diff --git a/notebooks/Gallery.ipynb b/notebooks/Gallery.ipynb
index 62b27fed..b2b6a704 100644
--- a/notebooks/Gallery.ipynb
+++ b/notebooks/Gallery.ipynb
@@ -41,6 +41,26 @@
     "| **Radar Visualisation** | Shows how to visualise radar data as a time-series, in 2D and in 3D | ![Image showing a top down view of radar data](https://pyearthtools.readthedocs.io/en/latest/_images/notebooks_RadarVisualisation_10_1.png) | [Radar Visualisation](./RadarVisualisation.ipynb) | 23 Aug 2025 |\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a6f26875-9a0c-40b2-87ad-39cb1f8037e9",
+   "metadata": {},
+   "source": [
+    "## Working with Station Data (medium requirements)\n",
+    "\n",
+    "Working with station data and integrating it with gridded data is quite complex. This series of tutorials demonstrates how to download one of the key open station databases, re-process it to suit the time-series nature of most PyEarthTools use cases, create a Data Accessor, and then combine the data with gridded data to form the basis of a heterogenous machine learning pipeline. \n",
+    "\n",
+    "These tutorials can be run on some laptops and workstations and do not require a GPU as they do not yet include model training, but may require larger amounts of RAM than devices, and some user modification may be needed to run them on less than 36GB RAM.\n",
+    "\n",
+    "| Title |  Description | Image |   Notebooks | Last Tested |\n",
+    "|-------|--------------|-------|-------------|-------------|\n",
+    "| **One - Introduction** | Introduction to station data | (no image) | [One - Introduction](./scorecard/One-Introduction.ipynb) | 5 Nov 2025 |\n",
+    "| **Two - Data Download** | Perform inital data downloading | (no image) | [Two - DataDownload](./scorecard/Two-DataDownload.ipynb) | 5 Nov 2025 |\n",
+    "| **Three - Small Chunks** | Group the data by decade in small groups | (no image) | [Three - SmallChunks](./scorecard/Three-SmallChunks.ipynb) | 5 Nov 2025 |\n",
+    "| **Four - Make Large Groupings** | Group the data by decade in large groups | (no image) | [Four - MakeLargeGroupings](./scorecard/Four-MakeLargeGroupings.ipynb) | 5 Nov 2025 |\n",
+    "| **Five - Data Accessor** | Integrate the data with PyEarthTools pipelines | (no image) | [Five - DataAccessor](./scorecard/Five-DataAccessor.ipynb) | 5 Nov 2025 |"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1f72b9c5-1d2b-4212-9009-ab147685ca83",
@@ -50,6 +70,8 @@
     "\n",
     "These notebooks start with the basics and work up towards more complex examples, showing how to work with the classes and functions within the package to achieve objectives.\n",
     "\n",
+    "These tutorials require a high-performance computing environment and work with very large data volumes.\n",
+    "\n",
     "| Title |  Description  | Image |  Notebooks | Last Tested |\n",
     "|-------|---------------|-------|------------|-------------|\n",
     "| **ENSO Prediction** |The El Niño–Southern Oscillation (ENSO) is a major driver of climate variability, influencing regional and global weather patterns. It has been linked to extreme weather events across the globe, including droughts, floods, and shifts in precipitation. Weather centres around the world actively forecast ENSO to anticipate these patterns. |  |  |   |  \n",
@@ -136,7 +158,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.7"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/scorecard/Five-DataAccessor.ipynb b/notebooks/scorecard/Five-DataAccessor.ipynb
new file mode 100644
index 00000000..009f717b
--- /dev/null
+++ b/notebooks/scorecard/Five-DataAccessor.ipynb
@@ -0,0 +1,317 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ca22f992-1245-4b56-ba15-d30d6e27ddb3",
+   "metadata": {},
+   "source": [
+    "# Integration of Station Data with PyEarthTools\n",
+    "\n",
+    "This tutorial demonstrates how to create a data accessor for the station data which will integrate with PyEarthTools pipelines. In due course, a similar implementation will be put into the relevant site archive packages for people working in supported facilities, but this demonstrates how you can easily connect your own station data archive into the framework from first principles. Working with custom station datasets is relatively common (e.g. hydrology-specific data sets, different temporal resolutions, region-specific datasets, data sources not included in the global data sharing etc) and so seeing the entire process is of value.\n",
+    "\n",
+    "This tutorial will show how to:\n",
+    "\n",
+    " - Create a data accessor on-the-fly in the notebook\n",
+    " - Access and plot data\n",
+    " - Access that data alongside gridded data, and plot the overlay\n",
+    "\n",
+    "\n",
+    "Note! The data visualised in this notebook is only showing a small subset of the total number of stations, as it was developed on limited data in the first instance. It will be updated in due course with the entire dataset.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5499eafc-803d-46f1-9b12-5f6fff5f3e64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyearthtools.data\n",
+    "import pyearthtools.pipeline\n",
+    "from pyearthtools.data import Petdt\n",
+    "\n",
+    "from pathlib import Path\n",
+    "DECADAL_DIR = Path('/g/data/kd24/data') / 'hadisd' / 'by_decade' \n",
+    "WBERA5_DIR = Path('/g/data/kd24/data') / 'wbera5' / 'by_decade' \n",
+    "\n",
+    "from mpl_toolkits.basemap import Basemap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e0072369-5c34-45ed-8582-85a401d2979f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyearthtools.data.archive import register_archive\n",
+    "from pyearthtools.data.exceptions import DataNotFoundError\n",
+    "from pyearthtools.data.indexes import ArchiveIndex, decorators\n",
+    "from pyearthtools.data.transforms import Transform, TransformCollection\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "\n",
+    "@register_archive(\"ISD\", sample_kwargs=dict(variable=\"2t\"))\n",
+    "class ISD(ArchiveIndex):\n",
+    "    @property\n",
+    "    def _desc_(self):\n",
+    "        return {\n",
+    "            \"singleline\": \"Hadley Integrated Surface Database\",\n",
+    "            \"range\": \"1930 - 2025\",\n",
+    "            \"Documentation\": \"https://www.metoffice.gov.uk/hadobs/hadisd/\",\n",
+    "        }\n",
+    "\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        disk_location,\n",
+    "        *,        \n",
+    "        transforms: Transform | TransformCollection | None = None,\n",
+    "    ):\n",
+    "\n",
+    "        self.disk_location = Path(disk_location)  # Location of the large groupings files\n",
+    "        super().__init__(transforms=transforms or TransformCollection())\n",
+    "\n",
+    "    def filesystem(self, querytime: str | Petdt):\n",
+    "        '''\n",
+    "        This is quick, no need to cache it\n",
+    "        '''\n",
+    "        files = list(self.disk_location.glob('*1990*.nc'))\n",
+    "        return files\n",
+    "\n",
+    "    def load(self, from_files_list, **kwargs):\n",
+    "\n",
+    "        ds = xr.open_mfdataset(from_files_list, combine='nested', concat_dim='report')\n",
+    "\n",
+    "        # Arguably, this should be a transform, or handled in the pipeline, but it works for now\n",
+    "        ds['temperatures'] = ds.temperatures.where(ds.temperatures > -1000)\n",
+    "        return ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0b99769d-6202-4cf9-94ea-7d4f3e4bb492",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "era5_source = pyearthtools.data.download.weatherbench.WB2ERA5(\n",
+    "        variables=[\"2m_temperature\", \"u\", \"v\", \"geopotential\"],\n",
+    "        level=[850],\n",
+    "        download_dir=WBERA5_DIR,\n",
+    "        license_ok=True,\n",
+    "    ),\n",
+    "\n",
+    "station_source = ISD(DECADAL_DIR)\n",
+    "\n",
+    "data_pipeline = pyearthtools.pipeline.Pipeline(\n",
+    "    (era5_source, station_source)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "533f2964-c4dc-4d94-86c9-7b5dd69e7551",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<xarray.Dataset> Size: 34kB\n",
+       " Dimensions:              (time: 1, longitude: 64, latitude: 32, level: 1)\n",
+       " Coordinates:\n",
+       "   * time                 (time) datetime64[ns] 8B 1990-06-20\n",
+       "   * longitude            (longitude) float64 512B 0.0 5.625 ... 348.8 354.4\n",
+       "   * latitude             (latitude) float64 256B -87.19 -81.56 ... 81.56 87.19\n",
+       "   * level                (level) int64 8B 850\n",
+       " Data variables:\n",
+       "     2m_temperature       (time, longitude, latitude) float32 8kB dask.array<chunksize=(1, 64, 32), meta=np.ndarray>\n",
+       "     u_component_of_wind  (time, level, longitude, latitude) float32 8kB dask.array<chunksize=(1, 1, 64, 32), meta=np.ndarray>\n",
+       "     v_component_of_wind  (time, level, longitude, latitude) float32 8kB dask.array<chunksize=(1, 1, 64, 32), meta=np.ndarray>\n",
+       "     geopotential         (time, level, longitude, latitude) float32 8kB dask.array<chunksize=(1, 1, 64, 32), meta=np.ndarray>,\n",
+       " <xarray.Dataset> Size: 17MB\n",
+       " Dimensions:                (time: 1, report: 50, test: 71, flagged: 19,\n",
+       "                             reporting_v: 19, reporting_t: 1140, reporting_2: 2)\n",
+       " Coordinates:\n",
+       "   * time                   (time) datetime64[ns] 8B 1990-06-20\n",
+       " Dimensions without coordinates: report, test, flagged, reporting_v,\n",
+       "                                 reporting_t, reporting_2\n",
+       " Data variables: (12/30)\n",
+       "     station_id             (time, report) |S12 600B dask.array<chunksize=(1, 50), meta=np.ndarray>\n",
+       "     temperatures           (time, report) float64 400B dask.array<chunksize=(1, 25), meta=np.ndarray>\n",
+       "     dewpoints              (time, report) float64 400B dask.array<chunksize=(1, 25), meta=np.ndarray>\n",
+       "     slp                    (time, report) float64 400B dask.array<chunksize=(1, 25), meta=np.ndarray>\n",
+       "     stnlp                  (time, report) float64 400B dask.array<chunksize=(1, 25), meta=np.ndarray>\n",
+       "     windspeeds             (time, report) float64 400B dask.array<chunksize=(1, 25), meta=np.ndarray>\n",
+       "     ...                     ...\n",
+       "     quality_control_flags  (time, report, test) float64 28kB dask.array<chunksize=(1, 8, 12), meta=np.ndarray>\n",
+       "     flagged_obs            (time, report, flagged) float64 8kB dask.array<chunksize=(1, 13, 4), meta=np.ndarray>\n",
+       "     reporting_stats        (time, report, reporting_v, reporting_t, reporting_2) float64 17MB dask.array<chunksize=(1, 50, 19, 1140, 2), meta=np.ndarray>\n",
+       "     lat                    (time, report) float64 400B dask.array<chunksize=(1, 50), meta=np.ndarray>\n",
+       "     lon                    (time, report) float64 400B dask.array<chunksize=(1, 50), meta=np.ndarray>\n",
+       "     elev                   (time, report) float64 400B dask.array<chunksize=(1, 50), meta=np.ndarray>)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here we see the pipeline returns the ERA5 grid and the station data as two separate datasets, at a matched time.\n",
+    "data_pipeline['19900620T00']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "95838f20-084a-40d3-bd1d-6934c78f2482",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid, points = data_pipeline['19900620T00']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cb011eda-fc95-46bc-a798-97fa90676fb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transform gridded data for plotting\n",
+    "lats = grid['latitude'].values\n",
+    "lons = grid['longitude'].values\n",
+    "data = grid['2m_temperature'].values[0]  # Replace with your variable name\n",
+    "lon, lat = np.meshgrid(lons, lats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "24cdb456-b534-4216-9878-53a82508d16d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x14b7b4bc1990>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CEhISSEtLQ+/evfH+/XvExsYKhWu1bNkSurq6QtcDgEAgwKZNm9CqVSscP34cjx49wtixY/H48WP4+PjgwIEDxeT28fGBoqIiEhMTMXr0aPZc27ZtYWBgADs7O8ycORMtWrSo7Nfzn6BNmzZIS0vDnTt3REprW5vUqgJv0aIFhg0bBnd3d4SGhrKJCSZPniz0kMrLy7PmH3V1dTg6OiI9PV3slV5PKG2lLRAIICEhIU6tKOZ/hnfv3mHhwoUACldq3t7eJVbv+vr1q1C0za/06NEDVlZWGDJkCDgcDpYsWYKzZ8+iR48ekJWVhbOzM6SlpTFz5kw2+dWvXL16Fc2bNwefz0dycjIuX76M+/fvY9y4cbh9+zbrlf4rMjIy6Ny5M1RVVaGjo1PsvIKCAiZNmoQjR44gOTkZixYtwh9//FHRr+c/wdu3b9GlSxcQEc6fP48ZM2agTZs2dS0WgFrcAy9ixowZYBgGHA4HN2/ehLGxcbE2oaGh6Nq1Kx48eACGYdjc6WLqL8+fP8exY8dw7tw5fPjwoVgsqxgxvztEVKaD5pgxY6CqqspmnfwVZ2dn3L9/v8TrRowYgZUrV6Jfv34AChcw27ZtA5fLZZO3aGpqsik84+LiYGZmhqSkJACFxUq6d++OSZMmQVpaGqdOnUKnTp2QlJSEpKQk9OrVC2ZmZkJjdu7cGeHh4WXGNRfVsJCRkcHHjx9x4sQJcLlc+Pj44NChQ8WSc/1XadWqFYgIpqam4PP5uHbtWl2LxFLrCvzmzZvIyMhAy5YtcffuXQCAk5OTkIL29vbGly9fIC8vD0dHR2RlZf3Pm3HqM/7+/pCWlsauXbuwYcMG/Pz5E5aWlnUtlhgx1QaXy8WlS5dw8eJFvH//nj3etWtX3LlzB3l5eXj06BGioqJYp7Nf6du3L759+yZyKFKjRo3A4XBw48YNuLu7w97eHpMmTQIAvHz5EitXrsTVq1cBFFYSfPjwIYDCbcrv379j3LhxWLNmDbZt2wZZWVkEBAQgICCA7T82NrZEOf+NrKwstm7dimbNmkEgEMDe3h4CgQCDBg3Cu3fvRLqX352JEyeiZcuWePLkCcaNG4dly5bVtUgste6hoKSkhEmTJrEPj0AggIyMDLuXA4D9guLj4+Hg4IBz586JzbL1lJSUFAQHBwuleMzKykJsbGwdSiVGTPXg7e2NqKgoSEhIwMXFBYcOHcLVq1ehpqYGDoeDwYMHIyQkBJcuXcKECRNKLTMpLy8PDQ0N3LhxQ6giWFksXbq0xOODBw/GtWvXwOPxwOVyISMjgy5dumDt2rUwMzNDt27dcOzYMcycORNt27bFp0+f0LRpU+zatQvTp09HWloa+Hw+du3aJZIcsrKy0NXVxeHDh9GqVSvEx8fj3r17MDc3F+n6/wLa2tp4/fo1vLy84O7ujlGjRtWLNN+1rsDHjx8PGxsbzJw5EwoKCkhKSiox5AIodNgwMTERK+96ChHh0qVLbIGazMxMXLx4EbGxsZgxYwaISPy3E/PbwuVyERERgcWLFwMA5s+fDysrK6xatUroue7Zsyd69uxZZl9jxoyBvb09unTpIvL4P3/+RKNGjSArKyt0vFOnThg6dCj4fD5kZGTw+PFjhIaG4sOHDxAIBGjdujXrkQ4UTh7U1NSwfft2jBs3DuvWrcOSJUtE+m2eOHECvXr1grS0NDp16oSHDx8iOzsbXC4X+fn5Iq3i/wtISkpi8ODBUFZWxo8fPxAYGFjXIgEAOCRCNfbMzEw20X11JLAnIpiZmSE5ORmjR4+GiorK/0SlsZzMXDxx8sar22/BK+Chp0Y3TF49AR16tMXTp08RExMDPp8PfX39cqsU1Qe8vb3Rvn17NskEl8vFt2/f8ODBA3A4HOTm5mLKlCnQ0NCoY0nFiKk47u7u+PPPP4WykN25cwcZGRk1HjJpaWmJnJwctGvXDnw+HwMHDkTv3sUr/Lm7u2PAgAHo3r27SAo5KysLZmZmaNmyJdq3b4/JkyeX2paIsH79esybNw8+Pj4lOuf9L8Dj8eDm5oa0tDRMmjQJvXr1gqurK+bPn19jY4qqc+skyI/D4bCzSH9//1LNTv8lwgIjsX3CQWSkZhYeIODzi2+4euYOhq/pj2nLJ0NTUxOZmZm4ffs2Zs2aVbcCi0BSUhKUlZXZzzIyMujXrx/rjHP79m1ISkoiJCQE0tLSkJOTQ2BgIDQ0NKCgoFBXYosRUy7v3r2DtLQ0mjVrhpSUFFy+fBlr166FhIQExo0bV+a1SUlJsLKywurVq9GyZctKjW9vbw83NzcoKSkhISEBr1+/BpfLZX9bANjkL8+ePUOPHj1K7Ofbt29Cir9JkybYuXMnbty4wWbALA0Oh4PFixfDw8Oj3Hv+rxITE4N9+/bhzp07uHDhAuLi4vDhwwfweLy6Fg1AHSlwAJg2bRpOnz4tUrag35383ALsmHQIWWnZwC/2DkbAgIhw+dx1TNMrjO1s2rQpcnJy6kjSitG6dWukpaWVqIyTkpKQkJAADoeDJk2agMfjISYmBiNGjICuri5u3LhR6ZebGDE1SUxMDCIiIjBjxgwAwKNHj8DhcJCWlobk5GT8/PmzzPwHDx48KHFFnJubi61bt0JLS0sonvjGjRtISUlBdnY23N3d8erVK+zcuRN37tyBvr4+2rRpg+HDh8PQ0JB1ZFNUVISysjJat26NzZs3Q1FREQMHDoSfnx+mTZuG6OhoJCcn459//sHKlStRUFCAWbNmQUpKChwOB8OHD8etW7fK/S4GDx6MQYMG4eXLl1i/fj3y8vJgY2NTma/1tyQoKAjx8fF4/PgxO3lKSEioNwlu6lSKZcuW/U/soTy/9ALpScWTO+RSNpIQC3nJVvAwuQW1cSqIj4+v19sJoaGhuHXrFlq1agVvb29cvHix1LY5OTngcrnF4ve9vb0xceJEXL58GfLy8jUtshgxIuPr64sXL15g48aNAArNyF+/fkWXLl1w48YNzJgxAy9evEBAQACkpaWLmVHv3buHdu3aIT09vVg6UhkZGaipqaFRo0Z4+fIliAihoaH4+fMnpkyZgj///BNXr16Fh4cHZs2ahTVr1iArKwvNmzeHqakpDA0NMWTIEBgbG8Pe3h5XrlzBy5cv8eXLFxgbG2PevHnw8/PD0KFDsXnzZnTt2hWpqam4d+8eVFVVYWlpiXXr1uHz589wc3MrMT68JDgcDuLj47F8+fJSE2plZWWhcePG/zmfl+bNm+P+/ftwdXVlj9WXGHCgjhX4/0qFm7ePApHFSUcOkwk+eAA4kIAkOACU0BMchoOAR4FITk6Gi4sL+/Koj/j7+yM+Ph5r1qxhE1iUhKKiYqn3ISEhAVNTUyxcuBB2dnZ1XmM3NTUVDMOgVatWACB2vvsf5efPn4iPj8f27duRkpICaWlp6OjoQFtbG506dUJ8fDwYhsGdO3ewYcMGvHz5Em5ubmjVqhU0NTUBFJq1O3bsiDFjxsDa2hpr1qxh+5eSkoKXlxfevXuHvn37IiUlBQkJCWjdujXMzMwQHBwMKSkpfPnyBQBw/vx5dOzYEWvWrAHDMPDz88OQIUMgEAhgYmKCJ0+esPHez58/R7du3dC/f388f/4cgwcPxo4dOyAhIYGCggLIysrC0dERDx8+RGRkJFtKVFTatGnDJmr68OEDYmNj8ePHD8yaNQszZ87EjBkz8P79e+jo6EBPT6/6/ih1zJAhQ2BnZwc9PT1oa2tjypQpJfoi1BX1ww7wH+fzjw8IZT6hP0agAHmQhgykOP8/oxJDDBL50fDy8sKyZcvq1DxTUFCAM2fOIDo6GnFxcRg+fDjU1dUxYMAA7N27F9OmTcObN2/g7OxcZpWj8ujevTvu3bsHLy+vOt3vv3r1Kvbv349ly5bhr7/+QkpKCrS1teHr61vM+1fMf5t3797B2toaqqqq2LBhAy5fvow2bdpAU1MTnp6e0NHRgZWVFebMmYPQ0FDo6emhUaNGcHFxQX5+Pho0aIDp06fDyckJMjIyeP/+PbKysuDr6wsVFRV06NABw4YNg4KCAlJSUjB48GC0bt0a06dPx+TJk8Hj8VgTN1A40Q0PD2efQxcXFwgEAigrK0NDQwODBw/GjRs30KpVK6HQtFGjRgndV9H1HA4HqqqqrBm+IowcORLOzs5sLHtISAhkZWVx+vRprFu3DsrKypg/fz5ycnJgaWkJDoeDVatWVfZPUa8YMmQI5syZg/DwcHh7e4sV+P8KWVlZhftMzQRQkugFCZJAQzQSapNOKcjhZGFY/xGYOXNmHUn6/wkKCsKOHTugp6eHa9eu4ejRozA1NUVERAQMDQ2hqakJTU1NiBC8UCZZWVkoKCjAokWLMHPmzDpZ8aakpMDHxwevX79GdnY2zM3N0ahRI5iamsLCwgJTpkxB8+bNxQ53/yNoamoiIiICX758wYABA2BoaIipU6fi5cuXEAgEaNCgAQIDA/Hz508MGzaMTS41Z84crF69GufPn0dubi6Cg4MxZ84cjBo1Ctu2bcOAAQMQExODmTNnYt26dWzs9okTJ+Dh4YF79+5h0aJF7MT91yyGv04i//zzTzg5ObEOa+VFdxSNEx8fj6ZNm5ZpMROFBQsWICMjg61TsWTJEkhISAhtFbRs2RKrVq2Cp6cndu7cicWLF9crhVcZPn36hG3btkFKSkrkGP7aQqzAa4jMzExYWlrC0NAQkydOwfxOa8Dn8kFEICKkIgH5yENzKKA9umCe0Yw6kzUnJwf+/v5ITk5GdnY2fH19MWzYMADA6tWrYWBgUCwTXlUVbkxMDBYsWIBp06ZVqZ+qcO3aNbRr1w4NGzZEw4YN0adPH1y9ehXz5s3D9evX4evriwYNGqBVq1b/s164/0twOBzWqjRt2jSkpqbi27dv6N69O/z9/eHt7Y2lS5cW2wOVkpLCsGHDEBoaCjU1NWhpaeHWrVvo3Lkzhg0bhjVr1sDBwQHv37/HuHHjICMjg7CwMISHhyMlJQWNGjXCzp07IS0tjZ8/fyIqKkqo/6ItHQUFBcjKyqJHjx4oKCjA+/fv4ebmhjlz5gg51eXl5cHV1RVZWVlo1qwZ2rVrh8TERMjLy1e5GEezZs0wZ86ccr/HcePGoXnz5rhy5Qrat29falKa34FBgwaxitvY2BjZ2dn1xndLrMBrCFdXV+jq6kJGRgYyrWTwt6shDs05hRzKQiw/EvJojY6S3UAMQXulJjT1R5XfaTXz8+dPXLx4EQ0bNkSHDh3Qp0+fYjnna8rJrH///pgxYwbs7Oxw8ODBGhmjPC5cuIAPHz6wn0eMGIGUlBTk5+cjMTERw4YNg56eHh4/foxLly6hbdu2aN68OVRVVSEpKVknMoupHTgcDlq2bImCggK8ePECS5Yswblz5xAZGQkJCQmYm5sjNTUVCgoKkJGRwapVq3D8+HF8+vQJHA4HX758wZEjR0BEuHXrFh4/foyhQ4eCx+NBWloa9vb2GDFiBDgcDqytrXH06FHMnj0bSUlJePHiBYDClZ+fnx/S0tKwYsUKyMvLIzs7m/UZadiwIa5fv47IyEgIBAJ06dIF7du3R0xMDIYPH47hw4ez98Pn8/H48WOsWbMGx48fr5Z8HuWhrq4ONTW1YnnYfyeCgoKgoqICJycnLFiwAD179gTDMPXHT4ZEICMjgwBQRkaGKM3/p2EYhi5cuEAHDx4UOs7j8WjOjHlk8OdfpNtiEU1prE+bRu8hL4+XxDBMrcvJ5/Np+PDh9PTpU+LxeLU+fhGurq51Nra+vj5lZ2eXeE5HR4cyMzOJiOjWrVuUmJhITk5OZGxsTLa2tmRmZkZZWVnE5/NrU2QxdUR0dDRduXKF0tPTiWEYSk9Ppz///JN27txJiYmJRESUlpbGtudyuULXOzk5UUZGBu3du5eePHkidG7GjBnsu3Xt2rV08OBBYhiGunXrRlwul/h8PpmampJAICAnJyeha+Pi4ignJ4dSUlIoLS2NVq9eTX5+fqXeR2BgIDk4OFTlq6gQcXFxNHv27FobryZITEyk/v3709mzZ+nmzZtka2tLf/75J/38+bPGxhRV54pX4NVITk4Ojh8/DlVVVSHvUyKCs7MzVhgsw/jx4+tQQoBhGNjY2CA6OhpHjx4VqgNcERwcHDB16lQh03p0dDTS0tKEkk2URVJSUp06io0ePbrU8SdMmAA7Ozts2LABcXFxkJeXh5ycHLS0tNC3b194eXnhwYMHyMjIAJfLxapVq8Sr8v8w0tLSSE1NxcqVKzFmzBi0adMGQUFBmDJlCnr27IkDBw7A19cX7u7uAApXvJmZmZCTk0PDhg3BMAwePnyIFStWsIVHihg3bhwSEhIgISGBZs2aoVWrVvD09ISjoyNbPrRNmzYoKCgoZl4vCjmVk5MDAFhYWJR5H9nZ2VBSUqqW70QU2rZti9GjRyMvLw8NGzastXGri69fv+LLly8wNDREdHQ0njx5gsGDB2PIkCG4desWm2a3rqj1amT/ZZKSkuDs7IxevXoJHTc2NkZkZGSdK2+g0DR4/vx5DBo0qNLKGyg07xVVQyrizp07ePLkCWJiYsq9Pjk5GZcuXarTPfDZs2fj8uXLAAqTdXz+/Bk+Pj74559/EBUVhUePHmHLli2YNGkSpKSkoKenh/DwcHh6emLMmDGYOXMmli1bBgkJCbHy/o/TunVr1kwuKyuL8PBwPHjwAMrKyjh16hSaNm0KU1NTMAyD9+/fY/To0di4cSPOnj0LW1tbDB06FLq6unjz5g3mzZsn1PfQoUNx//59bN26FV27dsWyZcvAMEyxHAtSUlKsws7KymKPx8fHlygzl8vFz58/hY7t2bOn2CSgppkyZQpsbGxqfdzqoE+fPnByckJ8fDyUlJRgamoKJSUldO7cGQkJCXUtntiEXp0kJyfT2LFjKTQ0lD32+PFjWrZsGf348aMOJSvE2NiYtm7dSidPnqxyX9bW1rR7927W/B8XF0eampq0bds22rVrl5A58d9kZWWRmZlZvTA/P3jwgB4+fEhWVlZkampKISEhJBAIyNHRkSIiIoqZQomIAgICyMbGhpycnMjR0ZFCQkLqQHIxdQnDMHTp0iVycXFhfwN5eXl09OhRevHiRbWM8fLlS/rrr79IIBCQra0t8Xg8unHjBp08eZKGDBlCRERnz56l+fPnC12XkZFB2dnZxOPxaNq0aULn1q1bR58+faoW+SpCWFgYmZiY1Pq41UV2djaZmJhQVlYWCQSCGh9PVJ1bJ8VM/qtYWVlBWloavXr1wqBBg2BjY4NXr15h5cqVGDFiRF2LhydPnuDkyZPYtGlTlUvhBQUFwd/fHz9+/MD379+Rnp6OBQsWYO7cuaVeIxAIcO3aNaSmpoLD4aBVq1aYOnVqnacl/PHjB9LS0qCmplancoipW/Lz8+Hv74+IiAgMGjQIffr0KbUtEcHc3BwLFixA06ZN8ezZM0RERGDevHmsObs6SEhIwLNnz9C2bVuMHTsWQGHuAklJScTFxUFKSgqpqano0KED3rx5g5YtW+L+/fuYMGECOnXqhJ8/fyItLQ1jx45FamoqFBUVMXHixGqTT1QEAgFsbW3Rv39/DB48uNbHrwq7d+/G4sWLERYWBk9PT2hpaRXLLlndiKpzxQq8miAiTJgwAUeOHGEf0H379mHKlCkYNGhQHUtXiI+PD0aPHg0NDQ28ffu20v2sWbMGgYGBSEpKwsSJE6Guro758+ejUaNGpV7D4/GwaNEiHDt2DImJiYiPj4eamho+fvwIHR2dSssiRkxVuX37NjIzM9GgQQMMGjQInTp1wtOnTxEXF4d58+aBiJCQkICOHTsKXcfn8+Hh4QGGYTBo0KByS4rWFPHx8QgODgYRoU2bNlBUVETDhg3ZiURubi6SkpLQuXPnOpGviEGDBmHhwoX466+/6lSOihAfHw9tbW2MHDkSP378wL59+5CUlAQtLa0aHbdeVyP7L/LgwQP2RxIaGopjx47h2bNn2L9/f12LxjJy5Eg4ODiI7GRWGubm5sjLy4ODgwOSk5MxZMiQMpU3AMTFxWHixIlQUlICn8/Hhw8f4OzsjK1bt1ZJFjFiqkpGRgbmzZsn5MegqamJsLAwjB49GpMmTcLw4cPh6+srZGGSkpIqtp9d3QgEAty9exc+Pj5QVVWFtrZ2sSJAbdu2LbN+gpycXJ0r71evXiErK4tNE/u7QETQ1NSEqqoqWrVqhcTERGhra9e1WCxiJ7ZqID09HXv37sXz58/h4+ODz58/IzExEdevX69r0YTIy8tDmzZtsHLlyipnUmvYsCGaNm2Ktm3bljshSExMxLNnz8Dj8WBnZ4c1a9awaRb/XfChrsnPz8fz589/uxeNmMozfPhwNvb6V7p16wZnZ2ds2LABEyZMQN++fXHv3r1qHZuI8O7dO5w5cwYAkJaWhhs3boBhGACFk+URI0bg0KFD0NfXh4ODAxwdHeHg4IDQ0FChvrhcLhITE9lr6xNDhw7F27dvkZSUhIKCgroWp1SKogiKKPr7pKSkYMqUKfVKeQNiE3qV4fP5WL58OXr16oWPHz+yFYUaNGgADw+POpOLiJCeno6wsDB8/foVfD4fAoEAWlpayMnJqRZzX0hICHJzc0vdOy4oKEBubi7c3NywYMECmJiYsOanhIQE+Pj4YM+ePejevTsCAgIQEREBZWVl9OrVq0yvbh8fHzx79gwxMTGwsrKq8n38ipOTE8aNG4cvX76gWbNmGDJkSLX2L6Z+YmpqKpJpt0jRjxw5UuS+nz59isTERAgEAsybN0/I5+Pu3buIi4tDTEwMuFwuVFRU0KlTJ6SlpWHw4MFYvXo11q9fjwkTJgj1effuXWhoaLBFRm7cuIGcnBy0b98eoaGh6NGjB/744w+8f/8eX79+BVBYWSs5ORkaGhrgcrlQVFREhw4dRL6P6kBZWRlXrlwp07+gLrlw4QJ4PB6Cg4MhJyeHkydPYurUqbh16xbMzc2xdu3aWpFDbEKvJYry4z58+BAzZszAxIkTYWlpCWdn5yr1KxAI8OTJE6iqqpZZe/jfXLlyBTweD0SEFi1aoEuXLliwYEGNZA0qbxLg7OwMBQUF6OrqwsbGBnJycpg/fz6bgWrMmDEwMzPDoEGDoKGhgalTpyIoKAgeHh7IyclB06ZNSyx0YmRkBDs7O5w/fx6rVq1C3759MWbMGKirq1fpfsLDw1FQUIAOHTqgQ4cOcHR0RJ8+fcST1v84PB4PzZs3F6ntyJEjcf78eQwePFgk61FiYiLS0tIwf/58ZGRk4PLly2wJUj6fjzdv3iAoKAhXrlxhs3utW7cOW7duRVxcHA4dOlRMyWZlZSEmJgaTJ08GUDjpnDx5Mlq1agWGYRAUFARlZWUsXLgQy5cvx/z58/H582dcvHgRZ8+eRUBAAJo0aYJPnz4hODiYraRWGxw5cgS5ubm1Np6oeHp6YuzYsVi2bBlUVVVhZGSET58+YfDgwTh69Cjc3NyQkVG8JHRdIzahV5GfP3/i4cOHSE1Nhb29PaZNm4YZM2agW7duVerXxcUFqqqq2L59e4Wui46Oxrx58zB//nxoaWmhd+/edZLy7+nTp2jQoAF0dXXx7NkzZGVlYcWKFRg5ciQrT8uWLeHi4oJBgwahb9++kJWVxYABAzB37lzIyMiw+dj/TadOnaCsrIwLFy5g/fr12LRpEwYMGAAOh4Pg4OBKycvj8WBmZgZZWVnY2dnB09MTPXv2xMePHyv9HYj5Pbh9+zamTp1abjsiwu3bt9GwYUPY29vjxo0bZbZ3dHTEkydP4O/vj8DAQEhJSQklDpKSkkL37t3h6+uLR48egcPhIDs7m03Z26FDByQkJBSrQ2BhYcFObN++fYvs7Gy2FO6NGzcwYsQIqKuro127dqwDbceOHVlz8MCBA9GrVy9oaWkhLS0N3759q8jXVSV0dXVx4cKFWhtPVD5+/AhtbW1s2rQJ48ePx5o1a5CXl4dVq1YhJCQE48aNw86dO+tazGKIV+BV5OLFizh9+jQkJSURGRmJBQsWYPPmzVXuNyAgAHw+H2PGjIFAIBA5Ucj379/rPE8vwzAwMTHBvHnzcOnSJWhoaGDBggXF2snLy+Pz588lOuBoaWnh7t27kJGRwZ9//sm+xJKSknD16lXw+XxISUmhX79+7H7+hw8fMGjQIKSmprIZrERFWloap0+fBlD4oo6Li0N4eDgGDBhQ0dsX85uRlJRU7go8KysL5ubmkJGRQffu3TF58mQ4OjoiIyMDDMOgRYsWICKEhYWhS5cukJSURGxsLLS0tNC+fXs4Ojqif//+bMXBpKQkeHl5YcGCBfD09ER+fj4A4NChQ1ixYgWaN2+OsLCwEkMs+Xw+W6Pg7du3Qlkfs7KysHr1ajRt2hQFBQWsc2mzZs2gpqaG9+/fC1Ux09PTg6mpaa1VDIuIiEBQUBBbfrU+kJSUBENDQ+jo6KBZs2bgcDg4dOgQrl27BiUlJezcubPe+eoUIVbgVeDFixd48OABNm3aBElJSRgbG6NPnz6QkKiaYePmzZuQlpaGlJQUJCQkcOfOHejo6EBCQgJ5eXnw8PDAwoULS1TS8+fPR0hISLFscLVFQUEBLCwsMGDAAJHKF5bmPduyZUssXrwYAQEBOHz4MFJSUtCiRQtkZGTgxYsXJb7Y1NTUcPHiRTx69Ig1L1aUa9euoX///ujWrRvat29fqT7E/D7ExcUhPT29zElvSEgIPDw8oKCggKVLl7Kr6NzcXLi4uKBNmzbIycnB5MmTcebMGfTr1w8rVqyAkpIS6x/y8uVLPH36FIsWLQJQaKEaNWoUrl69isaNG7PbZJ06dYK7uzs2btyIpKQkKCkpISsrC35+fpCXlweHw2FzOBQUFCAtLY2d3IeEhKBbt24YOXIkfv78yZrqi1BVVcXTp0/x5csXvHv3DgKBABwOBz9+/KjQIqEqdOnSBRcvXsS5c+ewZcuWGh9PFBQVFdGnTx906NABUVFR0NbWhqOjI5sL4NSpU9ixY0ddi1kiYgVeBdzc3CArK4v8/Hzk5+cjLi6uWirvZGZmwtjYmP2ckJAAa2trrFq1CgKBAN7e3vjjjz/QoUMHBAUFoU2bNmzN6pSUlCrvBVeW79+/Y+rUqbCwsKhSmtZf0dDQQP/+/WFtbQ1vb2+4urqW2paI8PnzZ7ZaU2VIS0tDYGAgXr9+zb7QORwOJkyYIK4L/h+kUaNGZW4zffnyBe/fv0erVq3YyAmgMKf4ixcvoKenBz09PeTk5MDOzg6pqamIi4vD1q1bhaw3y5YtE4psaNq0KbKzs9GkSRP07t2bVfRr167Fjx8/MGfOHJw7dw6vXr1Cr1698ODBAyxevBihoaHQ09MDUGhxGjhwINtnZGQk+vbti8GDB2Pu3LmIiIgQupf27dujV69eSE1Nhbq6Onx9fdGwYUP06NED3759g7KycpW/T1GQkZGpVwocKEwNLSUlhS9fvuDu3buYN28eFi9ejLVr12LUqNqvFCkqYgVeBZo3bw5XV1c0a9YMP3/+RHBwcJVXbXw+v9hMuE2bNqwprHHjxujevTu+fv0KaWlpvHv3Dvfu3YOuri6GDh0KdXV12NvbY+TIkejfv3+VZKkIW7duxdOnT+Hv71/ttXKlpKRgYGCAtLQ0bN++HcePHy+xHZfLRceOHdG1a9cKj7F582YEBQVh7dq1mDJlitDf4NmzZzAwMECjRo2go6MDVVVVyMrK1mpRCDE1w/Xr18vc//bz8wOHwylm0ZGTk4ODgwNrCTIxMcG+ffsAFGbuUlVVBRGxK/vWrVvj6dOn7PWTJ0+Gubk5pk+fjkmTJgn1raSkhE2bNiEhIQE8Hg8DBgzAx48foaysLBSy+f37d6GtqQkTJsDBwQFZWVnQ1NTEs2fP2HMMw0BDQwOrVq0CwzAQCARYtGgR+vTpg+/fvyMyMhJRUVE1nqDk58+f6NOnD8aNGwcfH596oxwlJSXh4uICIyMjBAUFYdmyZXBzc4OKigo+ffpU1+KVitiJrZIEBATgx48f7KpMXl6+WkIM/P39S0w1WBTbyeVy0bx5c2RnZ4PH48HX1xcDBw7EH3/8gZCQEHh7e2PVqlVwdnZm99VqmpcvX4LP5+Pdu3c1Wuh+586d6NixI1avXl3i+ZycHEhKSpYY01seHA4H79+/h66urpDyTktLw5kzZ+Dv7w8HBwds374dWVlZiIiIwLZt20otJCGm/lNQUABZWdlSa94bGxujZ8+e6NSpE8LCwoTOSUhICG3jNGrUCM+fPwcR4eDBg2jbti0kJSWRmJgIoPD5+nfErp6eHg4ePAgXF5diY3/79g1qamoICAhAYGAgiKjYxD4xMRGZmZnsZyKCjIwMCgoK4OXlJTTe58+foaamhoMHD8Lf3x/Hjh2DtrY2tm3bhi5durCFfLhcrojfXuWQl5eHhYUF1NTUsHHjxhodq6LMnz8f06dPh5mZGSZNmoTY2Fi8ePGiQlFAtY1YgVcCgUCAt2/fCnmI83g8hIeHV7nvsLAwdO/eXehYQkICWrdujaioKFy8eBGNGzeGvLw8bt68iT59+kBOTo7Ncayjo4PLly9j48aNcHNzE/qB1wRfvnzBtGnTcPLkyRodp4j169cjKCioxHOZmZnw8vJCly5dhI7b29vj4MGDZfbbqFEj2NvbFzveokULODg4sFsCd+7cgYaGBsaOHYv169cXq8gm5vchOjoawcHBpUYutG7dGkOGDEF8fHy5W0JGRkZsrnKgMHFJVFSU0JYah8MRSrLSunVrnD9/HvLy8vjx4wcAsGbvK1eusArbyMioRPN2kyZNhEIcJSQk8O3bNzRr1gzPnj3DtWvX2HP9+vVDREQE3r17B0NDQwBA3759cfz4cdbxdsKECXB0dCzzPqtKTk4Ojhw5gqZNm6J3795C2xJ1QVxcHIYOHcqGiC1btgxTp07FkCFDoK2tjVGjRtXr1K9iBV4JcnNz0aZNG6H6ttLS0tViss7Ozi527Pnz5xg9ejRu3bqFdevWQUJCAlFRUVizZg2GDx8uVKa0efPmaN68Oby8vPDjx48aefgKCgqwcuVKaGlp4fDhw2jXrh0EAkG1j1MaWVlZJa4UOnfujAYNGuDJkyfsMR6PB09PTwwfPpw9lpeXJ1SOMSsrC9OnTwefzy9xvE+fPiE/Px9ubm6st25+fj40NTXrR0lBMZWie/fu+OeffxAeHo5Hjx4JnSMiSEhI4OvXr0hOThZ6pkShWbNmSEtLg4qKCntMU1MTt2/fLtb2zz//xPXr18EwDKZMmQJPT0+sXr0aaWlpyM7OxtOnT/Hu3bti15UUaTFo0CB8/foVmZmZrMd7EUFBQVBXV4e2tjaSk5MRExMDDoeDbdu2QVtbG5s3b8aWLVsQHh6O79+/18hv+uXLlzAyMsLmzZsREhKCmJgYXL58Ga9evar2sUShXbt22LVrF6ZMmYKfP3+ykyYOhwMlJSUcOHCAzZJXHxHvgVeCJk2aICYmBsuXLwdQ6DgGFK7WUlJSiuUqFhU7O7tiOYu5XC6ysrLg7++PsWPHgsPhoGHDhujatSsuXryIxYsXF/shF+3XXb9+vdwc5ZVh/fr1MDIyYpXZ9+/fcffuXda5piYxMTHBokWLioV1pKWloUWLFjAyMhJyFvLz88O+ffvw5s0beHt7IyUlBXw+H/fu3YOOjg64XC4SEhIQERFR6p72sGHDwOfz8ccff7DHGjRogJCQEBw/fhy2traYMWMGmjVrVjM3XUnev3+Pt2/fonHjxkhJScGCBQuKxRTXJFlZWWjSpEmtjVdZtLS0cPnyZWRmZqJJkya4c+cOEhISMGHCBCgpKSE8PByzZ8/G0qVLARSujq2srMqt6rV161ah+2/Tpk2JE/SiPfZHjx5hwYIFbOUxc3NztG7dGkSE58+f4/Xr10KZAUtyvJsyZQpiYmLw4sWLYnv7v5r8W7ZsicDAQADAgAEDcODAAdYh7siRI1BVVYWUlBT09PTKzLNeUTQ0NHDixAkMHDgQq1evhrS0NDZt2oSlS5eWmvehJklKSoK9vT2aNGmCiRMnQkFBAZcuXYK8vDxkZGSwYMEChISE1LpcIlOdtUlrk5LqNNc2PB6PrKysaP369cTn84nH45G1tXWl+rp06RK9fPmyxHNWVlb09u1bunnzJhEV1qPes2cPOTo60urVq2n58uXFrvn06RM5OjqSm5tbpeSprwwfPrzE41OmTGH/b25uzv5/1apVlJ+fT0RE379/pzdv3rDnAgMDCQDFx8ezbSrK06dP6dq1a2RqalpmDfTaJjQ0lK5du8Z+5vP51VIHXlSeP39OgwcPpuvXrxOPx6u1cStLfn4+OTs7k52dHX39+pWysrLYcwzD0LNnz+jgwYPk6upKHz58qNQYDMPQ6dOnSz1//fp1ev36NRERvXv3jszNzYXGcnBwEGofGBhIzs7O5OzsTHfu3CEioujoaBo5ciTp6+tXWD5NTU2aPn06AaAfP36QQCAgKyurCvdTHrGxsRQUFEQMw1BCQgLJycnR0aNHq30cUfn58yf7/1WrVtGWLVvI39+fiArftZKSkrWu+0TVub+NCd3Ozg7bt29HZmYm7t69i1atWmH79u017nRRFlJSUmjTpg1u3boFhmEgJSUFDQ2NShU8SElJKXUGumjRInz//h0ZGRng8/kYMGAAdu/ejTlz5mDFihXIyclBXl6e0DX5+fmQk5Or14UDKgoRYcOGDaWeLzL5DR06FHZ2dggJCUGPHj3YuN3u3bsLlXbt168fPnz4gNatWwtlyBKVsLAwWFhYYPr06VBTU8OpU6cAFG4x3L9/H46OjnB2dsa2bdvg7OyM48ePs3udNY23tzcmTZqEq1evIiAgAK6urkIJPGqSqKgofPz4ES4uLnB3d4e0tDRSU1Ph5+dXb53+ZGVl0blzZ4wdOxa9e/cWcsb8/PkzYmJisHPnTsybN6/SW2UMw7Bpjkti2rRprANrXFwcxowZIzTWvy1tffv2xfPnz8EwDF69eoUVK1bg4MGD2L9/P75//15h+R4/foxNmzZhxYoV4PP5OHHiBD5//oy7d+9Wqzm9Xbt26Nu3LzgcDhQVFWFtbY2dO3eyFoHaJC8vD/Ly8ti/fz82btyIvLw8HDx4kM3AqKysDIFAUG/fo7+FCZ3P58PExASysrKIjY1lvS2LPK179+6NRYsW1Yi5uDyioqIwadIkGBsbIz09HQ0bNgQRgcvlYtq0aSL3k5iYCD8/PwwdOrTYORkZGTAMg/Hjx+Pp06eYNGkSa0IeOHAgrKysipmUpaSkYGZmJhQn+rvD4XCEyjn+SlpaGps/XU1NDdLS0rC1tWWTXpRGZV7Gubm50NfXB5fLxeXLlwEU5sjOz8/HpUuXICkpibFjxxaLR//48SMCAwMRHx9f4t+5OmnZsiVu3bqF4cOHIysrq8by4RcRGRkJAwMDDB8+HGpqatiwYQMCAwMRERGBgIAAeHh4YPz48Vi2bBlcXV1r1ZQvKiNGjCjxeO/evfHkyRP8/PkTXC4Xbdu2RVhYGD58+ABlZWX07dtXpP4lJSUxbtw4vH//HgoKCmWGIZaUVOXnz5+IiopCp06dABT+xocOHYqFCxeCYRihBFJlRWKkpKSAiNj0q0VwOByMGjUKo0aNQtOmTbF3717IyMhgzZo1mDp1KhYvXiw0Aa4KLi4u4HA4yMvLg66uLrS1tfH333/j7t271dK/qDRs2BCnTp2ClJQUli9fjrCwMMTFxUFGRgZEhPXr12PUqFGIiYkp9n3VC6pzOV9TnDx5kiwsLKhv377k7OxMioqKdOTIEQJA2dnZ5ODgQFu2bKHc3Nw6kY+I6PPnz2RpaUkTJkygzp07U1JSEoWHh9P79+9Fuj46Opru379f6nkXFxcqKCggJycnkfo7cOAA9erVi06ePFmn30tNY2RkRNra2nTmzJlaG3PEiBHk7e1d6ettbGyIYZhqlKhuiYmJIQDk4+NTbtsjR46QsrIy2drako2NDTk4ONCNGzdIIBDUgqSFW0tHjhyp8HVxcXHk5ORE1tbWtHr1avLz86O8vDzy8fEhJycn8vX1FbmvW7duUffu3cv8XSYlJdGKFSuEjjEMQ/PnzxfakvD396erV6+SQCCgy5cvFzOzl8S8efOoU6dOZbYJDg4mDQ0NatCgAcnLy1NsbCwdOXKEnjx5Um7/ouDs7EzR0dEkEAjIycmJNd3XJnFxcTRjxgwaO3Ysbd68mQwNDcnCwoKioqIoMTGRTE1NaeLEibRkyRL6/v17rcomqs6tlwq8oKCAWrduTYqKijRr1iyysLCg/Px86tevH+Xl5VFkZCQJBALKyMigz58/E5fLpXPnzlFQUFCtyCcqBgYGdPPmTfry5Uu5bR8/fiy0P/srnz9/psePH9P169cpNTVVpLHz8vKoX79+ZGFhUSGZfzcmT55MSUlJtTZefn4+TZw4sUp93LlzhxITEyt8HY/HqzVFJwoREREEgPbt20dOTk4UFxcn0nU5OTnk4+PD+rGEh4fT7t276cqVK3T37l2h3zGPx6vyZCcyMpJev35NsbGx5OHhQW3atKG7d++y593d3UX6Xnk8Ht29e5fddy76Z2pqShISEhQZGSmyTHl5eWRhYUFGRkZUUFBQ7Ly7u3sxvwyBQEBNmzalbdu2CR2/fv06jRo1ilRUVMje3p4EAgF7Pzdv3qR9+/ZRSkoK2/7mzZvUtWvXct8NJ06coKVLlxLDMLR3716aOnUqbd68mWJjY0W+z5JIT0+n06dP08aNG+ngwYOUk5NDUVFRZG9vX6V+K0p+fj7Fx8eTra0tzZ8/n6ZPn07m5ua0fft2cnJyosTERMrNzaUfP37UqlxEouvcemdCDwgIQEhICBITE9GrVy+8fPkSpqamyM7Oxrx585Cfnw8/Pz9cv34dioqK8PT0hISEBHbs2IHOnTsX89SsS1RVVZGbmwt/f380a9aszIQAHh4e2LRpU4nnZGVl0bhxYyQkJJSadOLfNGjQALt27cL+/fuxbNmyChf3+B24cuUKGjVqVKumrby8PKHwwcrw4MEDkXO1379/H2lpadDW1sb9+/fZOGIejwclJSV06tQJ7dq1Q05ODho2bFhr20gMw8DS0hKJiYkVTl0rJycnVE+7S5cuOHjwIFJTUwEAt27dQkBAAIBCU3JmZib++OOPStWwf/jwIc6dO4e3b99i27ZtyMvLQ0REBC5dugQAePLkCebMmYPY2NhyE3acOHECy5cvR+vWrYudK8s3oyQaNGgAJSUlhISEICUlhR07OzsbkpKSGDx4MB48eABdXV32Gh6Phx49ehTLyz1t2jR2u87Z2RmNGjWCoaEh+vXrh9zcXOTn56Nly5bYuHEjsrOzYWRkhF27dkFWVhbnz5/H2rVrS9xeGTZsGGbOnIk5c+YgKysLkZGROHHiBO7du4cVK1ZU6H5/JSMjAxEREThz5gzOnj0LW1tbBAUF4e3btxgxYkSxPBg1haysLCIjI/Ho0SOoqKhg165d7LmsrCw8evQIy5cvh5ubG7ttUd+oFwqcz+eDw+HAxMQEf//9N9q0aYMrV64gJydHqGjHjh07cPHiRcybNw9NmjTB06dPMWLECCxZsgQvX77E6NGjcf78eRw5cgQ7duyo04pcALBy5UrY2tri48eP8PHxYRM9SEpKYtKkSUL7gI0bN0abNm1K7KdLly7w9/evsCPJ7Nmz8fr1a+jp6SEuLg66urrYu3dvpe+nvmFpaVksfremadCgQZWL1XTo0AGPHz/G2LFjkZCQgBcvXoDP56Nhw4ZCsbvv3r2Du7s7tm7dips3byI9PR1r166FtLQ0+Hw+Vq5ciXnz5uHOnTtIS0tD+/btMXToUKiqqlb1Nsvl7t27MDIyqnTIZEkUZTUsCtf6lUePHuHWrVuYPn06FBQUSq0e9vz5cwwbNgzR0dEwMzPDkCFDoKmpie7du+PEiROwsbFBgwYNMHDgQPj7++PJkyfo27cvwsLC4OXlBaBQUXbo0EEobBAozLFQHXv3jx49wvv373HixAk2bWoRHh4e8PPzg7GxMTIyMuDg4IBevXph6NCh8PHxwaJFi5Cenl6qHAsWLMCRI0fQt29fNiwuOjoaHz9+xK1bt3Dy5Em8evUKurq6uHLlCgwNDRERESFUe6GIokmWpqYm/v77b9y5cwcvXrwo5jBbUUJDQ5GVlQUej4e1a9fi/PnzMDY2xtKlS2t9oTF06NAS/VFyc3OxaNEikfxo6pTqXM5XhqVLlxIA6t69OwEgALR9+/YSTVoCgYBcXFzYz/7+/rRr1y46cOAAmZub05MnT2jAgAEEgIKDg6tVzmvXrpGDg0OlQmIuX74sFKqQn59PV69epRs3brDHXFxcyMLCgg0H+ZWAgACytbUlBweHSpkT37x5Qzwej6ZOnVrvthkqS3JyMk2fPr3Wx2UYhtTV1avcz4cPH2jYsGFs2CHDMPTjxw9ydXUlZ2dncnBwoPv375OtrS09efKEEhMTydnZudy/n5+fHzk4OJC9vX2N+j5s2bKlxvoujfv379OVK1fo5s2bZGtrK3SOYRi6ePEizZs3j/744w/q3LkzZWZmEhHRzJkzicvlEp/PF/r9WFhYEABycXGhs2fPCvX55s0bcnNzIz6fzx5LSkqiU6dOVfgdYG5uLvQ+8/X1JQBkbW0tJA+XyyUAFBgYSKdOnaKMjAyKjY0lY2NjGjp0KOXm5pK/vz8pKysTAMrJySlxvMOHD7P/379/f4l7y5GRkVRQUEDBwcHUvn37UvsiKtzCmzhxIk2YMIFMTU3Jzs6uSrogPz+fdu/eTQDo2bNnZG5uTnw+nw3prGs+fvxIqqqq9Pbt2zqT4bfZAwdAffv2pR49epCOjg55eHjQ8+fPaefOnewPsIj4+HjWicLHx4cA0IgRI4RiNokKnd4AkKOjI/Xr14+MjIyqFIsaFhZGnp6elJ6eTn///XeFr8/PzydHR8dix/fu3UurV69mz2VkZNCVK1eKtUtMTKQ7d+6Qvb09eXt7k4uLC9nZ2VXIcYaISE9Pr072c2qCwYMHC+3r1Ra5ubmkra1d5X4YhmH3F8vixYsX5OLiQhkZGcTn88nV1VWk/t+/f0+bNm0qcX+1qkRERNBff/1V7f2Wh5eXF8XExBARUUhICK1Zs4ZVPP369aO2bdtSQkICnT17VsjHICIiosT+GIahly9fUlpaGt2/f7/YnnNsbCy5urqSpaUl+y5KSEggZ2dncnV1JWtr6zLjuomInj17RiYmJuTg4EChoaFEVKg816xZQ8OHD2f7TUtLI0NDQ9YRkMvl0oQJE4iIyM3NjVRVVSkvL4+ICice2tralJ+fTydOnKCvX78KjamlpSXkbHb16lU6d+4c6yuSlZVFo0ePZr+j/Pz8cvfDo6KiCAC1a9eOHBwcqpxfIicnhywtLdm4948fPxKAYhOz2iYmJoZGjRpF4eHhdSpHvVfgHh4etHfvXjp06BBpaGiwM11TU1PS0dGhxMREdrWdnJxMDMPQ8+fP6eXLl/TlyxcaOnQoaWholPgHX7t2LSUkJBBR4ar92bNndO7cOYqOjq6UrKdPn6b4+Hjy9PSkDRs2CJ17/Pgx9evXr9hsLTIykiwsLFiHk/Xr11N2drZQm4KCAsrPz6e///6bLl26RAKBgNzd3Wn37t1C7ZKSksjDw4NSUlLo+/fvdPv2bXJwcKC9e/fS+fPnRV6VL1q06D+hwOPi4mjJkiV1MnbRS78y5OTkVMqCkpeXRxcvXqSdO3fS9u3bRb4uOzubrK2tRfIOL4+XL1+Sm5sbOTk5kbOzc5140f+qwIkKvxc7Ozs6f/48rV+/nsaPH18j43K5XHJyciJzc3NatGgRLVmyhHx8fEggEJCzs3OJ1zx58oRsbW3p7t27dPv2bbp79y6tXLmSTpw4QUFBQWRvb0/nzp1jJw1v374tZjncvXs3PXr0iPbv30/Dhg0T6n///v3k4eFBmZmZdPHiRaEV9OvXr2n06NHs5+zsbOrbty+lp6cTUWEkQNGigWEY2rZtG02fPp06duxYqtXm5s2brIW0skmPysLExIR1iKwr+Hw+derUiZ1o1SX1WoEzDMM+DHPnzqWRI0eyCtzGxoYyMzPpzJkzdPv2bbpw4QI5OjrSiRMn6NKlS+z1TZo0IVtbW5EVEsMwZG9vX6anqKOjI3358oV+/vwpZPKKjY2ljRs3UkBAAHssLS2NFixYQL6+vmRnZ0dEhaslR0dHMjY2pkGDBpFAIKCjR4/SiRMn6PHjx2U++F