsNb0fBzpmrKhUV/3CphYXJzplBD8cZ1Lrb9EjJkZMTo8qN8LaNYDGgqLEjBCgxI9Z/c57NHt71jzhG3ihX1wu4z4frIZtTTZMTeNQ7pSE12gZHN+O4fXmAUy7HWfXrmJwJsMBOlXRINPGoBbXGFgyTYsOo6SlsLeZKy0hyQsrsL84noYMz6adU4cTgpocRg6lnVD75Fo/Hg6cDtsl3C0rocbAROz2+igNOMmkXXaCtH1ySarXJuSDJQ/Ou3We4Fu5tzjzqx55Xw0ON9hOzfMsbBF1NfRYgJKcNGGa1lBYAN+Sz3kkIhNNM3ikNtAGIdpQ/47shNpGMzzNdVUQqOn91Q3peaD1XqSul2z+4owz5KSedCwdkT8eH5RS20EmGWX15F4UTGJB7ZjF5aRXtt7sbMmBMdOIpuyjY5WkrpvmaOdPWIYBuOjK4o4Kk2yjoTwISbufH+W8+fPn0hT52u+5mvwrGc9a/aYxz3ucbh06RIA4IlPfOL0/Wq1wkd+5Efiz//8zwEAj3zkI/Hbv/3bxbl33303NpuNYQLNlR/+4R/GddddhzvvvBN33nln8VsI4YEHfl784hfjec97Hr7jO74DBwe+7sRDuYSu37JgKvoZoXc2YLkMslk2hpkFEcKCJp80phytpqRU5xqabJ8M3bnqFSnalQ2LqEO9KkwVNiAcD7711gQLPtTqqbGG5hg/nuCwE79sDTr/UhrIqAmRTlTdWSd4XxcyhdqQ1xg/vCHWAMgwGuAcAuPTVem+Vwt7nOehAKzwdEpASymbmbgw9HZzrOuvGMIpBIQm2rZoj1EFkJr0e/TG04SmyLm+NkFhMHP9kqlB16eNCQn1Aj0PMahoQzxc2KL58Xgztq3inSiMS36W/WAp6Y6ncvye+nXPGjusMdNcihZMVgDnBLhRWFFqAgKibYsO8QxyzfLceJQFV1V3s+jrFF6vU+WeMCwKgJkndQa9CQzgfl06HmXQmAtjdiFmx8gGdlDft1d4o9qYDGdm4y4b+qt07sLOgUZcsllaR8CRHvMSw+R4umM095SS0izIIBCvL/LOFf3taKgAKAXpO/Vci5AEy2Zzvau5SHa1DYc0ynzB+y+tK9UGDG1Ad1Bec3V3ZjepccCgBYMTgB1zU7IHxq2aYJg6lj0UbNYwBV7I/a3Pl33TXh0wOFnDtNhztzfWy/o9ItxcgGXcdtm36+l+sL8Ddmw3x6kQcwbG0DQpwiZqKap0yxijZ6Der26vQWoj4lUbhj6erEF/enaOFpoJhxHdJ2PnRBfsLsoOsurEGjW2UXBZ1sPBdn6e1rTahli3twL8FFIDWqdnho3haQdpZ8o03/JhNdHpIsNrAJJlRkyizHO+iI1lRhp7pKbbmBzQhQG71oqPe1qAVgstO3BmHCkC/jJYLfZBUSePOQEaCsBG20p1JkuK0SanaJxzHc3R8Vj6TGH/o5aOD+Klwtawa4vREzX37QOJwWHimXb2yY5PPbjkWix/0ESfga73SmJKOPuDZBaEh3hJ2OnwOlEd11BuuOEG3HDDDTuPu/XWW7FarfDud78bn/IpnwJgFGL+0z/9U3z4h384AOCTPumT8MpXvhLve9/78KhHPQrAKPi8Wq1w6623nrhN73nPe6a/k4DPfwP21zUDP3/5l3+Jr/u6r/uQBH0AjC+ubBJN6vA8qzsbMCkGzZdzZT+hGQfsxREPjp7Ei5Sba/f78cdQX+DlECfUy01paurOCwAPRB2DLF6vSvy7LgaA8gAfPYlr9X4WDu6HberrqTFaDDCzc2pekBlP1/SsKnHgRfgPp71uGrMQFUZZ5y9K2w0Be9HKe/TCPqbYYSf2fCpHx0gXKOa7pqvjhK+x4RQ31PdixOiigbiFZFJx2CUbx5Onn72ITnO2MaHaa80SGne9l37WodQDFhwKh5tZqrF4XY0xKgYTh7foeO82jt7FypwyLyC++92aNkbMxtj0CMS+MJuebrBGWZHKtXfrHjOBbCybrghhzddiZ6J4RXUYqeO5BazHdSpz02CyIT76PZ4AJw6Vytlg0kEd9E+yITfAef5dXZbnyXjYm/6XUKXpHGGJsBZKP9hxYsB12KKem6RiH/bYwB7737AVtXNCHCIstJxBBM3g4XAJGYM65KnQdtG6SOyFD8E8p6jmgC6DqdxftTJoptGVHv1exJrApuDMz2791nFeFOkT3sh1exHLS5xNhqqOMHO2BkQYOJKyPhfRbJJhQmnQLU6af3UWynQdw6iyrAudHUyvS0Y8vQWOHs5UvO2fy0v5naflX77n9UQnc5A+5jBDeSbB2ZxPpXVAvAp4aUpKPgNal8YCkC4YZOyE8d0wzgaH9WrCU08QFjQBKYWQrzpA95PRswyIh6TrqJ0pFYbgVM8OwVwvLGqac+dC3Rq74Tfrovxh0nQHZ40mEKYJZg4v3qPKWpxCcLO7uSF/xrHjfN/4274iEQFr7Uw/2LGdFhS6PKNTym0fZkIRgXFs8powNeuSiqwwTK22Hp44VeCPf7dw6Ox+i0ggs2arpnPj/thm/Bszu3khndMxK5+Jl9oGw5y991Asya5v96WOB6KcP38e/+pf/St827d9G26++WZ8+Id/OG6//XYAwL/4F/8CAPC0pz0NT3ziE/HsZz8bt99+Oz74wQ/iJS95Cb7iK77iROwjXV7/+tfju7/7u/G//tf/AgA8/vGPxwtf+EL8y3/5L6+57dcM/Dz96U/HO9/5TnzkR37kNV/soVCKOMtKaJNJka31QWSyOUnMrcOOSW0sJoR4ueKlqoUiFBdwFtAaq4baYS6XkkWo1XFiVEXOZuaIsLEI7hS/ric9fX8K7DHZIjaOWLHmWUqIVIVyWt2sA9uFncMTdnnxKscUYXXLyeosD9qI97xcbMOe+pwFmtkANQwdwN73STIHyNh1gD4jYGwWbYdRo8GIDCYZDZUQxroccfCpCLW35p1sdjOLCmaPkz0FAOIlCuWMoYi3B8goy5tlc54Ufk7aKG4bhEVj79vjCrMnydNoMboJ4z8mJGDdIbUliMGgdUjJpLst+l42/JUQQwYjdHhQCnlTznRnYY/MUdfl2erNd/F75e9cjIisGhOiTxJZN6xxDGwA8cq6OOb4+oVhM/T5HdEAgSceygYQp51vj/y06ZszC6M3ARLwbQ77WRBxyszFWgdD3ph4Y0OK/MYbQCeMi9/dRgAnBbDorGsF2MPr0pAMC2hQa40wfo4vEJPtaDxnQ8CazrDVHYznLK6U/XF8YRzzB4otxeyt7pzNgsbGqYQDMqOoWQ9Txq/pu2MacwzQgkMjUz6vbPv6XDOOKZ4+tZ8pj9GWtY1y5i8tytxSyncWwubvtDPbZOpbBMOUWp/Vz3JsDwNxUg9nU9JC6akNY+Y7Dmv3ki04Gbw4PJ5tqIl5wEvhYOdds4FtW4SaV0rXSXNBCAOQ4uymdhLE5ndaEkjoNYzfLWGCV2ydYj7wRI45jb0GC3KfGHtFHK4z9xSONiOI5Om2oAQ3XN1JBu0Y2KvMkSnafjTgpdPsgsAhZpWZY0edP7ZZBhXev2X2VMaKNnl5TZ0cNBVtHr3YMrsnRmvj6VDEHfZkvEqOQWWPDuf2EI46BAbCZC26ouwpyijcHHa7k5vItdj+GgYbMWE096wj3QvHc9fFYSg0I4HS1oy5j408xPoQ6B1b6rT8rZXbb78dbdvi2c9+Ng4PD/GUpzwFb33rW3H99dcDGLUff/EXfxFf9VVfhac+9anY39/Hbbfdhle/+tXXdJ2XvvSl+O7v/m587dd+LT7pkz4JwJgx7F//63+NP/3TP8UrXvGKa6rvRMDPz/3cz01/P+MZz8A3fMM34A//8A/xcR/3cVgQw+KZz3zmNTXg73SphQ9U0kkDmCZvzm4lG52i8AI3pdlU5+ljVHtsyvVov1vzwtTYRVVILdoIMB6JfIyhUqq/K6lS3Tj3Snx28ZWamItsZgbVj0Z/omAFic5ExYt0EsaPBbyEqqyNSwIG2tb1bkzNygZswwt5BsFmF9SmAZLjVZho4epZsk6PowvD4NBkIHj17wgn9DLHFUUYWMYjntvFLCNtdFRSh3raSca4ai17qxjPDhV3+xtsPLYyAiaNBy/rGWBZIlR3WrZI+wT0idaN2nRwpghXPJTDytqK1ysleyxtcFITLMNOt1GeM8faZ/A8sSizGk+yuWeApbtuZKMV7AACCafUvWoMpYrejwnrClZoV/dNFGkC1m0RJtIRh1CVHub2cCgYLnJNLnzfYTPY+zRskAyukyHbHvamvYnAg9g580WBs1fWOmAco+zI0Frz03xYHiLzSHH/PNcIaKvOLTZvel3iqRqwwv769+m38oEvMlCjASb+PLK3gMUl9jSn6Xd9rC4j6DDv+JGU7V4WL5OB0mGYRCMTojZUeRwtWGuqGd+rlgAhDYAJ+MVssya36ei67Ry3IKBGgMqkpjL9zut1ie8pdgmHN5Tz50rpO3UTQ6o4xOhpSdHvYLMZEJIzN+b5qbm43WwZDcSQLHORmZKyjgz8/g520DI4umgQOLW9mo8m8d4aa2cGYBQnHIfKpjNLc2zV3tAhnUXIlW2jLpxdsqiyEjZbFZQunCyLMWTL6NPlNVg3xVkb4yG1i9OvTyGv5fehTybJAIMwsYN5Hl7ombWvG5dVqZ+JzAkmhfwE2O/WhinC7ZQtoe1rk42qd9ieuu1O5saxYVIBnatf8RDGqdkkQcl/aLtih+YUYOfhaY5hllTTOACs814e7e5TY4vGZrzezJ5H7tZ7p4eZLJYPyZJgHBH3qY4HqCwWC7z61a+eBXIe+9jH4hd+4Rf+Rtf5/u//fvzQD/0QvviLv3j67pnPfCY+/uM/Hl/7tV/7wAA/n/u5n2u++/Zv/3bzXQgB/UnYDn+HS6Hxc8LUegUDKPlaHdOCMGtgR2CgRbjwlMz0vacn48S2VkXH1HMNx3bS8uiohQihLCqeIBqDG7WFXRv/NTTfC43iNKramzFtPnaAT1K9Ah44hng6dXDSpLrZWMhg1FRbWWjYmyIUaw4P0iF6KY118bN0PG/Gy88eHTjg0JGwLjhsaLAaOWzgeR4iZzx72Xg8r5evDUD1yzl6M1YBOAsB5uL56XAfMsQRzPtVpPacgAi6p5z1rZrxDgA2A8KyK1lAwMQiDIX2CW/kZuYSOUfwLgbC1p0Nh+M0s3DeQ61tIjRu1t1aNAjDYL1huo8nan55ajPpcGz1gBgAkfvWorf9nh8q5BbD8tu2SxgPhlWRn39PIF4RitQN6PciIm2su9yvheBsx3PDYL3YFSCIPc3NYaf6TQ62mx5+lkXIIXvopx8wvjsMlrXlfQMwGWHk3Q069MBFb7bAAlDO1f2B2qAQyDOsGqe+7bnH1+cMVcRM2ZyVjHnlqZoJ1lzpMbQBPQMgmc2lQ4mYobO41BuhVwbrZIw2V8tnsjnXFCFnAJAICEJKVvdHpy/P12aNH6HVdyteC8vrAzDZzOQczfLhcDC5R2bfTJfX6zubBAPQXiWgU7VzeVE29sSKyO9UQ954jc0MOZyWx380f9h1KWx6xEqo6fYCvoMoNQGBdfh46RK2hy6q/moIfRyZBXa9VG0Tp9dVa5sAKDMNEfvG6NyB1gIN4vF63/WzKcDFRDb27OT4jP73U7sbmIXDCfXiErrB3eDzMV49KYR6KnU5t09m/Ggm3lQ3jzEAiM47obWn9i2jCVAJA2b0JLWDcqpPP0v1ThndohBMsouCOcQyC1OduZ/5d83+bpz5G8pu1vsGBheHwdiygbFGaadxFjeGkW/eaU/jR+/FjsVDRHZV6oDlYhYsm54r285hPlnBQ7I8yIGf/1el73t8wid8gvn+1ltvRXcCUJfLiYAfTtn+IV00Y4E3NPI9sxKCM7k7KRsBlBOFJ2K3v0DQRrw+Xk90Jq22410y6XWH+sSivb90TFznbAjm+239vSygHlbG1+S2O6Ebf6PiiEHzJmryuPPioRdb2Qjz6yFsIe0pYdbD8RqBvCepUdlTZMGupLs0y0ZfGl2pCYhsVE6ZymYAgb53Qo/8xdsshl1vMjC4QnZHVJ+euPZ8YDRs+rGuDS2Gqv4w+MyZqe9123hOy78lBW7UM8dR/xxtkDhTKOcVBux9Z5TVAIAa5KmwhHBVAszPq/qYemzfebO5rxmrIZj4e8NCcbx9hRFY8+qJtgVvctR8MfU9t88RZDQaPyKWrENNdVYjjZHyqzAEC+qo46cNdIWZwkxOzapMbURzZAW3BdSw2YG2xfuNgQkvA9R4XLBjzBV6Z1aQ41GtADMMuhRe+MnrTM8pz3E6+yDr4chGTDOWtGh9PFZzR3DmcMNW3B4jgA9vmtsMtkg411S9vsc0/s6v+eJynp/VvbJ2jKc9Zefk8XwBWqY2DLBsSJNhy87vGtyaQr1Yfq2P6JdASxGp+pFPeji8XubvezVkOC383kbmO31RVU/xTtMNBGBztrxmo9rZSzp51vi5LNfkjTpV7706DsPEzHewbIBq8gvee3tMVN5Yp1QFkgBs31kGBbouv/MV1oy6fiIB/UmvTN8X22TT5lvN94UOnp5wqQ2LpXUYaJtM2sUMGmFvGf0nnQoeY18Y/R7b156UggH2KhmrTpJW27yH4WTOGFMq/uVCS6e2dnhaTNyGjcPI0g611QwwP2A2dfk0Jhgoy8828ATKe6dhsGMbzjio6GwWxcnMBcCCYp6480nKnBi6lCEB/WDBUH282BBnKiSB0/IhV770S78U3//934877rij+P4Hf/AH8SVf8iXXXN81a/z8h//wH/BFX/RFWFGWmfV6jZ/4iZ946KdzX7VIFXG0KkChFlDJ6sUbrsmQKlJTOkheSmWYk95oLAgtp/PcjFpFI5zFccrqpby/TEEUw97xBkx/Sxpy7ptucDw43IgdgI/+3WMPcT9qy1Puz8nWAcDG2WoNGOljjmSZDEZ1Xe7r1dI+I91fohnC6dwl/pdDxzTjJ2TPklnovCwBtGCGOCvYO55U8Z61jofG+8wGkZNNzgg2xjiOdX6+2giubE4nI0bfhjPWART03YKJU4gwsvfHMU4Kj1mm41O4VjXag+O/PU0DyYikb9Uwa9qdXshpA+R4rQ2DyBk/BkBQbZj6wIDJo/imMbJ1eJzYdkYMc/xBh7LwGN5mutl+pwEF3WdGGBeo6n0BCs9jXTK5R2YfFXN1j2EZbba9vCnXwJrR+PHCJVg0OJ9jMoItogX0uc+ik5ZXlyggCTMNky/uXNnQF9fcs2thT6E0k8daVdEdbPt0dVXrQlH9S+uF12C4jK3Nns8SaYltozVx+r2I9kpvNHg2Z8b27/31sTqvrL9fRaP3wQK+ElK1PsdaKBnQ1ffEgOzgMAtUmNqURY7Am9SMQMniUjl+jh6+XQOnMLErvoPo4K+3368JtBJQRmcE6/WUqJq8Plv26+qio4lXgBi5Cmb85LXM5FZQfTgsc0p2no9kcpgNTe5tGCS/qrXNfp9sUgA6ZHR41IFoWRtcBnQb7Tqq1tpqVi8R8K6EbhVFh3QFZy32Sjc4AIjdxJv5KK+Ls6BLbQpz+scwbVOwOi0c8iNaQdyGDmBkz9xhZ8H54h2sgTeZ5T8bUlVLmy59qb/n+eLYceypKIXQ18O5vYymwcukyAwaAfHYkadLG5FWiyqTOWh9IH5Oe0v7jJh9I+vkHrF0g58RrzjXs1+0gzRV5o4mju2Yc75O+xWyF3fJJDwES0gVUP4a63golNe//vV4y1vegk/8xE8EAPzWb/0W3vve9+LLvuzL8KIXvWg6jsEhr1wz8PPc5z4Xn/VZn4VHPOIRxfeXLl3Cc5/73Ic88IOm2YIFnHWrsjYWNPnKIBQjtJjcWWBsGHI6d3Vd7Vld1r0s3sbBjTOtxRHPLbYBOaVvXeRwwlo8ZX1Gv2vZHNQbrMV0a5kDpusx26azXmJm/EzWFS/GNfE8fUnpKydTxVQWNvOE6w1izEVYQnPsp5TGzR5verwsZFz//mons2ryADkhMWZjyIyVTWeBJO0BFw+VyZ4SR2DJq09KLSW73Ld22NeycBU6S1uDIN57dXswAzN7wRicxbsgoV58zZNkTxG6O99Tn0MU5zyI/WA2QrXNPbfFA5tMlh5PQ0iBW5PX99jJ3OEYsgVIkirMgtyu2Sjb3M96Q1ykhVfD29PXYUbGoPUOBv+YRsC9Cii+rSyhJ70d2RgWoV4kxjwsG/M+9/u0sZY+a8r+bpxseEZjY9WauUALb0rGQTaIU8zPksNBdciPaInQGPY2MnEOfJL6tEaZDkfxwg6ZHaDWyH458+7APrtCcDgBaRGMgLHNZGWdPGPIB8x3RT352lpQGhjFnq3wMbO5nHGnPk66Xs7ee3MQEdekeaTacPSwHNLI4Wb5epsz20pZK2t5GfmY7bm18C4Wcg6D1e8pgN1sP3UE4k2bRFprdZa4ZjOM4ry8HjuhRWb8L1tjr5hxKOPcC0dhhwELGqdkNSPVw5xsUJNlKgLd4Aj5WgeeBeAzE49ZPsVBjqNSM7b0e+zYTKZufUhl0z3NFxzWxglDFo39Ltgxb2yMAU7acceBCth1r20sk4+dCi3ASdaKImau50AdBgO+Fezzil0q+k1zjJ/JRtN17DOFWSp0HG9sk+n5jp1RUqSrDiiDrA75W446mDXtuULryrAqG8Oot0Cif+6oj1kJ8ZNDnL0SlJM4VDK5Att9XFH0OjZU+v60fMiWP/iDP8A//If/EADwJ3/yJwCAG2+8ETfeeCP+4A/+YDrupCnerxn4SSm5lf/FX/wFLly4cK3VzZau6/Cyl70MP/ZjP4a77roLj3rUo/Cc5zwH3/qt34oohnZKePnLX44f/MEfxN13342nPOUp+N7v/V7ccsstUz3Hx8d4yUtegh//8R/H4eEhPv3TPx3f933fhw/7sA+79kZtemCQ8CCf4mtCDy4rcUCZZKyzwZxrvA+OcVJcR9He04FDE9yBFidPdGyqvE6rlLbZxXA7vGQTZmKmN4648wlCCwsPtj6eN+UhOFnWNDVYVlvaYE5p3uttmcSdOaNTBpZm07kfrYGGnpG+VM3rlRdGI+Srb7HrELtkjZ+TAA2Hx8C5eYqplwVC6jfMAl7486a/KDoESjaHhlI8IC1a+yy5bthN7eSl0+8WsS76LGhZvNO679W9GpZUn2wWCDVupvAfMs6mDHHkcSr6TEDRGuCoQQxmh3nADLczqWN1GQakSGPMUOyj3bgUYG/cHqfPy+OEMy6ljdo0CnjD8+SU0hc7SyEArQ3+Ha+BEZbV4UTJP0bmIA0WAiiyGKYQ0F7pCr0hYEzRPV5HN4Lmo6MNIm0sWNtlYqjwJvdMiwWPAx5zTn8OWq+oRtlvgWHZ2o2HHgeS0ZEdJdlTojNt8bOZdIB6pRtRMDw168M6MkwSBbWJEECDNXNqY0tv7EKX054zWSJr/BQp56ldzXqYmEFTfbxJzO9GJNaRlxqexZ5TDKa+sj35PGKnDYuA0G8zdElplUBy3PjnLj44zuH9artpYaBmcm4VYZc+M8yAnMtgwTJNyMxgFotax4ruVQEe5k1cLRnC3Hdh6JEIQTN6O9n2MY7CaHW1XIF+Lp7Go2dDLRoL6KtMjdO1+Zh9C/hwZsYJ6PUYtyifsUkA0DmAl1cPr5fyR4WtC+T1oU/23CgAYPVUhL53tQWLjzWG1aKZZ0zmMvDY0O+l2CksMN2EMcqAnnERyjyBeH4biv49ga5jYUfrOZTZPDHasaeHUy0MTOqf25MM+X/GGZ3fJ499L8dshmI/BFh7VewvdzTNaMNNhW9N25RTkhJm68Xx3eHHVLDL898NrdEfavo+p2Uqv/qrv3q/1ndi4OfJT34yQggIIeDTP/3T0apNfd/3eM973oPP+qzPul8b913f9V34gR/4AbzxjW/ELbfcgne+85147nOfiwsXLuDrv/7rAQCvetWrcMcdd+BHfuRH8DEf8zF4xStegc/8zM/Eu9/9bpw7dw4A8MIXvhA///M/j5/4iZ/Awx/+cLz4xS/G53zO5+DOO+9EMyd65pRC3Jnny7lYV66HqY8yP+xaPHq7mdtW4htRAGwok3fMuquKeOpJlrU/wpDGiYzrU38PooHBi1qEE6JCE57D2DhJKAGAkcbKoRt6AcjicsbLIovqTLpzWegTe3rEkNJZvQw7ZgOEEtkvFme5Dl9fUq3yWDtShtkw5Fhi9prmBVPr4jjjzXxX6V+zqPXDmLVAFyf8yBStKC7HezHbm85Sg4v7zudS6JhsHgvQkdoum/vCoNPH6BAmEzIZ7aKswQ7pbwOK5QkkzRj7xxuE46UVQZS2aBHNK1ft7/2Z8jODyXK6k+I1LGnTeRzNMXPvnmz02QDDXgD6ZNPDO6GtccNtkFTD6hbYmBZvW6s2oVqKQu+baBORvF2/syE1G8ncTqPxo48JAUc3rrC8yNSF3EYt2srp10OwmjP8mk5sk7Jty3vs2DF95j1HvRcTPSqTGWV8zlZsXtUnjhJeawUsc1gypmg8Vl2ruTzT306mnTJTUa6PsncJgGY0lJQGVVqMWlAbCvVaXMmsq0r6eSCvxUyeY+p/fh6b8+VcFrtk2sVMgxRgNJeSauci697weEpNwNDaFPWFAG0lDHJzfpzDNMuHWTvS97z5nX5W1xnose7/1RpH1/Fz0g9z/IeFuuVZmnaTYK23DgpoUobtcps9fZTK+uk5XhzA3VboMx7G3xwWb60dXASYr7G89SXP7O08RjtcNODE83pqY124WbfLZMmqrDWFzl+P1DZuWLJprwmHXKC5TMrjFVvbhNqnhERJRCyI5ABPeo6tCHUjZSeeCbnffp5CYllCQhhwhROL0XWP9qrq0X3J9pxn++j65NkvyaavOfAK3aJ+DJ02OoKyH9C2OK2pbXRCu+jZZlaTYeVETPpzU3H2dkYv0QNknRL6ZN97DR5L6J3DWKoBew/ZknAiJ9/OOk5LUU4M/Ehmr9/93d/F05/+dJw9e3b6bblc4nGPexy+4Au+4H5t3Nvf/nb883/+z/GMZzwDAPC4xz0OP/7jP453vvOdAEa2z2tf+1p8y7d8Cz7/8z8fAPDGN74RN910E9785jfjBS94Ae699168/vWvx4/+6I/iMz7jMwAAb3rTm3DzzTfjV37lV/D0pz/92hql4yx5MhDk3GyI1aaxz+mIPd0O0OThaa1ElDoS2piODnAgH0+Q1QtOuNa0eOnNj0OjDcNgQCtXCJnnTwcwsukb03Qdt+hF6iQxsHrxEuDgpLGzDnBhKOJiMOp28X3v780DVk6/j9eXUC9aAGgsetlA0lrapb5kg9jTleJ+F52hhcNyYX6/V2qZHoD5mO+us+09CXDrGfA1j07heVG/6+/5ml2PYFIAbY8fMvvOGCcrX4w6MB/cY+IJmKW/ZuPKo1nXqNdcYjCGptk8ebphHhuDPYptTk9LlyzSdMu1DMMk2mMrXlod6uUJ6gJAijwObOy+HgcTs4DFjIVtVgvzwDjnH7zvCN0Zu5kf61ZtZNJdSiY1tdnw580sb6wbh2XHwu/D/sIxsNVGTsBU87zH+a6mszTey8J+B2xDGzRGZFhN0d7TCQVSU/QZMtO1JOSJAR4x+s+y1lZ5brNOBmgQkKd4V5hIkhxmE4flroUhyyhPsptOJ3uWCVNTtyKp2I3AcZ+Axoqbt4fbsSIiyhzqJfeoQaNjAmoWGZfWfabFmHXoXUMspuPrF2hYN/389vi9u30QUe61pSxk2hGW2jy/8nzn6cI4c41xhLEdJA4iL1SH5mN2KqQQRieRLmqtnzQYHZssNZbxxscAMElPPUZvpE20Kx2g69DzDYc6siPANCz5x8l9z+nWpTSygmv6hkW7ynuKV52sXhX7woT8BSsMXWsf1bT9S8ZJjWk2YydPIY18OZkH5h6Xp/Gj70+HY3Ha326o26jA1k51nFLjsaxhRqGDAwBeo09qq+/KFizvHtsGIdi93QnA0UJsW+rm/mqakTDAwKQGwsQu5DG8jEgfYqyfU42fB6acGPj5tm/7NgAj+PJFX/RF2Nt74OMQP+VTPgU/8AM/gD/+4z/Gx3zMx+B//s//id/4jd/Aa1/7WgDAe97zHtx111142tOeNp2zWq3wqZ/6qXjb296GF7zgBbjzzjux2WyKYx796EfjSU96Et72trdVgZ/j42McH28R4osXL9qDvDAWwG6eVaaibYaTHXHMsJNLON44WZNONhEEJ7WnJ4Tnii8DO8OEUozGgIBCzSdxZ0bSnWq5bwbRCtHGrLZa9STKRsFyYb0L+phzGcDktUXSvDM9VD3zwQkhAqAYBxqUgy1sVPR2IffSpnqlMA7bFqmJW3HK6Zjcrjkq7tmDnQur6LiYMLiuA8gzmGK5gw2Xrtr4cQeMMPoT/TAaFA1fUx0obCNP2BxAr7IkmBCQSZdJp4fW1F3VT+ydd94jnda7vScbPZVxaESfdf9nDRUrwOkYpI632LBCalnCjNhvdMDjE3isdZH31blmcgzZMgV4Nv5ZiFKeiWpuLfxVAynFeJpl/Ng+0/VPm+KaET4ncgxgfX5hDRGpUs1vgUCeuBkMe8Vm0hr/0Rt1YARTwpoNWQarg9XXcTY9JnSkQQ7zpQP1BkqYhlwXRFh/e92m4/se76U5Ugyail5FoiuEAZZJU+ibjX8367IvRA+KMexWLevtlR79XiwEnwEF/Khn09KeZwwtou9MKFJuIgF03UFjRazpvUwxGB0gLwxneW9pp2zONgjHFjTi6wMwrDVpfyHozH6lPP3p9utwsCJMi+ejJsymmZdrMdAnnw24p8GtIf/OOh4rC6oYRmtKxqapOfU8Zo1JHsEZNJcLf67nur0wtegANB44YZiD1t5KHK7l6B8V96fnR07ekJJl/GgbIIehelm3TN0gm7XPQIRZCyWLlD6R6m/tmmTWFtEgPEHYvG2nvaarC+owfjA4z1KzhWqOwumAGbDcS65RXGeGZeIlA9FF5iYzBiScaX5spz0b4paW2bGjxZ09xwO/N5UYb05HPwp+7XYuWCe5zk7rO/XGZ+nYc7ofBn/PFdbdiTRGT8tp2VWuWePny7/8ywEA73znO/FHf/RHCCHgYz/2Y3Hrrbfe7437xm/8Rtx77734e3/v76FpGvR9j1e+8pX44i/+YgDAXXfdBQC46aabivNuuukm/Nmf/dl0zHK5xPXXX2+OkfO98p3f+Z14+ctfbn9Qol5G0FKYKbxZ10ScKQyIDhFPlZ5EB0uPGWmyflx1AWg4C5MBdfgQR+l0yrwyR2MM/vfaqBGKopksHXE4Djeb4qp1G2rtOUm4nb6eLOQskClx+bQoeGBO4s1jBhgip+8uDnIWaP3sJwZK5XxD11Wfm4jUBEv3z+NSpxc2oX19b0RYzbjpJRSC+mxv5egbULuZlQK4wB0/j+H8PuLdl+25utSeff5ea4wYsT7pB+9dBe3TdnmSnPoBWACktoiTAZ2axuokSOikfsYsYt472VNOmk42wtyXoRl7/a3ZPTLGmfGzahGONtPcYo5XZeAsT1ds+wd+F4Txo57BoAFCXYXjDrJC7+r6wlbguS6/U+bdIaO52QymvQJytVfrqckBmM08t1PqiRxNu3ZEXs2mZLAbU3XO9IsX4tEnmIlK9X1NJ03mlSHWzRCZqzTo1V7Rmbysx3wqjd0Qe1o3vOw1h+NzaJjVokMLhnHoMPOlEY0fvVTRPcVusI6GypBj9laKFjTikMm4SRbwVc2cBModgeZmPRjRay2EXEvnLppFgzq3lqJet7/U+1H3QIyf5jChI2bN5kA9+0oIprwzCwJSey2k3jssP2zHkx5XPB+FbtjJ+JnWHUdE1gAxvEa00WSkKgCAGgCRkg9A6WdbYahMzNTCtqQBJefqcaXuu3jvTsLQOElolpe5DCjt3iY7LJhJJXbeTLtSjAacHqgPJ6kC7nYnS5MnsG4ZY+p98bKpqbYZW9Bby2d0gIq69CkeG0qXuWfjMZG0nSCAhlnPoj0WADNkjDSEPkfbHx4jxwhRV5xnu+wYwOgnArBh814b2TE/pPF6DCDrkqMQWJwaEXYx+VAoH4r3/ACXawZ+/vIv/xLPetaz8Ju/+Zu47rrrAAD33HMPPvmTPxk//uM/jptvvvl+a9xP/uRP4k1vehPe/OY345ZbbsHv/u7v4oUvfCEe/ehHTwAUAEuPrQhQX8sx3/zN31ykSLt48eJ4b+vjLUU9+syFsK6P1LjJG1+mmYrnUC8Mjkc8DEN5jJ7E9eLDE07T2AmajQYnzGcyfopJjjdsGONiK0LEAADJCsNxsX1vJll+KmHXZnUuNWLvMJ0cum+VgcXgnt7QZE9fImMwXsoT95zBs8MYqrVnSr050DMonv0wGvfGOMkASJFxg57HpavAuTLTgtWFyUYUA3St83y90CJegJ2NIWuchKP1CGwcHpUHaiBp8kKyh0iyVtTBS+nvqOPA9TFz80kTraGp+nWi1PN5UqcBLkpgMrWOB0r0UTQLxBhRu1fMarY9J3ynJ6AmttFqY2lR+RnnVFq0s7owEzOyQuvXBm3L86SEFmmP9Ul0ZABgCEjMhtEimrJxqrQrko7AcK7sjxQss+b4wtivjWKVGU02b3PKj8zTcQOwOdtidaUEofnZDsvGCGmHY2cjR33dHIv3cgZgzJsbL3sKAAxqjmVQrL0qmk7b6+rNWKvAdZ5vhyaguUrMPs2qkv4iAGQCmwhoa9e2P3rKIrW6R8Sot23hLG7NMFjhY3q2/b5sAmksBstq4axe/TJgcdl5TqoO/e/2BwBhK1A91af6R+53ST4NSe++CUp/qWJdaoBHAzW6jesLJDB9mEzGLs3UkjYy4NXkvjIAHTtFmmBTqztj3tP1aK5SZ3jZ5QCr39g4aaM5LKrrTfhz0GueZLJ0Nr6hjWbtdkEFnocls5je8FdCbou1W/eTZnt7IeFsszFrB7A6htI3/D2DOU30r8nFODasjo4JlZtCjekZ98Ew7zgcOMU6wAuoZ8NtDWFkhzFgo21RcaxSvzbCGNQAeS1lvb4nQ9+HPQbw2ULePoQdfvL9QPWRzZUa2OeU/50FF70wdHbIiy3ZMEs9mpT0PH6GEBCYraQ/CrvbsMoiJg3OWhFGPO/PTpD05iFXEv7mwM8pcGTKfUrnvtls8Ed/9Ed4whOeAAB497vfjec973l4/vOfj7e85S33W+O+4Ru+Ad/0Td+EZz3rWQCAj/u4j8Of/dmf4Tu/8zvx5V/+5XjkIx8JAFPGLynvf//7JxbQIx/5SKzXa9x9990F6+f9738/PvmTP7l67dVqhdXKyW60WE5q6ybWXv44IhExb3Ks0Vh1lbxnHvJ/+himzOpjVUltsFRRPtcLKZFN/tzGN+SF3gBVKhOLAEg1bRXnmuYy2qDS5+n+5sk4BF+3pna8nCbt9dKrShEwizdk8iw1eOEZMWwg6ucjYAHHX8uCacLEVF0CQLLWQKaRh7PbcW0Mwa63XpYKc8k8ua6fT7deK7pvxOjzUsyu17Y+DQRJ2B4DibnO4bqt0LHJsDKx+BbmO4AMQOcdYYBQawPErqIjVdOFuaxiQ5aL8XmwzkO+x8K45Po7m6XEeKPFLuTjjjYIZHg0Rw5Yxn19pDboN5zPJ1qDNTWOJoIXqnYCMMuEPDmluNYOQ4A323qsTEwHDs3J/TqcodBGyjwyrCLay2WfyeZUg1mLQ7vBNOAA679IBiMCuWJf8f5TMZujE8zNkgmSNyVxY98XzqQ3AXTa1ufnLZkytSZLBejnbH5h2UxhW9N3hT5eBm84wZmkUr9ETBr17nT7zQgsHZX9scm6QItLKsMmPY++seFazKARUIO1bsIwpnQv20XjdYexXAMI+72A9tiCJBoAC/m2DJCSQULtjzBMqnxMp7LaFdm11PhvSJOnORrQU6ij7jMRkubQGRGJbjY8D5bjKfROFsyQ2c4z2YPG42guYJaWGGxsL64dzToztgMQltVjtkkoTLPGOc8L4ZXfxVnB4H1mNxTzQS3Dlv5e2yDKDgi8Zg+Dtcl0Oz1WsJwH2DXAsa3CxrdZ5oAXxOiCLn5buN7gRBLRO7JO81m9Js0lh8Gakp2fPT09AwA6bed2CkhRY0zPhe31g2HpFKCRjA8zBvwkHqzxk1YLu+ZIlsiLh9svTdQA7DhhJ2QlxM2MV8DV0DTi0Z6z1nPYeWNKv/dHp+LOp+WBLdcM/Pz3//7f8ba3vW0CfQDgCU94Al73utfhqU996v3auKtXr05p26U0TYMhv3Af8REfgUc+8pH45V/+ZTz5yU8GAKzXa/zar/0avuu7vgsAcOutt2KxWOCXf/mX8YVf+IUAgPe97334gz/4A7zqVa+69kapUC/zossL7YEPuUx0zipt1T9vrBejsToTCz8VjgQaBjMRGkOnaWx9YrRrj6sn7Odlt1CaJsP5vfyds+CzbgQvfNKmSjx50BM4Aw9db8Nk5sLW5JC8qTKeI7Uo1ML2XOE3XvCbHbHRch4JAE8p5NmI1Bo0zZhqOPAzaS3t1niR9vdMCE5a02ZWFjyn78x9mvAmBxxyvB+uzsTeykkNq4xyMVhr2jHR+U6asGfDCYssJVp/xqPee8KdU7uslsn4xXiPHB4UU8m4GvYXiMfELsxKp9rbb8Zq2xg2gBH8jv736BszPo2WyLK14F9B8xZGk80ehKZxNAu0hzl/xe0V9pa6MGvfeMKjVcaPUbhNSMT0GLQGU1L3oI858HUpCiM+YkznziFE8rPanLLIcTwuw1MAC1DJRpg3uc2VzqRStwwv2NCPghnps3ZS8J0CBRtQQHT2mopGWrH5pzGWn2XyNrtUJzMQw5AsKKhZYPlSnMVKAB4W4S4YQMkHUOQY3V4OW4p9wmafNxJlPaGShtwT+eax2Bwn8+7qtsr5m3Pls2yvDuj2orkv/VnCwBrK2CXaP5rJ1FI7RWhZ16dTtCc13liMOUWgob28zho2afnUNtaG4Vay61ITpvE4lcYZ82yrONdMJA4VD8kRKMctGpty2gvRZ1aEzmJVs1/y/ZhrF2tgY74DMNmgxTxaCzMqpAka+zvgZ5HqfbsGmLGr8nZlToMKISBs+iJtffHzLu//DFBWHOaEcXEx2fdaK5xeAL4y3nhrUAHXPRujImND9dFaL2FMun4NkujjPdAtUH36GE5JLkXmtV1aSckPwwSApIGRhu2a6GeQ1UWcfV4mYwMa8XwUbeimp8/KQM1yMa6XlXcOgGUSyTWX7am4832s47SU5ZqBn8c+9rHYOJl3uq7DYx7zmPulUVL+2T/7Z3jlK1+Jxz72sbjlllvwrne9C3fccQee97znARgXvRe+8IX4ju/4Djz+8Y/H4x//eHzHd3wHDg4OcNtttwEALly4gOc///l48YtfjIc//OF42MMehpe85CX4uI/7uCnL1zWVzRroKwDPtGA5iL38mSc8q5vieLKZbRKDPa5Im6v/psqaYIxLAwQBSOxVkNSaevH2GDkeku2I/ZnFq+9tuBF7HryU49dCe2Skn8KiAGtMiKjmnBd8mtwrGX6K+2Kl0H7YMU7oGlM9GYgzbIm+OIY3G9M1QRsnLz6b2R811o73vHc9lr6f93KGyvsRw9g2zgimdTeOhbXDtGJhDQzmu6l6Yb/pY/SzrgG10taeDHhlFExClrxoZ8PE0Mm1sZDSKHDLrCt53kUYgvWssoeoDjhzyIF3jMOE4Wepx4T0MYfByW+8UVdtEzDACA57jJ8KoK03zaWop6rOZAxxNuGOuHNt/uYQAK3/EYYB3X5jjP8+gwW6N/j9HVatOc/Q+o8FFKbNCzODnDKK8/MG07JjuISELCJL19Q7ENnQBM7gkv9RtzqQgG+YNHO0d7tsd62NKQSTsaUI/5vARf8Y3kQVjJTjhG4ZsCRWkGT3axTbkzP+jSypsu5aVi+j69RY4WYeB/0SaDgrtZ5SZLwRMLM5iAg9DLBUhJad8zeY0n6tD3RM4Vr7Hxj7pGeRZuc+RCR7akOfbIibHk4ConMUulST/P4FxrEb+mTXQlnXa6LFyGsKdUZtjnXFeff8vtDXM4LSRZ1+ttTQDeN7ufK14cbr+w6cKbuUbm9FH7JYU/UOq8jQSJtmj32o11o5noHVyQlZCQHTx1R0+IpskJxEZNlOYvLbgyr3zTZSO8wDINN39FHfihzvTNdh0wP8KFX7p2HODi1xijjyBtuT8zkF2FOx908QMnd/ldDnzJ8Vxk+x1noadnMSD+qzEVTHFkgr6tMlAuBtsG5DZ+0zAMB6GIEdkyBEHSeA1cz+4EOmJJh35j7VcVqKcs3Az6te9Sp87dd+Lb73e78Xt956K0IIeOc734mv//qvx6tf/er7tXGve93r8NKXvhRf9VVfhfe///149KMfjRe84AX4d//u303H/Jt/829weHiIr/qqr8Ldd9+NpzzlKXjLW96Cc+fOTcd893d/N9q2xRd+4Rfi8PAQn/7pn44f+ZEfQXOSVNBcQsRER5yjq+pSbM78kCeh8c8CGsNI96ymjdQbPyMwbRk/1svvbKo2TtiQmZCc74DyvuX+vHCXXXpMovdRS8etn0ONLaRLcbwsIv4CbxcP5W2T7F0V7YpZb8ZgAS+ojVGoZQ+q6NgUYnER48aCh5IXIuEtJifxGnnnJutpBmdxa1vnnvTn/LejKRDk/KJ+tWgLKHb1sDwmn5P0JpwZEJOwovpe958nWizF0c9yhf+MplbuG8OAondtGGyol4g7a+OYAc7NZmeK2QnI4LDAYUDalZ7Wi1Nfa82VzFyieTLt2dThAHyxUSNCLICQNuD9Y2p1GwC8ONn5Sj3LbWhF5XnTI26u6A3myKZgktEkmKtAChEYno5ZdwikJTZQyA9y1zcUUoWUMLA+0xGvXU6/6c2YhH86gtyhGxDpeRbhohXQHyubObE55DlRQCMF2CiQqTD+aUM47Ld2bKjnJgDHmpgv+3nO6i+U99oUjKyAxeUe6/PlMZztCnAYIdE6YXhMdGedLETAqPXEj4k2ubFz3gnN2pHMY7zkbXLdDCTtK/ZSJUxM+rJXY3R1rw1TA1AY4jo0S4M2DIQtLnW4ekP5nLS+0vLSkO/BYT/B3mvB+BnSCPjWRJTn5sFN7ztAnGIYnylZ5545yZsnVTuFjMg6PZt+nIp4XtfOJbGrvExiAMLV+sZ6WrP0c9JOGb1OexotLIegQ3xkPjBZmZK9pv5e/vZsSseuNuywvrdMamZ65Ha62bio8BgOgw3p9NYijxmcHL0mbXduwWoGv7d1TIXn4XUOLdc2ZFexd64V5BH7rCYQbWzgksHshStOThbNljOs4sE6y2qhbE7SDbMWUkbWcLxw9Ln0AbnNLviX4BoaUsTWZCaSE152Wk7LfSnXDPw85znPwdWrV/GUpzwFbZ7cu65D27Z43vOeN7FxAOCDH/zg36hx586dw2tf+9opfbtXQgh42ctehpe97GXVY/b29vC6170Or3vd6/5G7QEwTog1D4zMKyYOtjfHGGS+dWjFvGh3/Wg86zW1r6D03gRdmUCnn4dh60Hy2p6Ll60A3WBpjWoynu7XM0QqSLwpNc+bNhoY3Nh0DuCh+njKaECXkr6Z0aiRZ2X0LZzUmi6gZ4AZ/be/wZwK97WnY8TrnGj/7NjQewaq+9lbMPlcNjIOj8aQrbJCdfzMgtg5YJnuV2HB7EpPCjhhTNkILsBgdS09lh3KNYe7FKFh8l6yMPXCihDbNuRxVqP066awYROXVTaDFHfeAcb31FD06eKeIeWJbXsZyXpHs0Az0RxtFwCTLlahlWBYa+Jt237F+jjTdTp+bk54kP59MqS5nswy2q9Q2wEgBIQhmWciTAYD2FARMGCqjpo5hZAx+HTYG9FpC2jDGJWaAj+FlJiw3LzJ8rR/5FzpTx5OIo4/k4lxeq901InatGvNFWZbhc6OMQ0ySRtb0unpzozjlUPmjLe+CVarx2FGcYje6MWktdcwAcZ6eEx0B41hg3H/DQvLKCp0evKawpvQbj+iPRwmBtr2hO2foj1kMkbmMa3r7A/Kalb32OtqgWYdJsZje1hE0969DyrGg3QT62BlkKu94md5GisHEGBDvaZ2KY0vLxyMNXIYPJixZ+YywALw2bb63a2IrqdlO4IFHOLmMJaHc+WDmjJgaUdJJdlEcZ6+Pt9X0bhUhucDpf2Sw7RsWHIGLFmwWoNZIYwSA9znHvDg2MMmrI7LnO1k5kAO50zmvfT0ysz46RPSsrUZI/UzkfC8Jb+7tv5qNqravc/qe9qQqkJHSn5jp5Q8b3a+F5mJAQxDPQnEXAbfNs7/DmydCk4ooklK4mbb4zlcfa5dO6+Vxgmit24yJoxN2SKFHePzIVZOQ70emHLNwM8cCPMhUVYroMkbV7NxyRPUDFgweZV4gRfWbcGsoZNjGI3b2hqsJxMvRMkwFdg6dM7Lpdgweoe00davQzfYwzxTzKToxJyHXrVHL+oeIGK0kjS45od0pcpGx3u2vOGYRPrUYmEWr0PYjZCT1cgUCb1jKmpBM+1MfeMXJzAyjp3j+Ll63r5cvxW65F1onAUIa1TtsO7tebotwBaQ5XZ5fVkZYxpYKt5FFY5ljMO2tXHZvTMmGQCZ2FvUlqsqbX0IiHsLy/iZ2qI+GAai1euwoJ7/fRgGE/ZZvHNQRrYu+hmFrEvEoV6b6I6Vopq8WTZZw0QXRn/NXs68qSjEeJWgbLFZ5zm2sR5GPR9tmZk8xvIxM+AJQkC/jCaURjblnAWGS3uZNdmIZZPP5/Te3dkF4tGMzhkyeMlzVLGxkPALer+lGl5unMyTvAbIxrLQ+GHR7Gk+UEwdxUQo5i8Gc9tgwsvisWZvLc31AWBxcfQUHz2i1NWKKstZczxgc67Bgp6JpEgvwD26p7geHM982QaPBbZtNzMVHKCeGS4KNJlAJdq4tEfj2F/eS95t1T/H1+dnRhtpYWo1x5pJ47MbCraearp+L/gd6c40BUjE505AcW1jbnBO1R8LoDkEIjEEJlBRvdM2a2VnnGVGZNwLnZqOpTmbN5yrhbU5NNg5hbVyuFkHHKxMAolYOEqkzzgUPr+vBUuEKWl9cezYCPVcNYuIx2cT7Vql+1BsMk4cIO2ZsU+CsH2YAeLpUjrhO0a8m69V0SszYaxOCYMFAXVo5DR2vSynV44dIGB7TwIKWYajw3TieV6cNcyckqL7xNsf8HhvrX1txo9cc0azJvQJqQHi5dJpMYH3WoNxn5yJnY1wqGUkczPdsX3NzK8mIHDeF70/kffaY8p1w3xkRx73aY90sPrevqsP9ZJg1rL7VMdpKco1Az86jfqHZNEaP9Vj/LAM/fdOzwIcI2aQTUnyjykmk/mJC4Cj5eKweyQFuDZQmJEgjB/e5Gt6/qoicDvAhk2Q8TOJDetT9QSoKca8uQacEJg6RdQUE1JlmQZsLMS0sddhA8JpZyFYLc/GYzABCByWrDfgMWT6egX40bo4nNnNG5eGNm0ZTdXClG5gXnRPmBOOqCnSsCNLWG77Pgkhe4AXA1S9DWksPFfFOHGMQ97IeTpKTigWACuAXejkjCw/Ayx5DC8jSOh4Pk1YSOVZOuwt43HcdD4Yx4Xmw7S/8sXm1fUkm5HRUhj878t6ct9oUmCFOWc0foYAzlLiGmm1jCG8wSCafHulM5mmJj0T9dwFfNDnMgjAOiiLiwIW0Hx03NswFoe5Y/SUPGDOAavdDG36GAmJNSFPEjahNjCckczRKiv0c2aYCCFaxk8BIEl4EvVXdzaP80o4NjCCSnGTrIjysbNpY3FnT3+N+rq9Mr4zLDAdu2RZcHy9Dgbo0Lo6IpLMYXX9+RapsaLPGoQRhiFn3RLGj74Oy9o1ua889lH+NP21Pm+Fp9vj8qaOrtuOmzMZFOronZDQu7k+2+lE1+vASbI+1UK/DLvB2ZyyPbIjbCm1jr2j6jGMIV2/3JYRbJL2qu8Z4Jc1q8bCLpgbVH/f277Q65kHROj2zOnKAeNcbDIu5XPPkW1A9ZgNPoMkleQabjuoDA1g0qTr7hPWMdMIU8q2Oq2F2l4UcJHYpJMtPaNT5dpotWfPdpl3z57CNNsr8tmwrtTYzg724SwlFxHGkm4jO/echBvV9OiOnWzWM3ZedU7YlT5H3jvOeCbtmNO5FMetx8zaFX5/Wk7LCco1Az8A8Cd/8id4wxvegD/5kz/B93zP9+ARj3gEfumXfgk333wzbrnllvu7jQ/eYiatiqCXmqCmCZoNb6lqJoXmFL+sNisFWKA3WCbkwHoBuUhqXreoezCb5gGjt4TFWvXEJbfCXotFs6U2OtfS5xZGl65He4uMIdXOW3YSs23CtWoiuLYTTRapKXZ+e//sIUXXIXScsUjVKc+ewYLpYOc+Vd1jJZVnqb82gsAnWFhqKeWHwTIomNK92dgFU49bSefO7Wib8biTxJhXAKkiLW9F38pNdwuUz8GM18aCl3qBnthjFfDP3KtmMYWxT3n8TJtmCypv2xWtgcpZk2rMshBsH/GhIcyDeNIeD4zth1kPpsxVJs210LP1bVTGrM6eVbtPZiWkkKrpwovj2IjM/TrHmkmLgPV1Cywo7GSTz10eajbK7jYwyCAbar6n9op957zaue3x6hZ8cjc6uZ2hG+zvnTMuWQcorw9lWnpaW2S9VB51zaZplUYKP5NhEQ2LStfenRnvd6DwiFrInZ6T+r0GQ2tTOK/uFralCkM6Ji//XmPYNrU00wxYpAALmJqwKAtEN8d2PjKZ47qEeJxMaFpUTBsBdli/Z53BTH2dtE/PMs+JjYp41USnQU0TPIZjl7A+W7arVdcSALunZynvMj/jQuQ3JSAGN1U7QPaOyabpjJWTirB6mYd4PU5pylZZtFdKZZ5IfW73XgUYApQgvWNnem3VRd6FgglecULypn+xcBjpqv6Ns7EHtvawSf5R0X4s2itGpAVLplNjtLatl4UW1vGQYPvRhHolIJl4Ud1/+StHMzKgtyGFJ7CFxAlQaLrVzivsaE3F81ld42/NLGunGj42OQ/ngdTQDQhrAqY8R4xxkg+2XTU9IY+RZhy1jnPO9KN+L3Pd3sTuOAqLulYZ6OJxMJNg5CFbThk/D0i5ZuDn137t1/DZn/3ZeOpTn4pf//Vfxytf+Uo84hGPwO/93u/hh3/4h/Ef/+N/fCDa+eApe3vbUC9epGVx5I2O9mh67BVsDZMCPOHJbDGmQA410sNKIeO8qVq2xtq3SvXR1/pAafi7WRkAG+ag06+LeC5nMBpgY4Rr9etjNN1Zn+8xEObADHk27GSpbHR0W6c2sBNH7lsxT8w9OekgS7G+vJGsGW5M7b6irOnVqO0SawCkZmKdK1OHh8O1NSgYpBRvopdhiY0TAiBHI5s6TOvCZCPO6KC0mfUy5ymU8V+579lxNWlQqbZrJoACXkz8dwxmvJSbhdxfFUBhLk0r0IztZl0xz64w+lyOR7mmFcTjfdmY+YefiRvH7hneDquOU89z22TDZjJZOSACezmnUC+1udbgQtRTEDc3JDsX7Eo5i+28XQtNGz8kLO/ZoN8r+0NAgAKkcEAxo+1SSVHPzJ1h1SAQFd6ErGyGurgttnOd8YRmvQKzAdZdVnnvvHTuhg3jdH0xDvVGjtaf2CXEGfFcGVucAUtANxNmpe+9TwjRbr4kpHBxeduWYcUAqgWMGLwRpo8OawOQw9foRuida44dVpBi4nB4mpT1uYjlpWTAsl6lnhe2EGsZSfr1wxu24z3ynln2euqVKPAdzXi7XJ7c70WjxaQZRZvcxoYZV7md8Yg99gqgigGhH2zItozbgmlsx79nRxWHCEvEOMwGG7bHJnk/ILCxqG0ccTKdFBQo3hdx4FB75b51nTxfe1Oitsk00Gj0fHqnPgc0qjGnDFtCNUacT7VztR6Qp7PCGFgNyGdnXwjWviZbtF8GM4Z1RPg0f7EJW3mWerxO7eTl3mOIMHCydGzMqL6bC0uKsc6kAbZtXy79Y3a9Oy6zytoO1kkVbGQCZ3utLetNY0AWqxUXEUhI/kSSApPUQZ3pVNW5bII97yFeTjV+HphyzcDPN33TN+EVr3gFXvSiFxWZsz7t0z4N3/M933O/Nu5BWbpua3HUvDVmc6qzI4z/sJExedXYE6XLukNYNHWvxpy4s5Pe0BgdG2ej6NRn2r4ZkJoGoaHFUHncpwXTZN1yruewggAgrCt9owE4b0M5t/mVcC325K0rsfPBaQPb0Z5oHHvcN10dXNJtdgQIc4Xl93rM5WdtWGV5o1WIa5q+dhbVZWXjRjTzeLyx4UCciWK1dLSr1BcCNNbCBo0Xx4thdwQ4UT5L3qgO+6ILowRttXbAsdodsqEcrYeoSOcuba5puPA7QZpVoU+j1oMuHsB5EoCiEopkwhX7DYZ9+o4FXT1dL88wYb2FZTtmUJkBGKe+d7RogBLwMsZtvkcdUqKP1xtPeKFeZnyqc2tGmWzuWPxVbzQWjQUBAIQM/IioMLAN9fHqkWJCvS75HoF41E/smqk6M14jsOb1QG0s8viwwv4BYRjMex81wC9942QeGuvUOkjUeGfTU/SN7hcvNNowa219hjEj78iMVlh7pUPXtGjoGNH4wRV/7AHjeDXACYes5XthQe+4tuPAALJO0eNWspiZDF1582o0qPQ6nu/XgK25eh0Ox2w9OUazgnTfb5TGcEcZ65pDO9/pcydNJA51ScKwZkeYZQbxuyvAZHN561QxgsPNbntDQnK8MLFdiRbQOHOsA7h7G18vnJbnIwBOqvZcf8GqnAlrkUvqY2Zs0bQXEFhsXj2fyVlUWS5N+FqhRRRHe7AWcjvHkglhGi9TqTke2F4cBmtv0XOKm2SjoFT10/jznlnbzgtsS70nmD/rTHBrFwMobUuP3XOS8EcTGp2fISf6UM82tS1C11lQU95x3R8zxLapWRyuKPWxc3UYrHPvJOHe+rOMhROz/5w+Y2fKooEbRndaTss1lmsGfn7/938fb37zm833N954Iz7wgQ/cL416UBed1YsnEpnomdqqNmlhyNZNDcWvCazlc5Is6PKVnqC0MC3TTvcWu2OjnWvKxr1oF503LCLa3mZcKjYOZ1ZFfUWpMaekHg8g0dfS98UbYo/yWVSemUiVTFZmAdD9UFmIp3vU44O9Km4cuo7dz2AXp1qV6895WXJWi8hei6ujsRUubJlI5nkcrREOiKXDOkCih7PgkK0OgcHEY2r/kCwDxBEuDpQSJmz6DLrOjOGs9RQS93U2vCtZW4DtRrXoj1q4DY+xGI1IZ9DAT81gE70aHmOFftUG6cIBcIVS1OdzY3d++50J2+vNu2qYHjKcmIm36WYZIGO7BxuKqPUnpL9pnozACGhxaKjOnpOv1R779RehgDwkskhrGNQ414C6rnLgZ2zZErofJhHZCphsPe/qkNQhDAvbj7KZ1gATa8esE/oDFtQtx5WwTRoD0AU71/D8kTzvqgJBBbxhp0YGmee0T6ZzzAZQvNRqM8rhERL6qd65XqWUL7Xn7PvKekf6HiV7lU4LDgCrD+QQNGIr6mfZr8ZQLwZJ4LCwGCRpLm3ApheDQ21mriTSEwyDBY0MEyxidsMhoYYbB1RK0WpJBc2cE2YNCy0L8KLuvz2kdyG3u9chzbVmMhbRJ/S8P1PA7upee/25ovssxQAkO0/HTP+cY2GHNJhMX2aOXTv1YAQRzOzDYzg6abxbB1TxNqbBglnF/CTruONg5OuYtPOyhtc03vRGlkWa0ToZQe0c64W9AbCOVe8Yz/EJlPqGDkN9V4jtNjmL4+hhYXqym9zU7brrZW3jdXfRIF5dzzsKa+2V56PvtRa2l/ReogLicek6h9GlNTQlwoFuXqIiWF9IPdt4VLln6Se2LYv6nRC0iqPJjO0YrXOPnZ99snOsvsWaxs86+Vln9fXkXJbrSM41H+olwawF96mO01KUawZ+rrvuOrzvfe/DR3zERxTfv+td78JjHvOY+61hD9oyqJFYQ3x5MnNe9GpKRV0cECOw9VMR1EsOvdzWP4NYy1dCx1cZL9zQlBjNQpe8EA8nVSUX49GZPFO1eGH9vXPfczHsQRZbnyJuxZWdNvAeSsLaFBXU0LyjpaIWbC9pswFJKvo9vLlmsAbYglmF8cd943gJud/F+2/YUCegug69BQscUNFkOBIh4bmFzwllA+BqbxldpmyCaxAm6bGk6+TNadfPGgFTljdur0M/B2CA49D3Fhzz2D18TBNPlIYX8J+lzSBBc88ucecKSys1y1HAcuZZTuFKFQM+FK+wX0+sCHXPOs08x556T2VjabJPZQBiIPFQPc+n2CD0CZEusslaM8t7VOY4Iy45OGm8iTWSw1n4uO6gRbuLDRad56GBe0ntzMCWtIH7Tc9lMp4ZxMj192e2Xl+jySPaWwo80cBH4VygtSVuegdA2x7fHoxrW09rpbCyzDPWbJZlwPKezZTlSsoys670uRyu1e81hm0TGd/M4IsJ2/NYdifQpBocj7jJSLYIoxOHxk8Z6iUOEQJAps3j9rvj8wSoZV2gqNdFrTVcpHMv6+/2YwH0ACWwJEwfk8VQphFmbug+i+N6YNhgMm9r24dtuZTuc6YdN1SadWdamDmw1A1zwtFy3akNJ8umWtPEO8naoTf0XjYnr2y6eRujrwA7NRYFh1U7TrWt7mHlPPi2qAnpEva5ZzvvyCYakgV4PeaqpzWVssxDUQbnb57mvXtiG9bTo6mxfLhtnsaPqzfJYbN5rfP0/3JJbRyTB/B7KfeqoxCMPQf7PBK1U65tACG7Z2DB7TAMdvxokEf+5vtOOcyxBkwC23HuOO1Os3rdxzpOS1GuGfi57bbb8I3f+I346Z/+aYQQMAwDfvM3fxMveclL8GVf9mUPRBsfXKUfthNIVfDuBB4JKhNFek7Aq+9HgKV2TLFhJApl39uFnKNp+mS11mSimaHIphAQNj3Sij342suQ/zViqhYwqOra6H7VfaDP5wU/RkdIzaFKMyBVy8SkPVMSdsKsAXmWmunFYNbxGqCYY81sGqTNhlEhFI154z810eopyXXm4olPJOyYwTLW40jJbgDNZnLjZy7h4mU72d8rM7gBZf+0Faq0AFVF+JXPMivGcJHFR53rZRabyw430bDZgMle/T0Oj1P32LZj2/ZLLSZ5UYuNLdOmU9rpJZQNkGFspB3pvYHxnvmePKFNmg9Dn3xv9NoyfgyDZo9F660xLWOwpgWjN5sGjfMkBfQ7LymQK1k5amKgYz0JQxvNZl6eoQY3EouFOx593gjHHKrF4S7t1c6sK2mv7Pt4bBleui9kLpllEFTO3W5KKiypOcbPxoJZw4HSWXN0iHQxgKZaI0Ujidkr7b3j+9dz2JB6Ju1hj8NHrDCw5uc9wjhQbaAN4LCyzIJE/ShZ7bRAOQCkRZj0dGr1pwbOpmT7tzB9Fld5c5N1kRgk0ULUDjsNUECVuo/lB3wWWKemO12PHs/Li8S+6RMiJUPQAtltBte6/bJhEpUfzSZc31P+jgHqkxAsnHAts1GdwlNn2iCFswedNFSHNeZkY8oMqLnQSCpFZkp2qMkaGP05tvo3MM6VJlzLYTyYkO18zJyzoR8yC6VcC9PBuH5q28c8p8Zm9aqBQycRVmbgeHDey2KecOZDAGO42TAUzDuA7BkZY7XGFOAYOzPFyalfRtUPOkuqYTsH+6IU12pt3cDWjp7TD2oaoO/s2idt0G1pqW/a6DjaTsaYSq1lcBvtp9Zh4un3RUAldsa17ZiGnllU2n6shLmnHPFxWk7L37RcM/Dzyle+Es95znPwmMc8BiklPPGJT0Tf97jtttvwrd/6rQ9EGx9cZRgwzQKGLp/UMao4jB9LmZdj9eRL9XcRbgpQ7zrOHGc8ogQMpBAcj4H8q8PVyCjbzIQBSMmnuJo5HF7G+is5JKCMIdcrpoOWS+n6+VCyPOFyWNRJYsKnhdqEuHVF3W7x2FC6XXJLxoicOrL8vgh9SeP5lXWu2FzxQu6xVxh8krY7Xj1jtJxEXLjCWivKMBo/NbougO0iy16k3M54qMa7ASzEmFDXrzALvOxZNtOOel+kbkN3dlLi8uehHw0MBry8OcBhDpmNecVoNsBnjI5QMQOczlykx5Y8G/ZSpdHjZYCGYrMgx/pzw2zmLRE81QxIdXxBuT8JIUq1K1a0glPFS1ccEwLiujc6P9NGW7WF+37YX5gQHw4zEsPTpGUHLMhPRuuwvwLmsjJO9ThGfkrzkpPSbh5PecwV85HRzMmMU3VPhcaPpwsl9TfBAHt6jhVQgIHD7sIy11d+z59X92y2ekC5rC+Mc8/qA4r1a1IsJ4SW58ny4zZDG43/tQNIGZYUZosAXWYJaQJSE0wIWFF3fpacCl6AqrVKBX98XVnP6h7RdFLPTN2f9p8YTZ4BJlRGh3UJ8MTHSFib0bTS7/QwIDURiXXUaiLDlXqkeEKw9QoYQCYApwnzc5Qc74RajolAHEeYHJLXSy+L1Fin+orvU8JQNLNP21lzkgWITp+eYD2rFB2GE7phXINovRQmb7qwdaDERBv3Hr7jUBcJj2PhYDi2D89li1Bm14Mdr15JcQQxzDg6AQgwjcU59tYcO2s8QDWG563oMLjVtabMpfzyilOI7XWVCfdwPTri+JrCurpav6fQDbsz5km7WZdSxtBMCZveSlLo9VLqNplvk581TNtlfWXeiWkG2XtollNx5wemXDPws1gs8GM/9mP49m//drzrXe/CMAx48pOfjMc//vEPRPsefKVtgJi7rZZucoZWPxn0ZkMM+70BLBwxQNokmutItV7ssme0VLxAyUOk5bKLMQ1mPHL0XKTePf++w8bqkMxnOcqlyDihJm4T7tKU2c6AMjRMDIJamlazKVeGkyxSPLk7wAgLDgdH16YAGmqeELkWA0cUXYgAOw49qrRZVHfEHwPbPnaE+YxguJfVwHheLF2cPf+pbUYmjEed5Wo8thBA7wqH9llwsaD163eRjb4QjE5PMZ66vClhxkM+x6au19fNKewr4XbF+3ofNLwEdLAbhGD7aBeoBxDLzYbYTe2MznuuL583syY7Ua6zTMdcHjKBQ5rlU7OReSgGp70nANmm50/jYDhQ70gT0B205nmLVk8BPjv31NJm22Sdqnwf+oTIm1oGSZYNAqWZL+dYmdP8UCwuxff5OSTKZjYxfvQ8WdH46Q+27S9C2TTAymDZcmm07vRasrw4vtOHN5QsMgE1+gvkKFFjsTtokdpgUr93ohuhN80EYrSHvWGm1Erke3JYA4ahE+2cUoz/Sb+OQbaIxeGASJmxtIi4tDuQUyrkMaw3tg1lMJK1QT8nvV7o+9icK8dKezSYLGFl5eO5PTN+Dn3mYBm2FxGPnfCgSeRYzzU8lw1Wu4cZjsIo46UrRATOKEdaIuHIYcgWgDb8MgBYWCdaMSfKWK3oG6YZUHVKVKDHmadTAvgOUi/EXIpkgKqxkU2otUbM8zns/PEEto0jphk1WPQl2Z6psfkHmCyL3GdhsGtVEbZaC7kbBqAb6jYhsJVkYPa59IfuMs8mA8o1Qc8xXd32GbOoMYih/maHFdfP+jqeY48dq50z9qikJlhmNqeWlzHCY7F1EujwHqVpTJr5QhPL61Ng7KuThF0D9rhNfzJH1UOpJPzNwa5T4MeUawZ+pHzUR30UPuqjPur+bMvfvVKjnM7Eb241EfydSEHfdFDhcUKrLF6c8lmXk0wY0Xp2psVIk1E4BCQloI0WPFGT+tboKy+ZYqwueHy9qi5SQfn0smzMMAsqQMO0uHhhPVIkhIj7bLquui8vhIQ3X17IQy27lYknn2HxSJHQIk8c0mtzrT5Z/L3r7Bpn7vNRbagIwYaURtDHxCKq40RAu7KwFuO2C+5vQ7ShRACAM4ruzEK5fao/J2AL6LGVXvMqaUOnbTEsWzQc6jUI82WuPls/h5X1i8o8FCMGCqsyWUv2FrPU+EnriualtGyAxtEs8DTGOGPRQvRXdLvoWWaQo8jKpseYbjJfMtn6dOlzWmzTF7I5c7K9bKuOWFzpsDnD8UH5d82EYCBx2RpAh9spIASHejXr3oZ9Gm9l76SlVteSMczC/u3I+LHAOUyxzgj5d3uuHRNyIQW6qP5b6jWGs0g1wbRDb+S2DCnryACsN75g2jQB/TJgoI2QhGEV76UBEuGAspVNHwt1L1rrweQNZu+BTRoskAHHG9OEbhWwoLkx6pCqSROvPFcAl16ljbfjMwMKerNbAYEWpIsUjwcMD6dQr2N7T4GX60kTjO6V2MjBC1POHa3Xd1fUfYeA/vYmrHOpyraRj84cW4QjS4hyxYnnCUpPp2ZHndWOycfod4VNqGsRmTV6Pg6jQtc/afJV1tS5fbPYCZWwapMBTFffp3pCB26bG4I3f2rcpIllODVXs9wcYBTINnIc5tdaGQcEEoZuY9tbs/lqDCJts7HNt2iBY87Qpp3Evf0O2IIbxvG4tU+SADBOmBWgwBWveBIPkdddcUhYaYkaGDr97mn8HDlMVAcoRrQZdwtQT+ZWtjOHefDvtJyWk5YTAT8vetGLTlzhHXfccZ8b83eitM12EvNofIBFlofSyPDKhPxrTyFrf/Rp9CZpQ6SYoJR+wUliQT0jhufSxoIuvPeejC4WFNaLkHhcNk5crGGmUBvcRVu3UX3w6J18n5oBNDiGOraLSzrDbCH15+RlYeM89xlmyt7KSQ2rF8z8TyWdvPFEbfSmBBCqd1F/Hk/aIDe6TCkZj2XaX5lj3Db0yT479lauN/MaWHJNo1sRRkHswNlAlEdJxgenDz+JJsKkKeOz5kzmh+LkZN7Vwlta01LI70Li7A2BAJc22n7MA7Z4lpQ5bkwxS9fkZyv2CYO5TnMHYmw0l7zxaYEfsC5P0/haZlpEOW+yG9bdkFTtrb4OjZVgDehhvzJ3eIX3DI6ukAkhynMBi+qzEOvQRsvGyGO929+eu+SMMCx2DqA59hlAzWE5x/arBs2ud66x84W+xYlFReNkYqTwmlCESc6HzBSeUnpXJA39oNiFkTO9zZS5MJtJR6riOeZxVbTreEBcl6FNALC4JCw+dSyDJHuOR5nrz+dwVjI0wWKVJgTE1qfDAoXBw+yY2CUMrQ31EmYUAAx5rW05rXwGz/R1GPiZ2IWVW9eacczQGxbBOI16FXraZlCJGVICHBunkToshTCCj7we576PV7ZrTE8ZL+Pa2hdmXZTbMuEuTqgslxhHgWf6zlyrAj7NbTCnd9kwQOQA1TaeY6sUynwdfazzDhpbUTu9RPuwRgyem78HIHisZdnkz4D6KQQzBmwSjfwvOyQcwIj7HrAgsx7D2+fhgAVNY6UU1Nq4tV8qG4w5lrfMf/o5aXtD2+y8FqZk2VX6vnt/fE7P40yZvbVwNrRtjgr0nXTFmHEytqaDlfmuaEItgUQI5p0ztnobYTIzqufjZkMGkMJ4T4ZBpG9c7G12spxZma3lQ72chno9MOVEwM+73vWu4vOdd96Jvu/xhCc8AQDwx3/8x2iaBrfeeuv938IHW0lpO+FwOncxTr2MSlJkcmXKvIiGVjaecr3QL0qPkp7o54yICGsQesbCLhoiHO/ZMPaJ8TboiTkvuCbkbLDtMMCALBLaaaHbWWjjsNc6Wm+D3kxOFODykK0HsRKbDGVEeag+yFNosvT0NrbY6S/jWZPnbfSUynYObZhSAk/1r21okW2XZThZymz2+noU7jAfUoIQdsfRw+v3fBxThz1DrkLT1Zso3khHJ4So2JAWAIszbRoWkBpjwiYyBnDWEuH0vxSKGDrHgMn3qLMgmbTwniYSXWsSSPU8pLyfdJ7JrOdXDud2NSPAbHS1dBHgs2Kna2FXE8bhiKlWwTePeMHDRz3baWNZO4aZZIURnNDvRaPN0+exVaRhd56HyXREzKH2is9ODP1gNU4YoNo42l5eNhcTlpsZDzyv60crz4PBVscragB4CUcpsoRV+onDGI8cFlOxARrnqp7Da6qMyZKdMiyCyYwlIUqNyqbEzy12gwmVMvc9bejL7+PxYL4zjJBoQSvNNJAxzKydoxta7N3dF+nWgTK0LhwIyxV0UJ5jFYNwfba8qYO/GvtEs4JYWHtqL7W/3SQLYmssN98Lh9DFKc08fa/nzT4BQ7Kah8IWUqBR44yngddxI5Kex7Cnt8MhvEzq8LL4dKqd4mRyNFzSsnWEgtV9Tw41Gj/3IW04gKpYtMtU4XVbMeCndhmbLLrfFyL0cq5he+x2NoVhKDfg41XLj62dswAA/Q6dM+R3nG1ubYvV1sMhX8/RcZr+zH08sCPPY8kbNov0t2rbVWVn6b7rjspzyRENoASKxJapgP7GZtEaP/t7GM7tWdbv9F7W7Ydhf4V4lfUPqEzsrZJNHZxQRBZ7Hs7vm74tnp/cF9fTxPG9r8kRANO+Mi3KdsVLR4j9jnt6qBXHHrtPdZyWopwI+PnVX/3V6e877rgD586dwxvf+EZcf/31AIC7774bz33uc/GP//E/fmBa+WAtPBFfueof52iLGJaILFRFbK0zsTFQouvRoBGfG4MxaGvpS8uDUvkv/y31eIu+asMW2HIMce5HXgB3GSLaOGI6p2c4HatFrRGPoLM5BWxIVdHfeUPDbIkho/1au4JBseM1wBRT3f+LfF3ehIi4IIdOUzaluKl7E4tncHhkf+f+rwE1J2GVmaxoDs1bt1OO5411SuNv3B+6DfJcExni2QMY1eJtNkvT81aGsd4Yedo1us0z4GU4HNtljHj5zOFvGnzrh/F6/Jy8vr96WH5um50peSeAkcGnPtnvTIak3tm4KENWNkScqSaLG6a5Tbkcy+9fHruF0LERwLXeNva0bs+lz9Fe08uOx/1a0zMqGFDL1uj0jO2UOtW5jl7TLqZLnxk/LQNhQ7IUcbIfh/0Fmiv0pRrDNWZBCgD6ZIS/PYFpYyjLeNIhJUbjQV0nl/7s9v1eftCC+Nv2h60WiXNMTVh1SufOy6PaTPaLEWxo1v48ojfPHHoX1zbNPHslhZXDIOHQOiAbneuBKbp+Ce9MlDFsdW+PFICWtJ40iCT6PyY8Lv/bKl0fq69k21fo+pzZtmf1QRorg31eGgiSc5f3+u+I1UZUbV8EtFdtFsOJRbFw3oOpnmjerRoYbsK6YO1AZnukZoeJ7oVvA9vNpadjI0UcQw6IMdahr+O3u5Am0NfS76Kn5zMDYkyHGSZSKNvntC21cVxvWUphApA1q8zav4EdXyexkXXbVOH6h0Uo3g/5ztTtOTJSmnfKTrpddh0f26fZ99T3Hqh/oECHwrZ0wu+MXuIJgEOZS4/JkC2E/geEw02d4VXsdxwnNq+h/GzFUW8yLMIwrN13ekabNIjng9lnKSGt2rr9CTU1GbZzgxR2A5in5bTsKtes8fOa17wGb3nLWybQBwCuv/56vOIVr8DTnvY0vPjFL75fG/igK4OCIGuLAk8wFE/ulcmTXGiD0IyX2Q7lBKOKnqBZPNKJlTXZqFLCUNOr4axR5gYcI6bQnckgiWc4UVVG36Jvp/ZNRV9L97f3TDhA3VuYjKOnslHUuiHSBrY15R71MDAhUK0dC4X2jpxX8Yzx90zN9ZrvxVXrVJ3ACB6YmH9mUVXatukt4MglDXZHpZ+fpwEE5PHrACwFHb3x2yufZ9LXioFUhObU4v093ac5EGzqdzZkLWV5bGfJ4guDYyh7sd4mQ0bvZAUqQYzpdwZwuh6B0ieHBV1zvXbCXTXYO5jvxh8ypYBBEgUMiEc+MjMrgzDNpv4st9miKs9En+ph3TSuCxZYrUrxvLOYqt5opTSldNdlyKCJpGMHLGMDxwmbCyRCfMTzt9/+YdGgucKgnTN+aroY2LI+mN3QbMbxWWTM4zKFaPhAXwEIMZvOaWZxf3PizovGai7pZ5l/YhBjSue+Rx5XBfK0mwHdXoOOBaulbr2f8xIyMDuJ2rAFWHjRjlNYVa0MTcDyqJyjjq7fPjcBqzgV/eHDGuzd3Rswq1faUjJWeXxK32jwxjiIBJfXvq2Vv6vTgtIA3PlV719lQ90dlOdN8wRhmnr8N9LMGhCd6hNG2PSWyWbCsLOzhtNxRyfrFpduM7vmuexTYFyLU7L6g5otNLGHKztrDZKapBmWxVTYpUUGVqd+ni+cUFRjT9a6yktqUNP5U99zVi8Mg2UZGTZgJWFBnwwzxYTnBnteoT1XS6wSghuCXwDmXoZEbMdi0POBYc45dqF2NOlQLs/O4f6nNc89Tz6vKpqKQJbVsF+74s6ejWpYX2yvjmPQXdLnGDmAK1ERvAHKdlUII3vIsNLVeyzP2Yzh4UOPvXLK+HlAyjUDPxcvXsT//b//F7fcckvx/fvf/35cunTpfmvYg7boUC8GeGo6Cp44oFnM8796sWNPs2jwDPSd97cXx8sbIWdiqVLj9ffU9tg5wnNAyZLwmDfIk/iOOHc3pKrQ+5lBwb2U0ycQJQ6u0QcXZDL37izgbqiJ6X5Nw5aQwIowNY8N6pvgPRO5b90Ul1VWoQJP5zjXlK8Ms8lhxxixaLtZcEPcYgMMfthZ0a493jwK/Vt9x6KpomWl266f2cz4N78DBWttClfhc2pZKeaEoqdzhTk48+6cwEs1jQUTXtlbAWPPgDcGl2P0ee+bJ9rohGwaQ9bpCwaHvPevAAt2ENWMBo/2ate6Wzzgho2hqRYJKafMLq83/qs3+ZEZiDGivUzaPay5dCS6HmX9RfpzaZbDPqiK1EMxCtifEPJv/IidNS+xyLcIdc8wOrfJBRTrRM9rGiM1m4vKvCunZtDBsG0OxvmDQ+dKYdwe2G+wOVPe0+reLrdxW2nP2OgyOu80bQiF2WbC4yxoZDY0gRhx2KY115dix//ycgJCwPp8OX9qjaKJncbmRQaHFle2lXK41sQuLOwX9buq06S97gesz/HgU22U/ubHLZ/Zl0EsnngcjD6Q9ejAevnbaO2oSqixeedCQOrpXNYo2l/YcB39HlUFeSOSp9enL1/LSuqAG6wF6Bb9XIs51BmvnBF0Z0pxKB2b5H4P5Pmi7+sZd/V44/kuWTHsauHMbSnZocegUTfvVKjZnGnRAB3sOFDjaai1W57JnGi1J0Rcy9bmFQYF9T0mp25dDDCinZ9hZOLz9SdNIvUOm8x3qINNUuQZ8thrG8vUYfaik2WtsFkn24frzunpub26VMZB6NPsevZQLAE7zbUT1XFaynLNwM/nfd7n4bnPfS5e85rX4BM/8RMBAL/1W7+Fb/iGb8Dnf/7n3+8NfNCVhO0LyaJmUma8/3XlfudgTsF7PHp/Am/EpezK7FTRoinaVpug9STLG8IsJGzCj7ThIBtrDpNJyZmQqX5vwS/CuzQI5HQkT7J6oZLMXF4aVcC2TTNSZEPDgqdz2iXFgbygqaqlD6qieCS4fO+Vol3DfmsZXbnOwjPFi2psbLt4nCe78AIYF1Dufq5rf8/RfLHPzGQmauI4Xk17Vf+s5sX8ClFMuqWJyaDtjkJXRLXZWbSNhsq+Sj8tITTMFBLDwwg3lwZEWjRWsDILORfX5ec0JNuP3M4p45g1zt0MNkUblk4WEQV4ifg5tWs0ZAdnrOhj8lg1KVjz9SpaTIADGgL1dO5cBmucFyCGNMcTNocFfoqNVwZ9mMUhG2R9XdZqiOvePstK6JdJK+yAgyaMzwGCyo2fv9aNQFazFYSVooHP/C5YvYk89+o5gp+Tt17WwE7WcTnunOxr289tzhyl2TDAtr8GmmuKsIzYjs+S1vE+g0mtmo55PPaNI/Bd2VvxWOnaBomyu+FiWddQydQ3/b50ABiMTKAUgPaQGD+KfTOdw/ZLrrPbqwOsjcheqGM0wKNTUTNzZ3lPZ3WQNPFL3iG+5pHVbhsPLD8i2HdX1oSmU3QhngP7BOs18plzHI4COPYQl3XnMF0Uy0bsEfYVdHndMJn0HEchAw1Spw4/qTCTSh017eyYB6dYM6WwE8TG8cLXACRmDur2pISAhV3PvDBiE0pjNZGqGc8cuQMblkxsVYepV2SnlP5mR6YAUjNz2fSueeGuQMESNO+Cxx6uARPscGwisCTWju6HKXSfriFjjBneOjQ6BMABHKcjtJQERwns2f6q7tc8Z82cc7JSinAtSbSx2QE+ydcOaORmFtvhJD8tp+Uk5ZqBnx/4gR/AS17yEnzpl34pNnkD3bYtnv/85+P222+/3xv4oCv65TNI927K7HRMLZbY0zuZrj3MM2T0ZMVzXLApBE0zu8GIfsKJ5TYlYhJILL/XAEO+PyaqtNGo9luAyrlmjfXExYtB1ouNGBm8Yavdrxb4lPtj40qYQDMZJIrzpRRpbnObPXAFqNOegW18vzFS87PUQsW8aT62fTEbXsVl17ExzgNxsjh7gJfHqDtSRrngPhUNocJIZZqutFP3qyMCDsBlYXFq0UK/RsYYZe8S7Z9Zhk+MY7vZGPRAPDZWPBp1ZUyasegJbrNGWNtYOr4bBupcswnz721tfOdnosEN8+4K4KTfy4rNdxJ3kJ4LJgfmjnd7ql6DJyGWOj65CHig07UvLpYoeugsG7OnuVqyeTFTpd9v0R7NhB+jsgFVzyBmsU/jEU9p3EDNZGacnsNJ5hHGGj0PZ/F+2J+L48yGWIem5Hma94h5TuwPWAB7e2530CB0CatLZb8eZxCp0BLhlO+HvQllMqnjReOHNYTagOZwbvIfdYH4ndCix1EedeB1a+wTZgttFAgjQM2C2tVckfCx7bnH15X3uLic1x+1GdL3nTRWcMxjPZqxsbi6/UJCzbhfZX400UDqPUwyF5mwKFkvLRNxe2HYkA+jkSfH2g23ybpFzoEAOOu4rkNYO07bPZ0ttdROOj3UhpjrLEK3KkBKYbudxC6tHesCM9e++UY/jGtEbYM92HV5+uhoIgXGqqtsjN4wGo12TLTvuV5HprnOYcenJljgXAnET1lFeaDLc+ppvOu2H83PJ6XGDx8brUNLO6nkeJOMROwtficUmNPEcVLicSB9ptvFAut9solAuB4BZzy7ktd3o7VpQZhirZI+qbwThm1WaIfmv5093Il0NR9KJcGC9PeljtNSlGsGfg4ODvB93/d9uP322/Enf/InSCnhoz/6o3HmzJkHon0PvnKwDzR+ur2pzAjXTeKPNFHJRiZpUIKzPC9aDHttaVgogeCgWTne5t3EvJKRtIoW8Mg7HX1NBoeGJoznMadd3bdkVGqcLAOJ2QzsZZE+02u2dpWolCXWQ2ONqSL19gQKUJtks84eB/V5OMj1sC0obVD3yu2KvWU8aC+/ePfDPi1okrbdY9JI2XRIbcRA4tFT5gcFFiT2uDivMffBxN5yhCM59XX0WCP83ujnX9lYpzZrQc2wMSb2yc6QRse7K6l7Peo1AES9YXRo3hySocGC3O+Bs0yIB5M30/od7Iax7hrjQtvd+8QCc7J68fOZPOVe6A+95z2F0MXDBskTe5TzRXeA0qqmxSg6zfT0gj1RHQdt0W7AGlFSrw6TqabmZjvWwyL0PCbCuBWwyVDu9Z4xxrEdbPwL5h/tfCklNNFmLzthntIT6fmEgNCzF37755BBRM95kBaNZX5pQznPg4k9w9N16iCe1KJBsXLTvr1upLWlP1g6unPqWpJNbe1vfBik06BCezhgaAM6AoearM2jQTkO12rXlr3CzoMpG5XZ0CeThr29TO1OdoPplUgi0MMyjGFdPD1fVeFbuT3crmmN1s4LHudybjGV6fdYVcehXkMq2UQA2sPtMcK04msKIGS0zhiYG6yjZBtmqL+084jZSB/bdRFwbJPQGuH4xMkpBgu6FNebMl0RQLhoxnd1DviUH5kFmuz3PNN4Ibe1cH6z/rbRaqF5tnTNATUHGjUNEFq7KXfmwIFtq+PerLP22VZYlsvWOgMY01gEI7yux+Wsc3UY5n+XUrHxi3BlEzqYz/Ec1FynB+I5AsZTqWSDnAonONHVhIC015r2ig2gnXeJw/sB4NyB/U6XGrMMjsOB38FVYzJ4ahsx1ICtmPdKZgyruvIYNM+7s9k5H+rlNJ37A1OuGfiRcubMGXz8x3/8/dmWvxul7zGJBTP6XXvZ1eZaFiDrFRo37nqR97wsYdPXU9seKYSbNoHG2wQHde66qvGjJ9+wK554OldNhOIt5fo3vRGcTZEBC3+zNRUF7ITB8SywEaBpxtlYiV25SMiGwT4n1fd7FetanolOnep533gjpzWR8mJkRFMnvQx6ntq70fWIx731mOV70SEinMUiHG0QCByoZjDicIW2MSlxDaU7BFfwbio5y0O4nhbtlEYPj5OtbqpGxhEzivJuIOjxw/JZosNTod4XG16zabPHF++ovDu1kEYOu9H3OAxjOnemWE96Gfo6zlgxBnD5PEQfx4QFrjsD/BjdmY0N7SvGijBvjEBtj7DpETkbXm3j4BS9mJvsQc4zrMXGG6MgBes9V/XJhtRsrCXbHqXpBrGOUgwItCMQQGF5r5+OHRjHROD5j2UIsuZPvMyGctydrWYY7AZN96OX6SfXEzrLXPCKAWHE8NbzCI+naR7enluk4lZ1mqxMi4hAGy295omt3VNo1FKYHvscwms34Kxjs7xkn6ERnW6jwzIq64mHvl4TYsJgMhYxs8AJO9GHZzCEr9mtIpr1gJY3pwoYE6FnG6omm3J1nYret2ZR6PAX3ZccrhbXFpAqQsYuJVNHrV0Aymx7ceyLRP06Cf47guDbz9G+S7V3geegxgH9qYRND5P9kK8PlM6CfK20aCzAoq5XDTNzABbDTJK5ulgX1d9zIWweu6eQBchrG2c+qmmBqcNGQWtaQ4EtQ7bQ6eLMcYMx6ZnxU9XhcdK5m/Bvr/keY6q2VlHYcymmne3Cmo1cEeEG1LNcWIcon+uyNuekGqSemh4qs4WK0PUWoU/WOSm4ZKHvSGNl2SIcl44245wYRHTdOhnnEh2Mn51+ZP1JrwxpZHoTWFboVMm9nIZ1nZYHqNxn4OdDtgw9pp1eDeDZkGdfgzmVBXEygIrJzBHG3VuWG2d9jAqZCey5QDTtZWApdEOBWuu2F4sM3UPcjBRZZtYU2hhz2Su4HbTBHLLYZnHdmtiu5w2ay7ImVTjibW7RKSS92GhsF9KgUlUmDrtZr014kDZGY827lZ934J2nI45odDwc+qlJY3+8BoYym43x8kxiyRwmtoZJFcPtYs0WoHx++R1iwd6w7sfr8vm6/XOCeQBwoJ+B7xEsnqkW2y5EeqnerrdG38JuSA0Qlz8bU02/R5sNwmbf0pands8YB13ngIsnZIkMg6MPQcesnYwzhUB5/vuI2h5WLiilDd5p08UbNgGiZza2ArRpfRTNDNHGPN8TmuRky3LaVZkv0pLdvOUxYUgnCxPjjUWMJrSLN7kTS8RhNzHD1DANmgZhQ89Jh2tVMhiGIQF9MoBvcYwAnyw0mzcpUYUuDWd8VpAOadSZJ+NVfV1nrDOTRs8fkl3OE+yFA+6p0q/GsL1Amzlh9yyu1DcBw7KxFAp+jYThxA7jbsBAgJTRt4gwDFaXdWxAvHEt7ynTls5mJu2KxGqRsaffJ80U0vXoTGh65gzFu2KBJR3axe2X6zOIJ/3QsPNCA+ZDvrZh4mUAQn/JoUAxAke8xlUGDgsCd4NlX/B7v3T5HBJrAAEAAElEQVTYK17SAZrIwrrzASN9vWvYWEaawyeQVd+rnidrqd2BUW+GnRT6mODo12FmQ0xCxGHTWVtA5mvtKPKA6B1MuQlYYfDVAVyMfT3Y9bfI/reRMGYHIRpg+1EzWuW3asKIGRvZy6Ras6890Wx22Oi+EdvbTJQybhmgU+v0phsZtNwdYncWDl92Qkb7rh5W5mQCYUI/1MXB5Zhlb+1rPcfI8QYwSmPdbAvqPpZ3jRncXX9NzrGHREmwa+V9qeO0FOUU+LnW0g3bIHmzcRFAqLJRBrYvOE9KXsppR+MHQLnh1m2YWWxDSr5YmP7oearE+FEG8xBoovRQf67GYQ5N9RMwY0ASr736Vjz6qZSum4/XzQaCMTLks9msW/DJMGIkpEcDX8e0qep6SyPW4oip4hGRccKhP3qhkjTexuDKoNEcWBDDbm9HhV6OprFeEKO3M9Sz3wFbDRcvFWeP+XEmIVg1UeK+YpiOFxz/rb1DuxZcNgw0Q06Ewmux6lyGEsxyAUbvXTUAXbIpZSvGvpkbhmS8aIZxMlMfoIDHxHPdsHO+qIngT+Nj7nQxiCtGcLFHcg4xYSyF59uOpfFzhSGo6ej9OM/w+zeFtWimPQOfmx5xw+9NOQaEycehRc1RZ5+vk5Vmtsj5xFBIITfDFb2VyqXP2Mgf69I0fQOo5XtqWrX+sLixFMM+c8Tm9dSfWTUcrhXzRp6zW7UKyIgYp4zFpfKeupzlq2QwEJCyGcBq2JzqexqDBsBxQv52ZbGrFXpmsUsui2n1we1cNtQYP/K+qv49Pl/e90qGuTqmUfetQRvLxLMZbfQ9dpk1tzjkuUYcauW5OiNeGBwQGnDD1+yaakNwbBpyn1XpFp6zHG0vt7BQcQjjXDN3zRqLT8jZc9edgHkHhALK+2AToI02WYEnQO9tmgEnwYOuPG+sa1nC9HvJbCsnnfsso7WodrcN3B4Os4kDJnuSWVpN47PxNXYmocG1R7ZLBxOoA5a6eP06x3hLdm0bv8/XYltQj4NFHMcAmzAy/9fC0aRNu94bsbPY+9M6oVjmXDj7Px8ELUoMOUMts4U8G48+N9H58kOgPEiBm//23/4bPu3TPs397R3veAf+0T/6RwCAP//zP8dXf/VX461vfSv29/dx22234dWvfjWWtdD3/wflFPh5IMocNXcS/iVNEM9Q48Vx09lNk5MNAYAPhvCcwR6JlE4UQ8qTVGrCaAjwgsygBbdR2rDg0K4KPblYJCqCc4YWunBoprrP/Im02g+6reKAYg9N1jTRYU42+5GTrUN7tUUTyRkDYwW82Kr627zB5HEodemx5glf186Tkr11DNCNoUa0iDJAESzzzH1+/Ar1edwb8ETVX8uEJt/r57ThNgiTQxmGwf7u1u9l+XI0eSyIYb2QY32qv4fkG8p53Fa1a6ReIzBJBoc0ycSwt/ZcE0IXfI+WHC9MJ3on06IZ3w1PKHWqJ1+iBlhoUNWwJ4I55sRZvXoH+FQl5VuphS2Z4ws2W0Rz3BvmjmxOo9aPMGMlOSAAXyv/6wB0BmRmanwTLPMs2gfCTKFJKNh4ty3bymxCZf7Uz3JGk2dboW6A+toBlC1YsD1hc3Z8mKzxM2RQoEiBTqW9uMHm3MJm0JKPMzZA3Az2WfIxXW3DBDM38loVu2SyjRXsHsHhOJyqG8GVhuqXftJFQDMpAqRowmdLOj2TiLmas1i3Z9sWGp8xGAZWqxhAbRao5UjvKAOEGEqN8v73+y2QHLsmOhtM09B0MmDGq6fvbXIFs77BhKC5hcd+qABGxbyZWYqBN6HifFEs78r7XT1mBngM677unIGy/7iOmriztifEPt7F/pVji8+7T5H7taAUbLY9R5tplnFbE6SugUoF6JaPNQ4cWUg1C6eydrGN4RUv9fkM+3ayZcw71Pj16We+6UY9KO6zSUqhknwDuR/MXoPbnh0PrOMZguPAYXsb5p6SqmYKj+W+aaKP3RQAoPPMMK7vH2oaPw/m8smf/Ml43/veV3z30pe+FL/yK7+CT/iETwAA9H2PZzzjGbjxxhvxG7/xG/jABz6AL//yL0dKCa973ev+NpoN4BT4ufYSUJ8oJzFA3uw6hu1J0HUuMadN3xWbDfiLpAe66I9zi0tBxbXHuUaGMkamutnQce6FU6tPnjc9zxcbfnW8lwnCeKPVMTWbrva9XmwEZ+CFVBaq4hk7m+ZaPDawZQXU2ExzxuaQ5g2v4v7pRg+Pdo6TqR6zca8sarp0XTXrFoD6/TZx1LnZQcF12yvHnCDcsBojr4thQM0bZZPhwiFP+bzUEoDmbZDNhiADVXPTyGLh0OWJnizne/ouJwkL4zmwyABSGcN9Hp+zGyr/a2FSVQE6YHo+RcYUfSvX6kHSoUUZ97BZ0PIxtWxxAFJuF2sldWeyMLQCfr3b72ljkSi9u8wZi3vKUONhzwEmjdCsA3g57z0DmtNGuxIuNbYrPw/enO4ygLHt51I02H+ALO7simg2dkz0HDKXGT/9mXJsayZYbAKGVTTsMNH46ZVwrBWSj3YMVgSCGdgdFsF8x+PABTn1HlFErQn8Pj4fsYjA4jKDOmpc5nM25wnYzs+kUVPcEWX1YsFm/k6DaMzC8liq3cH2+PZo/JcdaEZoOZdCqD5V7Bc5Va8bXopxOnegdyQeju+jmYujY8sZNosTsqLnlIzieSFjqU2zuotVLZ0J8FLfXSPToNSfofrbxva3Wjck2UdV1JrvSfVZaptxjTsJm51LdJwzHHIo7K7kzHdzAMjUWBoD+r2UENOaGH+NeQ1sAWK2X3K36vFxIhtHr9n6ujyH15hV0+8VR+Uki0Ehf3sqvKltXKBsGhc6mY0H9DHLnt8tcUis2dG8A8wC/PVyjhk4VZ7G686xhfZqyYMepNSXB7A8mMWdl8slHvnIR06fN5sNfu7nfg5f8zVfM0WMvOUtb8Ef/uEf4r3vfS8e/ehHAwBe85rX4DnPeQ5e+cpX4vz58w9M43aUU+DnWkvCzIYmTyDMMFEe7+3kXh4ixmxNY0TqDz2h/zUAgNvmaWrwJqXrkBig8BYh9v5sxvSxxiD39HAMldbGuxovrbAxarRiXTxQgVk9GkHKhpAxRKRJMwj7ZLTVNtezIWg2RjgpbYWaiGA9k1y54R41ceiYXJfOGGSWy7a11zALXC3V/QCEHVNKE+35vX0ehs0QI0IMAIvnakNc3rtFJQyuYPzUQ3KmUgNSjbBprI8fYPsMmQUkoXfmGROFOcKnMoOMODZ+1uudXtOJCTAT6jBd0hHMNAwiR0TetKsJSE1jWSiF4KqAYmX9vKkCnIV9AuDVV9pGVs/GpOvdP5mVwJttc+3pAuqemgb9qrFshryRHxQTqDlmgG4w2kOoZKNioCd0yYjEG+DHG/9OaK3RNUjjfG1AKL0RlbCQyPed9bzUsQN5X3cK7M6wutxsLar9EgZpslTmzwZsUON1c3aB9nKP7izpFkl79Hg7CSOkEjrlZQridO687g7LYPYIrQJApM5I97e6t0fs7TUble55SjPPKaBlPlKn7n/Q0QKkY4o5QC/vvE88GtDdRMxB3ceib0Xp3KdxQ8+gOdJjrkGKwbyXnnXsOqrMZt5ntrnhHLsAim5wgBPL8jbvzaTDR01R9Ytuj2UdiZ2gMsbOiTVLuwodNT3JOuuIp8U4XctPhS1JPubEhMMmh0TxWuVpZhlQLFmbuwaGn4Qt74iu2/XBtscDqcIwWI00rfEj7xFPNZJRrqaPCWztL91ltTB3j63N9pYXLs82eM1W03unXawt/dz5HUGzTdc+tYttNJ9ZE45tWJ2xo1KySTl0hEONteaJokt9UvJ7GRxb/8TswodKSfib4135/IsXLxZfr1YrrFYr54T7Vn7u534Of/3Xf43nPOc503dvf/vb8aQnPWkCfQDg6U9/Oo6Pj3HnnXdWQ8Ue6HIK/FxrWS6AmD30nAKgxrLQ3o+KF108h0XaRQYOPBE2PTmo0BtDX3ToombB3wTfEwyUEzLXs8isGl7odAyjnOMk3Qrcj0arBLbU4slNOvoE9ORV0AuK0D3ZU+iydlA88+lZmdhr0XqqL0xom3qWiqKyE1I7i7HRIS2banx/MQZNeB6sYVAT7T4J28b7fBLvG5Ut3ZkXb3WPMolXFscipIQNbnkWK9/YLTyYjDQ0wV7SMc6tZpfPMiqE2SUenN99Z6NlxspyadOOO/RhwBqbIQ52U9Jaw3NONyzNpXL1vN363Mqw34YY2HvYtkuyian2q5CSIqSfh2Lv1Kf6cApN4WFdSbVbpHfPHmWzQRCcivWY9CExGnCFtXxEPLehevq91gBmPDek4IR6FWFqdn0azxv7x9yTfo9qWe1kL6bZQZUEBFqoVm9y23sOt23xAGl2IqhrxUmnhM7J1+r3ifGjGCkpAiFFA4BsM7Rt1LF2A8igixlP00aOnTVWg8cIWDsZsHTqdnmHmK3UHzQY+lSkSQeAQTkkpnroWUrfDMvt9wM1YtlZ/Tad2l2zhVoCt4ZFMO3ywsQ4xE3aFcn5X+guDUDh0JsOcgBkzvw18F36x4zV2fZa5iAdEIK1f/S+WuwRx/GQlq0N0dJz7MRe8dfL4GX141KAb6phvbP5l+Jp8HiM8lpoFwvJa2eQhB4ZmyzPQfpdpPkzpWQn/5p2mfO9cXowCSsCwYjjq79rm/qUxixtM+G6E4uU2+UtKbWIhFo4u55XjVOwObljsDzIb4te2PsBab+ugzJpJ8Lud4Zli4bbwWG5NdkEz14xadktuz0p9vNUgwmNy1IKc6CWzJPMzBqs/XZaTl5uvvnm4vO3fdu34WUve9n9Vv/rX/96PP3pTy+uc9ddd+Gmm24qjrv++uuxXC5x11133W/XvtZyCvxca0nDdvM/xyTRpfD6jn9XBXbnNuVD3jToDWylmZxGOy0bRwvAWRVYONKj+++KN5ZzFYVy2M8GmOc5on40tE0v1Kv4XVtmO4AHLiLKehKaLhexuSgLyjRhB2XV8lrbtjZNZVGHsFQq2Slm2FUYUg73unaABa0VaPa0pgA47KoTjAuPIquvV6MOi8YPg4S6H2rtyteb85bImC7CRWaAovLkNKsPML3zzNCQdrFRrYG2SfTdB0mKDSRn/nIoxQxA1kLFfIAimWMM+Ndq4Loidj+xD+tGjOiucFiU6w09yatb27iYTb/DXNTGuTAza8yeGggMZL2CwQA4Ah642XpmigW8ZPPtgN8MMp/Ec1ikoheUhsdT5VwvfTA/ukkkXjWLjPMJ+FFGcHNVhS0s6puS0A07tY3GSqhZGTjbnK84AwC0V4YR5OFhkN9HLao+EIAUNz1SLJ8RC6dLuJmfCt4Cz8XtxGDHht5PyXrlAJDtcTKgVKdCDEO+YX5HJGSrvapBHQKHJIubqj+ohvYrdS6BW2Gw9UX1ak2i2gyCybXYKVeIDwcgWEeYlxTAhNIMBO4Clskr9bImSAgIiew0sgnCunN02RR4I3Xz/Ja/N2wkPRfLOQyAyDFzc4RHENTvonaUUAavtGzAzOBahlSvfo8JMR0TAgIGC/iKDTPMzLG91WviuTTIZEXrUkgJnNCdx5OvveWAgSYsKr/zHDKq5hBpp5mPvcXRhNntSEOubQ+2Wbt+PnS/Fuo12fwzzz0EeA7rLSBbHwcuQ40BQ0nswvZtLEGl8Tvei1kGFrPu3TaGMNblsKenIk4TA9DN20wPxXJ/hnq9973vLUKramyfl73sZXj5y18+W+fv/M7vTDo+APAXf/EX+C//5b/gp37qp+z1nfkqpTQ7jz3Q5RT4udbSK+CHDQV5mXlC0BOhaBbwOy3zn57c2QM8DEBDwmPFQq4YCoSUj2JndC+uJ54+S5UnGaSekBnX6wEzlKLb0DbFM1szDvTmkxehtplP5y5oUs0zxpOvJyDKlE9Z3PXk7gA4xuuuDadaOKET3zxeVFv2m3ETeFg5d05Tp+tn4+/Ha1XGgpcVg9u52TiZyhzDwqPa9smCrZ7hMKfpVDlmAnycdxXAvJBgD7vA65ASAWFr9N65zHEhjpvoSkawYh5xQuiMIWjC1PJlnHfLaD8c8D129p60kX+Q5yB6/0LfjsKmnFXFKQasZi0Lp2wZJuq7wuBXfzobGJNCXvVNlGFSM0Y9sXRdovXgp2znaZCCNxzNYWeuyQK7U30MIhz3RpTZAjhe2J5dW9w3P0YLKBZzjE9vFzHUOcbPlhXnsyeLEDC2w5ftbJr5KdECh0plkCtuuI+2n4dlROwSNhTqJVm+9Lvjphp2QKqyEWUbp+NSQk/A4YLObTbJjGGtDyWizpwBCymNgMvA75xmueZ5p+Io2ZxR7KCO19Tx3wLU0SE+et9EwE9ztTfvatRRIVezLhM7YaQabq4G9ftk2dXqmEIXh1+5YDNnGaZtZb30MhaxVo8bQuStUdywbgCW9r0smRz5b6Mdk6tUc1dgG8oDuvU4nNE7Cd0wr6Eiaymz/2oafcUYGkbHIbPZ87nlvGRtPvO+8bOVNjggnrlPx8nCgKx+F2psjtAN41rpzAXTuVGcSLsBNAMSSj0e68qcewKW90mKaPtwBl+dHGLVjjZx7R0Z6s8Sq2idZQb8zWPC2KPDyYSuK+/NbBGnlwHvS6aTV391nnoolwQ7d9+XOgCcP3/+RJo6X/M1X4NnPetZs8c87nGPKz6/4Q1vwMMf/nA885nPLL5/5CMfid/+7d8uvrv77rux2WwME+j/ZTkFfq61xLCdQJh9IJMFTyROekPrMZZ/1eTEm+ZuPL8IO9G/6wXQS8/MLxBPjP1gRQIli8GMMG7ocqr4uRAhCU3x0hgaemrlTdfH6ebEmQXLM/yLPvZfgWkRNlR7B8xi+1kYHpw5qqgn2D7Vxqj0genTjTl2PE7VFZ1MCJXrmLLZ2AWtlrnJY3nxsdz//Y7nXUm/O7KYBgMSuiLpNcHAkwiqa3ZHr8avtzBLSQm86hdhZXJuJdTL6vCo9369Gas2nraKJ61Wz/RdxTrxvBKGLeG8CzPpgrfp3GnjG4JrZGtvqGy+Dfi0d4IlawKZVVt0NaHyPQBEZ57U40ZsZH53J/ZKHQBJGO9LZxQCgF4AMqXDYvo6JbRXy/M250hEdi3MITvOOQzKy2AUj0tRaK+YzUkYn/NAzyVoTaFaSImshXPv5cTEU04NNS7TzGYlOEw8PX6knoEZWFlfqSNxZ88ArTGeNKuL64/dYECMata5E+Dc/J72i4CWX1U1nppDP5QgLSKa48HMR1GNy06An4q3vT3aXodDsYRhXNP4KcJgePpvo8nYVYhFi25RZd3jd0K/X8PeyHKNm5IxOQGJGmg8CYuWx2G2qRKHAjVO5k2vOrInDUPBuSbmsj3yOdxnYr9ojUZmqgjuwzawlON5p9fsWlx5GSagdk5QOKWtzo8ujThc1XjjUz0xeF67hHVk5n9nrWLwybNrdf1N5Xlk1oupT11v0iXzQGYAzb36XaE2CHNI21ZFqF4R61ieGyyIXQKTuW7Dwham2oz9kJKvMSr9pOxrE7qfkq9H5BWP4X4SgIWdJ7qtUiezsEMc34e5eSTbr4HfGw8wOi33e7nhhhtwww03nPj4lBLe8IY34Mu+7MuwIM2qT/qkT8IrX/lKvO9978OjHvUoAKPg82q1wq233nq/tvtayinwc61FM352hELMfm8WmPyvnjx4gVuvEfpVfbLcEa5UDU+QEoNDX8zt0ROho6OTYiyyK/A52zT21CiPzVAzClJl8SqQf9r0N00pWkftmgKxjaensrEuxLflGnSMzPVFn1Ebug7pgKiGjgfHgn/CKpuh14r3qZa9YW7xaKK/EOrSV8Z/tF4vl/Fj9GpmGFu6zU0zayhMhsuy4kXSIZcmE04en8WmUh2jjX++rmOUFboQIuheY2/NGRmyeWUwSy6t5wsTh77b+BcvvMcssMc6BunMNbepbyubsRnjfwtikHG1EUFs1QyTalVoTPoYVUchYkkXTnZDrOdE0SQxz1s2oSbFazmG4npwtNXGey3C2rhdfcKaMikxo2IK3T0JIOuEpDEDKxxp4FPYMdaLjZQQSIxae9yNM0GKaJ+0dYFF6SstfF2AAvo6HGKzaGaBQhmXrK3UyybKMAK2f8d1j/WFBRYXy3uTkKO41gPOuTgvxfzIhHXB+kBtMBpOnlOGgaVOhVG1vTg1ymP6vYBmEwqGDxeZqo2OVJ5Pez310n0L06hg/Kg/9XhmtlVcD6Y+HfoldcZKFi/Tj0oEOoXshKno7eiEF1bEfzDrmQlHzaEbBkjvE1Lg+Y1AnlULtGQ8FIzlyrxTcbQV15J32WSCSv73+lxh4p0kHJpCvUZ7gtql77uifxIkS9PcplnuiYR9t+H3DuOJz9Xnka07zXMMNMTovMRUfWvTuRcaPxvRwKqsIQZ80wucVMJ7kvyvxyaX4tlc+hnMOb08vaYi21jFSSj1czIQDrU7WFbDFZurylHhUXfZZuK+kTHiMcTnHL5AZt/MPO9JMJv6q839ZcSidZ9lkJL3Wd2A5N3nQ7g8mLN6SXnrW9+K97znPXj+859vfnva056GJz7xiXj2s5+N22+/HR/84Afxkpe8BF/xFV/xt5bRCzgFfq696PCGmogZL3Sapi6e0JrjXS8+lc1e1cuv0eWzB+VvzJSA4wU+2jhU1woroagnjotobWOL7X25nnIGAmjhdzNc6Wo0ks/IUgjAEXmyTxCutfWGMStqd6ayyXireVDkuDkPkdxqbSzx99pwijEzvOa9V2M9nGK8sV4WXtjlXgyo09eBQ90GHte6b7Kb1dWCYiad1wbA1i9AkFqojafSo65rsEfHfHM/LlpHU0uNaammIiLIHtyg05DK83b0vgDaTPA72nUWmKmkczdU/iGZDbsJzXLH07afahv+ie1jUufaZ27EfqWf9SacQ2InAMS9/M5iDDon1MucI1594+VVXtm9JeJxb9KEyzPQKaZ11iEASKsGLWf1olLTj0tNsO80izSvGhuyqtoua4XZlARgWC3spk9t3L1MVwC26XR1uJYBxcY2RQUsFWFwWkvIETI3641mmEg/mXDC8ZyesqNpIkLoBsTjwejOTHWoZ5AWHojqnrYtsicnLZHuoK2ybaZr9zbUS28wRYCZj4nrhG4voqV3ToMwsu9gYWipS7NwOPRO+kTvXXQ/6TAts7+J9t3Tz0Pq4XtyN/xAOS8uRuA+bSq2T+GQ4XBIR+Cey5Q9yDrMZtkrwKiTOOewk3BMZjQBY7g/sxK8dbUG3hf+MTpPUq6vK+tiwYx2HILOGmpKbfM9x3KNcexvXucE7JwLD2qCeb6cLa2R/mMApxvq6eelriagMRo/uhJ5L3kuHQD0VhNEfW6Oc4ZEtkPEPtLPvZKIoGystskqzxXwnVa1fYsu0na2zfXclhYIR11d1UY78jzBag4jq53vvV+eU5OKcf5o+1LGmTdXNw5LqrA7fXDxQ7Ik3Gc7rqjjASyvf/3r8cmf/Mn42I/9WPNb0zT4xV/8RXzVV30VnvrUp2J/fx+33XYbXv3qVz+wjdpRToGfay3XXwCa0UNZjcmdeWGH83vjH4xsizdsbztZRZpU02KMeQ16rdVaPnvX19vdWuQ8Ghpra4UiZbOnNoZeNqoUo80kpidmodp6KXYp44zJHCOMCe0F1LRpTa/zgIDz58rPekOTUX+bRjh7o5mVo8NR9sdzbWaj3B6t8s9jYrms3mdRjMZQ9oDvlYta1IZT1yO1ASnR85B7VWyXdP5McUi4dGjSG6fe0Yty2hs6y2Zg6z80jQWDtC6DaNewNy6E0XPKhoLz7K049fj9cKZuCEQBdgqdAi0cO6MtM6RRsFIX/byFtUDAieiPpANqV3HvK/TnlggdAbmiFaYAIZMBI6xKpoRzjHjKE40nxM6mUqd7jPvLum4RRm8dAESeU5pmvEeaj4bz++CSCPASQ1ZvuHu6x3Amv7OFDelvKk1UQXAABC+FPIdK5WfL2bO0JlEYBqwv7KG9UhrGErLVKgDLE5i2IT0cojTILZSnLprdekqDA0TrexHg3mihYQwJmfPCyziqbIz0ulR1PKhSMKMK5md5XEjJMp0KjHkcnyZLVp6DGmKP6HmzO2hdJs9WqFs9y96CtibUiz/nNvUmRC9ZQ5b6LAWYYzygSev+ACObLW6c8a+Jq1dEsJ3q2hOGyfZcZqR5IJnu+wKcYiHnzp6rxZ3lPe736Z1wngdA88qQxzU/prxeRh22yMO8r9uB0ynC5OAN9wAD6PvA54zTbU7w2EkFX4wV6ZMakFgATJV5Xtlbc+yiotqUrBNHg2tiy/BePoMaZq2Nem6PiMfWgSll0ELlHDuIEnwfP9P712Wbj57lsHIAWTPf2XdLv5eTfeB0Y+gGC1Rps+nQdzzEK6MTqb+wXVtZPyvuCJ8u2JwsBN462TmLA3bsumNpfxo22+A4pfKzHa7bnjsYWzogBbL7a2RydmYsrFQCz8/DopkNw22O9ou26npCl+wzUGt/d3DdeKqzXnbdAni3fx+n5W+nvPnNb579/bGPfSx+4Rd+4f9Ra05WToGfayzrG89gaDN4w/ThGmKvBcsm7YrymO5gnLj681ugoT9nN6qpicXkWIQ8zNsftr0Oxd6kNRdmQVyZ76a2n20ALBF5kdRGdp4ku3PlNUMChpmUjYDyZFfSHceVAqUcmuyc8SP3uzlftqHh0AWniJFgvKt5AS+8/WyshGDup2ACyE+P8OmARjdis21/isDmwtJQ9mWDrLUrQio32+n6fWPsLDiNd4UC7o0/0zc7surI+7EhVkRzbulmwiti9muGbKLfYTeUw17e5FY2yAV44YwxNjS1gSkbIH5mKea+Z6OiVUZaHD3hzVkCH3J7+gIAobESAjraCG3OlcccXT/+Ho8JdAnWQNwclHUtz84vH+LBb47tPDMsLPCjn8nmrJxL7IOHj+N8fU4b+2XfrC/Ydh1fUMern82GeAi4elN5fnewNTCPz+d2PZJBqvEza76UG9uE5jjh+GEEpOY2dPvb92xznkHCwdRtMsTIGO24X603uiewcVhG9DTGNMtG3qGB5oZ+GdCd33NEmbWWTg4DWvmWt36HGWzZtndbn2abNPqdYiO7DSjyhZvryh8MUOQ56ByH8JRtjl0yAIUADToVvA0dDBa8MfOkMFiobU5WLy8blQkp8QA0DhXMotCcol5fT+wUEVPW5wLlmGRArZW+UX1WbHz1o+Qle0hWT6nQNMz9xdFauT3WIUEfQ7C5LxwHB9eTGgtERMoItLnxbL4HmGJAR69U1rOxUv+Umo6aLl64VvF9ob9EY85j8an70xljzblNMNkaC72avInn9WdixBhHW9n2zfnVBIRMh8hzc5yRup7F3YfFVwueUyZB7PLUCAf4oufWHg7Wvqa2A7DSBMjA0kxmRukz1o/rrhv3Ku0l5TCjNdW1yfR9a2CHJWuA+YyRFeBnuuaMXEZaNOjP7WFypMq5+Zjm3u2zCmS39QdLxCulk9Cw68TJaML0HUYOJ2JZLWYBryBhaJ5UxMIKdev7bu7NwNY+rV0DEPrdOnwPqfJ3gPHzd7GcAj/XWJqjDk0rYn1scOV/WGBNveTipTUhDJImeCYbCFLCsCwXUr2ZLRYyZ2LxwAdd4rq3Hvb8USPSnnho7OzGRANe02/s9UvJoafyQpIXfE9fB6CUyQ612WOPTMf73uiazkFxrYM8Mdeo3toQYkOnT9Y7o7On1DJbSHyzyQSl+hpAOBjKjRu2aZD1poQX/ZCsQRS47fKzk63DZeroj5veej61AZ8zzbRUT3N1s/XM6qINxoO6lgJA4Fvg5521Y1SIjW6np3cy/XY1WdZOQVnOmyIGE+URcwYn8obGTWOEfad7VADZ4rJlJTEowhu29fm8kTvkjZxDx7+hBHCW91pKt54buvw8GERNcTRUeR4pNqJh7E/ehLaXx2uuz20ZUAwWyL1oI1tv0ANlLSpvIKG9Wn4lmZqALfAzMR+mC+S2UIhWAfbFgH4Z0RzR+iAMEz3HXrVzikktXNH44c1F3Axmc9eQUYywMnNUcY6k4qZws9glNIcbx7OpmB95IxyPmUFm10L2uAfnnnToke5vs8F0vNF6zu2yY8Vkn5o0zFB+r0mml7pxLa9tTnVbeBnP6cPnyhTqyv26Chj4WTqMHzsPl78DW5FnKevzLdqjwQI2h9omyX/wxjfTgpIKZ6rpmVTD3NRlPZC/pXdCg7nLSsjdlEKeNqzsPHOTTEwbcXUsN76z2Tl5TW0v+vZBCgHNjvCgcNTVNXiAKosBwAk08eS4E4BPBmxszbnFfe+67hxbQn4zTIzMbprJ1Bf6rMhUCY9bP+KsOtY6BJmxyVqA2znSPlNP8FmXoQ1mrime95QxzFSN5pKdY3XpLuy5bWgkm5kOu2ab7JBfDnvMVDwn6lxkl4R9meyWYncziJeKY5pLR/XEBPrYnu0XZ/x5qdX1v/p7k6mPnlsW3C6KvqYwslm2YnBY8QB0JsVa+GcYHAbfQ7z8XdD4+btYToGfayzNxSM0TR5JTFetxYyqEq8fFx5jiB/lGF3lLQq0iQgpAef3yklITfTN8dH0t6vHcbbcuDHgEq+u0Z/dK7+bxJ3r99QcDYjrvqREo1zUpD2shYIQEI5o4WGRUS9jTgX4cVH4uTj6/BwWDHbkc+LVsm26/W0t1E8W2UJTxoKBRuh6f/t8xIvYXD6CV3gxjLoP+wHNQWvAq3g4bvaao61x01ymDWA3IK7Jw8J9Ks/BoXYbyjnrhng6PdrLI8wbBn4uHiE1jmaHMt6kDwywJL/PxbZPQsTaeNW/++2Vthp9FNUP4nmqhoDOaDikJmA4s0K8VPFePXLL4GnuLhGLMAxI8WzxXfuB8pjFdRmcoXc3Xl2bOWRxL4VRXD62Br7qPjlbe+aAMRyqifY56XktZs0C9mA2F8e6Fle289TiYtn2CeA8sx3HrQaQZgQSwxAMqLP84Lb9zY1triLQMWO/mrG3LDfCsbde+CmLlGL8NDTMm6POaBlxVi8ZwwZg3JVNBuOGhjdUxXgVPSqjj7bnhnjGq/Y953ekuZoBWQX2GGZKngfaKwqQ1ZtNfa+8aV53Zo7Sx/cPtyyAsf7cj+uyLRrAHBYRCBYwHRyjfqCQKvN8YI3TLo/dxgBtgxvSVrS/td9pls0E5LDjp09oji3wUzC18lhi9lacwHUFtjZcv4BD2+8aBeIVx7OjvI1FqngAWFy1jqWartCukJPQDTuzMlXrmdPrmKsvJYBBR2Y1e1OVs465LNy5BA/6ML5vb12vMTf0O13TYOQSHTuwCI2WkG261gk2vakJxlEFYAtca3DdcdgFWgerejiO/hDP69yPzSZZVrTGh4VBzvZWSmP9zDYrQtx89ooAV82lcg0uSg2ckaLt57mkIlJ0P0zZu6ivJXyLE3FooKTNzBsejjtDG0b7OlYYPvqY8Q96B9edlWHgNoQAo5tKiRwAZ8z2g5vswoQvwhmfw2CBt9NyWu5DOQV+rrF01x0A7d7sMXPIf3/g68JISIb2OETjyQtIbVOkBdWUWh3Ha4RZPXohfe7P79lFX6rRX7No3dIuekC5gPVZX8UwX446Kwhswmayp0eLL+okBepeTWaalKyQqc4EIwYre3bkWG6bCp2RZ8kbMulDre/BGwIvi46e/KcsJTVvB6+/XFcTAZM+NuujqI2IMUbbON3X9NXdBOi1NpQNyPdtFkgWsnU8Hs5aZnRbMu01cVSgBgAdijqwfcbFO+fEXpvvC8G98h6Kc700sLqIc4uRPjmnoWfnGZBObP34mzqWx/kQLIOP6MOOzMF43GphtT687B8Nv1sKtMptYy+q6PuwrlaZ1aqyecpaREW7+dhJ0FWfqA7XGJBzGZvmWmf1Gf8V5tH2HD/Nb8EASgkptEXY0njS2K/Le7fgLW8A47rD+sZS58mEw0ziubThTjbUy8yxi2YL8sshWnS5JtCMPM/zMyiE6p33r7i4biz9JvOzysJUpHZflcCaLq6enHMDbNRPICgTLfS8mUO9JPRp+0M+VTN3Ow4PioYFZFjC+Rl64XFxR/bOuHZCfAq9lvxeMkiySQas0W0Z2x6mY8trZiaqYrNFWn+m59PYOQIoQSAuzXGP5WVijKm5pjaPCdOvIf9G8d4PI/vWjM+JvaX1DTnsZDDzugFhpFrvmQwz2nH53MAbTPXi17S7wqYfNbrmMg/pduiPAuZH+w7r+k0pQvXqay08NrUDMptm9v76XswfTfDXYgEBdN1GkgG7hY97px4pDFTReBraYN6bIpnTUX0sJFdwWP/trHlFY+w7PP0k/dCVa9VUNDjjrSMs5qxtAiepzNjeEwBqaXwvDQgjvkGV1ctEUOwlm7iEbWnpE0cXdFa3CHlt4fdG77nmnM1OVq9w7I1Zek8WDYqNz4dCSTBr5X2q47QU5RT4ucbSvvev0MY8ERqQZMciDiA88obxXzKylxntL+JWrxJKHyLCeTL+dUrDu9VEd0AaFFfXBTAEwHh443FnBWLFaNfgChnUi0VEc3Vt2R06bjV77g1NNyUEjsVleqQILGt2gbYrLl7e/u0p+R8Ta6ds/Ni+fZ/lEi9R3IdqQyv3y6COPBOtKcOb5uM1cKEUtmsUaydk5lXt+mGfwEc9VroezdmV0RqQdjWHW2ZRvESMos0GiBfK8zgc7Mj3HgUoxo58xyElQ49QW/SBaRHm8KB4uBkZdnMZwfbyfS19XQ+d2tmEgGSAqhgbBe24ONhWzou0JteJgWDSQ0soATH7mBXVBAT22GWKdFQsvnCFnmVKiOeIwUcMtmbtb8rj1bWJMWf6tEcRL3pGwkQvl21PywWwaNDQ0NbPJJ4VIfJyrLQXx/EUZ+wquZdi8627Xu8xmByzSoYpVxjksrHnzEcyr83E7oduQHdmacL2NueF3aE3WtSu2BqANPEwz5dmlki/aix7lE/tnFAXNS6DZPXiMxPcTVDB4qxlv5S1RWdKrDELVH/rkLfFB9X8Yhwlji6GGghNfs5GNDuPe8PeUfWHYQRJegox2vvgxrTX0/wz2adoIyDPkJ0K3ZmTmWxGhFvPPfI3CyivE/pVKEK7AGBzdtvfk5aPYcdkxoHqso40wSSTYiHirPeIqj2Lnp7J0oJl3b5mMclFymOmTGKV78d2tmiOewu4C/Cj7TwG/Ztgxg9v7qdjvS9n5gsAwKIp0snrdgGYQDQ3W6r63b10DYid2H3a8UH1ZCC2mtLaA42lrFqHuXsCFodo/zCLr2AaDUirdj7Vtpw3lx69VmQu2xGiNx5Lm/sBdWAGqIvgxzg6ETlLZuHAzPYLi5iLg0jPZXyfsleogRXFeGOnXbJMoQJkzv1VC7Pi74dyvUQIxok9PTedpMWz+3nd4f2aMMtmsnNVy66xIr97fdo6jkIP+OTxc7xB6E+QMe0hVEJKs8mSTlrHaSnLKfBzreVgDxCh49rLP4f2ytxey4CgX/aF3cCmRVNOhHoiVsi89YpUqMC6bgf5lnbq0BXPs5qaAJA3LOlQJ0l3zN7/waFYG4Q/e9OLb1Uf60nfS+vIIIleMDLQxQyTVtrEi4fKnjWxJyoUXx1+ZZhIbbONk5dj1H1P1N0z1HZhtXCYTGGkOWLS2Bp6eqEbCBgI3dIwfgxIIhmguKv7ZENdmBGSRSeLUmRZG/vEzdTULRB61tJR95LP5bh8MYaKdNkcbuGJgOpNu6amO55bZtLoNgwZKI6cojvft+kzXVeMoyZTouwXUrfOVrVn5wseB/358nnXBHeH1cKMWdZfac7vz3qCRcjdLDIhYFi2ljml5g/RQmto4ythQb0KKWERUAmT0c+gOF4/YgeT4PGjs8HVxFjlnTfvpRZ4P1hmkVr2muY6gvmqKAy8DZwCWXDElXNTTDajK4SUrJdTz6WTp5sZlf7m18sy5IkQA+VaxSyw9t4RzAwbNTfq+9OebMcjzmMjKFCPs6JNp+W5wbwb2pFxPKDfa7C4QuNzIWxPtSlzmCQGOGTgNbOa+J1DsvdkAIsmWBalJjpkgMUIkWfBdR7ji8sqxE2yjVH4mtSlAVnumyYDSoPuV42ta7OGRb4TsD5fXrM9VOwiAfE4nKOSsarI7pTDL5nxNpkYBbDgsCWoI41YcrZ9TEv63obt8ZwIx/Hi2Z0eE6N3mHj6EMmS5eiQACgY1ub9zuOzOFc/njmn12awoT8eE4fbnvshprp9nUJAOF47m/5c1wxbbgyN5rBMX76AbbcxQ9uO9zLYMVCwDSsaP6Hvx/AjR1Nx+lsctoz/HTrOaCOO7fS3Hk8e03f6zWFYOexC2+8CPrP2jzpCQB92hPE5dB4An4XKRRwP5CBFCG7olTmmkj1W/x1YqaGJPjPeAZfNPqhpMJwElDotp2VHOQV+rrV03RbFNqEboofDBoRmqowbOBYEnISh9aTME2o/WONcT4qKcWSMA2/y5k1b19cNeK3DwgwXCaU5rGtETOwBNmK6DmBtCWPASwrZiodKM63MgpwsS0Rfaz3+1rKon6crBACaXioTc43yqe7LCLYdHSNwP+rQsLxpLhhdwHQvYUWZkjStddMhXrdvwkWENaJZIh7jp2GQihgb4v03fdN1lom0YTaLI1KoAZb8/BrWDLl8NAKqM4yfRq7NY3263rZtxiMoxm5NxHmGih6GAeGqI5AobZffeF4Qw4PfaX3cMADpDCJrMcm5CpQwugmbDSKlSOdMF806Z6Pi9/J4Y8FQSkMe771qn6UaE60AoHdfLJt+5gDtlWNfh0zOzQLkRqcqPx/NSmDNlPaecbxuHr4FyxotUOuhPfoatOkUXSEAaI/GMcRhb3FibxE4o8G4BCAlG4qVr6dZCOZ59D36C5QRsTIdctvaK2sLBND7NaxaE+aggSajwybXCvld4rDSIkwyXzwQCLlvdZy8DEBACcbp550I8C6asOkR+b7VbUt/byjz3dQuYk5pELU7aEYhWRorXc50l9SUacbKcW/GAINQQ84yaeapYMFH1hAKA1xQXooIphtQJDqACxW51uJiOdf0mQGiwctjAmr2pOqCCayZN9vjN2fKsbL64BodefVbpfHT7/mbU9FpMgkL9BwrY4zDpb2NI0/hXoiY0X+RRjrAKpuQLIHY7QBJGofRIe0MOzbl0ifchsn2obYWbc8/6jmjsBFRLUFfe2qvOleuVckWZUKmdemGEfSpMUmcsMWpXb0DvHkZn2Dt97RyWBwOIJs4JMnbx9fmrJlQMi/hStFO/fz4ohvr9CpYx9p2NbZ5sI5uDQbWRL7FTp/JgBwwMn6YzTPZ0h4AlEtqAsJi/n2uiSiPIWaOZATVFRjoU0z2yeabA810/WqtTZV9xUj8OwEr7aFUTkO9HpByCvxcYzn8mJvQ7tL4mfEq1HTJjm4QvZhz6lhrmJlr9dvQL01Nd+ltbD8ws6MbqgZSsQDS/R0/bInFqrHaGOoUyTjEabpDsgKullEx/lNsAvX9nds3x05NXcSqfsJY6fj3hlJTT8YxIf/6nmSDUMuuUxhCnufRMK7sMd2Zh5VfVNYR3iBszlpx5+ZgrGt9/XYhXXAWnZRMXyzOOjRa2Htyxz2LyHop2RtrjHWUNro9WIz3OEPbnMYu40oOs4BDKGScF1fVLLeZmO+0aDDslX2kdVyisBgqukJGO0fjTU1Av4hormfWWr6O3vCEC9SwNLFfpnbR8xbB1M11xPxKe+Z95jATV0NFp2TP4sOLM2XfDIs4jh1jyG7PPc6i0zym2tyvko1svCbdo7yX6tz1Bc2mU9fkObEP2JxjYGg7J28O7FgCgJ6E871rAeO7x8CSvPe9EneOJqtdKgSOgTFExStW7LcpRZBhgZzgMPGK9SePb6s/NX5n5n4FpMvmg8N85W6LOnkOEcxIh45okGStzt3BhBnbYMPKeAxMYtscVqWaltoAxICO50BvfqNjurMLA95w1imxE2wKc5hNLc/zKVhh5UZpSAiTic/r9xqkTTJi6RKKCGzXRZPG3tFtWZV475RFTANVGuzR92qyHy2i+U6Hei0vpXwPPCfJM6Z1SM3/QxyfJYeVTiCAfnWcrJUDAUaSRGH7RQZnDHhiAV7DGvEEdzVDVn5njKRpfDFkbZOIA2dHJs7xWBrnTtiVnj9CUmOIs3N5GdQ8DRo+T0Lv5gRu24g0WDap9Jl2ZoSObLtl2BnuVQuPG5aNLwau69+xZ+/Pj2uIZ1d15/fMGNb2fntlnECGPQbvx7qOLygHEeNTO2yyMpTOttusG0WmtwwqmTT2+Rxuiw7pbSNCSkZ/U84p7EbvtT8hRsJzGeCEkpN9PSyDtec0pnrshxGPThIPULbzsykB6DYt8Lv+zw/FcprV64Epp8DPNZbLj1mgqWiISDFGi37PKwbd+uz4w9D6G4ipnoATIZhGQ0DOLb6cP2e2Hap0+wGLq7EwLscKt38e3pC9qGt7THp43fMPbPuK9ThOWoyDX+8zxNvHNlmfqZp9/RWR8JGa512PAxN6l5JPtZcmCovKe45O4bo2B2G6BynNeryXjTKY1wTqhCGZTcnmLBtgqLZt1xgavWr134f8apmMMF0zPn/uRh310dvvdHtX92imQHlQlxlp4WHb/tBaDbHbgov8DFIMNvzCYQu5OgCw98rtH9pg3hs5Rp/b7VuDgY2aSMbu8bkcUvVwHgeWWdCv6PMyuH0x/d367UoRQLCpbfX1JKIv0AWk/k7hVA0J7Eq92ojs9vTGQldYth8B2Bxwe62ByaCL9A0bqYM6rDlOSBEI9EymOWgmnBbDYA3gSjFZoxKMXhOi40UlQEozEjkEa/p+EUbgk1PN6038pQrjUijtzIrSTc9AZTEWVT/pdrGmWVotZo1/2TiZtOPZEzw0xAJVbQhdQuySYds02cHhCSJvT3auyUynfD6DfcMymrWKQxO9e/bWls05+0xTA6yvI4eQepYyzlksWd5xnXmL548p81YFeNVrhwGlFhENR2RoYmS+PxZ5lgSsBnxTgHhIqQBdp+9lftIOMgdA4rHfXaB3ayObQPv+2rA9c4gLxEzNkbA9nscchstJi7u+O8yz+aKccc4c2xzRXKPnapnPK6CqcZDptoVxXBgnVP6oQYp+n50ijRUJ5qxr8jvf/2ABCpvVzjlGvYLiFPVsqBRh+kO/X11myLG+lIwPcbwCdq5xQ4O1jayuy2SrMCSr++YUowmW+6bfc66tz1sP7vgBynB9T/C7xoKaqpFH6bBMrb1efuyHaAE0zVoTkJLHeQay5uym5SW7hkyfa7pcp+W0XEM5BX6usYQ+7RaOm1kjZXNqPM0nATdinqy057GyF2DEOkWfbbGzyCnB+S4XyVbgpjzMRTJ2eMfMMaQA3Wf+cUWWHq8N3Bc6LF2yxpjwnVRc271uKv/dXmA388UFSLT9IvfEe4aKIeuigfcT0s2L1NQGZ72vHXvS+reebgv+8dgHyuctz9UumOO/x9erTSIZSBI6pA1SbayI1sh4HdagiliTsa+fm+hhmMVewJuF3145JzXWsJnYQnq8c3aZUJ8fpAy52WsC90Ky84WwXbZtsAZMwVaSiIHK5iYM9fuW+csaSPl9VX22PlceE7u8ab66fX7dgTNYAZdBp0EioDTOaxo/wlzg30umRUC3F8wGVtqpWUzMOmqOFlgQgBK4LUHOLTc0zfGAhsL0OLNY0wEDMUD1hjZOmh7MCAX6MwvjpSzn5BGlY7BMtF4W927bxiyb1Qfye6feaX3M6q+253L2uGEZ0XBa+SLUa2wks1DTJPpJGzcFRoTkhxd1GUAoxZ3LY7zMWXyMsMK4z0KfLBBAa0uzToaB44mbG9bj4IPMeh6Q52w3yOO/OgST069LPbMZ+aSJ1Eft1X4S4Jeis4CJTdZUmFPVTEYYn0dzPFhh3MYCgyZEjzWYAMOKGPLG2s6VwYJ4DJZtkmXk6L4P23so6glhtG14vTxBend5PnrM1JgwRX9UqrZp2ecBrxrjoba+F6Hiafy9ZitqMXYXYJlZz4DtfO45r/i9MQDLMjgM0+3fR9k56rXBu56uXxwkxl7Nhxxdr5JAzNhQXplz1AHzNl9NXL3aFrPet3Zcid2tgRYPqDF10bWH8l9durr/PZ+043fdDn2a2LAzbavZGADQr++j9/vvanFs/vtUx2kpyinwc40lxVAVDJRyklAvw0pwN3L2GKMl6GyaAUxyQ/o3Ng7cBaCCrs++PPmYaBy75aYHsIKPKaad6LrXXv0MhjkC1q6XPtG/9P3cwrRdOCrepTnQqHcMD/VFdDT5dBlaC4zo68fOgn+idxCV0K1hYMEukDVj2tgnqb64bj/b6+l+EAPGPNOUQVc6X7dtercqwE9xr9R42aQURqfWotDhb44hosWDx/YrAzP3wVCZbY2xYz4HWAs+16nD10hnxQN+zHhuyn919ZyxyDDnkmP/qPplA+EZsgJocXulMLtISpefj47u4jAZYQtpLQi92Zxj/KSYJjCsaLDUnZlGRw8jvZrcXsOGVOBA6MdsTgygrTPIc/B/VVYzQy2P2DC4yCW/G8xC2Zxt0D/aMrqKUxd7sxuAWqai9dmI2C9sfcp4jdkbbQBAYU8pvTK+7ys3W1FzveG59JHqd2c9C0NpwRfhAWF7nC5HN4jgf/lDd70KF2yAxRW7sR7OjZUuLylvtBO6wddkTZvJUeJsqpiFsL6OwkyXwYTKavBg2tg7jNMwwG5Wikw7eYwxKCZ6SRXh7bFh+Z70+FTX0u88Z/Xy2EmbM2odOw65jrJjBYgysik6UV8M6M629c3pjMMstcGyQnhMOKEk44HO8/U22bxmagZGHicBtg1e6DgKsGD8l9vQ5r4pxmgFLCjCu3Q/6DHvrPnLu2nSVscLA8XaVdl+4bBCbfc2AfGotwB1Bnn7MzpskfQHj7pSON4pUXQsObxptcDmOtJg49DEvWUBVgJlH5/531nInjb3oe/Rn1ma98kD3Uwobh4DZ/9caVOajHzRfK//7g/q604YrO6odoxFSepSAw45iYfWulm2IzuGNdJy/3Rnt8+S17x4PLgs++JaFds/pLRlndWKY+uepEwOTCouIGmyuwV0O9LMP9TKaajXA1NOgZ9rLGf+zwYtZ+GRIgAIhzBoYzNPxJZCOj6K/b+2qYN1/YYirjdaevJzWEnGm+S9EBXQShv+PKGuzzVY3duhvVwXURYvdHtIi1o3+F4zVeR6mtqp+0FT/DkkIR5tXIbG9sP4j+iCTPXnfmhIsFpbkGJEGO/nlG5S9RlnmnLSJ+vnN90z9ddkcPHGUNcfA/pVY1Oi58+anbK6mzj7gKGuN1eJaVBZfFII1jvJNNpFY1kWOr5fQnnOlshPe2mDYdWYMBZtoEwGTCVV5vw4y89Sh3dVmH2WuWNZHPrdWuRN4PLuUkhb+nFgnSUS+tucX5kU4FLWF7b9tLyHdFv6Ad250mhjBsSVDxsp+Qd3leNgTD1ODArawCzvXtdTAgPo8zPk8TOsWgzLaOZAbYhfeczYLjaU25xh6Oqjtve199cU4pPfBR2ypefkoxt9XZHxc8B1/1/Zj8sPbp/bBz9upBftfYAytEkWvgq4AWxDvXgOFEaYFtZlNgYGi7LxeJ7mDEp/OzQBe3/NyQT8DUJ54vbPRRbMZgHcq4/ex8F7r87qW0ybJdaMkjBTzeahkJtlZgPpcAQ9Dpf31q26fi9Ww0XGa8X8L+++8xpNj6A9UpvcAcYwB4AmH1NkveINRwiGLaTZaQCmtSMww2QZWCPbrOP9MqBd8Hm2PfxuTWshtS1SKA1gARbRVCntBGrXvsOK01WrLuioDc16MHZV74TTsSNDrtWdY0CotGdSdID5PA70PfExJ9lUiFPA2yjOhXwA9TXInH9SBkJxsn/uFKai9YBOkupclV0plI84vF9f33OCwnEguBcexxE7zrxzQ7Ji+f28isO2XsdZY5wVXhfMOHkOH3YAt4Q8Bh2Hk5TVvcN0bHFq/vrqTWfdawK7x3DB3vSewUy7qix9mWsYFNUfE7C8PDiOowzMK9PGvDddrAOu0zEza8dcu4CsbUeH6OoqVXusdalv+ruyvUwxoF/vAKROy2k5QflbBX5+/dd/HbfffjvuvPNOvO9978PP/MzP4HM/93On31NKePnLX44f/MEfxN13342nPOUp+N7v/V7ccsst0zHHx8d4yUtegh//8R/H4eEhPv3TPx3f933fhw/7sA+bjrn77rvxdV/3dfi5n/s5AMAzn/lMvO51r8N11113zW0elmE7oVQWw2HBEPL2uHU2QGpChez5K6pxkGCPVjm2k4y3jdVucdkZhkae26e81Eaw9OyoJ+NR2Ld1Z2+f0TNpZsXhgK3xFtSFtQGfolqtqf2bh+0bd19B884bFqOFshHgx7+fsaJcB+s8OCKWRjQ72M2dvm8B0Zq/ulheM2chA2U6wJHauA8Dug+/CQ1lcAoXr+S/HrZtK9ffdQjXny++ipeuwi0EqIW+N+nnDUh09RiJ9UL08zg7ns+C3/F4g+Yy58ZECSxJF1fSnqZma1hxxjPxIur0rTqdZlRZuxhIRBOMcG1/YXst2bTy5nh63jNCl8AIeDaUfU00W9qr2zobyvwV1p3N8HR3+SybG0fjNzKFOFlDk+em9kpE2q8vIRKuFCmUZmjGjaMFyLdjumb8hLzh1DpV4XoCezey+dZe5O3vBa3aYZbd/THluxXX28+ivV/MOdganlxf0MM4jp/7lb+7OPeX22fADLLYJRtKwIZrJetNc9hZIIlBZydTjgaX1w8fxzN7yYdmBHt5DtTjToAfTk8rbegrDBBAeb8VIKTTgRcp7ukW4iahJdBRvyPdhXGuOSJ9KwGb7DqkgcQBmzONAYdWHxznCR1ux/oWQxOqoV3bhmbQgUO2NhaQYoCfQROgXGvkvkx4UBxZobyKa5BH3h0d0jX+IOD69uzI49UJrdAbQz3mFwSEhcE+j0JHSsAZbnx+Lxmo5T6M3WDWHM8J4QrhcpY1BlFr+8vg3JPDrPFCxKRUQaUwHzIC1PtsqquwM9m5RceC+nQHmMBzf6nxk0OjmbkryyXXTfZw3FjAQeoqruuAU3tGk6X8vaozBDs2OHTw6qNWRgezSIByxRdOD8muB+O5CjCf+ozalJtQXHdmPE6HaOLeDvb5SXQXzSnSLurvMuIhoF/VIyyKZ0z3NCwCFpfnn6W8HwYASgmNEaMuDzHAEB0z1emwjIY2zL4ftfc2DMOHHOPnNNTrgSl/q8DPlStX8Pf//t/Hc5/7XHzBF3yB+f1Vr3oV7rjjDvzIj/wIPuZjPgaveMUr8Jmf+Zl497vfjXPnRgv8hS98IX7+538eP/ETP4GHP/zhePGLX4zP+ZzPwZ133okmb9Buu+02/MVf/AV+6Zd+CQDwlV/5lXj2s5+Nn//5n7/mNh/e0G7TXVfW1RotHthOijxxSAjB+tzMYp29GayNMdWtF2GeHwLu0wsgk9kcOp6aMAq9RmLWqGZKPZ0RsbRGmGmDTPBqw12LiXXj6Oe6VAApmugnb2igYF/VfPFg8oI/ON5vu5g3RohSb4CCCGw+pkzHPXkh555HGAWZQ1+e26yvA1COsfaxVD+s8dAeliIqNZ2Ak4g7e+msCz2cSshMGPbHzQZvWJ0ZrNY+/V7ymDvzZyMoptO5F9RnBTqxFx4xGjBL07X7nBXnmDx6cq/sZeRwz9ADzbqWNUoBIBTOk5rg9E/ZhuPrxvOv3niWDzTzW09YY3dgw4O8d82ELea5iOcxPXYkxl70eqY2SNroGd2dSWhZvV+bGeCai7kH5/gayL33Qbth1ddtDy0FXZ6hTpG9dIQpO9LusWLb4w23BCIMy0WRzh4osykBwOJKXxUYBrYbP2ZGDsuAzfml6bOCJSEiqjzHZt2h7ux2DmIxY8mqyJkGp+MVsMOshGEZTdiE7nvR9uG5WVhHvI4X/TOMfWbSsOfPi4tboHigcIZmMxgx4ZqeELNvQpfMJtzTAeINlQZvpG6+ZrcXgWAdRLofZI1ivanurMR1qvOM3kgyx9Q2OT07U44HLC+WFR5fp8DASj3SD81Rea5mtQ5tQHult0BDXscX927BQsuys5MD9/1UzFwZ0DNoxODlOln/on7+1fg03+DTh0/6hpXkAlonqvp+a9ZVzc4yaznQXmZKzrati0sV5q6w4Bz9P12PcWIAkyiu1jFj509qo2V+Uxu01l9xbtNgmHGCSDsNA8ZxHnvjJx47TlM1VpYXfTtWnsneX209mOyMC/dezgdrqo56rueUzcAMtDaabHWhUzbU5XxdLzsdYByFBYh3tEH38LNm/pRjlv/nXre9ANBffzBptdWKrA09scuHNpoxZBwnAJorrIGg2r720bK0WgBOVjqdFVacj8ZR0id0vWXoP5TLaajXA1P+VoGfz/7sz8Znf/Znu7+llPDa174W3/It34LP//zPBwC88Y1vxE033YQ3v/nNeMELXoB7770Xr3/96/GjP/qj+IzP+AwAwJve9CbcfPPN+JVf+RU8/elPxx/90R/hl37pl/Bbv/VbeMpTngIA+KEf+iF80id9Et797nfjCU94wjW1eVhCmIbVhc5D56VMgsDM7M5zZ03bYjrf04aR33QqROMds1o6Xnx5VQBNz43OhrDbD47Gz7aIJ5w3cgBmw9f0BTWYVfM0MVMgdLa9+pheGD/0JvQ5DSaL3uq6RAS2nkHCb68U1s7RG9XtZrmC/u/gPY96TWwoCCNAXdPxXNQo7147y+Ms4GgzH2AWgBQKPoMMcSOGEzF11HiaxhHdkrwL4o0f2+E3YjizBViSFrdd1qfKYdUaz47eIOy9f2TZsDEgzKGOUqlr9sTQBqzPNz6Qi1Lrid+/lNIsCw/Y6uO0nJEv1VmJUlb3OjRsVWQzZkNKkmEDAOVGtFnZ8KexvVn0VmWCYi+q1leRokMLjh+2vQ7r+aQWuO7/KztSh0NeetxoBB/83/LlFdChZ20APScv48hCuddnFmj2CrMP4KRAZiN/ARFgJoDlqLesRNpctPeuC7bbWL8CovP45uxgm7MN2ssb493Wm4vNdSOw0zGbNTezKxgltLF2vL267Vc+bLspMWE4PZAaAkyd8bqhcb24LPWV32/OKsdDAywv9mb+PMoby14xMnWIGICsb0XgitknjH3CDqIYgxmzJotoEww7T98LA4NSZKO/vIfYMerZSpZLHk+yBhf3UQn30fNBUq+qFlXne1reu8HhjRS2qt57ATF4vAzZFtss6jE8KQasr1ta5k4j67vSEuGQ7SHtDNeqh244gBXXFex3J0kCAVScIh5L3bsmUDCs3XTZlesW7fJKAhaH/mYemLefd9ad8S6T3CRXuTk7D1Tt0lYMmTpqWUe2XVb3DNjQ+qDrOb4Q3XbVRIhrWp9eObxx67wzNll/wZ6gxslc+GYKux23gLOvEF/uETemrJsF4vW54TFqbncE73c9S7kvD4w7fjQxes07DrATbS4BznTI4O+z9OeJNcnNSkC/PgJ+36/7tJyWk5YHrcbPe97zHtx111142tOeNn23Wq3wqZ/6qXjb296GF7zgBbjzzjux2WyKYx796EfjSU96Et72trfh6U9/Ot7+9rfjwoULE+gDAJ/4iZ+ICxcu4G1ve1sV+Dk+Psbx8dbgv3hxDIm56f/3Z2hjnnB4shHvD9PmldJy95iHu9cTQcn9u+rIPAAMe4tSbFEdE1VoT3+hZHLEw81EbZ/OJcClvfuq0XBAn+vXkzsxWtY3nMHi4jHCVUIytHc1sx3aewmxjgDIY8ApCwX51veqAYCa5szYuI2tf6O8xBdGpkN/xhewm0sJOW3uObRIztFph5kFFIHGSdU7tUsyilSM53g0c88xYHNuYRY08UIfPlJ52K86Y4yMUfbCyzNkEMbbnHrGf9or+3pQng2h1fOGqlkP6A4aa6w54pKJz80bHa2zxOLn4uljz/DURu0hJg2kFG36T+2p786MArQmW0nyNysGuGoDGgZHc/sXV7ffsx4UQkBLQDKzGK7eNN736l7WTkpm88gG3oK9tlSkPc1FipcMAZvr9y19eqU31hk0ok3qQkCYxyjPbS2Di7p3naWj2BA5m6pLjy2NvsMbtyfIpmR5sewbyRjHITCa1RT6hPZKh82Fsn7RLbr04dv3kkPGUrQptNl4lM2O2fClRXUjNF1vFREGy/7bHpCBH9rwr89FNEdLM7a1NomEHplwWgf8Y0NfwgyKUBDF9NAhQQx8eg4Szc4UrSXWFRJgie+1FGzGuMGshDPtfUCtMayt1yccX18+4MVl8jBnEIZx+aGNWF+g0EYGcpIFW3Xf1zJd9auA0PuhYtO1KuLOkz6Qnm+oX8Xe0NfVAI/uS65/fd1i0mTySl8Lv/faBbtp7i94KEn+RzuZHKcUv28Mrsga7oEuu7RwQu8AAervGkNW2EWzbOeaCSHjYw5M2FX/3G0F69jRdzUBgBVAyksyUNSU6gCOvmcz9Tv3Yjb8i3rbdmeomgdJag5dSdIwV/+UBbPiuC36yLEv7EV1HTtQJSpuBjKeonI7B387NLVhlJ+oXEc7kQ1YForwbsB5vnO3tYsl4gGy+ucZ89xlHAfnd6cN/bU9ir/7JWH3szhJHaelKA9a4Oeuu+4CANx0003F9zfddBP+7M/+bDpmuVzi+uuvN8fI+XfddRce8YhHmPof8YhHTMd45Tu/8zvx8pe/3Hz/f/75h6NZZQDlhN4OL+SJB2OXweP2w1VYjVc/D2JvwqgcN5sqHgBgM6i416F29csxFGV2spNMIr3dXBhPLZ8r31cWr9l07slWmGp9pooYDnNhIdNvlQbPemQSABIXLKjgM568k5Rh4XiIsrWl+zs47khjPHAsUhifoS+YuKNhzvMwvzslJN8IhjPmPb0DoNwYct8IVVp7d/VmmUFSrp9TgOs2CFOGjd3pOcyFSwXzR1F/kRGtd1zLM/MFsA0xPbyBmBbBGs8MRly5aWXfLfd9saKV7lzh3DffU+jGTtyoafLwERxi6vRDrPzttIHTw2t9sy73lxG+zpeMHbVF6x0MQFy3Ttawse8P3r8dlBrQA0bjfHU3M8YI4MwsIdZ86VeN0YqJJFo/7C9MKFY83l5PgFEObdkcLBEGYEksJt3HEmZmsujkJh0+YjsPNpT1UVg2hjUml3HAIynDMlhdOyfbnmbyAFvRZ6Njo6+7SSODiTbuiytj3x8+QlH3jYaEnS94NyvN5me8OdvYkDwn1HFxuTzGC3Nl4eBaqG4BmohONzPxpvoVS5JDoHPfNyxkLddRz0b6cds4y5zSRRiLiTIbTuGTHB6nwiZTAA4ud+DEFhOzQId5OKnETfgXh9Dt+5o1JynNxjKK9DMSINoD4mI/z/jcpkf3fy+1mE7a4rFoPTIPjGDWsL6nZp2dXhUmtW1oeUx7ZLN/Suigtn3MeEqWFV9LomGBK2B5iUB/5z2VDI7Td3qenMZwWW8YMGWErNUvYLXJBpnvZy5LlRcaP5eZuDh315AWsHMHwGmOx/islpcH815KKYBgAlVbnj9g38vtuXRcDOZ8Bmj7RbSJTFQRMN7oeDpOQsCG9HplaD/0snoBJ9hTnJZrLg9a4EdKYHp7SuY7LnyMd/yuer75m78ZL3rRi6bPFy9exM0334zlZSuyado8h+pXaJub9dgWndLXK7FPVfReZ7HxEHa7IbZGTC2rxByosz4bsDhMs6Feex8Y2SbsXfXayouOeF5rbAzWA9DFXfAccWw2nMSTbLyp6jBpF2v8iJhnKUBtvYCc7UhvqsTA4M3SBAixgaLCiNKiQXdmWQqfAmhzZp7Dm89P33HIR+yGIlMUYBfRyRPuLObMKOE2xKMeoafnqBhZfQ6VYN2K5rhHe/HYxJNrFo/EgvcUEz55RZ1UzlORTCaOMQWUmZiY7p8ikChLifawyyaMSV/SHkOfVsdJ2vNaqJfn9SvasSMJhNQr4S3FuTsy2DTHdk7R9zKBUzNAeHkBe4xHqQfKMElmwsg8pPusU3hzp3AoDyjeGUoAew9TauSZFPWpAbBEFYw7uk7N3/yK9AmFsJFT+hv831P0gKQSgF9cGRzxyy0gM2mhGdA/jHpBxMbQc7gAK0PLNCY5WH9JzKE8n+gNU6E5owxmbluzSVNGvemeHOYLAxTCvpnb1IyMH7tRFJacvq5htu1Fy06iNW9ikNHmNAxWnykQfbHbDziisVBkzPLlwkZgsneEd9USJNdmhpLctxZ37lik3hFd1++CfneYbbg4HHB8gTZ3Sm5F0tfzxneTmbxGw4/Wve7GpWXu5HOagolExzRhN7ieh727gdlxbuzsMboeASc9cHEXS2R52Z/oBOhqOG26rqex66VmNGlBcwOetAHtpbpWiRw/EPtc1vpamncASDGiuXxk9VFyc5IC6Uwa8jbOO3gAHD0yM9dJ6idFO6aY4WhkA0CMt4rYdvj/s/e3MdckV3kwuqq6e+/76/mcTw/YMe+BJBAIIeZADNIRBoJBtpwcETm8I1kmQs4PgxwLTAL5wkgxIdhAJBtLFgkCAkp+RIoCJnLsyCgJgRhjhPQSchKc+D04xzNjz8wzz/PcX3t3d9X50bWqV11rVe/7HhjsjO+SRvPsffeuru6urlrrWte61hgnsAxTEYUdUhPEzno4ApSqAn21oJusTotjs3+ifqvthOmXaC9K4DY6T/2+r257ZkA9tbBy2i/A/SHZ9pb/YWmryTbu+XqEmojGPP90enNonAabLEFsnOZNWcnwql2159s+Z4GfRx99lIgmxs5LXvKS/P2nP/3pzAJ69NFHabvd0p07dwrWz6c//Wn6uq/7unzMU089pfr/zGc+o9hEsq3Xa1qvtYXUH7icN65ajlLXr4sdFhUNS09iK8uOVpgqNcekiB5gWdPOabFKrHzg6kg/CujJ5kdH6zsjtSeoETFfAKeydRDRjd6g96OhnO53I+576VCJ75UT7QwBWh31xd+F2sItGmtDqIoXxvOxHEUPFrglzl013LC/UB7THzlVjcEn71dGcC2DEWnxvrcpWRctr3qpVnOsQ0ft+Vo9S1PYu2LYSE0IfEY3fj/p8IgITZlGKSw9YD3EVUvNpmS19Nfme8apE5I5QkQ0VujyyPjhvHCrSbBHAdJud6R2m2jc3Yn+LRoe/WE50Jsf36qKRZJZwu88tugdrZ/raYBqX1I4cnsjMT0qIPvZA/N7iQ4MP2c5N+TY++ti/oMDPnRER09AeplYs85vTec9+mSZ1sqpTtW9gaZ3uT/0tH52UN8TEZ0+Utch8X2kgydLbwOFSBnkRnZP6IwoI+p2dYYIvhT4dBWQspkir0sC5V3S7UKAzkoxqIGNEsCUAFmhF2NEbmv6KkTz3MI15ex2Si1eEJH1Y3JQK6mtUQjoW5F6dAoRrGYAA/VOQqf3M71eR/Xuyj27SUEmVSo8TkCDKpsuWVLpN5h2NVckmr/vFKOC0m8FKC4AyV44ai2kre6qUMWpqipNOaUyt6fIlivtLHxniOZ50R8tpJU6zeLQz3s+tjyBFtK+yH4p5zkzxyz9xqkSWv23DMhq+2h6CZfKufN9qIIFki1rgPer+3ZVRKIZMKkBFGhDSqfcj5GGax35jW2ES6YXijtP6zO8hwoEta+72QaV7orPpD2Pi9oubAdbVcvaM4PxI8bPwSVke/L7KK9L7QXRCO4WYFp9UposQfnbCiuG1xF9TfMX7elA7UmvGHUspbC5LaqAqvvqqF2qGCbGZmlK7dJateam/M5XwBsXpnd+Kdg97tfPHXaQHl50LUblbz6vPq5a0T5ngZ8v+qIvokcffZQ+9KEP0Vd91VcREdF2u6V//+//Pf2jf/SPiIjoFa94BXVdRx/60Ifo9a9/PRERPfHEE/S7v/u79GM/9mNERPTKV76S7t69S7/5m79JX/M1X0NERB/5yEfo7t27GRy6TGs2RM2OeaTK7sqoVg28MfpUUd8wsWp8EWWxx6AEH71TUTpsq2fPVMnpXNlIltzeKzft7e01NeejrnAhfsNUbBMc8nWHJx9DZeRIMrrjUoUrb6DrRqQAqffsPFosivzbSu4zP7eiT2OjQLpwUUVnR+SptqESTdcXWm1n8helc6X70YLMAPQtSLvUUjK4tWd6IbdS4gYwolyINqNFGM5WNSeieUOW4AaOMxsXQgZLUoMX9U+ISg0sKh0XZkyNJ+U1sfOE60WhseESaIE+lJFmqAywoOnnuAa0iVmw/xSUgo96DmJlpfMH2wuJO1vPbdg3QCExYXMVLrgmXvu2M2lNGdwsEC5/K53NYn7ABbigWY9yfjKAdPZwuWbxO7G5Xv5WXnv009pw9hA6PdP/1/eEFpxh/N1/+fIczOuNgXageDc+t+2hX2Rv5fLNYDGElnYK0HJ6HIrZZ0Fe8dumLzvi+dvdhR/z8VhZpTiBV3pco2ASsjOLgBszHTDVS7FCT0bNgErvrkxB25k+BccTzdFoVQp+5VUlsQ402I5fuqf6k+djFhE6RsN6KjOv0gLF0sDAJqZibRPQvcT6bQxGQ8FYlE6f0sqKdH4TqhaK22AVKSCaBcVRr0kGm6K3AbW8bwhwygJH20rqGrfVsb0nukDUoMaeSrOOVc0gIqLz213qC4Nx0RTelWv66rnKu5POJx1rbNvr6b7KNaJgPMj+9O83Tfle2unNZB6DICnuEe15tNOeqZwnbtQ2hkrvhL1g74lU/XMAILFtqcVxQVfHLz+kDqsrir2eWeaYnhc6ovPbfnEe5HG4ci3jedGI91JpwyWtnJqpu7SWWeLmJoCExmj6vHqmDGbIAgNujLR5+FDPh8T6lvqUCvhsnNYqU4GB8v9ybKpIDDzLXXZuLiVvzGVTD6qw/RMTyTjF51uq11VVrxemfVaBn+PjY/r4xz+eP3/iE5+g3/md36Hbt2/Ty172MnrrW99KP/IjP0Jf8iVfQl/yJV9CP/IjP0IHBwf0+OOPExHRjRs36Lu+67vo+77v++iBBx6g27dv09ve9jb6iq/4ilzl60u/9EvpW7/1W+lNb3oTve997yOiqZz7a1/72ktX9CKaXvjGaYNVtqUSx5lhUjEyCrBAGdNOlYSs5WFbYp5cNSAfDy/E8Rd0OwXqrDbsO9p7TtPXpYOfxeeMUudo1KvUqRRxs0oqTn2KyDRWurmnjZuCkpwW0uGoNHL4HqORUajvd8nYrQjvSsPaKrOJv0Oww+p7NgoMhoZo41pHFXgDl/PA2sAQOEGgp6YNEJ0zHVbZzm8u/rmqm+QCKQaTauz3qo1c97lV0dcU5Ze3Q/xbOkNaG8PplVQcsr3FaRDlwNg41lHZcg2IjXHt6RAJ8Frsnu318pwFo5CINremjrZHUFaVNHsFWR1HnzKMEKMaCFYRcoFo9cxGl2kV7e7/YeejsFHZns7fre7j+5eMXfHqS8Ow1ArT83+AWxGEk8JrHDoIvL6tnwNgTaxt40FL5B2dvAQdqgQm3ZbOqB1hL75TUWebwTKNG4EFWFcMRoXsPzMLoO9hz1GzjdrBLN6jSpT8nMF1EVQAAz6z5dZ2BZdhfwbDVMnpQYN4cvwMtOE1ba9xoAHGIlKsQjuBBzUxVQmu4711UTvzOg0hqn7yccr5wkpoRK06Zv4ip7KpsTtqT3VZczk2Hpcqt572OifWDLUeMUtEvCK1wArOg+5sUAC53JfyOojXzZ9hzd5/Wohvu6lyolVam6i0ySz7yKo8VIz9pGK3rNxi1VCidK9q10RER//3cRoHzPNuAj0VC0U827NH9tJ3ZB6D+mSy8TVJIEnabUX5cKO09dkXgJCa3JsT27PGQMT7WeyBQ6TVnXPyWGgkjfPkS27Ov0PbxzstII7MyJSK7k/Lw6hxdPYS0LIDYCa2ml0i38ushWatYwZYIItu+HO7BHhmLcv5Dyc4/cJJ27Nm+6yfnpFfhwzfTa/S7yUoFpJdgezUXAUTvi8AWufID0HN7QyGC19iNIAfTFXEObz/qQTigd4dNY7ufvmtsj9YEzfXlt/5/JwN0MGPpFko4hoZSLbWmvH5in5etasm2mcV+Pmt3/otetWrXpU/s6bOG9/4RvrZn/1Z+pt/82/S2dkZvfnNb6Y7d+7Q137t19IHP/hBunZt3jR+8id/ktq2pde//vV0dnZG3/RN30Q/+7M/S40w1n/xF3+R3vKWt+TqX6973evoPe95z/Mac2gcuR0lklVpZNGy7gUW4EmL0pJ+UPREqCZf6IHINR/9WrfM1CBKjCJ08lP/ljZCPlWcIsYqbagQakvOzQNoZRjsFbwFhpNby+/VTk+3aGTXSoBzWoKiw4vGFHiPUvvZ0akDUhSjKskuHQR2ipQ4qfE8rLa9ZgA/aX+TUXsszGVWwjH0oogMQMhFHTZSjA1jsOKYnDKAJan7SH4TqEGxPJmOUnkvmf4sNZUUm+VmcixF9+392Xj0Z7PxE1ukHhtCgsIAW33GHFbWG0Djp4i4h0jDUacp3MlAOntwRmM6K/KsnO3ymD4BnqrktJEmg8XxxrUWz5XvERs/G0Lng8g/3JjgHreWb3eFpiudcpWawiWcRXpXoVMg0woMxxpT2uQ9nI9HTaf0/wY7FDo5vNQoQHb6/83/IaKfcNnIWCAygI6sxwbPsnHUHoNxaxie6KDJfsaDVRp7OfiTLzyg1f1Ri1ga7yI686zZUaQow/xk5ossby/HUIhUo6PVNEpYS17j6Z+YaGOSaUU0V6vbXiufZWaS0XzvEZBjQJCdVyIiMkA2ZMw0ACSyCDUGQfD+EBG1oBV3+tg+rZ8tv5NadwxUqJSZpG2hBLPF+8LvFlaDZIdZVS4UjQGnIj1X+nnyPcMUt3Wj1hr53tdSwbK4LQQkkIlnifXy3lIE2ozlSAlpwzM7+p/3pv4w6DVGigB+o2PtNr1m/w7z4LdfeHM6Jzq5w0jtc73WlRO6OYo5kxoD8lLwH+8vr5G1wOBwMBcKUXt+jHT0X5+xf0hEe0fTmhmRFZTYe8cvLZF5yTSPnujk0aOqHS1BPAvg1Czs8nN/mMAdw0bdpafnR6MIhPi4yoru8LthSg3FfUOmGK7up/Q8JaieNK5k4APn0y4BZnGfIrANh709DerI9SIB92pP8vpYHOcErusJZqYdG+DoLi3W7fXrxVhkQxYfsqtW94y0PTF21JbL/YyR3Kj19IrfPj0BUkqnioiGsa6N9aJskUzw7NJ9XLWiuRivEuAu0u7du0c3btygP/9/voOaVYqUqIjE9D+kcVtVpPDF77mql6h8bBkZE1PE7rsYCv42Gk6O+pHxXepnCTQa96YIPC7u1viR7k9k/E4BFimCX6FXLom80Rg1CCYW2VwVAyMxtdLIRj81OnVZ2UIDP/idHANHVmuVLZb0J8hNWiT4zLhPaQQ3SjxUj0tFTji4akTklG6UFXFfWHHYwMSUEr+NU/9Lc6y2lCVDwdJx4La6kyamGK5ME2nvCadcRVcb8sclZbkoWc+6H7CRc3qZrABEBHPOTdorNSNGrjXI4tiVh040O4F7z+pS8EpPyQAbLZ2wPLaV/X7wd0tzmK8LAXK+D6cPzYPDVIphreeQrEi2uTn/uz8C0Gg/0kv/XfmdLFt/+sj0rA6eslmKxTwhKtCy6Bwdv/xQvU/saPfCkLfKTy9VsJqOsf/uR4PFZ9ilKGTfCjH9kDxIBCBPH1lTex50lSdx3PYWg0b2+AowA6PaCbCSwJcEmU6+sJ7+Flqjepb4yHvRgHoO6SOCodIptEpzE83OiJxvau+MpLPx4Lo5vQgZArHRQANW2+uOY1F6fhqvHmvAdNqkPbSk8WOnC8/vbiEiDfd+7zkN/MhzyfOo+TREuv/S8kJl6m4tTYbB0PUz5fta6IvF6d1TwGR+ljZQxb/VABoc4+zvoyNaPwvrCM7/p08UQ0OCN9sHUoVNQ4fFhajmVVFt8rAC/HCKrgTWKvemFmxZSvUybVbju13v2NJvMc0qHyPuhxXUU6z1mn1tPMtd1+lCCR5j2/9MmgsGKD+uvU5nkmnuucy8PeCCfQ6HZG0heU3yPi1IKfgxKrBJ2mi1Ocb998B4KwLZ3hZCnn2SaP5uGpf9rsrW1NJSo9ZgwxYaMp63XCfT/wz73cW670gk9gvjto/bc/rtf/F36O7du3T9+nV9wIuksb/9//x//wNqO81Gv0wb+nP66L/6uy/6e3aZ9jmr8fO52m7/h09S61MUFyL/LPoajkpjVEZ1Th+bEB40bLiKx56gIFuAgouxiApJI9ifzGhwOIDUpc2oywqhfsDhyqClk2oB+tnc7mjv6a2uQCV1gZIT7Y3KXBhFUI41VwA4k9zu+Z/NU8/NH/ZAMHltMH7E+N35tNmON0qaLlNnwxot3Lkv1jypVrJaYKPExlH3XIney8gJb57tsyfl79LYw355nSjEGP2BMiBm4Eec5wKwr2YoVAAUpwVinZWSZkRn8hjTM8Y0IL8ZadxrdI55URUjpTDA/GSBw/VT5b2Urb+d3tnK/Riuz5uP5cCODx4VnwtDPA0HGQQMwFnCnPlccZpfuF64bPzYThT3sysNYZuG7cLycURCdye1pq8z0ohmsMGy2ybgYcG4Mgx1olmYVpZEx3HlLuRSIxyRopy68dNn/5ShYMz/TP2cPlS+f+wIu7BspLTnpCxSfpYHnxaVoJQGnK1xJVstzTIOROvnyrUZARxL2F++W65L4KWK0BJ19wddKlccx8AZOo+zcLOMKOM1JA0T4Uxsrs8PsyhTjmtDcHT0yTP4Uq/Jm5ug8ZP6RCBMOgPN+STmapVEJyJaiRQ/rALogmYt4B6yupd0wSD1ZNjz6p3Ye66uG5K/E2lqOZqtHEzKFYRkk9FvZjIpQVQmDYDjJhs7p3JNqpX9Rq2n5nyg9d1y/SxA7+TYRrhfzKRSos/3ZzsrrBtqzh25Duf29FkC6pYeyEVLnVslzPvrtt4Xt/7whlqj5PlQWFr2bemvyPlvsZzKsdQBrwz0yfkvziWr0yr9oT4WDL5pvGIfR/A895OeMVT7KsSE1x2FdZt1UrDJa2Ibb/7C57Sn2th5PiHD0YWoniWWIR/3vA72iUvJgtmGpIHfjsoulnN6+wVpP6oAP72sbguH5H26CKKJwyVz3eh+KSic7x+ma6X1TqVBwpI07jkFxlkVPxGItgWUy8/rO9PAMZjoDV0tbOO+V/3J9zsYbFY+V3c86ipkYg/MQvQwhnHtaRgWRNSu2lW7YLsCfi7Znv1/vTQzfmptCc3lxRodLaY09pboqejHBW2YcZOVhaxFT+U0QxsODIFP1h4SRhYuWsM+0dntvWUQgdd/g5Wxa4HmJjeYYpx/cna6rci2rvZS/t36XbvZjTLPz7L8njcSeb91OXddQULS5LNB/DDQmlM3CAQUIoQh0tg5Q+cpbbaiCtL6OdhIQlTATnO3dJ7GW0x31tGM5n55rKIVb/uqcUJEFBIAN1D5HrR3N7T6dK+MPlnytb+1Tt8B8JNu8/ZLbuTvECRhxo90iOXm3wjNAAtIRPCGy9ITEW1SJRF0zGv6Ewoo8UZk1WCVtSDy6oZA0QNACMZodzbdq9U9/V6qdAuwa2/8z60ygKVB31+3SykTTbpeCO61x/M93t6e5n0NLDh9ZH7uB0+Wc7i7N30eRVT/5NF5Psmy0HhNLhDd/Hj54uyJtJk7XzKNaw2i2Vt2ZpWTVt7X9iyqecDXiPpLRT+jo71nyueLZb33n5rGub0BqYhRv4co1D3s61RdyTDqEvMGwRDfRxoOGqNK5Ty2TdK4wmh31oURX7d3y2fJa5F8lt39+T7IFCZsLjhRnZHHK5+tK/6Xx5CAKuy7YKQlAAZ1uzgFSqafqBSN613VkeaWmY+G0LgqRAAgybDvNLgo+6+UUmZgC5+x3LPb86nfgyfKoAUHduT9wKg5A2pRpLXJ9Uvuf0pL6kCbqmb6ZkUAVzlacu2JkZrzQA3gDTXwoDzI7azmxEE9bNHp9QdtldVxXGSHZa0xBAWa9Hxx2xDnY6akKhOexi+12XBcZipLAfrXg15jp/fLYo7xMxvwxZney/GovqeFVUOxcRQdvLvpWTL7kIhUVVXXB9r7zMb8HbeBK7wZ7J7MGubv8LrXK82eFinLDBwtVS6tNdaWq6Xl732qHvTiwLSsYhqEHyIBMnwmsXHk72NtezFBWe8HguRxNV1r/zAEzPBa7xJZgudEpc4Y2nyxcbT36XJcCsR7+n4xlvkPTgXOFQizv1cyIaHlzA5DSmFzq63qahERhUfr68W4/fwSd75K9Xph2hXwc8l24/dPqG3TIosbU4pIeBQLE5vH9guS8wmLEG9IuPGU/Uca99tiQykcL7EOKefUa0FGdAa643GKLBQHJSPgObGIwqK1feCA2tNBRVDkRsIOu5Xn3tw5Lr7ChTgcrlV/UkAurufjsf/h2kpHWcT9Y+cJnVx2UqxSj/NB6Zx4iJG/jKlLNFosIDnG6f81EAwdDrm4Re9o3HMUVXBg+o0UMIwNJrXrVbK9aQtfW5T25gTm7waMofWKCHOXhUaOf/ru9P/VA+XINz2d/YmbRqqXiPgn59NHcE7TbW4/LRhxe5h2lQQlRXS+YDxEMccafI+sCN383V5KMxj3kEnC/dUZP9Q4cr1OpeFnICuv4Psd9xolQoxMDy7pu/cMONxDoC0yITbA9LvRKseF3GxM8xzGFE03EDWbhga4H7Gdf7u9nth0SCNP112UJQYmFV/T820oiM06CUTztTDIwm2VnD/UoJLzoL/eUXtfl7HPZWWl4wtrSrMNig2jAIWbU7+oTzCunapChs2NVunw+d9M2cf3bzjwWYNCNgk6S6BZNjbyGagj0oCUXyfnVL4jAviQv1XVAsdI29ulczeKSj48t1SFsxtczrr8PhTaUC6X5i36T+/gSuy/GOzwQ1QgmGLrpX5XAIQN+40CaXEM6+diriSYxyX2C+4bq3qRb1TaBlFpS+QKhfDusuMl5xDuuz6V4pTzsxiXjF0g0N3Zeh/5tzkIAyyLNK6lCH5mFIM9N6RUXOlUWk64CizB8sOsBksbcVf1zu5eb1Ronf+dASx0+FtnAu4S1Nt7MoEBoFHHdq20q5R+VkphtQJ5RETDDVkeE3/rVLU9OdbjPzGxbmql6DEFs7R9pncJQU0OtHb3ZrtRM9QN+wXnDYs7bzUAcvYIFCRAtmpvMLBEm8Fe6MY7ajZBX5OYO8wS8ZvymjLwLfsEsT5+FlGUTS/BNFEJEdi1vg80PlCCNzKQxTa7VQWNiJS/EYO0vwK1n7lvBBjTnnBzZmdhpWE3jLS9tRy8bYz3e+pfp8GaTKcd7y6R1mTzQ6TWCOTJ/ZOfs5VOOPS7Kpy8uNpVVa8Xpl0BP5dsd7/4cCfjR5b/JCoXDY6iYFSN6c+r5xZSLhwR+dJAKYTUFoQVp5OCg4mfQ6RRLaBM1RHXDMZVf9RMG4+qYiATyvm6NTgUkeUEGxOL+hUpYVHS/SUgBDoV97c631t0s7qbwJAjcHITc0EZwuKenT+Yxo1RRsM5jYM+Rjnl58LATuXAFdCRro/ZFFaLLrG3EFBI1y2jfjqlxC2Wip9+5PJ5iq9DpOHgRvmlYfSplBJpuLmb0xhRf+IL9k3qOmqhENXTj9pzadiUv+FnUQxX3PvCQDFSvdCQlfOwSUbZsA9g0z7TgStMIB7/qqkaIjLNKYAY5kT3h74QUEiGiRSyJprSRrc3bpbjhXt96z8/oQ1jcQOHR6a5gIBwXLU0HOkKgpIlwuK0NV0Y2dYAlg/XdInjdjPfm3sLwl0uEh18pjRG5Vr93JdM790pVObi+zigkDWIVQ7rVRa5zeNNzJLCETeMytU9AAGABcHgDJdangcxpw7lr7A0+f1h0TlmB90U4ndOveeSQckgUHsMjDSuvnZajyhvb6T7LMVJxZoswUkldr2NKu2p0C1JfhrqTHCK4P5nyvstQQo3JiO+UspbalsoJtUmGEwwuzw6tmYbVGqaxTC20r24cUlpBNmim7a3BsYmmU/8LiiAM49/PraWeijXsiLNVzjTll4ZAohyLmZnGV4bZmChk1+suc6RG0Y19/JdLoILuKBG6m+XaeKYuj7yZ7VfknpvlJPindJJKQIS6d/ITouNo/UzGx3tlmNgwEep9ifbR9hqqE/HLJFQMNvmk8m0JwvIsPTnuHGQo1YpENeRohR2iOT7QG4AMCutp3cFO5ycoa0Xy2eJz2OVmJ49oFK+jzuZBQwWlycQ1wFjlWPf3GgWWYJc2a9mu5Wi1uXfrJS/4r0U26ulWbN3B+xYAVAhCzn/rDJOObeHax2dPfJQ1V7sJCiONlSj9yR8L1mHzipMUNUmSq05D4tBSLb5VFDQuYmJDQHqwo8z2I1EYh25alftD9mugJ9LtqP/34ZaRuZriyxswgXDJBk9HqskJCOpv96avyOaFrzueCg2GC82OOm8YaWh2DQ0HpYGIy542+udiirk1CJZOQTWsuawnTY1ZHLIfPKErntgHLgYdXWJigMix1uwY4SxiekjodPpa8XYk5GhWEHpWlxT3833EoOkTB8QWkunIu2pg4j7+Ybi7VJoLIgIW9jnKg22Y9HdBwANmVrNvnIe+VoHITysxAxjpAEprAhscfUgY1xLmgJEkyGutTh2G7K+jxNQgjicMBQYvNGCw9MYpPGvBAkZmDwX2hzyPp3Ikqbwbq0a8gC6BBE92zyQSpru2aCAihgXA3O2IHb6jQSZdbUO7QAiy2ZznRlvpbZB9EdKTBgNxPtf+Yh+HmIMrD+hnDg3gUhKH0UaP+lVQFHrVXL+Th4WAqfguN/47xODUGpmVY0muPnRRepBJ0Qu1S5NIbym89vJEUJijTzMTcb6yaO43qVx/w/BfMNnGUkxhbCxUKylEaGMXtDYGG6sFGNMRiG5b1xft9c8rZ/dLkay27ubNAZ4R9LzkYATjr1LjgPqys1jnPtUQ2icqj4lPzNrBbctBqqQSVYAOPxuQQyI96Wj/++89us0BK90f7iEfB56AgG318sxuKjfQ10oQutilOKh9sMa15NjqgJIUkOFdXpW9p4tQfcam6Vg9sjjF6o0jJ2zgZPUeK1CcJrtKWQoybHFxlF3X4+TbRaZHqRYce1uHbVaqXki2qnbZQGLhYD2Qf3codurpsoSER2dKkrwNExOt5b2IjrWfP8qjJ/VM7IKZuW38ju5Z3OfisU0XWv7NDwsSC2KN44UiM1OuSwughUKXbCDWbKt7tnrUWx0hUtLj1AzBOfP1z8+7VtYXTF6T6sba0NvSLDesw4mPFMum34o5vAerGuWJqUY5tnDwGSS3Uc9B4vM05Tmq+UcpmvZexqAIal5FIlWdwcNnqS5ePqSeVxo4zdbQw4BGu+JqpLeqAPz6rde+wwSQGXgXjGWh6lyotZZkutg7aSRhv7zDPyJsbrGXKqPq1a0K+Dnkq0/bCl2NhWcmQBYQUIuSuyMIAASU/Rh7ylZ1ksbaON+W0YJpJbCI8IiRKeq1UyOXar30xhS3w8ICxcWPHYeOdJutfVnztL4gVEUIq3+4NlyGCBcHK6lVC9xz+Sm7U5FSAKjV9ttUf5UtTZpEzx6q/w+3T+VmiZaf8OuVuOvpe+H2ZlW6UEh0vZGeS+kEVAr/Tx3uMSamSjuqnpQyidficgpphLExpGDvDQsNZzT4lCE29nViGTb3G7VPCvej/R7bbBGU0SwMNSS8601nfgLAeTA+3Htk0njRzg0kqnC6YbTH7B/n+dCPr6ohDP9T6ViVdL5JKjJ12KVniWa2QlEWmQxOm1kI02eDdH1szqKKnV0pvHDe3/D62ov4hJ5TvYY+XfTNSqGiXQIO76G8pjz29MJeoFTIWPj/NYEqEqnahDL1ygeZWz0e3T2IAA/gonHrwY63wyYH3xGMwJy32EC7hHAYRBSAoODchDK91b+jtvZF6YyyArELisgERHFplxj954ZFpl+Z4/s5b6Kca0dbW926p2TwIZPDBWr0iQREQkGS42hJo14Oef2PiNSkI1qOAiwyDWL0x7RMWWnVYm1ynF5R63FRmMARAINOKwY9dpccTS6+/r7FhhjuP9sbne0/9QGvtNh/e4Y00I0sM7j5cZzq7tX2jjMQJViw6piTwKpi6CNrEwkhuhHPReXKtNxWpYC/bny5o60DCs9NGv1yZQarILZR83wVk6ofW43ROU0K9aaoVEor3F9l0UKNUtk/dSpYlgX6ykHHiBowe9RcS/RxtlqsEDanNJuwgBj6Ly2qyQ78/7GHldq28dulF8gc92oLsqfJQsN95bQGVVn4dGdcsBhIXiVv0M7xxACl3P47CUT4KY0FjdhuocL7NeBbc6Dcs3i9U/qSeI6LwOB8+DFP+VwLulEY4DE7FN2L4MAIVJ7si0kHogoy0YcPDH3rTXmnAp24L7IDOzYaQblosQDEZkVbF35dyK9j5Obxlqz54igIl5xjKex+fwCfq5SvV6YdgX8XLJ1JwO1bTKYlCGbDIUzKNEpc15Ziw8YGk0/ve2YMlJ25Ci0vppXLQ0jZzhVanG8hBRGQUVXefQr8n2k6LXhnY9JG5LSt3BEw8HD5c8qm7ZfV8QGBdVaVVAxyqYXKVjpeCzp26YNCw1yuWlubtaiGcZ58E2LegOWC/5SpYTpJPARtTf2HDl4wG0yMiXtFzd9F4wSwujULWyKSvAOf9uSDTDy4Qx4GXPcFCc3wFHNOpp+J1MFlMjx0xMwKQ3lwniVufNq03ZqvEg/x++IhCDqQopmbNwknot05CxmKoal3m+jJLvKLZ/+rzSjiGh9rzznBrRvDp/QQt3y3rOor2X8RWNsBfDDFXXAaWJWl0xnkpUQiYiac03hPn7ZXGlxe2M+sSLWOF0lSVYe2aSqRvsQqezuJEcb32kRXY3eaXYjzSDh3lMziKFENI11H9e6WloLkXZyrPnaAWW/0Ggzxk00Valsj0dDr0wz0TBd0RLHHEFDgh1tyXwpAyn74ns9F5WOjWi8nuA+yAwdBIflGjuufNIqsYFuTJnGfhSwDWNnphPOg7BqFFClKmXGTs0zuaazs4lA9LjnKbROa3KJz5ye2kPl0nysGC4LznLjdaDU9RHzRDxYBKLb04Hu/4mSXiUNei7nrkSIkyYRr0X5OmSQZZyAYatiF17TEns4/wym3LDHmxpGJHaXjTabJLjwXm6My710pfdR8ZFTdPB586J48PFnqkM4+z9up5PofY5ot+yAAuIk2xO18FLLjryyD8UxvC1WgktSIF/pr/RjBri5YVZwU5HgdNHY5wxzBQFfC2zHNKPtjZZ8Hw3gXHzBzwEDgul0R79/Rx+bGgsZF36KYB05UbADg7LxYKWrqAofhp+lAsbSuFbPnML3wsZfd1OgG3wi3tdXn7orxgXg1bU9iqD1qGUTtBD/9IVmtuKae36r03agPJ7noTFXXSQDQBPrc7Vwl9auumpX7fm0K+Dnku3swRW1na2vkpXcF6iRHO3CF/icDZMd77WLVG584p+4kenxlQtVYzAEzNrLVFJbldPAUVkwQMtyi0nDCCsfeFKlPbExS6pIN5PlP89sZ50oOS+IzLciKvnQRB1wA9Bf0wbdIh1abB6rZBcpyjAL2onNU5Ugda6g3xKVub18fkzJ4HMpeuqxYIqFSMNjt5TQn08i2v1jN/N33ZP3ynFtt4r95O9DpYttuifIrgqBqIPrBLbVePNQCStbbTjUmkvds6caaJDPG8cDx8jUH+Us8bHSEJORbjFn9Px31DxXGjCSIeQqZeY5atrdgfdGUr1jpHC0R/4YKmckgGrz/3gwf8V55fNvtXZVewK6JevJgVPVTEKc9VX4mlAX5tgAGMT9YyfRKnvaH7oLVT/EUu0+pcSUIrIQ5WyZLjR/N0qNA6k/A2kvYR3nCl0wFqJZYPb0kfKc/cune6Wi+wUwx/3BIayBcyI0qIDe7mIpLkxkgNxpbVrS/+KmtZ5GIjR2hfB7m8qTYxSV3LRGKkajeO95rQprYBIep8i+FA9FYDL9Rhrt8rqL9E2IDFtaCnL9Pn8srf3KBk9zDK9V3lbvyBkisuwMrJ4VKaMAsDSnA20eKG0ETEvg3yAgNK69ToMF7TALTCjSolhnUAnghmk/xrVRgtjpJ8hKYN3BrHtHOgWEU0qKalzi3SpKOyMr4ZquhCZTUVfPJR011MNJ5zr4lF7f8hi8myLLCK6nsRUpNQjeNE6lzmBr2D6xUtgRzAUnt72rS5vL94X3SeWouum6VDqtXGNZ+Br93nTvz7/oth5vaiyqjmnuuQ/x3lnMTo92lWSK8v5XM2fxnZa2UOtpuLlf2ookUkLlUBCLWDcKKMY5sXp2mkcopUDO0cnLQOQYmgsGeCNl3dKepgJrkdMwy69lcJBtf10tbfr/yRfPNh2CEUpInUALrZ0ZVhZ7wipZjsdbTDYiorP18j3rTgadVsf23BfcnMeIqczbUa1RKlia5q43AiX+DAH9sq/2fqeBJGG3ZdkG9W7ZNqrc72PFjg3rhoYBKWkv8hZpp098oT6uWtGugJ9LttW9mfFToyuqBcGIhOCGnKO+n54deFPfJcZyYRXsorgQZYzeUwCgAYGBKY3MRrELIUUzBc2r65YRXaZSt2d6UZM59NMJyo9sFBd6DlJIrak75TRGcrBbSadk9dSULz7cLjVOcuUqY1PgtnkkUXNVJGH63EgnGzfk7aCvUzjuc842lheNxd/NFka7BPhKG4lcPj23YU+n42FaIhu6Snwx0niADCI0pptqyVGi+d5hhRXfBwp7q0KDhwgiU8yiM1I+iIicKAOLVSBQfwRbUUXOaQ2M2GpgTzXY0Iej6V7pdBTZ9/TM/b69VMvoKBo6rg/a2FGisskJBAai60dag9ZAdwxA5X6j1gL5LLNw9Kl2DrqTxqxyk/tODDzUuOiem96H0y+c5+3+p8t3xJ9Mc2T70HzMKHR7ToU+UDQwemT8rO7O9+H8wUQzf7Kch+ygDdegw0J8v6GwcopFOCbHvRCpNzR+IgII8F6yI9ceg/7XGNRewnMvD7MPaj2Sa+mYAEQc11RBsNVzWJgWq2cTm65SMbKg9+N77/TcCYX4pfg3spK8UynVcs/jdQgBtdyW0hiHaR9Glh2nDxTsILw3B61OJcDy0wmU8U7fbzkfiQzn22mmR1EJKp9b78UuRK2HI/EYvp84PXNlLBGQAWCJ2Q6jGIwcwSBFnw22VTXtmYjGBIYpnZUE7EZYPwsgce2pOR/JY/W1nllEYn7ivR6lnPXUMMDW30ip6kaqjpobMC0wADL1P/+b7cZqZcgFRiY/q5rmngUI5HFxWn+0bc6lEuQuEhHKAlT0Zcpj0t9xXLhfDlEJ3+d7Jp8NAiArX7ChiShXA+OW5zSaAM7R4SehZDrMp+OXH9XTXWmeN7jn+T5Sd1+D640IeLKNphlemm22JF8wj0WwYkXwyGSOIvYt11xvz8/MAsX0d2Qjrbx6R3h+rp8SwTbE94/WCgxVbKieK+Jp3VG0Bx0GtcdYtTPleCygpzne6P5lkDiBRipQ2AdlF7/Y21Wq1wvTroCfS7b9P7hLbTMZ+QqZTYvbcLOkQZfRNjtC0nD5YinOhkySMVI4XBf0aJJMBEnNx4Ul6AVDOW2tneNvHVv0HacymXhtjSChcKqYxWBRTjjc1qylI26kFILsH7Vp50REsTnU34lLYSYAGjkcuV+qijQwG0EZTulrL5xA6/Yp41n/Lfr6tRXnlAaEsyNEbrxGRKVOhx+054uGV7uxGWx2icvlcTYbbSgUC3O0gS13o536jvX7kR2iylRt78zRU4w4hgN2bCsRTCG8bUVkGkGHxmMCA2nwDrHh5nt4V0X0xw2BhsNOlz1N/TcCVFER685rFh6AOfy8HAJqQ6BwswQFR2AWrD51X0Vfw4GM+KfSt6jx0DpqzsfFdADWJMBjNqlkuRTDLd4zIopeV11Uosv5YP2VEmS0xEgxFSutty2w4+Ta5sZI8YzUPOD5L50VVXXuuNfpC7hmJeAYWQPNWVAOa/dM6aRsXnJdvTfy3nf3p75VOpVPwNIC7f30pVNUV4Ed6XpklTOsbpWdZamVJDVX7glGBN7XIRQi60TAOuK1Bp2GdN34PjWqlL0GLxnslEBYi0w851RFRwRJsoGvKtroao0eS7d3jhykVMt0niyEDOmdoZmOc4oJJANXNkDO1yjnB1b14vkrA0JyXJ24J3jv27ORtjdgXgu9nZzeucX5yc406BmJdau9309MyMo7vZjy6Fwh/E+k14bmuMnHFv07R52qkgfBs8OVIdov7t/9af7LwhDTHxxtb6wWA0RcIMICmYlKWxS1gvI6L99LKwgDxxAR0TBSPEJV9PkYDsIprb6avon4OjpHfqsLSORxiXnlYS8kw+FXz6wCZETvaHsbUhFRjNlI05F2J+87uMaOa0fdsX6Oci2bAVmkMU3X2z+g7eD8W65AVejRiTksrqPBd8TrvaoA13KfMP7UZ/fUcfm9tCeco7DXkq8IyfcPijRfeE7jvqf10xg0hffyOQDqRP8IAiNjf7i2WmSS5IphYGfGrqHxcK1Sx4IoPY/VX/NvPdFgFG64alftsu0K+LlsOznN3pLDjYgXs4oYGRFRf2tarNAZ4kVkPFpKE3NT5Es6hnKjkhsvjsE5VVbZwWbuN71OSWKhv0018ZTceEjdc+e6GoFYjDn1BR1rN0ZNFTXK3xKVEe4iclUB04iSsbCgc9JsEq0fjGA24NfPQlK3kf6DGwobTO5kdkriClg0/ZBFwnMTz5LnAT6zLBoHkcAC+GkcbW90KvWPKetStHT1nKaO9hCNw34yRRxtEcPI1Mbb8jE8FzR9WP0s/WH+8fZGq76bOktzeJhBCZVemEVZxbVLfBUricgWgsoxlwbkljf1SoQIjYBCeKZxNHaOmk2F8SOc8wZAEj9GOr+NFTzKcW6vs5N7HY4zrBpYsraPXdsBCPN9B9YFNRMDp1ItjmgW2dSVTKZBbIQwutLKSmwhKRYrRceV3ha0swfK+7i9NnfEqWce0nn5/UNhfwnCuDES+bL64nRMEmUWYMEAIEl/raHuPjI7YODssBkgoeUYytae9DqtQe4hySBXYNO2o+ZkSw2AoXIvaO+nCCaKbbYaYME0UBbAl2w2mc6zvXlt7s9YaxBgMfX2kH2b1jcEHWX/TR+pP9QVI11itso1UzEqxkgDVI5D52l1z17whgOvxPbVexRKQJiovGc8ZkzV3Nxop8pDCpAVz4fTUeBZcmWrUgi27IWr5cl0GskMkVpLXhVD0NoXMpWMHWN8ZhlIREdOzmPvqb++1hpaxpxXGj/e0fjYxYIzVmVGZJjU9q5a84MGuflcVmpRmerFYvB2F06o4GM/ObVI9CeP8UUhDgQSSVchLcAb+5xkgMBERCS6cs5N97CmgynXF7z1WD2KiPwpShPwPAebaN1pBqZi+jUKVJVHMFBhVWX051s9h+WanuZqAEFsHm93R2QSIGvtXgJf5BouwJvViZhjyFprPA0QIJKAmk/ApBLgTucaMUguU54aT+2zJ9SoEvIxnzt/tUKGl9f2KL5bHDRX7EhfTbeSzdIT5TYmBvj2JjDg47SXqVRu8cz5SlSKm6/YZS/mdlXV6wVpV8DPJdvdr3kptV1aCGs+zwUYDRhV3txMEfzNsj5D6Jxid+R/L0R3XLA34KKNRg4yjNtq2xvtlB99XAeHWL+GtSLkINp7dXCFSGgDSGBJRB7cfYHcQ8pNPFzrqI3cHNPiur1VGlCsHdE8C1GBMwHm3EhRFNhseZzxQKQW7SO1eU9pcUijNVcjCQAopEtBIcFCs8BNny0jlag09LQ2gFPzF8Vis2i0JcCMedXQ2jsnWoepoN0nEXB4Hn4zTmNVEXDRd3pWOoKZItw3ZiMDWRFz9Zfi2/n468JAUU5Eo50GYWCunmHjpxwW+1MYeSwcjVVLm1tralF3g1MRhWAMRrWjI+pO4V73OPbpXMgM8kNUIvX97dJIa04G5aibKaE4JWKk5mwk16HBNf+WnyFqn/B9aAUQhroMXWKAF6mmQrfn7EGxHuzp+bTGVC/hhJ88Ns3PNaTbcNRUASPSARomgw9TKfO4783rZ4t6bOcD9dfAiESAvNdsE6LpWW4fqEfYiaZ7r6qUiDHwc0DHenvkaX24UlFqL6LRmc6PlYHS+GU1SD0GvjbB8hEMF6l34zGWEqJiEMn1iZ8VCjEz2I8MN1kZx8VUUalSaccCmLiN+35nhTa+DwrIOBk1I8DQmFGMyUGOJ6oxEk3Mq+idmZqZf5u26vWdcs/mKLV8HjgGLoUtrwkDFvn7rZ4HmnmnARnF3solmwFAFnta6Brq7p7r4FbDVX/ENSmwelQpvBZTu/rbiuBsPl/b6DRxYftwQE0zUDwN1zozvYxbvjd4iMW6xS3VYPhFeZDTzyZ/bhx1d0DUV7SGgyzoqLOzjAFX6UR7R/Fov2qvcuCViHTKk3PqnSdYc1efSeM2dB2zthx/h4By4zSuJ+c8PyuDTTS2exo4F4exdp9Kle3TnBbfq3m4n/YGuI/zwGUgykgPxb1E3Nf+0ev5GsqDkh+ELGXZfyTqH76mABC2owpfAqfEvmZJK0Ctt2UcnI9lFVfSQB+RliMoUkLT9XXIKiMjYAqNr8s65zBW1MVfpO0q1euFaVfAzyXbwac31HKUqlLJRznKYgPobyYmBzjIHPkqIoW4aTsidz8Ui2VJr5Vqb9ohU/oT0MKq0RFMDrIsLVZxMu63txf657xxPMa5nWABtxrrwR3Wzxv2Wz12sQnxpoKMn+iToPT10tmNUhOnsedB7klSZ1GMuR9pfQ+EGyXjJ+kxKcYPd7MkcNg4On/4QIEF+T4X+fQWhbjsW+kM8GUbcwJz65VmwVGnI21innN1LTSa/Gag5v55oWmlzv3Q9dQf0pGn6ykAFpUGN33hKjor7lRsuHjPTFFrwVBIGg8INrFxEBqbPTL9zdFw4ClWqr9Ip1IZSKTZWu19oB6307m758q56M62NIL+U3MClXaeOzX0FubPpy+/Mf3jJqRiJeZiba2ZPvD4wMnliktSSwQrfyUQppFV2UbhtEt15YhGdqQVMGs6wdBpz1JKLq7f6dkiTVs+t+Y8UHM+qv0hJodBpsEqrYYhUKvo7LaOBUaeo3O0/3/fKftDfYvr+8uszizQDGm57dGkdQNGtUwJ4jWm3ejUCiKilajgpACLPl2jBKHE814/Uy/nHvY6xSyQa8NwfW3+jnWAVNU0wbSJ3pEfgtY8YhZfkcIBa/9x1CLEFX0ZTEEe97wyZHsIdrgQNUjSSadk+j9e33DQ0OruYIAIYlxp3uNaw/o9S+yYXEVNAL4yNVjuAxjZbs9G2gB7sbjGdN+VYHXkcaMelsGMMAowEAFDwkgFUuxqOGZmyMLcjq1ilGBqYly1+ndiPrH+So15iQ63BMD2nkpBLbUXjvncc0flfc32iXzvJeNBlhU3Utz6h0HUVxbrGKZ9R68Ftp3owM4KXaN0r3htk/sGi8vnITSO3BZ1wkgdQ0QUV7CndV5X5IV71vR6z5OHbB7cS9egPVUX4iKIx6XglZNrBFBwreHAa43lUlQ3RbZQjNQ8A+laYo3NbB0Efiop9QX4tF5R3F+VgVqa3y9pz+F7Q2SAodBPw4wfZHrFqFL3cQ63RJo5JY9J1+dwzwuB4nqlwT2xZnielwCyTXZTfY++an/87b//9/9O3//930//6T/9J9put/QVX/EV9A/+wT+gV73qVfmYP/iDP6Dv/u7vpg9/+MO0v79Pjz/+OL3rXe+i1Wp3EY4Xql0BP5ds2+sdhY41O+CPaY3DkuVF85yGgEZXomTKXF6HYASRG30pJizEK2pl3onILAOPG4xF+c2Gp7xWVc6aqD0ZdPRTHLe5OY1TVR5zRiWOWhldb+e+OnEvlSMayBTiy0NMBiFWQuENFJlARdcMkCBIkT4XziEMy29Dzs/P45IGI1/yqQ10KA0n6VyMgbrDlY54pGP8dp4zOuUwEh3a9zn3wxFj/EOgUqeIrCit8YxEY8FsVQlnrzEdmuJZ8kY86g2TiMgJ0ENpNyUj159eIKKCkaLGq+plMgWTQRV8Zjz2/lYduHQhUncSTGeeiKgXZa6dMYdVCXlgeHE1weYcxM0jiH6TFiLtr3c6Cik+dinC30AlsbhqEjBQZ8cwc6BW9rpIEYHrzqk6Yg5J9k8h0qvYSEbapxCkZ8zIAtmI5jLcc3+SzdZQaLVGC48zICtHNEvUUTl1Z8wSQS2UQRv18LG/uVbaSNLRyGkv8LvtNU9uXOk9oTj/9FwVw3FP30MEk/eeTkLdN+w9tV3IwPQnW63JYqDVNdFgFDku5udhSyE6TeJLz7YthPoxBTOQg4USCwTktHGYT+5uUJX6unvlHnH2yN6ik5j3FphT3clkQzgMxBS6PUnL6hSBlOkai4pjKnWKbQn5Ls7HWCm/8m8oessFI4jmuarYbgygIegu53bnabhW18NhQXmiGRSZf6ydTnQI/XF6PgYzSAeEYAz3zjR7RfTjuRIgAlL9OIErSLQQ9364bqcg52Df0jt9ZjigUnvoWO61OjVRsW2sFHq0TVL3S3aT2w7UPtfre8bP+9q8xlmCw27AwAgyZpNDD88tUEcNpu/AO729pfWaivfUsJWIprnfX2sU+ICV6aZ/4LNMgKi09SsMryLoJZ5Z8DZDlce8FdW11BjyuOxzKrBMHuIpzRUEDqf/H/5/np5PY1RZW0pDJyLaPnKtHKM8hxJLB+DndCBa1/28bHMikzNGcttBgVDFb1O/Sm8qEMWq5sGLtEXSc+f59PECtde85jX0J//kn8ygzj/+x/+YXvva19L/+B//gx599FEax5Fe85rX0EMPPUS/9mu/Rs888wy98Y1vpBgjvfvd737hBrajXQE/l2yru1tqWZvASGEgMjQWZBSs4d9CBCWBCOvPzBRYhSh7T9uHDsyIAFFpdDVgvMXGVX+Xzxf0gtf0zISoaxZE30053UoIbz7w4H9N1zUiI8Q7/WIirsSAhTQERmkoSOo4AAM1QcDUmnuM7GOe8nSu9jP3yh9IR+5a+g2KbTJYI/OVseJaCIqVJSMlLlm+NYAHN45irsRI41oLj7aJaSDTH1pgHU06JLDJgSZJZgBVhA+L74wKHMqZFwNlZyRiGss2UPf0sT6nkbanIqQhRen2ZsdTHcMl1yuGSMEcQHZPjFrjR0RL/d1p7rsW+k7zo7kLtPdNyS4Kt6+R/8zd8pgU5W8euzV/9eRz5THDQP2feKj4qjkpn2WzTcLpaDCOUZXEJagKuHrmdLFCCFeO0pX2AjXncTFCyuk8HaSBNmm+DocP5O9UWXleW6XArmBrSDHtAfAtF7VT28q0hJdNDEAE4vgZyjk2jUVEP888DYcaLMsiq5Jpj0y/GCnAHEMnlxlAilFkrYGYhvOZU5W+UDhjPJ9V1ZUj2vvUiQKSCgenFpDIGlcLa3TqZy2AVXk9qCsnmxnBltfEjA7cz9J4rNRJbs3pSOQ1AMjPkgqnDMFqPSxLmJ2IiDA4s2oMFhAyv7SzJB0/BlVw7KFxFIyqixIgyoEbXC/S9Rb6Pfh6pzEVVe0KrR3RHzrB3qlgWVmkwavviGawicXJ5x8LlsgYyW8GUxdRjQvTO1rN9EDtR+9r+1JQVQVxzjbH5+bv1L83aIxOQvNLgG9Vx4SZ62KvwGPzvSoYXiIAJ9Ym1J0hMtITxSVm/asai+kRAKjlVImTXIGydY00d7+F1NkxaoAdNeZ4TVC6co1OW0KcryHyYNyWVdZCHkc5rkDrZ4cMiOXfSlZhYouqSlAsO7BUtZTnpwygFsFlOV6Yi61XNmSRGpnGXK06h02usdtAzXGvhMV5ng0PXxffld34PmjxbqsiGel3mojKIgDG+Lc3FsAwIq3byN9XrrtIefU66MZt6Dui/2vx1C+q9rmc6vX000/Txz/+cfqZn/kZ+rN/9s8SEdGP/uiP0nvf+176L//lv9Cjjz5KH/zgB+n3fu/36JOf/CQ99thjRET04z/+4/Sd3/md9I53vIOuX7++dIoXrF0BP5dsTpQHV3oENUNWGkl58bGjLKNwriy0uDkdigWsKMnu5t+q0s4rgz6KEYrWqdz6vDhe4OXB8vBFdCnlSqtzjlGxO/C6uYx7YZCKARU6IzBON4bFMrD97WmRxbQJF6bNRkUz5DUloE1VAbqlKbvqGkPUlQhEBJyfn0NdDwYXUUjwoNTJcNGgBrMvIRlpUbMM+iOohAMOZo7comCf5dDAvelOBmpVlFM45alCyvBgyUBpnz6ZjFxg2zghmh1aLZJLNL+PQUTKtQ4QM9JEuo3UUjiSFrMGSRRIKx1MBopUatN0w4YHStq7P5+fSVi3FFYNNcgWYnFEkVrUHEDKWN9qZgqAN2xoKWNzCMr4Ucy51leN82lsdbHt5nRQoo1O3Ht+ZgOMl53+grqOugZc4UfMvfOHhN7WAsM2ulLLhYiIHjUEhFUarr32O5lduGooNl69uzkYIKKWllOmI9S4lvKBMLQQNLUc01qIlP5TwcxI8w+dj9AQDTf3TGFU1f8FAiU451jzRDrXMvW0cE4XmIR4zuIrnJ6NDSLI9c7FSDQQUQN7Faciiv0XmUNERINKLQZHInWLDFnfh0UtplqTYtKzPhz00zmTWSPXcA5QNRhgZ70mcV04zrynia8HIwVt+q2+hhGEkOX7zXMX7wSvE4r5KJ3TzlNYeWUzZWdyodR4sxl1oQDUf0nrmtuRekJEilEUG6eDHd08V2rFQGLjqDntdSBMsMjZoVbjYCLzQX2/HI8Mx1cc0h6LAINRLa0FjR+ZEsPaiIqxwWlWCELJ++0cDddW2j7qOJCmbax8XkvnDIFEfpdVAMqrpQV/i3YiUZnqyMARAvcUo96LoX/edxQ7JmjQEY/hSl2ulgov9zQMDnivmWxGU/ZFulntXai8Bc+Sgq5I6TitU7CzMbAa9rvFoAARkTvlRdZggSodyvLvq7uxHtCgeS9SrNw0v1R1TBnM4H6N7v1wler1udIeeOAB+tIv/VL6+Z//efrzf/7P03q9pve97330yCOP0Cte8QoiIvqN3/gN+vIv//IM+hARvfrVr6bNZkMf+9jHipSwP852BfxcsvmzgXxjv3y8sasFR7JE9u3qVk1iYRRRdoX8j0l0TFpOtgOkSheeBa1Cj4KHYdTGM4+9oGeDEdW4yTBfcAJz5B6d/hDI3YUc4f0SjODSocV9FedqP1UXd6YQiXq7MhYR5Tza8aGb5SHslNyDXF+xQPtbCZlHfIU3KkOcLfd/PlAAzaX2VFYA0poz07mmkymDT86xztO48samkw5d2Kf9diQ3QBoCGFGrZzmqjQ5C0N+hJo/3esOUcysbfeUhcb8jd7o1QYTcTbpXTSVy28p7ZDi+RFRGl+T7JN8Li5KN91QK/aV7ElF4nH+D+gnC4I6Np+GoVcAh359Cj+kh0E3wRoTu3AYLlJHdegUAYjnu7ulI5OvPcpVSJPzxuTomdg1FWqA7s+g0sC7YYdg8KCrOwGuWWYUSpMXqOfnH+quAukIyep267m9AdbQHklbSPYhOisi234wmQMLrsjL45TBDqLNCUuPnZaYwoM4SijEPUV23dMSZiaqdLl3CnoiKd8QNQx5HMQYGZAVLCgXFucpeUfFNrEcoOI5NORyyEgwb53C/+HmgXhPqZETvVIoW9yXvparqFWIhUE1ESo9tFgTW14RsXo9aboeNIfw9f84OLwLRfaRmjKpIg1x7VEpaajyfpHOL5dybVGpdVqyTFcpklS5dcSlqR8gYioq089TEPVrew34SXa+lERd7sKFppsBeBBR4rzF+6yCVG4Ga4eaBmSo1d67tM6IUUDNYHsWagVWnoP+C5Q1jaBI4KG3OWAMIFBtw1IEX+V4yiI5pmjC+fLwArtwQqPvMsWJT+3Qv+utr8TsMXDkN0sLzYEYfprT7IewEYF3Q61EoUpDT2oEMsmFa+xVzVn5Ot9vhguF4HxUAuQISU2C1WNsrz5XQDnFaU1E+y9oUq211sq+mIfKeIlZX5OcmMSIMxvWjZvLU0v+iviYs6oJM8HC4NplC+fzJjlQaP86ZAtmyDbenoKfJkvp8q1AVog6yPZ8+iOjevTJ7Y71e03q9rHu71Jxz9KEPfYj+0l/6S3Tt2jXy3tMjjzxCH/jAB+jmzZtERPTkk0/SI488Uvzu1q1btFqt6Mknn3ze5/7Dtivg55Ktv7VHsa1rMRCRjoyLDaFWqvL8gelRjHsz06FGj/SABXBb3ZkXd7V59MEwBnVH6nejkSNvvIfjWgMN8j6wMa1KXDqidkGcWY4THefchfwe03DGQBTRM4TNhXSknp3+8XbJPJHXmJ1PBfzoGxTAaTYjmsII4HQTNPK5dc9AxAzAxnHvpkpHYUaWFyLZqL/ixpFaeEZKrPw8zTMU3Ws9qcmPKWkn51pMT/793nRdDVDB/f3zKaqzRHvnCBCmVPFvpIDeQiUtc1xyA1c6D5EIU3xEiSF2bNGQ5SgeMuUKfYTznppbB+TgWbITsX1gdnxbTM0i/T5jCpHvp7UM0/n8picHpYKxNKk/3+pNWRg141H6PYAFMUUJsSKYF8+A2XSoK8QaRTLdBUGx7tkJrO1vzuNf3xElZmVJe1iIp2pN5fNYfXo2AvtrN6fvngOwg3W9zgHoBCct3txXOkuWfo8yZDe9AorRyW2THgy+9xQ1I005udb8NwxrB6WzXJwcBwVQy/V0TI4c+iSsl7GuG3YswMrg//RdJeqJAM6q0cZ5oQmWUoiHcv4wkDWuS1Cp0CTyEztGsVPTOr+6Kxh/cN3jfqN0qQrQg+ZKcjg3ttdbVdERmQvj2lN7Vt9vWFPPSqcKjmgEjUJLz6qDammj4aNj+XUep2SdlKlC4p3G9GnvlM0k7wMDUkGxt5iJZzvERDTrHxoAHRERiXUK0ybdGBTAEkHLCgMnxRiOgNGLLMg+LAbUskAxgkOBJrYcOopy/LXy6Bl4EfMA5wrPCSnKfLJwnaLFBPwX34k5NiIrlY9J82OpRD2tG4o311UQT9q8KMngz4fFoBTRrOfkIR04rhu910L/Uo8vHyMzyO+em+ekGMltejPNMP+bwUVk4vH1LrByspBxoTckOhKsaqVvuAuIyJXK4PxsA65RAkH2N1K4tldNV/SSIYySCNtBs1rH5T0wfz+MO8EZCqTeiWIM6brUGLyf5iJ2v5swOzFOL8DuvGp2e+lLX1p8/qEf+iF6+9vfro57+9vfTj/8wz+82NdHP/pResUrXkFvfvOb6eGHH6b/+B//I+3v79M/+Sf/hF772tfSRz/6UXrJS15CRJQZarLFGM3v/7jaFfBzybZ69ozaxs5zzyJpzwGDRR5yO+X0KT2Fa7n/6m/9JEJYVDEYbQPTjA4j7ResJLcZNXEhbRxeivkpnYeGVs+eqaowRfWJa5