++fKHbt28Tj8crMRTk4cOHFBAQQPb29hQSEkJERBcvXiQXFxeRvbDv3LlDJ06cKPV8fn5+vfJyLg0fHx86duxYXYtRIVJTU0leXp4A0LZt2+jr168VUoLv3r2jO3fuVMqSdOHCBTp79myFr/sVFxeXOs+o9m8FXgSXy6V3797RyJEjycfHh969e1djMiQnJ1N2dja9fPmSnjx5QqampiQQCKigoIDWr19PFy9eJEdHR7p79y7Z29uTra0t+fj40KxZsygkJIS4XC75+PiUuJiIjY2lQ4cOkbOzM33+/JkOHz5M1tbWdOvWLZo5c6aQkj5z5gwbUpqbm0vu7u5EVPieGzVqlNAKOT09na5du0bfvn0jIhJ6D/F4PDp27BjFxcXRnj17SFdXt0TZQkJCSE5Ojtq2bStyJIwo5Ofn09ChQ+no0aM1HkZ27969YscYhqE3b97Qvn37aOPGjRWKJKhJ6rUC9/f3JwDUrVs3AkB//PEHHTlyhOzs7GjPnj1EVDhrtLe3Jy8vLyIqOYTm0qVLFUqpyjAMrV+/vlSz16dPn+jAgQN079492rNnT4mpMXNycsja2pqOHj1KEydOJAsLC3YFHRAQQAzDUGhoKDshOXv2bIlm8ZLw9/enLVu2lBhilJKSQocPHxZS7nw+n8zMzEQOvyj6cf/KmjVryNLSksaOHUtKSkp069YtkfqqSzZv3kx+fn51LUaFsLKyogsXLhBR4WqrNlcaRSFLL1++rNTK+cOHD2RiYlIDklWM0hT4r5w4caJWQic/f/5MO3bsYCfwHh4e5O3tLbQQcXV1pR07dhAA0tLSInV19TL9EmJiYqh///6krq5OCxYsoOvXrxNRYY6If+d/OHnyJK1evZqICk3kdnZ2lJKSQlFRUfTo0SOhtgDIxsamxDGL0sTevn2bCgoKyNjYmF68eFHic5KZmUkzZswgAPT06VORv6vS+PLlCwGgVatWkby8PP3zzz9V7rMsRo4cSZcvX6bU1FSys7Ojo0eP0l9//UUrV64sdZulrqjXCpyISFFRkQDQihUr6Pjx46XuOTg6OpYaRJ+YmEhmZmZCDmLlwePxyNnZmQYMGFCioiwy2+fn5xfLzxsVFUULFy5kZ7AMw9D169dp2LBhQs51VcHc3LzEH4+zszPx+Xy6desW6+jBMAxNnz69QibdX01rRIV7T+7u7iLH8NY18fHxNGnSpLoWQ2RycnLo8+fP1K5du7oWhVxdXcnAwKCYz0h5MAxDPj4+5O7uXqcJaERR4IMGDRJyPKsJEhMTac2aNWy+7F9JSkoiLpdLISEhtGLFCtLU1CQNDQ0iKrSi7N69m/bv31+ipSs5OZkAUHh4OHXs2JEMDAzIycmJTE1Ni1k/8vPz2bwRZmZm9PXrVzI3N6etW7cWe5fu37+/1Hfoo0eP6MuXLxQfH08HDhwgIyMjMjExKTV3RWxsLBkYGBAAunHjBnl6elbaavfmzRvWEguAFixYUKl+ROXs2bPUvn17atOmDQ0YMIC+fPlS489KZanXceA9e/ZEUlISgMJKQMrKysVqOBcxZcoUPH36tMTYQEVFRaxZswZWVlYwMDAQaWwpKSno6+tj5syZ2LNnDwwNDdG+fXv2fFRUFAQCAW7fvo38/Hz2+KJFizBz5kxYWlqyebNDQ0Nx7NgxHD58GJqamiLff1mUdh+xsbGQlJRE7969ERMTgzZt2oDP5yMqKqpYBbOy+Hcomqqqaq2EHVUXly9frtf1eX+Fz+dDR0cHEhIS1VIrvqrMmzcPXC4Xc+fOhbGxMVtNrjw4HA5GjhyJHz9+wMLCAitWrKiTvAKihIWqqanh48ePUFdXrzE5PDw80LlzZ0RHR6Nx48bg8/nw8/PDt2/f0KVLF4SFhcHQ0BDa2toYOHAgOnbsCABQV1dHnz59kJWVBXNzczRu3BhEhMjISHTr1o2tSb1t2zaYmprCy8sLPXv2ZPOj/4qsrCyb1rRFixbo3r07fHx8kJeXV+xdunDhQnz79q3Ed2hsbCwmTJgAIgKfz4eSkhKys7Nx9epV7N69u1j7du3a4dSpU7CwsGBjz83NzUV+//7KyZMnhT4X1QsoLx6/MqSlpSElJQW3b9+u0Wej1qnO2YCoNG7cmADQ6dOnWc/K0oiIiGCrcJVGaeah8mAYRmjlzDAMOTg4kLu7OyUnJ7PHTUxMaPfu3UKyhoaGUuvWrSvt0FQRgoOD6fLly0RUuJ/5qxzOzs7UpUsXsrS0LLcfPp9Pffv2rTE5awMDA4N6Z+76N56enjRp0iSaOHEijR8/vl5V8SvC3t6edWqqCFlZWbWeMasIb2/vch1Rw8PDSVVVlfLz8yk/P5+MjIxKDM2sDrKzs8nf35/ev3/P7m8TFa7CJ06cSI6OjqVu9TAMQ8HBwUIr3YSEBMrJyaHu3bvTlStXSEdHhy5evFiuHKmpqWRvb09BQUH04MGDEttcv36doqKiih0vMs2npKQIvWdLc877lRMnTtA///xD2tralVqFDx06lBQUFOjKlSu0a9cu2r9/f4X7EJXNmzf/Viml6201sm/fviE7Oxvdu3dHkyZN0KBBgzLbd+7cmV2tl0RUVBQ7wwUKK+qIWvGJw+EI1eeNiopCo0aNICEhgdjYWACFFbAUFRVx8OBBIVnl5OQwZsyYWqlh6+HhAV1dXXh6emLChAlCcujr6yM8PByrVq0qtx9JSUl07dq1JkWtcZYuXQoLC4u6FqNMxowZgwcPHuDhw4d4/PgxmjZtWtciFWPWrFm4f/8+YmNjK1TRr3HjxuBwOODz+TUoXeXp0qULbt++jX79+mHixIlYsGAB3N3d4erqWu1jNWrUCAMHDoSamhp69OjBWiVatWqFdevW4cyZM3jy5AmbPe9XOBwOevbsiT59+rDHWrduDTk5OXz//h0zZsyAl5cX/Pz8ypVDXl4evr6+UFZWRocOHUps065duxITsDRu3BiXLl3CzZs3oa2tzR5v3rx5ue/RrVu3ok+fPrh37x5mzpyJJ0+elCvrr4SGhkJaWhp37txB9+7dayzJVGJiIlq0aFHlDIr1kVpV4F+/fkWfPn0QFhaG4OBgkV8CZZnOnj59ivHjx7Of/fz8sGbNGsTExJRatu9X5OTk2P8rKSkhLS0NWlpa8PPzw+vXr2Fra4tevXoVu65t27bIysoSSf6KQETIyMhAcHAweDwejI2N0b9/f0hISODt27fo1q1blfqXkZGpt6XxRGHAgAH48uVLXYvx28Pj8fDw4UNcvHgRN27cgLOzMx4/fizStVOnTsXp06fRuXNnrF69uoYlrThKSkoIDg6Gl5cX1NTUYGdnBwcHB5GvFwgEcHNzg4WFBTZs2FDmAqI0evfujXfv3mH37t2VzgiZnp6OCxcuiNT28OHD+PnzJ5SVlREZGQkOh4Nr164BAHJycvDo0SP06NGj2HXTpk3DvHnzsGzZMkhJFe6oZmVlISQkRKT0oaqqqnj8+DGuX7+Oo0ePgsPhiPx9paSkoEuXLsjKykKTJk3Qs2fPGpkYLly48LfZdqsotarA+/btCxkZGXTt2hU3btwQmvGVRVn7bY0aNUJOTg77+e+//8b9+/dx7tw5LF68GO/fvy/12sTEREhLSyM/P599cFq0aIHc3FzMnTsXLVu2xMKFC0stoVddtciDg4MxadIkaGlpQUJCAvPmzcOrV69w5coVLFmyBOrq6jAxMcGKFSuqnB722rVrbMrI3xEpKal6u/r7ncjJyYGSkhK6dOmC2bNnY8GCBcjKykJERESZ13369Al+fn7YunUrtmzZgps3b+LEiRN4+PBhLUlecSQlJTF69GjMnz+/zHbR0dG4cOECrKys8Oeff0JeXh7a2tpo2bIlTE1NS3zu0tLSSpzI9+zZE/PmzcO0adMq/ZvlcDgi16lv1aoV+Hw+AgMDkZiYiCNHjmDGjBmIioqCqakpNmzYIJIcRAQnJyc8evSIrYn9Kzk5OejduzebwrhPnz7Q1NTE3r170adPHygoKMDW1lYkmX19ffHq1SvIycnB3d0dt27dqnKa4n9jY2MDb2/vGrOC5ebmIiUlpUb6FoVaN6Hr6+uDiJCYmChk+q4sU6ZMwe3bt9nPkpKSOHbsGI4fP46TJ08iOzsbzs7OcHd3R0xMDNsuOjoaHh4emDVrFqytrXH8+HHY2NggIyMDrVq1QrNmzdCtW7didbZ/5dfVO1CYm1tHR6dEk1lpEBHmz5+PixcvwsHBAcHBwfDw8ABQ+AO+f/8+bGxssGXLllLzP1eE5ORkrFy5skasB7VFmzZtEB0dXddi/BYwDAMDAwM8ffpU6HhRARR7e3v22PTp0/HmzRt8+PChWD95eXm4cOECYmJiEBQUBABYv349JCUl4eXlVe+fp127dmHMmDHFHLO4XC5sbGxgbm4OBwcHtGrVClOnTkWzZs3w7t07eHt749y5czhz5kyJJuWWLVviyJEjJY7p6uoKFRUVkWWsyHujJN69ewc/Pz94eHggPj4eampquHDhAoyMjNCsWbNyryci2NvbQ0dHB/fv30f//v2Ltdm0aROCg4Nx8uRJ2NnZITAwEM7Ozti/fz+CgoKQmpoqcn30a9euoXHjxvjy5Qvc3d3Ru3fvKilwPp8PNTU1+Pr6QiAQgMPhwMHBAZmZmZXqLygoCBwOB+bm5sXGISJwuVw4OzvD2toaR44cwa1btyote6Wpzg31srh37x4BICcnJ9q2bZtIJel4PB7xeDyys7Mr00miKDaaYRhKT0+nnJwcSkhIIFNTUzb5AY/Ho/v375OTkxNZWNnQtiPnaM0hW9JasJ6sLj+kvAIu7dy5UyiPcHloamqy//f09KRly5aRr68vbdq0SeQ+vn37Rvr6+qWeZxim2pMLHDlyhKZNm0bOzs4VCsGrL/j5+dHGjRvrWozfAlNTU5o3bx5du3atxPOvXr0S+pyRkUFz585lk3UwDENXrlyhCxcukJWVFREVOnW6urqSmZkZGRgYsPHKNY0oTmxloaurW6xGQlxcHPXp04fs7Oxo48aNtG7dOpo/f75Qgig+n09jx44tsc9Vq1bRqlWrKDc3t8I5FJKSkmjLli108uRJsrKyIgCllhT+N56enmRmZlYsOVRR6dALFy6U+V4piZycnDLrifP5fBozZgytWbOGvn37RgoKCiQnJ0c5OTmUnp5OACoUy926dWsCQOPHj2dTqlaFomx2AKhLly7Ut2/fSqcSLigoYPvavXs37dixg1xdXdm8H+PHj6dt27bRzp07KSUlhfbv308fPnyo8j0UUe/iwAcPHszW4BU1ZlpXV5ekpKRo0KBBpKmpWarS379/P1sgBADrDXrr1q1iqSe9P4TR8NVnqd+8fdRp2AwauNyENJaZ0MRN5vQ9OknkONe0tDQ2JzaXy6WePXtSdnY2xcfHk66urkh9EBUqo127doncvjooejjNzc1JRUWF3r9/X6vjVwc1lTbzv8bt27fp+fPnrAIvKTmRm5sb3bx5k86ePUszZsxgY45zcnJIRUWlzJz7tRkXXlUF/uDBAxo9enSxGgu/vtdOnDhBqamplJqaSm/evCEjIyPavn17sVSm/8bLy4sA0JkzZ0SWJycnh3r37k36+vpsXXtRFU5R6tG9e/ey6aR//PhBhw8fpl27dlFiYiK1bdtWJC/2X3nw4AF9+vSpxHNFGTSL+nz69Ck1a9aMLcICgMLCwkQeq0iv6OrqkoqKCi1atKhCsv4ql4GBAXXu3JlmzJhBLVq0oLi4uCo9m3PnzmXvycrKiubNm0crVqygI0eOUHp6Os2ePVuofXx8PK1evVooeqkq1CsFXqQwEhMTi4VulYRAICAOh0MzZswgIqKfP39SampqqRWQfn1IDxw4wGa8OnDggFDoxPfoZBq88jQNXGZCSiNn04ClxqSxrFCBD1pxisYbXqCsXNHy/IaEhFCbNm1IR0eHhgwZIjSD3LFjh0h9EBHp6OiUWQmoppgyZQoRFYaurFixotbHryrVUVDkf4HU1FS6efMmOTg4kJ2dHV29epWWLl0q1EYgEJCUlBQZGBgIZfHi8Xh0/vx5NsVwXVNVBU5UmNNdVVWVrl69WuxcYGAgXb9+nXx9fcnFxYXWrFlDdnZ2Iuf+/vbtG5uIpTbgcrl06tQpMjQ0JBcXFzI2NiZnZ2fy9vamnTt3UlBQUJmhU+np6bRq1Sp68OAB+fr60qlTp8jExITu379PRETPnz8vNuHbu3cvASCGYYjH4xEAmjRpEgEoNYStLIpSqFYl//jixYtp4MCBtHPnzgpl5iwNhmFo5syZNGfOHFJUVCQOh0MAaOvWrUJt/k1WVhbNnz+/yuMT1TMFbmtrSwDox48f9PTpU5FmaTo6OsWOFaUO/DcHDhwo8fiTJ0/YB+PRo0c0belmGrj0BGksM6E+ukbUU3sdq8A1lpnQwGUm5P70vcj3VVBQUG4ce3m0atWqyn1UhqLvNz09naZNm1br41eVPXv2/BZpX2sThmHYf0VcuHCh2KrOxcWlWDY2MzOzUvudM2dOsSI+v1JbE9DqUOBEhd/T4cOHaeLEiex3xTAMzZ8/n+7cuUMmJia0du1aMjExqdC9MQzDlu+sLctETk4OvXjxgpycnOjUqVPk6upKDMOQp6cnOTo6lpny1MPDg/z8/CgyMpLc3d0pJSVFSG47Ozs6c+ZMsap/Ojo61L17dyIiunv3LrtSrQx5eXkEgG7fvk09e/as0LUMw9C4ceNIS0ur2la+vxIREUF6eno0YMAANg9HWfj5+VVbPvd6EwduamqKZcuWITY2Fp06dUJERES5scheXl4YNWqU0DE7Ozs0bty4xPaysrIlOtG0b98eVlZWAABPT0+kyXZAgOPfSIv8CNkm8vgZ9q7YNd4fwkS9NcjIyJQbx14eqqqqQrHotUFaWhoaNWoEAGjWrBkYhsHly5drVYaqsnfvXtjY2ODGjRt1LUqd8+PHD/Tp0we9evWCgoICTE1N8eLFC1y4cAHa2trFHDEnTZqEI0eOwNPTE0Bh9rKyMgm6uLhATU1N6FhaWhpyc3NhaGiIFStWsMd5PB62bdtWbfdWE3A4HPz999/4+++/0aNHD3h4eIDD4cDFxQUqKiro2bMnNDQ0sHnz5mKOquX1a2RkhF27dlXI8zwkJAShoaHg8Xg4fvw45OXlERkZKdK1cnJyGDFiBFq1agVlZWXw+XxwOByMGTMGCxcuBJfLhZ2dHXg8ntB1KSkp+PnzJ4YMGQIlJSXMmjULEydOxL1795CcnAygMOeCtLR0MSeu69evIzQ0FBwOB9ra2uDz+bC0tBT5fn+l6P2po6ODkJAQbNmyReRrBQIBRo4cCQ8PD7Rs2bJS45fG27dvMXfuXMTFxeH69euYNWtWuddYWlqK1K5aqc7ZwL8JCAgQmp0VVb4pj/z8fCGzbmBgIN2+fZv9rKenR1u3bmVnXfb29iXOwPLz82nnzp306dMn8vPzox5j5lLf6VtIdc4e6jJGn1r2Hk4tew0ltQWH2VX4iqNVq3NbUTw8PGq9staePXuKZYhasWIFTZs27beoRlYEj8cjTU1NWrt2bV2LUufweDyaPn06Xb58maytrenu3bvlrgKDgoLo9OnTBIAtIlQajx8/JkdHRxowYACZmJhQs2bNaN26deTp6UkHDx5k282cOZOtllfdVNcK/Fe4XG6FSrSKSmhoaKn5x3+FYRhavXo16evrU5s2bQgA+fn5UXR0NMXHx4tkAWAYhq029++iJ0SFRUj+fbxo3ISEBLp//z4BoB49egi9r79+/Uo+Pj708OHDEu9vx44dNG7cuHLlKw8XFxfWlI4KWC/S09Np586dNbplER0dTQDI29u7zHYhISGkrq5ebZaXemFCHzlyJF26dIn8/f0pJyeH3Nzc6NChQ+Ved/v2bdb0nZKSUsyz0c3NjQCQq6srERW+vM6dO1diX2FhYbR3716Ki4ujRf/YU+dRc4XM5n2mbiLZZq1IY5kJDV5xikwuPa/QPVaVnJwcGjJkSK2OuXfvXqHSqEXUZaGKivDPP//QnDlzKCMjg549e1Zh56H/MgzD0J49e+jPP/8UqX1iYiJduXKlwkUd+Hw+q1x+Ta06cuTICvVTEWpCgVcXXC6XEhMTycjIiHbt2kX9+vUjSUlJ+vvvv0Xuoyjqhqjw++3Rowfp6urSkiVLaOXKlcX24i9evEjLly8nc3NzCg4OpoKCAjp//jx7PiYmhk6cOEE5OTmkra1NlpaWQn/nmJgYOnjwIJ05c4Y9npKSQsbGxmRra1vu++Dhw4fUqVOnKqcoLXIAxP9VTRP1PXT37l2aM2dOjUbS+Pj4UI8ePUqdSPF4PLK1tSUdHZ1qrZNe5wp8/fr17B/l8ePHdP78ecrJySm3j8OHD9PMmTOJqLD6V4MGDcrN2fzy5csS93qmTZtGkZGRlJeXR87OzrTOaDf1nvKXkAJXGjmHOgyeyu6BR8RXX61bUfjw4QOtWrWqVsfU19f/baqPERVWLRoyZAiNHTuWpk6dSi4uLhQZGUkTJ04kAwMDEggEtG7dulKdHP/XSElJqZQiffToEWVkZBTb8yyPIi9oIyOjEp3Dqov6rMCNjIwIAP3111+UnZ1NkZGRdO3aNZKRkam0VYvL5RLDMBQQEEBTp04lADRixAiys7OjgoICMjQ0JHNzc9Y5+OjRo+Tv789WWbxw4QK9efOGtm3bRiEhIXTo0KFSSyn/CsMwJCkpSQCElLOHhwedO3eO9Z8o8koPDAys1P39CgCR9pl/xdzcnEaMGFFiZEV1cerUKTI3N6fZs2fTmDFj2OMfPnygc+fOlRjVUB3UqQJfuHChUDzejx8/SjTD/AqPx6PZs2fT+fPnKT8/nxiGoYEDB5ZoGt+4cSNboKB379709u1bOnv2bDFnsKJJRNEXzDAM7bd9WKis/y98TG3BYZJTaE8ay0zo0pN3It1fdbJkyZJqqa1bEf5dUrS+4uTkRBMmTKC+ffuK5F1qZWVFEydOJFlZWTIyMiIbGxuytLSke/fu1VhBi/oKgAo59uTm5tLIkSNpwYIFZGZmRjdv3qTs7Owyr7G0tKSjR4/SvXv3iIiK1ZqvbupKgd+/f5+OHj1aZhnW7OzsCluwsrKySEFBoUITz127dtGBAweKhWwxDEM2Njb0+vVrcnV1pfv377Mx/mFhYeTq6kqnT59mf0fe3t5kY2NTquWlyLls8+bN7DEPDw8CQLNmzaJ37wrflQBo5cqVFbrvkijSF/+O0y8NPp9fYZN7VfH29iYnJydydHSk9u3bl1iOurqoEwX+48cPWrlyJQGgT58+sfGEXl5eZYYJFO0z/LrPPWfOnGKesVOnTqUzZ86QqakpcTgcevjwIVtTNicnp1hFLh6PV8yUyDAM3X7xmebtc2QVeee+GnT41AVRvopqhcvl0sCBA2t93Dt37tCYMWN+G5N5RRk/fjy9f/+eLC0tyc3Njc6ePUsAaMmSJXUtWq3x7du3YvXsyyMiIoISEhLo7NmzdOLEiRIr7dnZ2ZGJiQmdPn2avLy82OPh4eEVThxSUWpagZe2Ug4MDKQNGzaQm1v1+sesW7eOzp8/Xyx5iyix4GUlXDl+/LhQZbHExMRi9xYfH0979+4t0/y8c+dOkpCQEPLUX7BgAW3cuJGt3w2AncBVheDgYAJA69evF6n9rFmzhHJ+/NeodQWelpYmVJz9Vz5//lxmspDhw4fT9OnT2c/fvn0rMXPUp0+fhMbYvHkzCQQC2rVrF7m6upKjo6NQ+wMHDtDatWtL3ZvgCwQkEBQ+nAsWLKDjx4+XKmNNIBAIqFevXnXyEF69erXU8LvfndmzZ5c4O16yZAnt2bOn1EQVdQnDMPTp06dq3UcTpcRsSSQlJZW6bXXw4EG6ceNGMWuXlpZWjYeT+fj4lFgSs7qYN29euW2Sk5PJxsammELMzc0lVVVV9t1UFA8cHh5OEyZMKLEvR0dHdiVdtPe9bdu2MrMMzp07l8zNzct1qiIqdDQ7evQoubq6Vjpb3tmzZ2nLli3UoEED4vP5FBcXRx07dqSHDx+SsrIym9+jOjhz5oxIYVjnz58nAHTixIlqGbc+UqsK/Ne0c1evXi2mkEJDQ8nHx6fEa0NCQorFfH/+/FnI+YNhGMrPz6cfP34IKXAApKKiQiNHjqS0tDTy8PBgr/n48aNQKjxROHbsGA0dOpQ8PT1Fal8dfP/+nTp37iyy6ag6qUjGuN+Jb9++0bp160o8d/fuXWrdunWFnbZqkvT0dNq9ezcdOnSIBg4cWCnLCMMwdPv2bbK3tydnZ2eys7OjU6dOVaucnp6edOfOHRo+fHixc7/GVNcUNa3AJ06cWG4bgUBA+vr6tH37dnr+/Dl7/M8//6SuXbuymbuKlFpoaKhQ9rJ/k5ycTCNHjmSTVrm5udGaNWtKbLto0SICIHId94sXL1Jubi6ZmpqyE4RPnz6Rl5cXCQQCMjY2JjMzM7KxsSEvLy/KzMyk27dvl7h4WrRoEQ0ePJjS0tIoKyuLtLS0KDs7u0QrTWVJSEggLS2tcicE586dq7Z46/pKrSlwc3NzAkAtW7aktm3blvgjFggEpWZfi4qKKnGl8GtoVZEiXrduHWs2+TXvrYKCApmZmRWbOAAgOTm5ChWK5/P5NHnyZJHbVwe5ubk0fvz4YhYEMZXj1KlTQtsx/6Zv377sC622KdorLco1TlSYw/nt27clhgCJgouLCy1evJhiYmLYY1wuV+QXvagUKaElS5bQ0qVL6fjx48Tj8cjf35/U1dUrlYmrItS0Al++fDlNmjSJ9u3bV+ZkJCkpiaysrOjJkydkYWFBtra2tGzZslIn/jExMWXmCL916xbNmTOnTNlevnxJACqUb/vZs2dkbW0t5Ihmb29PDg4OxOPx2MgNPp9PBgYGtGHDBgoJCaHAwMASo3p+/PhBGzZsoE2bNtHJkydrJIPj+PHjCUCZ/hcMw9Do0aP/s1uARLWowIuUaPPmzUv1Xk1LS6NLly6VeG7hwoUl5lr+1TwCgI0pt7e3p4KCAlJRUaHRo0eThoYG/f333+Ts7EzPnj0T6kNPT4/y8/PJxsZGlNusUxiGoW7dugm92MVUHIFAQP379y/1xx0eHk5t27atVZni4+PJ3NycbGxsyM7OjoYNG0bm5uZEVLg/OX78eHry5ImQBUkUnj17RlOmTBEKHaopDA0NhXxSXr9+TatXryYANGfOHHrx4kWNy1DTCrwINze3MmPDb9++zX4XfD6fMjMz2b9nTSEtLV0tE/xz586x/ZiYmJS67//HH3+QpqZmmX4BL168oDNnzlTrRLFI15S1r+7t7S1egf8f1abAvby8SFNTkz5//lxiH87OziWaRgCUuBrq2LEjMQxDUVFRpKGhwbadO3cuK0+7du3I0NCQVq1aRadOnaLTp08LhSJkZ2fTnj176PHjx0I5nusrsbGxNHToUHr69Cnt2rWLFi5cSPb29rRo0SJyc3OjK1eu0MuXL+sk9Wp9h8/nk4WFBWloaAg5V/0bhmGod+/etShZ4Z7dryb748ePU0hICCUlJdGKFSvo/fv3dOPGDfr69avIfRaFt9QW169fp5MnTwrtczMMU+kQosTERDp48CD17NmTwsPDKSkpqdx43tpS4CXx+fNn+vTpEx0/fpy1Dubn59OZM2fIwcGh0lWvRKGgoEAoD3dVyMrKYt8fs2fPLlX55uTkEIAy3zVZWVmkr68vZPmpDop0SpEuefr0KQUFBbHnmzdvTkpKStU6Zn2j1lKpdu7cGQAwZswYPH36tNS6s9++fYOCgkKx44cPHwYRCR0TCARITk4Gh8NBx44d4ePjAwAYPXo03Nzc0LRpU1y9ehWqqqoYNGgQGjduDE1NTWzcuBEDBgzAvXv3gMIpGg4ePIjdu3ejU6dOVb3VGqddu3aYOnUqNDU1oaGhgePHjyM/Px8HDx5EXl4eIiMjcfz4cejo6KBfv364cOFCXYtcpwgEAqxZswaTJ0/GlClTIC8vj4cPH2L06NGlXsPj8dC+fftak/HatWsYNmwYJCUl2WMMw8Df3x/u7u6wtLSEmpoavLy80KNHD5H6PH36NO7fvw8DA4OaErsY06ZNg5+fHxo1aoSQkBAQETgcDvr161fhvoKCgnDhwgXExcVBXl4eaWlpUFRUhLy8PDgcTol1t+uaLl264MCBA8jPz8fAgQNx9epVWFtbY9SoUVi0aFGxdLUlIRAIMGnSJPj5+ZV4PiUlBXw+v9jxhw8folu3blW+BwBo3LgxGjRoACLCsGHDSk1PLScnh9evX+PIkSNYvHgxjIyMSuzL2dm52n9PRITp06dDRUUF0tLS0NTUhLKyMoDCZyc9PR3r16+v1jF/W6o6Gyiq6RoSEkKmpqal9vFrtqZfuXnzJh05cqSY962amlqJdbDj4+Pp9u3bZGRkRP369aO3b98SEbGrEYZhhPbPGYapkUT3NUl6ejoNHTq0zDZ8Pp+OHz9OPXr0EKpd/Dty7NgxmjRpEo0aNYoiIiKKnQ8NDaVRo0bR5MmTSUdHh3R0dGjChAk0YcKEMktdloS6unq111cvDT8/vxITmyQnJ5OamhplZGRQXFwcnTlzRijki8/nU15eHuXm5tKlS5do48aNbBatK1eukIODQ63v/zEMQ7du3aIhQ4ZQz549yd/fv1L9cLlc2rlzJx07dowYhqHc3FyaOHEiW+yoffv2pTpq1eUKvIi8vDz6+PFjpba6ispuTps2jZKSkujGjRvsOVNTU1JQUCjmx8MwTJnv1cqSl5dHDRs2LDO2vYiUlBTatGlTtctQFgKBgK5fv06pqansilxXV5fk5OSqVFirvnPs2DHKz8+vPRP6nTt32C+4KHFASTg7O5d6LiEhgTp16iRkZoyLi6M5c+bQ33//Tfn5+eTk5MTGHV64cIGsrKzoxo0bdPjwYXr16hW9efOGrl69So6OjqWmVbW2tq71pCmVZcSIESK1K0q48Gvc5++Et7c36w2fnJxMvXr1YnMGhIaG0tChQ9mY06rCMAzNmjWrVjzQvby8yn3WuFwuGRsbs/uMeXl5FBwcTJs2baKFCxfSrFmzKDU1lRiGoZMnT5KWlhY7Ya0t4uLi6OzZs7Rr1y5yd3en2bNns05R+vr6FcoyVlBQQLNmzaLw8HC6cuUKERWaYbOysoT2ND09PUlHR4d27txJDMOwmcb8/Pzo8OHDZGpqShYWFtV7o7WEnZ0dpaWl0enTp0lTU5M93qRJEzp8+HCx9gKBgM10V924uLiInIa0tFTFs2fPpq5du1anWMXIzc2lKVOmlBqm/F8CAJ08ebL2FHhROr1/FzgnKgzlMjMzI29v71IzYfF4PHr//j0ZGhqSoaFhiTe0detWmjt3LgkEAqGVx927d8nY2JjWrl1LQUFBtHXrVsrJySl1dRIQEFDj4VqWlpYizWrLIjc3t0IPKcMw1KFDhyqNWRcUrUR/JScnhwYPHkxr166lfv36sXuLdb3yKomYmJhSV/MdO3Ys9/oLFy6wz4qhoSGNHz+e9PT0yN/fn4yMjITaZmVllZm8o6bIy8sja2trWrt2Lbm7u1NeXh6tW7eOrK2tae/evSJ5RYeFhZGDgwOZmprS2bNnycnJiby8vCg2NpYA0JAhQ9ga1L9ib29PFy9epIcPH7JZ+Yriqtu3by9UNCU1NZU+fvxYrfde0xTFe4eFhRGAEn0gUlJSqiVRSkm8ePGCjhw5Umabd+/esZavf5OQkMAq1JrOZREWFkbLly+n2NhYNgvcfxFzc3P6/v177SnwX+Ot/83o0aPp/PnzbPjJu3fvhDIp6erqkpSUFAEgeXn5Es2nRIWew1u2bCFtbW3at28f2djYkIuLC23fvr3OwoFK48iRI1X2em/UqFGpzoClYWtrS2pqarW+Qqssnz59Ijk5uRLzGDMMQ3fu3KnTMBGGYejFixf0/PlzevHiBd28eZNu3bpF3t7eFBcXRzdu3KAZM2bQ06dPSVlZmc6fP88+i0UOQGXlE7h48SL7vKemprKewfW1GlxJk9Jfq2CVxoYNG6hNmza0du1aUlFRoZycHFq6dCm79Xby5EmRZcjPzycfHx8yMjKinTt3koWFBSUmJlJkZCQdOnSIli9fXuffn0AgIHNzczI0NCR7e3t6/fp1ucl5EhMTac+ePSW+X7Oysmosv/yYMWMoKSlJpLabN28u9ns8c+YMaWpq0uLFi8usJy+m4tSaAi86928FfvLkSQJAampqdOLECfrw4QMtXbq0WLrFu3fvUvfu3UucAPj5+ZGvry/Nnj1bKGF8Tk4OGRsbl6rwS8PJyYkAVGu2q18pmswkJCRUuo+YmBgaMGBApa7NyMigzp07k46OTr2PkXz27Fm99abPyckhMzMzunv3LkVFRdGPHz8oPj6edu/eTcHBwWRjY0O3bt1iV3x5eXm0Y8cOGjZsGF25coUMDAxoz549pabevHbtmlBq4aLUpCdOnKixUpw1QVZWFikpKRGXyyVPT08qKCggU1NT8vb2pu/fv1NOTg4FBgaSQCCgJUuWsKv1rVu3UsuWLSvtuc3n82nnzp20aNEiOnjwIGlra9PNmzfZ7bW6gsvl0pEjR+js2bNkbm5O58+fp127dtGMGTOKpUsVleDg4Bq5p/j4eFq2bJlIbQUCAQEoZm4HQJaWlnT37l0CUOEiOGJKp1YzsQ0fPlzoIfv69SsNHTqUMjIyyMbGhl68eEFjxoyhU6dOsUkniApXIQMGDCg1H/iMGTNo4cKFtHTpUqHj7u7uNG3atAopqaKHUFpaWqgIQHVSlCO4Kk5S06ZNKzMJiSjY2tpSjx496r0Sr48wDEOWlpYVnly4urrSs2fPyk3TmpaWJpTUKDw8nPT09MjAwIDOnj1b7clXapKMjAzq0qULHTt2jKytremvv/6iDh060IYNG6hr165CvgaJiYnVXvc+PDyc/vrrL6G9UWdn51pPiPT8+XOaNWsWde3alTZt2lRuERgej1ehEpzDhg2jc+fOVWuinA4dOpQrZxHW1tYEgJycnOj27dvEMAzZ2toSAEpLSyMul0sHDhz4bYok1QcYhmHLIXt7exfzYapVBe7t7U1r1qyhp0+f0sePH+ngwYPsnsjly5cJAGlqatKzZ88oICCAFi5cSESFK5HSyh5++PCBFBQUaOPGjXTo0CGh/b/U1FTq2bNnhVfS7u7udOjQIVJXV6/QdaLy5csXAkB79+6tdB/NmzevFicrW1tb6tSpU43np/6vYW1tXWHLjqhwuVw6e/as0N/33LlzJBAIqjxpqwsYhhGaDIeHh9OtW7coISGBNmzYwB7n8XjUvXt3NhnTr2UZq0pRUg9ZWVlq1KgRTZkyhQYNGlTpFW9lCAsLI0NDQ3J1dRWptOSiRYtoxYoVIu8bX716lQQCQaUz9ZXEp0+f6NSpUyJPJPLy8lh/Jz09PXJ2dmYXYkRE7dq1IwB1voXxO7B69WqaNm0ajR49mnr27EmvXr0q5pRZ68VM/Pz8CAAZGxuzM7uiHOa/msc3b97MBuWfPXuWxo4dS0SF1XV+9WIXCARkYGBABQUFxDAMGRoa0rZt29j8w0OGDKnUA+3i4kKurq4Vvq488vPzKSAggJYvX06NGzeudD/VuQILCwujcePGiX9UIhIdHU13796t9n6Dg4PJ3t6eTp48WWwv2dDQkI4ePfrbhTqWxfPnz+nevXs0aNAgunLlCm3evJm2bt1KjRs3JkVFxWrfJvj8+XOxGgm/TiDqGoZhWGtYkSXwwYMHBKBCZW7d3d2rdfsvOztbZDM6EdG9e/do/fr1JVaWLNrCkJaWrjb5/qsAYPO5BwcHk4qKSrHtpDqtB05UOBvX1dWl8PBwmj9/Phs3+euMz9XVlbZs2ULx8fE0c+ZMIQ/xO3fusHWJi9oSEb169YrMzc3p0KFDIstSFn5+fqSurk7z5s2rUqhSVlYWmz3Oz8+vWmSrDubMmVNmCJ+YQkJDQ+n8+fM1MtmxsrKiuLi4Es9FRUWRqalpvXpmqsrChQvp7du3ZGxsTHPmzKG3b9+StLQ0q1wrm72tNIqUIgBq2rQp+//qLLRRWV6/fk16enrs9oFAICAPDw86cOAAnT59muLj49m2Rc6PpT2DMTExbGhidT2nPj4+1Tah+vDhQ43XhP8vsGDBAurduzcdOHCADhw4QFJSUuTs7Ez29vasvqu1TGz/hoiwa9cuTJ8+HZqammjRogUOHTqEBw8eAAAaNmzItg0ODsabN2+QkZGBiIgIdOnShe