MR3aFj5H129nPDyjG8eMp7Ji9PbhZWicYFITVrrESUGVn+aSjnLkCFbHQhGGEAECrHeghKkFGyeBggq6VQDDcgz70oHzzlquMcYMCmPRUsBMNRRXFyNHbjDRvwImfTp2VrDlfVFEkioialeikA8mhNHkQdpx9bYV8wntP/rQio+o18t4qopbgOb1R/gecURcnu/A4BG8SleR7A0B1lWtnRmmLjsj5Tbgksau+LZ4lV4ohofAiASzBE2AFF7a3YeQWGIjhw8iW3tQirYZh06BDEFCFVIN78b2YCIPuA58fm1g11Hm4jgwSi/8Jpl9MDq3rtyGnl61V6IEy1h7VLzmMXUhS+YuTJ9wwFfN1wgQoQ7LAp8X2vjdwBI5GGoSvYXKFjNpFmnrneKEsdZLkmHo/N+LGuAY8pziueUVMRvydKa7rBMM1937TZQswGRHC+EY5cdI5i4xRLan0nMQLE11hSPvRRCVtjGkjWOkNR0bOgAQoQlleC/VQyC/JcwKk+RmrPRi1qLRhFDKCggLgz1k/FDF4tO1ONCJg0hmZNgGU3GqAR3kd+X7X+irgf3lXu2fTjVuiMmelZFT2Z/BH1unJnUbPDEacMQYPr8h4zOIPDbxxtH7qu07ElYJ4Ld9gOiNzL0Q7J90sMTdpQMrBisUkXhXdr40n3GcWd8R0nIz2ObcdRVGn1sKeGVUtBVUqE9y+Bwop90485ZSpfhir3Tbk4wdy/OH9mN2qmStg7MHVsauPMLe1Hzd3Zr8Aj8zosAZY434ccwCF93dE51aElLK/Y51ZAGZobIo37bTUot15gyA4397X9i5UTjytAW+N0NUgE1FZjZrLm/qQtzX6IEp32FI/21DoShL3IgUBT22us73cvyhbJJBpcug8i+uQnP1lo6tTYPt/zPd9D3/Ed37HY5ctf/nL68Ic/TO9///vpzp07ud/3vve99KEPfYh+7ud+jn7gB36AHn30UfrIRz5S/PbOnTvU971iAv1xtivg55LNbfpMF68h0eEhcErE+pOp/0j5ZDpjBdQhIqI2RXMr1DfJKFBGlp+EG8uLgYXFe03bTKWG5QKNC3F/1JAf1tTeB0NPHMcU2+45GLQnike3ynNicGCfDfEa4DVrcKjF/VxHoSzhNdT4yfnWlZQgIqIhpeSo6HwCZYoxGukXKsVHpBZkmrmqaJDowJXy3nyu7TWvIq7tZnoGUoehuQ3RybFkHuF1EIm5ZQxhew2rp5R/bw4aw0kU6QHX0nyD6w7rhpq9ztSIyv9mIUN8L/neF6mQlRK88pmJZ7+YL77tFdOsSLvM1cbQeE4pMPjOdSVgGNaNSu3LkWwJ9iJzzmuHB6vENZtprnZ3SsDZbYeCZYHXRETUPnOqDeDtPOlY/NzfBfB0b0U0BgpwTfIe90fTRorpTFkXRup7oMaPUXHGyzK9skt0oqNhRBelhqf/Y6ogAxA4T9y5cE7blsKNlU4PYgBeilvfhRSQfiyMQyIiAhuwlrrkz0fNyIFm0tYlmy6NeYQ5EX3lt0ZpW8WExWgvGZHbZNzL/azQVJHvjhIcDkUaWR4w/51ZIrgt5uqHANiI/XVK2yPyA9zr9JuC5YsFEzYjbW7B/MEy5GlNbJBZY1S21BWrNABPB03xdx6HbJzahpo1hVBvRUukOdXX3WJaKa9F4hirshuRtl+iUxhtAQTNj1Xf6+n3tuPJP/abQQEIWeJHOniI+Q+DDiwhu45TfXG+WylP0MLBSoM6kkxXYceQc+SHqITFvWBJsTyVYn7zeGW1LJgrI6dMFe+cAK5lur7BotHg9PzPzOiDqcHzvL8FgK3sK05zp6aDJee2WmtCoPZuXai4+B6F0VetBnosoBOfd02vE87V3DvTv5XAJ+9HFcaSZIsieN/mtCY7ItHKcu0Wc0c9y5LlytdQHMJl5jHwKNn5bUv++r72U1iTSEhEuA410vYXZRry2HG8fNwuZk0gzWSW4DoHwVVgMpC7d6JMZ38stCqPpvmNmmBkgGwv9uaIFMPv+fRBRHT9+vULiSk/+OCD9OCDD+487vR0Iix4mAfeewppDX3lK19J73jHO+iJJ57IDKAPfvCDtF6vsw7QZ6NdAT+XbSenmZ9ZfQevl1ob0jnaJE0CdGD7RPVeycotCGJsR/Jn28K5cDKadC7SD45AGT5EGm9A5SpYdOO6M8RJkxFcqaJFROSHltq759XS47If7fQHcqfAkIIXqWOgTS6EtYoPmFoUo44YbMUCzWBBUzIjOL1JR20EO4WNIaz8kFgJpcCvBt0GSN2QlHqO+NZo4i2QpIpzNY5is9bimimK2ohNTbExAlELc7MQaqQ5RcmKxKDTgA5n2O80a0D0kx1M0Ndo72+JApE/gxQbmd/P51JoEzvEErwEqjnnufcWUwHmPNpH+yutZbSWG/lkBKvoFuffg4FSMJvGkdy1A/JPPl0cw1pW/Rc9nL9qnr2v+kdx8gbfG2aJqNKhWvMCQVQzGi0AsNwnMvgaR8ND11UKiUxr4VQvBH5CO62tvUiNaY+BocA6K5K5UEvlQ4zHGYwJY61RzhJrb90Ap0TeQz+xJ3RVxOXIO9EE6iM4je8fs6OUJojZITpG3qiIKICOtFc0qOEV9qY1sVL2uGhWyh9RoZ+A7BUWrS/WQXFPw0KacGy8WkfK8yeWCC6Bad5gSl5xzLqZwGlwEJgtKdOu9bgcNVBEQYk4c1qU0uEJah40UPI9ej03JJjvt7Yl3WzDJHIM81/2H65Pz6M93l1WuD8sB79moWU5lh2VRrl1JwN1pxrgwn5wz8v6WcAclOu5So3MnfKJFrwt73VQB7TLYqX72LYUEbfBV9fCVGWVNd5v8b0c45SCiTaZJM4xq6wSwFwCpbonk16Goc1DZDBiRIut1+uDvMe8BsB9383bpul9HHSFWW6jLKSgtGkacnDDFbOTRe6N/nUVXvjYR73fWLinEgcPk21eqUo1dcQonm1bcdGM6YcwCAYppF0mbUC5xhmVH+MKC0YIu4n3ftxb0txCEeVy7M2kBYgBawbFaX6W6JOMB92i7U5E5E+SnWWAPGofx8tudZBcHtPU2ERtO7FtkWErrtEfT/agqgRMRG68Enf+XGmvfOUr6datW/TGN76R/v7f//u0v79PP/3TP02f+MQn6DWveQ0REX3Lt3wLfdmXfRm94Q1voHe+85307LPP0tve9jZ605ve9Fmr6EV0BfxcuoVHblFoljV+/DPggElH+54GBYiIVsmY86JKiYoeb3uKh3Bu2c9alKtGpkoIWqCMgO1xtiFXSYuSy1cEZTs3TEaG0m6RYroJLEAn142RAgBSuIGObNyLQchobIHsg4hr2O+Uwy2jTcPBQRofOKGJIWDp9eS+U9QTjU3WSJDOiiqRuxkMfR1B3d2VY1wpp8v9NGejYhZw9FiOpbtTRrj8ySan5cnvinMxWLbSLBeVu4/G23OnVcYaEQldgXIMrh/JbQeduy+T2pltsl/RCBC6QUrzxHJUDfFEswXSUV/J3GOnGRjqLPS+EVWjsPkhUGg9tYePFd+zwSidwOYWiDuHoIETYI1wFF0KShMRub1Osa62t8r7uveUNn7k8xlT9SUFMK9acmOkFsAyOVZO9UCNK9Yicg8IAWwYJwOqVfFFmZ6AGE8kpUMm5zSzDDBtsOG1eiEqy3NCsTED9ynYKEYKndICgrWJgQrfIzUiEoEujAL6vNdMhSKlJGkWIFjHDiYa9vI4Q4yTaH7n5LuHOliZ9t7ZIF4NqCWa1h6k+xfpESwujQyptLcgwNIKAGRcN9Qa6UGz8Ki4H8Zarks22wwFJaY9Bg2uW/Mcp6EAezh1C9+b7fWWXCBaPVf2JwGw/Ftk4jGZRwA5/hj3KD02mSY1HMrzaC0R3ItlOth8v+BZslYfpLJKRtRw0FFzNmjNBQsshucRGq3tUmuWoPquanhW+q7p8KuTxSRwuwDQcTVBOIYd7SBTvrECKbNuitRoycJcBvQsVmb+7aoeWCLSdnGx99a0MxiYFzZAYwQha/IN87kTYIHdh8aoyGYA9Rh3EY+f92Drui0QrmRBcpWGCqNIzlsELHbZml19HpgAmGQ7nyU2UW2eI6hTgE+RqPXkzyHYx9co1z1cw3v9LPH5UCVzw4WgxJ0VcHW4pzWDJGOLrwvn+RgmP20h+yLcTOLORnXocEGg/EXTos6MeF59vADtwQcfpA984AP0d/7O36Fv/MZvpL7v6c/8mT9D//pf/2v6yq/8SiIiapqGfuVXfoXe/OY309d//dfT/v4+Pf744/Sud73rBRnTRdsV8HPJ5o83lNnWtdxrXEhRcJi0scYLe+EkqXLcU3SpyDeVm56kly+UFa4dE5EWT2JhXxK/dUTkHIVrdd0ZNvYQLIjOaUomLIomei6rSUjDCx3RflQRPWk4cXoasm/4XEj3l411OJQgY1qc5W8RyAnrVi1I0pnMVb2wVHxOQ4AHKjYhNwRqV42u9pL1c2aAAHWZaDQAQkzvaAx2DNFknOOzxKjaXmdXOsiDtI0QN8Yp73oLKTDCQ44JsFLgU+qzVhVO/qZMIRQOJtWNDBqjikpJYyJTk7egV5PuQ4NVr/pynOPNQ2ru2Lph8QtvzR8COmydEkcm0I5BUdl5/DpdpLsHItR3tR6ZfLe4koplTDf3ztS8Mt81I9KmBwtjSO+OdNxlRTKJW1tVkxYb+/WYitJNc69G15/O6yl2XlUqY2dEOoGqRDQRDUoItvw7gxpKC2KMBiMA5n8ICvgp/s4Ok9J4mAxqpX1isPhU6oNVWh3ff8NCkc5bK9mvMH4fI1GlTPp0rsQMW6E3Nn1u79erW7kYJ6BbMZ3Suy7T5La4znv17FR6Xi5ogNFug+5vpQSg0yM+Dqn6WwP7w+q5gcLKVysIEs33rIU5zOCHBItxbbE0vKOlPQT9EBHRXqvSm6XO0P7T07NCfSKXtMIUy64AmdO9VgGi9HexPCt2zxBUFUlsed+xKlLifFf2i7GeSEZFCoqpQiCNp/66Zc/N/+4SkInMy7yGLwVosq1pr6vNuFChcAwK4C323do5+bqRYCJ/6z1REzTIzP+X4JQBdKuAKa5RfC6ruhuC6QrMNQKJMjWS08wMoXzXGxV3JYM5PcMak6ocmwH6E5XXWmFVmho/Sy2zrXGeJxsZ7U/R3HZKv6xlIURMfYZxKRZ2JdVLga/OabYN2up7LTkMsBQaSQkgRHZ710xMJpj/ZqAX7Zo0ts+n5qJeu59PHy9U++qv/mr6t//23y4e87KXvYze//73v3CDeB7tCvi5ZAt7KwpNKi+uqH7s4C0YApzzqkrITp+RbVH8tPXT2y9SJ+RCXESMlYZQs5znTpOjZAmz4bGWkFxsfDX9YeqAB6J+SbHC0MhH8KYmwTJJWZYaIitgLpz3WshU/j3lBntIR+FzKYdbOme1Z8WOvoyYIztmOyymKXCrbYyY5y6fjwtTuWl3WD7LNjnChXhzgBS3zajLOleAFKzK5AJRuF2yt5DVFDudfy9brv6GaRZhRW7Vkt8CCCWNAjZyjEjJ1Ld4b2qgbUXcuXjXL5JJI4dVo2Gnea8MOmnQcPWrB4EVxOtIIToNbIx+zMY9t/a5EqzhEuAqvSnGxZQ8ogkMUEwFywmyqho0jcFm0Aw5ZLlwGmXTzyAzAkbNiU6zKcprS1EXKzi8ZFvxo8SS2imypxwaeT9aT83ZaINXeBo8ZtTsAFVhqLedXDIcGsUS6RqlGVQIIfM/0fGNqS90YHv9vqj0DwaEJOCtAKrp/klgvgbGK8drV6SPjfPK+6zWNyleez7Q5qF95Ry3Weum/oxd0HNMadGuWW8HUt86Xw845f71dUvmU3Y+YYjDUUN+E1R6cQEapdQ5ZLkweCKZs/4+AHGJoSN1fZx0hMXcQnHnZjMqPaDtNZ1mVGOa4HwvWH2NIz+GrCe11NR+7JxhV9nVg7AE+DSQOmOSiKZgGs5Pg0WoxGfHkVbPGMUQpLgzp8EZVQCnPuob3SwQboMFRZDFAiYXRNfzPo5gKDv8C+CMG+M0r9FmYdvcYOnlY5zbuTY7Zhgr/U1j0zDWSiW2be0/uFbz+r3ARspps/uYG5oCmDK4VCtGIr+XRUwqQSfrWCIiszoY2uAcPTd03opxulFVKuN7v8R882PUQI+l5UPGWt01i0EQomk9USwsaS/y/Mag8HlPrhmJTsFukunOSb7DSvXy40Z9d9Wu2mXbFfBzyRY7n6ObEYUW07vebEGARVBpWYwPI8PZqKnR2InIn/RTmoT8XhgZfoHO2Xiv0jxUiWg4fzlA20Cbxt5M2kNYvUIuhPucfqGNK5WLiw4hG3UyJUAu4kWKGyyWjSMa4Tsjz105JRwEQVBKRkIqm5Zj0QjxbJRz75yiLPsCKEsbNubop41khRRSySYJkUL3gKHxw3oAgu5szAF0HHEMOW8bK684R/4MNjqMqt0dNNVYgmmpGlB4oMx/9ffOKB6sdUQrzA9TVuwoxpvu/SD1PsAI6O5N1yifaQFEyLmNhk6MSkQ2iE2bK7DVIqvVihzcV+tUpJB/W5Y1RyAu6lLSyMpLc8RMfVMR6vKjJVAu3y0GglXkLXY03jjQUX1ZaYfHhcASg8AycotCnmz013TA5NcXiQaJMfBzqEYqMSXiQIKNlCpB2YCaOxdrC6aPxKgroyhGqJFmlPpCd0YJm27GRW2gvD6iYx2nsVnsv/zbSmpoZuJJgAnfkcSWkcDkcDjf09Xp/BxchH2ka4rUaSLYfxJwr6Lwoz33ikP2O1o9u9EAteOx2MzBfBg6BBXWqMW60NFz+LvTxxQCzWSnUfpNmMB7pUUmz532JXCgeW3tpeNZS2uR34t/S4FrZPe40dG4XnbKibQjzfcBbZ1i/HFikblKSrjUuBtBRNbUshlAb+SI1yNjwMg8RV91iPp34pS8DinmbtNM+/Fp6SjKEu0c0FEgsBWoQOauISaMRQnmsej0oAbSx4OolJnZt8guyfvl7nmgnkvgeTtWj4mN07ZOLTihNNKMIIj13PA9L4SpDUAjjz9QbMFdK1iVaVyIS3JfkiWGa7/F+Kk1PKZZlnjINjCugb2hK4TjTOxwBEByxUi5fmFQ5NBAWXGcm1ShDY/zjR6XYlBq7dAi4M/PFRl7YTT7dyIVew4I6muIiPC+2Fuki9lou/q4akW7An4u2ZrjzcywrEVELHpuaj5R+NFx4PLFUk9FbcjcrwRUaqkFaFCs3U6mgtsOSqQzs5hkahVshs35VHlCGhU4hpyaZAh8uk0ZmcIcbY7WFM5EBe13G3AyV1oUr2hpI9ZRu7QJ34Nc30ECbdxHeUjOmRfzAMt40rZXAtzFfeU5hgYdOze4KRTjj+Q3o8qjZ9G4lTiPhzQjN0ai6wAOoEBzAjmsXPpF0UYiGm8cLDqYPP9VutZeN0Wm8DmdzePPmjEWpZ6ImqMZGFI6ATWqtHUdZxBJ7TplYMv+W75efJZswGBZyU0pxBhvHJF7DnTD0rwdvnCuPtA+8Wx5TNMQ3S6ZQhi929x+aDoNzBU3jIbBZRh4SyKeVUFlR+1n7qvUUGl0j/vX53PIZkwdjPL7XAlKpk5KoEj8VrFjnHKIC2CC32NkV92fwP44gJaIPM45Gm7sKbA1CxBL4fh7KGLu1LqLpYez4YmpXmcx60zlY5FZcLBaTnNI90CxFJ3BtKHymeR+kd3AkX0p01OptifZB919WzhZreHbQVffEaWJuYLguIb9hlmH+FsBRkTvqCWtB8XNi2puFnNIsecQVE3WqlUCXVU2NNZTVZlLgqyVVzY2jlyM1N0t594gKkfxPobsXpcA+O5MAHRwX5klFypjkY6yxz3VGLOsWpmrBGHkn1kiqjSymJ+Np+akVzZZBiZl+qoq2W1U9YIxtCn9ORwYVQwNUf2iq67RgIIE+9MaHa5r7UffBxqv1YV3a8Bm7r9glNgUtVrQomAgG6dRMgONBooCCp0xrm0EOOQ4/WbUjnoCFSVo3JzDnOjHnaxBrnKLLXRNXVMuNd9HrQcpNSn5nlrlxQenbPMiFZ3nDVRxC0lmwgzywnmLvmWq+pKXGKK2E+R9YJkL1JriZ7ugdePO+8lGqNjvRSAYA7St1+fExqLU6G+0TaEHSaR9sbDfZTsj/04yGTlQiDIfsaHYtTrALu53FhC30gkXshdejM3FuCgUf9E+rlrZroCfy7azs9kqwRKCNYBBVj7gxXsLRhFvwkUZXCP3tGtKzQA5qaUI2j4472fbqYyyPCc6osNIsa0I4y6UNQ+rhsLhijD5Whp4HGFDkUUiIn+9NIgwUsKaGK6RqSDCsJBAGzJ+zrdzhICbvK/p2fgeVfwrm7jTG/WFIGUUlgtBbUyyfGN+DhDF5rHHA0j1koyfVI5SMbxGLVraQKWUSKNyMPHZ5+pRuKmFqME/TNXpz7URLu9DZZF2Q5j+hsCePL7RDnS1YUCHr1mgAtVxoaEfgv5OBkuTgRFvItCXDFxwHkuh7kjjQavekVymVTiGHkAeCvp9c2M5bziKjo6D2w4atAMB6Oae8SwtujM2BlWR6WfowuCrlSPcMmBv0eyhVfO88XtnVAG0+sH1gVmJqlS7OK715IagnzezD6QDo1IdZ7Hs+YflRwYsVEqYc1pTRhm7lsZAOQ+JDAO4dbmCUNmfGCZXSMR3e5ieZZDvBd4/45qKMYhxq/Rp32ktNNF4T3LAJOFzqdRlwfD1pwON+21VTFj+1pqfCvhRQrP2hA2dK9OjjBa9ywUG8riA4UIEDEia3qnQOl0MoXXq37WUGFk9DN+5vFbJed7Z7xqm8/ntSAMUbmhElJz7RsZMrtyGz0ncj9A6akYNrGZ9kiX/sWn0noVgATulOD8P19oBNIDuiBpUEsRO19EYtqIFMMqGRRvkb4v/k8F8Se9VATpKOQBhG1yoyqBotRLq3FRlSqsPFcBM/xfzYAT7yO8SOaZ5bVDi7CGo61TgliO1ZhfrcrD7ZnAQWV1FqG/FOnEwruQTSBtOMUl43BJ0ORFZCxiYkq0f9LqOYA4ZQWEGpLZ1jR9qG/LHG5Uel9tGsj2haIX3u+1A47qJiFxwKhBmBYbVd0WRgnR9mAVB6X3A/VgM36V0Yax4Fr3XAtVX7ao9j3YF/Fy2BcE9Q7X5rOQOjACxMDYp6oMLgt9MjlfBRMCXvGsno7coESwWhz2xQCPV23vt7CKY47Txk1kdMm0JKKfRT9FmZEs0xiLVHMNiPARypyASC4AaG07FvZGGsXwOlsG8oCnDABkauz6V142HUHFMgAFZnBYW8eycSsAP87bPtss01yxkCEAc04ExmtGXqV5uDIrxwyk3hROF4F8/kIfn6+5Cjjc7QAYbSRl5PRzTtjO1OJ9ARDBT1ToTfGo9EVa1E43TmDCFyBLOQ6eOtW+K8UuDQ75zOK9bI/9est2YbXEX2GMM6iGFW87nriP38HVdZSKtNcPtWaOpKANPRBQCNRC5wjnRnkzXghR3d94rY82fwzi3vX6WBSBrby8uBIp7HQUVVdPPCdkSfL1SrLlVugnJYZPvX0GpF/+0bH30UwwARBnPaU6oqL7UYFu1UylviBQyoFaC2VonRLFXgA2SwQI0eH3UzAJoZqqXBdxhlb7txPpSDLriPUr/x+2ABT7FnAwIMqdUrfFQroPiPi2kY8XWUQATpwAxKmAdi0erdClZPME7ao/7LPCfx3tqpDLjbQy6QhVWYMzfAwByETHypo9Kg6qsZJn0g6CEfGwmvZoBAMZW6LT5noE4YBakudgKxg9q8rDGT6EDJNKF5fdYNSz2QWnkSGCJ5z6Wh2dgRH0v9kBP3g44WOLjSsB3JCzBp2yoiq5j9FpIGwG/5rRX1fuK42uOYIwTKBVsps7UeWVNMNKD0HYMOyonlmn5BtCgNHJ0cLSW5lu7v3nszuky7JyiKN4LjynIzqlxKc0xTltFYAn3cNLPxkWqMqTy+Ym0XlAIkybeIQZIhd3EFR1hvWC7tntmth8Uq5JtKrn/CPCmCMigvtq6W2bWpKCsO7fniQpsyjk5BopdWxWUXloKXQg7NX4iU5kMllcNBC7GgALN4nNmrlv6pQtV54iI4h4zfqD/zlPYxWJ6sbVAOzNVLtTHVSvaFfBzyRavH1JsKpteLfVLfM9ggYeXnxduqRWCx8TGqapehSiY2Mci6s+kDbE4JxjZnkjTSVkDp2Yk8LlDUL8NlmgnUnCd06lxuInuMkSMCMM8CF0Jqthc7k2ghj8CQIGDAQhKibH6ynnd2WY+N393jptLUKCOuZHVoom4qUm9KZ8qnoAR40mDXEpM3GuHx42lAPQ8BtiYnFMGohv31DFK50n6iCcTeKHy0LfD5Cj2dXG79k5iIiHri09zbQbxlKPK9NpWO/nTHxYYP87pOVgYDpyCgwZdOsaI1hZdnWyITkA3jCuryXlrgIFqjUKW2QImqpwca4JiNTPBC8/GMT4P/h4NIPk5jQuFZhlUbc7rQt08v5A5ZTVL2FdFMIvIt9lNvkZLw2w+lyffGylbLLJaRH/hxyFo5ohiziXHGp6bKgOf+iuaJbYt2RsM8pqaJkEbzzJVoCJWmfeopUC7qXchxigASxShd/2oUvJIABPx1vTuICjAwNwAaTKyClZoPY2NVylVvLZKB0w5S43bCeCE7OQiWOA0E0k5NKQ0BGW6Vndsz9HtYUfdfQO4Eu8Ol1zHql4MsIxCW0Pr7XB6kBy6M4+33lfJ8CHS7z0RqSqGbUVYWdo+0TnyjVMgNr9vVnXS/LHzWusDAQv+iOt/Sscqv4Nx7nVaF0a+E3bW43TcEBTw42S1Lcuuohm0kes/Aol0qtOql9hF5Riifq/Fu5UDZngMg47YX1F4Yeo7AhvZJ5Z8ocuIt36vKe8PGaydCiOegrFfQoue1PMtnm1FvNqFMO175wZQxd2k60JQp7FsNbTBkwRAPaVKXgQ+k2hWLVZN2T6pH7T1Cza+s0WtLQYZ+hBdo+0hTJHlceNvndM2AFblPVyTg8IlJvsNfQ+20w2dyNwP/xZ9OOeIPs/Ena9SvV6YdgX8XLK5080c4Kmk4agmc8SZXotOOdPOpeOwxYUlKqez0PgZjM2zGB+cE5xf5QzLsctogIr6Borekz8rFyUZxRyTg2qKol4/Kj6qNIS8AIrfFo7FwmLonEp7KzaSBL6oNJYUaY5HkFJVlJGvMBq4f0l9bhHI6HUJ8D3dn6LI1gA45aQ7I2qqDVnN0IlEyOytOMJmmg9Y+zin3DDqjVvO+UQrVmNv/eRALonuMcBQAX6Kd6smsCtBVQlcLkVa2sYopys+cwQTl1tO28OokKKMt+SAecbvutR5UeBlXFOA+eOAkcJReWXgHqzV/FYpIAdrU4Awn4vTg2Adi9SRi1ELl8tnmZxunAeOHdMi2o8nTs9Svqsi5W2xvGdwZm49/rtWOS7uAeMHnPRxr9VVqdJ4IlQaKlrvtINmiDgTaac/dk0BkEwHkWpLoFWOtivWQjKCLf0n/ifPuUpgoAAyKqkvXjBkCxFZsWbqKiukxJflCPj4sG877qoCkDTOgxbbnv6QTl3oSyGzxhvPF7rhlBKcq0YAR63DkdR7Ly+85qD7baBxr6H102WwowjgMJCivG9mVAiQ6KACiIgfF6XirVRPHsOqmdhlokltpi5h/vWUTrB9BAMh7DXk+mCkIqZjJXsF710oWUtE+p2f/6CZHKoh42RXOXe2IfB9bhoiI/2oPH/qDkXRDfHk2npXShOI90PulzuKF+Ax+Vz4PtcA4gi/bVyVkbk4hONzLaBcS+fBd7BrNPvK0kBE4FDOf2aQG0B97BpVTKTQnuN3VNVRSGtsLa2vOFgMTu4t0uYy9IdqZdKJaGZ8YyEc65xE5VyJcbqnFemBwsfBY7qG3AZ/h1pECeDE6lnO6XHhXByjtiFlQILvAcqBEBF1rdZxkreYAUDLpq/dx6t21S7RroCfy7YYNODDbV2JBox6gXYejZhEyZRf4gbsje/lArKAvMeuUWkTyqlatbqqChveZ7bhTUQ0rjy1Fi1a0nf5XFtt2C+KL6djpkGIsUnjaq+egxy9N9KihDFV0+ZIY/f3kI00X3tgsKhibC7l48aDdVUnZDpx5YcVo7IwEGMk3+8rx4XZNFIvRjnfp2cUHrpZ9l0REJeaS0Q0UXMPFvLBiaZrxHkqN+T0rJSA6Rin578gnB73bQeT76tMF6mWc6/ZyVKzAOZTbJzWqxFLQf4bAsPcDwqGQkWguG4oeNDgyaKllfHmDpajHZwWZ5X7VmwDTKHrGm2cSCmR9Bg8VNWLq8nwQedUAiJjTh2Be50YkTItBFP7HB8j1rcixWeJYeJjPnfuX9ybwJeC9yYZ5Qq0Bh2J5nyo0/2lxgLOMe+N6jvwc2b8IJNz0+t3Ak/NqSHyu41e8y2n1m/6RackA39Au/BG4QA8JlfQrIHsCxo+rmlUcKNwarkyHujM5FQCXCLkc3PTGqXS79LnRlQbGxHQ904zKHArYL8agYAYFZiltEWcDYLlf7JuEqY/xYlht71VrjUyNZHfNY9Mk3RrZHoXMqn4t/K+BpGyKQFcFNZuzgeKDlnMpJpit7HpAHuhXBvcEKbniUCfAZCZzMFdjIRK1aTYNfq5e+uYSn80z//YoNC7FjbH33IBiYj9j4a9hS0Y7JRCgqD+U9YfqjYGFw3mi9VikfPnJvmAaLOK5d6qUohWrd7/a6mkaNtZYNPykjudU+xLFtOKiChGN4MgssmxsilqgAXTWBYGk2ybsrjI7j2q+p1c+2s2MK/rCFAbfSuwleeHsDWVNEDTUMAqr2qe2yBK7C6g29U1CqwnmdLI8w3t1SYxBCvBKiJhSxggc1w0Xl6ELVLVv7pUH1etaFfAzyVbvH6UU72qwqYLm1pGq5V4WPrHUj6itehKsTqRtqM2See00XKRF4Ij2TtorESkK77IFJ4EHqhNrXHUPH2//B0CA3w/a6U47wv9E0yf8q5klGBLBjqmM83RSaS2ilQvBkTQOE+bXQGYqH56itchhUr209mAVAYPVElfcbOHgdxoRDC5tK3oU83h1WqZ8UAiyogUd+c0iIef20YDcfJS+DrwPRjG6T8U6pbpPFxRqQKoydTJmhBj8X3NEbfKKSvjR/eNxhcz59S7KjWYiMgNHfln4R1JkUkv3jkF0G235PCdBMedHSKMCE6GOWgKgCPhT7eLeguc1qoEDlNEDcULJdDQbCsgOgPRwqlEJ5pZhZINJZ26gtVl2FG6zLUYF6ebIHDI7zzmlMjqVq2fhNXxt3ntF98rhsKWQlM65AgouOREYPoROc0WQsclrloFohTMGgZ2lN4C2aKT8jVKayZuNzn94kCipBhR5vHam5WM0CtHznCaC+2RxAKTujRE8/uoUpUEWOA3I437rQIUGCSR74XS22kdRdR2qbIZADzpvJ4/mLox6HslARle31tYS8e1p9A4BbZKxk/DYsKQ6sVrQyN0vYrS7kTUpnkps498sU7Kf+PN146QTP3i9xjTHLt7tnhscY+8m/bySupkMWcsMGXBiZvGlvYWZL8aIq9qX/Je7+PRGA9q2rSe4qpVILC0F7PGjdJs5P1KfIfp35VqqPnvw4K96EkDP0Wab8zXUIwrMAgMNqRKL48aFE99jiiaL4/ZDhqsUeumwXRKnxXIvCMFkIjKNCo5DtlPmgPqWcrsLRYx3+iAAREVPokJeBEVa6UEseTabmon1e4R0WzLY9DLa1YuHpdBIXy/OFNcANKYhRA8KT1DXfWR2Ugwt7d+d8DsYKXGXuqzpr8pFuE4vTv4DBoBarMtQXquft6lLUV9r55XH1etaFfAz2XbOBJrdqjoEjuHlUWOiMixQCoYyt6IcqiIx3aYouxiQ5RLVnNX6IAYUeV4o0TALfFfRXtk2rtI48rMCj7v0YpcP5J/GkSAZTeP3kpjBM2cGJejESSccvmdBIEk4wf7YsBANouCi5FsXizUwm1oIKjSoamvSsULHpeKVsux8z9rQuFIIYVqV9E7DSgkY60Qd8aIxHZLbthX38nmUq69ykHuBx3kWmDozMeIcVao67FrJ/AF76O8Rr5XSNfmPsV4FWDB73KNGi+fMerk4N+JSr0Rjv5cpFIcft5uyQ1rPQ/T8y7m3o60ACJSoFtOzTAiXPhO+A2mpwxa9kcCcbx+wdxkLQ23BsNJGqeVKlJ5TDKtoBKd8xIAMaoJEhF5hSMajkwhHpp+h+LLma1UF91w3k8Geo0hWDBfwDBcd1qAHhkK6d6rcsd9II/rCDKWVmQEHSQ1gyexTity46jKHBdpY5VqOXl/KxwieI+4jPyRSGmU+jmSHYdAosV6kHtncv4DxiuSw4GMFRkgHg478tugnQkGmcW7qDSX8LkZjUV/GwvwqpTW5hY7HSWXbBrWKtJASiC/DUa5+Pk4HhfqH/F6LZ3w1f2a8LDsWwDFknmFr3TnFTOrAH+TAHIAmhaPF9MfC5ssMbe0U5tYbpYQmGxKB8YOysQDuGeDUQnKoImoGVAIp2uQZvrRlEqrHOuLVF3kLuS8ukiql2TTSLDMEspdSOOqprpw21bGks7lN71eP3kMonqlOzeAOGx4jyqmqtsOhT4nkbYpuWpeMS5DbF6vsZFoHMmFBTuZTfkae16CeLBru9Nk28vn520JCQy8udVK29dyfdpoLSh1jGzSftg4itcPDTs5/V9KPGDAd4xa00dpibKuowGw2KMTY/A6aGRJA1igQz+o4HbxLnKgxKpjclXV66r9EbQr4OeSLe51FJuUSmBQ8YhoruJjNStdi+bo4qI46DBSDKEo51psLnJRwEoQnUFjXUo14q/4GOnkG0ZGriJRaVl/wtrMdyGybCRIo0X+Rjo1yBayot2y0tTBpJ2iq3Wk/lEvpmLoyJYjfKM4jxJzCzp9QfbH50GAhzcUZUyJz8mQVekXaa5IpoCDKlnuYK30jtwW9GVY5wTBk3WnI+yjNtJq4CIRZc0OZDPExpNHw5lKLQt2WJQWjbGLY2Uld6bTTgoDHgUkZWubusFFhsHBjechhvvlGhDjdG48Z2YlyPloGK4KQEDdnvQswWhVQpKkdUP80b4JGOV/sm4SaGzFvW4xRYdIAD9ovPHvFiwzZj5FQfVuikioAACxHxcNlp3xnitQlVMfQONnLO9HbJxK9eLUndJn1Wlw6LziO8LOL94z8qSrPBpgowo0SEeAmSzwDk4lip1ykIq1gftVzEFf/F/9joia42ltl4ytgoEixZ1V+kWjUzClA5Q0aLCyEt/n/ka9gmB3vzefJTMKimeFTs4YdZo1vg+Mx15AMNfSZFGpy3KZ4ApHcG9CN1WZ8sBCkGlX+b1EwWHWKBTXhVW9WFS6Eb/lCmNEVFQ6Q3AsOqLupPxOlo5nUAjf58xMUlotYj7tt9ScbKtpq8UzUHabxcKGj4nxpZgpMSo2JLJZLL2RQiy8sYH72E6sBT2vSpDEbPz1gj2X75W8frEmShBEAbIhLIJO4Sg55AhO83PAe14I48bJhrhAlN8MGOB3NcALWmwa3R/+1EodLJhn6Tv17rqpuAp+bxVPgcYspMKuwXnO4ExhQ4o5JgNpFmCDtq2lh1gDq9G+BTuWvCGezteKFW9Fi63BQq2MUzOuRy0fgdc0dMpXK+Y0Xx/OiS4xfhXjR4KsSd8Q37/GUazpyL5Im4toFz2/Pq5a2a6An0s2f7yp6pRl5wwXEgFGcDpUWJeLGRtAo8xLNYyK6MvoXeGMyT0E6aIWRRmrMq2dNmySloaTpS9VegfZzqlY7LPmAQIDQ9CsFmRsJHCiGhGvaR4RTZvHYvUgeyHNm8HWporLftDYZO2KYrPDTafvTUpv/vcOTQ7V5AbqBvKboVrOvTkTleOegxSiMVDT3ix/d4xlwtOGiS9CGPVGjhvVZkMOKpMX50pAW4fpc/dOrcPJRz0nmopBgJU+ilYzKrkZFWTmwTn9zlsGokp7q4ynEeNncEgZlem9XDJwY9SpBPfL+9hsrk/fYxrC2VbpNUndEiKaqoEsAZDc4P123k3zYgk0YiMYusrvSEESgX44pVE4SHH/DyGKKMEC/reKYCZmwXGZMiejiW4kokM9N9n4l+ubFoDW5dy1A5T+b6SPKGYKGrJbHYWXx7BDiiLRfrtPpoistIFrczSJzZMEoiGizE5KwXKtiUErnQdHuFkXBjanlOBtzMA9gA9ybocE3uA06NnJl2AJgk/6XqNzw2CBcgI7TyqNxdBdUk6m/MzOJuKbe56ac+2UF3eVGW+VstHyPEqgOafTCoBHCCNLEAjTLd1QAj1EVKSGZa0+WKdHTuc7rgMss5i3zQwu1kbUwbLmduVdcI2x3qk0PQNARQBB2hL8fhhisXHPK7ZZscZsbJvMug7FXjnjiVDZL5eqfRmnUyXZoQ/ZDwowF79NpeKtlDkikCPAdNdWp7uic18Dy5yPuQJubhew4aKY0zOYpm2JGJxOrZfjrxGVebxyHuCcZZDDYpJhU7qjrpRakP0RLVfcJdJVr3Cf2nizeh8RLbJfXD8a6dhgy/O7jNfknB3cK37riMYFUD5LFkA/Z+dT/xDsK+ZwZwdKKBjBjRd7u0r1ekHaFfBz2RYEBKlKavP/66X6WEsDN/juaHJ42zvCMTOi7vFor0xzkMCEpD5iJavGGxo8aNg21RxnqaCvyhG3fto0UfFRLFJsgLTHBssIwYJdYs/Y5IutqjIEvbnIY7h6Si3VCxdaGV3a2tHEHB2Qz8Y4BqnBVk49Ot/ZAapVmyAiCoHGg07l5TcGUyceQNWyraZJq02Pqf+4YfZhR1lzmtIEd+XRE9EIzIImM1/gQHnP+LlihLHnClid+k59LoxIMS75rFB4d9WQU2KnBuiHzjaLKi5pAQxJe6LyTiwKozun7zWAYllc1YpMWY409o9Og3jX4kEClQzx9di11RLruW/SzqPVtLAjOwhinsv7tLS8xOWqXjk1rFKNJKr0R/HMnaPmbFDPjJ1TeV4rXWsEoWgEFFi832LZNehEGXo4ShOhSFdkRpNhAFvzU97DBDhblPqp7/mfWnurVd9L56wYo6HREiHcV2g9pfVRCS1zA6dWssbCylNsnRIh7u4Ze7ZRiRDTyJTgdxaeRrZEVDolARiak+5SfZIz02fcQ1BgKi9eZVrQDFIOhzAXebwidSXgVpDu8yiE2RsB/Mh7osqy94OK3LaSAcQgHozdpf7xPkpG0bhuyMdogy5ExfyMK1yrjfUbyYyVkHNsDRZ2papdOXjxbxZINwEoA4cv1kF7jc3XYzGQYZxVRpp8p/G+coXO2rh4nUC2Z77wBbaocxPDFgE1Fnu+ti6OLY+JBsgG9mRNYNq6Dwp80oDmKE7oNlzYwnDumckkxyvZpLwOVsbnzhcAg5yWVFsHpZEF82wIRGBDmlXAakwVZH+Dzl3ASqV8TqJcrIRIB/XiqiW6BuPCOcGsTCNYpdjyRpBKMXKkb5Bc64too6qx1dhC3l/eN7pqV81oV8DPZdswzBYNIr78Uio9BbGw3J6cISXuaIgoOwJgYAxTmUKqLOLSsEYj22K+gMPkN32daSLBKyjJ7uLeNH5MixrEplbL246RnBIjvQC9Vm4QRbUvMCha7cAWLVViC3uY3pTuMS7u4rxZA6AGlHRzipS67r1ObfCjAOvYkAhGiXciO8qbm/dTNaiK9oCsCuPBmYz73U5mQbVyl1GpDDfHpVTG4lwY7Fh31KDAMYzNbVLkCYEG1t0oBoaOl45Gk4wwS0cNhS5HIypRiG2P5f9x7A7mhgQMm5RGho4c6zUVYtQ6ZaxK58dmsQt3MUycZgjKNSDWUnxWLdEQdPUpKS6ZrnfcL49p2emXzmiNft/uPkbJd0QiVe1I0uSZcKTKBVccAvl+pQolNQDEC+Mc1wY3RnUN6HwzI8vSr0LxbpV+MQSdFiKvief8RQ3PInqcHExMebTmHKZ48jGF8y0YQjI4go+g9fWqPDSDAQossNIoicA49+T6QC2IqXKfVTCJaJoHGDOqjFOlVI1RsW0aYJn522tqAMQrKtyxuDMINIfWE4Wozik/cVpcex8d9/R36QDvoY2TxifujUyzk/ekg7E1m9GoxDj/k+9fqMxPxaAr/pZSSjr7eQehC6PmimFXIZvbW0EFIiLnaNyHwMtFNGXkq5VsMdPBDPV1j0gAqIp1ZDBucO/KhSLEd7IfuXYolrfT66VcE2v6ivx7VaREMkX9FFSo6BsWzUjbU7NHsUkb83s3hLpcgBzGQupmfla4p47jxL4Et6LQRap1arCntMhxRYcnD3oh+NI22uYWdn8Gk/B587gwKFvoXDryJ0aFLeyDqAyA83fIRMLGe5ph4/vaePnj9X1yI7DD5PNm++y0XJuJaAomIyvd0pDEIFIYyI0LMiIvwuaCYaM9jz6uWtmugJ/Ltm0/O2G1CKZiPcyfuaS0h7QJjhwWRju899RMOgzS6ZCVimQNaeVwj+Oyc0qUyyyXY+c/SscXIo4pn1yJy1rCm0pBf1jM1eVxTU1SroVTF8TvLcALozZGCpbSEmEgbr2QR8wAChj/vGAXjhQaP2dbiqCvIyPubEQ0mGaVI5s6bz63YSR/Y08Zu7lq0rV6yfVJpLA0RpUmT05lQ8bPqPUNTtBaoWXnkZ9NhDSju6fT75SDLebYjalKWo0KPsgy9iiwyvc1VtLERJcqgukcEWrnCHadZ1DpDAS4K6VMJbDm+pHCXkvNGp4Zj1fOd2SQDY0CjPBZsiGqAKJO6xapqPmuUqyjMUYee6OBSXm+7GCCrkcGLyUZBVkoSXxeOmlYoj33Z3yNmi8SaMiMBjDg+ZmpqoXyHjZusapMwaaBoEKMUT0jVaI6/V5V9bLSxAxjWqcHzeOpgng+7RsL61GtFHsW7ZdCsLhvcYljcd+iADidHCNWgvKk1ygrsorPkitUoQ6YWHv8drpf+AyGxL5xQbCSrNxwReO3nRtcw2PjlT4QzrnQEA2QUiifLadXWdpnw0GbK3floQqHJuSAhB0QCYLNg+8cV0+LRSl1EVAS7iuykcg56g+BXSXYwzldC9g1vAZo1pX4Z+cpGkLLeVxyXzbSlnZW2mEQF4EG76mBIJoJVC5g99Xqo/z3Svoa0TxvcF+yGBBqr8psqErgwSpUwS2QTlsSmlhWMQ+imYWhQZ0yRalayp7KeWCxdJCxqSpZnttgWVwZukK4vY3RCJ7IsSWAHMG/pqFwoMEsOTaeB+qaeO2RfeIxh6DhiE3aEAgAxrgMYvAGi6AqN8tez+NMWQpKN9IAyKx999a1sm8sFGEE26vNChQuMX6Myoo85rhe6cpqMoDJzHwMzoyRFheDF2O7SvV6QdoV8HPZtuqIfFrocIPkl/ccUF65B55z+XA7AiUZESqy0Q/k1l2xmVdFUvG3rTeMf8PIrggcFj/DvPExTtXGjiB6JctAVqKocb+r5ifnZlT4Ka5lCUiIUSPnxSArhmE6p4eKRGbVIuySjTmxSZplO5HuLI0rHk4FTHPIrkIW1hir0W7prOjc/S15dL7uQyU2TkODMbhh1E5+TT+gOEaGbhM7x2I0mcwvcY/53Wrtczb3xeaK+f08p+X3sqQpPivRnCHAWQCyFeM894lzD9YPd7jWKYdc8lMaYheoXofPI6dXoD1h3H6MnNhMJ/F+tGmMp+X8iQf75ELQGlTimTdL1HSiUggWnGPPFQjFNUlHUoI9KgsjakBFrll8v5RDlUuwlg2rTrnOVwEb+SwdgMluGGi4fVh+hw76UDH+g7PfJ9Gi91obSD7b3nZeXUj3wmMEXILe6f9qjrXqe3RueH9DJtA8cDHfDM0O5TSI4xnYGfb1fkZUghhEJcDYnPQ0ItBKMzhU7N1OzydVEVT1wwC/ZqHgc8fmR2P9FJfCQSb13gxTRS98JyQQyqlZyCjiggCy8EQHYwjZsZWO4fxvmd61VoCvXsvkOBmUxqpJLYtOL7EWaiW6LcfQ2M+Ug1Z713AuhqCBSfRBR52CVrCOEfTmlvqN692OYrhWETFfSMHMadPyGPH+RaHXZAo5G6mmeHw4sFObVJCiKMJAk5g87kvWMzZSQ1XVqloKtzFXdqUlu0gUUVjLw9hrfZNx3eIzv6OqMh2znYu1HJ7HxmClFAFlAyznZgmAF9Vs03iQjczrGqwjBYg0BnKbvhpMKa4J7N+4tyKHQUfF0k17NmZoNI0uEoPMy84Q8y6OD+bvyGt7iIim6mg7Wmycqsh21a7a82lXwM9l22Y7G1E1QwEWUicYCI5zVmGTYEOw2DxQ4ydOwovF4i6dRAESqIWk8bmMcj6nijYFLfBpGDoO0gb8/rTR+mNY0KTxn4xkLZ436tQ4SNWJ+0nlXkY4pCMuf2+BcQjEycaUZcglnkuyw8J9gXLupnGAgMi21xWWpCPBKSXACsobisphL/UO3Bh0uohhgJlVPxAgNDQqiIgcgmLDQM7XK+FM44+aGlz0b1OpKYTp3i9VNQg8HtROSs9YOuGK3gv/J6IC/QBhV9lia1X0EHOVrxfnQBYHBwO0iJy6aS4i8OMMhqCKxoWyAgyVkVWieb1RAOBm0GsBAgqDwSK0GgKJIaVHYH/i2TIbwWP5+ZzWUoIgRf8G00hqK8j0BF3Vi0iJ51pgPL5/rGODaZBFFHByequ6TMAOKrppOi2oq/Rf0rEoHGkh6zuo7OqYDJDbY6+ugTSvTaqMcJrTRSl4lTZrOK5yXi45JUaJ7rLqXFoX8H2uXaOsRnVjRa4PRunuNPcks8KoaIMpH16ldE33TFduCzpIYaXMqdRQeS5mbJbHDOuG3BgV00mmZs2MNdSbimm8GiTKn8/TNUmNHwECtad1cLI5G6k5L++RBHlmMBXvTfr/wjoV/XRPVLEFTuWWumVY+S4EVYJdM/OSxtwu3RBrnI0z0kploCSdC/f2cQriWSmdeC7FTuAgnWTOwhzLqaNybOJdK0TureqxSxVlmYXdwxpoOfziOnI/Y9ABM35nZGDiosElY5wWk2pRb4+MvQbHU7HtKOlPOeg/DmKNSSxXtWfzGrsEeFmaMrV1FQGccdQ2sTyen0Mt5RztGpmyteomHbaaT7JgC7phWO6bKMs8qGuymOUW6wjta7k/10SYB5reGfy77D/Z1a4BJtYwqvSyF32LpOyx59XHVSvaFfBz2bYgtJo3BWshSS0bExd5gZVAo5HvKkWcOxFxQWpuIGVER1BfNMXtDOV73ODCuqH2Xtyt4E96DBPQsFDWnEig53KwYgwL6VgTeo/gjVh0+R6jo8UABCL/lmimErxL/UuAQx0zkBtKQ1ACcVlzBsE0/js6mPIe9j1F7xTQN4s5yogMbHDb3jAiL5gkO4wafFgQx8ZxEdEkXk6k9F/c4R65rV6uCjCQKeso9GdUPlFaQxY4JMWdDSc4fzQq2lnUXXUf09A1aARzcdUW4PH0ZdS/DXXHCX+383vv9LuqsuzicipN7gvWnSTu6foFqjT/U6Wepjks/ZfKeiwNsXAonRjxT4Pxo5oZ1cSTpXFtcF7J1ApHfjuoCObIDIwzmR4EzuNW6zU5ZNmMxjpJaY4ggI3l3buG/ACGsXwe6EhxC3Fan/GdkPeB+2kq63yov1v5/ZYFDZYAXGwLjlzW+EEAjUEMSMuT4s5uiEReO3vKKSVYb4k0K9Iadno+DTC/rFTBKuu3+N18jcxaQ9bRuHbU3Q+FBhyRKIlO83TW4tT6+wDpHQ3jJ1LEXKyxRWl3Q3A44vI/ln8nqjjXROqdkM/cbycAW80lPqSo0LYAOvAxuLfwWo1BrwuwYaP3ugKf/HtOodd/c9thrpyXvxPveJqHal3hdz0Y36kmvrd0evB7omkfxPVIromjDYC4iuizFPbNawOubynFuQC+MHVq3WUx8GrLKdbQv3OaxY/7XucVW69gstVEfUk/IyKisC9sQA4UwrvLAFWxZuJ+ucu+k7Yx2hjO6WdpaY0qfa70WaWYQyr6utM+CePW0jZWzLlGF40BYfasL4RB8nGs+3h5EBdEE9AXJJruxZLPY1VGzd9/fqV6uai1DZ9PH1etbFfAzyVbePQWhWYXqwEdvPllZbFSf14u0P1RKjsqmSeNoUofqEhdkmXbl5YqFxXJ1Iy6qAoPTF6RIAY4LjkiiMwamSrDmyJSfFuvSvhqIVvuT+Yai79DlKBom42dl4xtLPOB831FGqyM0FSuKbfG2ADlMBaE/qqsMjYq0QAtxBTj5MDi82XjbRdFVTE7wOhjRpalBYAi2QNc9zASLdDPc7oWVqAjSvO/4uwS1Q0nFha/JsZWGYK7yPtk0chR8LmgO3OECiNQFQFxYFK5TU90DEKFDRvtYpRo6PT9IhNj6oDsFqIBBhnHVR0CcTwwvJxzKk2QiGyDR91rThkV/SlBcfZO5Zfi36Hyb/4NEjSEQGNsrk/fGUAuEVG8cQTjLat6DUcrLdib+pK6KR5S3cKqzdW/8jHofHOkXIGvGmzA8bvzQa+T8r7yHFUaEnbqgww8ZGcL5jk7IIXGD6bP8hCE0S6H7gU7RVXg2eFAOWMuEc3zB4EReVxz1tNwuFLsGEtbRDnWRjqVYh0xGHgCrMr91igPj06oGgK1cj4x3qYqXUUKnaP2DHWFJHvL/m0WQhZgD6aGZpaTuFaZTtf09X0pOiqFhLH/9G8Vc+O1XwWcxE9XDTVDUHZIZvJY70E+SJcOv1RDhxiZBT5qoFC+g/zeGUGe2BhOvrwPtXTDVr/r0cO7tSvFpwiyGMLQeE3ymGCvF3kfVSlacm7HqS/cj5lRX6TetvoYPCcye/jPyELxUWvFINjoRBAon1NORPv68nnxt3Jv4X+qtSYBVXLO4DVy4FSuK96YY0SVwF0FmSGa5zfa9DyfFEgKzHUL1L4I9hGjZparxcF4v2vNKGyhUzCNfnAMRNPcVPdDjI39GRUo3AFGXbWrdsF2Bfxcsvm7Z+SbCiUzRyoWFpKbE8CAC1p3nESfz2YnQ0WPQ5hSf2QkVh4jKMnOKpG+Y4GLzmkqM49TLkKYc+sdmSJvMtrNTCdcLGPUiyqybFiEUDJihOGg2BCyta1G1+Vmw/cJ9y4WGERRXVmFwBI4JDKNMR1VO1cgVVHpjY+vpAepCJy8pnGcnFeVC28Ya+hsd60GldBJd9qA575URNRKKcFKDoYRqaKjJ+cpUrigY8P94PNIho2cezXRy2L+yxLSUmzXiuyh4yYdTH5f8Xc8XpzvIxiy3mtQh8v4ygiiEblbBBeJsnGvBSX97qjWjr/nPo38ftcPFLu6yHg1cs9zWNqiFSO/ZAhW+kMj0qAWF+zJyOMDI5yZlwugf/Se2vsbxSxgPfHCoUcbdTNSe1IOLChWZ+V5hKCvE6tO7a+U0LWzUqqMyLMCXKgE4yzGHRFlJ3RpjmZQTOwlZfUUOV4E+A1dOyMFywPgMM+f+vwe91pyMSp2jPkbDLCvGg02oYYNb7uHKJyqGUoKjHOk91Q5Lk6ZgXc+tA21Z+OiVklMLLNa1bKyJDswfrJo6fy934rjRTUsB8+t3YwKSCqO5/mJJlOaey2mzYvu3ZYWq78tsZ1do9OplFAP13dHcNQ1umonLh/nW1NTce4kmn3nvyGTWe5LlVTjnOpV2QurY5H9SDbiRVjSsllBAflTfAYqPbQxQOyofqtGZdmitSq3wLaMrbe1jOQ4Bx1McZJVXGOZ+AvsxXxqfE45nU+sx6pqGK93kglfMshzw7V+s9WM+yD656mL9ip3o8B6Mf7Vapq/+G5at9m6P1jdFdfm3n4vTTtZvbtO66HKdyv/A9dqPx2H74S8P+l+qqIqRBRrKWQv1haNAOTz6eOqFe0K+Llki3sdxYbBAjvqVRNYI6Ld5ayLKBascMMOxsaS3sEFmtv21QosS/25MSQjQ9Nf8zFsTCAAYgFGFVpoYbTIe1PbsIh0Xi+2XYvC0kJbi9AYudpmuUlkPxWOarpmNPrYWMPv5b3nSDYafek3oygxrURkV60uqbwtn0+Vem4t0pY+0AUi84omfbAmd/d4eV7z+wFVdPjeB1HNzA0wrgyklAwNs1nRTgQfrHfRohQT7aZax1gHF5do2ETaSKqImKv5dLaluAbGIfY1DBqQsu5ZJVKl9CdOLeAK5gFfr7y9FdCtqDxUWzJxaI60Por1OzT6KmWVC8fIE4W9Tt0jZk80Yh4oYNKqglZhDmkWkzfS7Ui1RXFSPpeZyqS1nsqUivQ3JfyegB8LJMVz7NLmINLXaAFKRcqY7nv6Iq2xMPc86KNE71TlKqssO/bvt0FVosN7n0WijfRKZL4oxyga74Rkb1TWNT9G2t7oqLsHQqniPjCjR5WCTwCY1N3hKl7c5rS1+dqDTL8Q42og6BUbr4BJ1gwimq8Jn3l+Zgssgdh4cn7ULI4LOAzmHKtp0Bj2oj8FQMoQHMbCAjK1idd/te5weXGcG3IInAZUq/gn7RcENQzQqEw7Ft0ZKcPq3hr2i2qc5qukAIz7XX0GdtfzuZfTtWrf7wJ9iKZ3Et/nor5KZkbCHOjH6T4b38/9sIQEsj0XgMF83vSuFc9A2tRikGjHNt4I3IrjmTF/0fQytA2dUyBbtr8WbPvYenILmmHTQWynwzO27Fscr1Ewpiy0waCSMX+22wvZTcqPbHYH7190LZIN9F22j6tWtM8q8PMf/sN/oHe+8530sY99jJ544gn6V//qX9Ff/st/mYiI+r6nv/t3/y79m3/zb+h//s//STdu3KBv/uZvph/90R+lxx57LPex2WzobW97G/3zf/7P6ezsjL7pm76J3vve99IXfuEX5mPu3LlDb3nLW+iXfumXiIjoda97Hb373e+mmzdvXnrM7mwzv8sVcWdlkAtHItyaUgHUS52T5+UPcUHtpk002JuRI4FwWyBMRfNl7t+YDpw7vKRL4CYBQtz8iqhvPpfxFloRG2ucYvhSUNFZ5XJzX63h7BqU5Ms4+dy4cgZuyJaxYFRnUUajMJCykCE6OWluId2/ELgeR6IHr9mCvURFVSEl8N0P5NCIVNGayj2xDGX87bbXET35vrAQLN7TENKzvICOVMVxL4xsBN3WBphbUPwrjAOanpUC8aSxm8UqUXAyMSGwMgtoFoR1R801EDPmCKa8VqusaoXJlj937DDBGFadjhbjOA8AGKLSEcisOWS4dM0019UzkFXtKu+lwRpAoIaBS0mjL6r9FGusugTNvpAlw/N6CPexVu4cS283zhD75T9GcZzWaoh7ZV9YwQj1YOQ51TqidGeMNUuKbafoo5VWFFcGS7ARnyvlrHOTrxzoAOX1qQZK1SLylO6ZMtAliy+xiXAdrjj8Ulx7XDcUWq+qz2UmzALQ4uIFKgBxTAHF2RuXS8bnc6KuR9RrVBD3icEbZDr5bSDfB6MC1nzPugTmcMq6/C0RUSMYPFKzh2hmAxVpmnIMUuPb4dwe9DXJtLIMVpQjzyk+6rcACntPsUI0kUCbLiVu/KBSfjpapRKxWRpCmKYp7ar08lgMVhOMkPMpp97Z12QJQcv+cSy1kuHa1vXauZY2Gq+PKg0u6mOJSrvQ+ynQg7YsB2UrWmH5MGS8VwAv9TvnNKiEt9+5zJjLv5NrOL+juIc0cZqfuzT3rN9yxUhZ4QqvwUr1qjVk91gsKTknGMhBZmRNX0faSJ2n6L3au/I9FM9Ys9Zot+ZoTRqgbYwKavDbxhEFOKfc1/m6LQCybZeBuBq7bjtWq0Fftat2mfZZBX5OTk7oK7/yK+mv/bW/Rt/+7d9e/O309JR++7d/m/7e3/t79JVf+ZV0584deutb30qve93r6Ld+67fycW9961vpl3/5l+lf/It/QQ888AB93/d9H732ta+lj33sY9Qk4/nxxx+n//W//hd94AMfICKiv/7X/zq94Q1voF/+5V++9JjjakWxSakjNX2LJUPSiKYQCWNILji4eQ1aqM0U0SRSzq8jolBT8+efN3rz4o1QbphK3Hnlp8wQdNblGsWMCqsCQC2qD+fzS3RVbgoYGI10I/k59VMzxNFJk1k4nIKG1UBYSLCIyOC977QjJFOL2MGtbUDIloB+wrrR2ifsvEmnBBleRiUQUyjcaM6I/kQwFByRBn6K/P5kUCKIyPMcS2HKuVNj1vBYZMlYfN6WESCjmeI9UwBI0BHXIlq6UK1jGssCMBnC9L4r3RYDIFN6UNpI2OVw5uNipIgAF64NBuAlP4casEU0zSHLWBN9T//AiDWzRORAYOw8d/alY2gfr3RDxh1siZweVNHDcXVjMzo3VWXC9TMxk4py4WcIEjrymPp7we3bDfqcyoF1mlG0m/c0zSe3HfQ6IkC3nAqGS7YV5aww2+TY5L99WGB0No3WMSmepf5OnhP1e6SwcnM6TBF8fEcS0Ce1edT6GQ1wEbeoNDYUix67RqVoVRlQsj9xPgSruPXXO2pPRvV3qS01HE1rd3e/ZKrwe1HoIEE6WAY85ZJVwRYU+BRJsSW8fDzp+AAMLGbCBpWWUc6DODgtKJ1TFO09gSjd+x06avne4/feGxpseM+WU4jy7w2Wedxr1bwqbL4qEJv6BH2yna3Snbk3os0nx8L4B2pscT+4h8kCD0OguOqqbK2iGISV7o3vIa6JNSH1dWcDXNBQ2+siu3FsW3KjTsEs1nRmflU0fhZBEAvwkb5Hwf5BW+WCQETNV0L7WoI53hO1nkILVX4tDTb0GdYNxe0Oehdfy0VYe1ZTQR5hX9ZASs6OWGrJPlbvTeMojhcIfL6I2pW48wvTPqvAz7d927fRt33bt5l/u3HjBn3oQx8qvnv3u99NX/M1X0N/8Ad/QC972cvo7t279E//6T+lf/bP/hl98zd/MxER/cIv/AK99KUvpX/37/4dvfrVr6b/+l//K33gAx+g//yf/zN97dd+LRER/fRP/zS98pWvpP/23/4b/ak/9acuNWY3jORiWuwq0eglnYe5THhlM7dAIm5tMyH/MsIhyn0XoIoCdQxxYSPtSm0uvCiKTQ0ZIURpg1F5sOLflRQy149qU3AOtG/a/If5y5ouBN7X0ag0VYyT04OgHx48blhyoxtshz6DBBLIQf9ks511QbiJnN4MKmHJdB47fF0IAodAzVmv07hS1MfLVK8z6KgfFI1VaU1lBhY6L5FQ3wrBRSLSz2Op+lnuO0z3Xonqir7YaFClMhPA0tfL02aDTuoABdsrwSg8eUeEzpTMz64Zzfz9UunQMZUZxmPSu14ABJYQOZ4b37XkoKvN0WALoeCw22xNsdN8fLqnmKpAXUtkAEvFuTgNAR3A9Gy99Enw/ctrrATfKudCjN4QRJVrYhagxag+z5sR9asE+OQcjYdr9d7w+dqzCogPx+XP+HxOksA0arB1zc6UEgphGUBg5gTotriQxqrSLwSrsK/slzw+CergPpH6lUCbdOLL9BJj31XzWgOlqpz1BdLNwh4D5OU5GXho7sl9HIdgAF6YZpTGhDpObogqTc8CcxUjVP6N10O47mYzsX0WRcQdp2xj8CftV+LQAaroNIktVDxvidvLW6b694Yo+vL5p+HyteLckP/2tkAzA/NSWxD2xsk+MsAg2UZeYxGFuUB60DBqYXZDP8sUag5Ur1ZKNLMHFFuPwVbBPK1oxxTjksHBxv5+Hhs8S/ke8XzD4GVtHZE6dyFODHu027JtsGBfW06mCrz5fCz+1pQxkIe0TmlQFe9gRa/JjSkN0eH8l7+t2BjWc1LXuAP4qAGEtb/J+cpM9Fp2BDLOxTviaEUxRh0E3PI7Ld4zZC96T+4M7CG8zpwyhj5D0KlcGAxatYavIY5hRpPy4drJdsMsBQkQsR6ZKwGvizBFX3Qt0sVAuF19XLWi/W+l8XP37l1yzuUUrY997GPU9z19y7d8Sz7mscceoy//8i+nX//1X6dXv/rV9Bu/8Rt048aNDPoQEf2Fv/AX6MaNG/Trv/7rVeBns9nQRjhS9+7dm/4xDEShgrrmdK0dKVWkDZEmlZGsiTVPBzUT6CKdUOm4SzbE4QEMYaBwBAsJCuhue1Vtx3N5yyA3ddiYhkg0jhpEEECUS2CDgyiqicZC7m4GmmpMJwl4If3SahJo8Fr4t/isWDmGQ1Zje9TmCU1OID4PeS52bjyyYyrphEVutHfTnKuAY4V2BUYkghGF3BHRLA5FR0UJlGtAoQAPalXLvJ/ArQUHc47g2NHdIiJToXCb2jz4bxxDPy6+87l6Ua1KgzJkxfxk8AbXgos0b9D91bOtfD9qIEA55btAPX42eN0uVV1ZsCezT4SgjmX4VNLsSsNYdiK+xksIZUoPUblOZkcV/Sy+RmR3wDj9Wa9L7iZGQFE9CbHqfqTxhkGzl0Pft3NV/AVYCSbjUT63XFYdmQVTX0spEewQoXaYlWar06Kme18Fe5ZYCSGY2if533zNFX2icLQ0Qd2UtgdrjWewXTIFcI5FDUQqMKjmx3Vepzaq645G1a35n7my6Bb3vOl3HkpOy1LweQ3D6ZLuoQRysEoXPzcvSmbLql4IZMrmh1iMg6hMVTOrF4lxKSazFGxuHVHvjGdg2HNKi2nc+S5lB9XSgVso1c6/WdS0YUDWSp3aDsuaNjXcKQNVwn5bYptwE2CLk66FZeNZeyi3Wjp3jQUqbdO2mZj4Nbup0etS7t4A/7T2Xdqbcbsfdep+vABnsth/aoHjGM2U1oK9VJn/PgUTCuYSrrEMNNRYPnK+WZqNivVjBL1URS3+/QL4ud2SOzPmgp//Po/LqMamgFYUpk7+HeoKNUYqoppz+rviHeTrjdBPiBPog/exsC/tdzq2TVWf7apdtcu0/22An/Pzc/qBH/gBevzxx+n69etERPTkk0/SarWiW7duFcc+8sgj9OSTT+ZjHn74YdXfww8/nI+x2j/8h/+QfviHf1j/wbvZYVPlmcN8TK3tVWh8eSGUjicsDklBvzAEaiwfK4K1K8Bkofd5s13K9zbOT6QXVCK9gVlOM0S48gZtbHREVKr3I6V122svSh7D97hC21SMFRkJaCqGCHctF25c6Pue/CmmVM19Z4o/ViPhZukxyb6cq5sd8ppwg9tsKTZQkrr2PAzjZxfoGa8f1KtbEeV5qwSmh5Zcc2ACb3M/lWgcp4sYTDVsRbnXmpi41RYAqfzbPXDc+dmis4IaVCHod8syADCKFMbdYoBc1ctKXao4xLnFqAGpYj4lRhuWNOWUQLTLxG9zqgueM90b6bQqA57BxgNxv+XtkrcXARYr8F/otaTjaml7KMRa6BZ5CgcVIREqRdcV+NQ49R069zzPkOlH3rhHePMbV+imEVEp0smOvcXctOaikeqYnSY+hFNp5Humggrpecu02YL5qce4NC65BvD9w/Sg7CQh40DqgwyB4kg6FcQEJuGzIwXeqNLxnM4Az3xcrxSAbDqsOHa5rlV2htg6oqhT3ApNodQvavxYgFDo7POgNlXuY2GpskAhZ5xLBaUS9QTZTaU2jTcZM9yXs4IL8vOOiHReAwzWgwJsMD17f6UBeMEm5b/pdMI4vRu4rsvh1yo68ru+gBrxfCrut0zTkSA2MuqI9L0w3+MKiLfQ3DCB3LUKgktpc2bq3UJaWdFC0IK92Maor0HatNsKY7Ntp2elUggFeMnAT6WKoZxDaq9nu9Oo/EdE5T5gsbxR9w00Cpebtovn83YU9zu1dzGTuPCPEPjpGu0/4Vi4Yq+1b+zYS8zrKjQh07lVMZ8K0Cv9gwxWwzlD2G2HvtiaxSZ+Pn1ctaL9bwH89H1P3/Ed30EhBHrve9+78/gYY+GwW+kmeAy2H/zBH6Tv/d7vzZ/v3btHL33pS1O6Sa2qSOoPNUYsHRJ4gVl8UW7ganx7K4oHq5IeeX0WfS0E3AAYyIKqsuGa1Hh1TMzaFbKSBDJcJodAbaqFaC9HBGEMTSCHaTIYGbPyiKXxK19sVXWgWQYLBiMiQvMmrDV+wCknqpebXFpwOr2hmccj5ZS1XbAqDKQ8xNZRxMpVufy8BA4RFGu144jpQbUy9k1jl0gt+hrraXpE5E5TFOYQ2FDOTWNVaVFirAw04vxMx6jrKsaeHIdVJVIp30UEwpxTel+ynwxEIABSYwgWQpcNWTpY+Txy3ioBcc1IqEVT1fw3yj6Z0SY0dmX/fF14DIt7IkPFWCeVEWzoBV3EGCoYFjK4hkvZLj3I2qnS2JXhbd1vBTJPvykYGNbSsYP9lNdftc5bwIDhICBLRDoI/BwM/ZLo/eI7XWsWoK/S2dJ7JPWPCjBkgfETgR07jVeCucxsoOoxxffyGhPtHvcmxbQxxmWmYSGuO9r7pR9CIdRMpMG40LkyPYuItgfzb7LIMgAwUxqZFo+WQFB+Dvi4WYevr+95PO5ibGIOSwBOATgbUtdU/D3fUngnmL2ELCkq1824bqqpjtEKYPHfOq0jhcSCKlPFud36eUPQa7MEQQ3wlIiIfDoOgx0X0WTh8cr3EtcQ/tsFqlnZQQr4LFk7bLvWcCeVMQcBtkan7vHzlvPKgz0QG71eqJHXWDnOLeqJTeO0wAJx3Wzr4RhinNLZcB2RKYhc1asWhDTmTP7b/ZQpULExCvsPA6uWjmARWKxUIuYgFbJy2tL+cttBFUvJtvkSc/0izn4ltW4C8cB/QjZXq0FCmS2R/2at9d5pu8C69zhfLlBt9EXXAhkv4fPo46oV7XMe+On7nl7/+tfTJz7xCfrwhz+c2T5ERI8++ihtt1u6c+dOwfr59Kc/TV/3dV+Xj3nqqadUv5/5zGfokUceqZ53vV7Ter1W38f9fYrN9H21rDX+Rm7cmXK6g84qj4XPtZSVeKDHm/9msR2w2o6FRmcdBGn86WnjhqDzquXCFdI9U/orpBe0XZooRMuLPjb05uTxvMDjIn6B51MzFPO8KLRa4JrON1pcWz6jmv4LMwtQZ+Zc5DP7SURW3etMuxXfIbDUDyplxG0RyKwAXkTLotOUnHS1EBvOm5Vr37VEHoEfScNPf1OR5hTtk6mNuJFzNaEaiCDfdTTMnFOgjuvFPcvzrVLGfmlD9yn6hPPAmvPqee94L0g4Rkaq11LFJCKyc9WlIcf3EqNe3VRpTwHFspx51syxwY5QVPVCp8cVx16mWcCO7IcFYFVKEgvvw2+L48ZIrg9aQ2XLLCYRlTVERMN++R4ic0KJhPP30ag6Z2kuLFRlpC1fN6wNY5yOg5w5vG5zXOm9L3Re4JjMfBEpo0W6ltz/cF8ao3a+5G/baVyY8sSpsEvpa26M075edbTEuBCocU4De4oVxIwfEJjuvAaOYM1qNrHKqiEyWHTc98pTezoYVeeEs1xZL/g5BeEw4Rjak/S8FyryVccYIg175bGr+/o6EMTL3+N+KVrsprVI7eee54EU9gem03bQQGFt3ljFHBSTQO/3inlTML4Tm67B9TsdtwD0ZLZQLeVpSVS6JnLMf5entfauJQ0q3msrc0MHD8EGiFHfVwaH5Gl3aPJMJ6sweZHlYulERXy2unvTKbXsfrNSrACx2TdQkgUpsFmAMXBSiz1esNzEb3Hf6Fo9x4oU6wq4wvaSEj8u06XcEIgGgzFMVN4T9D1WbRkUJFLPI9slGJgy7C3FWvPe0JQT/2Ywzao4HCKpEvVy/DwuJBCEuBzEvmpX7YLtcxr4YdDn93//9+lXf/VX6YEHHij+/opXvIK6rqMPfehD9PrXv56IiJ544gn63d/9XfqxH/sxIiJ65StfSXfv3qXf/M3fpK/5mq8hIqKPfOQjdPfu3QwOXaa5vp8N7FquMTJWzucFIlyfSiBXKae7cjiXotsLjo6LA0VMNbGAgYohKyPtGlBI/1ebk6Q+poO20H/ribaXp/QWC7NMN0Fnl/VuZJNOfDbqQBPJEIidPguDPpdDhbGlKJ7rhYGLUZaxU9G+MqWEo24VAAoNJ/lMxsmgVowxy5AyKNdK7wg31YENWaM/jPhaz7ICZhHRfL1KN2G0KyKMhoGirjudRrJwKsJ8haClBCykcWRVaoBNujBOE5rg1P1KrBYEy2TUy7kEeqHYYDLaCx0iyzjUX5XjrNwzPu9SX21rvPMG48eKmDv9XkoR06zTU5krS9H/rClTWUtlNSAEenYxfnL1IsVEqtWCFu/0qqGw1ygNFX7vpfYWisi6IWhBVFx3mCVliYPiocCMjPsr450Q+1YqImAJlpoVFr3+9xJzYj7WcI6pfEfk+YqqVyi4TaQBKaOqF2rHdEmYGQG65kwITK8acuNUPbE4J+ujyAg7sNSiN5xM3AKZVGlpFCHQgE5o66gBNoMcZ9Ycs/TqGgPMEp/HlIJbE0vvjueXa9hDrbU0Fsm6EDbLWGhcWfOsvEnDvpgHad+pVVlT81bOg/z+VV7+IhUZ/uacBpixnxr4csEUhCVGY3b4VcrqBCqpNdBg5ul3mtfehTX2AmnT8wkQhAnarrmAnZCZQBi0KbACtwimVPXB+DBYoy7CoCEiimu/UzMorLwCEExdK2vfCsEGtrFhf7mv+Vh1TbWKW9yk7YPHto3tR+TxVOYJd6kYw7CWNY5oBQFtfpYLJepj48gd7Ju/w99oXblo2JnGbxHflb9ZSiPzTvsVcgzJllSs/kEHWF7s7aqq1wvTPqvAz/HxMX384x/Pnz/xiU/Q7/zO79Dt27fpscceo7/yV/4K/fZv/za9//3vp3EcsybP7du3abVa0Y0bN+i7vuu76Pu+7/vogQceoNu3b9Pb3vY2+oqv+Ipc5etLv/RL6Vu/9VvpTW96E73vfe8joqmc+2tf+9pLV/SamgEk5FYLN8mFkBc22NSMDUDmchdN/rRWhUip0Gsaq94gdYTUQuzRQAqtT5RnjIYJKipHzywWQU1Dhj+z3o6M7so89wXGT2y0YVaKqLbpO3S+bRCvcC74nuDexlR6GZFZiB4vfadAgdSPElEeRYWwYZiER9E55fEUziiKMffasOpsY8iMfqJBaLAIlujtVSM30GRgoFEtwYI0TmXIshO9IOwY0nwq0kgEmyjaFeLToI00SjnHaoZVZmAZUTRxTOwacgoATBR/GVXHuToMi+CK/L0lgqu1BjCPvl3UOGFmYIOVcNYdUT/qdA5pP6ZnGMD48Tn1p04T4XkZKylERbVBXDri7Cxif0SU9drVPL0IuOGn+aXmHq9vwtFRJbcbzdpRqbqWc0M0iUlD9RTcW0yn1wB0TIfPG8cauigq/cIQ7dcpt2m9M8o9ExHRWd0ptjQ7Cp0eDt6jCHFy6trjsj85F9vjLY0HndpbcuW7Qo8Ox+i1s4EpSnlsAOCsGp12oFIVYlF6fjpI9h2L/+dRJQ0S/L5IX+MtBDVz0jp//tDspKkKW+fpvIKdUoBAC2liREQtPGupIZRZWzANmXlVFW6mNCcMdljW+JGvDr4SFqZjpEBPHSLAYgHidkWwnU2lorQ7teHyulZjli8AcebeVUuNRkH4WhW1PHYOGODDrNjdcp2PhtC8GJsEUv2w2/1RYuHMDLbY3gtxz+mzfpZFACPLKtjXqcrby7Hxn1QKL/9BrLG41/eMgFdYPjKwinM2Rg3eWHOiBn4ulJmPjZvmKK6VzFSTDFi1BpJKQ1N2bLLL8G7HrtXZRQYg5cDwj1JHqQY2eDfNbxXYFPtS8psUg/fzkfFzpfHzgrTPKvDzW7/1W/SqV70qf2ZNnTe+8Y309re/nX7pl36JiIj+3J/7c8XvfvVXf5W+4Ru+gYiIfvInf5LatqXXv/71dHZ2Rt/0Td9EP/uzP0uNMGR/8Rd/kd7ylrfk6l+ve93r6D3vec/zG/TpGeVawhZdnkhXFJAL0LUUOYWNo2HDRQI590/KfkIgeuBGeYx0KhcWaDc4iiukK5Jq6ACYGzTuLenlrDq4JBZCa1Ouachw4wiUpBfLTapIwwGgodERieKppfuExnk2FFEzSLBAuESy2kgZPJDnVVUHej1PpHHFBjZWxeI0FryPspx7jOS3o5pjvKEUqRWnUPLy5JQc6Ou4k9PyGL5nmOo4jOQIEBK8f+NYMlqw1XST0m9VE/1nhhM6e2l+xRszq0s5bOmZ1SjshWivGZHbnValHAJ2wJAJdCYiWauVvfmxyLFkjxjHXLQKhDLsjapeDp3+oEutFsfz75E2vR2mcal0KQ2qKpFjLvM+VJwMopnh4m20rpj/OKUiFWk603fGXKwa51ivV4KNzQSeWRoORAVLQ6WdOA3kWppO1njdEPQzMB1WOGWR6sVaDQgQTlo38SKJ9JXKiQUTT4mM67EUMowQaQAAqNNJREFUc1XuFarsr1HVSFZzYrBOEfGmLxB0lMeN++30Gf1hvvcLab5uHCke7TC9IrOOAKDzOpDhsQCAh0g3lXN+2J+uqwVwMTSOmsHpykDyfeC1tJIG6gWjd9wrj8lglJifjajwNRwJEB/mdnMyaHbeBZbcuYP6WhO7ZtbEKU6QxikdOqwa6g0QT+1ddnqQ3/RGME4H7JQNYKS8K8bPMMxOpvxeMtGWijQQgGVqT92huyif30Jal9VqbLK5Pzg3BEpoGLX2TMeV7GTKqHFfFxxyIprBdSuQt+syo54bxbtWCwQRTWsblnOXeyCPJ1Set3x+Z3Dd/HwkuCHtSVk8xdoLscCDZPlwShdeE9uA0tYhIsladm0zvTO1vVZWEcZg8WFjpN+DLcoNfZ7NVtvrcI1u1er9xkpxwzm2jeQwhYuoBMAsBi1Rej6XWfSu2lWz22cV+PmGb/gGigto3NLfuO3t7dG73/1ueve731095vbt2/QLv/ALz2uMqq1XRD4thJhDegEnK5czr1FI5YaFwEDgiNyCs8df72N+qHGQZehjrjifS/pkcE6/DYkhANNJOlgbuwwmObdc5Um0gkEj/1CkesEzON/ohVQCD7wwV5yeuC7voxNjy8wCjAolh0Gm+CnaZui0MbqDhly0JXruapWER+250ZyKDVCVgTWYYUjXZaMAqbTeXczIw83QiBQi4MjVOhSQVDinaVwVbZvCWK0IJUoj2xISVuckStVTALyUH/KmXhFGXNIKO9+Q69rSEJNjWJozho4OgrPZlnw+Vb3Ozu20Nz6e37tzABdX3fQ3fG+EwZ9TZqDCUDTAPVXliMEh8T2mV+VjleNufCfuTS5Rjfe1VlVFHnM+kCeDNZMrD0nGJvw2jPp36CBV3vewasnv0lFzTuuLmJ0h08kRhbBYkWkJHCSiYj2IDaa4GZVuhsr+pxgbjXZqJbCb7oEEH4jsOUZEJOskRu8odl7pA3EqVCvHCGMwU+PQ4Te0jXjMyOZRbK0xqvkuU73aY9sZC2tPfoyKzSMZP8y06+6W7zSDZFGycCpOc1GGXVx3UeYdK9btNQoQWN3XgRIEYbKUhkodM9gklVSQglFiASlWRSHZRiMtl/vFe7SLNUJkVgBSrIW1ZkpUO7wILoP7eqtBvGK/LFi8uE4Z90ze+1r6cdbEW3BbOEiCTBJjv7TWpVpVrPyRn5eSDzBSsVSAlNSDkqw1c12gaQ10MWp/qLj3dhpctqckSFJLs5PPeF2hOFvai0uBvJy6j+mpaZwYMCs0AlMasfIZOBVxwW7yxjlrTYF9QetEWhXCcK7IIGrOlIBjKkUSCvuRn4PX+psUKuDVi7VdMX5ekPY5rfHzOdksQVNu/I6jUy7LXHKOPJS2zYbOkmjxME6RXxkhLSi1wulCMUN/gXxvTqcxWpG2BaBSdJSMf9hIhQEZmOlkMSpg41ApJVYKQy2PGO+ZwfgpALsdYJ2ibAoHqsqmMDbwnc4PGQASnI+I6lRPOZaQ0mggJMpzpXCiVJrcBZaEowP7++g1wGVVo1caPDLStEldGYaCM0LslrFUK3VvRWTwc/H+yWsR76slLol0dpnexECEEn+M9ljkMx7H6T1VDncag/z6kpHV6TfGu1VreEzjtWEjbxn/yQIdhnEG0Ll7OXf4XOhoMZi6EI3Oc0c4VTIlTgI7yBqITrMNyr7T31TKxzQuTRuX71pKKcQpnNbqIg3BEApVVP9aZT8VnY/aKTGet2YuiP0kTbRgRStj1O/EBfqf54f4bS2lRDY5JwptM7jucVBOeKHhlcCRcGA7bJj+WxzSBwqkK2yhts50Ig3wK1AEf7Ko17f4U3LRACgkqMqxHHBuuvtTijCy7CRoxCmIyETi8UrGz3BQjqG7N51YairJdV6+o6u74OA4p1LBCkZRIkdHzDph8WNMM5QBnNYRbWIdaChAZr3/qLXTco7JsiW8purorECzimRubDMY758779XaWwTPLIefZvurCEJWbLJinkmwR/5W2WReB3BkS+e6iD6ROm8/TDY6ph8Z2oEKSLcYPxX9I+sZ1yqJFX3hz+SczmmUeq+PzpifMj2oovW0qAkqx7X0XaEjaNyfpepT/J7V7FZV7rzs2w2D/U5gQ9bOdrh4YB4pn5YuKKarN41mjEm7M7MFjesLQTOUhE+VQTq05ywh7Rd7uwJ+XpB2Bfxctq3XRE2lehZvcO0CNTc15eTz3JSTFJkAY9CpUpLmLZkvymj1hkOg+1eCwMZLo6tf1BZUY/OwDNpK6kP+s4XcF+Xc5T0x8mJrkTzZlCOXfoMGitQG4E0VHQ6LBo3P8uzcqFI4L/7ZKKlVSkNwo7jGiXZcBZvkeBXoMO6uhlRjG4WotWhwo9r2em5K4I/TyNBg6Qcb2LDel8auzlJct6WJQERuEO+QpedUawsi4Gp8OC4LUMkDi/bmx/3L6zBAQnUfkUmVxq2cfud0lFwxVSLRiGKPQLsn0te9v0dmk680C66iRlEWbra7IKqAWLLvxd9qdlChD8TGeYXNoFk56HA6Q1A6sTuEw61YHGFUqUeWoOU0tvIwMioWaZ0HQ+hUaHExE07PpzjdW0QjihSstIbimnWRNMQMyIpja2l+UCklHK7Jny2s3/zM0O5PKbwBtciAZea8V+fMQGKo70nTvV7QxSCa7QH1jL2ae0rHyNjz5Bxm1o6lFeRirFadm46Z/t9AugiDZBJI1Sl09jXlP4vnqvS/hqDKzxd/53RJKB7B74xiTkmnmeeuWrON9dlY16up3rkb+35PwtCQymoAvqb4LP+ZU88M+yW2mvFWONE8/3FdYXJMhcFDRPP7WNhDUh8v2P8mmu7HElBcAdFzWtvSutGmFB+r3Do0JWswDHpcuF+mZ6vuWTSAGcNsxUBDAchy6qu1Nlt7mjwujVPZ79xk4QBrr8fxSnvGFwfb/dcaM5X3wGey0svk9/xvixnM74wDO0kOcwg6QI9AUI2p5HS6q9vq937RTuYMB0wvy4AkzCtcn4g02DqMzy+4d9WuGrQr4Oey7fiEyO/IFYWXPQpH2SVkF42D7GxUGDxENC1cjSsXCQl6SCPCcBR1pQw0SAdS6Sh5UxAbB0aPQ4r4wsJaOA7sWFjioJAJohxFK8qyq4Q7N+9KR4GovGc5ulUxFNBZkWOr5WSH9HykEYHj9Qe62otVzh375mMQUJGbxBimzbrm9Nc2eKIJYDHAoKLx/VRCf0GDQlaEAvuXaUwMXii9mtSPAoTEu8aAAoJ1nDIp5zumBJzrKEthRIXKe8ZjXZiP2ZivRYbxe6w6t8DEK8AZTAeLUQOtmFJSY7Ccb9Tct3Rn1O9EFMwEQIkmo6httFEtrjuDIwZ+TUSlKOZFwOpaxNHCoZVxLlGj9D80DJlSj4xGiDA3p70Wdz7s1LiVA9h6pT2kGD+VdDNHRHQOhvHCOPNX8vlxNTPlsJEdgR31OqkA/Rpz1mqF+GUNeC4/uk3FYeLDE8u2UfeVqzJih/KgOOntwL6R9/ECaASABffP1J91rqhyQ6JmcWDKVuuow2vy4t1Kp1IMstZPwKT6rYz4p+9Q7NR4L7Hy3lzyXYAFFVYegfSH7wMNUH2twBoZu4EUsea8okkk7pnvU7ADqzJdIEg1gTcLoCeJ+YppLdZvsQ2jdual3hXv+4ZjSmMwwAjxmdObQwXglNeKTigDILUCJEsAQQjatpXsCd7H0ZbglNilc3lHJguFVbjlOgKMDWfo6GjWUWJvgaMe9zoNPhh6PshaCwaY6Tbav3Bj1MCnnAeGXhqRWMPl3oq2Sk37Jp9nKVLiNGvHCgRjundm9MK8UhWRe5UBMOuoSTsJul+35M7A/lAMGmd/T9EGYuQYdgX2+J6olMMpa4PcQuA2ByTAxvh81PgJZLzwz6OPq1a0K+Dnsm1PaPxgYwQa6PAyEhISOOSainElFyEEHYKf0GgZiRQLixP0VqsSU9iDSB4YV3G90nmrnGY11EGlXAUHo1fHsyM6PnRzOucZojykN2kERCpVMfJ5JPKPm9AY9EbUdup4VYpz1BtmOtk8rooeTd68liiGmw05D8yHIMaeAR7omz/j3JAieSEsL3byb9jPZqPvBQI8fI9VKqJhaGLbUS44r/KWGGDsdRRHjpXnUQ2EqQE5NINuMr2lrGoiHVkE8XZdk3F+onluqfkJkTyrCoTVMKo2jPp9hsbGpC5J7HfbGCn1tOxQOnLJSDa0h6Y/1K8pO+Xw/mUDV2J4CATkeSCZOjIyLTusDmFuUiuoVkY+zydwiKUeyxAo7nfGWmO8rJazhxFqlaJReWBWNBoP6QzRaWn8ssOjBFGnZ4Ll58u+kpOoUot1sEM5LqNOkyzSVeSztzTTakKZcnQAFqwYEMFtvmDNNBOooBhp6X2SVb0U4OWKalZERA0IQGewDKt67TUqFVFdYpy1hnBc6c/muPw2EMWomWXGO63A0bR+SEe2O7HTmArn19J2kYPkn3ZekcoayURK561qeSkAXLDruoZocNUqUsXcs4pf7GCX5PfSYrkspfkafeF3tdLr0bmLrSs8DtkWKi1hP5Ygv/r3RdKN5J4dKjYOM34qlU6JaAKLnNN7Ku9B8rcIFhhroHqWZIPYk7gzBqWQzUWqFXOaU/GxMEsIFEPQQRypPVVJg8s26lK6Fp8vVN7FpRajDgDKZ9mfpe9gTvEcW7Cvab2i2DaLRWOqLRh9qyAu27E62K38J+MdrzKniGZb7IIV/srAbcWvsFIRX+Ttqpz7C9OugJ/LtsWcQ079qW907GDiERwFK8qUnxkCKc6VkWHJbpDfK9FeLfRqtpoRI40NWCizUXkRKr81hB3sHZfBp8r4i98rCoIxBhnJau2f8e1beJbVkqRW7rxi/BjGlXRUGYhAh6o2HvlM3FTNA52xXAazW7jfKy06rVotUt80+jqDdZ0L6TC16+Pvl+jUDLhWNBbkRo1XGPZW6VBhTBVU6vmfcU/Pc04PyYeLymg53URpAzHYizRlqa3QTuVv93WlnqIPIqOsqi90Y4hIVWzL44W54qPWhQkQ3XW3rumxSycp3VN383pxTFy1iTEB90Oufel5K2ZBdjyFE47vCB9rpLdMfcrx4o9KTRMiKvLv8zqN2jGJVRauQUU8afx3ZJcyTu+IvFYEWOKqVWwGvDctA+3G/hT24fkeAwDfNQY7TDzLdA+Ug+Hd9O6g0yzXan6uldS0MmJfdpPXsIJ9ICn+Ot1h/puhbXSB9M2cZlgJghBNAENsjdLqDPyIPUZpBYWoIv/KiWBGBYwheqfYaxq0ddSclFH84YbQkEjgJQJIw0FLboyZJTP/QKybWEEsNQaa5Fh6qFzW3eN0QQn8zP8siAXWHIZHubonGA+jfU019LrU+GmmNRrXAr7uBSZebL1mmiHQveCsqdRQHMJmMEAMedNSP8qZ9xR9u1hFStkWeEzBPofnwcHH2jEyIAkoYXRuqpxUNHEfeL+uMD2UHYjBUks3ptWFOExZA2V+oC2RvrVAdgUgAKPVYJ1GY85XNaOW0oj5WhTQkNYyks8D+mc2jgxY1yrSWsxZVRRAMt0rQAeDOagZV4C/U6q6KhqTmmQuKVui9brviwCaRFN2BPpPeF/XncFGFmPnfb8WGFsCI/oKW8gKml61z2r77d/+bfpbf+tv0Uc/+lFqmoa+/du/nX7iJ36Cjo6O8jF/8Ad/QN/93d9NH/7wh2l/f58ef/xxete73kUrSy/xj6ldAT9/mPY80FdXW9ytPpVeylSe2dWOkWwFJbI76kgJ6h8QGWXHd4uJuTA5cepuWAutMmS9WmSVEVPTz+DzjwvGfGtUt3Dl5sLjKBrvV/vgyMnoJzsVaIhYET50wIN2SkqhRE4ZqBgBeG8l2yMkPQR4vLl/CeKtNTNMGTU1sWSjaVFRQ/NHiW1rg1E9j65VZZGJyEynU4yKfM+koVC5AEmfFuWOizSke5DKudfp67ZKc6MxkZxWUztH/DuuGyUuyH0VTiWKgHatLaxcfE7/Rx93B1OIKDkl6NSaYK8GFNwYNUtE3DMGrBrUEGr4nkmwAJkk6WtBmY9OOL4SxMPLdNp5lPeipg8UDxLIhY6WeMdjEo0M6NDweORjxzl8PhB1paHgLSFhqzlH/gSAHnXPggJ8w9EMNs70+vJn+X7U1kAiotPEqlQgDPdts3lkv/IdKdZg6ciZYqrITtUC4kosuVZdB9K3QqsjsLwXFUwk5bCRet9UWll2vnEMen0OKr0jKJ2eQqcqvTsI8AyrzrZLxHfbWxNLtbsH5Y35HRfnaU9R64bHIrouWJULjIMQ1b3oD+f7ur7Dk6k8xmS9iLHkcbS+ru/SiHuJ72U/7kzlywEXVbCANFsRg2p7BkPQ0hmyjnGOwgEGjsQ9xgqu3JeV9hkxncdgNTfariKiWYdIHqfY7NKuYkFgGFdFWFm+97FriNoDU7+SCOaYNdd3Bb14zQCphrh2+hmggK/TAaddQu3TQY7iqrHFwbnvrO9WYx4vXDdXZ60AKWW1WeN9wvnYGe8L2gkJQI6VQBTRdC0cJJItr+HSf1H7uKN4BIx6AyAv+hO/VZp46Ld0jUKipb2R10PrXT1Y6+yIc3EtbMeixpxzl0uPfjG0RaLFJfp4AdqnPvUp+uZv/mb6q3/1r9J73vMeunfvHr31rW+l7/zO76R/+S//JRERjeNIr3nNa+ihhx6iX/u1X6NnnnmG3vjGN1KMcbES+QvdroCfyzZJt6uVCxzB4y7SUQzDkMQGsMR+GMfJMZeLspVXSqTzeL0rnNHpu/Kj2446osufZQlWlRsdl0srkoiCKSHKoBdBXOhTid8i0iOP2dpRSCKaDIqlCFy6l763K1U5TE2TKSOV0oxW3n5EQyTGRWcpRyTRAGarGeeGzNMexkkoFu81GytSUBBzmYdRC6IiYMHnQobJMJBDFFvl6Yfl8uUDR4WB4RLClM6m0ob0RujwO47C15wMIopNOq/cgKUBszC/3WbQG7ysfJdFp9GpTWkseI82pcC0318R3Ts2z+03h/OH+yfluIaBPDA93HGZ39mcX5u+x/XobLtbf2Ic9fMV8zDf79Ozsu9xRXG90ucUc9EbJdl5XERUlH725+V85X6juPZWgHjRCWaRwfhpzstnLdkxfkz3C95nf2+6xnhYGpvyGmPrKa5aJb7MTB5Z6Qn7dyFQcwqVINcV516tR0ZKnmXAI3uLxBgyeIqpWHG6RgQKkTVCet/IrbFBnepvZBqVvGegjWGnrwlAlvXdKsLsCuQrGJme/GZU7DCsiDWdAFlSBrNAsaHSNcLYpmpi0J3Sa9rT5dxFIIjHiHPfD0kHsAJmTb9N9kslJUkCqcN+eW9Wd9MaK9M0C62u+Z9KQ8sA/WVaWRapV1Wsdgfn3HYkCqRsmMy6FSwnTMMxQRLUaMmYFN5ww1Y4O4NjlquGMRPPo6PaJP0gZYvKcaZ1EkCYXFHQCJDlxqlyBctBzEPDxsqtpkfEP+X3b2UzllWTGprb3mb8GOwt1UIJQpvH87xXOn9NnQGemlXJz2L8YHNDIBeHxWBM3g9rQdOCJQnHMONHMtukrSLBBrw/bas1gjZGgQyldZMAtOfgt/K8zpGjvbKADRE5nq/S7kef4doBubulPaRs6XRdzkoLxH0OGbg3jjRoK7MvavZijOT6Xtut8h1J99ON4J5vt+SCIZXxYm4hXhAd3dHHC9De//73U9d19FM/9VM5U+Cnfuqn6Ku+6qvo4x//OH3xF38xffCDH6Tf+73fo09+8pP02GOPERHRj//4j9N3fud30jve8Q66fv360ilesHYF/Fy29SORN8R7ieYFEF/qtXACd2VbLW224+TMl2KjlUl9kcmuIiqGocyb1dKGyRoSNQ0TIhEhMmjkS4wceRpJOZeOy1I5dyJb9weORwMuG9NLjAm+JzWnSiz+GP2hELRBJ4EgPr4mllyrkpD6JiJNiU4bZTF/FLAU6owZbrWUKud1fxjZOz1bZomwV4D3tGun/9Zw3VLgj39j5GwTUckwwXvDBpIwNmXk1ou5rKjHrS8BIyIKginWGIDb9LvEXkFHS97/BPQ6MIL5GguHbg2gW+N1NGuvPIZLIqNDQwdOpXSGfWCc3DvTDr808Pi6cFyrjtxmS2EFYKvFEMTHlNKGopEWmceV2C2jeH6jSPVatCMc0QjOfCsAlmwzq+i9V+MiohLkYS2oynpXzANMJxzCYpWpqf/UHc7tYLAQECRZdypdUYriLzLAGreYUpWBODyGr1c6RDh2ZniJZ+IlqCTOE/ZQWy/o/da4DAUO8LNFEEG8v3470njYUgMColzmvL27oInnvR4H+vYcKAcALXaNjj4jU6vTe6qlAYJsN2aQKf0eUSmL9wJ8R/idkgK0zaa8+Vg+vjYuIqIG09mGoGpqFMwhI8WOaNb8UWusFJV2jmLXkANWXP6FJBRfQK9GabTwnq0YII4wFV0FLRJzpzxIfOZULyMt0QSljHQslb5mMd7w3bVAmLVMJxR/t+QDlu6joa1UHZd13nEkrcPHwKRkyGodl6AKnlQceFfuxxNzvcKKSi0YFR1NXSSL4WIwH+Q6z9d1ET0TtUdZgbzCzl3wN843et+Wl71UQtPqD3yG2DhysK5ndpOogqulFLyuHopgDD8vfCZtq68JPxvvV2Gj8VZqAUAWYUAyMplpi6+N93pxvmqftbbZbGi1WhXyEPtJkuHXfu3X6Iu/+IvpN37jN+jLv/zLM+hDRPTqV7+aNpsNfexjH6NXvepVf+zjJroCfi7fuobIc4oPioelF3+DLBH9slZLUg4alJg/r/UGIBebs4XN1hiDMmKGUUVZ5oONDWpXuwhAFaMGZlS1qB2bmXT+0YgfjDLgFxhXFsVDJ9+IaqkNIJd5N0AJbtt+uSR7zTCqscwMwEtXBWABY3EejE4Oo6Y749hrAKd3+p1Qz7YxUp60MaocQI6mLFWgqAE/PPcLnSq4N4nlITWzivdDgo74bIwIpj+vlIWXLc0lNcckQ2YYyK3bUrybKF+j3865xKpyhtNVh7DiGUfcPc6Dfig0d4iI/PkFWCNFlbWVeU4aBqKuM5iDAlyrRepzjrzxXe4ovbvi2hsrylxpDTIjJaskM+cRdJnOiWw5FAgP1/YJ+f5sG3sxXovx43al37ETiGCfj8qptUoGL2na1PTVeC1RDpL124BrgZGaiZdoVLuURQrk/bYcRcUeFesnaibNf7CB88Jpi0TN2aiq8tgAi5GGg0Y9sq1Yswa1pJxTALKuLBbVuHay97j/VpeLV+cnwQrJJ2BnXRwL96JN9kkomK3z3yUw5Ad9X1FYXWYR83rhFaDPHeLXYi/wflr/FBgX9W+N90CBqriusV4TplWTfi5K1yYYgbFCEDv9H+dP0rdSdqYMRjHjR50zgWWSWY56k7w+W8G9izQ8VqLxfH2wJ2VmhkrLB0Y9MpGJZpCr0MfENDtjv6xUaNO2lZG6jM05iv4C72HNvsZAjLCTGVxUVQD5vZTMU7x/15INEY1nsDQeIqLWCGTI3+6nMaKtwGydFQYwATD3XrEq8zMaF5j+q1YHTdXY6+XcF68pHaPsQHmNaPOUB5KqnizmBTOV4rVD+rxvf4SpXvfu3Su+Xq/XtMZA8iXaN37jN9L3fu/30jvf+U76G3/jb9DJyQn97b/9t4mI6IknniAioieffJIeeeSR4ne3bt2i1WpFTz755PM+9x+2XQE/l22S8VNxOCxNi3wIG5UKvTcmt3LKB6Ibh2V+saQ7yoXO2vx26HhcVD1fGcSRpmvExW5XxImItDCcdULuRxo8Muol076Q2mxElwwQqxopQWdaGjop0uR85RrkRm3RtVV5SYMtofpMz0xtpDLfeTLeNUCxo2xnGpcyfhC8qTGdeqPqFrZhNASgo/432pAh3S9M6ys0ftK5UTiSKxL1IkJ0EY2tmlF70Wop+DcEvGql6+X9tjSqxDHFO4xRKacrT6hWAzLGoA1sq/IXAnEW4w7n4d6+fU3SKOcphiXfjTKrak0xnk/tPqiAZDSc8F4azek7fEeyWCWkJ0DVm+Zko0WzDQBCATPbOOn8yGP2dMR6GqMGGrSgJzJJ2mVDltlpqEHSuDQv6ikS+VoqgEKh2QPANp6PaCEKb6WJ4e/FMZzyNEKZ8JqWnAy2+m3S0cEKWwbAaO4tuBdjP2kdNMEplc6ktUQQvBz3hJhxrqqHTCRHvh9VSuEgBMuZUYPMhQzaiGfslR2UwCyZzRHsfyOA4wKpOgFWhb3+sHwnOMWzvVcXrCYXp+mLLClmUcj3AufGOOo1qqazYoAzes5eQLfLYPyYFYCM994C9TTzOI1BpuqhGDkDKbVxLdkaVqVVK7B4keqc2Lyf1mLsPwHEUvfFIyg8ElFFzHlni7G+Lslj8PbLMfCcRwA+hKlyIoBGxRVydTcj5W/qQ+ytOAi0cbF549lwaxptb8nj+W/4LPPeUqnIyW0w9q78N1nR0dCS3JXmySCTtUfUUvzy5x1MvKXKvpZvZrSLsAtf/O2PAPhJ7/RLX/rS4tsf+qEfore//e3q6Le//e30wz/8w4s9fvSjH6Wv/uqvpp/7uZ+j7/3e76Uf/MEfpKZp6C1veQs98sgj1Mj0dWO9ijGa3/9xtSvg57LN0fyC18TWVFll7awoR+QiGww7gRfZENGAWHdqoVJGwAIzqVh8FXXdTQYILo4y4scRTByD0V/t84WMIsugsMqocmPH1dIessYiW1OZB/xZGj/IpDJSPopNm/+GBhQ/+x2K8NG56oZZPINdOe1EOjJSuWfknGYxIdXVAhctnQCVSpPmPW6aowHOVFhrZapgRe+qVvmhUs2m+hvrfUGQhL/H9UJeY4zTu1OLTi1F5saw23hY2nsQ3FjBMxl0OesCLMgRc+P9C4FiC8LphoaXcmpruiyyZVHW57GxRlpkUzec9nKRd4uoSEmNrafxcF1/JmK8SmeLNNBTKyerWATe62Nx/I1b1p1hcVQFAk9rpUoVlCBeSoOMwCCrMSatccaCDVk52HomuK4L7Zt4mIB79JkTaKKmgbqHRA6AHt6jilQixerwSsy5ln6odJ32uiqAVpxDgRiSTWPvo6HzFHyj5k+hF5RFnMvfZo0dwTTCMXDqV0HukD4lpF9hawB8GI3UMQSDcp8Lcyw6NzGYlFPLgL08Ke4bXgHbmg1iAEi1MVk2paoCKD6vaoBsEgNWVcjEHmjpvxARtUYqd2VfrOpNLmn8uKDBBtk/X4sB+hKRcT9k326yaRAU5ICUzKI2iipgyvaFK0E5p/dLg9SE+1YB+PLxRkqXmR5qaIfq+cPXLdY9ywbHVlT1kinyhmi2lfKfj+cguQHMWN9DmrsLgeJFnCMMCO4dar8LbQa+99b31nXKttai04Vty/sh2nzO2QHSokhHa48rBG2vX7ULt09+8pOFpk6N7fM93/M99B3f8R2Lfb385S8nIqLHH3+cHn/8cXrqqafo8PCQnHP0Ez/xE/RFX/RFRET06KOP0kc+8pHit3fu3KG+7xUT6I+zXQE/l22NnxcndEL5pUTn1sqdtSjcROXibpXyTvnouTsSeazSOd3bXSrOKuGsog35YBExxGMcTb/DcxqCfSrS6Tw5uI8q3Yy7qUUe5KKJz+R8s1xKnTeHWrRPofOiUhCLPyoHOf3GqDiVW4g6cisrAHEER92LtBmosp2yutto6m44zHkmMgEkxTbATa+m8dMamyFuXl27HPGogapE07PE8RbGqVFilmimeVsgEfZTHCP6kQagBfSpsuOChs1zpgbYqoiTeI+dS+mlGDG7QO64ZbNeio6/43PbaoNIRCXZGVHiiW1zccE9ZTwnJoQsz46aJynFrHgvC90B2R+cz7KCxXPLKTRW5JNI69jIVltbWZ9EajYgGxPTEoxmgvQ0zXsFmu+ishOVlSHZr0CAxxkOFPSfNbFqIHpTWdflb+Q0k7dma+wxecyRIggMW862SoviOWaBz3wMTVF4FHMeU2pDLTWOx4k6YQggzfZAOYbQ+WXQk6Y5HeGaGgl4cdfgHMfGkdvGQjeMqHzXmNHUHpeO1rBqir/j74jEmGrDl/dXBZf0c8KxWy0/hxo7myhXwMI9Netg+fpa4/rRWFNtYELbTUb1INBRc2dbPWcl2MIBNZzm24HI6/dV7oF5PBdJA1T7+g7QygIlZFN6dMYaUivKUQu4EqWS13U7pAA1DaBGVVJCrI6DkApQa/V+iwyvYOsuqYbBjpUjtx10ipYR9FHHGN/r6mAGAHIRyQdu+Czlc6sFrPg32J/cp9hOVOxXw2ZXvpgOrCqdqtzhAnMnj1cHLdT