0jOTkZ69evR35+Pm7evAlFRUUAwNChQ2FgYIA2bdpAIBBUWd4hQ4bAx8cHcnJyQrJVhI8fP0JRURHz588Hh8PBkCFDqixXdeHi4oIFCxYUy3Yn5v/j5+eHwMBArF27FhIS1fuTICKkp6ejbdu2JZ7v2LEjuFxuvXpmqsL169dhaGiItm3b4vPnz3Bzc0NycjJ4PB4A4OLFi5XK3lYWEhISiIqKAgBkZmZCS0sLAPD48eNqHacyhIeHY8KECTA0NARQKOvMmTORmJiI3NxctGnTBkBhhsC+ffti/Pjx4HA4JfaVkZGBrVu3ws7ODtbW1rh+/TpycnKqJN+wYcMQEBBQpT6K6NevH1RUVGBnZ1ct/f1X2bNnD759+4a9e/diz5494PF40NfXR4sWLTBq1KiKdSbKjKEiK3BHR0dq27YtdejQgZYvX86uvC0sLCg2Nlaora6uLp08eZKiUpPI4403+SZEUA6vgG7fvs3OTBmGKXYdUeHeelk1xitKVFRUpc0/Li4u9XqvuaTyj2IKSUpKqtEsU87OzuVGJdjb29fLGuUVoSjZkpWVFe3fv5/09PTYECWBQEAaGhr05MmTGpUBAElKSrIr8L59+wplOyuN0hI/VYXp06eXmtve39+fAAhl39u1axeNHj26TBN5aGiokCUnKSmJPDw8yMHBoUIr8n9nL6yuLcVPnz6xhafElE2bNm2Eqq4xDCNkVa7TFfjly5fh7++Pdu3a4ciRIyAivH//HlwuF0DhitXKygppGRn4MaADNJ9YYXu4NxZ6umDwjTNw/PACO3buhIGBAfT19XHw4EHMmDED8+fPR1JSEgBAS0sLjx8/BsMw1SJ3hw4d4OLigvHjx8Pb2xv79+9Hjx49sHfvXrx69arMa+fPnw85OblqkaMmmDRpUo31bWxsjF69esHCwqLGxqgpiAi2trbYu3dvjfXPMAxat25dapvo6GhIS0vjw4cPNSJDbWFpaQldXV2sXLkS1tbWuHLlClq1agWgcNX59u1baGpq1qgMz549Y61yO3bswPr16xEQEABTU1MkJCSUet33799x+vRppKenV8tqlGEYqKuro0+fPiWef/36NXR0dLBt2zb22KFDh+Dl5QVZWdlS++3WrZuQJadVq1aYOXMmJk2aBDc3N5Fk4/P5GDFiBHbt2sV+Lnov3759G15eXiL1UxIqKioAAG9v70r38b9CSEgIgMJaCdnZ2ZCVlcWYMWMq3pEos4XyZgMMw9DXjBjyTfpGJtbniGEYioyMpHnz5pGZmRkBoEmTJtH79+/Jz8+P7OzsyP/De9L0OE093A9TV7dDQv8U186gWXYni4VB/fjxg6ZPn06HDx+mGTNm0MaNG2ngwIG0ePHiaiup+O7dO/rnn3/o1q1bxOPx6MaNG6ShoUHDhg1jHZDy8vLozJkz//NhWkV5iufOnUvDhg0rs21GRgab0Kc+UJRjuqY8zokK05CWl07V2tqaPD09f8sY2qysLLKysqJu3brRgAEDaMqUKdSpUyeRPLFrii9fvtDTp0/pzz//JF1dXQJAq1evpilTppR6zevXrwkAjRw5kv75558q/66LMinu2rWrSv1UhBs3bpCDgwN5e3uXKf+4ceOEYratrKxYC+fHjx9p9+7dVZLj27dvBIBWrVpVpX7+66SlpbGWIjs7O1q/fj0ZGxuzTsw14sSWVEICf9+krzTL25iGPNhBQx7soB67p9HQv+dR/yEDaeDAgdStWze6f/8+9evXjxYvXkz79u2j5ORksv32mrr9S3EX/Ws2dRS1WjuDfBMiio334cMH0tbWZj9nZWVRdHQ0HTp0SOSsQhWFYRhav349derUiSZNmsSmf6wJUlNTadu2bRQQEFDhbGxVJTs7m9atW1eha7Zs2UIASEtLq8x21tbWdOrUqaqIV20wDEPnz58vcWumOgkMDCzTNC4QCEp0MPT09Kz3deEzMjJo9OjRNG3aNOrevTvt27eP7Ozs6losIhJOLlX0T1VVtcxroqKiRMrpfe/ePRo4cCCtXbu2zHaXLl0qpkh9fX1rdMJIVJiD48yZMxQSEkIBAQFlJsvx9PQsdVJdFSe5goIC1lmxNuoO/E7cuXOHvn//Tu/evaP169cLZbDj8XhkZWVFRDVkQp/t4obE7Gz2s3fSFxi9c0R0bgp7rFGvdgh0foSv4cFo3bUjFi1aBC0tLWzevBk2Njb4559/0LJlS1wKe8deI8jOgyA7DwCQ9ykMGbd8wKRlYp+pCb5+/cq2YxgGJiYmGDlyJHuscePG6NChA/r374/c3NyK3I7IcDgcdO/eHd26dYObmxsEAkGpJQErS0ZGBu7du4f9+/djx44d4PF4ePv2LUJDQ0u9JjQ0FOvXr4evr2+J59PS0ljnofJYvnw5pk2bhqVLl1ZI7pMnT2Lx4sXIz88v1VGuaJujyBGxLsnNzYWZmRn09PQQHx+PVatWVdkRqDRK+z6KkJCQKLF8ZExMDE6cOFEjMlWVyMhI2NraolmzZvD29sa0adPw/ft3/PPPP1iyZEldiwcAaNq0Kes09vDhQwDAp0+fyvwtdOzYEY6OjuX2raWlhTdv3uDUqVNltps7d24xZ7QlS5bAwcGh3DH+TVJSEpKTk0Vq27t3b8ycORMpKSmIj4+Hm5sbFi5cKNQmMzMTDg4OCAsLw8qVK4v1MXz4cEydOhUFBQWljsPhcEp9votKq/bv3x9SUlLw8fFhzfT/y+jr62PKlCn466+/cOfOHfTp0wcNGjTA3bt3YW5uDikpKUhLS1doW1iqIgJEZ2RgzfWbuLpgPhgQTny5AYLwH5EjAeQnZ0Jp5R/4+Ckc87vPxB9//IG8vDwEBQWxHqjxeZkoiEpAQWQ8JBo1xE/Xh5BfoIXE445of2I9ZDooQpqRQ0BAALuX9PLlSwwYMAAbN24sJluvXr3w+vVrKCkpVeSWRCI7OxsbN25ESEgImjdvXi19EhEiIiKwd+9eyMnJYfLkyRg8eDC0tbUBFHrHq6qq4vLly2jbti0aNWpU7PrHjx/D2dkZ//zzT7H+nz9/jvXr1+PMmTOYMGFCufK8fPkSvr6+kJeXr/C92Nvbo3fv3qV6z/r7+6NTp07V7uFdGYgIEhIScHFxgaysLIYOHVrsuwXA7ktJS0tXeqxu3brh6dOn7N7gr7x79w4fPnwo8cc6f/58XL9+vdLj1iTm5ubo0aMHli9fDmtr67oWp1TOnDmDr1+/Yty4cVBTU8OHDx/K/VuOGDFCpL45HE6Ze9WlUbTvWRbZ2dmQk5NjfysMw2Ds2LFYsGAB/v77b5HGad++vVCN7l9/d7m5uVizZg1sbW1LvYeXL1+KNE5gYCAuXryInz9/FtuDJyL4+PigQYMG2LRpEzIyMvDp0yeR+v2vQURITU2Fq6srVq9ejePHj+Ply5e4cOEC2rdvjy5dukBfXx9AYZTVgQMHsGHDBpH6rpACZ4jwMSER7+PiwZPNQUpB1q9iQlKCQZPWDdBcuR06TOiBjI5NcODcCYztNxQODg7IyMhATk4OiAhc38/gSwNNRqsDAGQ6tUay1U20N/4LTGYOcl8Hgdp1AbdlC1y/fh3h4eG4ePEiDh48WKJsPXr0wIsXLxATE4MOHTpU5LbKJTc3F4qKijAyMsLly5fRoEGDCveRl5cHb29vhISEQFZWFgzDoEGDBpg7dy6mTJlS6rgBAQFYt24dZs6cCX19fUycOBGfP3/GixcvcPHiRaSnpxe77uzZs3j58iU+f/5cqlL9N4cOHcLYsWPx8ePHCt8bUBgSGBkZic6dOxc7VzRxq8z3Vt00atQI69evR0xMDPr374/9+/fjr7/+grKyMho3bsz+kPT09LBy5UrMmjWr0mM1bty4xNX9tWvX0KRJEyxZsqTESQ2Hw4Genl6lx61uwsLCkJ2djf79+2Pt2rXo1KkTVqxYUddilcm5c+fQuXNnXL16Fffu3UO7du2QlJQECQkJtGzZsq7FE+LYsWPw9/fH1atXoaysjCNHjrDPYX5+Puzs7KoUZvirlUdOrnBRlJycXKX3pJycHNTV1dnP7u7uCAgIwIABA9hjsrKyGDx4MADAyMgIfD4fUlIVUjn/CSQkJLBt2zbMnTsXffr0webNmxEbG4uZM2dixYoVSE1NRcuWLcHlctGrVy8YGhrC3d1dpL4r/G1Kcjh4EfkDXZR+nc0S+PEpyIlORm5MOgT5PLxd7YSBVovA79ACzduo4KW5OTgcDgwMDNC0aVOsX74SpkE+YP7PDCPdqgXa/b1EaKxNI2ZifLseWLZsGcaMGYOvX7+WuYrT19fH8ePHsXTp0mpV4oqKipg6dSoEAkGlZt4AcOLECUhJSWHkyJEYPXq0SIq1ZcuWMDMzwz///IMvX77A0tISr1+/Rs+ePbF69WooKSnB09MTY8eOFbquffv2Ij8ARYwaNYp9aVSUIlPa8+fPi5ngr127hry8PCxbtqzSk4MiCgoKkJWVhbi4OKioqFRpRS8tLY3x48fj+vXrePbsGYgIz549w+PHj9lIhO7du1dJgZfGjRs3RDLX1jUZGRlIS0tD9+7dART+nWvCwlUTODs74/Xr11i1ahViYmIAAMrKylBQUEBQUBAkJSXrWMJCGIbBzp07oaCgAAsLCwgEAiErX3JyMpYtW4bLly9DWVm5UmNIS0sLKc9169bh5s2bWLduXaXlzsnJwZEjR7Br1y68efMGb968Qd++fdnz8fHx0NfXx9KlS2FpaYnJkyejefPmOHToUIkW1P8qRRa2vXv3spY+hmFw/PhxdhKsoKAAbW1t9u9z7949kbdoK6zAORwOBMSgucz/CcMTIPn+O/CT05DiHQrlf3TQsF0ztBioBAlpSTRQbILXTARepgRhRCsVbNu2DbGxsVDuroFLYe+Rkp8Nwb/2UiQ4HPSXb4c/2vWAhIQExo0bh8WLF5crm7e3Nzp06IDAwMBqX4UnJCTgx48fiImJQceOHSt8vYGBATZs2AB9fX2RV8VFKCgoYMSIEVBUVESTJk3Qrl07AIX7cX5+fnjx4oWQX8DMmTMrLJ+ioiK2b99e4euAwmdiyZIl0NDQEDr+4MEDKCoqIjs7GxcuXKhU30BhopXv379DRkYGzZo1Q5s2bXDq1CkoKChg6tSpUFBQqHCfrVu3hrW1NfLy8pCfn4+8vDzs2bMHRIT79+/j8OHDuHr1aqVlLomCggJ8+/YNN27cQEFBQaUng7UBEeHPP/9Eo0aN0KFDB3z58qWuRaoQKioqeP36NZKSkjBixAj0798faWlpCA4ORmBgoNBKsS759u0b/vjjDzx79gwxMTEIDg7GpUuX2PNKSkoICgqq0hgSEhJwd3dHnz598P/YO+uwqLavj39n6BYUQRFFxQ7sVmwMrh0YV1FMbK/d3YWCAiIpICogYncRBq2AKIqEdMfA1Hr/4Md5HakB8XrvdT7P4/M45+w6w8zsvdde67u6deuGESNGIDk5+UeHjj///BNEhJ49e6Jnz57MdSJC27ZtsXXrViZU7sCBA+jatSuaNWv2W03gANCiRQv07dsXYWFhYLFYYLPZKPifL1l8fDxOnjyJiIgIZGdnQ0NDA7NmzcLYsWPF8peo0QSeFxwEed2m6DJhHHo3aAplKXkEmJ+Feo+m0PuzHxQa1YNKay2otBaNeyUhG0ci7gECf3x5/gZdOnXGsrxmuDxsDtYEXENwZhJTlgVgpE4bHOo5FtJsNoiIedjqiIqKwqxZs2p1jlsdvr6+P1Tf2toakyZNqrUzB5vNRps2bcpd79OnD+zs7NCvX79fesZsZ2cnsquJj49HSEgI9PX1oaenJ7LAqAkfP35EQEAA1qxZI3K9zGlx48aNOHr0aK1U9FRUVKCiogIA+Pz5M/Nj1Lx5cyxatAjBwcHVtCAeQqGQMaN17doVaWlpjKPPP5V79+5h5cqVmDFjxq8eSq2wtbXFhQsX4OPjg9GjRyM0NBT9+/dHfHw8fH19oaCgUGmc9t9JWloaHj16hDZt2oCIEBkZCTabDS6XW2efERUVFfj5+cHPzw9nz56Fnp4eli1bhiFDhvxQu7q6ukw8+bewWCzk5uaKXOvRowcyMzORlJRUrvx/GTabjVOnTmHmzJnMBA2A+S0/cuQImjdvzigJllGVA6FI+zUZjFrXblAuyMeg5nqQZUtjZJEuhCU8qLTSAi+nEGzZ8usBLl8KybnKiMoqQFRuKoo66+Luo4eY9dQJ7rFvcHnYXNwYuQB7u4/GwZ5j8cR4Gaz6T4aKbOl5qaenJ/r161ft2IgISkpKiIiIgJ2dHY4fPy625+bPhs/ng8/nY/r06WjdunWdtz9u3Dhcv369ztutCD6fX6E377eTd2JiIg4ePAhVVVWMGDECgwYNqnV/Ojo6FXq4s1gsKCkpYdu2bbXy7P2e5s2bQ0FBAWPGjEFsbCzk5OR+2OQPAF27dsWNGzcAlHpHT5s2DfLy8jW2wtQ1QqEQZmZmlS5MjYyM/rWTN1D6w1lUVAQbGxtGnOXRo0fo27cvdu3aJSKi8isZPHgw3r9/j+7du2Pr1q0IDg7GgAEDmM9MXWBsbIySkhKcOnUKACAvL/9Dzpm1pWxSz87O/tv7/lV8+vQJ48aNw7Fjx6CiooK8vDzs3r2b+W2ZN28eI3j0LeJuSGq0A/+0cxuehIVD6n87vaSHYZixbineaqYj8fIr6E4VNaEKhUBGnhIILJTurf93/X+e6+djAtBOXRvGuh3RTr1itarx48fDxsYGb968gaqqKrhcLthsNsaMGQNlZWVISUmBzWbj+vXrkJOTQ2ZmJhYsWAA+nw9ra2ssX768Jo8oQteuXXHs2DERBaky78oOHTogOTm5Qg/j7zlx4gSMjIx+2o92mZm6sLCwQo/quoKIYGFhAWVlZSgrK2PAgAEiZ6IvXrzAx48fUVRUBAMDAyxZsuSH+5SWloacnBwEAkGFDjDa2tp1psb36NEjzJ49G5mZmQBQJ+e9bdu2RUhICIRCIZo2bfqPUexLTEyEvLw8+vbt+6uH8tNQUFDAu3fvmCMvOTk5+Pv7g8Vi4cCBA794dP+PgoIC3NzcMHjwYHTs2BETJkzA8OHD69SRcceOHTh79iyWLVuG7OzsSh1nK+PGjRv4448/4OjoKNZxZkUoKCjA1NQUampqtar/b+T169cAgAkTJsDMzAzKysqYM2cOOnfujAULFkBeXh579+4FEcHf31/sSIgyWFRdsCpK4wbV1NTQvEULvI+ORn5+Poz/MEaAfwBUBxtAe3g7yAiKoDlAn6kjELKQka8EvqD8j27uwyCoDjKAlIwM2qtrw2tY5R6tRIQ1a9bgyJEjjEmppKQEZ86cgYyMDOLi4tCuXTukpKSISGISEQ4ePAgzM7MqpSwrY+fOnVBRUcFff/3FTLxCoRAWFhaQk5NDSkoKOnXqBA6Hg169eqFt27YVjp3FYqGoqAi+vr6QlpbG5MmTazwWceBwOPD09ETXrl0hJyfHOB7VJQ8ePICWlhY6deqER48e4erVq+jVqxfat2+PXr16wdfXFzk5OVBSUoKRkVGdLCa8vb1haGhY5bHIhQsX0LVr11qfa3p7e2PLli3gcrno1KkTdu/ejZycHOzYsQMXL16slc/D9+MrKirCzJkza3VeL6FuYbFYmDlzJi5evPjLLSFlHDt2DOvXr8eXL18QHByMiRMnIj4+/oc/e98SEBCAe/fu4f379+Dz+bh8+XKF5TZs2ABtbW2sXbuWufb27VsMHDgQ48aNq5HFKz09HV26dMGUKVNQWFiICxcuwM3N7V9t3akJZWGrkydPxtKlS0U2g9HR0QgKCoKLiws4HA7Wr1/PLKzK5tzc3FyoqqpW2n6NduADp43EuD1myExIgeryAWi/fCCEcvJItvKB3vj2IAJYLIAIyC5ShEDIRqkQUumXhPgC5D4KRuGrKBALUBvUBW+zk1Ei4ENOquKhsFgs6Onpieyy5OTksG7dunJv1Pf15s2bBwcHB/z11181Nhk9fvwY58+fZ77ghYWF2Lt3L9asWSOyIBAKhXBycsLRo0dx4MABuLu7Iy8vD4qKiuDxeFi3bh3evHmDIUOG4Nq1a2L1zeFwICsrWyNPWQUFBRQVFcHDwwMJCQkwNzfHhw8fUFBQACMjI7F2k3l5ebhx4wbGjBlTYbz7ly9fMHz4cAClZ9ODBw/GtGnT4OLigl69euGPP/4Qe7ziQETIyMio1qdhwoQJuHPnTo0ncB6Ph8OHD+P9+/eIjIxEfn4+Hjx4gM6dO8PNzQ3e3t4IDAz8oR/RtLQ0hIWFYfv27ZLJ+x9AmenSzc0Nt27dwpYtW9CsWTNMmzbtl45r3bp12Lt3L0JDQzFhwgRMnz4dX79+rfCzl5+fz/hu1ITevXujUaNGjCWzIo4dO4ajR4+W0zNv37499PX1sXnzZrH7IyL06NEDFy5cgJ6eHpycnGBsbPzbTN5A1YI3bdu2Zax+rVq1QqdOnWBlZQUOhyPiFFgVNZrAI/WLwM8thkJHPcTe9Ue9LrpgtWwBzZlDkfoiHCrtG0OaRSjmSUP4jcibkMdD4eto8DPzoNynA+qN7Al+Vh5SLL2g2EEPUW0j0aVjxSkGiQiFhYXVmkkrWkk3atQII0aMwMmTJ9GpUydkZmZi9uzZYj2rv78/QkJCUFxcjJKSEjg7O+P48ePlPIfLVlcNGjTA7du3MWHCBCYWOisrCwcPHkSHDh3A4XDECtOKj49Hs2bNEBYWVuO0i2ZmZiAilJSUIC4uDkOHDoW2tjbOnDkDc3PzShcEHA4H165dQ3FxMfz9/fHw4UO0bNkSq1evFjH59u7dG8+ePcOgQYOQl5eHiRMnAig1c9+/fx9Dhw7FjRs3kJ+fDxkZGXTu3LlaRyEiQlRUFPT19cs57dy7dw99+vSp9rnr16/PnJPXZEclKyuLs2fPwsXFBUDpirhz587IysqChoYGNDQ0fui8LigoCJs2bfpHpLWUUEp+fql2ha2tLRYtWsSchZcdyf1KQkNDsWvXLgwePBjS0tLl4tWlpKQgFAqxatUq5jybiBATE1Ohg+v3sNnsCnUavmX9+vV48OBBubSWbDYbnp6eaNq0KYDS9/HDhw/M73P9+vWRnZ2Npk2bQlVVFWlpaXB1dYWXlxdzvt+3b18YGBiI/4b8x7lx4wZu376NhIQEjBs3Dp6enhg1ahRatmwptvWyZib0+f2hOag1FHXVQULC15sR4HzNAWlrg5vNQYNJ/SErLYBQyIJAyIaQ2BAIWBBy+cjyfo4G04eKtMtPy0GjIqBfKhs9e/ascBXs7u4OQ0NDJnSqKioTEhEKhfj06RP4fD6uX7+OBg0aYMKECZXu7JKSkjB58mQcPHgQb9++haqqKhITEyv0uKxrCgoKEB4eLpbjnrjExsbC19cXq1evxtevX6Gtrc14rL98+RLh4eGYPHkyNDQ0YGtri44dO6J9+/ZwdHSEnJwcpKSk0LBhQ8TExMDU1LRCSdSYmBi8fv0ao0aNQv369VFUVISnT58iISEBs2fPrvDsNz09HZcvX0aPHj3w+vVrKCsrQ1FREVOnTkVUVBRev36NsWPHiiW8cfnyZRgbG4t9xrx69Wr4+voiMjKSWZQFBQUhNDQU8+fPh7u7O2bOnAlnZ2fMmTNHrDa/x97eHgMHDkSrVq1qVV/Cz0FLSwtpaWlITEzEy5cvcfToUQQGBkIoFP5SkzoRwc/PD/379xcZh6urK2bNmoXMzEw8fPgQQ4cOZb4Thw8fxu7duxEcHFzhMV5N2bx5M/bt21el9S8kJAQuLi6YO3cu5OTkcPXqVaiqqsLIyAg3btxAdnY2WrRoASMjI+jo6CAzMxPnz5+Hq6sr3r59iw8fPvyUI75/E0VFRWjSpAkePHgADofD/N6X+fqIa0Kv0QQ+4PoySCuJ7kAL4jKRdCcKXIEMNGcPYz54ZaZ0Pr90osh7EgrFTi0hXV90MEd7jodxkw7Yv38/MjIyMGjQIGaV3LNnT8THx2PEKCPkcjlQkpaFgnTloRU3b97E7t270aZNG2ZXVdnzXLp0Ce3btxcJb7p48SJMTEwwaNAgeHp6Ijc3F3l5eejZsyf4fP4v8dz8UYYNGwYDAwPMnj0bEREReP78OZYuXQqBQIDbt2+jXr16jG40ULqASEhIKCeNmpaWBlVV1RqrqSUnJ8PLywstW7bE0KFDUVJSgocPHyIvLw8cDgeLFi1i+uFwOLC1tUVhYSE6depUI5N8VlYWvL29kZSUhHbt2lUrwKKlpYXt27eLODmGhIRAIBDAwMAA3t7eGDJkCK5fv47Ro0eLtYCU8O8hLi4OzZs3x+fPn3Hy5EmcPn0aHTt2/MfJfY4YMQIPHjxAYGBghWps8+fPh4ODA1JSUmrl61NTvnz5ghs3bsDc3By5ubmQlZVFXFwcPD090bhxY9SvXx8TJkwQqbN06VKMGTMG79+/x/r16wGUHvldvXqVkY6WUGrVGDNmDGbNmoUBAwagU6dOP38CBwAhAUmxAhQGfwQ/twD1Jw8CW5oNFqvUE10gKI3nznsSCnn9JhDkFYD3NRNde/XEkZ7j8fz5c4wdOxZ6enq4c+cOBg4cCAUFBazdtAHp2vJ430oRRQIu2GBhSKM2WNpmENrXq/gHtbCwEEZGRigoKICVlVWlXn379+/H+PHjRbzIV61aBR6Ph/DwcLx48QJRUVH48uULRo0aVd1b9I9l586duHfvHuN96+/vjwcPHqCwsBCHDx8uVz4jIwOmpqbYt28funTpUidj4HK5yMrKwtOnT8Hn82FsbFyhJ+rly5fRqVMnTJ06FUeOHKnVl7vMmzMsLAxz584tZ4oSCoXw9fXFmTNn8ODBA5F7PXv2REBAACIjI8Hn8/Hs2TM0bdoU3t7eVS4IJfw7efr0KQYPHoxZs2ZBSUkJ3t7eWL9+fZ06jv0oVlZWSEhIwLJly2BkZFThsdrFixdBROjTpw8aN25cpfn15MmTmDFjBrS1tcvdS0pKQsOGDSEjIwMulwsejyfS1urVqyEQCNClSxfGKldUVIQnT55g7969uHv3LpKTk7FixQomYiQtLQ1aWlrIyMhA/fr1QUQYOnQoTp48iaFDhyIzM/Mf40j4q8jNzcXVq1dhZmYGJycnTJkyBQkJCWjXrl3dOrFVBguAbMN6kDHqjtyHISKCImw2wGIJIRSyIKWsgMI30ZDWUIXqsO4wEGjBw8MDmzdvhrKyMogIXbp0gb29PYqEPFxPfgul/n0hEJQ6XAhBeJwcDZ+rXlig3x+moycyZzJlKCkp4cWLF8jNzcWff/6J6OjoCpMIqKqqipg2X716hdOnT+P69etYunQpgFKvaxMTk7p4i34JAoEAx48fR2hoKFgsFp49e4bw8HBs2bIF9vb2iIqKEjmj5vF48PT0hK+vb51+qWRlZaGtrY3p06dXWY7D4UBJSQnr16+v9cqcxWKhf//+4PF4iIqKQo8ePQCUvhcrVqyAjY0N9u7di3v37pWru3jxYkhLS0MgEODjx4/o3bs3+vbtK3E++49iaGiIlJQU/PXXX8jMzISGhgbu3LmDhw8f/uqhMZiYmODGjRt48+YNY0lzdHRkFL3OnTsHc3NzpKen49OnT/Dz86syK1xxcTHGjx+Ply9filxfv349jh07BjU1NeTk5CA8PBy+vr7YvXs3s5jV0NBAy5YtMW7cOMaJLi8vD1evXoW6unq530oigpaWFk6fPs18h1gsFh4/fownT54gOzsbmzZtqnAj8bvAYrGwZcsWtGnTBlOnTkXjxo0xd+5csS1+P7wDJwJ4Ajayi5TAS8sBPz0Xrdu0QZoMgYsSxjOdm5KNwojPqDe8G4RCgM+XgudgU9y2d2WcoiKi30JFXRVzZvyJncE3cPXhLeS9iYG8nhbAZoElIw1+ei6EJXw0H9MfU5NVoaKsjMWLF1c69o4dO+Lt27ci1/z8/HDt2jXs27cPcnJyuH//PkaOHIlTp06JmJP5fD62bduGJUuWVOv88U8kNTUV8+bNg6qqKpydneHh4SGSWtDd3R1ZWVkYMWIEWrdujSdPnmDIkCHg8/l/u1Z0XFwcLl26BD09PWhra+PLly/o06ePWM45FZGWloY7d+5g6tSp+PPPPxEUFIQ1a9ZAXl4eixYtKlf+6tWrMDAwQKtWrSAQCDB+/Pg6X8hI+GdiYWEBLpeL69ev48WLF9Wmga0JRUVFGD58OB4+fMiIc4wePRp37tyBl5cX4whaGXPmzIGuri72798PoDSL3evXr8HlchEZGQkZGRmMHz8eHz58wNChQxEXF4f09HS0bdu2nLRxVQiFQsTHx0NFRaXcgjU1NZXJ9lj2fTQ2Noa8vDyKi4vh6elZoSxwdnY2nJ2dMXv2bEaz448//sCtW7cQHByMHj16YPLkybh69arY4/yvoaioiE2bNsHc3FzE1+enhJFVBIsFFHFlQQIhsj2eoW+PXtB5Eg8ZKsbjgBdQ6a4Ptpw0SuLTIN9GFwIBGxCy0UezGcLvPoG6ujqSWRm4kfUA+T24YLFZeB78Dl/TCcWfkqA5YwjYstIQ5HNAQiGk+yhByOMj/nEwum4/irhbfkhJSSlnEgoMDETfvn3LmYE9PDxQv359rFu3DsXFxZCTk0NJSQlOnz5dzolOWloaU6dORWRkJD5//vzD0oN/Nw0bNsTNmzfx4MEDyMrKlpNanTFjBiN407x5c3Tr1g25ubm/JNGDnp4eRo4ciQ4dOuDly5eYO3cubGxsAKBWk3jDhg0RHh6OuXPnQk9PD58/f0ZERAQ+f/5crmxWVhbYbDZjkXn79i2GDBmCyMjIWieQkPDvoVevXmjQoEGdq7N17twZHz58gJqaGuLj45nP8e3btxEVFYX27dvD3NwcVlZWlbbxfdKbbt26QUVFBY8ePcLBgwdRr149RmMiISEBSUlJGD16NG7dulWjCbwyD3WhUIh169Zhz549SEhIYHTOb9y4AV9fX4wbNw58Pr/CCVxBQQH5+fkYPnw4FBUVmTSl3/4OeXp6oqio6B8jcPR3k56ejoSEBGhqasLQ0BBPnjypUf0aSalKsb4xjf8vttu4cR/cHroBywub4dk5N/getoa15VkMb22Adq1aQygtByGfDZlWelDqaQCBgA191YY41uMPvHr1Cm0nGOAS3UaRHh8sdmmbXCEXBa/eoZtJC8jIlfYppaIAabXS8xhlBSGadVLD9YcuMJ44Gvb29gCAJ0+ewM/PD7Gxsejbty8yMzMREhICoNREvmfPHvj4+GDHjh1Ys2YNUlJSSp/B2BgrVqyo0AkkIyMDeXl5ePXqFQQCAWxsbLBx40YsW7YMFhYWYull5+TkIDIyEgEBATV5uyskIyMDt2/fxsOHD8HhcACUmocfPHhQbudw584dWFpaIj09HTt37qwwwUvZIsXHxweqqqpVrvZ+Nt26dYOcnBwGDRqEnJwcTJgwAY8fP65VW8ePH0d0dDROnz6NT58+ASj1Mh83bly5sm5ubiLXhUIh+vTpg4MHD4qtSSzh30vXrl2ZnfC3FrgfJSwsDBwOBykpKeUWoe3atQMRwdLSskb5EcLCwnDq1Cno6Ojg8+fPsLa2xpw5c6CpqYlhw4Zh6tSp8PT0xOXLlyuUPK4JOTk5WLx4MQwMDKCoqAhVVVU8fPiQaXfYsGFo1KgRjh8/jqKiIqYeh8PBhg0bIC8vj23btiEkJAR+fn7M83bo0IGZzNeuXfvbTt5A6ZFv27ZtQUTo16+fyPsoDjUyoW/yt0ZIyWcISIg2KrqYpDsAQxp2QVpaGsaNGwdfX1+kZCYi4ONt3L17F8GPYrDx4i584GoiJDAImYHh6N+xC9qqaUFeVhZ3H9xD7mAhNLqW7p5JSBAKBChIyEPuhwzojmyNLI4iUgpKz1vkpXjoppkAbcV8yLD5eHf9CxLD8yBd2AB6Gu3w4P5DJCcnY//+/Zg3bx60tLQwbNgw6Ovro3Xr1pg6dSoMDQ3x6tUrsT02iQhmZmZQUFDA2bNn4efnh379+kEoFEIoFOLYsWPYtGlTpfXfv3+PKVOmQEZGBq1bty6X+L4mxMfHw8XFBdu2bcPSpUvh6OiI3r1748mTJ9DT08OHDx8gLS2NkJAQpKenIzk5mZE9LEuoURk/Ei5Vl9y6dQuvXr1Cp06dICUlBT6fX6vsat8/LxHB0dGxXLrT6Oho5OTkiMSbu7m5MZ+jO3fuMDmNK4LH4yE/P/+nJNCR8PfBYrGQnJxcoXPXz4SIMGTIELF3Xl++fIFQKISnpyeGDh2Kbt26oaioCFFRUYiIiAARoV69ejA1NcXgwYNhaWkJVVXVCp1GBQJBhdY2Pp+PdevW4e3bt3BwcMDLly8xYsQIqKmpwdPTE8bGxsyOe//+/bCxsUFCQgKUlJSwdetWaGtrY/fu3Xjz5k2lIaA8Hg8+Pj5o0KBBuXTIvyspKSlwd3fH5MmTUa9evbo3oW/uMKPCxoRCIQ4dOoR9x7chUe4V0r7kwmBsY5jO7YwceV80VpDFcrNtuMqth5YtW2LSpEnYu3cvFp5ajiM+p/DlbgyEPAGKMziQUZYBr4iHtrO6gMUC6slzkFqgDGm2AIN1PkBFphiNZXOgzC5BLCcX3fvK4M39OBSochGfEIdnT1/g7t27GDVqFKO6VLaLKygoQElJSY1WprGxsZg+fTpGjRqFxMRE6OjoAPj/ZAktWrSotK6fnx8WLVqECxcuwMrKCrt3767J210Oc3NztG7dGqmpqWjYsCGTopPH44mEuBUUFMDR0RFjx45lrlU1eQsEglpnSatLgoKCoK2tjV27dv1wW98/7/3798tFJPD5fNy/f7+cXn7Xrl3BYrHw5MmTck6S35Kfn4+xY8di7969MDQ0/OExS/h1jBgx4peEibJYLGRlZYldvkxR8VslSkVFRXTv3h1WVlZo27YtSkpKmOiaJUuW4PPnz4iNjcWuXbuwceNG5rvh7++PQYMGMZKtZb+LQ4YMwebNmxmxmMePHzMLAKFQCIFAwPT9559/Ytu2bWjRogU+ffoEbW1trF69Gnl5eThy5AiOHDlS4XPIyMjUamH+X0ZbWxtGRka4dOmS2HkkfugMvFiQh3c5dxBT8gR5DbKRIBWKviZ6EKAR/j95CaGomIMuLfpj/V8b4Orqihs3bmDAgAEIi4kAmy0F7d664Bfz8OFSONqZDkDczWjkJ+ZCTksTORkqEObLQkMtF/LgooVcOqRZQrBYQGx4IVp0VETok2wsP9kSj1M2oW+vPZg0aRLevXsHgUCAZs2a4eDBg9iyZQu6d++O+Pj4GqXp27x5M7PjLnNo+vr1K3x9fREfH19O1ITL5eLZs2f4+vUrnjx5gpMnT6JPnz5QUVHBpUuXsHnz5lo7RhkYGEBVVbVcn9/+8JR9CQcPHix2Nra8vDxmYfMrfsTu3r2LzMxMSElJ1bmkpZOTE1RUVPD161eMHDmSuS4QCHDu3DnMmTOn3N/j6dOn6Nq1a6VKeN7e3nB0dISpqSmsra3Rvn37Oh2zhL+fXr16/bJog4iICPD5/AqT9YjLs2fPMGLECIwaNQo+Pj5YtmwZk/2rpKQEwcHBmD17NszMzODg4AAAGDhwIDIyMmBkZARZWVmoqanhzp07cHZ2xtixY8HhcLBu3ToRTYURI0bAysoK06dPR0BAAHbu3AkAWLlyJQYNGoSXL18iLy8P586dq9BZVELVNGjQABYWFj9/As8o+Qyv+HXgCPIAED6/zYGyuhTk2DzwiA0eSaFsEpeRY6OhvjK4/YrgbxGAgwcOoGHDhnj6xAvynZSQ9iYJnKwi8IpKJ5+mo9vi2eYAKPTWhUyDemBJSSEhSx4p8Q0wrstrGDQtzZ1qurMZFnUPxgqLlmCxgHSOH0rqlzpy2NnZYfjw4di3bx+0tbXh7OyM9PR0ZGRkiO2in52dDVVVVZFc1MnJydi+fTtOnz5dLt7y7du3ePr0KSZOnIiuXbtCQ0ODUdhp0qQJtm3bhgULFlSoZCYO8+bNY85zKyIwMBAnT57EwYMH0axZM7Hzg6urq2PMmDHw9PSEQCCAQCCAtra2yIRXl7x48YLJe8zn89GtWzcYGRnVeT/29vZISEjA27dvGS/eMmxsbGBiYlKhaVFZWRmfP3+Grq5uuc/K5cuXsWPHDvj6+koU1v5D1IWKWW3w8fEBULWFrDp4PB42b96Mmzdv4u7du5g3bx6ICAKBAIcOHYKenh4GDhyIQYMGoVevXggMDGSOjOrXr483b95g48aNuHbtGnbv3o3FixdDSkoKxcXF2Lt3L3M8VJbjYcOGDSgoKEBOTg62bduGT58+ITMzE4MHD4aSkhKeP38uIpAlQXyioqLQs2dPsa3EtZrABcSHT8IWFAvygf+lBtVpp4L3/llI/1KE935ZEEpJodMYHcgplXbRxbgxeCqxaHV+HvJl8nF6yylMmWuC0MbxUGxcKqsXcykMRISEWC0oD5uEjBs3oNyxI5Q6lJqDeAIpXH3dC/mJGTBol4u0hBKMnqeFPmNKV84EFj7l30BPzfUoKCgoJ3+ak5MDHx8fNGzYEKNHj672OePi4jBixAiRa97e3tDQ0EB4eHiFqRjLZF+zs7PB5XIZfWU9PT24ubnVevIGAH19/SolCPv06YNx48ZVadavjObNm6N58+bM6+joaJw/fx6tW7euc/NwdHT037I6l5WVxbp16xAeHi6Sh/3u3bvo1atXhXl4y5g+fTrs7e3h5+cHCwsLXL58GdbW1hg9ejTCwsIq9LqV8O/lRx2+asvo0aNhZmb2QxO4UCiEoaEhFBUVmZwAQOkGpHnz5hAKhVBQUICrqyvS0tKYyTs7OxuDBw+Grq4uVFVV8f79ewCl34+ZM2eK9JGbm8skOLpy5QpatWqFhQsX4siRI5CXl8fEiRPx5cuXCpMgSRCfrKwsHD9+XGxLaK0m8M8FASjgp6OkkM9M0LLyUlDWkEHQzRQMM9MDp5CPwKsJGDC3dFKQkwe+3IzGlHGZkJJlo/VYDgqV/CH7Ugn83hoQgpAWlIQmYwyQlaYKKSUWFPT0UPL1K4TFxaXxamw2BPl5COzSEqyMx4h7V4TJK//fs5oFAoefBgAV6pbXq1cPc+fORXh4OLy9vaGmpoZBgwZVaroqywfesmVLJjuMubk5zp8/X22SDXV1dWzYsAFbtmzBuXPnwOfzkZaWhoCAADx//hxqampITU3FiBEj6jQnM5fLrXFSj4po27Yt2rZti9OnT9f5BN6lSxfcvHlT5Iz+Z8BisaCkpCTy/sbHx8PLy4sJUfseHo+HkpISsFgsmJmZQUVFBba2trhy5Qrc3d1/ew3n/yIlJSW/LJGJrKws7OzsfqgNOTk5tGnTBrm5uSKL0rNnz4LD4UAgEKBz586MNfDKlSt48+YNk3dBQUGBsQQAYDIOlrFz507s2bMHr169Yn4Hw8PDER4eDnl5ebx//15kgSyhYiIiIuDu7o5x48aBy+VCR0cHLVq0YI4uWSwWCgsL0bJlS+Tl5YnVZq0m8PepgXh4Ph6K9aRw+8wnDPpTFxlfOBj0ZxM0blPqMZ4WV4J6jeXByeNBVY2F/sb1UVzAx9snWehqpImBJo3x+nosPvhlo4PiCHzIz4Cyjhrin2eD1EonIAX9VihOiIdKl64AABIIwJKSQh4A7fbvkJWSBHmlb1eubMhLVe8NXJZx6vjx4zh37hwaNGiA4uJiHD58uNwOuUePHuXMqK1atcLNmzeZ3K2VERMTg+LiYty8eROhoaFo3LgxHB0dMWbMGOTm5qJbt244fvw4VFVVxYo3fvLkCWJjY/Hnn39CSkoKhYWFkJeXFznTT0tLQ35+fp2Fg/Xu3RuvX78WO71ddTx69AiZmZk/1YGFz+eDz+eDw+FAR0cHp06dwtSpUxEcHIygoCCcOXOm0rEFBweLpCU1NjbGs2fPsG7dOrGSqkj493Hnzp1ftgOvK9hsNurVq4fo6GhGxXDTpk2QkZHB1KlTmUVsWR6I1q1bo2vXrrC2tka7du1EvNG/dbbNysqCnp5euRDVzp07w8fHB127dv0hq+LvwsmTJ+Hh4YFnz57h1atXaNSoET58+IDAwEBER0dDIBCgSZMmeP36NQYOHAh1dXWx2q3RBH7s2DFoa2sjQRAG1fqy6DlJG32n6oDLEYDFBmTkSj8EH15m4ZH9Fxhv6wg/l88Yt1QHxGJDXlkahTk8CIUEthQLvSdqQVVTFg5rL6Nn3/5YtXsVTm25APaI7iApoPhLHOSb6TH9s775kPn7pGOimeZ3O00hmquIJ8E5ePBgDBo0COfOncPs2bMhJSWF8ePHIy0tDQUFBViyZAk2btyI+fPnw9fXVyQN6eDBgxEYGIj+/fvDz8+vwvZjYmKwdetW7Nq1S8QJpEymtQxra2sYGRmVkzYESk3+Z8+eha6uLvh8PjIzM7Fo0SJ4eXkhPz8fcXFxyMvLw/Pnz7FgwQJ07twZOTk5dRrL3bt3b1y6dIlJHVj2fpd9oZWUlDB+/PgKd/wCgQBJSUmQkpICh8NBYGAgtLW1q0008qO4uLjA1dUVDx8+RGhoKOzs7JCYmIiOHTti4cKFFdYpKSlhQmSSkpKQkZEBFosFd3d31KtXDxwOp8aJXCT8OygpKalWEe2fjpSUFGRkZETCY2VlZfH+/Xtoa2ujYcOGcHFxgYODA7hcLoYOHYoFCxZU266Li0u5Y8Qyfobfyn+V7du34927d5CVlWX8AyryoeFyuTVKP1yjOPD09HS8ffsWUal+8PW3h9HS5hWWd98WhRbd66HT+KbI+pyP2GcpGDC9EeSVSifwQO8UaDSWR/MuqqinVXqWGGXTFoJiFpoNMsRhe3tIq9WDoKgQiq1aQ07n/83kRISit2FoVXQP4xc3RKPmCmWPAh2lgRikfQTR0dEICQlhzNTz58+v9Nlyc3Px9OlTESGPsoxUJiYm2Lp1K65evYqBAweWixHdt28fEzrRtWtXRlc8MzMTkZGRaNq0KWRlZREaGspk4amIXbt2wdzcXGQlm52djdWrV2PUqFGMYlpiYiKjlpSUlAQPDw+oqqqif//+aNu2LVxcXJCTk4OVK1dW2tfbt2/h4+NTZ6lRk5OTcevWLaipqWHEiBFgs9ngcrm4ffs2hEIhdHV1wWazIScnh+7du/9UL/eyHO7q6up48OABFi9eXGEGp4rIy8vD2rVrUa9ePezduxe3b9/GrVu3YGNj80uU6ST8fYSFhUEoFKJr166/eii1xsXFBSNHjkRgYCDGjx/PXG/cuDFatWqF7t27Q0dHB1++fMGsWbPE+l4QEXbs2IHU1FRYWlrWKHpHgii7du1CaGgoNm7ciMTEREyYMKHK38Kfkk40O2ETijgseN/hocAgCgoNuSAIy5W/ceojBMTGsJXtwOWxoc7Ow3O3JHQf0xD1tORARHj3NAufw/JgvFIPLBYLE3RPwfqQJ8wWL0b/deuh0K0HCEDWg3uQ0agP4vEg36IlCl69RKtOyphjkgA/3wwMntIQKvXk0FJ1HFSyjPHieQA6duyIPn36IC8vD8HBwXjx4gUmTJiAjh07ltspBgUF4fLly1BSUsLKlStRr149hIaGoqSkBOfOncOAAQPQunVr+Pr6Yu/evSK7sBEjRuDYsWN49eoVOnfujJCQEDRr1gyfPn3CsmXLmHLr1q3DkSNHUFxcXKHqEJ/Ph6enJ5Ps4+7du3j//j3evn2LsWPHwsDAoEot9s+fP+PevXvo1q1btabuuLg4pKamij2xiUt2djZevHiBmJgYNGrUCFOnTv3bQtJsbGyQmJiIrKwsWFpa4vz585XmIK+KJUuWoKioCBs2bEDHjh1RXFws2XX/BvD5fLi5udVKyIjD4eDEiRN1tiCuLS9evEBCQgKmTZvGLDiJCD179oSsrCymTp2Kbt26YdCgQdX6xwiFQhQWFuLKlSswMzMDgL8tXel/lSdPnkBJSQkZGRlo0KAB3r17V2XSmZ+ihW53wR7NdWWwZIoC0lkd4J3HBk9YzEziLLAR/y4Huo3bw9kyAF+G9kEHjWQY6UZhyJwmiHiSiVc+KRg0SwcdB9eHZlMFPL/0Fa1714O8nhpiY2Oh26gR+qmq4nFgANjSUpBv0RJSSkrgxMQgz98P8lKEQ3Oa4l1QETT4qlg75DVWrloGQdsmUFL6IGIiVVNTw5AhQzBgwAC8ePEC1tbWkJKSgoKCAnPmpaenh8OHD4PH48HU1BRKSkpISUmBoaEh2rdvj0GDBiE9PR1btmyBp6cnZs2axbRvZmaGTZs2YeHChbC3t8eyZctQWFhYbhJds2YN3N3dkZeXh6FDh5aTVZSWloaMjAzs7OxQVFQEXV1dGBkZ4f3798jMzIS3tzcUFBSwePFikS/fo0ePkJqaCh6PV2VCl2/R09P7KYlZ1NXVa5S/uyYIBAK4uLigoKCgnOiKh4cHYxrn8/kASjMulSWOqAkaGhqwsbFBmzZt0LFjR8nk/ZsgJSVVayuLnJwcXF1dQUTYtm1bHY9MfCoK22KxWHjz5k2N9B2uXr0KCwsLfP78GXZ2dr8ksdF/kXbt2uH169cwNjbG0aNHyx2l1pYaTeDDBsrDoH2pybsh3mN2PX28Zc3Eh/yn4Ao5aCDXHA2VmkJrYFsoN3gD3/wsfJbWgoCiIS0FGAxrgOz2yrh64COm72wNzWYKCL2fgVceBQiRjUViYmKptGAXAywdMRL7r3oh0NUFhW8joLtsOUZMbYBU78sY2usFDLrIYKSRHFq3ZqNjex3MnDm70nHLyMhgyJAhVSYjkZGRgZGREaZPn44DBw7g+fPn8PLyApvNZjwsS0pKMGPGDLi7uwMoTUm6YcMGfPnyBfv27as0LElHRwezZs0CEcHW1rbC5ByTJk2CUCjExYsX8eDBA+Tm5kJOTg5SUlJ49+4dRo0axXwhvby8UFJSgi5duohkF/svQkQ4f/48DA0NYWtry0ikpqWlwdbWFjNnzmTC5mRlZXHhwgUMHjy4xl74RIQmTZpAIBDA1tb2ZzyKhH8owcHBjHJZTWGz2Xj27FmVObh/NTWxhC1ZsgRHjhzB6NGj0ahRo584qt+LqKgo5ohUVVW1zqIeajSBN9eVBhHB5Uo+At5wcO6IAH3U9dBX05Qpw9PmYeHChRgzZgxm9p+ES/HheJuZgi6a0QAA9UbymLJZH7et4tCimxra9lNH3/pLkfI1BQcPHsT06dMxwfQPFCq+wYS+hIFa01GcNAZDDD1RTzEfB725eBPaEPXrl3qfmy+XApttD4FwHqTY5UU5asKAAQNw6tQp8Pl81KtXD1++fEHTpk3x5s0b9O7dG2w2G05OTkx5NTU1GBkZITU1VawvMIvFgoqKCg4dOlShfnpSUhKkpaURHR2NTp06ISQkBFJSUti6dSuaN2+OO3fuQEZGBgcOHPih5/y3kJaWBldXV4wZMwZcLhe9e/fGp0+fsHLlSsTFxWH58uWM2lRUVBTu37+P8ePHM3KTNe3r5s2bMDc3R5MmTfDhwweJUMtvgoGBAc6fPw8DA4Na1f+3RydwuVz4+PhgypQp2Lp1a5U+QxJqR0REBBYtWoTQ0FCxZKvFTXxVK/WA7BwBjEcqA5ACFd8TucfhcCAtLY07d+6gj05LnOo7ETb9z6JXAzNIsUqdIBSUZWG0uBn4BTJgx/TChCFmmDVrFjr17ADVwWy4vrXF/aRr+KQWjj1LdqDb5Gdo1bwIDbVY8PfjYt3a7G96FEAozEBh4eX/vyIQ4N27dzV6JiKCuro6srOz0bBhQ0hJSeGvv/6ClZUVbty4gXv37iEqKkrEkaN///4oLCysMh3g98ycORNt2rTBwoULkZOTI3LPzs4OTZo0wYULF9CkSRMsXboULVu2hLW1NVxdXXHv3r3fQvj/69evcHV1xYULF7Bo0SK0adMGJiYmmDFjBp4/fw5fX1+EhIQgJycH9+6Vfv727duHkSNH1mryBgAtLS3MmDEDAODg4MBkqpPw30daWrpCRb7fASKCnJwczM3N0a9fv1p/fyRUDhExqavz8vJEwlQrK1+V4ub3haslNzeXAFB2TAsSJLf65l8bEmSvFCnL4/Ho0KFDxOFwyrVTzM+n6Jy7FJp5hV5/9qUVK5aTo6MjEREJhAI6HLWRVgfPpHpNVWimxyiadH4oKTVUoIFDFOhVsCZ9TtCmU6dVic0GffisRZ8TtJl/X1P/YPoJDw8nHR0dcR6NGfPIkSOpSZMmZG9vT0KhkC5dukRHjx4loVBITk5OtGHDBsrJySlXVygUkrW1tdh9lREREUEmJibk6+vLXIuNjaULFy4wr5OSkqiwsJB5ff78ebK2tqbs7Owa9/dvwsHBgVJSUkSuTZ8+nVxdXUWuTZkyhe7fv8+8Pnz4MMXHx9eqz+fPn9Pbt2+Jw+FQy5Yta9WGhH8vERERdPv27V89jL+dgwcPEgC6f/8+NW3a9FcP5z9JdnY2AaCIiAiKjIykR48eVVleIBCQl5cXAaDc3Nwqy9Zev+9/sKRFlamkpaWxcePGcg5AFy9exMmjlgjw/opgn3ysnXMEGhr1Gaewd3nBSOJ8gRBCjLc0hPfSx3h2NBiFaRykp/JhdaYAAJCXRxAKgfQ0gUj7Qipk/t+pUyfxVzAoPcc6fPgwHj9+jIyMDOzYsQOqqqpYt24dWCwW5syZAx0dnQq9AcvM4unp6XBwcMDp06dx6NAhPHr0SCRrz/d07NgR7u7uiImJgampKTw8PNC8eXNGgSc1NRXXr1/Hnj17mDoLFizAhAkTEBgYKPaz/RsZMmQIrl69yghK5OTkwMHBQUTesbCwEFevXsXp06dx7tw5ODg4oF27drh7926N+8vJyUF4eDg6dOgAJycnyRn4b0jHjh2RkJAgEoL1O1CW0XDEiBFYsWLFLx7Nf5Oy0NTi4mIIhUJERkZWWd7T05MJSa6OH8pGBgBQKC/KQUS4c+cOtLS0GHNBgwYNwOfzYWJigtTUVNSvXx9jxoxhZEzDsl+BDTaEEEJJUwHdTdvBYHorWPW+gqET1LF6YalT0p9zlcDhEDyvcrB8pcr/epSCrLRoRqiaxCympKQgMzMTqampaN26NcLDw7F3714RvfRp06Yx2uDfm7E/fPiAtLQ0rFy5ktE0/vDhA5ycnFBSUoIlS5ZU6FTF5/PRqVMnFBQU4NGjRygsLMSwYcNw6tQpEBFMTExw584dAKUxzmVp/b5+/YpRo0aJ/Xw/g7JwiJ9Bs2bNsGzZMpw5cwZ+fn7w8/NDbm4uNDQ00KJFC0ycOBFKSkpwdnZGeHg4zM3NkZWVBXV1dbx48QLv3r2rVtmuoKAAu3fvxuHDh+Hi4gJzc3MAgIqKCoYOHfpTnkvCP5uFCxdi69atKCoqqnEI4r8Va2trRtL42xSlEuqWPn36oHHjxrh582a5tMbfcvXqVSQmJootklOjOPCs9y3AKSZExvAQl8CFnOpE8Nm9oaioiIyMDGRlZUFJSQmJiYmYNm0ali9fjlGjRmHfvn0ASs/HPTw8kJWVhcaNG8PExITpw+7TcUTkvmFeh7q9R/aXfEjLScF0UxOMU/v/M+2UZB769soEALhfVkefvnJ4G7oVhYWaMDY2ZhzKQkNDwePxcPfuXXTq1AkaGhqIjY1F/fr1weVykZubC3l5eURGRqJv377o0aMHfHx8kJKSUqlGeUBAAN6/f4/evXvj69ev+PjxI5ycnODv71/he1cmuDJ48GB07txZRHf94sWL6N69O1q3bo2ioiJ4e3tDWloa9erVw8iRIyEtLQ1HR0e8f/8esrKy0NDQQHx8PFgsFubOnYtOnTpV96f7KcTGxmLx4sVYs2bNT9Uzj4qKQlJSEvr06YO//voLV65cwbRp02Btbc2UCQ4ORvfu3UV0ml1cXKr1zg8KCsKwYcNgYWEBY2NjJpWkra2tJA3ib8zz589RXFxcqfrYf43Q0FB07doVV65ckeTn/om8fPkSb968QWJiYqnuyYQJ6NWrF2JjYxEcHAxZWVkkJSWhc+fOGDBgwM8Rcjl7WBP6zWWh17wT9DuuA0t+GIqKikBEzKTp7+8PHo8HQ0NDhISEID09vVxayjJHt2/DG64nueNx2g0IvxGGibkXDyFfiLZjmsFIJRrt5FLBYrFAJISTQyE+xQqhrAxoaPRFQ81xWLBgAezs7NC+fXsEBwejVatWaNGiBVxdXcFms2Fqaoo2bdogPT0dSkpKUFJSgouLC3r27CmSTtDLywsdO3asVKCfx+MhLCwMOjo6YoVaFBUV4eHDh9DR0WEsEuHh4QgKCsK8efOqrHvixAl06dJFZFdIRLCzs6tUFvTvICYmBjIyMiIZzH4GeXl5aN++PSwtLSuUbBUIBJCWlhb5oFtYWGDVqlWVtllSUgIbGxusWLFCpL3i4mJcv369znOSS/j3IBAIcPbs2d/GnFxUVMT8dosxFUioA4gIs2fPRr169TBhwgQYGhqipKQEioqKTMy9uBN4jc7AZy70x/DJb9Gqx22w5IcBABQVFUVCqJSUlBgh9mbNmuHjx4/l2lFQUCgXm9i3wRCRyRsAWo9sijajm8Fz4SM4PK2PhOIpYLN0wWKx0K27HDZt6YojRxywc8dtrFy5EoqKilixYgW0tbXxxx9/4OHDhzAwMIBQKMT69evRtm1bsFgsNGzYUGTM3+cCNjY2hpubG7y8vCp8H2RkZNCjRw+x4yQVFRVhYGCAW7duISsrC3v37kVWVla1k3dZX9/Hr7NYLHTp0gVHjhyBi4sLoqOjxRpHXdK6deufPnkDwPr162Fvb48JEyZUeAxR9oH/1uciKSlJpEx0dDROnz4NFxcX7N+/H+rq6tDS0hJp7+vXrzh48GCtztAl/HeQkpJC79698eDBg189lL+Ft2/f/uoh/HawWCxMmTIFiYmJMDQ0hKysLIRCIaSkpJCZmckcHYpDjc7AWVINwWJXnShDTk4O6enpAEqVrcRV8dGU04Zxo+m4kewBFlig/+UZBwGtRzbDh6vJ2OvBRsz7FMTGRkC3sRTYrHrlftR9fX3x9etXvH79mpGq69KlC2Mi/Z6K8vDKysoyu/e64u3bt5gwYQJcXV0xbtw4sWNOiUgkiUhmZiY0NDTQs2dPSEtLIygoCP7+/uUWIf8FXrx4gXnz5lWZurVs13D79m2sWrUKubm5IskAYmNjERQUhIULF0IgEKCkpAQrV65ESkoKVq5ciV27dmHPnj0IDQ1FZmYmgoODf/pzSfhn06tXL5w/f/63OAsvmyjKVAwl/HyICDk5OXB1dcWlS5fA5XJx7tw5TJgwAaqqqjA3NweHwxGrrR93YvsGgUCAu3fvMqZdDw+PavNmf8sI7QnQkNXEvdRrSClOBAAoyihjzfK/YKQ9EbJsuWrbGDBgADw8PLB161ZmAo6NjcXx48fx119/iZT9dueak5ODjIwMtGzZEkCpvvi3sqk/QplTn4WFRY0UnzIzM6GgoMAsMry9vSEUCsHhcMDlcqGoqAgzMzO8fPkSly5dEvEp+C+goaGBO3fuQEVFpVKntLKFzYQJE+Dg4IBJkyYxJqdt27ahXbt2iIuLw5w5c/DhwwdMnToVTZs2RWpqKs6cOQMWi4WRI0di8+bNEq1nCQz9+/dnkgVNmjSpxsp+/wYePXqE8PBw+Pr6SuRS/0aeP3+O/v37Q1lZmdHfX7BgAYgIlpaWkJGR+TUTeEREBPr06cOsWlNTUyErK1sjhaPuGv3RTb0fcnnZ4BMP9WTqQ5ot/jA1NDREdGZfv34NFRUVZmIGSnPcenl5oVWrVswkzePxcPjwYfj5+eHt27fIz88X2fn+CAUFBRUmUqmKvXv3QldXV8QTvlmzZnjz5k05J6s+ffrAw8PjPzWBx8fHo1mzZli7di327t1bpVf5ly9f0LhxY8ZBsKioCCtWrMCLFy/QvHlz6OnpoUePHti2bRt2796NRo0aobCwUKLzLKFS2rdvj/bt2yM5ORmHDx9G3759YWho+KuHVScUFxfD1dUVJSUlcHBw+NXD+W0gIjx69AgZGRkYNGhQuftlIck14YfjwL/FwMBAJP55+fLluHz5MpYvX478/HxkZGSI5SjBYrFQnMVFYUpxjSbvivDx8UFeXh7Gjx8PIsK1a9dw5MgRmJqainwhNTU1MXPmTIwcORK2trYYNmxYnX24VVRUkJ2dXX3B/1FYWAgpKSkMGzYMTZr8fyrVZs2aQVpausL3sFWrVnj//n2djPdXk5qaimbNmjEZntq2bVtOta4MDoeDnTt3iry/SkpKCAkJwbNnz/Du3TvY2NggKCgIAQEB8PT0RIsWLbBlyxbJ5C2hWho1aoRNmzZBTU3tP3EuzufzMX78eMyYMYMJnZTw88nLy4OFhQX09PSYrJMVUZV2SEXU6Q6cxWJh5syZ8PLywtSpU8Fms3HmzBl4enri4cOHkJGRQUZGBgCUm5zKSE5OxrVr16CnpwcFBQX4+PjA3NxcJPxKXLhcLr5+/QplZWVcvXoVaWlpGD9+PAQCAfLy8qChoSFS/unTp1i9ejX279+PAwcOQFFRERwOB+bm5j+8E2/cuLHYZW/fvo2FCxeWS45Sv359pKWlwcvLC5MnT2auBwYGIi0tDV++fKkwUcq/jYYNG+L169eQlpbGzp07sWjRIjg4OGDNmjXlyiooKJRbaG3evBkHDx4Em82GoaEhDA0NMXjwYEbb/HeQo5VQt3Tp0gWTJ09GWloagNLwq2nTpqF79+7/GvO6tbU1bG1tcffu3f/82f4/DWdnZyxfvrzaeUxXVxcvXrxA586dxWq3TidwoHSXmJiYiKCgIAClZnUzM7NyA3dycsLcuXPL1b9+/bqI8Imamhrc3d3BZrPRtGlT6OvrQ1tbu9yXhsvlQigUingjP3nyBMXFxZg7dy4MDAyQm5uLZ8+eIS8vDzdu3ACfz8fkyZMZHeRdu3YBAM6fPw+gdCJVUVHBrVu3fjjeuSJnue/Jy8vD7t270bBhw0pjMvv161fOy7ply5Zo2bJluXC96pg7dy7mzp37jxMuYbFYCAkJgaenJ1auXAkDAwMoKSkxms3VceDAARQXF+Pdu3cYPnx4lXl3JUgQlxUrVpRmSxwyBCoqKkhLS4OTkxOkpKQwePBg6OrqipS/fv06CgsLISsr+8vP0WNjY7F06VLweLxabYYk1J64uDjo6+tX+r5//PgRt27dQoMGDZCeni6iOFkdP+Uv2a9fP7i5uaGkpKTSzDb6+vq4ePEipKSk0L9/fzRt2hRAaTjQtx/0rl27omvXroxJtKSkBI8ePUJaWhpatmyJ4cOH4/Lly5g3bx5Wr16NoKAgxMXF4a+//kJkZCSKi4uhq6sLNpuN48eP4/jx40wIG4/Hg5eXF2NW+j7F2+jRo8HlcpkJ/Udo2rQpjh49irVr15Yz3fJ4PDg4OCAkJATHjh2r1FwMlO62v1dM0tTUhJ+fH7p161bpWXFxcTFiY2NF7peFVBERE8bwTyAyMhKLFi1ijjxKSkpw8uRJZGZmit2Gs7Mzzpw5w+yYJEj4UQYOHIjz589jyJAhyMrKgpycHOLj47Fx40b4+fnhyZMnIj/SampqmDFjBpKSkmBra4uFCxeKtZD/Gejr68PS0lIyef8Cnjx5UuFmtQxLS0scOXJERD20TFK7WsQRYy9LZlKdsPq32Nvbk62tbbXleDwe3bt3j+zt7Sk6OppJbvI9oaGhFBQUxLwWCoWUlZVFHh4elJmZyVyfM2cOpaSkEJfLJScnJ3J2diaBQEBEpQkr7ty5U67toqIisrS0rPCeUCikQYMGkYWFBfn5+VX7PJUhFArpzJkzzFi+xd3dXeQZKuPz58908eLFStt/9OgR7du3jxISEig0NFTkflZWFi1atKjCuhs3bqQjR46I8RQ/h7lz59KVK1eIiOjhw4ekpaVFBQUFRERUUFBAJ06coIMHD9Lr16/FblMoFFJxcfFPGa+E3xd3d3e6desWOTk5kVAopBcvXlB0dHS19TIzM+nUqVMVfv9rg0AgoKSkJLp9+zbZ2NjQmDFjqGXLlrRy5UrKz88vV97Q0JCEQmGd9C2hZtja2hKfzy93PScnh2xtbenmzZvl7ok75/60CXzjxo3Mj7A4FBYW0osXLyr9gF+8eJF4PF617dy6dYu2b99ORETPnj2j48ePExHRixcv6PTp08TlciusVzbBfs+FCxdo/PjxpKmpSatWrSJLS0txH0mE48eP09OnT8tdT09Pp40bN1Zbv7i4mPbs2VPp+MtwcXGhjRs3ko+PD3l5eYn1nv1qhEIhde/enbp3706dOnUSuVdSUkKGhoZ09uxZ5u8qQcKvgsfjkYWFBb148YL8/f1JKBTS+fPnxaobExNDz58/J6LSxbijoyO9e/euyjrJycn04cMHWr58OQUEBJC3tzcFBgbSp0+fCAABoNevX1P37t3pxYsXZGtrSwDI1NSU1q9fz2QuvHfvHiUnJ//Qs0uoHfb29iITeEpKCl28eJEsLS0r/X0Wd86tkZRqdbJu33L//n20b98eOjo64pkCqiEmJgY5OTno1atXleU+fPgAS0tLnDx5Ep8/f8batWsxYcIE6OrqYvjw4VXWjY6OZs7uFRUVkZKSghYtWmDbtm14/fo1ACAsLAwJCQkwNjau0fgTExPh7OwMXV1dSElJMcIJmpqaMDQ0rNapxNPTE4MGDSrn2PYtPB4Pzs7OMDMzA1AaiuXs7Iw+ffpU++zf8+HDB9y4cQMaGhp4/fo1LC0ta1S/NrBYLHTo0KFCdaj+/fvD399fIvco4ZcTEhKCoKAgtGjRAkpKSnj27BmMjIzEcjy6fPkyFBQU8OnTJyxduhRXr15F27ZtK80RPXr0aDx8+BDPnz+HiooKbt68iVu3bqFRo0Zwd3cHUF4CNSYmBpqamlBRUcGxY8dgbm4OeXl5eHh4VJsjQELdEhkZifj4eIwaNQrJycnw8vJC/fr1MXHiRMjJVa5rIvacK84KojY78JiYGHr58qXY5asjPT2d7t27J1bZjRs30ps3b+jYsWN069atWvVXWFhIcXFx5OvrSxs2bKDHjx8z90xNTWvV5pUrV+jx48dimbK+LePj40OBgYHV1snMzKTt27dTZGSkyPW7d+/S8ePHqaSkpMJ+vs05LhQKycXFhdzd3YnP51NOTg6NHTuWQkNDycfHp9ox/AgxMTG0ZMmSCu/Nnj2bvLy8fmr/EiSIS1paGjk6OtKuXbvI0dGRli9fTkVFRWLVTU5OppycHOZ1RTt4NTU1AkDe3t4VtsHlcqmkpKTa3NL5+fmMFXLUqFEi/Ur4cR48eMDMc58+faLLly8z927fvs3kmC8pKaETJ06IfYzxy3fgCQkJ+PDhQ515OGdkZCAkJESsLEFljiXv3r3D+PHjsWPHjnLa6zVl4sSJ8Pb2BlCaWebt27f4448/0LBhwxq1ExERgdjYWEyYMKHC+69fv0avXr3w8OFDfPr0CaqqqmCz2TXKFFSWUc3Y2JixgMTGxsLKygpcLhc9e/ZERkYGZGVlQURgs9mMBSA6OhorV64UCfFLSUnBoUOH0KZNGwwbNqzSJC8/iyNHjsDT0xP37t1jIgYkSPiVuLu7Y+bMmRAKhbhw4QKmTp2Ko0ePYuDAgWKngiwjISEB/v7+THzwkCFD4O/vDw6H88NOb8+fP8fUqVORkpKCy5cvo0uXLn/79/e/zLZt29ChQwcQET58+IA///wTXl5e4HK5aNOmDRPWy+Fw8PXrV7Hzzf/yHfjLly8pKipK7PLVUZMdOBHRvn37qKCggDIzM8nd3f2H+79//77I+Syfz6ejR4+KOLbl5OSQo6MjhYSEVNoOj8ejZcuWVXgvPz+fAFBYWBidPXuWMjMza+2IxeVy6cqVK3T48GE6dOgQBQcHM/cEAgE5OjrS+vXrRZznNm/eXO5vnJeXRw4ODmRqakpcLpdWrFhRq/HUFqFQSAYGBhIHHAn/KHx9fWn16tXE5XLpzp07FBgYSAKBgKZPn16hE1l1XLp0iTIzM0koFBKAOvu8CwQCGjZsGAkEAnr27Nnf/v39HeByuYxvUpmfQkREhMi/V69e0f79++t8B/7TYgqEQiFSUlJ+WZKN0aNHIzo6Gt27d4eysjI+f/78Q9mzhg8fDk9PT4wfPx4+Pj6QkpLCunXrEBgYCAcHB3C5XJw6dQrz5s3DkydP0KVLl3JtEBF8fX2ZkLnvOXjwIPz9/dGkSRNkZGRAWVlZJLSgJsjIyDC7dqFQiEuXLuHBgwdo2LAhFBUVERQUBBaLhStXrkBKSgqysrJQUlIS6e/z58+4f/8+5s2bh5kzZ0JGRqZG2vZ1AZvNxqVLl6qNoeXz+ZCTk0NJSYkkVEbCT8fY2BjNmzfHiBEjcPLkScyfPx8+Pj5wcHDA6NGjYWdnB319/Rq1Z29vj+XLl2Pp0qV1FjPOZrPRpEkT7Nq1C5qamujZsydzLyoqCmPHjsWnT5/qpK/flTLrbnx8PIKCgrBs2bJyZYqKihASEoJz585h0qRJ0NbWrpvO63I18C1WVlZ16gFd0x24UCgka2tr5v8HDhyok1XtkCFD6MCBA3Tq1Kly9zIzMwkAOTs708OHDykjI0PkvpubW4UhJ8XFxSQQCCgyMpIMDQ2JiMjb27vcWXZdwOVyKTExkY4fP07r168nQ0NDatOmjUiIXhlXr14VeQY+n0+LFy+u8zFVBp/PJz09PbHKpqenk4ODw88dkAQJFXDjxg16/vw5Y+kbNWoU2dvb17idT58+kb29PY0aNYpiYmLqdIydO3em5ORkkYgXoVBI/fr1q9N+fleEQiGdPXuWmWMePHhAbm5uzD9HR0cKDAwkoVBIV65coevXr1c5H/3yMLKcnBwmtrcuqOkETkQUFBTExEMnJCSQr6/vD49j/vz5ZGRkVC7kLCMjgzZs2EAHDx4kgUBAAQEB5ObmRiYmJrR//37avXs3zZw5kxYtWkQ2NjY0dOhQWrduHTk7O1P37t1p//79RETUqFEjIiqdvE6ePFlt2FhtiYyMpJEjR5KBgQFZWVmRq6srubi40Pv375kyR48eFalz5syZcvHlPwuBQEAAaPTo0URUGn/75MmTv6VvCRJqAo/Ho7Vr1zJhQYmJiTR16tRKNS2qouxzX5HD6Y+QkZFBAGj48OFEVKp94eHhUad9/M68ffuW3rx5w7x2c3OrtCyHw6GbN2/SkiVLKj1m/uUTOBGRpaUlXbx4kVJSUmpUryJqM4ETEdnY2DD/37dvX4UB9TWhoKCABgwYQLNmzWKu+fv7U7du3ejGjRtkampKiYmJFdbNycmp1Coxd+5c6t+/Py1fvpyioqIoOTmZTp48Wa2XaW3R0tIiFxcXGjp0KKWmphIRUXBwMLm6uhIRUXx8PONBSVS6cz98+PBPGcv3JCcnEwDG5+Dx48cUFhZGdnZ2f0v/EiTUBA6HQwDo2bNnIpESgwcPpj179tSoraysLOrbt29dD5GhbNe3adMmEtMAK0EMbG1tRaJ5qprAX7x4QdevX6fw8HBycXGpcF77R0zgRKWr029NC7WlJhP4jh07mP/fuXOHzp49S0REsbGxtGfPnlo5mXzLmzdvaNCgQXTq1ClydHSkhQsXUk5ODu3du5ciIiLI0dGRPDw86OPHj2K3yeVyGWEGS0tLunr1KoWFhf00IZbCwkLq1KkT7du3j3JycujevXtkYmJCX758oTNnztC+fftERHWEQiEtXbqUJk6cKHa4zPcUFRVVu7P4/PkzASArKysiKj1e2LlzJ71584bCw8Nr1a8ECT8bgUBAdnZ2NGbMGGbTIBQKqWPHjhQRESF2O4WFhbXaudcGABQfH/+39PVfZsiQISKWS6KqJ/C4uDhG0IeI6N27d2RjYyPyu/rLndjKCA8PB4/Hg6urK4RCIXr37g2hUIinT59CSkoKjRs3xujRo+tMI1goFGLPnj3YvXs3gNIsYIWFhQCAFi1aYP369Thz5gzWr19f6z66d++OhQsXonv37mjVqhXmzp0LHo8HPT09dOzYER07dkRxcTEePXqEr1+/YuDAgdW2KSMjAyKCgYEBunTpAmVlZSgoKPw0hyxFRUUMGzaMSdmZkZHB6MyXlJQw18tgsVg4e/YsAgICYGlpWeH7R0QICwvDzZs3YWRkhLZt24roywcEBGD79u2MuP/IkSPRuHFjmJqaori4GImJiWjdujU+ffrEOByGhYWhd+/eePv2rUSEQsI/FjabDTMzMwiFQibjIovFQkREBA4dOoT69eujUaNG1bajoKDAiDz9KAKBAF5eXiguLkZ6ejpSUlIwefJk9OjRA1JSUhJRpDqAx+OhX79+IqF5vr6+VdZp1KgRXr16xbxu3749WrRoAWdnZ/Tr1w8dO3YUu/+fpqxfWFiICxcuoKSkBCtXrsTs2bMxZ84cxMTE4MGDBxg6dCgWLlyIrl27wsHBAffu3avyAyWOV+b58+chJSUFKysrAEBwcDB8fX0hEAjw+PFjAKXJUspU1X6E6dOng8Ph4OrVq3B1dcWFCxdEvnjy8vIYM2YMsrKyEBwcLHa7YWFhWLRoEWbMmIHc3FwApcpKdTHmb8nOzka3bt2QlJSEEydO4NChQ/jjjz8AAOrq6pXW69u3r4iC0PXr17Fp0yZs27YNS5cuRVRUFDZv3gwej4dz586J1B06dCj8/PyQlJSEp0+f4vHjx5g/fz4Th37u3Dnk5+eLRAt069YNUVFRYLPZvywRhATx+D5L3u/IrFmzsGDBApFrGzZswIULF8SqHx8fXyde4fHx8di7dy9ev36N+Ph4pKenY+PGjcjLy8OJEyfg6OgomcDrgEuXLmHlypUi17KzszFjxoxK68jKyoLH44lck5eXx6JFi5CamgoPDw+x/zY/ZXv35MkTxMXFYfbs2eXk4v744w9kZ2cjMDAQrVu3RuPGjWFmZoYvX77gwoULaN26NQYNGlRhu35+fiKr228fUigUYtOmTbh48SJYLBasra3RoEEDbN68GSwWC7a2thg4cCCkpaWRnp4OIvqhUA0ZGRl069ZNRALx+7zUAKCsrFxj8ZF169ahf//+zKpu5syZyMrKqrNwj9DQUHTr1k3k/Xv16hVOnz4NHR0djBkzpsr6LVu2xIkTJ9CiRQt8/vwZHTt2hEAgwL59+5gyffv2xa1bt2BlZYXly5dj8+bN0NbWxoIFC3Do0CE4Ozuja9euOHv2LNq1a1dpjm5paWm0b99e7Py4En4NPB4PNjY22L59O54+fVpj6d7/CoqKiuVkkdlsNjp16oSvX7+icePGVdZv2rQpDhw4gP3794vVHxHh1atXUFBQgIaGBpo0aQIiQq9evbBixQrIysqib9++yMnJgaWlJVRVVdGuXTuUlJRg/fr1iIyMxK1bt2r9vL8zHA4HUlJS5cS8fsRqOmzYMKSkpODZs2fiVRDHxl9mjy87l6yMtLQ0OnfuXLUC/QKBgDmX/p6oqCiysbEpF0KVkZFBd+/erbLNrKysSu/Hx8fTwoULiYjo+vXrlJCQUOUYa4pQKCQnJyeRa2FhYZVKIVaHk5MThYaG0qNHj8jJyanOHE4+fvxILi4uxOFwiMPhiNxzdHSkSZMmidXOq1evyNTUlBYvXsyE6T19+pTS09Pp7Nmz5O7uTq1bt6bevXszzj2ysrLUqFEj2r59e51lZZLwz4LP59Ps2bNJIBDQ+fPny33Gfldyc3PFTnoybdq0ar8fWVlZZGNjQ66urhQZGUlhYWFkYmJSrlxISAjjpFpGeno6HT9+nAoKCmjjxo00fvx4srCwIKFQSHl5eWRgYFArf6ffjYcPH9KAAQPI3d2d+efm5iZyvl0ZZc7ClfFTnNgeP35MR44cobS0tHJlPn36RHZ2dmL/MD98+LBKxbJnz56Rra0tpaSkkFAopLS0tArTfdYEZ2dnIiqdbHft2lXtQqMmREZG0qtXr5jXPj4+tGbNmh9qMzAwkHGEO3HixA+1VcbixYvp0qVLFd4rU4Fq0qRJlW1cunSJpk6dKuK4kZCQQEFBQTR+/HjicrnUvHlzMjAwEKmnqalJ+/bt++FnkPDvwMXFhby9vSUqekTk6enJZCN8+vQpHTt2rNKySUlJ1YZ4XblyRWRxFBMTQ9evXxd7PCUlJeTo6Ehbt24lIqKtW7fSoUOH6M6dOz8cqfO7kJeXR7Nnzy7nwCYOdTWB1+hQsVu3bvjrr7/g5+eHa9euAQC4XC6ePXuGwMBAmJmZiX1OmZWVVaUm78CBA2FmZoaAgACYmJjg/v376Nq1a02GW4709HQApeb3nTt3IjQ0FOHh4bVuj4gQFBQEJycnBAUFoUePHsy9srOmH6F3795o2bIlADBHB7UlMjISBw4cwJgxYxjN5e9hsVgoLCwEh8NB9+7dy9338PCAra0tlJWVsWvXLly4cIF5T5s0aYL8/Hx06NABWVlZaN68OUJCQgCU+kNMnDgRGzZsgI2NDdLS0n7oWST8Oxg9ejTi4uJw+vTpXz2UX06fPn2QmpoKoDSz3rRp0yot27hxY9SrVw9xcXGVlsnNzYW8vDzzeuDAgdUefX2LrKwsWrduDX19fbi5uaF+/frg8Xho3749Vq1ahYULF2Ls2LFwdHSEnZ0dPnz4AKFQKHb7vwMqKir4448/EB0d/cvGUGNjPZvNxvjx43Ho0CG8fv0aBgYGMDAwqPTcujK4XG61KTTZbDYmTJiAwsJCzJw5s6ZDLYeSkhJKSkqYc/mZM2fCyspKrPNVoVCIhIQEJCYmIikpCUlJSVBQUMCAAQMwd+5ckXLXrl1DcXHxD4/3W2pyLvY9X79+ha+vL7Zs2VJtWUVFRXh4eFR4hpmXl4dFixYxr83MzBAREcEkrOFwOGjZsiUCAwNx4sQJxsfg1q1bcHV1hZSUFKKjo2ucAEYgEDBSqRL+PWhoaCA1NRWTJ0/+1UP55TRu3BiNGjXCiRMnYGJiAl1d3SrLDxkyBKNHj8ahQ4eYjQGfz0d0dDS8vLxEkkQlJycznuU1oW/fvujbty/zmoiQnZ2NM2fOYOvWrdDX14epqSmICO3bt8fcuXOxevVqkYXD705t02WzWCwIhcIfdsyt1Wk7i8XC5s2b4e7uXuVKsro2xKWuvI+nTZuGtWvXYs+ePahfvz4AoFWrVvj48WOVusVBQUEIDQ1FmzZtoKuri169ejEhGt+6/PN4PPj4+KBnz55o1qxZnYy5jBYtWtSqXkREBK5cucKE1YnDsGHDcOXKFbx79w4dOnQAUJql7PsdQWxsLOTl5XHz5k0cO3YMkZGRCA4Oho2NDdzd3WFvbw9FRUX07NkTtra28PHxgZ2dXY2fwdbWFnw+Hzdv3sSVK1egoqJS4zYk/P2wWCwcPHjwVw/jH8OAAQPQv39/ODg4QFVVFT169ICenl6FZWVkZODu7o7w8HCsWbMGgYGBaNeuHTp37gwdHR0MGDAAQOnk/eeff9Z4Uo2JicGhQ4egr68PPT09xlHWyMgIycnJ0NHRQadOnXDhwgWEhYVh69atiIyMRGJiIm7duoX09HQUFxfj6NGjP/q2/Kvp378/3Nzc0L9//xrVa9iwIdLT06GlpfVD/f+yrA/0C0IYUlJS0KBBA1y6dIkRnB8xYgTs7OygrKwMX19fqKqqgs/no2nTplBRUYGWlhY+fvwIMzMzkbZkZGTA4/EgEAggJSUFgUCAAwcOwNjYuM4nb6B0t5uSklIjEfz79++Dw+Fgz549Ne5v1KhRcHBwEJnAmzRpAg6HAwUFBQBgTIKdO3eGk5MT+Hw+evXqhcePH+PWrVt4+PAh/vjjD+jp6WH16tVYvXp1jccBlHp1Ll26FPXr15eY8ST8q2GxWJg/fz4KCwvx7NkzPHv2DCYmJhUmLdLU1MSwYcMwdOhQkQ1PXFwcVq9ejd69e+PVq1eYMGECli5dWqNxtG7dGqtWrYK1tTWTeKl+/fp4/vw55OXlwWKxcPfuXcjJyZX77ZswYQLS09PrLiHHvxgiqtVvUpMmTZCYmPjvnMC5XC6Kior+1j7Dw8MREBCA3bt3w9XVlQkjY7FYmDJlCrZs2YJevXph/PjxkJOTQ0JCAjIzM+Hl5YXFixdX2Gb37t2xd+9e6OnpQSgUYsmSJT/8B6mMUaNG4fLly+ViDivD19cXubm5mD17dq36CwkJYXwO1q5di5MnT+Lx48ewt7eHubk5WCwWTE1Ny9XT0tJC69atkZaWhps3b8Lf3/+Hd2FlpvNevXrB398fo0eP/qH2JEj41SgpKWH06NHIzs6Gl5cXCgsL0axZMwwdOrScxfF7a2VUVBS6devGxBpnZ2fX2HwOAAYGBuW0GsoW5wAqzWvetGnTSjMqAhBZ5P/XefLkCZKSktC3b1/o6Ojg8uXLYlmMmzRpgvv37zO+Rvb29vDw8IBAIEDLli3RoEEDsfr/oQm8NpOwUCiEjY0N5s+fL1Z5gUCAvLy8GvfzPdHR0cw5etOmTREbG8uYzdXV1dG2bVvMnDmTmSzKPqSVOc7l5eXh2bNn2LJlS61TftaEdu3aieUUFxsbi2vXrkFJSQkLFy6sdX8+Pj44fvw4jIyM0LFjR+Tm5mLv3r1IT09nks1XxB9//IEOHTrg3bt3CAwMREJCApOCtTYsW7YMPXr0QEZGBszMzPDkyZNaP5MECf801NXVYWJiAiJCfHw87OzsoKKigsGDB6O4uBiPHz+GjIwMdHV1MXjwYOTl5eHr16+Ijo5GQUEBoqOj8ezZMzRq1AidOnXC8+fPoaKiguTkZCxYsOBvn0g9PT3h5eUFV1fXv7XfX8Xx48ehqqqKQ4cO4evXr5CSkkJUVFS5NNr0ne6IkpISvn79CqDURyg3Nxd3795l7j98+BAHDhyotn8WiWHLLvvBvnz5Mng8Hvh8PoqKimBsbIwmTZqI/bAA4OLigjFjxjBn0NVx4MABdO/evdLVoLjY29sziwYigqGhIezs7BhP+HHjxuH69evVtsPhcODv74+PHz9i9OjRVa5E65o///wTdnZ2lTpz5eTk4OLFixg0aNAPC5/weDzcuHEDzZo1Y8RqDh48CBaLhU2bNlVZl8ViITY2Fqqqqrh8+TLk5OSgp6eHYcOG1Xgczs7OmD59Ory9vWFkZFSlSpwECf8FeDweHj9+DHl5eQwcOJCRZI2MjER+fj7mzJnDbBpCQ0Ph7OwMc3NzZGZmomHDhtDS0sKHDx/QpUsXPHnyBIaGhr/4if598Pl83L17F2PHjq2yzIULF0QstJ8/f8awYcNw//59ODg4QF1dHQ0aNEBYWJhILnYA0NbWxpAhQ8Dn80XEX5KTk5Gfn482bdogNzcXqqqqlY6hRjvwyMhI7Ny5syZVRPj8+TPk5eXFnrxv3bqFESNGlHvw2sDj8XDt2jUEBASgcePG6N+/P06dOoW+ffti9uzZaN++PTIzMysdW1paGm7cuAFFRUUYGhrWajL6UU6cOAEbG5tKzehFRUXQ1NSsE9UyGRkZTJw4UeRaeno6evXqVWH5pKQkfPz4EUSEPXv2YPbs2Vi9ejW+fv2KvXv3wsnJqVbjGDhwIDp37ow1a9YgIiIC+vr61apZSZDwb0ZGRgYjR44UudapUycoKCjg7NmzkJGRAVB6FOnm5sZY5sosiuHh4bhx4waOHDmCgwcP/u0T+NSpU7F161bmbP3fCJvNrjbq5a+//ip3vFoWPrt7927s3r0bcnJyVVpoCwsLYWNjg7Vr1yIlJQU3b95EgwYNxN4Y1mgC/z6ZRHp6Ou7fvw8Wi4Xu3btXGdcNAKdPn67RH/XVq1fYsWNHTYZYIWXhEc2bN8f+/fsZp4OXL18iODgYhw4dwpo1a/DixQuMHz9epO67d+/g7+8POTk5mJqa/lI9bk1NTaSlpTGOc9+jpKT0wyEeRAQej1fhh65evXr4+vUrHj16hIEDB4LD4UBVVRURERF48+YNhgwZAgDw9/dHfHw8evToAaFQCBaLhcuXL1d4Zl4dzZs3x7Rp05CZmSnyQzRjxgy4ubnV+jklSPi3oa+vj40bN8LBwQFycnKIjIzEmDFjcOTIEXTq1AnNmzeHl5cX1NTUsGXLFhQUFPwUh9rquHTp0t/eZ13DZrOrlQM+deoUhg8fDmtrazx48ADDhw9H06ZNoaamhvbt21cZLWNnZ4dTp07BxMQE27Ztg7W1NaSlpTFjxgwoKSmJfWxco9lozZo1Iq/d3Nwwffp0mJiYICoqCmfPnoWdnR1u3bqF9+/fw87ODm5ubnB0dMSJEyfQrFkzmJqaYsqUKYz9vzJycnLQtGnTOpkwQ0NDMWbMGIwcORLS0tKQlZWFrKwsBg4ciNGjR4OI4O7ujrS0NAiFQoSEhODcuXNwc3NDQUEBFi5ciDlz5vwjkmkYGhpW+t6pqamhoKDgh9p3d3fHnDlzKry3atUqFBQUIC4uDsePH2c+D/fv34epqSmcnZ2hq6uLzZs3IysrCy1atEBJSQmA0rM+gUBQqzHFxMQgISEBSUlJGDduHDQ0NKqNo5Ug4b+IlpYW5s+fj1mzZmHWrFn48OEDHj9+jDFjxuD8+fPYvHkzli1bBhaLhYSEBEYI6u9ESkqqVk51/zZYLBY+ffqE/v37Iz8/H7t27WI0zLt27VqhgFFWVhYGDhyI9PR0REREYNq0aVi+fDnk5OSwYMECKCkp1WgMNdqBb926FfPnz0evXr3QrFkzGBoaMn+o8ePHM4HpZeZUMzOzch6UDx48gFAorNbLrqSkBKGhobUKdvf390enTp2goqKClJQU3L17FytXrsS1a9cwYcIEkbKtW7dG9+7d4eHhAWVlZbi4uCA/Px/Lly8Xqy8PDw84OTnhzJkzf8uXZdCgQfD09KxQ2CYxMRGampo/1H5ERESlxyRqampwdHTE2rVr0aRJE7Rp0waHDx/GsWPH8McffzCpVC0sLNC8eXPExMQwK8mnT5+Cy+XWyqnm+PHjWLx4MRo0aABvb2/o6+sjNTUV27Ztg5mZmUj2MgkSfhfat2+P9u3bY+HChfDy8oKfnx/mzp2L8+fPQ05ODqtXr8aSJUsqVFWsDh6PB0NDQ5w5cwa6urpQVVWVCLhUQFxcHExMTKClpYWxY8fixo0bePPmDbS0tPD582eEh4eDxWLhzp07EAgE0NHRwfPnz5n6Dg4O2LdvH+rVq1er/ms0M2ppacHY2BhqamqwsrIq54lcNtHq6OjA0NCwQrGWa9euQU9PD1++fKm2r379+tVY6vTcuXO4fv06E6Ps7e2NDRs2ICcnBw8fPqywTmxsLGRlZdGoUSPMnTu3wslbIBAgMjISZ8+exebNm5nd5PTp03Hr1q2/baUrJycHFRUVxMTElLt348YNjBgx4ofaP336dDkPym9JSEhAVlYWVFVVMXHiRGzcuBEuLi7YtWsXJk2aBIFAgOnTp2Ps2LGIiorCihUrQERo3rx5rSbv/Px8NGnSBNeuXcPatWsxdepU/PHHHxAIBIiJialRqlYJEv6rTJo0CYsXL0bDhg1x5MgRJCQkQEdHp9yRYHWUlJQgOTkZ1tbW0NXVxZcvX/Dnn39i5MiRiIiI+Emj//fy9etXyMvLM4p1jRs3xubNmzF//nzMnDkTX758QX5+Pho0aIBr166JHEPv2bMH48aNq/XkDdRwB75r1y5GYq9evXo4d+4c5OXlxRYKyc/Ph6WlJYYPH15t6FVWVhZKSkpw+/btGp2by8nJYdy4cUhMTERKSgpkZWXBZrPRuHFjnDlzpsI6JSUlUFNTg7KyMr58+QIdHR3cuHEDMjIyzB9ISkoK+vr6WLp0KS5cuPBLTUTGxsY4dOgQNm/eLHKdz+f/UIpUoDTioKo2unXrhvj4eGzdupW5NmrUKNy8eRN79uyBq6srhgwZgpiYGBQXF6Nz5864du0aHB0dazyWkydPYsuWLeBwOJCRkUHbtm2Rnp4OXV1dvHjxAvXq1YOfn59EqlOCBJRaE7t164bz588jOTkZ8vLyKCwsrNKLuYzk5GSYm5sjNzcX69atg4yMDJKSkvDu3Tv89ddf6NmzJ96/f49du3Zh3bp1SE5ORrNmzSArK4vExERkZGQgMTERJSUlSEhIgLm5+d8SXvuradSoEc6fPw9HR0cmhK6Mb52vHzx4wEzUxcXFSEtLQ4MGDRgp21evXuHjx48i+SjEQpzMKWWZUQ4fPsxcc3Z2JhMTE5o5c6Y4TRBRaYrMjx8/0pMnT2j37t2VluNyuXTgwAHi8/n0+PFjevPmDRUUFNCpU6fIzs5OpGxxcTFZWVnRmzdv6OXLl/T8+XMSCARkZWVFFhYWVFxcLNbYnJ2dKTs7m65du0YuLi6UmZlJ6enpFZZt3bp1hRnZ/k6sra1FxhcZGUl79+796ZmE1NXV6dChQ+WuczgcGjlyJI0YMYIAMP9u3bpVqzEVFRURAHJ0dGSueXl50aZNm6hv375kbm5OACRpSSVI+IaoqCiaMmUKXblyheLj4+ngwYNi1TMxMSGBQEA8Ho+Sk5Pp7t27tG7dunIpnL29venatWsUEBBAkydPptOnT1OHDh3o2LFjpKenRzNmzKB27dpRSUnJz3i8fyx8Pp+6dOlCL168IKFQSMHBwfTgwQORTHyXLl2ibdu20caNG2nnzp20ZcsWSk1NpS9fvpC1tbVI2teUlBSxspHVaAfeqFEj5OXlQVFREcnJybC3t8f58+fFqltQUIC8vDzcunULAwYMqFTdLD09Hfb29liyZAmkpKQwaNAgODk5ITw8HIsXL0ZYWBhcXFzA4/HA5XJRUFCAWbNmIT4+HoWFhYzIf/fu3fH69WuxE2CYmJjAwsIC5ubmVSZZ4fF42L59+w+fNf8oI0eOxL179zBz5kxwuVzs3bsXM2bMgLy8PDIzM8VaddeUqKgo6OnpITs7G/Hx8SKhDvLy8rh79y6uXLkCKysrtGvXDrdv366VSZ/D4WDBggU4deqUSKKYYcOGITAwEOrq6tDV1cW7d+9gaGiIq1ev/jQFPAkS/k20adMGY8aMwYMHD8DhcNCkSRMsWrQIR48ehbKyMtatWwcWi4UWLVpg2LBhaNeuHT5+/IiOHTuCzWaDzWbjwoUL+PPPPzF58uRyR4Pf+hBdvXoVALBixQoApWJTnp6e6NSpE+Li4qqNSvovISUlheDgYLDZbJw5cwadO3dGs2bNcOnSJUYxr379+jAyMmJ07Hk8HiwsLNC5c2ckJCQgPj4empqaEAgE4h83irO6KNuBJyUlkZeXF9na2lJGRgYREZ0+fZpatGhRYf7aT58+Mf8/c+YMxcfHU0lJCVlaWlJKSopI2czMTFq4cCHdunWL8vPzmesFBQW0cOFCsra2FmeoRFS6K/f396crV66IXaesnrOzMzk5OdHr168rzGN86dIlyszMrFG7P4u5c+eSQCCgXbt2UXJyMl2+fJm+fv1KR48e/Sn9paam0t69eykrK4v27t1bYRk7OzuytrYWyVVcE4RCIXXr1o0uX75c4f3Xr1/T0qVL6fHjxzRq1Ci6ffs2nTp1qlZ9SZDwX2bfvn3Uv39/cnJyoqVLl5KDgwN17dqVTExMaMmSJbR8+XI6efIkXbhwQaReXFwcWVhY0Js3byg+Pl6svoRCIT18+JAAkKurK/Xp04fJf/47ERMTQ99Oq5s3byY3Nzfi8/kUFxdHq1atqrBeVlYWXbt2jUaNGsXsvFHXO3Cg9Jy1Xr16jODJihUrsGLFCkyePBlbt25FixYtoKWlBX19fWhqamLp0qXIzs6GtrY2E/pjbm6O8+fPo3PnziAiJCYmIjMzEydOnICysrJIf0pKSjh+/DguX76M+/fvi7Wj8/T0RHFxscjuTRzk5OQYJ4N3797BxcWFOZNYsmQJEhMTISsrCw0NjRq1+7M4fvw47O3tMWXKFGhra+P48eOYOnUqGjdujOjo6Cqd0WqDqqoq7t+/Dy0tLSgrK4PL5ZY75/o+8UFNISJcunQJrVq1KncvICAAhw8fhrS0ND58+IDr169DWloaN2/e/KE+JUj4L7Ju3Tp8+vQJ/v7+UFdXB4/Hw5gxY7Bv3z7s3r0bfD4f3t7e5ayozZo1g5KSErp161apP8zXr1+xf/9+cLlcXLp0CQYGBtDV1QWXy4WMjAw0NTVx7tw5JCQkYObMmT/sm/NvoWnTplBUVGQslEQEZWVl3L9/H8HBwUweh7Iscg8ePABQmhhKSkoKt2/fhqysbKVS1eUQZ1VRthpISUkhJycncnBwKFdmwYIFlJSURETEnA8HBQVRWFgYHTlyRGRHlpubS4cOHaIHDx7Qy5cvxRkCERHZ29tTQEBAteV4PB49ePCAvLy8xG67IrhcLp0+fZpycnKIiMjGxoZ4PN4PtfkzcXNzY/7v7Oz8U/oQCoXUqFEjunnzJu3bt6+cJeVHCQ8Pr9S/wMTEhObOnUvr1q2jZcuWEZ/Pp+3bt1dqDZAgQUIpaWlpdPz4cbK0tCSiUp+V2bNnV/pde/r0KVlZWZG3tzc9evRIxBrp6+tLPXv2pLCwMIqKiqK8vDwSCAQiO8+1a9cSEVFsbGyNLaH/dj5//ky9evWis2fPUkhICHE4HNq+fTutWbOGKXPjxg2R98ve3p75/4sXL2jVqlVi7cBrFEZ2//59BAUF4f3792jZsiUOHDiAtLQ0AKUynmV2e01NTdjY2CAhIQGvXr1Cu3btRGIIL126hFWrVmHYsGGVSnNWxLx58xAaGlqtCIy0tDSGDRsGDQ0NBAUFMdc5HA62b98uVl8CgQDW1tYwNTWFmpoa4uLioK+vL6JZ+09DXl4eeXl5CA8PF1uutqawWCw8fPgQR48eBRHB2toab9++rZO2Q0JCMHfuXCxbtqxC0RcZGRnY2tri4MGD6NSpExISEhAREYGcnJw66V+ChP8qmpqaWLt2LZNGWV5eHn/99Rc8PDwqLD9o0CCYmppiyJAh0NLSgoODA6KiouDn54edO3fC398fnTt3Rtu2baGiogI2m82EmZ0/fx5jxowBAMTHx2P9+vXYtm3bL0kh/SvQ09PDy5cv8f79e8TExEBWVhZ79uxBYWEhkpOTAQBjx46tVHQrLCwMT58+Fa8zcVYUZTvwEydOUFZWFnNdIBDQH3/8QQcPHqTp06cz965evSpy/v0tPB6PTpw4IU635SgqKqK8vDw6ePBgpe1/z/HjxykvL4+IiBISEigqKqraOkKhkKysrCg7O5u5ZmNj89M9vH+UsjN8f3//OmmPw+GQvb09FRQUlLt39uxZcnR0pP3795OlpWWNLCkV8enTJ1q5ciUJhUI6cuQI1atXj86ePVuhHwIRUUhICI0fP548PDwqLSNBgoTKSUtLoy1btohV9tOnT/Ty5Ut6/fo12draVln20KFDxOVyRa7dvn2bLC0tafDgwTRgwACytLSkYcOG1bkF75/Gp0+f6NSpU1RSUkJCoZB27NhR4e+VpaUlY6UuLi6mZ8+eibUDr1E2sooyo2RkZGDJkiU4d+4cYmJikJ6ejqVLlzIrjYpwcHDAvHnzRK7xeDzY29tDWloaM2fOhIKCAogIL168QFZWFt6/fw9ZWVkoKSmBy+UiNTUVu3btglAoREFBQYXB8MHBwbCzs4OCggKOHTtW5TkMEeHmzZvIy8tDZmYmpk2bxng2v3r1CiwWq06SqvybKCwshKenJ2RlZcHlcsHj8Zgz7tDQUHz8+BFTpkwBAFhYWGDhwoVVevBXBJ/Px+PHjzFx4kR8/vyZ8e6/ePEiHj16hHPnzokdSSBBggTxKS4uhpOTU6URQRXh5uaGyZMnV/mdTEtLw+7duzFu3DgYGRkhPT0du3fvhoWFBaSkpLBmzRpoaWlh1apVcHBwwIsXL+Dq6vqfPSfPzc2Fi4sLBg8ejMePH2PEiBG4f/8+SkpKoKOjw2haNGnSBLKyslBQUIChoSGaNGlSt9nIKqJBgwbo378/GjZsCAA4evQoUlJSMGnSJLi5uVUov6egoIAPHz6IOCo5ODjAxMQEcnJycHFxgaysLNLT09GvXz8MGDCgnKJQUlISLCwsIC0tjfz8fJiZmTFj4HA4CA8Px+3btzF27FisX78ezZo1g7q6OmbMmMHU8fHxgYKCAgoKClBSUoKxY8dCR0enXD/R0dGV6oP/l1FSUkKTJk1ARBg2bBh8fHxw5swZrFixAgYGBli2bBkzgffq1QtJSUnlnM+ys7MRGBjIOG98j4uLCx49eoT58+fDxcUFGhoa2LNnD3r27InTp09LJm8JEn4SX758walTp2o0gSsrK4PD4TDfy7KEUN27dweXy8WHDx+Qnp6Obdu2wcrKCkZGRggICMDOnTsZ8atPnz6hTZs2UFBQgLm5OYDSeSQ1NfUffURZW9TU1LB8+XK8evUKhYWFyMzMxPLly5kFS1mYWUlJCWRkZMBms8VOZlIjE3p123miUvPz0KFDCQD5+vpWWKagoIDOnDlDERER5ODgQOfOnRM54K8pXC6XFixYQKdPn6Z9+/aRu7s7PX78mLZv305ERMbGxvTu3TvKzs4me3t7cnZ2JgsLC+JwOPTs2TPy8fGpsN20tDSytrb+7U20ISEhjNksICCAzp8/TwUFBXT06FEmzOTixYv0+fPncnWLiopo586dFbb77Nkzev78OQmFQnr37h29evWKETD49vhCggQJdU9+fj6JOQUwFBYWkoWFBfNaIBDQ4cOH6eXLl+Wcm62srIiIKhSUCQwMZI7d7O3tac6cOfTq1ataPMV/E3Hn3DqfwIlKJ+jU1FS6desWEZXa9L8/EymL+xYIBFRcXFyjOO+KOH36NBObXsbz58/p5cuXFBkZSaNHj66wXkUe9UREGRkZZGVl9dsrfV25coUGDBhAnz59Il9fX2rZsiUFBASQpqYmcblc2rZtG3E4HPr48SN5enqK1aapqSk5OjqKnNWvXLmSOQPq0KEDxcXF1XisaWlpkh8BCRLEIDAwkABQcnJyjeu5uLiIXHNwcKCQkJByZZ2cnJj/v3z5koKCgigiIoJcXFzIwsKCcnNzKSkpiXx9famoqOinRc78G/lpceDioKSkhOfPn+Pt27eIiIiAjIwMdHR04O/vj969eyMzMxOKioqQl5cHm83Gxo0b0aFDB9y+fRunT5+GlJQUeDweBAIBmjZtCnt7+2r71NfXR//+/REdHY20tDQkJCRgwIABcHFxwZ9//glnZ+cK631voiUieHp6QiAQYPHixf+IFKK/kilTpqBDhw5o3rw5IiIiMGvWLDRq1AhhYWFo164dWrZsiQsXLqBLly7IyclBfn5+hXlwL1y4gHfv3mH48OEICQmBnZ0dk2rU398fkyZNYo5bTp48CSsrKxw5cqTKsX17DJOYmIhly5Zh7dq1dfwOSJDw36KkpARz5sxBYWGhWD4rRIQHDx5ASkoKly5dQkFBAWbOnMn8Nnbs2BF3794tl7Piy5cvEAqFyMnJwcePH+Hp6Ql1dXXs27cP3t7ezLn3woULISUlhaSkJLx58wY9evT4GY/9n+SnHTjY29tj9uzZiI+PR79+/dCtWzckJibi69evmDZtGnR1dRk3+pcvXyI+Ph5t27aFj4+PiDiIt7c3evTogbt371YaGpWcnIy//voL169fB4/Hg5aWFs6ePQsOh8OkmqwufSlQGgpnb2+PqVOnSqQ5v6Fdu3bYuXMndHR00KRJE6ioqEBDQwMfP35EQEAAVq9eDVVVVaxbtw5hYWGwsLAQqU9EeP78Odq3bw8PDw8YGRnh0KFDUFJSYrKrzZ49myk/aNAgeHt7w93dHTNmzICnpyeTIrZFixaoV68e/P39cfXqVUYIwcnJCX5+fujfvz8MDQ3/1vdHgoR/C3l5eTh48CCTklecVLxcLhc+Pj5Yu3Ytjh49isuXL+PatWto0aIFgoKCkJeXh40bN5art3jxYpw9exbFxcWYN2+eSArkpUuXliu/du1azJ49G5cvX/6xh/ydqMvt/LcAoMjISMrPzxc5Q3Z1daXY2FiRsm3atKGRI0dW2lZ2dnaVJvZz584xAitFRUW0du1a0tXVZc5gqqJM/KSoqIhOnToldnG5mXsAAFJPSURBVPKT343i4mI6d+4clZSU0OnTp0X+pocOHaIWLVrQvXv3yv1tiUpDB8+ePUsCgYCio6OppKSkyqMJFxcXcnBwoMjISLKysqJBgwaRvb092djY0JYtW2jt2rW0YcMGEZlHT09P0tPTo9mzZ9ftg0uQ8A/jR/xDTE1NKSsri4RCIV2/fp0sLCzI0tKSEhISKq3j7+9PFhYWIsda4eHhZG9vz4h3/SgCgYCOHj1K+/bt+0eLZf1d/NIz8MzMTOrVqxd9/Pix3L2DBw+W+6MDoGPHjonV9ve8fv2axowZQ0SlE4Wuri45ODiQnZ0dAahwQvkWNzc34vF4dOrUqVrrd/9uhIWFkbe3N/NaIBDQ4MGDaeXKlZXWGTBggNj+BPLy8sz/mzZtKvI3TExMrFA96tixY2RkZMT8DfG/bGjv378nFxcXev/+PdO/xEFOwr+ZCRMm1ErrIS8vj3bt2iVyjcfjEYfDIVNTU9qxY0eFeR6CgoLo0aNHItdevnxJw4cPpxEjRtCoUaNo7ty5Ijksasr8+fNp5MiRZG5uTtbW1uTi4kKPHz/+bb+r4s65P+WAd9SoUbhy5Uq5TDYAsHz5cgQGBpbt/tG7d2+sXLkSf/31V6360tfXR4cOHbB7925YW1vD2NgYcnJyeP36NebPn8/or1dGfn4+9uzZA1NT0wpD3iSI4uzsDDabjdzcXCQkJAAA2Gw2Fi9eDG1tbTx79qzCeqqqqmKF4llaWkJdXR1AaTzpmDFjcOnSJdjb28PS0hItWrSo0FzXvHlzNGrUCEVFRUzd4cOHIyoqCrNnz0ZKSgo2bNgAoPQ45cCBAwDw26hDSfjvMGLECCQmJta4nru7O8aOHStyTVpaGvLy8hgwYAAWLlwIGxubckqX3bp1Q1xcnMi1Xr164f79+7h58yZu3bqFjRs3ok+fPjh48CB4PF6NxnX9+nV4enrixYsXsLKygrKyMs6cOYMhQ4ZAXV0dLBYLq1atApfLrfEz/+epy9VAGRWFDrm5uVF6ejqFhIQwYVszZsyggwcPUkREBDVs2FCstr9HX1+f7ty5Q7a2tnT//n0KCQmhnJwcAkCJiYl04cIFunv3boWhYHl5eaSpqUmvX7+uVd+/Iy9fvqS5c+cSn88vl8WIiKhv377Uvn37cmGBXC6Xzpw5U2XbDx8+FDGB379/n+Li4ig1NZUWLVpEx44dI2tra9q8eTPZ2dmRgYEBGRoakr+/P3l5edG+ffuIiCgiIoLGjRtHtra2BIA4HA7l5+fT8ePHiYhozZo1ZGRkxIQenj9/XiyFvoKCAtq2bRuFhYVVW1aChH8adnZ2Ff4OloXXEhGjFkZUGmZWZs6+ePFite0LhULy8fGh3r17V3p/7ty5NHfuXIqMjKSJEyfSqFGjSF9fn5SVlUlTU5MMDAyIzWaTlpYWc5xZUFDAWNR+F36pF7qCggLU1dVx/fp1jBs3DmZmZpgwYQKsra0RExMDJycnAEB4eDjc3NxQXFwsklu6JqSnp8PPzw9xcXFo1qwZunbtCgCYOXMmdHR0MH/+fMTFxeH8+fPo2bMncx8AbGxscOjQIYnXYw3o1asXo1+vrq6O2NhYEUtLfHw8WrVqhfj4eDg7O0NKSgp6enro378/FBUVERkZCUVFRejo6IDFYokIN+Tl5Yko+Pn7+2P48OF48OABhgwZgpSUFCxcuBBsNhtEVC7zWe/evREYGAg+nw8nJyfUq1cPUlJS6Nq1K6KjowGUOiru3LkTZ8+ehbGxMTp16gQAeP78OR4/foyUlBQMHz4cAwYMKKcMxePxoKenJ2LVsbOzQ2BgINauXYv27dvX0bssQULdU1JSAllZWQQGBqJ79+7Iy8vD/fv3kZeXx1jHWCwWJkyYACsrK3A4HLBYLGRkZKBDhw4AgI8fP2LatGlMlNDixYuxatUqpu64cePw6tUrJvoHAN68eQNjY2M0btwYEydOxI4dO/Do0SNcv34dXbp0Qd++fbF79258/vwZjRs3Rm5uLlavXs2MW0lJCcXFxZCXl4euri4MDAygr6+Pfv36QVdXF1JSUujZs+d/VsmtSupyNfA9jo6O5ObmRpMnT65w5VeZwEdNWLhwIeXn59P8+fNJKBRSSUkJE3/O4/Ho3LlzTFlLS0tKTU0lIqJ79+5VmnNagvjY2NiUc4DhcDi0Zs0aevjwIa1evZru379PRKWfowsXLtCGDRtow4YNNGPGDJo3bx5Nnz6diIhat25N79+/Z9pZs2YNxcXF0f79+8nZ2ZmOHz9OgYGBIn09f/6cjhw5QseOHSMANGXKFJo/fz7NnDmTIiIiKDIykhYsWEDGxsY0e/ZsWrduXbnseN/i5uZGMTEx5ODgQC4uLvThw4dq34OCggKaPHkymZmZ/faiPxL+uWRnZ9OSJUuouLiY3r59SwcPHqzw3DojI4MAUMuWLSknJ4fOnj1LGzduJGNjYwIg4mTWrVs32rZtGwmFQuLxeFRSUkLW1tbMjnngwIE0ePBgkTwSQ4YMEekvMjKS+T6amprS169fKxx/69atyc/Pj4iIcnJyKDAwkJ48eUJRUVFkaWlJ7969++H36J/CL3Vi+5Zz586Rs7MzFRUVUXFxMX369IlSU1PpwoULdWKK1NLSopMnT1bo1JGTk0Ourq7Maw6HQ9bW1hQVFVWlw5UE8REKhXTmzBmRhCdpaWlVJjwoW2iV4e7uTg4ODrRjxw5KT09nrpeUlNCBAwfIwcGBDhw4QJGRkaSlpSXyY8Dj8cjT05PevXsn8sMSGhpK4eHhRFSaSGHHjh3UtWtXMjAwKBcZ8S3fpmQlIjpx4gQtWrSoWm/bgIAAGjlyJC1ZsqTKchIk/AoKCgro2LFjNGfOHCouLqYLFy5Uu9h89+4ddenShVgsVqXmay6XS66ursyE365dOzp69GiVDsGFhYWV3nv16lWtxbOuXLlCHh4etar7T0PcOfeHk5mIw7lz5/Dy5UtoaWlBV1cXERER2LdvH5O44kdYuXIl5OTkcPToUbHrrF27FqNGjcLIkSN/uH8JpXGiTk5O6NChA/r16wc+n4+TJ09i/fr1P9SuUCiEi4sLuFwuDAwMcO7cOVy7dg329vaYOHFirdpMS0tDQEAA4uPjceHCBRw9ehQjRoxg7pfFnn8Lj8eDm5sbhg8fXk4rPzg4GJ06dcLWrVshEAigoaGBrVu31mpsEiT8LD5//oyIiAioq6vj+fPnaNGiBUxMTCosKxQK4eHhAYFAgC5dukBXVxccDgfa2tqVth8TE4PWrVv/rOGLjY+PDxITE6GlpYWOHTuibdu2v3pItULcOfdvUY6fOnUqAgICMGvWLBQVFWHEiBFITk5GQkICunTp8kNqZy1btsTHjx9rVEdDQ4M505Hw48jKymLhwoW4c+cO5syZg9TUVMyfPx8cDgeysrJMEoOakpWVheDgYOjo6KBXr15QU1PDlStXRPwYasLFixfx7t07HDx4EABgYmKChg0bwt7evlx2vG+RkZHB8OHD4ePjgx49esDAwAD79u1D06ZN4ejoiEmTJkFVVRUdOnRgFhZubm5QVVWFsbFxrcYqQUJd0rRpU5w4cQJnzpzBwIEDqywbFRUFXV1dDBgwgLmmpqZWZZ1/wuQNAOPHjwefz0dsbCxevHgBaWlp6Ovr/+ph/TT+Fp3QBg0awMvLC9LS0mjYsCEKCgrAZrPB5/Nx7969H2pbXV0dMTExyM7OFrtOTk4OFixY8EP9SijPqFGjsGPHDmzfvh1hYWGwsbGBnZ0dzp8/z5R58uQJIiMjxWqvQYMGsLCwYMK/2rRpg4KCAujp6dVqfAsWLICHhwfzWlNTExwOB58+fWKuCQSCCuvq6OjA3Nwc0tLSOHPmDMzNzZGamgpzc3OoqKjAzMxMxCowZMgQuLu7w9fXF5mZmbUarwQJdYWUlBSaNWuG8PDwass+efIE/fv3/xtG9XOQlpZGmzZtMG3aNNy+fftXD+en8rcJfV+/fh0KCgpo0aIFunfvjo4dO6JXr16Ij49nygQEBMDR0REbNmyAr68vhEJhte2amJhAV1cXe/fuFXssK1aswKhRo2r1HBKqRl9fH9evXwebzYahoSEWLFiAwYMHw9bWFi4uLhAKhUhKSsLZs2cxYMAA9OvXD6mpqT9lLLm5uXB3d2deu7m5ITQ0VKSMnJwc9u3bhytXrgAAo89eBo/HQ0pKCvP68uXL6NGjBxo1aoRt27Zh1qxZWLRoERo1aiRSr1GjRli/fj0KCwsxbtw4ODg41PHTSZBQM9atW4fU1FSEhIRUWubZs2do167df8KjW0VFBS9fvvzVw/ip/G0TuKGhIZ4+fVruuq6uLtzc3GBra4vLly9jzpw52LVrFwoLC1FYWFhtu7KystixYweePn2K3Nxc9O7dG2lpaVXWycrKEvlRllC3HDlyBJs2bUJubi4uXryIO3fu4OnTp+ByuWjYsCGGDh0Kc3NzrFmzBmZmZrhy5QrCw8MhFAohEAhQUFAgIlTx7ULu06dPKC4uRnZ2NpKTk3H8+HGMHDmyQvGIqVOnol69eszrMlP3t1hYWGD16tV4/PgxgoODoaSkBKA0PPHRo0eQlZVFo0aNMHPmTAQHB2P16tV4+fKlWBafLl26wMTEBI8fP4aKigru3r2LyZMn48uXLzV9SyVIqBNGjBgBf3//SsVWEhMTqzzr/rfRp0+fXz2En8rf4sRWxuHDhytU0UpJSUGDBg1EYoLfvHkDRUXFKmNrP3/+jK1btzJn4C1btsTZs2ehqKhYLsvYt8TFxeHatWsisYYSfj5EhFevXsHPzw/q6uqQkpJCXl4eLCws0LhxYzRu3JjJaObn5wcAOHv2LO7evYuGDRsiNjYWjx49AlC6mygsLET//v3x+vVrtGnTplyCBCKqdidhY2MDFRUVtG3blknM8Ndff2HDhg24e/cuHBwc0K1bNxARzp8/j8mTJ0NFRQV2dnZYunSpWDsVgUCA1q1b4+HDh+ByudizZw+UlJSwaNEidO/evZbvpgQJtSMnJwc3b97ErFmzyt0jIhw7dgxr166tte/KP4Xi4mJ4eXmJJFH5tyDunPu3TuAmJib/1959h0Vxvf/Df8/u0kWqClhQwIaooCKWqKgYwV6xoWJLrFFjSYzmk/iLMfbeQOwKxoYlWAELKgFFVFRERARpgpSlL+zuPH/wsF8JxUWWLXC/rivXxe7MnLnXwN5zZs65D06fPi3Vvjdu3ICdnR0aN25c6T4dO3bEjz/+iL59+8LCwgJ8Ph++vr5ISEhAq1at0KpVqwqf5YhEIpibm39VOUIieyzLQiAQ4MOHD7h79y4aNGiADh06IC4uDtHR0bC2toaBgQGaNm0KMzMzxMfHIyAgAElJSWjWrBk6duwos0To5eUFTU1NpKWlwdjYGG5ubpIkXVBQAHd3dxw4cACxsbHYtGkTNmzYAC8vL6xbt07qc2zfvh0NGzaEp6dnnb/FR5TTH3/8gdWrV1c4gPjMmTNISkrCokWLkJOTg9evX1fYkxWLxWAYRilvtxcXF2PZsmX4448/vjgATxkpZQI/duwYJk2aVGa50Mrk5OTA19e3yvrZfn5+sLe3L5fk3717hz///BMREREIDQ2t8Ng1a9agW7duGDVqVLU+A5GfFy9eIDAwEGFhYThw4AC0tLQAANOmTUO/fv3Qpk2bL46olTWRSIR169YhPz8f7du3x4kTJ2BmZoYTJ05I3cadO3fw4MEDtGrVCi4uLpLa74TIS3x8PCIjIzF48OAKt3/48AEXL14Eh8OBiYkJxo4dK9kWEhKC58+fQyAQwNTUtMw2ZXHs2DG8efMGf/75p6JD+SpKNY2s1IgRI3D27NkKb938l66uLuLj43Hnzh0EBwdj2rRpaNKkSZnb7MXFxeBwOOjatSvCwsIk75uYmMDa2hrPnj1DZmZmuS/IrKwsdOzYEUuWLKEErsRsbGxgY2OD/Px8SfI+duwYTExMMG3aNKipqVV6bHFxMbhcLrKyssDj8ap14cmyLBwdHeHn54cGDRqU2cblcvHbb7+BZVmkpaXh4cOH1Z6m4ujoCEdHRwQHB2PIkCEIDAyUfD5C5KFFixa4fv16pY+ZmjdvjkWLFiEjIwO///47Pn78iCZNmiA5ORn29vaYM2cOoqKilHKGxcGDB2FmZobp06crOpRaJ9cEXt2exuDBg2FiYoK4uDh4eXkhPT0d8+fPR3JyMlq1aoXMzEzo6uqidevWZY7LysqClZUV2rdvj9mzZ6NPnz7Q0dGBWCyGQCBA06ZN4eLigsTExDLHrVmzpspbodI8UyWyp62tLflZmj9KlmUld3msrKzw9u1bLF26FOPGjUPPnj0BlDwfqyxpRkdHw8XFRTLorCIMw6Bx48bw9PSs7seR6NmzJ4KDg7/6eEJqolevXrh69Wq5Fco+Z2hoiL/++gvBwcHQ0dHBqFGjJM/GTUxM8M8//6BXr17yCvmLYmNjwefzMWfOHEWHIhdyG4UOAI8ePYK5ubnU+9vb26N58+bgcDj43//+h61bt4LP50MgEOD69esQCoX466+/4ODgUOa44uJiREZGIjY2Fu7u7liyZAnmzJmD7777Dj/88APGjh2Lhg0blhuJXlUxjy1bttSo4AyRH4ZhJPM/V61aBWdnZyxfvhxXr17F8uXLweFwoK2tjd27d1d4fNu2bTF06FDcuXOHlhsldZaNjQ2Sk5ORk5NT5X46OjoYOHAgnj17VmZgm56eHtq0aYN9+/YhNDQUQqGwtkOuUl5eHm7evImlS5cqNA55kltGio6Oxps3b8pU96lKUVERdu7cCR8fH0RFRYFhGKipqcHBwQHOzs6YO3cubGxsYGdnh8LCwjLH3rhxA4sWLYKpqSl0dXUl7/+393z16tUyrytav7wUj8fD2bNn8ezZM6niJ4q1dOlSHDlyBDNnzsS1a9dgZmaGdevWYevWrcjIyAAAjBs3rsJjDQwM0KxZMzg4OODly5fyDFth6EKlfpo8ebKkBkJVGIap8Hdk+PDhcHNzg4aGBnbv3o2HDx/WRphflJ2dDXNzcwwYMEDlR89Xh9wSeEBAgFTPvksJBAJYWlpi0qRJld7W7tmzJ8zMzDBw4MBy5xIKhWjfvj22bNlS6ZeTq6urVLGoq6tDU1MTY8eOlaqSEZGfw4cPw9raGjNmzJD8f/7999+xe/duuLu7V3jMtWvXMGXKFNy5c6fC7UuWLIGBgQGcnJzKPWapi549e4ZJkyZhz549ig6FyJm2trZUs3E+fyz1Xw0bNkTnzp2xdOlSiMViHD58WOrZRrJy+vRp/PHHH+Uep9Z1ckvgn/eEZcne3h7du3dHcnIyfHx8cPz4cUyaNAnbtm0Dl8tFmzZtypTP/FxQUBDu379fZfsvX76EtbU15s6dC4Zh6tXVnbI7ffo0Zs2ahVatWmHNmjX43//+h/j4eKxdu7bKWvcpKSno06dPpY9EWrVqBaDkGd+TJ0+wbt068Pn8WvkMlZHnnZ7OnTvDx8en0sUtSN3m4OCAR48eVblPXFwcNDU1v9jWN998g5kzZ5a7K1rbTE1N5T4jRRnIJYG/evUKLVq0qLX209PTcfToUQwfPhxOTk7o378/1q5dC3Nzc2zbtg1FRUUVHhcfH1/lCGI/Pz/Y2NhInqcmJiZ+scobkY+IiAhMmjQJAQEB8PPzg6WlJdatWycZY1HVLWEul4uuXbtiwoQJFW7/vPLbqlWrsGLFimqV6pWFjRs3YseOHXI7H8MwMDY2ltv5iPIYPHgwYmJi8OTJkwq3Z2dnY8mSJXBycpK6zdjYWKlKYcuKiYlJvexcySWB379/XzL6tzqk+QXYv38/rl+/jujoaPTr1w/Lly+X3PbMyclBfHx8ufrWQMlcxtJR7p/LyMjAmjVrYGNjgx9//BFhYWGSOte3bt1CcnJytT8HkR2WZbFt2zZ06tQJa9euxYABAyTb0tLSsGnTJojFYpiZmVXaxuLFi5GUlIT169fjw4cP5dr/7xeBhoaG3JclXLt2LZKSkmo0MOjjx480yp1IZeLEiSgsLISnpyfS0tLKbHv//j0uXbpUrR5u37598fjxY6SmpkpVErumDA0N8enTp1o/j7KRSwJv0KABuFwuYmJiqnXM+/fvK70Vc+XKFTAMg+TkZEyZMgWHDx/GnTt3MGTIEPj7+wMouXCYOnUqZs6cKTmOZVn4+/tj/fr12L59e4Xt/vjjj3jx4gWioqLQpUsXyTZ1dXV89913Un8GIltFRUVo0aIFli1bBgDlZjQYGhpix44dUk31GzFiBH766Sfcv38f3t7eGDhwIAoLCxEcHAxbW9ty+zMMU2bhndrWunVrtG/fXlJStrqmTZsGExMT+Pn5yTgyUlf16tUL7u7uCA4OhpeXF7KzswEAnTp1wqNHj6p1Mdm7d2+IRCI8f/4ce/fura2QJZ49e1YvyxLLZR54YmIijh49CnNzcwQEBFT4PJzD4cDV1VXy5cswDGbOnIn/9//+Hzp06AAOh4Nu3bqhdevWyMnJwYgRI/D06VN07txZ0oauri7c3Nxw6tQpAMCRI0dga2sr6VHdvHkTd+/eha2tLS5dulQuhtKSnoaGhhV+jtKBdUQxvL29cfv2bXTq1AmhoaGwsbEps53D4VRr0BmXy8WkSZMgEomQnJyMsLAwPH/+vFxNdaBk/rmXlxfmzp1b488hraqmNVYmOTkZR48exdChQ3Ho0KEqi90og5s3b6J3796SRWSIYqmrq2PEiBEQCoU4deoUunTpgo4dO+LEiRPV6oBpampK7rpGRUUhPj6+Vh+jpqamlqkXUV/IJYGbmJhg4sSJUFNTK3PL83Olg9DEYjHS0tIQFxeH4uJiNGrUCOnp6eByudi8eTO6dOmCt2/flkvenyt95q2trY2//voLycnJWL9+PSZOnFhlaT2GYcpV3voczQNXrI8fP0JfXx/5+fkybZfL5WLZsmWIjo7GrFmzKtyHx+NVuUCOsggJCYGJiQnev3+v9MkbKJmyl5iYiDZt2ig6FPIZHo+HqVOn4tSpU3j06BGMjY0REhKCHj164K+//sLKlSulfua8YMECbN68GStWrPjqeAYOHIiAgIAKtxUWFuLdu3df3bZKY6XA5/NZACyfz5dm93I+ffrEHjlyROr9T548yRYVFbEsy7JisZhNTExkWZZlk5OT2QsXLnzx+DVr1pR5vXfvXnbLli1SnbuyON+8ecOePn1aqjaI7EVHR7NHjx5VaAzbt29n09PTFRoDIfJ2+PBhViwWswcOHGBZlmWdnZ3Z2NhYqY8vLCxkvby8ahQDAPbKlSsVbouOjmZDQkJq1L6ykTbnyqUHbmRkBDMzM3z48AHNmzf/4v4cDkfSe2AYRjIgycTEBOnp6UhJSalyzdrS23EvX76Ev78/XFxcpL7C//DhAwQCQbneVlhYmFIW7a/LWJaFUChESEgIXr16pfDyiPPmzcOuXbtw5coVBAQEqEQPl5CaUldXB8MwEAgEyMnJwenTpyudUvbmzRuEhoZKVikrXWWwosdS1SEUCivt8UdHR6N9+/Y1al9Vye2esJOTE27cuFHjdmbNmoU7d+7g4sWLFW4Xi8WSBB4VFYW2bdtW6/acgYFBhYOgqnNL8s2bN1i1apXU5yT/JzU1FQUFBTh48CBWrlyJU6dOoUmTJvjuu+8UXodeQ0MDK1aswP79++Ho6IjHjx/LrG2RSAR7e3tJlThClE3pokJ6enqVPk4KDAyEm5sbpkyZgsmTJ2PGjBkoKCio0eOn3NzcSqfvsiyLqKgotGzZ8qvbV2VyS+AcDkcmQ/0ZhsHEiRORmJhY4TSzx48fS0YjDhgwAFu2bJG67ffv30NbW7tcxSE+nw9ra+sqjxWJRPjpp58gEAjQsmVLrF69WurzkpI/xEOHDmH9+vXYv38/xo0bh82bN8Pd3V3pqit16NAB27Ztw5kzZ2TWJpfLxU8//YQff/wRQUFBEIlE6Nixo8zaJ+RrlY4+d3BwQHh4eJX76evrl3vfzs4O0dHRX31+XV3dSqeFHjx4sF4XIJLrqKxRo0bhwoULVe6TlZVVbh5iRUaPHo2DBw8iMDBQ8l5xcTHu3LmDbt26AQD09fXh6OgodXxTp06Fm5tbuffPnz//xTmQXC4XLi4u4PF4UFdXr3IwHPk/qampuHz5Mry8vNCvXz/s2LEDP/74o9Kvke3g4IC8vDwUFBTIrM1x48ahR48eGD9+PM6fP4+nT5/KrG1CvhaPx4NYLEb//v0RFRUFAEhKSsKbN28AlCTuxMRE+Pv7o3v37uWONzY2hrGx8VfX22/dujU2bdpU4TZNTc0qH6fWdXJdTpTD4cDe3h6BgYGVjkY/dOiQVMvTmZmZoUuXLjh37hw+fPgANTU1vH79GitXrizTg27UqBEuXLiAMWPGfLHNYcOGVVjvl8fjSZVQqnOxQEp4eHhg8+bNkjmnqmTatGnQ1tYGwzAyqzo1d+5c9O3bF6NHj0a7du3QqVMnmbRLyNficDiSGTjFxcVISEiAt7c3OnfujJCQENy+fRvTpk1DaGiopAzx5ywtLeHv7w+BQIDc3FwsXry4WrfUSy8UShUWFkIsFuPQoUMYPHhwzT6cipNrAgdK/meOHj260gRuZmb2xaptLMviypUr0NfXx8aNGyXvnzhxolzPt1+/flLXsTY0NCy35jdbQWUuIjstW7ZUyeQNAN27d0dWVha2bt0q03atra0lPR1CFEkoFJaZotWlSxds2LABW7dulSThZs2awdHREY6OjvDw8ICpqSkaNmwoOcbc3FwyPfPFixd4+fJlmQJZ0sbh4OAAd3d3BAQEYMCAARgwYEC9n34o94nNWlpamD17do3auHDhAlq3bo2+ffuWeb9t27blFoFYs2aNVEX4AUBNTQ25ubll3nv27FmtFiCo7+S96IEsMQwDPT092NjY4LfffoNIJFJ0SITIFMuy4PH+r5/XqVMn2NnZlelB9+/fX/LzrFmzcPLkSVy/fr3CAZmtWrXC1atXKx2E/F8CgQA7d+7Exo0bceTIESxatAgXL17EDz/8UK6QU30k9wTO5XIrXVwE+PK6xI8fP0Z+fn6F0wa6d++OiIgIyevCwkJ88803sLCwkCo2CwsLfPz4scx7ly5dknoNc1I9QqFQ6osrZebq6goTExP89ddf4PP5lMhJncHj8SRTf5OTk7F79+5yHaf/7j9//nx06dIF58+fx40bN8p8p+vo6GDBggVSDWZ++fIlPD09MXbsWKxevZoeJ1VA7gmcw+FUOh3oxYsXyMvLQ1hYmOS9gIAAxMXFAShZFefVq1eYOnVqpe1zuVzExsZi27Zt8PX1hbu7u9RLmfbp0we//vorXr9+jfj4eKxfvx7t2rVT+PSluorH4yE3N/erB7cok3nz5sHU1BSDBg2San1lQlQBwzCSC9Lw8HAMHTpUqlkhjRs3xpw5c9C6dWvs37+/zJRLAwODKqtaikQidO/eHZGRkVi0aBGaNWtW8w9SR8n9GfiX9OzZE48ePULXrl2RnZ2NrKwsRERESMroVTZlQCgUwsPDA6mpqThz5gwuXLhQ7cTLMAy8vb0RFBQEgUCAVatWUfKuRR8/fsT8+fNhaGhY6dKeqmTWrFlYsmQJPXIhdca7d+8kU7h4PF61Z9dYWFjA2dkZz58/l8wOAiAp9lJRvX+xWAyWZalwlhQUksANDAwQERFR6TzXvLw8xMbGwtfXFwsWLJBqxKKvry8mTpyIc+fO4fXr11+deBmGqfIWEZGd69evA4BU1flUxeLFi+mij9QZubm5kgFpQqGwwqWZv+TatWtlKrHFx8djyJAhyMrKqnD/jIyMMgtbkcopZHUOR0dHvHjxotLtnTp1wsuXL7F06VKppxt07NgRPj4+iIqKKnOlR5TX9OnTcfz48Tr1bMvDw6NOPBIgBACePn0KOzs7ACVzv6tbiOvOnTvYunUr5s6dC09PT4wePRrnz5+Hjo5OmcFxn2vSpEm5sUikYgpJ4Lq6usjLy0NRUVGF02X69u2LYcOGVesKrF27dli4cCHWr19fbp1oorycnJwQHBys6DBk5tq1awgJCVF0GITIREFBAXg8HmJiYtCsWbNqT/9atWoVoqOj0b9/f9y9exeLFi2SjHuZPHlypccpW/VFZaWw9TEnTZqEzZs348CBAzh9+jQKCwurNaCJZVlcvHix3PrPN27cQO/evWsjZFILTExMalRmUdl07doVr169UnQYhNSYSCSS3AG9du0anJ2dq92Gjo4OuFwuJk2ahBMnTuD69euYO3cunJycKj1m/fr1dWJ2ijwoLIHr6Ohg3rx56NWrF9q2bYt//vkH2dnZUtV/ZlkW+/btg52dHS5cuCAp1PLPP//A2NgYTZs2re3wiYwwDIOePXsiNDRU0aHIRHJyssoWpiHkc8+fP4eNjQ0SEhKknor7OS8vrzLVKc+ePYulS5eiUaNGlR7z5MkTdO3aFdOnT/+akOsdhSVwoKTyWUFBAezs7DBu3Dh8++23Uh139+5d2Nvbw9DQELm5udDV1cXHjx8hEomo962C7Ozs8PbtW0WHIRPR0dH1vrwjqRvatWuH6OhoZGZmVpl0K3PmzBmsWbMGQMmAVV1dXZiamla4b2xsLFiWRVhYGP39VINCEzhQUkqzul/e/fr1w+PHj7F9+3Z8//334HA4uHLlCoYPH15LUZLaVrrikaqLj49Hu3btFB0GITXm7e2NQYMG4dWrV1LV0vD29i7zXV461/vjx4+4c+cOUlNTK12gp/R73N7eXiax1xcKT+B9+vSBv79/tY5hGAbz5s3DmjVrYGhoiHv37sHOzq7K4gBEuTEMU2WFvq8hqwVGqoNhGJr+QuoELS0tGBsbIyYmRqqLUg0NDcmzaz6fj/z8fADA69evMW3aNLi7u+POnTvYtGmTZBtQkuCjo6Ph6uoKW1vbWvksdZXCC7kwDIMBAwbg7t276NevX7WOYxgGgYGB4HA4kjXAiWoaPnw4PD09sXDhwq9u4+3btwgKCpIMvCkuLoZAIIC5uTkGDBgAHo9Xq8k1Ly+v1tomRN40NTWRnp4ObW1tqfb/vPCKtbW1ZF2KV69eYe7cuQCAJUuWgM/nw9PTE4MHD0b79u0ly4HSAj7Vp/AEDpQU8viaQUxPnjyBUCiU+tk5UV7a2tplrsqlkZycjH/++Qfa2tpgWRatWrWCu7t7mSQtEAgQExODX3/9FZ8+fYKXl1eZNvz9/ascESstlmXh4uKCw4cP17gtQpRBq1at8OHDh2ot/QmUXDh36dIFxsbGAAAjIyMwDIPExET07t0bx44dw5IlS+Dl5YU///wTPXv2RFBQEK36+BWU4p7z0qVLq92Dvnz5MtLT0yl51xG5ublSzUAodfz4cTx69AizZ8/GlClT4Obmht69e5frYWtoaMDa2hobNmyAv78/fHx8JNuSkpLw008/yST+3r17o3PnzrCyspJJe4QoWmRkJFq0aAEdHZ1q1ffn8Xhlpvf6+voCANq0aYO4uDhJpcugoCA8efIEO3fupOT9lZSiB75t2zacO3eu3ApjxcXFAEp6UTo6Onj//j3u3buHoqIiDBgwAJaWlooIl9SCtWvXYtWqVeXez8zMRFxcHN69ewdTU1MIBAKkp6ejbdu2cHBwqNY53r9/jz179qCgoAC5ubn47bffMG3atBrHLhQKERwcjIcPH9a4LUKURUFBAQwNDTF48GAEBwdLvagIwzD4999/AQBZWVm4c+cOgPKPmLS0tLB69WoauFYDSpHAtbW14ePjg7y8PMycORPHjh0Dl8tFXl4e9PX1oa2tDT6fDx0dHYwePbrMYvGkbtDT00NBQUGZ906ePInw8HC4u7vDxcUFL1++hJ6eHkxMTL56pHefPn2wYsUKmJqaYsGCBdi1axeio6NrVPnJx8cH+/fv/+rjv4a3tzfs7OwqXFaXEFkoHRQcGxtb7Wlk6urqAEouAlJSUnD27FkMHDgQhoaGAICioiK0bdsWU6ZMkW3Q9QzDSlH6LDs7G3p6euDz+bWaPB8+fIjXr1/D1dW12qveENV37NgxBAcHw9bWFpcuXcKGDRvQqVMnmQ48y8zMhKGhIe7evQs/Pz8IBAIYGBjgt99+++o27ezsEB4eLrMYpXH58mWMHDkS27dvx5IlS+R6blL3ffz4EWFhYRgyZAg8PDzw/ffff3Vb586dg1gsRlRUFFiWhYGBAXR1dTF27Fipl3qub6TNuUqVwAkBSh6ZsCxbq+UUP336hB07dqCoqAjh4eHo378/fvnll2q38/z5c0RFRWH8+PG1EGXlxGIxHB0dERQUhAcPHqBXr15yPT+p2zZs2IDly5eDx+Nh165d+OGHH2rU3smTJzF8+HDo6enJKMK6TdqcqxSD2Aj53OfzSWtLYmIiLC0twePxcPPmTaxevRopKSnVbufmzZsKWbeYw+Hg1KlTAEpqR9MKaERWiouLYWpqCh6Ph/j4eBgYGNS4TW1tbclzcSI7lMBJvdS5c2fMmDED69evB8MweP/+PSIiIqrdjra2tsIKCDVv3hwjR46En58fFTEiMrNkyRLJSHEfHx9MnDixxm2OHj26Ti1apCzor54QlIyIzcjIqNYxLMvi+vXrtRSRdE6dOgVjY2OMHDkSMTExAEqm/1T3sxAClEzn5HK5aNWqFQDA2NgYampqNW43MTFR6lHsRHqUwAkB0LhxY4SFheHkyZNlpruwLIvvv/9eUlWKZVkwDINdu3bh5MmT2LFjh4IiLqGjo4MGDRrA29sb58+fx8qVKxEWFoaNGzcqNC6imng8XpnxFLJI3izLwtPTEyNHjqxxW6QspZhGRogy2LRpEwoKCrBv3z4YGBigYcOG4PP5+PTpE9zd3TF79mzExsYiOTlZUv5R0ViWxbp166CtrY2VK1cCKFm7eerUqQqOjKgiTU3NMhewaWlpUh9bXFwsmfrLsixiYmIQGhqKwsJCzJs3j9YIqAWUwAn5jJaWFpYtWwYASE9PBwDMmjULQqEQ3bt3R1BQEHR0dBQZokRBQQEOHDiASZMmSd47deoU/P39ceTIEQVGRlRZaa/748ePaN68udTHffr0Ce3bt8e+ffsAABYWFnB1dQWPR2mmttAtdEIqYWRkJCnQwuPx8OTJE+jo6ODatWuIjY1VcHQlo/UbNGhQpgJcRkYGJW/y1UQikaT06blz5zBw4ECpjy0dizF58mRMnjwZPXr0oORdyyiBE1KFimr0m5iY4MyZMwqI5v88ePAA+fn5mDNnDng8HuLi4hAaGkrzwUmNREZGSgoD8fl8GBkZVevYBQsW1FJkpCKUwAmpgouLC9avX4/ly5cjNDQUhw4dQmBgILp06aKwmJ48eYJ58+bh+PHj0NbWRkJCAgwNDREWFkbL6pIaCQ4OlvxuSzNqfOjQofDy8kJycjIeP35c7RUFSc1QAifkC8zMzLBx40YUFxejYcOGWLZsGa5du6aQWFiWRWhoKEJDQ8HlclFQUIAzZ87g4MGDcHV1VUhMpO4wMjLCwYMHsXDhwnJrE1SkV69emDp1Kl68eAEXFxc4OjrWfpBEgh5QEPIFgwYNwoYNG7B69WoAJYN1rl+/jm3btsk9lps3b6J3797Q1NTEnDlzIBKJMG/ePLAsS8VcSI0dPXoUCQkJCA8Px++///7F/fX19XHp0iW0adMGpqamtR8gKYP+4gn5gqZNm2LPnj0ASkbmrl27Ft7e3nKPg2VZxMbGStZN9/HxwZgxY8AwDCVvIhNdu3bF8OHDAQBt27b94v6NGzfGwoULJVMYiXzRXz0hUti1axe2bduGCRMmYM+ePWjZsqVcz19QUAAOhyNJ3oGBgTAzM1Oa+ehE9RUVFSEsLAyJiYn48ccfpZppkZ+fjyVLluD48eNyiJD8FyVwQqQwbtw4hIeHo02bNmBZFvr6+nI7d1BQEFq1aoXo6Gj07t0bEyZMwJQpU2gBEyJTampqmDFjBvr06QNdXd0vLulcVFQElmXxyy+/0IWkgtAzcEKkwDAMTpw4Iffzenh4QENDA0+fPoWJiQn27t2Lxo0bIyAgoFpFNgj5EoZhMHr0aOjq6mLNmjXIzc1FcnJypc+2eTwexGKxnKMkn6MeOCFKauzYsTA0NIS7u7ukhxMSEoLdu3fD2toaurq6Co6Q1EW5ubk4c+YMPnz4gKtXr1a4T2FhIQ4ePChZtYwoBvXACVFCW7ZswYULF3D+/HnJe2KxGA4ODgqMitQXnp6e+PTpEzQ0NMptCwsLw6lTpzBs2DBYWVkpIDpSinrghCihFStWlCuJumvXLqq0RmoNwzCSBUesra3x6tUr9OnTp8w+YrEYT548wbZt2zBgwABFhEk+QwmcECW0ceNG5OTkYPr06UhNTYWfnx969uwJOzs7RYdG6qAnT55IfuZyufj333+xYsUKhISElNmvtJgRUQ6UwAlRQitXrsSwYcPg5+eHmzdvokmTJnT7nNSa0hK8VlZW+PTpE1xcXPDLL7+UGyipoaEBDoeDwMBAZGZmKiJU8hlK4IQoqYCAABw+fBhubm7o1q2bosMhdRjLsvj333/x9u1b6Orq4smTJ2jRokWFMx3Gjx8PCwsLnDp1SgGRks9RAidESc2ePRsjRoxQdBikHggPD8fYsWMBAJqamggKCsLMmTMr3b958+YIDAyUV3ikEpTAlRAV6CCEyFOXLl2QmJgIALCxscH58+chEAgq3LegoAD79+/Hhg0b5BkiqQAlcCWTmZkJDoeDv//+GykpKYoOhxBSD5QmbwCYNWsWbt26VeEUMrFYDE9PT8yaNQtt2rSRZ4ikApTAlUzpohQTJ07E5s2bFRwNIaQ+aNq0qeTnt2/f4tdff62wB+7r64smTZpAS0tLnuGRSlACVzIMw2DOnDkYPnw4VqxYoehwCCH1wKVLlyQ/x8fHIz8/H+/evSuzz6hRo5CWlkbzv5UIVWJTMg0bNoSnp6eiwyCE1CMnT56U/Ozr6wsAcHZ2RocOHQCUjMvp27cvbGxs0LhxY4XESMqjBE4IIfXcmTNnAJTcAZw8eTI0NTXLFA16+fIlLCws8M033ygqRFIBuoVOCCFEwtvbGx07dgSP93/9O7FYDKFQKHUba9euRc+ePTFgwABwuVxER0fXRqj1HiVwQgghAID169cDAJYuXYq7d+8iISEBCQkJ+Pvvv+Hv7y9VGwKBAL///jtycnJw+/ZtiMVinD59ujbDrrcogRNCCAEAzJkzR/KzpaUlYmNj8e7dOxQXFyMqKgpv3rz5YhulS5CWPitPSEjAr7/+WjsB13P0DJwQQghu3bqFRo0aSV6vXr0aLVq0QFRUFIyNjWFtbQ1DQ8MvtjN69GhJMSqWZSUrnBHZox44IYQQ2NjYlBm4lpWVhcTERAiFQmzduhUtW7bE1q1bq9UmJe/aRQmcEEIITE1NUVRUJHl99+5dODk5wcrKCj///DOaNm0KExMT5ObmKjBK8jlK4IQQQgAAampqGD9+vOT1/Pnz4eXlhcWLFwMAJk2ahP3796OwsFBRIVbJ3t4eDMPg77//VnQocsGwUqyckZ2dDT09PfD5fFrMnRBC6oH/3v6ePn06jh49ioKCAhw/fhz6+vro2rUrrKysFBRheUVFRfDw8MAPP/yAqKgola3XLm3OpR44IYSQcpKSkjBy5EjJ63/++Qd9+/bF8uXLoaOjg1evXiEuLk6BEZanrq6ORYsWYceOHUhOTlZ0OLWOeuCEEEIqFRMTI+llm5ubY8KECVi/fj24XK6CI6u7qAdOCCGkxiwtLZGXl4fc3FzMnj0bGzdupOStJKgHTgghhCgR6oETQgghdRglcEIIIUQFUQInhBBCVBAlcEIIIUQFUQInhBBCVBAlcEIIIUQFUQInhBBCVBAlcEIIIUQFUQInhBBCVBAlcEIIIUQFUQInhBBCVBAlcEIIIUQFUQInhBBCVBAlcEIIIUQFUQInhBBSK27evAljY2NcvHhR0aHUSTxFB0AIIaRuSk1NxbFjxzB06FBFh1InUQInhBBSK9zc3BQdQp1Gt9AJIYTUqry8PIjFYty4cQOvX79WdDh1BvXACSGE1Irk5GR4eXnh8ePHsLCwgJ6eHsLDw/Hzzz8rOrQ6gRI4IYQQmRKLxTh27BgKCgogFovRtm1bODs7Y8CAAYoOrU6hW+iEEEJqRCQSSX6+f/8+fv75ZxgaGsLIyAgPHjzA7t270a1bNwVGWDdRAieEEPLVsrKyYGxsjICAALx9+xZHjhzBqlWr4O3tje+//x5NmzaFWCyGpqamokOtcxiWZdkv7ZSdnQ09PT3w+Xw0bNhQHnERQghRAVu3bkVCQgIiIyORnZ2NU6dO4fz58+jTpw9atGgBU1NTRYeocqTNudQDJ1XKycnB/v37cfbs2Ur3YVkWcXFxcoyKEKIsJk2aBJFIBG1tbZw9exZXrlzB8+fPERYWRsm7llECJ1Xy9PTE9OnT0bVrV1y5ckXyfk5ODk6ePInCwkIUFxfDzc0N4eHhCoyUEKIIfn5++Pnnn3Hq1Cls3rwZU6ZMwbFjx9CxY0eIxWJFh1enUQKXASmeQqgsU1NTaGtrw8LCAvHx8fD09MShQ4ewefNmODk5Ye/evQCAkSNHIiwsTOp2WZbF69evUVBQUFuhE0JqWemFfEpKCrS0tODu7o4zZ86AYRj06dMHHA6lmNpEz8C/0rVr15CRkQGRSAQ1NTWwLAuRSAQ3NzcwDKPo8GTG29sbkydPlrxmWRb5+fnQ0dEBAHz69AmBgYGwtrZGSEgIBg8ejGbNmlXaXlxcHO7duwcAaNOmDR49eoQJEyagUaNGMo07JycHDRo0AMMwEIvFiI2NRaNGjRAQEIB+/frB0NBQpucjpL5JT0/H+vXr4eDgAFdXV8n7UVFRCAsLK/O9QapH2pxLCbyanj17hvDwcNjb26NDhw5ltiUlJeHq1asYM2aM0iUIlmVx/fp15OXloU+fPmjSpMkXjzl37hy0tbUxZMgQqc9x8+ZNfPz4EWpqaujXrx/MzMzK7HPkyBHMmDFD8losFmPfvn1YuHBh9T4QAKFQiAsXLoBhGIwfP