77AUzktP1q/sZpkaL+cQVW7GicONttvmLuf0Rpnpdv379QmLKDz74ID344IOXOgWDOD/zMz9De3t79Bf/4l8kIqJXvvKV9I53vIOeeOIJeslLXkJERB/84AdpvV7TK17xikud44+yXQE/l21W/ie37Hiiky82WxYmA0M+R5mNKGtujVcvQqEhIR1LNJysCicXiEBlAER+WXEe1aYmRVEz3R8Mp9EwnCpGXLF5SYNbbhZoQOytjeiS6D9tQGrsFbp/IS7Mtw9BI37G8hFbxvpFNhzFoGE2Az4DWUVqoOidphKykLXUWAAj3wXDaKpFvSyGGPanaORhOdWr1rp2epZLDmyNXsvRaLF5O2DwZAN5X6Z0CT0EydxAo8iTKu9azGF+hrWqRtgOREWzECaHTAGAU59L5dypD6rUs8d7zwQWNGxaTxGAXBQXdueDoaEiHf4EhhyiiLMv/p6Pl+VRK4YsAyslawDePz6veAahABfkb8uhkTOAJHFveJ0OwL7xmTZedhigXG1YeSVSy8+AQQMiUlWZiEinemHFtkqqlx81yKwQLu8VCibndDhI12Et/RCMUH/mNAt0Aoy+lPaW1WENvIR5EA5W2kGTQRNOk0EcN/WDwIUCSlpPQa39eo1VDCxyuzV3KiaG3440wtxr4J5FR9SclXOl0MlIc6E/Kp9Zcz5O6V6ocWWMBcEhBsC2N/W7kru5n5xmccNHSYkXc95DZT0X46JOrFlNjeZUOX+un51qlXVEgjMqncd7JXSs9ryavWhVUINU1qrmIjd+9gqkjJkxUW18bgTUDIBFsS54rZEBjhoj27JhVFUsgyELv8u2qKoyCrqLztm2M5V7hbPsTIudJFpoGNitpL/LpgJoUQGyJeMnnVvNsQq4bgFtFQ2hJcZ+tqnwOVnHK/sh6mdpsX5rtq4qxFHaM3HV1oMeIpBpgaoqSFjzdzBo572eY/h51VZBwWnslWCH9/Z6IOcBs2uNQMbnHfATItUjBZfp44Vp73nPe+jrvu7r6OjoiD70oQ/R93//99OP/uiP0s2bN4mI6Fu+5Vvoy77sy+gNb3gDvfOd76Rnn32W3va2t9Gb3vSmz1pFL6Ir4OcP19TiX3E8raYqJ6X/L0QKaRiJ1l2xeRWirJJWiIJ95/3Fov01cEjmCGOUKm22F6nCoFNsDCqz0hJJQMpY2byK0pNwzr7XTo8EgjhKHsA55f7xWQpKKZcDxkh+Nt7kdSlWh6bImlHLmhCy0uwowS2/GfR9TGljsvyvEm0cBm3c1oTC8fvNlpyHMuw15o5s0iHMFdHgd5s+bXwLY+O/HYEoHlcz22hgIX9Mv7U0ZLChURDbRs2xomoS3wOcA/xZlROV0Z9hWhNURQrDyEa68xjqWjTY0HkZRiIH4prolJ9tCKnHTjxLz+8tlHOnriNqvNJYkPMui7VWHBfpJCpx2wQ0yKpeqppKpUVH2s4Y9biU0ceO50JKVWxb8lu9JjLII8FFJe48BBoPgA2gWBHe/G1snPbdwYB020EbSDJdi/c6S+MnBJwqZd+VNIQsRCrfdbw9RoS9xuRCYVMX4yIgxQwSpSdTcVak+LBr3DRX0N9go13OZRyDc3qtR6yAh4DzvydyK50Kgm04rLBmxfFYOWs6H6nS4HKeDalqmUpZSfdKVjkbDsrrZi2lUiTbDmKFPQCWNqMG3gwGMGolZa0jeJayMlxYpWAHMn44tUymoBlCzspJxPWc2SYWcrVUeIIMEImoXPsZnN/Cnto00760wJLKaU21NC4ZVKzapWI/WShhrT7j3JNl59n5Rxu535rfl/2Mk52DgBm/l8Wapll2u4BnV6kEFVeNBoOMvQsB/WAV67AwyfNes5F2nKtoRXAUTsDPv6iUK6680GA0Am+qCI0RgFaVXJlttmDXhJFo3WpQhwsKCLvCAcgT9zptDylGdPpsiYjX0gzlccoGF+fjuYDBjs35dL+xf5nGyjZVA6lIfxTsl6v2R9p+8zd/k37oh36Ijo+P6U//6T9N73vf++gNb3hD/nvTNPQrv/Ir9OY3v5m+/uu/nvb39+nxxx+nd73rXZ/FUV8BP5dvZ5sqql5NYQmGQW/QQFWzIkcxFo471fKqsT+nhV49IunWwmKUc8fN0ffj5DiiMyHHUFlI3XZQABqmFkWOmtfQ7mJjuoCHZ1Fka2U8F5yhXD65FpFYquo1jKrvItWDDUy8Zqv0JjbnpqhFpXxlXBKRDlGLw9Yo12jorLSOlDbwgt4Ma2l72GLURl8hvMlV9WDDN6KmyjnuDBDGilwRqbxtkyXViU2brw/vI+s3IVVaAmPskCBYxg5xARLp52RGjeQh7GBaACeytyxhdjSm5DiZJYI6VV03AVBo0InxcwRfnXOXgKbsRwrNSoNbdomByKjXYsl8YedzJzhqNBfCBErBdQ/XpuffnNX7iK1VoQrXYeP9JuP9JdIgjPfktMjP/M+croLgCpGVJlOs9+z4Iss1MamK9JM1AkuJvVUBcKqAEBFFF8imKE2N58SAqU2JUYNOWiFKPMTJqb5AypzWwYqqmINKp60Iv8eVZu5a7BUUdx4OdLoSClGHlafQ+lySPo9NVpvL2ls2y1XaBqjJwxo/Umi66siofZdoBAZWJ98Xvl3IqKgAksU9a9JzRHYhM36EY2ixpJVDjvtnCq6Y1d0QdBmN+aSY1KABR6RSxMhNFaqW3MScbog+OVf7WovUcKjINwcExZeSMSKfn9obW6IBqlTKxvcE7zX3v1SExPtlG0L0qcBX5/RaqdJmK3d0HPVzgmZW/JNfWfeUj9vr9Pom+6uBYRcBCnjtKQKVcg0wmEWy/wq7arFx5WNkGcl7mOZw9b7Ka0OQxwJXara8pQu6VOjAOj9ReS1Z9sPQ+LFYUtImzoFjw/+7SKrgi6nFoIKLz6uPF6j9/M///M5jXvayl9H73//+F2wMz6ddAT+XbV1L5NOiWCstrVJNpIHC1NzKgmyAEkU/IZQ0cunsFQKYlU1TngqjG4M2Mhy/NAsbSGz8ZDxtKwsrCSPqdIdoGpERHeB7JsYmHS/5HFQ5d0sLReZ6sANd2eAR3ChKhxr9ybZErw3BGKuMuCxsHER6MZPjjHHS9agANoUBYUSpdgoC11KWrEoeKpc56u9kqWlmLShmDwNxFfYRkZHOAuMVbKQIlXzyRlyr5CWfH4JujVfGqBTbdMfJyL0oTdd616yoKdGyIWAKliKtOf0f33unyzpHc1z1a8rgiGUsDgORg4gWGH1ExhrF/5fLJI7LElOt3CYl7DuSvtdSe6RWdS6LHy8Y50Mg2m8oGCkA6rfW2o9OCAxhdsrh+02wjUj87RLjpwZepnGjXlzx98p7mUsQy/SrWuUvwdCoRe3VXOkNZ0x+TtMvgEgwjyGsMVCCa5J2EHLVTrE/4b1xjQZOHN5WTk9FhskY1F6u5lzrdP/WPTNYO3476nkmATCryhjN75F8n4a9cgxZHksyaOQSuyTG7ohGfB6i5DvfEStFkkiDY1I8PbYG65jmZ1iMF7xytxmUaLkuE84MFjjBEIlUFSkNEqqxSbSA9etwDng/Bw0qbWbi2XuEZLBgxVcGjYq196Kl3cew7JRnPch6YKD8rbSB/dR3hX1UrDWKGdmQxyIOGITkZ30RXSTr/Av7dW3t48qJFlCV26AZTcX5ZAo2ApAMtEmgVj5vWXzCsvEWq7QG+xh+/kup++neaz8l9bkU/Gw90dmOd4uvxQJ+atpE3Kz3yvrO3Mfdsmg43yurv883cec/Qo2fqza3K+Dnsi3SPJFqlUyWhFgt55vILueunH5PsV3IeV3IZTSrPOGidL4pnOPasahnoqKV3IzUBQUqNN5Y/A1H0Rovt6VFmhF2qz8iEWWpXMNCGk61zORFWEdWmWIZjeJboCKC6RillyGp0hy9vEB6ED47a6FVLLD0G6xGRYPByDEALyW6Z1SNqIgULjJ+mG2ixmU0ZGxYWgkF0LjwTI15UKRg8jPETZvv45JzGuMEMNbmmjQWLxINUuy6BDooQO0CfTm/qFuUxYat61ut9HiL99Jm1qADIs9T9E+lkV8cs3Bp0WkwqDCa2Y6urMGmdgi3xBDBex2S2Hwo9GcQkWp2Pt8sCI9OYNfolE7DQXBonAsjPzYV4fQkWGqyirixAX9Qvpf5mcjhqtSpBA5J4Xu5Pyystdn5LTrU99D39nqnQbz5vH4z6ajhexLYEe8XniUZIMdQfo4JjPLHJdMvNo1mxComb9A6VTI9nNcMfMc9EQWnmMDWnF4EaVJDBhuPoWBOyeVL2kA4tkjUQn+W2LNiI29Sn7jNSUAt/P/b+/I4u6oq3e+cc29VpTIAwQiCYRJCE2SwgUZFG3wyCCgyPfE1g8hrcGAQG0RwAloFoW0bxIYnioYGGvC1MoiRwafSzCA0MgVbCHTQZhDUJFSSqnvP2e+Pe/aptdda+9wKJKncqvX9fvxInXuGfc6e1l77W98qF/0s5KMqb3Au71sN6QSIzZcJn7MThdHL56Wsln2gOmD8b41UdwhxcGdH5aiKj2Wq7UPPrxsPNNCxpxoXImxVvonJhaTbbWkf+fLQWwo2Ty6ZhdxJ778n74ONZv2mVHUD9udY1qRJgsQVcEJQm1zs2z3X5/JZ34gdKUITq/UMTRBC3qWuLjV2j3aMt3PFISVQ6PZRtYm8nLDGeH23lY3V2P37mL2Y5woTSUpUiA0Wajj4bzpWgXJqR/lvxbOo5tIpbzC8FpjjZ1XCD/S8swe099iOVGlkUDYPFycbGalnY8SyXqFjfBYJ3z3kVMVm3JkRCJcpmVIUoUJanuq9CsXhwe/HdzSVcLOooj9fiCaKd11zgHCat9+5FYtTYnRFJq2kagekrnhPixhq1T20d667njsLnJNx+d1E5QDhK+qcE3E+aU4j/m3FbpAyIQffycebK8ZJlsZZdsBoO4hRj8cyadL+peysApC04ixVjCPSJv09Rba/Rvi7VgagI7QsGFBjeBfnZOYSfkoaMcAK6QAU7UlZ+Kp6LUKnpNVx/PD6oN/Y31YIv3f+pmLN0V1OUpZumZCC58YWbiCGOv9ezYhjhH6zZoqimYkxvGIp0OFbWUwKgdXITqhYlBeQ/bAlQzfETnAwb5XF4MZomujRVPRekdANP7bW6fB4BGPjsNxI6ZzUnSkXiLvG2oQWRolw8eQaqRA4Du5Jn8PqsmhmgpkiGadlXXIB9IZ0AGpOt5RplRVK9kYOlyRIEof21PDcQPMnwsSr2FDke3OdnkYl2BxZkNHDShg0d/xQcWr//WT4WuecRp12W5aoArouxj7l14oNNH1hLRbc2gJZc4YLu0peFnM4i7BPypDz78ezsY4hXLFy6gfzZcQ+01gPYwmRjKF2I8917IBIyHkSKyOgs3i4cLrXreMsKSVUkI936UghBOPpPFb1KT5l5+VczJ25lFXsHeRc29O/Ex3LYqFYY2Fs8Wu10H3KwIo4b0YLWNMO+iKZv/w5dIzgdv/UAcn44RtvsTkbkLaa0j7HJJwe0woS34zaTZG+kKYQ3qaJjrVc3LlXYY6flUVfE0gjRpRfBLUjgxzIRMyNQT+o00GIL6qazc4uTqEslDs3H/13LFwqKC/7O0ulEe4ppHTXWxhIEeOfopqAmEFXFHoqRvpnJe5MDkapkpyFJUMkgm9TsTE4LdSfy0WZyXnNiJOi+p28l+b5r9ntGE3nztpapfFTJ/TnOgsTFmKQjnT+Doz2foUdE1lsj6KpH3eFTHvJdlE7mblqwueqrGXsuzca+k6e5gDUnH8cwqArn0dZCVQ/hNL9+0Mh8KQtwy/ot6/eLkZdFxm76E5ep0+mkQwAQRY1znRyDo5lAOLneEeGWCC4ppKRKrxXY9lwkFmDl91nBUsGGYuwkQGttsgkFgjilvUjwqL839Rg5vpcyvEg/XvNWJXkicyaRDQ8vKEuvldLhi0BbKHhnO5YL78ZZRxoO/WJC8eQohF+P18/giXVbXceUJlIwcKlfJZw/hVOH8uUuhTv5NsTLR9ffGmsIB4+50/loYkNyfih36JIJQOFPtPVhDC4LEXaKqqsUR7c4VKVg96+XQDMKcLvk5Vjg94O+PzI25xcAFKHl38WZxukrQJIgGwFZwWOliEvsx6KkCrv76LrFyVdfOcdCPsouE/NfNiXCWdTcxndhPE3DK+rHHH8mzGdOy0sUBUlF2N4gYSHLgumQaofH2nBMX23hOvTKWUPEm348E7GBulkQUtQt2hKKk2wmjnVI+NtWDoaEp5dy0MwTLowov08zt/bzxd1C7ki75RVY5aDOX75tSm6Om+iTJUxhKS4VIZg8r+rctBHViwjPpaR7+3HADZHVOzIwOHN3knL6hWMyXQe4PZ1FymFmMM5JiERJHDp0k6o7aHoniX8+pjtKLR6mvUbq+U1YlznOoyAHkYGKPMvdV5KJzqAUhd0JZl0vQ4L9VotMMfPyoKKTXGHRcy7HexIlP8fo4ESnpSUu7dk55EMBPSpXBtAe6Y6wYtBUInFFdm7ErlLAbCwmfL/fMGtpU6MZZmgkxctJ2WB8DrRGA/Ke4sUyH4RJsKSyEQWCUHzYo+Bs4zvxIy0JGNI05QRTqtIbDR1LPmJWvhranY4qjJ0SRsKxJ1umRKOwieqRp98Bp0cvSHL2QxVutIaw8zfJxJPHrAGYqFeudJXgWAHKMmVMvBFA+1/vj7GagDT71FlEdEn/KRul04TweS6HrGsL+1cWYSzcxqZzvrS/k1PSZLQweaPk/L7RXeqaQoA4SKc978yzXmMvZjWbP67zIksT2E4XeRChWUEhONw0i7g0kSEkrlyIV6QDYWUhWYl7QIFG9u4Ae/fV8uu2I3x5LIEyUj3uoyxc4QWDc3245lIEdZAXaiXX9HHmAWBQ5b3wXaBgqW8DxdA+vgdG4PoIi1b0UY+0BBOkopFQQx5MdYkiViPi/opX5c7WwHpJEo1tgfv53QqLh0y3OGVDpeMBN5UAvaSzipLvaOKPDfvC89pvFo6OMm3oYyHbqEZgslG674cL3KmK9S3eES8g7hvmnRC6Hi/8ZsvJBRQ2AlOCQ9iqMIsM1aXfU25CO+Wvp3Df3c+JzsHZElcjBiovkk0Dbu2IeOReuZLjVPMgzuWumgPxcO66xl6AEaZpPxcLTRxLKHRkXcSY1maKuG53Z08Qb+PlMdpm8IxCO2kcuOWyghwh4WW1Ys6JOlGmpbVqy6bWcxWrMb+Gtuy0cnopWWp7JSXHOdRAq1cfgtNa1A57voboxnv+LlVGTLBBHRkgyuJ2S3OdcolNprpZnT5Llq2tMmWzt2wWmCOn5VFlo0uXIWTpPycfPKm5/lBi23sVDvJgaHHdt5GciBLAid7IIcTsH/Y7oAiwCmyrDQk48cbDoHjQgyo5X9COJZamz78iU0SjVSZICNsJbpIXaE4OgCAi/b29Y1mEPCgrAdvyHIarSudN3wCIO9YGYZ8sacYGWKnPC9UJ1j1b18PEeeQcCRR51dRwGVSnNffiy4cUi5MmSbSkJ0WMjaqdiOo65myAGRsj7YSP62kDhWssmajY6ByR8XSV0f/7Sfe2AQfPLM7NT6hbbVORLpQdk21Z4oQycjwS+/VLDOGRXSsAlaCoMAX0nnD+2jMlmxkcee0R6st34k2c/9sJVQhWdGSY1TAsIoYOJUmEbmOp3MvxcLzdUjboz6MOvtZW1toIZhjCX0AmGB72Xf5rq9f5+Ty3OqchhyjOPwYUPC07yvayqJaGVdSPkaRs6swMp51q3xvzuyjiOjBVIZzwEbhGaUUBzx1qFPHGtdoUeaWQHR5wGcVi+zuCgYIY/y0HXKWhaxisNCQmn65+JHizpwN1bkmZfNPMdCUuiAim5nSJxQHgGiHA+WcwXWLlJDtxtLQgCnKzQ66IG4MRbREaFXSYhKGkKp9wsaL9sBof+hb3PlOXK/Jh3plI5zFRM/rJDQQ/UsbglhbLgaVkFWGSu+pS3i3eq3muAnsyXJMEeGdSUeHJOG2yWhb9JtbYr6v9EnItXycb/ixjNgSpFzBeykOWZG9jLYx7zyNsLSEXhnX+NG0vfyzY6Fp/D6xcyKh04kivq+ex1hwBbVX/dymMd6zrFYYusowK8Kxvd1NM9Px9/a2ZuTb1DF+irZc71CHyEiEoV4xu7mRQf5utwE0445VOhdpdrIWYqhBmyO6MH60MC81jFDbVNBCHekmXSz8PnZsIsNhTP2q6z0MAczxs7Jot3VWBDCaRlrs4hBD3MdTc2dumeo0XUYmNUUYNxlphztwlFpOFuJcm8apVP7wz2SkpU/S7N4y9bnOaKBiZ64oHSmasBzTTBET34ASZqCJAQLAcpYmVBOP/tPi0X+XTqBsWVgGdVcBCOvWO9LYOemryi6jYlQkLCwgScmiJCudU4LmXe7g1GUvcw5Jq5ACtOW3SUdGKbLJkiFZLu68WaZQWAG5+zA8LAkR/PsNj9RngSjfi2Zd6ZRheccoiInlAVJ7RzuneiZjS5SfKmCVtfS+JZBJ1lrQR3yZY6liOUuNOvEajc7CISKiHBhx/P3zQupTsJ2oJJqe3Mn2IxhF0nkThCG0fbYQ1s69JpIbg/YWQ9eMc/Q+1I8SpKQm9+PNqZC75NT5W13LnVbLO33ETQtD32gfdwNNpMtawhGQapRz8V0hxWe5QRxhHSVFIeeSMS5oqtOrcFsu7N99F94nA3B9+qYCdVRFExDEvg1tDwojoLYNe2YPH5r9AqxOtDdJkE/JwoxXIN+pJnxNS7Qgssv5b8Z1l9o5XMz+8PdPExRTGuJYVRzFEQV0nE9FI0XW5htC1FnQKVfO7j/qEB09lg+EZfdOmRj7zJGLhWOsLcPqGsMy1CthM5APvRMbWkQnyvV1NGHEGKjO2YqDs8tirKpDRZNLLNQ5S7PbQi+S1QsAXFuOl8E7VgL6uvZc4GjQMuYBSFRRQCAJMnXyBbjmzCLjc/VMrk8Uy+ol7RBpo/u+Tpz42n2UcXfM6OIscFki/e2BDp2+CdkpQy6dt9TR1qdvVPKMcyo8i4c6dUbCjURxLv2NhydmzBEHyHbtnUV1WjplhANnz1TjEd3Q5TZUkowm++gGTRYjps3j0S5ke8xl35KZeyN0Y/rt/X2c4ixbWdH0XoeFeq0WmONnZZESxg/3YvuFl5joiEMmkqqyEnemBoqI6U1VWmL1+1jKT6/lC3zFE13tspDMDyp1HZCGkkJtFUyVsbAxfDgwXZTHjACuObJsuawn6ogr6yym2SEdDcRwiKUdHYtXvt0W5Qo0T2Lp5D3Erg1bIORSs0DNdsV3cF4dglt3engvXgZfZ1wfqNmQE7mmt1MXHlT2LbHgATrtaaiGveW/CX9Pz8bg7ZairMtgh51+v6BuFP0AYbCTNlaF53G2W2T4pXU7tKwTFhUxMgLjXEuNytsAd/zEQkzHMlmqNG96XVkPmhh24ZTxgvQt75CKpfeuC13y9R04C/RTue/JNbTxiIw7vur5d6123tkNp1A2WwE30JA7t57lRvUyuLOgKUMJuAZNlaGNjVlFfxNZzHnrr3VO73Mc/JwC3XcmM/9+fLyTTh3B9vRMWLIYCpzC9Hzle4hQxjpxVwbBoqDD1IocGSAyS1UMWapBpYUWd3u2f19l0Sy0h/ii3AHpci7uPHpNVjo9RFasJEFSOGRDYX9NR0bbZXtqszzGPVXyHbIVzOlQtvOcOACpQzZg4vFP30ilIDZl/VV9iNlHPmSUL4iJ8ysdyTvOOP4CCiONoxO+yfuE7i1QM2rxe2t/10gHqPfE6Ngnykav9VpmMRYL7Sta1lmwdkztszomareQFW+fRXR6xFxLx7ss68yr/JuVzolaoW61fiLhW2NJnKHoi3HGT5tofUXZX80MyHOFSS5ZUuiPMIOJrSnCkPxcSh04tOxB0oUxtE/qdPNjfozhUid3kKUlE4/Nqf47UGePxkirS5lOy/Ba0MxkRGwwtyjf1EPNehZu3AJQN/4nXTp3w2qBOX5eDyJaIlERVyBqRFSx7nULroqCG7kfnSjFejuBQ+gVT3kazySJ01/JwldMQEl5XsHjYsnEVH6bhBXMZYkUMObwn5OOeYGzINH/DXRPN1k+W6VKA0h4anlahL6I4TS143wKmDLdMjfxMvifuXOgZIPw1MjJUvZd+xpIlo2BAcMnx/4+GZoRE9/WNBl4hpBlfBc1k7tGgU6NFO8F0KmnVjuMPQfCidX/xjN/+fLH+gr9LWYsUCOB3z/N9OxlHDVCyAFo244JXfr+RNse70ftthxvmFMsmtWr1ZJGJG/nzsnrNC0izTGqia7TW/tF6UiEURTYWMrCwpePltX/s2bNneQKA4SG+MQo9eVCRjBi6MI2K1kFzMnsmQwpCV8VGleKAyzq/OI6JCNKO1Daudj5p1ljIqFenetyqSmnaI4JxpKmXcEZpyNygRDs2mtpcP15fVmtJpgPr4qlVNZYFNVvRYEiayjZubqEAACl8OjYdiG1DY6Eh8nw+yeodKOqY2Ss9g6fbFlY3yMz+9Fc2hK6SIEYs3fIsmd6Zw7tWwULu24s77wLdRrRdh6E0rF2kLby2tTXvi1FHWp1IX/9zU4o2FjYZtrCV4SJs2d5p5PmWOD1yx3p2mYNdVFptpE/q53Xsm2q/j6WDE4RJlISY9zV6TU5p4QIK3PzWJI0AMFmHHJF2wWAmz618386pvGNoLH0ycr2YYeTRLLsBWOpyxge2aByKMcioVtEWe8RQWAP6oebwkKBqx/G4BTn9TbcUnQcuzhcgLGJFDc905+zjMqy0bGWO9mSpDuD2Pet15j1KcpOBUbZRtwOTFOdTRRb01FkGYAxfNuJhKLAytHuYvcwUJjjZ2VBUxjzydBPoKKzk44cEe2tsuuQhX7Kd2nTRMSHCg0aD03sj0FlufDz/IDUTYQuSRQGFAmRiO20FHrZAmjfNSY8KISiG9LrTg0F70Ef69hAJyyf8YwZKNWCoS50Q3nnhAiFeoHaaIgP37Whzogs69Qlb4e5Ui6+UGnnSFiYlfimvj3zsLq8AKYNymOi8DW7nLFsD61251tEQt865Srbc8SxFC4YWbnKhRJl1wYGWKDxw/pWs6FP8B6+zK8uD8/xbbguU1mWdthfsexy1AAVKbtdPcsJZNzhjgZeDkAscFTGEu1bVYgDv64UNxwDQzDWbzQdrdEyKAwLshDmm7jicm6gU10vnwmKLyT9ORHtn859SvFzzuRMJFuBhzqqiLD4ZdheITSoRPagPiUFcqDZUS7suS5Z0plHZHpvyiyIOC/HAi/MriUK4FAdjMwpoiU9YKgMerGlO/rPYqCJpFUIRpFf7GU0q5myky+Fo9kJPuR1KVsENjPB+NGyevG6zIhTsXLisdC7tOXgsiQeyg1U34CHXfkFLNXYKZgfumId0fUajQohjqJMy47GuzUtQmU/6XNLNy0dl0o2dXWLLuEVXRk/NQthx5zrmkhzjNUTQIQrln1WtCsyDkbmhmrMotpmkce6jDJJKPMmYp8BnbbNj2nOX76R4eeHGBMI6GQfbOfi/qNOLuJI42O1ItCsObqB7m1COycppCaY6szU5q+G4jDQ7JPIfBluHLBzhpVIBcrmpoxdvukFSHsoYIrFGOvld65jPrZacM0pYpVavUuNc9ElCRK+SchRMfoU+6RmLgTQmVO5DUD/8Jt8mtPB6zYGF5OrvV3Nnb6t9tgcZhMJFuq1WmCOn5UFpTbyBuUnbT440gksMuCpNH2+YFoZRXft3LoMDzH42wSTGKPsJ0k4CXkEDIvy/9wQ6UJlBuIGSgXKSNEmJv4daWywFxIcq1eYOt38bjRfhHojgzrLuFMsy+IUVhDHBF9ca86b8n4VvLCdxuIAM1S5U6fZEIwELOMOJP8cRWOkW4Y25+RE25SGrcy6lZY7PQoTyMMbKOK7lnUc0Jd53yo1C+iCI8YQGouQM+kvftGdZMwpNlQ6guocIK22LGv0mQqLjDt3+e/+gHDGpTKVdFPpI3W7srH4/mQESBNpOGkGLxcT9t+yLquXX+zRd48sNrkN7BJl8Ui+axIR4Ky+Q7tGU6EogCJBMY2x9crvnAYLp0ScUwwo4y49x2tEiNCfRKa25WGmWSIdXlSnpwo9De+T5q5Mac3aCl00lpsTIsW416Wo2+2sfiMLTHJ+UA+8DH0NiBZPmUMVMyw8ZVRYnx2n4Vtp0nGUxBYf2nOqcrr6bEsYZX9pwtCcZZRyHSPI0Kb2tNFx0rejbDgcY4tmgsSlyF4Nr80HyWZU2UaCtgogL+/ZnlqzI+33WJr6N9NCt4LLhZNt9O/mYm2BS85lYyzV9MtLjSkR0uzHnprFqUuSWpZf56SyTCIrWc3GnUeahkkGALbZUW5YaOK5fXIBGzyvcqTIkKTOTUldMna4m9IZw7hOYQVqB3CtlUZDSXSgfONYKI6myeIx3Or8HjsnmAe6h+gJB09U1gBwjI0hsj5lUturGMumsCfzsEU/vZePGIiN4dGMuMHz6HekbPWasSpL650Rvh5i7BveDqgN02qXotn6/J7UMcyyJO508qjqkl2rMeXGgpS1Q+3ehQNWDCmbYaT/VhvdCtv5NbKTDAYKc/ysLJL4zlA1kMR258lvgpLsfUn0WsEKSuSz6cDZ6jJAK7tCAiLmtrwnNSz4jnUsXXUQr+vpmdz4ccoOTmSBMBJZlNMBnnv5221ZH9oCM5bOnQ/QdBLyP8W0Suq46ZogMHGkqCKhFHwCYFo3RV+GlAs7lk6SwMDhE++I5sCLxZ7zMDRlouXX5m1JuaDfwdefCJdUwlUAkf6z80x90g7CCjjLu09eG4TpkN3xhH+jJJHfUQtH4XU2tZN1Suyw0/pJkk7YnhayyMor0N+Hgjl+UlbOwqdcFv0+EewIwZbIMmUXUumjPMRN+14MVcY8p2QBBFgaaHaxxgairPg62ymBWMzTXflC02IDRjPfcQ02+s2KonNdpE8XdPeftZU0ySUrcYysb5emSHK2wFQFLXmfU1grrJ0UjQRopJKVQP/08wV3YPo/A9qHzgQInD2xECwlnKeW4ROpBy/g6wbjTrxsRRtFf6PKGuXR98fl4lw5zqco2LdocM0c75NS6kmwhZT2XkS0PgAgK/V/qO4P0GEFJQ4oBtiYQRaTo88Kz/FOJDc0es/WdF3PLyki4zMNQxKZ1sJydM6n9/YH2cK67K+NIaZ5xJyoKotsDKyEpJ3DNTmzgDkgU73vuAxAxjOtsraiOG9CcfPyOhFOqDutwuys3iZj9px3IAcZLtn3akoGYHSDjo//2qKaOvL7vCwAQ91CvSpXh00hHGHVs8nt6hJFVOdzFm15De9zGjNI1HcixnBHnaARdlqSO1UQPlhD+PvGQthT8n1Z/0s0mysWwsc3EJyTzKyAUV7+nzuwPWNdsIZJP200On0nxu4NbD/F/uomIVGxplkda/OqwgQTDlnO1Ab0DXguncCvjTmdBvpe2+Z9L8MYP6sF5vh5PRBpeUsjVaQ7lx1Z7Ab49ZcyqQfnNdLQARJr1JpXXzisxkAb1NKw88VNVg7ORQ0ludIn4kwVxZklmE5SyDbqEFFCl7qyUAAZs+1FrWOaR/TfMWp3oF1Tz2rqPGwMhodvFDyF5qvLwlu1inqD0UPZeRAsERGPH2FyuAJIuS4MO6e/T5aLToTl7qBj7Jikr6/D6NFCwKqT/Gqp3pEIQIp2N6QOUMBaoEatskDmjtyCaGJVqYRFVi8f6lWzg+mNF/4ODSXzCq+nViu++1yiSiTHjdT+PimUqrUnwehShCAVQXptkRAasp1ycsdV9S3rNuT8fWK7//Ra7n8vUL/b53+KZEgUDI0gnXt5i5hQKiXtKc4NMWcIR4pO93fNVJ8P+N91OkDKznHnmQmSFS246SybmVa/fIe9PCcYbyJzlOOMRn8POhfy+m5mIqtXoPFRpaRmj4x8x8AZkSRIR3LhoPAp4gPGDffp5DkSnrGFISbU7RopCu7MUtjD6TKWBn7d0frxYY/cadVY1kbRlwpnU/PPo7v/njkks5l1rslJuFbKm0DZpzMS0pwPUCYXWZzy+TjL4uwqjDpr+Xjn9xikoDq5ti+Ba2fSGvbOZ9KGeBt2g/2S7cadwlVIODsvSaTdoWW+4/MGTX1etgUXCR0RDtkBOacJB7//nYZ6TtEX0UG/pH2BPpb7IpxT2IrEsesF0rnzxo+ffOOFjlOlzSVC4X2oKvkesTAuXtYAvm6V8V8whrhNkCViH4xm6qpsTj5meSdb3SZPDJ4RRm1nXk6vw6gJDAPMgaJsovJ+GWjveJrfGDeuyLNcI9PZzh7dtIS6RUzEIjfGYoenkGVTmJcCrlAZb4GtWdrMIiIgRZD5cFKgcMDrzcduLCkBc/ysLFwxOujznRI/kSxju350x8MPrlp2FPo7lAG6nXd2I+nhgBoZpoGWZWcdgBvKeSHDfCqFebmAGS2n6xg1Mc0jIBrqlbQLnQpMz+GCwb6s1b3pvxXBPu58UCig6YrIoM0HjTYVEy7LyddoXDRbQzuvkh5VoKk9/Q4ND13z783LFYip5kiKQi6+ylCoZJi0R+YwQruNZKqSGY2ibsIVWb1YGbql2KwE/dhHHR7uvLMQViYNqsqqpxsLYSpb9lupqUIFbgPDk5ZbhKo1hDBuukKZoCOOOGHI0nfMCyR9GfDqkH7t+tP0MgIdVlmdowwYTV3NjZZly4FsanAo5eVv5/KZ9Hm+fzAtqCQv0yfzEAlSNh/OItJel+Wki0TezqtzyIKGOltqRWITiIxFNDyiSknNF5h+oSEyQYXOAgBwUxjrqmIlRFLoAkCajkmfBlB2jNuKxo/muBJCs2Qu8lnqGFUuaTu4vkz2VyqirCUloPcgdVmIMDUX3gMInWl1u3lFocyzo9d6QeKUMWUrtlnEQQd0FlKukUi9KC2lPHfEZUl9qnhaBtYvi2ZDERaXz8ynx3fhPUsjY3NePiVD0nZVynZelgCR9UdGHEI5m+w9Yyefos8f1LGjtmFuO9DXLi8VjuLyXerCxbNyV1nUkxb6I7IYIprqvLqPX3xrgu28WFrGSI5A07Bk5ygZWl2ayvIq4KLrlZOXOlgjzq3AQR1LYqJ9RxHiIzdWYs4bufkT2j5oNORmQ9WnlXGJnjcGbZfO/ZTxtIvjJ+OsPoRaWaPaNdwb6Dp9vM4J4tsWd2aVZQr2OrpsBgnQetWySonNURqFEMne5ds1z/hJWX9ZCjc4RQmRLM+h7UN7b26b8PktosuHvJD6Oor8hBhT6P21bF7VxYXUoCpIe2r4cHa+/nNj0pYyGLrBHD+vB9oADciFMR0QfDYmEcblHSzkIDcik6QUCKWTrdwlV6/NEp1tQzHi5Dk1c81ouToTZlKXQaVOQ4fvGHAmlbYTEui10N1gviNRRCdDAKOhd2PIotO5VtLRhbHg2R504hHbyoi3ARAfd2ynnk/AQfhWQx6jt6DGMaedxsToKHx7Ftm+CiW7lfL3GHbaBNJSqFI4wkjf8rtFnA1VSIOKG8RVHcZYZXTXS9uBrVuM+XtyI3wsrDUv5s5py5XoOjnGnb2FIvAd2zXUjLc6IW2gZHjx+2k0ft5WXccpVLP49f2RMxfU3Ruxmy+d1XVsgeBWeaJ2VV4usTAdA0vENRK4ZoZ0mLNQmuKejo1jSUsRk4xQ/9VFLn8nrpSbQgrNagsjLiRcuLIN1oT5RhafRenQpwvPKMOyJsPW6Ln1ZQUQzHm+HtqDjK3kF9NTOUNJ9iXHH+EvoZmReb1p7yLO0dkYSduJjF38vYtGggZnItGwHZ/6XOkT6XAuQsACh4z/N++6pfMyJ9fy0Cx+3879yLNpX2HfI1Xm424i7Z2H+rGfPZe2jdI2SoruGj9iwZ/nXZkjoxnxuK4WxFyScD2TLBHHgnHTh4RHFoSi/dBrKwcPu0gLceNl8E5sGupF2CCaxldQLm4rkhD9bhnvomFciNuZ1ebKFHJQzOPKRolgOKZqGV0jRcKHOWG7yT4X/F1ptPExtgDKuSM4TgXkKzuWFd87zslmpBiPtKQYgX5OcHL4gCKXtgmtA7/pzR2a3n4VMgKMddTMpOPZvxN1rgg9pX4k3HkT2ywVayXZxrTsioLBSi/hoZEeaRnhwO+n6dypWdzGsiCbOHCugHudKexf7/UTEeb4WVnQmMOY46cuBXglTKbQfvm5zIGUtPPO7kaXDBUqxpIxJ03luKKFa4yVckomMD9ICgHXvK2zk+rKyctAF0ki3eSwEkpGRV/1ydZPgkJbhVhJldHD30nVyVG+Gc/OotWrJqAMyG/GRDVdI0XS4pO0snuuQBgYQkg6Df/voS1o+OTV1wx3aoCxta0i19sJ0zZS76FQpR1LWVwZxJFvE2j8DCvGoULLra4tDf5kBWcKRcRDKcOq2ejEk49FfJzXR3+zO0skixgZaSIddBqTSoiP053b8npuqJd/a7vUo3+UCwsWOuW8QUW/WWw3McL8qnXsNJ1cUCq77iJsI8KCc6ycLk3F89PlUiSeOxmSPJfCoFpYCKCGCIvy8nahtPti2ugqKS0XDtLZBLmBAIRjQaRP+3uGem2Rdk6fETjIR7+BTBcvx7Lg98yHQei/1wkwJ+UufCy7XLAoG+Tshvh1HkVEYDrJCxR8YSg0WpwUR6ZDbNtvWLBnZgnSZioYcZSV4BkeQgdoWM55OUvn7ll8ReDgHP092MsRoWQJshE+7pLfvV2lhTkCcAWf32lBy510vpDTnE1KP+nmLKjE5pV+oDm0+L2kfpbGAtLK1a4f//14202zEnrZO/cg/6aL2ax+fBYLaS0LGC+XP877dBAi2MnM5frD+V3VTBRDjbyOh1Vmr7JMpiWSVlE5skePceaX1OfSQno5nHdI1bWziF7T6INSea5Hu3zHlLw7HUvpe/EyDEPasWQDoepDXNam2ujmzhhq16f6OKlER3BbJXFO3puX3W8Q8rbIHfyAfMcsQ8IMBSpHmPj1lsYQ9Bv4FErClSobpr/nSL5yCX4mArR6fC33MAQwx8/KIklGB1wR2tTdIRJbdKsGhdgpL+AG+kMjPrCWSHWyCUw1vhW9Bpndxy8MawYch1JFv4a54wdwbtA1M0npjYhjB9RjqjlEB1fOCGk25DspArTRhXWdNkZMS0SLHdbqnWfTIIaCN6xFNp6xij1r52q0cR4al0ldDHFv385ECJ2y+6OxUOrS08bot81mZ9IT91PaP39vTyceHHUSRbO/BA5F+hx6kmwTtSl3KwObOwjLcnHxS+7wrWFhBZoQwihrdZ30/MJLOJm1Z4r2pAiPaudzR7hzAKRYZSDw6X2qY9Hp4ci9MzeyKK8ZypJcXhfUrR+LmJPWZ1YqWPsPRIlbOdKRdqUD41FM6dRl5QACFIZOqux8srJHwlkSpX0KHYm+hszY1R4dS+scKJ0b8sUdLWbMs1IuiGnZuK5UWcxg7CfvSZl7muO8TnfD9/VsOMISYEZ2wHxJEhSNRDhYKhZozeaMa2Zi0cnbnJbOvDpPsG3ks7hTMFcyJ2bLw/aSo1N+8d5k3vBOIM3RA7C+FbN1ArFY8s8a+ykp4v25U6CyfDxJQyzjEO3SaadviexoCoNOsCVyB+Ep44v5SqhemTe6sPi0zbjgO3jiBGPcdULjlHZGGa2xFOD+GvJewqEGBRm1z5x6HICefVLRqlPDUQGZSSxgzbiO7hivy7IO8mlEe4/3m6IQtpxoExXjWn4zUd4xbNBqDjVxn1J/rS70NCZ4r84JMZsgphdIz+d1p9jXQd37MO/Y5m43hlUBUGHq8NyaDUPn5PosliBD6Dg1VYZP8Og8l1If9JqYs8Jv5HJ7cSzizmnSxfAxGMYGc/ysSngjXyx0xqBJ4Ce1uoVamnYGcheZVIN4Web4aefSGcSRF0BsJzgw1uQOo7oApNdUu9GKId4lu0LiHUp00qHvTScVbugVrp4dqYT4AAjEBgMQemnSLrMyiXTuSopWzVMvhDvJojFWZv9NY9RZAHCl5pLQdCrPUZgVo/dXHDMi1ricUDXBak3wmZehzpkWo2MMj3Tquc4BmVXWf3hci5kWzLOS4UXZBCT7hXBOBtcWMnMPdTLFRP98u63TRUoSqMLslYglOZc74hoN6Thh7SZbXmoAKAsOKTLJqNNaeIHWzoUuU9KpxwiFGxjV2REhH2XYQFIzHnknVuAgIOUKbCeNTCgEYpV2EzHc6jQ1/O47T7Wdl9mjaB0kK6RWmUvYDmDKx+GSUcHYbBjOJQl1OdfPSGudJJUQLGNxOB9CzMsShLilwT348boFUvU96TlB6IHOBOqUIZcaKrTevDOLN8MahkZ1TpYgW9GWYr9lyFPQ7/h7N7OalMphGYTGlTKuizKkSehEBJCRMKtsmfemhUXIVuRwWYKUOcKoQ6bS3oosbGjfcjxt+3J5DmW80KcKhlomQzAby0goud9QYos7P47UsbeQOCQr2tLpqG0KKZkORV3yMNAafSHpQOCMpUw4krRwLVUjqihkn1YcqCJs1Tu3qcOWzW9qz+DZtXxZuG2VpWLOS6gj0ZdRsCjHuHNfOKm7VI65KbWBtc1WHgLOk1VEwmmLgT6FBSrHypRtfAbhj1UiFfatc9eZO+r6vdfy4W3Nl7dGoHxUd5Ru1EbYskqYXcL1bOjGrZ94uF0Qc27Q799sdsrNv6PKeOM2qhKGzuvbt1eRiTOT4YnCXtQ2cruH8CJNOkLpfKql/cHbotzB6Zxc30x0OIcx9/vaexgozPGzssiL0cUsH1iycuHOBctoyJPXq+GCev53GhvNO77X+4g1ZBoWpIU/cAN7ZYTC6IDPqcxpWSZBsyaUTx+Xm3MDF5JqybRjqnTudFILvOs1nvZGQw6WSfcJrnoXPqlNGQ1/qOqKTwJ+Qa9lN/BIU8XZRM7x7ypWbLqBixWhMKxTMtr4a4Lde5FmIpWGGp/sY6GMqZPGGl9gNpv19RFDmkTCjxTjlC98FZaGaIfeOUTFu1kq7lh5XV8DyXIuUtjdWeCpvOJaoneAkVaHVgwGz/ihO/mK1o1wSrJ2U5SLM834r2MtdMrQVBy5SnnULHqpkqmMOgv8ootdp9yLs1HSZZ3vSXVN6Hfqqg2i1O/oH2Vb4udExgIeUojcwfFU0opeBs+e0tGQYIsGFgZalZP7hJuZ1ADhmwBpGmZuBIK2Ur1fIRehHUYRKwsNW/Ptm9Pl/bhO3ksku1KM/CjjlderwnIJQ+/Ky3gf8UQI5frqlJEc+WBThll5PRfKLFLeQTg0x8jgL/oawu7gi/ukUISKA52a0hGnhIi5LEU+hTk3yDiQLY8xkeQ7cgdSNYgF7z42ozzJXeDo6VyqXMuL4R9V42hzSYakkcpFsw8RrmG7Jd4mqymXd8SK+ThNlc0ZVicrRmqFwKuMTzxstpXr8yq9NjZmKfMV1/9SN8yojlTAypNO7K4ZmTRUoZk19oIvJu9PZfmDMZaHtI8hBLOSBKhj+Pr78aQDztXrZ1XSATybWdGx1WvsRVelPNPLT9uwYAf5d1KY8Pzf4u40AsIjV2zzGAOGM4GoTdDOgf6GdML4tkPtUrEWU2xFPgf67ydYm7nUieSOJW280DL+imc6JEPDsrz8eYDsI3mB2jj1iYiiGIPB1gWm8SNgjp+VhBvsh8t4OqYOYnTLOrZMdVibcLihPNDsGJF0EM9plqnRwUNMjkkijHaha9OvMHeKiKOBnuINZB4uQpwmRV+ZnltLQcxDXWITOzVaaBxxHZPDM0WCZ1KNn8iul9fHmR5mNgr0V2LZOvwEGrCSJAPL8cGeLlS9cc4XaD7emrNE6LcvmQE8Xj1pddptsBDRhBJFenFeZ+VCtZ+FtRRNOUEOTgn/znOIlO8KxDfVDAwgrEvejjwqsd/4/b1hVICITNJdshqxymSkFTB86P0AAGX2HLF7v6xMXc+/I21jXsMoskMWhGXwdt7I5KKTpxGNGbp5ATeVvZPmTBY7WqSPel0F7sjt72QmEnWshVFGEISFCIezIsoaLMTpM/mNpQ4D3XHvJhKtLsCqPxLkU/ukSLDiFJdjSr3hH9xHWUyK3WhlMVuwbH6BZo4f68TuawLX16gPWfHjBXd2lE7h4JtF5p+g/si4lg7VZAksJDsmCRgPnu2mX87rKQ2yAhbInEPBF8TVyfFiJSNtJMy5ItqwD9VSdsoli0+GS3DnTaCHUwk0h9e1BxvIhgtpZ5O20y7Zac3FodZJlU6dmjqxsSVwupBFJZ3GWVhL4hzag2yOJu9dZdvjoeS+vY7EnTPpSMncGsMGhOoEEuEqui5fMVXajWLxyHWRhoZ1W646obQTtDA1bRNnLM6yWDhTcI0ME6P9uC70OSkKwWKizvDqm0TCgLrp1iUjbcHYrhwjQRtTGI687XNTqHIWMGdrpogQ83K13ZhWXFzXDrlk3wLsG/vqYAWuyqs4fqtzNE0lytKl87eSuVdo0dDv6m0vFno6miCE2+akveYdp2rBbKvKx7WElJGHQPU3o+1H/K1sRIq6ZP2+6G+KZDYBY7e07cQz263Oxl6Ns7jaMOfNKcvg8tfgMDUYGMzxs5JI2sWo7kpMO6YuU5L/p6Bny2eJSbuVww1mTM2fGE51BJ5mJsULGTOpsxiLiCDGhPtQ0r/TFGLWpBmwIiEQyUhbsgZEWEtp/FOjPRbiwr99mshQk0AQOGJkePCJTgktiNL2KVvIccdPW6Q0DUKL/GP5bpm/51TmUKEpsxtlemWRacrT9MlxvpBttaTBzidn/95aHDR3ELJU3kgzeT/OkAOkATbQ38mSxtvKMFn4xWiw/f1leUfrUjB+vDYHffeYs0eUv6WEJhDnrG8PvA+UDjVBlabtqdVC0m7IHbNKlBlxDC1HMj1sJ0KbxqeyVhaP4p0GWN0Ot2odwtU4xfpf0siAdi4XsErGPPF+vu0FGQzZc72RTNg2tI+mtJnwbp84mcCGXtv2z2eGf8XQq9/9T1qFYN+kLekYSVfI8bJgTCHOsqjKwL+HczJ9LLt3JwwhPoFE3y8BkOdyJziI2/FOAdbnfBuuW2Bq4Tak3dQ5/TtSUjWLZn+IOxn8/XlV0r7jdEd0qgn781AyLtgPBALKANAYKp1iimNVaH1wv/xIgcaS0CGWk7aTlfdurxM6nBvL2p1+z96hmCr7Edep8hnSaJ8W9oj/rHT6IWNMa/poGdMRGbIlxovAaeTnYnZKLPNmFm6ypK1c2DVVqBc9qJVBMKe4HVQe19JoR/SAaNlqs7G6yDiZOiDPpXiw5gyPkIprNWoUhwJ1wlIHmXACJUkY2oJwbKhYTFp2K0Cy5bXwNW7rVjp7YTkoXJJE606gyzgPQDKp0i6OpcizXCMFXBYXbgZpW2NhT3NUNiBl+YyODVT/T2MS1vXLqH3gn8kZMXwhlDsholyFBVK7Qmg/FtKu5N/GX8Nt4HZb2usMSSsXa5ogQsO3Iy2tuxa6T20fvxnNNxUmY8iShXqtFpjjZ2WR54DTY0OrgaVOGMxfMsJ2E7WsDFxXpN1G0hfqnARGGhUSZWwSpxg2YqIpnJLiUhRLpYgjz+vF1MpJXOxcaJ0ytksVaMHQhR/5N19cp5ksF3VG+B2zsaQLBhCwX7S0p4DOouE7t3khKaZk4qh2UcT7RCZ2WobC6WKV5UQZGEW8XFkaxtuXxwL476fsqDjG8BFZ3LJUOm/opO3Lw9uqvw8Xj6aTe0U/T/VzBkbZW441/qpdxrKRUAeTlslqBatLuvvpFxA8vWj1rnUGdsnC4+d0SSEMQG0rfFEXZQ778E16rXDU5EpmN0oXL9+XtzHfHzkDkewKFmWoV8aHC9926aIvpmUVo70H92M/FlKwl47nUWeFv0bQ6CnTKFXTLuflDm/jVeools5k4YiLZfsRYrNOMW65ZofSnjRNNUV/olY/BYgaXt4xEDCw2JhVhU71k/om82Jd+GaSuypksAINj+C70F3K61ibc0kiHD2j2YPoeBzex19LkSmaNoBSxw7Sa6fcvz2DjTW0OCUbiH+vfKCBxlBrTBm7ODzzqSDfm2v8ZKUzMyXftyDshpRm7VK0MVKW1SufIvu3YMrluqM2QBudOuftJ/EsIhq2xB0Dyn25f2fAZ3WUzlyxecJsFTelT+hnhenc/f+VTUItFTZtw/5dmC1aMfToUC7GWM+U1vtQMjRqYwkbIFE248g8npTMXeGc9raa+Obk73ZHA5OzMbRTZbKCRDJHuINfCeUE0HHI8qxzY1lw0lP88K3o9CTDrfrwMjVcGKOsRspa5bbVitK20ewwYDRDFXlO9WdRhBnAAD1DW8yOVezPAP2KtpEfY1fWySXWJ5Gsqs2mlNng6JfzsRoex/tHJCFG4Fwr5zfRDvIiTGozCeCKAu51hnpZOncJc/ysLBoNIFM0XIDRBT/f6tJ0CGJCgnSAHuDMoWbHw04XfRm9N41lqdklisD1ZTItojfEqYGihYtooTjUyNZ2z4Ay7Tg7yEWPSwM9+KyxXQXO+FkxLEOA6CRU7r4KA7v8rpyVE3zX2ETsz6FlFJoacjHvNGcBd3RE4pK1LAyC0ut1bFykzQCd78GNxaWKTg8g6zvPpSHLy99qSyOAfofSOaIKYuYFlz4J69bvrkxhz1QMMMedN4kU1AsWhrGsFADc9EEZh05pv1roHwX/ZrT9e+2syAIhyQdHjynMIZk5S58ExXntvHtWkrwIdHmq8vp7VgwU5ZnKrhdd9Fc7+HwxVt4/2MmOhTwFoTn6Dp4I80nr07lHxWLLc2T2G7rQaiOfPkWKRyvrAxFSom0MjGURCgDOibBPVWSfl0trr8pmR2dHmrNQiEOm1PgRwqBK+xJhHHmregftnKhwOilf7G8f4uAyPhZ4llG8bfsUy5x15RclGdUK474bzbnI4GIbEilExi7+HdsDGfr/GI5HhaLxkXCnapag6MtkpjJiOLfLrEgpdxb44pF22u4Lv70vQ+CMo3s51AHIbavCoWBhUF6YvnPB6HkBInNhwHzJUiTDTmg6VSLHQWg97/eZYOKpzB7IUK+kKOBYEg7BZikK6dijjmg/IWoCuGPV+OF9ThlHHN9M0UJFqRO2bu5IkkpzZ/Ri8m9/bYRJXUzhTs2QxZSuUFgWfjyipD0+/7QLxQ6M2H+CsZ92FfUumgky1m8KqvET0ZJK2h1GvbDn2tK+lhdLNqloF96G4o4Rj24pxNncEjjjXy3D1lk4e3RzhtpZzmHMWb2Eg9zJtUA/azfe2ahsogkZBt6c01To0ami66q+VUNuoFDnWkNfkyRqQQyGlYc5flYW7VHGD6fiCZFWD8XIEzub5aBRkEFUnNNqdybcGCWfLv61Z3KjhdtCI3mwswqMDuJ0ESB2DIpyt2wsMfJ8R2K4DXBmEx90/bwTm4Doce50y1IZbhQIyHXqLErx5QN3wKzxjA0eHufC3wFpxLTbcaOCPmc4omEhJjEWiqToelT3rNNSGV4BFEzXqEvWtdrjmjERY2iQ890A071pDXecQrwuFS0RQfEtDYDalOv+t1hK2hokK1rymdRp202zipeLfjP/W7dwSEBh3+RRp8jo33rR1N1KfqzZVJy9cmGhOk+zTJaN9LXK+RJpK3SRIbNyNOPvwKAynmq1LSJ1WekksPGNGefp8pYcd5W4fY2BKBZffCzlO85B+SRTJ7i3tujIlfFLC3FwCuuHPs87Uphj0oEtBgDZrv3ubkveD2CONp6GvKG0sYDtWC7s+Xzms6q0pcB2dZvlLbTX6a9C/0aPt4N7AxBWVpLnAMvQxtthoudN6rSLvsjmTImsVUjnYjBH6uyAKr27Es7Bz+GOGR9KJoSyaRm8M7elt1PKkuJaWqkrxLcuiGMpebUcF3g79owHbjPRz5F1wlpjWe3q0rmjqaRM5ynll3tGBQRkAgbl+8UWviDOKf5DQ8kAy+Edn0Jnxd+clEU4q7vbtBU0JyefL+mCvy3n4gB1oV7OdfRV2Du5PvldBfsyUdLAxyA0fpzow1LDDqKiAl0rz07TsgCuaAlnWXCeb/eRsNVax4+m8aOMFwB0e0II/pP7+HqN2UDcBubhU0Uh3mnUqULtJOX+WphV8GydxQpAJDShMgydaxMZMUG/WSwaIM065RascWoDl+17GgvTRwo32cSdLdRrtcAcPysLV1SdWjpSIjsVdCfLT9TMOPDZdQIdEm1hxO9NF/x1gnqKfo+2sJUGktJptOdoj6YDqmZMAPrkzgdibQeoFRn0xQSQAahJxT2gCB4DSJxuBAeTTcksEcaCxsrhr6mlNachfLHBKhYeRNtKuwzr4+2nNLZcOl2/N1AuyNkxHqLknS8KTddNHQwOBULFsftpRobWnlotuYtDDUg/WQtHnO+vtD3qfSXU8SDslW5ZPBizKdD4ycr3498rJqJJn+UFlLXwQfYcEeKWKW1I6CaMfVIUu+ZKOFhg0FR0Z2ZE9fchWT4sBLHRlDtm4pmeWaOkC6+K5Z2HPKNWdYJ+GABQKMxFusPur+V9y4c+cIcsc7K6/kzJBKXoUvC6S+S3jrFNVB2b5Xws4PR8hX6uhUKJMFx0dj8jzhMASPwH55pEGqMiNu5p2iRAuMsujGzUhkS6CLPAM4qEE4HMLcX0PqQjhQgl82FMoSYRq6dm1tWWrUIy+WJSyxDGjuXNFBkXGafNsBqHlHtliQztot/VMxb4UFCWNyPfI80VJyHCxW6Q1Yj8WzCDsxStKYzxQxxI/lquUxUVmCbHi4Em0uUjcHwML9tHSjNTKkkBtNTz4X18aJ2SBajbOi53isjr6IFisFNmTdPGpWnUmdW5SLdFq1NJeflY6jXx6PdI6Hei0yhnG+ZObAYEGap8v+M2ctmms6WhTUGZF4nfgOSaZp51OG10fBbswiRBUiuUScYeXsdpKplGYpx3smFTf0xM1No5uCl9si0Hjpr6cgeyEW1W335zkWqPxZIncDtEk7UIxovym8TsDM4E4owfXhZg1M7hdiQvQ10IfR2ckxuWfDPaOWWzmPzt552MM34KeOH14DDZKExi68tc6gNOeBQOKiV6ZWCOHwFz/KwsUhIOxQ3G0iOf8EwjSspGEd7kQQ1V3vH9oEuN3QghRLCRlFSVwshOE7HzWxk11CjTGEfKwoQOzNU1QvAulSEFbAGrToh04KSThaB6F3KyooN6oYQ/gbz3GBbGQoRbO0mkRE/qU5rGNG385MInJpJmHq0WVAG5RDGOu9F4tWdVonjyW4vJkDtq2u36nZi0PF/bTckL+cymZigotF+gnrLP7wGEfZEYhCIEs10IscoA/jexME3140F7zjvn1emGVQVTWCJ1rDKQOXUM7UB1CvNnahpYzMmQAJ3xpMYu82OVSAldLVZqypVFvmtVLnItv73iLAvq25P5GCMti7Awg0WPZ8bwecPXdyBuXRMu7M/hQsKeycEXgZqxqIxHCWcX1qVz9sfTjoNZZDOjMZnDPntdJHUvXWDy+yjNMhira9lZumNj9N6RNu+/I0/nLvqOQzrEQqrKthK0B8GWkHOl1M3zDBa+yy+zbom5y0kHSJto6Xh2DnfwcG2f6n40q1cp9Jyxa70zKSfpqhvL2TmFdObS+ggcP2zeTYfbgvFDxwD/W84yIjWWlXoZ3O6gDqrhUtSas+L8d81qbJ/cSbH2MWTQA6CGVGm6HnXXJd6e1Jw3WvgmDfXy9oCLOMvoOBnTZiyoHUaYN4T9LpjweQ7H2AxUW2fUVgz7lneq1oU8Re21bnOCf26X0OhKV4YzQFKnzOPMLswL4aynqOxIbk+O+KygNYx6TzCJhMeFWV8jLN0WGf/puEPtkRXKXMtD92kZvN0SC7ldweYcbuM3lcQWhXJPbfOsmw6if7ZgmaayDrj2VqZsKtG+GrOnfDl5wgv6h/+eIqV8jmgCE4NhJWCOn5WFI4Y7n1Qrgc+aRWAVUsJo1n4nrSazCorSWxxjLlCDYAypKlUnTyxbARn4xE59ksBlGRIuGkAH7Oq+cTbU6DG5qwYw46MYw2QPlDokrJnT8TQSulFpLIxFGCyWBcvRXRbFoEs5Y0aZqMSuQvmufBHFnSmJTEnp02rTFMQpf3/te8QWoUK/p5BOIj5RFYojjjNVoBgw7XbHkahlAPPwTqZYeEdNVXqdhnSF/nvAfhAGXgrHU9TTBU05gatZ0Pi9wRakrpP2nFOPR51ZOkMJQMdJ3WXTy4uxipSsK+ROrVjADPSp6V2re5daDBnTP3J9TSBVnG+a5gtPo1o6XIIFI7uu2gWnLBFqhNPTxSIakrJPxo8o07oKn2HhQSwMQcvS4+8ZMCGUsblOpwTAqCg7c44m2u6kUn7Xz8Mru4c9uBRAmtYKXYpQEg8/19GFdeQ+waZFzHE3hg3eMCNmJu8BVPWTMoHPoC7bBfLBBhKuC6OFjymLHsHWYnOYr//s1XBh5Poa4lze79N2IdkBlL3or+djvQOQyDpwGqtKhAp2zqHOnjZzJDWHym9DvgfVCsqbynNKFH2ZeKfGstFn+XEsxmCsS7XtsgTJsNxNd774Ne0qKaTNFP32inNGsoWVuo2JuIO0N37rJOm8FxPFc4q+n2NbVWobjjiro6FR1NEwhuipQBcmkkGw0tHjfUekJ08VO6Rkm+U1NjIUp3lME48zy/qls0A4jRzkHE0FtH37U+bFpJXXM37Kb+CEvagwqPkQ6+c3+l2DdkIdOSLbgrTfaDFjYYqaQ4o/F+XGMLchNYc+D81KEukMjelPajZvbB1UB20jV2QpizD2NfkFHrrc3zfKtJsscA61hvuY72GgMMfPyoKIGIvFkk8lOX2quKyCX7CxBUJrWqcqGovJIoM/uq/RMQjpoBRx/Ahkimgvz6qTJWLic+UiKl1GjvFFSdHZ9RKhAcHCyztSeLhCJrIdCQqucr/AedAmq3XOMGlkMoMEDceoslOwCd//gzs3qNZRrJzeoRNZrAAdJwDPiFFMI0LF5Xfii+boREqdWyMjnd1T1saK6UzkGkAxPQzNSleMVFlIqqKsMy0se2nsCUOEM2EAJMOsTQwqVnSwU9Kpj4J923SdqR2hQJ4KmRoiPouMMJQV46fFjLIumWsCJwV3PmkTS7BIzPXrvPHcjuyGAaPtj7drTwemRhkPM2q15aKE11HM8dmQ2Se47kZHALpGB8UvCkRmi46mBhdrpcKm/j7c0En9mEVJH8yISpd5MWHC8Io5h7n9lcj70TErxjqOiWvy8a5opkgjejvBcwTN21VZhqpn8ojG0lEnWAOtPCp6O3qxwtaj38F/dj7GO1SLTIogrXfF0uKhU7IsUnC1zU5m9RroPCjGdI24cyzhQZWyXEtiQIuvCByLLGJQ5pZWLgWUuSOlLGehhCtyJhK/f95M0RDsQuJcq8K1+Pt0Fpd8fqcsmqwM5xHO6pJ9ls8YnS95X6kIC3R9Sb4pZ/SED0jQWBF+byqUWz2L2zSe1VLHwik6ZRIi3/7edFziYUtaCGaDpbpfOswKWZ6nCAJ7e6u6dmi4u2NJg3OdOtFEiP29vb3K5lo/rkQFgzHqpKC2gpa1DEAnM1lwcTIq+lsVRlnMsw3Gqi5ZSBWNznJZIsNYgdF2UbdwLgq5+ca/X8yBmELaFLzNJbLuglDHKjyI3buRdhy+3BFG5pHUO4j5pqkiEi9C+7xjhdq61M6ltkyLz+WKky3QhvLvwMW8vdOf3Y/a9XnR6ZfcfvBrCWIbq0ksuslKVMxOuWnHs3pxtmox0Ic05VlzSXizb4N8DhloqiLiwdzry8U3d5NE3SSdyHCFg3udoV7OHD8C5vhZWRQ5gFLgkKVw9h5l6aEe7fx+kuW7S36njA7QgiLrHNy0KWH2myB2lwxEfKBNc4CLaXLGkrbD60+hOyVsgk8Gmp2FyXJO2ySTzfozOueKZ+bAUGgEJCvYZOudE5rYKP+35vwSyvqknGXdiMWSvydnu5BwiIpmzXeO/HekLBwej7xiGAlLfZ6+OurAqgxM7izw32Aqo0qzMI0kL6SBtLTjvXMbrkeeyQywV4eQTAsdRMJI87tn3BGQ5/UaPEBn8uLtjLbbwU5d81T0VVgLd+IFzK9IeF45+Wo7fNX9K+p65BxNuM8jTeVCOlhglu1Zy3DGzwVC+nNfx1AQbbgcY+qMc6SJfGfFodC5EVuIttrAMDM0tWfxBSwZs7xTkQvBJr4N5MxxTsum7NACo8YezR6ULmO0af/OVASYLirpMML9Ankin0kXfl5LhAmMJtpiAwBo888SZEqWniqNL3WWaMxPcYw7QSNtN4Vsn1qoUU24n/8m3MBO267zfcSmAi1XObexe1Ztt86W9QsX+m1ISttA64l9WNdoyAUBbRMxdm055qds/qWbNa4vQzKSIx8M59nGkBJaxBlY2uJThBboGj8oJBOJ9/G07eQcTZ1FftwWDou0ExLOypKSBa0PE2ssDucb76CiDq3W1LB9plxbBOH4RVPBpy3plErYwro9OHr/vtIJFNX7qmNj9DU6wug8VbJnhAVOf/Zdh9vSCdaKLOqYqHUCGVqkppFmrOOAWO0XlpyJkSSdd+asE8oyjoTheDswUTKFVvDjcyztNdX40lihdfXh783sjiqbrbYB4c8ZKTqOH6EdU9Ylda6zMdylaadO6DP5mFpJH4SHtTAx3uaKvkw4IAP2YlmHwmHktZrq5vrSxlCTpwDhuBcLF6K2Glmz1KVz78zjPOSYfJyYJk0VNcHWDLQdJwmSJJGOH/99aKIPnlRhcErcdmbXJJqN2uQMaBZ2mCTq+ky8Bwtl8zZUItZnpPz+m/Ew3+FhJEVE28NgWAmMq+Pn3//93/EP//APePDBB/H888/juuuuw4EHHqie+7GPfQyXXnop/umf/gknn3xydXx4eBinnnoqrr76aixfvhzvfe97cfHFF+PNb35zdc6f/vQnnHTSSbjxxhsBAAcccAAuuugirLvuuq/vBfhirJqE2XlkoK2MgIgXMpa9BACQdVLIBjuV3URnCcSOAd85aifimVWoE51cOVOlmXaWIHyAJnHQfjIbkx4O3wXQDOVgN5r8zmOGNe0hWm+JXh+VsVcnpuY983xHd9jJMvK6bDalAZPI7yU8/H4RUReGkedVCtAAlaZGDXU3yxSdgcjELuopVww6vuBUFpd0EiwdO+ruVlu7P7mfd7TFdrvrPP+K0HJg8NcJQ6eJWHS7oC4jTiv/HbkjKWBvtYCpA7Ke+L0AaeiMtIB1Q/ZhLNys1nEVQ6sVNaYAsjOrlRWQDBNyr+juf3n/OrkD/+2DMIQpVAGU/JM/JnWVUK2GSqdEiMFL1g4AGfKjCSErXUJr/wVjA4jsQRGnpetrSD0gfk4jlc4r2neVELtOwSRjoLxYeQj72IoYdaKMRwBj+fCsPpFHJkVR6/Cq2Aqc1e/FnRk7LmgXI3nHkRdjCtLnCLFfRR+Cj5O+avlnzBLpxODOuJFcsjaD3W59bklHcuQDDWTMaZQrYa7FoMJ8AUCZeA0mKO41hQLmIPl3Rhw7Ip177lD0heWlGcRGMyLxRXN5DmMx0Y0FV+ofJiwso/rOTfn+1Tl9khkZg8bUSfg4J+YCJdmCdm8ehlttZPDnkc24WAimdyLQfhlzCtN2Rc+hYxPvg3leLxLs36VbeKq/PWFGJMNlxiQ+R/uy1TFfUukMlmFk3mEhnabSXg//TJx0qgaJX7yNHNNrYlDD4/hg5usnSDQSaa/BGES+A60GkY0qlzYqtfF8PccYt0LDkm+aJ0rYf3kOde5p7bObxk/s9ySRazvO9HNOpHwPbKtKr0mZz9ptWQf0+2gJAoAyW+8k0/hxBV5/qNfrvH4CYlwdP0NDQ9hhhx3w0Y9+FIccckj0vOuvvx733XcfNtpoI/HbySefjB//+Me45pprsP766+OUU07B+9//fjz44IPIyo79N3/zN/jd736Hm2++GQBw3HHH4cgjj8SPf/zjlS80CfUSu3HV4pF79ZVOzcUctZ13LcV4XyPOdqGDH69ZZQAVuxmaoUyuV59Df6tbWNcxKWITUd096O26qffXZbboZrRxBgt1LEXqUh1o+HOWL5fGTy4XC4JV462JmFPHX+/TuVL4nTRaT0paSbFrJJxPNd+sWzaEFZK6Hgoq+x1+ZYJrZJLGzesHkO+UKO0zIgAd1GUszn0ME5GqRxVLXcqFAqnYpv8O2q4UWB/mxoq2syoETMv/8zbcagvRdZkhJpW0dLrg9ZRsPl40XMdJx8POxmLoawYkN86XlqFeZPEb6lXU3V/RX6FUd79mjjlZhGMy/CNp5cJRUkzxLArC+BP15sLsQoBwBPm2UPRzh4ViaHIBfc1RTL1iFTuN6T4liDONqpP8IjTiXAxSn7MylGNYIEpLnUB0jBNOKcXpH4RGl/fjl5WOj1RkFRp9brqihaLRlE6jchGaEbalCOPKoJRLbzdCdybN5Lns/kV/Jpw3oXCyPl/nUxpIcicZRNSZWzq/eMYzX07K6hnhbKhl3m6iLB8aUkLux+f0rJA2Ez3Fh+cxXaHMizvXLOj9XMn1atyAzNopHCzUHvSHxpIZ1V/LoTljuHlD59gqJbV8RqUpRkFFlCtWM+u/fg4O5svIJlVsIyEI91HO4cfo+f79xEaiZ/SyS+m9yj7LWSLVeF0TJpfk8ntJ265mLIvYFLWg7xKzRVPAJams40BHTdnUC84h4yfftPDsHhqmWhc9QJFlCsOanO8347gd6O047uin86oSbl4dB4ClQ6PHOMPLObkJzJ1Mvgya3k6d3Q8A0wflJsVYUbgoKx0A4Od53i+TJD6WTFBYqNfqwbg6fvbdd1/su+++tef8/ve/xwknnIBbbrkF+++/f/Db4sWLcdlll+GKK67AnnvuCQC48sorMXv2bPzsZz/DPvvsgwULFuDmm2/Gvffei1133RUA8J3vfAfveMc78Jvf/AZbb731yhU6L0YX9kIYtxxAuhl1UHYBtVS8fNDz6dxXlQNT2+Vn9x51QFBtG0XnwV9PQQczfz03ZPsyJEPMCJjCQo00cUT6TZdHFHl9Gep2u6tUqzpNN6nLOsDOrcrrJw16LS8jFQlXUBkqfHKpjBxloqPX504upP29Ys5CoOMg7ObI8kUQk7aTi3lND6cum5l/P/5tRlq6sDN9Rz/58/J6ujPVGtCcqvT5QGgQ0Pam7c4L41kxWLgzq8qmwZxZTKg7aSlMJ/9bt0ktQtGvfk4i50Fjd/BVruIopiGhsfC8LO30Oz6OkWtHs3rxnVVFkDcmgE5p9JRNQO+nba7y8DXFySOMZz9e1HwPACgGGyJMzI+nVNdKLPj7GnIBKDIVdU7g91d3MIV2RSZD+TTnFncKR+aicNGoO+ZVcVWxay5ZldwBVt1vhC/ulZOCkKfyfywExzMO6sIoK+cd7zb+vev0UdQkCrEPqTiEeLYgwWpSFqz0tZeX4U88axkKpK1COHWowHRe6sQJBpk3fUj7aCzjxoR3rpOyE6M+cE7xcEtF3DalpISm1DLp3LM8jrgNkI601T5e6dgEdgdnkuS1IcSda8qxQThnlAVgwft9U/Y56nz131RzpBSFcNQGiGVcUhDVJAn0JiObgyKdezHGLJWRua0uA6Ufh5VkF4A+5gSndRPP9fUlxnkXbtiQZ1Z/KizWgvRBdQ3gn6WVKwhbK/t0nW3lz+Wbc9XYU5PAoiqwVg7ez5Vrua3o2wqvy0gSi65lEEy8hkyIodkhGrT7a9lRBROelIGvGzzyotxg59+MlCWWgc6yehlWEdZqjZ+iKHDkkUfiM5/5DLbddlvx+4MPPohWq4W99967OrbRRhvhrW99K+6++27ss88+uOeee7DOOutUTh8AePvb34511lkHd999d9TxMzw8jGGim7J48WIAQLsgEwoftLyl6dggRxc0eeloYLHR7ZEyTWpOBgwec54kcPlwOHnRwScQOeY7+ClcxkVF2f1HWnANLhBbntumE2nYbPLhFIkblvcju54u7YSciJjwLB19iMfwUPi330GITfi0TsQOVx7+DjBjpbwFP8f/nLKBlkxgrqUP7smwj02vYda0CyAPnUEuHTWQPA07bTF9neoh3CnI7rVimYzvTzrfuSDnpmDv3W6hKMI2kNZlawseUEgDpeCTLeSkp1BknQuvS9IcyJQFE9U28n4dbtz58CDy3nEDjzrFyOGCfAMx4TfC6xAOAdWiI2ftxbfnhAtKUrVKdOqWt0Pv26B1ycUG2zmKkbBd8PouVnTaV7vFHJNFW9ixjvW/tLVcOrUDZkanHSW87GkBtIdlNplktM7zsg85Nqak5TiUt0b7Spv1w7S8D61v2sTaRLsoZ93L5UCLxdE78m1aZZ9tsn5ZjR+aM9Tfp5GhaGSSAfXq0s7/adsWDjtI45Z5rfxCVQhZOweRcVEsgDJFC42GbpTOEHZKq2ii0Vquhk1U5WqX9cF3Ub2GShB2E9FaGyF1RjVzqCYe74N8HgYAwkhxpZZca2p4XTrS0ULLB5iOGnUQpQngpOPQ6+G4nMx94rs6tNkiMGXnRBeoBeBGmNOOzV15azlaLjxGSShFo2Q08XTuZeYsLlrdnkKYBV4fr8Gdo+Vz6P2Yg6q/dNzTb5YTQeU80AYJx/88K7C8L3ynvldJ3fsQYa71UXp/Mzaf0GHQNVOkeVs41LzIcUqcCE6owTtFiF1n0IgNIk23peA2lGZjkGv8Qp+LWvdlSODEmB2kXM+9CD4X3i1/j22CgLwL3aekjSxgy3JbRemXwf3L8zlzKqbxQ8epvIWOQ00+AgCKgr4/d0YojkPOAPLPEmNxDuFbZOe0W6lM504lOr1GhGbnahu+5BP7cvE5ofo9CFXnba5sYzGbiNcfRaMpnRFt8lK+H/Mql0Sk8ln03hkwUkgHjZ/fqD21jEs8DAIjy8JjYl70kgX8/oWyIcoY9S2HhI8pQRbjiH6jj3AQdUDDiMt3yfg4kqFdTsCThcXSdsPxTZGx3oOvLw1rt+PnvPPOQ6PRwEknnaT+/sILL6Cvrw/rrbdecHyDDTbACy+8UJ3zxje+UVz7xje+sTpHw7nnnouzzz5bHP/lc5euzCtI/D5y/OnXd9txxW/HuwCrAM+NdwFWMeJNG1jU5dpYG12TWBvK0At4vsvv/93l92dWVUEUvJ46XNjl9/98Hfe+83U8upfH6dWBJ8a7AAaB34x3AVYT7h/vAhgMBsP4Y+nSpVhnnXXGuxirDX19fdhwww1x5wvzV8n9NtxwQ/TxUL9JjLXW8fPggw/iwgsvxEMPPaRnk6mBcy64Rruen8Nxxhln4O/+7u+qv4uiwB//+Eesv/76K12eVY0lS5Zg9uzZeO655zBjxoxxLYuhHlZXkwdW170DqyuDh7WF3oHVlQGwdtBrsPpaNXDOYenSpare7UTCwMAAnnnmGYxo8g6vAX19fRgYGOh+4iTBWuv4ueOOO/DSSy9hk002qY7leY5TTjkFF1xwAZ599llsuOGGGBkZwZ/+9KeA9fPSSy/hne98J4COp+/FF18U9//DH/6ADTbYIPr8/v5+9PeHIS+vOwvYKsaMGTNsEO0RWF1NHlhd9w6srgwe1hZ6B1ZXBsDaQa/B6uv1YyIzfSgGBgbMWbOaMPZc4GsYRx55JB555BE8/PDD1X8bbbQRPvOZz+CWW24BAOy0005oNpu47bbbquuef/55PPbYY5Xj5x3veAcWL16M++8f5Qnfd999WLx4cXWOwWAwGAwGg8FgMBgMBsNExLgyfl599VU89dRT1d/PPPMMHn74YcycORObbLIJ1l9//eD8ZrOJDTfcsBJkXmeddfC///f/ximnnIL1118fM2fOxKmnnortttuuyvK1zTbb4H3vex+OPfZYfPvb3wbQSef+/ve/f+UzehkMBoPBYDAYDAaDwWAw9BDG1fHzq1/9Cu95z3uqv72mzkc+8hHMmzdvTPf4p3/6JzQaDXzoQx/C8uXL8d73vhfz5s1DRtLvXXXVVTjppJOq7F8HHHAAvvWtb626F1nD6O/vx5lnnilC0QxrH6yuJg+srnsHVlcGD2sLvQOrKwNg7aDXYPVlMKw9SNxkyQtnMBgMBoPBYDAYDAaDwTDJsNZq/BgMBoPBYDAYDAaDwWAwGF4fzPFjMBgMBoPBYDAYDAaDwTBBYY4fg8FgMBgMBoPBYDAYDIYJCnP8GAwGg8FgMBgMBoPBYDBMUJjjx2AwGAwGg8FgMBgMBoNhgsIcPwbD68BDDz2EpUuXjncxDAYDgfVLg6H3YP3WYOgtWJ81GHoL5vhZi/Diiy/iJz/5CZxz410UQxf893//N/bee2+85z3vwcMPPzzexTGsRli/7B1YvzQA1md7DdZvDYD1216C9VmDoTdhjp+1BN/61rew0UYb4QMf+AAef/zx8S6OoQannXYaNt10UwwODmLBggV497vfPd5FMqwmWL/sHVi/NADWZ3sN1m8NgPXbXoL1WYOhd2GOn3GGcw7z58/H9ddfj/PPPx9ve9vbcPbZZ6MoivEumoGh1WrhxBNPxNe//nVceeWVuP7667HRRhvhpZdeGu+iGVYxrF/2DqxfGgDrs70G67cGwPptL8H6rMHQ+2iMdwEmO5IkwQYbbIAjjzwShxxyCHbZZRfsscceuOWWW7DvvvuOd/EMJZxzaDabePe7341HH30UL7/8Mp588kmcccYZePnll5GmKQ4//HAcffTR6OvrG+/iGl4nrF/2BqxfGjysz/YOrN8aPKzf9gaszxoMEwOJs2DaNYolS5bgiSeewOzZs7Hxxhur5xx22GH47W9/i9tvvx3Tp09fwyU0eIyMjCDPc0yZMgV5niPLMrTbbXz605/Gv/3bv6HVauGII47AFltsgccffxyXX345zjnnHHziE5/AlClTxrv4hpWA9cvegfVLA2B9ttdg/dYAWL/tJVifNRgmIJxhjeGcc85xM2bMcG9961vdjBkz3AUXXOB+97vfOeeca7fbLs9z55xzTz/9tJsyZYr75je/OZ7FndT42te+5ubMmeNuvvnm6li73XbOOXfPPfe4j3zkI+7GG28MrjnppJPcDjvs4B599NE1WlbD64P1y96B9UuDc9Znew3Wbw3OWb/tJVifNRgmJszxs4Ywf/58t80227jrrrvOLVy40H31q1912267rTvmmGOqc4qiqP79hS98wW2wwQbuueeec845NzQ05F599dU1Xu7JhldeecV9/OMfd9tvv72bMWOGO/jgg90f/vAH51xYP4888ohbsWKFc85VxsoLL7zgkiRx991335ovuOE1wfplb8D6pcHD+mzvwPqtwcP6bW/A+qzBMLFh4s5rCDfffDMGBgZw4IEHYvPNN8fnPvc5fPzjH8ddd92F73znOwAQiNmdfvrpGBgYwD/8wz/giiuuwD777IP58+ePV/EnDRYvXowZM2bg3HPPxU9+8hNcd911+NnPfoaiKJAkSZVmdLvttkN/fz+ATow6APziF7/ArFmzMDAwMG7lN6wcrF/2BqxfGjysz/YOrN8aPKzf9gaszxoMExzj63eaHMjz3H3iE59wH/7whysPuXPO/fd//7f72Mc+5nbYYQe3dOnS6lyPM8880yVJ4vr6+twZZ5yxxss9GdFut91//dd/VX9/6EMfcttvv7175pln1PP9DsiCBQvc3nvv7Y499tg1UUzDKoD1y96B9UuDc9Znew3Wbw3OWb/tJVifNRgmNozxs5rhnEOapthkk01w11134fnnn69+e9Ob3oT9998fzWYTV199NQAgTVMMDQ3hhBNOwN///d/jmGOOwYsvvohzzjlnvF5hUiHLMmyyySbVrsYll1yCBQsW4JprrsHw8HBw7rJly/DlL38ZH/3oR7HTTjth1qxZ+Md//MfxKLZhJWH9srdg/dJgfbb3YP3WYP22t2B91mCY2DDHzyrCSy+9VA2UFJ66evLJJ2Px4sW46qqrgt/32GMPpGmKV155pTr28ssvY/r06bjjjjvw3e9+F+uuu+5qLftkQ6yu6LEkSdButzFz5kx8/vOfxze+8Q0sWLAgOHdwcBDrrbceXn31Vdx+++248sorLQPFWoZf/epXWLFiBYCwfq1frn2I1ZX1y8kL67O9Beu3Bg7rt70D67MGwyTAmqQXTUQsXLjQfeADH3BHH320e+yxx4LfWq1W8PfXv/51N336dPfAAw8Ex3fccUf3yU9+crWXdbJjrHXlMxf4/zvn3MYbb+yOO+4498c//tHdcsstbt68ec65kJZsWHvw9NNPuwMOOMAlSeIuv/zy4Dfrl2sXxlpX1i8nNp599ll39tlnu3nz5rl7773XOTdaj9Zn1z6Mtb6s305sLFq0yN14443ukUceqerYh/9Yv127MNa6sj5rMExcGOPnNcCVXvF/+Zd/wU477YQpU6bg+OOPxxve8Ibg90ajAeccPvvZz+LKK6/EKaecgjlz5uD000+vROoeeughOOdw4IEHjsu7THS8lrq65pprUBQFsixDnucAgIsuugiXXXYZdt99d7zvfe/D0NAQgA4t2bD2wDmHT37yk9hqq60AAOussw6mTZsGYHTn0frl2oGVrSvrlxMXp59+OubOnYt7770XX/va13DooYfiiSeeQJqmcM5Zn13LsDL1Zf124uLUU0/FX/zFX+DCCy/Eu971Lpx44olYuHAhkiRBURTWb9cirExdWZ81GCYurMe+BviB8uqrr8YXv/hFXHvttdh5550riqNXuJ83bx7e8IY34NZbb8W2224LALjiiiswY8YMHHTQQdhnn33w7ne/G9tssw122223cXufiYzXUlfbb799NZllWYbf//73uPfee1EUBbbddlssWrQIn/zkJ8ftnQw6rr/+ekydOhUPPvgg7r77btxwww3YZptt8NOf/hTAaF1ffvnl1i/HGa+lrqxfTkz86Ec/ws9+9jPcdNNNmD9/Pq6++mrMnj0bP/7xjwF02oL12bUHK1tf1m8nJr73ve/h7rvvxi233IKbb74Z3/nOd/DYY4/hmGOOAdBxCFi/XTuwsnVlfdZgmMBYswSj3oWnQ3r8v//3/9xWW23lWq2Wu+uuu9wBBxzg9tlnH3f88ce7e+65xznn3FlnneUuvvhiQalcvHixu/XWW923vvUtd+edd67ZF5kEWBV15TE8POxOPvlkN3PmTPeLX/xiTb2CYYygdX3ppZe6H/7wh9Xfy5Ytc0cccYQ7+OCD3dDQkHPOuVdffdV9+ctfdpdccon1yzWMVVFXHtYvexd8fD7qqKPcu971ruDY7rvv7n79619X55911lnWZ8cJq6K+PKzf9i58O/D/32+//dwRRxwRnHPiiSe6JEncpZde6pxz1m/HCauirjyszxoMEwuJc4rKrSHA8uXLkaYp+vv7q2P33Xcf9t9/f3z3u9/FV7/6Vey5554YGBjAz3/+czz66KN44oknsOGGG45jqScnVkddvfjii9hggw3WRPENKwFe10VRVLtUeZ4jyzJ8+tOfxs9//nP8+te/hnOuYoAZPXnNYnXUlfXL3gNvB+12G+effz6+/e1v4+qrr8ab3vQmfOpTn8Kdd96JXXbZBXPmzMF5552HwcHBcS755MTqqC/rt70H3g7+/Oc/4/DDD8cOO+yAr3zlK9UYfdppp+HGG2/EK6+8gueffx6NRmM8iz0psTrqyvqswTBxYKufLjjjjDPwrne9C+9///vxzW9+E0uWLAHQ0afYcccdcc4552DHHXfEV7/6VZx55pm46aabsPHGG+Nzn/scAFTxsYbVj9VVVzbhrX3gdb106VKkaVppw/hQob322gvPPvssFi1aVB0zp8+axeqqK+uXvQXeDhYvXoxGo4EDDzwQ73jHO3Duuediyy23RLvdxv/9v/8X++yzD37605/iuOOOAzCq+2RYM1hd9WX9trfA28Gf//xnrLvuuthmm21w22234Stf+QpeeeUVnHbaaZg3bx7OPPNMNJtNfOc73wEQZooyrF6srrqyPmswTCCMH9lo7cbw8LA79NBD3dy5c90111zjjjrqKDd37ly37777OuecGxoacgcddFBAlfQUycsuu8xtvPHGbunSpeNW/skEq6vJg1hd77///ur5N9xwg9t8882NTj4OsLoyONd9fHauE5Jw1VVXuf32288tWbKkOn7DDTe4wcFB94c//GE8ij4pYfVlcC7eDvbZZx/nnHNLly51n/rUp9yWW27pZs6c6d761re6++67zznn3G677ea+8Y1vjGfxJxWsrgwGw1hhjp8InnjiCbfVVlu5W2+9tTp25513uilTprhzzz3XOefc/Pnz3frrr+/22muv4NrTTz/d7bHHHm5oaEjExxtWPayuJg/q6vr888+v6tCnGH3llVdcX1+fu+mmm4LjhtUPqyuDc93bgXfCn3HGGe6ggw4Krr3gggvcnDlz3H/913+t0TJPZlh9GZwbm13lnHPPPfece+SRR6q/V6xY4WbNmuX++Z//eY2WdzLD6spgMIwVFvMQwfLly/HUU09hp512AtChQO6222740pe+hK997WtYuHAh9t13X5x44ol46KGH8KUvfQm//e1v8Zvf/Ab33HMP/sf/+B8YHBysQhYMqw9WV5MHdXV97rnn4qmnngKAICPF29/+dvziF78IjhtWP6yuDED3drBw4UIAwIoVK7BkyRLMnz8feZ5jwYIF+NGPfoS99toLm2yyyXi+wqSC1ZcBqG8H559/Pv7zP/8TAPDmN78Z2223XRUm9IMf/ABbbLEFDjrooHEr+2SD1ZXBYBgrzLKOIE1TzJ07F//6r/8aHD/llFMwc+ZMXHTRRQCAj3/84zj33HNx0UUX4eCDD8Yuu+yCLbbYAqeddtp4FHtSwupq8qCurtddd118+9vfBtARIQWAadOm4fnnn8fQ0BBardYaL+9khtWVAejeDi6++GIAwIc//GFMmzYNBx10EPbbbz/81V/9Fbbcckt8/etfH49iT1pYfRmA7u3g0ksvBdDRRnzllVdw/fXX42Mf+xg+8YlPYP/998eGG25o+j5rCFZXBoNhrJi0jp9ug9ymm26KrbbaCnfeeSeef/55JEmCdruNZrOJE044Addeey2KosAGG2yAY489Fo8//ji++93v4qGHHsL3vve9IKuU4fXB6mry4PXW9dVXX42iKNBoNKpsUV/84hfx6U9/Gs1mcw29xeSA1ZUBWDXjc57n+Ku/+itccskluPbaa3HAAQfg/vvvx2WXXYaBgYE19CaTA1ZfBmDVjd9ZlqEoCtx9991YuHAh7rrrLnzxi19EkiTGol5FsLoyGAyrCpPS8bN48eIggxPNPuF3n9dbbz184AMfwJNPPokf/OAHAFClO1xnnXUwc+ZMPPfcc9WAvNFGG2HXXXfFlltuuaZeY1LA6mryYFXU9XrrrYfnnnsOQCd0CACOPPJIzJkzZ428w2SB1ZUBWLXjMwC86U1vwoEHHojjjz8e22yzzZp6jUkDqy8DsOrG70WLFgEAZs2ahS996Uu47bbbsMMOO6yp15gUsLoyGAyrEpPK8dNqtXD88cdjv/32w3777Ycvf/nLKIoCaZpWA2ij0cCKFStwzTXX4JhjjsGOO+6Ia6+9ttKdAIDf/e53mDVrFjbddFPzkq8mWF1NHqyOujasHlhdGYBV3w4222yzcXqTyQGrLwOwetvB9OnT1/TrTGhYXRkMhtWCNakkPZ649dZb3ZZbbul23313d91117ljjjnGbb311u7zn/98cN6FF17oZs6c6T74wQ8655z79a9/7Q4//HDX19fnPvGJT7jjjjvOTZ8+3V1yySXOOWeZoFYDrK4mD6yuewdWVwbnrB30Gqy+DM5ZO+glWF0ZDIbVhUnh+Fm8eLH727/9W3f88ce7kZER55xzw8PD7swzz3T77LOPGxoacs45d/HFF7vNN9/cXXXVVUEq4aIo3DnnnOOOPfZYt99++7m77rprXN5jMsDqavLA6rp3YHVlcM7aQa/B6svgnLWDXoLVlcFgWJ1InJv4Uu5/+tOfcOONN2KHHXbAjjvuCOcckiTB6aefjnvuuQe33347gE687PDwMKZOnVpd6881rBlYXU0eWF33DqyuDIC1g16D1ZcBsHbQS7C6MhgMqxON8S7A6sCll16KJEkwZ84c7L777lhvvfVw1FFHVQOiV7dfsmQJtthiCwCdAbPRaFSCaB42iK5eWF1NHlhd9w6srgyAtYNeg9WXAbB20EuwujIYDGsSE0rc+eqrr8YGG2yAefPm4eKLL8b//J//E1/96lcBhEr4adp57Yceegjvete7xqWskx1WV5MHVte9A6srA2DtoNdg9WUArB30EqyuDAbDuGBNxZStblx11VVuhx12cP/n//wf55xzv//9791FF13kpk6d6pYsWSLOX7hwoZs1a5Z78sknq2NPP/20c865dru9Zgo9SWF1NXlgdd07sLoyOGftoNdg9WVwztpBL8HqymAwjBd6nvHjSomiVquFXXfdFUcddRQAYKONNsLb3vY2bLzxxliwYIG47pZbbsHs2bOx9dZb4z/+4z+w66674u1vfzva7TayLFuj7zBZYHU1eWB13TuwujIA1g56DVZfBsDaQS/B6spgMIw3etbx89BDD+HPf/5zFdN64IEH4uKLL8aUKVOqc6ZNm4YlS5Zgq622qo75gfeJJ57AFltsgb/7u7/DzjvvjO222w7PPfeciJk1vH5YXU0eWF33DqyuDIC1g16D1ZcBsHbQS7C6MhgMawt6btT44Q9/iJNPPhn9/f1otVr4yEc+guOPPx4bbLABgE5srI+J/fnPf463vOUtWG+99TAyMoK+vj4kSYKiKHDjjTdi0aJF2H333fHoo49i7ty54/laExJWV5MHVte9A6srA2DtoNdg9WUArB30EqyuDAbD2oaecvz86le/whe+8AWceuqpeM973oO77roLZ555Jl5++WV85StfwcyZMwF00hw2Gg3ccccd2HHHHQEAfX191X2WLl2Kj3/84/jLv/xL7L333uPxKhMeVleTB1bXvQOrKwNg7aDXYPVlAKwd9BKsrgwGw1qJNS8rtPIoisI559wll1zi3vzmN7vFixdXv33rW99yb3/7292Xv/zl6lie564oCveWt7zF3XTTTc45537zm9+4D3/4w27RokVrtvCTDFZXkwdW170DqyuDc9YOeg1WXwbnrB30EqyuDAbD2oye0PjxcbHPPPMM5syZE8S1Hn300dhpp53w05/+FI8//jiATvrDBx54AIODg/jLv/xLnHzyydh+++3xyiuvYNasWePyDpMFVleTB1bXvQOrKwNg7aDXYPVlAKwd9BKsrgwGw9qMtdLxc9ttt+Gkk07ChRdeiPvvv786vttuu+Huu+/GCy+8AADI8xxTp07FBz/4QSRJgltvvbU6d/78+Xjsscew9dZb47bbbsNdd92FW2+9FQMDA2v8fSYyrK4mD6yuewdWVwbA2kGvwerLAFg76CVYXRkMhl7CWuX4ef755/GBD3wARxxxBP74xz/isssuw957710NpnvvvTc222wznHfeeQBGPet77bUX0jTFU089Vd2r2WziDW94A+bNm4fHH38cO+2005p/oQkMq6vJA6vr3oHVlQGwdtBrsPoyANYOeglWVwaDoScx3rFmHkNDQ+4jH/mIO+yww9zChQur47vssos7+uijnXPOtdtt9y//8i8uTVN31113Bdcffvjhbo899qj+fumll9ZMwSchrK4mD6yuewdWVwbnrB30Gqy+DM5ZO+glWF0ZDIZexVrD+BkcHER/fz+OPvpobL755mi32wCA97///ViwYAEAIMsyfOhDH8IHP/hB/O3f/i1uv/12OOfwwgsv4Le//S2OOOKI6n4WG7v6YHU1eWB13TuwujIA1g56DVZfBsDaQS/B6spgMPQqEuecG+9CeLRaLTSbTQCAcw5JkuDII4/ElClTcOmll1bHVqxYgX333RdPPPEEdtxxRzz22GPYZJNN8IMf/ACzZ88e57eYHLC6mjywuu4dWF0ZAGsHvQarLwNg7aCXYHVlMBh6EWuV40fDX//1X+OYY47B0UcfDecciqJAlmV48cUX8cgjj+CBBx7AZptthr/5m78Z76JOelhdTR5YXfcOrK4MgLWDXoPVlwGwdtBLsLoyGAxrO9Zqx8/ChQvxzne+Ez/5yU8qsbORkRH09fWNc8kMHFZXkwdW170DqysDYO2g12D1ZQCsHfQSrK4MBkMvYK3R+KHwvqg777wT06ZNqwbRs88+G5/61Kfw0ksvjWfxDARWV5MHVte9A6srA2DtoNdg9WUArB30EqyuDAZDL6Ex3gXQ4NMe3n///TjkkENw22234bjjjsOyZctwxRVX4I1vfOM4l9DgYXU1eWB13TuwujIA1g56DVZfBsDaQS/B6spgMPQS1tpQrxUrVmC77bbD008/jb6+Ppx99tn47Gc/O97FMiiwupo8sLruHVhdGQBrB70Gqy8DYO2gl2B1ZTAYegVrreMHAPbaay9stdVW+MY3voGBgYHxLo6hBlZXkwdW170DqysDYO2g12D1ZQCsHfQSrK4MBkMvYK12/OR5jizLxrsYhjHA6mrywOq6d2B1ZQCsHfQarL4MgLWDXoLVlcFg6AWs1Y4fg8FgMBgMBoPBYDAYDAbDa8damdXLYDAYDAaDwWAwGAwGg8Hw+mGOH4PBYDAYDAaDwWAwGAyGCQpz/BgMBoPBYDAYDAaDwWAwTFCY48dgMBgMBoPBYDAYDAaDYYLCHD8Gg8FgMBgMBoPBYDAYDBMU5vgxGAwGg8FgMBgMBoPBYJigMMePwWAwGAwTBL/85S+RJAn+/Oc/j3dRVFx66aWYPXs20jTFBRdcIH5/9tlnkSQJHn744TVeNoPBYDAYDIaJCnP8GAwGg8HQg9hjjz1w8sknB8fe+c534vnnn8c666wzPoWqwZIlS3DCCSfgs5/9LH7/+9/juOOOG+8iBTj66KNx4IEHjncxDAaDwWAwGFY5GuNdAIPBYDAYDKsGfX192HDDDce7GCoWLVqEVquF/fffH29605vW6LNbrRaazeYafabBYDAYDAbD2gJj/BgMBoPB0GM4+uijcfvtt+PCCy9EkiRIkgTPPvusCPWaN28e1l13Xdx0003YeuutMTg4iEMPPRRDQ0O4/PLLsdlmm2G99dbDiSeeiDzPq/uPjIzgtNNOw8Ybb4ypU6di1113xS9/+cvaMi1atAgf/OAHMW3aNMyYMQMf+tCH8OKLL1bl2G677QAAW2yxRVXeGJ588km8853vxMDAALbddtvg2f6dKK6//nokSVL9fdZZZ2HHHXfE9773PWyxxRbo7++Hcw7/9m//hu222w5TpkzB+uuvjz333BNDQ0M466yzcPnll+OGG26ovme39zUYDAaDwWDoFRjjx2AwGAyGHsOFF16I//zP/8Rb3/pW/P3f/z0AYNasWaozZdmyZfjmN7+Ja665BkuXLsXBBx+Mgw8+GOuuuy7mz5+PhQsX4pBDDsG73vUuHHbYYQCAj370o3j22WdxzTXXYKONNsJ1112H973vfXj00Uex1VZbiWc453DggQdi6tSpuP3229Fut/HJT34Shx12GH75y1/isMMOw+zZs7Hnnnvi/vvvx+zZszFr1qzo+33mM5/BBRdcgLlz5+Ib3/gGDjjgADzzzDNYf/31x/yNnnrqKfzgBz/AD3/4Q2RZhhdeeAH/63/9L5x//vk46KCDsHTpUtxxxx1wzuHUU0/FggULsGTJEnz/+98HAMycOXPMzzIYDAaDwWBYm2GOH4PBYDAYegzrrLMO+vr6MDg42DW0q9Vq4ZJLLsFb3vIWAMChhx6KK664Ai+++CKmTZuGuXPn4j3veQ9+8Ytf4LDDDsPTTz+Nq6++Gr/73e+w0UYbAQBOPfVU3Hzzzfj+97+Pc845RzzjZz/7GR555BE888wzmD17NgDgiiuuwLbbbosHHngAu+yyS+W0mTVrVtcyn3DCCTjkkEMAAJdccgluvvlmXHbZZTjttNPG/I1GRkZwxRVXVA6mhx56CO12GwcffDA23XRTAKhYSAAwZcoUDA8Pr7WhcgaDwWAwGAyvFeb4MRgMBoNhAmNwcLBy+gDABhtsgM022wzTpk0Ljr300ksAOg4S5xzmzJkT3Gd4eDjKuFmwYAFmz55dOX0AYO7cuVh33XWxYMEC7LLLLitV5ne84x3VvxuNBnbeeWcsWLBgpe6x6aabBqyiHXbYAe9973ux3XbbYZ999sHee++NQw89FOutt95K3ddgMBgMBoOh12COH4PBYDAYJjC4qHGSJOqxoigAAEVRIMsyPPjgg8iyLDiPOosonHOBxk63468F/j5pmsI5F/zWarXE+VOnTg3+zrIMt912G+6++27ceuutuOiii/D5z38e9913HzbffPNVUkaDwWAwGAyGtREm7mwwGAwGQw+ir68vEGReVXjb296GPM/x0ksvYcsttwz+i4VBzZ07F4sWLcJzzz1XHXviiSewePFibLPNNitdhnvvvbf6d7vdxoMPPoi/+Iu/ANAJFVu6dCmGhoaqcx5++OEx3TdJEuy22244++yz8R//8R/o6+vDddddB2D1fU+DwWAwGAyG8YY5fgwGg8Fg6EFsttlmuO+++/Dss8/i5Zdfrhg7rxdz5szB4YcfjqOOOgo/+tGP8Mwzz+CBBx7Aeeedh/nz56vX7Lnnnth+++1x+OGH46GHHsL999+Po446Crvvvjt23nnnlS7DP//zP+O6667Dk08+ieOPPx5/+tOfcMwxxwAAdt11VwwODuJzn/scnnrqKfzrv/4r5s2b1/We9913H8455xz86le/wqJFi/CjH/0If/jDHyrH1GabbYZHHnkEv/nNb/Dyyy+rLCKDwWAwGAyGXoQ5fgwGg8Fg6EGceuqpyLIMc+fOxaxZs7Bo0aJVdu/vf//7OOqoo3DKKadg6623xgEHHID77rsv0PChSJIE119/PdZbbz389V//Nfbcc09sscUWuPbaa1/T87/2ta/hvPPOww477IA77rgDN9xwA97whjcA6GTbuvLKKzF//nxst912uPrqq3HWWWd1veeMGTPw7//+79hvv/0wZ84cfOELX8A//uM/Yt999wUAHHvssdh6662x8847Y9asWbjrrrteU9kNBoPBYDAY1jYkjgfKGwwGg8FgMBgMBoPBYDAYJgSM8WMwGAwGg8FgMBgMBoPBMEFhjh+DwWAwGAwGg8FgMBgMhgkKc/wYDAaDwWAwGAwGg8FgMExQmOPHYDAYDAaDwWAwGAwGg2GCwhw/BoPBYDAYDAaDwWAwGAwTFOb4MRgMBoPBYDAYDAaDwWCYoDDHj8FgMBgMBoPBYDAYDAbDBIU5fgwGg8FgMBgMBoPBYDAYJijM8WMwGAwGg8FgMBgMBoPBMEFhjh+DwWAwGAwGg8FgMBgMhgkKc/wYDAaDwWAwGAwGg8FgMExQmOPHYDAYDAaDwWAwGAwGg2GC4v8DUwG0NvB40esAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.profile.isel(attenuator_setting_pair = 0, time = slice(200,11763)).dB().mean(dim=['chirp_num']).coarsen(time=10, boundary='trim').mean().plot(x='time', yincrease=False, size=(5),aspect=3);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "## Take home messages\n",
+    "- xarray is a great tool for storing and processing high-dimensional data\n",
+    "- dask works seamlessly with xarray and with dask clusters supplied by cryocloud, and allows rapid parallel processing, exploratory analysis, and plotting of large datasets. \n",
+    "\n",
+    "When attempting to write your own data to a zarr store:\n",
+    "- have a very clear idea of the data's structure,\n",
+    "- carefully consider the chunking you want your final dataset to have, \n",
+    "- you may need to design a workflow that avoids loading the whole dataset into memory at one time, and\n",
+    "- your workflow will need to result in uniform chink sizes within each dimension. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}