17yflJSEi5cuAADAwPJ9BZzc3O8ffsWjo6OePHiBTIyMqCuro7WrVuje/fu1T43IfXdzz//jIkTJ8LW1rbcNh8fH0yaNEn+QdUR0uZcKuRSTc+fP4e7u3uF28zMzDBixAj8+eef2LJli9L0xM+ePYtnz55h3rx5MDQ0RFBQEOLi4jBgwABYWloiLy8P586dg1gshlAohLa2NnJycqCtrY1x48ZJfR6GYTB48GAAQFFREe7du4d//vkHtra2sLe3x/Lly8slSw6HA3t7e3h4eEAkEsHBwQFdu3Yt1/a9e/fw4sULjBw5EmKxGBcvXsTdu3exf/9+8Pl8eHp64rvvvgMA+Pv7Y8GCBeX+/fv16wcAsLS0RGZmJvT09PDkyROcPHkSAGBtbY22bdtK7i78V1RUFNq2bSv1vwchdc21a9fQt29fPHz4EAUFBRUmb6Dk759lWaX5DqyrqAcuBZFIhDNnzoDD4YDP56NJkyYYOXJkhfsmJCTAy8sLCxcuhLGxsZwjLa+wsBDLly/Hzp07weVyy2y7cuUK+Hw+1NTUMGrUKGhoaEi2icVimT2/OnPmDLKysuDk5AQLC4sq9/Xx8YGamhq4XC6EQiEYhkF2djZatGiBgQMH4tixYzA1NcW3336LjIwMGBkZAQACAwPB4XBQVFSE7Ozsal14lAoPD8fbt2+RnZ2NRo0aobCwEDo6OnB0dERgYCD8/Pzwww8/wNra+qv+HQhRZS9fvsSWLVswaNAgfPz4EW5ubpU++rpy5Qo6dOjwxb93UjG6hS4Dr169QpMmTSAWi3H69Gn0798f27dvh7W1NZYtW1bpcf7+/khLS0NsbCxGjRqlkC/8Bw8e4P3798jNzYWzszPMzc3lHsPXSkhIgKGhIXg8nuSOgDQiIyPB5XLRpk2bGsfw9u1bGBgYIC4uDj4+Phg0aBDevHlTYc+ekPrg0KFDyM3Nxblz55CRkYGXL19Wuu+JEycwZcoUGsT2legWugzcunULampq6NChA3Jzc6GlpYVDhw598TgnJycAQEpKCrZv347WrVsDADQ1NWFvb4+srCx8+PABgwcPhq6urszjvnv3LsRiMSZPnqySyebzQXDq6upSH9e+fXuZxWBlZQUAMDIyQpcuXbBgwQJMnz5dJf89Camp1NRU6Onp4fbt2+jSpQsmTJhQ6b4FBQVITk6m5C0HlMD/Iy8vD97e3sjPz0f//v3RqVMnPH36FIsWLUKDBg2q1ZaJiQk2btwoec3n8/HixQvo6urC2dkZt27dQn5+PhiGAcuy4HK56NatG0JDQ2FjY4NOnTpVO36BQIDnz59j0aJF1T6WVG7v3r04cuQI2rdvXysXXYQoMwMDAzx9+hTbtm3DgwcPEBQUhF69elW4b35+Pj1mkhO6hf7/i4yMxOPHj8Hn8zF06FC0atVK7jEUFRUhPDwcLVu2xO3btzFx4kSpj01LS8OVK1egra0NJycnpXj+XtcIBAJs2LABgwcPRo8ePRQdDiEKceDAAQwfPhxNmzatdJ8TJ05g6tSpcoyqbqFb6NXw/PlzREREKPwXTl1dHQ4ODgBK7gRIKy0tDRcuXMDs2bPLDVQjsqOhoYE1a9Zg3759ePr0KTQ0NGBsbIyMjAyMGzcOvr6+EAgEyM/Ph729PSV5UieJxWKcPXsWS5YsqXQf+h6SjzqZwJOSknDv3j3Y2tqiXbt2le4nEAjg4eEBBwcHTJkyRY4RfpmFhQU8PDygqakJNTU1tGjRAt988025/QQCAY4ePYrJkyfTH40ccLlcLFq0CAKBADExMTA2NkZRURF27doFJycn2NvbQywWw9/fHxEREejYsaOiQyZEpho0aFDlHG+RSFRmeVFSe+pcAs/JycHly5cxZ84cXL58GQKBAJ07dy63n5+fH/Ly8uDu7q6UjwUcHR1hZWUFHR0dGBoaIiQkBBcvXsSoUaPK7KempgYDA4Mqb2cR2dPQ0CjznG/VqlWSnzkcDr799lts3boVWVlZaNOmjVSFcwhRJpcuXUJ2djZSU1PLzLp59epVlTXOCwoKoKamJo8Q6706kcCfPHmCu3fvomHDhhCLxXBxcQGXy8Xo0aOxZ88ePHr0CK6urmjYsCFEIhG4XC74fL5Sl/pjGAbNmzeXvHZwcICvry9SUlJgYmICoGS61Y0bN9C3b19FhUmqsGTJErx48QJ79uyBhoYGnJyc6LY6UQnx8fHQ1dXFyJEj4e3tjeLiYqipqUEoFMLKyqrK2Rg3b96Eo6Oj/IKtx+SewD9+/IigoCDY29vLZG7y9evXoa+vj4kTJ1a4dN3ChQvx+vVrzJkzR3JLffTo0So5HcjW1hanT5/G4sWL8fTpU9y/fx9z586lq10lxeVy0blzZ3Tu3BlCoRCPHz/G7t270atXrwqrzRGiDAoLC3Hjxg2kp6djwIAB6NOnDw4ePAgjIyO8ffsWXbp0qXJ6Z2pqqqSTQWqXXCfqHT16FPv27YOpqSlatmyJkydPwsfHBzt27EBxcTGEQiF8fX3x+vVrqVb4unPnDkJCQtCjR48q151t164dZs+ejQMHDkBdXR3R0dEoLCyU5UeTi/PnzyMjIwM7d+6EUCjEokWLKHmrCB6Phx49emDhwoVISkrCixcvFB0SIRIpKSm4efMmgJJn2Pn5+ZLv4ObNm8PY2BgtW7aEs7MzXFxcqmxLR0cHmZmZtR5zXRQVFYUTJ07g0aNHUu1frR74gQMHYG5ujvHjx6OwsBBaWlpgGAaPHj0Ch8OBWCxGQkICAgMDsXDhQgQGBsLAwACZmZno27cvWJbF2rVrAQAvXrxAu3btwDAMcnJycPToUXz69AlWVlbIzc2Fl5cX5syZI7nlLRAIEBwcDEtLS1y5cgUMw0BbWxurV6+WKvZBgwYhJiYGfn5+mDp1Km7fvl2dj64UFi5ciOLiYpqHrMIYhsHw4cOxc+dO2NjYKDocQgAAx44dQ5s2bfDu3Tvo6+tDTU2tzLgOCwsLvHv3TqqxNllZWdWumUFKnDp1CmvXrkVOTo5U+1drHvjDhw9hamqKW7duQUdHB48ePULHjh0RERGB77//XtKr6N27N968eYMePXogNzcXABAaGgp7e3up5yf7+fnh3bt3KCoqgomJCW7dugUjIyMEBgbi8OHDsLW1/erb4Onp6ZIa2oQogq+vL65duwY3NzeYm5urVKlbUvdcvHgRb9++RU5ODrhcLpYtW1ZmUR+RSIQHDx6gS5cuX0zOYWFhUFNT+6pCVPVV6SqXDMNg6tSpyM3Nrf1a6AKBoMwCGLUlLi4OmZmZsLW1lQymIETV5eTk4PDhw+DxeOjWrRtSUlLQtWtXGBoaSu5uESIvLMtiz549cHd3r/FdvqNHj1a6amN9lJWVheDgYNjZ2eHy5cvIz8/Hhw8f0KVLFwAlle4+X7ZZLoVc5JG8AZTpoVDyJnWFrq4uFi9ejNzcXOzcuRPTpk3D48ePER8fD29vb2zevBndunWDlpaWokMldVxiYiI2b94MR0dH5Ofn1ziB83h1YoKTTDx79gwRERGwsLDAtWvXMGfOHJldnFMpVUKUUF5eHlJTU3H//n2FVwgkdZuHhwdMTEygpaWFmJgY5ObmYtmyZTVajIR64EBGRga2bt0KGxubKgvfVETanEvLxRCihHR0dNCqVSuoq6vjxo0bAEoKZBAia82aNUNRURE6dOiAefPmoWnTpsjKyqpRm/K6O6usPnz4gJMnT+J///tftZN3dVACJ0SJTZgwAdra2vDz88PYsWOlml5JSHUMHToU48aNQ35+PrZu3QoLCwsYGhoqOiyVdubMGcyZM6fWL2ToQQUhSq5Pnz6IiIhAt27dJHUPSN0THx8PkUikkJUQb9++jeTkZCxYsACampo1bq+oqAhisbhergkuFAoBQC5jVyiBE6ICOnbsCHV1dURERCg6FCIjgYGBSEpKQkFBATgcDrhcLpo3b47AwEBoaGigqKgIM2bMkMtsBIZh0L59e5kNEtbQ0Kh3syhEIhFCQ0OxfPlyXLx4US7npAROiAqIiYnB/fv3MXPmTEWHQmooLy8PSUlJyMzMhJubW6X7JSUl4dKlS+UWMKoN/fv3h5+fH/z8/KCmpob58+fXaMCyhoYG+Hw+9PX1ZRekkhKLxTh8+DC0tLRga2sLT09PNGrUSC7nrn/3NwhRQQYGBhCJRPWuV1MXvX79Gj4+PhgzZkyV+5mZmUFNTQ2BgYEVbk9PT/+q89+7dw+enp4IDg7Gx48fJe8PHToUv/76KwYNGlTpOaU1bNgw+Pr61qgNVRAbG4vDhw9j+PDhmDJlCjp06IAOHTrI7fzUAydEBWhra1e5gARRHV27dkVYWJhUF2NDhw5FREQETp8+jcLCQrAsCx6Ph9TUVKSkpKBTp06wtLREz549pb64MzU1RUxMDAwNDeHt7Q1DQ0NMnz5dst3c3BwhISFf/fmAknoddXEuOMuyuHjxIlJSUgAA+vr6mDVrlsIurOvevzAhddCFCxfKVGoiquvGjRvVGojYsWNHdOzYscx7T58+ha2tLViWxYMHD+Dp6Ynu3bvD2tr6iyOf09LSYGZmhrZt26Jt27a4e/cuPDw8oKenBxcXFxgbG6Np06bIy8srU061OuRVpVPebty4AVtbWzRv3lwpLlAUHwEhpEoikQiZmZlo3LixokMhMpCeno5vv/22Rm3Y2toCKBl89s0338De3h6xsbE4dOgQRo8eXeHqjAEBAYiJicHbt2/RpEkTvHv3Dq1atYKzszP69esHgUCA9evXo0WLFtDR0UFKSgosLS2/Kj6WZetc1czS4krOzs6KDkWCEjghSoxlWVy5cgV9+vRRdCh1RnFxMby9vcHj8TB+/Hi5P5ro168fdu/ejUaNGsHV1RVcLrfGbWpoaKBdu3Zo1qwZjh8/jvnz55fZnpqaitDQUNjZ2cHMzAwCgQA3btxAs2bNyrTx008/IScnB/Hx8WjRosVXx1O6KEpdcuHCBbi6uio6jDJoEBshSig7OxuzZ8/G5cuXcejQITRv3lzRIdUZV65cgYuLC0aNGgVvb2+5n79p06aYO3cu+vbti4CAAJm23aBBA7Rs2RKnT5/G1atXkZeXBwDIzc1F//794ezsjGHDhkFTUxMbN27E8OHDyxyvra2NJk2awN7evkY96HPnzmHYsGE1+izKhmEYmcyRlyXqgROiZNLT0zFq1Cj8/PPPaNeuHU6fPv3VzyJJeYMHD8bFixcxZcoUdOzYEfv374ebmxvu3LmD7OxsAECLFi3QunVrCIVCmJmZybwgye+//4727dvDxMREpu0CwJAhQ5CXl4ecnBxs2rQJbm5uePfuXZllQIcOHSrz85YqreNdl4q4lD5uUDa0mAkhSkQsFsPFxQUbN26UPOcksnXnzh00b95c8nxXKBTi9OnTsLKyQo8ePSASiZCYmIinT59CXV0dsbGxmDx5MrKysvD3339j/vz5CAkJQWxsLGbPnv1VMezZsweWlpbo379/rffq/P390aJFC7Rp06ZWz1Pq/v37MDMzg4WFhVzOJ2tv3rxBUFAQWJaVFNLZvXs3FixYILeBa9LmXErghCiRUaNGYdiwYejRowdsbGwUHU6dw7IsDh06VK3Ey+fzce/ePXA4HAwaNAinTp2CpaUlLC0tcfLkSYwaNQosy8LU1BR6enpSt5uamoqbN29WWcxFFb18+RKFhYXo2rWrokOptnPnzqFx48bo06cPrl27hrCwMDRo0AATJkyAmZmZ3OKgBE6ICjpw4AC+//57KthSQ9nZ2Xj+/Dni4+PRqFEjNGrUCDweD/fv34eTkxOsrKxkcp68vDxERkaiqKgI8fHxyMjIKDeArDLXr1+HmpoaBg4cKJNYlEXpHQ1VvDA5evQopk+fLvn7KywsxKNHj+Q+iFTanEvPwAlRErGxsYiLi8Pjx49hb2+v6HBUilgsxvXr15GXl4fi4mIwDANbW1tYWVmhsLAQ+fn5KCwsxHfffSfTZ7M6Ojro1q0bAKBXr17w8fGRahGPzMxMPH36FD/99JPMYlEWHh4eSjdaW1odOnTA06dPYWdnBwDQ1NRU6hkglMAJUQKfPn3CunXrsG7dujo3f7a2Xb16FfHx8RgyZAg0NDTQpEkThcXSrVs3XL9+vUzRnYsXL8Le3h5isVgym8DAwABubm7Yu3cvFi5cqKhwZS4tLQ2tW7eWWy1wWYuMjMSIESMUHYbU6s4wQUJUlEAgwMOHD7Ft2zaYmprC2NhY0SGpDJZlkZmZiblz56JFixYKTd4A0Lp1a7x79w5AybNzDw8PNG/eHC9fvsTVq1fL7GtsbAwjIyNFhFlreDwe3rx5A4FAoOhQvsqkSZNw/vx5RYchNeqBE6JgTk5OsLOzU6krf0WKiIjA8+fPJa/79u2rwGjKMzc3h7+/P/bu3YvDhw/DwMAAQMmKckeOHJGUGM3JycGECRMUGarMGRgYYObMmTh48CDGjBkj14FfsiAQCKCtra3oMKRGg9gIUbBt27bB0NAQampqsLKyQvfu3ev1ILa4uDiwLIuIiAjk5OSgffv2sLOzQ3FxMbhcLo4eParUy6qyLIugoCBERkZizpw5dWo+tLRYlsXp06dhaWmJ7t27KzocqRQVFWH//v2YN2+ewhcOolHohKgAlmWhp6eHffv2AQACAwPRqVMnLFmyRLGBKcjr168xf/58rFy5Ej179kTDhg3x7NkzPHnyBImJiTA2Noarq2udu/VcV927dw/p6ekYPXq0okOpFMuy+Pvvv1FQUABXV1elKJpECZwQFeHt7Y3JkycDKKnClp+fX69Kp7Isi7t37yItLQ1aWlp1rgRnfffu3TvcvHkTM2fOVHjPtiK5ubnw9PTE4sWLlaZ+u7Q5t/7d2yFEyXz8+BGl19FGRkb1KnmXioyMxLfffkvJuw6ysLDA1KlT4eHhIVlHW5k0aNAA7u7uuHTpkqJDqTZK4IQoUGpqKoKDgxUdhkLl5ORAW1u7WlXMiGrR0dHBwoULERAQgLCwMEWHU46mpib4fL6iw6g2SuCEKFCjRo1gaGio6DAU5t27d9i0aZPKFv4g0mMYBlOmTEF2djYuX76s6HAAlCwte/bsWVy4cEElZwTQNDJCFIhhGPTq1atejjpPSEiAn58f/vjjj3r5+eur/v37IywsDPfv38c333yjsDj+/fdfREZGYtSoUZKpfqqGeuCEKFjpEpb1zcqVK2Fra0vJux7q2rUrYmJiFPa7n5CQgISEBMyYMUNlkzdACZwQhVOWka/y9OHDB7Rs2VKp60yT2jV58mScOHECaWlpcj2vSCTCxYsXMXbsWLmetzZQAidEgSIjI5Vyak1NsCyLx48fAwCysrLKbf/3339x69Yt5ObmoqioSM7REWWhpqaG8ePH4/r164iIiACfz0dxcXGtnvPRo0c4cuQI3Nzc6sSdH5oHTogCCYVCnD9/Hvr6+hg8eLDk/bS0NJVcEEIsFmP8+PGwt7dHgwYN8PDhQwwePBgMw6Bx48bo3bs3Vq9eDR6Phy1bttTLKmWkvFOnTiEiIgLffPNNrUwlLCoqgq+vL9LS0lRi8RhaTpQQFcDj8TBhwgQcPXq0zPubN2/G4MGD0b9/f5VKchwOB2PGjIGrqyuEQmGZL8uQkBAsWbIEaWlpGDZsmEp9LlK7pkyZgv379yMnJ0fmbaekpMDX1xdTp05ViiprskQJnBAloKmpiaKiIqirq0MkEsHKygosy8LHxwcikQgcDgc6OjooKCgAwzAwMzODhYUFzMzMlO4ZOofDgZqaWrllUR0cHNC9e3dcunQJo0aNUkxwRGlNmzatVqaXXb58GXPnzq0Tt8z/ixI4IUpAKBRKEnFeXh4KCwvh6OgIHo8n2V5cXAwtLS2IxWIkJSUhNjYWDx8+hFgsRl5eHkaPHq3wGuGFhYWSmCvCMAwlb1IhHR0dcDgcxMXFwdzcXCZthoeHo3PnznUyeQOUwAlRCoaGhli3bh1+++03ZGRkICAgAOPHj4epqSmAklvtpYmRw+GgWbNmaNasmeR4kUiE8+fPQ1NTE7169YKmpia0tLTk3ju/desWBg4cKNdzkrrD1dUVvr6+uHfvnuR3V1dXF4MGDYKmpma12ioqKsK1a9fwyy+/1EaoSoESOCFKYMiQIeDz+fD19YWGhgbs7OzQpEkTqY/ncrlwdXVFcnIywsPDUVBQgMLCQohEIgiFQohEIjg7O8PExKTWPkNkZCT4fH69rixHaoZhGIwZM0bymmVZZGVlwdPTE7NmzSr3DFsgEEjWV/+vc+fOYd68ebUar6JRAidESWhoaKB9+/Zo164dhgwZ8lVtmJqaSnrtnxOJRLhy5Qp0dXVrpYfs5+cHDocDNzc3mbdN6i+GYWBgYIB58+bhwIEDmDZtGrhcLq5evYr8/HyoqalBIBDAxcWlzO99dnY2MjIyVLpIizRoGhkh9cjmzZsxe/ZsmX2xFRUV4ciRI3B0dETbtm1l0iYhFRGJRPD19QXDMHB2dpb0xsViMW7duoX09HTJvikpKZg2bRqMjY0VFW6N0HrghJBysrKy8PDhQ2RmZkreMzIygr6+PnR0dCT/6evrV3prstSHDx9w+fJluLu717npOYQoEiVwQohU0tPTwefzkZeXJ/kvKysLAoFAMnqXx+OhTZs2SEhIkNy6FAqFGDt2bJ0d4UuIolAhF0KIVIyMjL44/ayoqAjR0dGws7ODkZER+Hw+GjduLKcICSEVoQROCPkidXV1dOjQQfKakjchike1DAkhhBAVRAmcEEIIUUGUwAkhhBAVRAmcEEIIUUGUwAkhhBAVRAmcEEIIUUGUwAkhhBAVJNU88NJibdnZ2bUaDCGEEFLflebaLxVKlSqB5+TkAACaN29ew7AIIYQQIo2cnBzo6elVul2qWuhisRhJSUnQ1dWluseEEEJILWJZFjk5OTAzMwOHU/mTbqkSOCGEEEKUCw1iI4QQQlQQJXBCCCFEBVECJ4QQQlQQJXBCCCFEBVECJ4QQQlQQJXBCCCFEBVECJ4QQQlTQ/weuxMgFVwKLygAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# First, just plot the station data so we can see where our data subset is\n",
+    "\n",
+    "map = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,\\\n",
+    "            llcrnrlon=0,urcrnrlon=360,lat_ts=20,resolution='l')\n",
+    "# draw coastlines, country boundaries, fill continents.\n",
+    "map.drawcoastlines(linewidth=0.25)\n",
+    "map.drawcountries(linewidth=0.25)\n",
+    "\n",
+    "# # Add station data over the top\n",
+    "x2, y2 = map(points.lon, points.lat)\n",
+    "\n",
+    "map.scatter(x2, y2, c=points.temperatures, cmap='viridis')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "46c3cc32-bc88-4e19-8665-45ea03197381",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x14b7b433f2d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Then, plot the stations over the gridded data to see the overlay\n",
+    "\n",
+    "map = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,\\\n",
+    "            llcrnrlon=0,urcrnrlon=360,lat_ts=20,resolution='l')\n",
+    "# draw coastlines, country boundaries, fill continents.\n",
+    "map.drawcoastlines(linewidth=0.25)\n",
+    "map.drawcountries(linewidth=0.25)\n",
+    "\n",
+    "x, y = map(lon, lat)\n",
+    "\n",
+    "\n",
+    "# # Add station data over the top\n",
+    "x2, y2 = map(points.lon, points.lat)\n",
+    "\n",
+    "map.contourf(x, y, data.T, cmap='viridis')\n",
+    "map.scatter(x2, y2, c=points.temperatures, cmap='viridis')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "957c60f7-0924-4ae1-ac57-b438fc99c83f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c54822e-297b-4f69-bd03-62540cf42e36",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/scorecard/Four-MakeLargeGroupings.ipynb b/notebooks/scorecard/Four-MakeLargeGroupings.ipynb
new file mode 100644
index 00000000..e3f822a4
--- /dev/null
+++ b/notebooks/scorecard/Four-MakeLargeGroupings.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1b4ae39b-4f5f-4dc7-bee0-a798eba46719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8bf7f65c-875c-47ff-9ea9-8ac81128be26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A spot to put the data on disk. We keep both the data as-downloaded and the reprocessed version, so you might need up to 50GB free in order to make this work.\n",
+    "\n",
+    "PROCESSING_DIR = Path('/g/data/kd24/data') / 'hadisd' / 'processing'   # We need to cache some data on disk during reprocessing\n",
+    "DECADAL_DIR = Path('/g/data/kd24/data') / 'hadisd' / 'by_decade'    # This will hold the final form of our data\n",
+    "DECADAL_DIR.mkdir()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "db156524-9c84-4257-b351-e960f8b1adcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decades = {\n",
+    "    'early': ('1800', '1930'), # Just in case there is undocumented early data\n",
+    "    '1930': ('1930', '1940'),  # Dataset begins in 1930, start by decade here \n",
+    "    '1940': ('1940', '1950'),\n",
+    "    '1950': ('1950', '1960'), \n",
+    "    '1960': ('1960', '1970'), \n",
+    "    '1970': ('1970', '1980'), \n",
+    "    '1980': ('1980', '1990'), \n",
+    "    '1990': ('1990', '2000'), \n",
+    "    '2000': ('2000', '2010'), \n",
+    "    '2010': ('2010', '2020'), \n",
+    "    '2020': ('2020', '2030')\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e205d264-92cb-4a29-b3ae-3ef16a7404e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files_for_decades = {}\n",
+    "\n",
+    "for ix in decades.keys():\n",
+    "    start_dec, end_dec = decades[ix]\n",
+    "    _files_for_decade = list(PROCESSING_DIR.glob(f'*{start_dec}-{end_dec}*.nc'))\n",
+    "    files_for_decades[ix] = _files_for_decade\n",
+    "\n",
+    "# Uncomment this to see values for debugging\n",
+    "# the1950s = files_for_decades['1950']\n",
+    "# the1950s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7cd6885d-635f-4028-bd75-19fed284cca3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 file groupings to be used for decade 1990\n",
+      "Loaded group 0\n",
+      "Combined group 0\n",
+      "Wrote group 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "decade_of_interest = '1990'  # In the interests of saving time, we process only one decade here\n",
+    "\n",
+    "files_for_decade = files_for_decades[decade_of_interest]\n",
+    "groupings = [files_for_decade[i:i + 40] for i in range(0, len(files_for_decade), 40)]\n",
+    "print(f\"{len(groupings)} file groupings to be used for decade {decade_of_interest}\")\n",
+    "for i, grouping in enumerate(groupings):\n",
+    "    loaded = [xr.open_dataset(f) for f in grouping]\n",
+    "    print(f\"Loaded group {i}\")\n",
+    "    combined = xr.concat(loaded, dim='report', data_vars='all')\n",
+    "    combined['reporting_stats'] = combined['reporting_stats'].fillna(-999.0)\n",
+    "    print(f\"Combined group {i}\")\n",
+    "    filename = f'all_{decade_of_interest}s_group{str(i)}.nc'\n",
+    "    combined.to_netcdf(DECADAL_DIR / filename)\n",
+    "    print(f\"Wrote group {i}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c2684bb3-0bf5-4b79-9c62-568bbdf5879d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Completed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e6f340e-b2cf-46cc-8157-60926434f31c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/scorecard/One-Introduction.ipynb b/notebooks/scorecard/One-Introduction.ipynb
new file mode 100644
index 00000000..83e698dc
--- /dev/null
+++ b/notebooks/scorecard/One-Introduction.ipynb
@@ -0,0 +1,92 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "329c0283-9192-45e4-80b0-910ce5625120",
+   "metadata": {},
+   "source": [
+    "## Hadley Integrated Surface Database\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51995928-e6e0-4e4b-a853-7eec52cf53a8",
+   "metadata": {},
+   "source": [
+    "This dataset holds the world's weather station data up until late 2025.\n",
+    "\n",
+    "![Image of weather stations](https://www.metoffice.gov.uk/hadobs/hadisd/v343_2025f/images/hadisd_gridded_station_distribution_v343_2025f.png)\n",
+    "\n",
+    "For futher information please see:\n",
+    "\n",
+    "- Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note\n",
+    "- Dunn, R. J. H., et al. (2016), Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491\n",
+    "- Dunn, R. J. H., et al. (2014), Pairwise homogeneity assessment of HadISD, Climate of the Past, 10, 1501-1522\n",
+    "- Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Climate of the Past, 8, 1649-1679  Smith, A., et al. (2011): The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704-708\n",
+    "\n",
+    "\n",
+    "For the product manual, see [https://www.metoffice.gov.uk/hadobs/hadisd/hadisd_v340_2023f_product_user_guide.pdf](https://www.metoffice.gov.uk/hadobs/hadisd/hadisd_v340_2023f_product_user_guide.pdf)\n",
+    "\n",
+    "For the website, see [https://www.metoffice.gov.uk/hadobs/hadisd/v343_2025f/index.html](https://www.metoffice.gov.uk/hadobs/hadisd/v343_2025f/index.html)\n",
+    "\n",
+    "It's an amazing scientific archive. The data is held in a collection of .tgz files, based on station ranges. These files contains smaller station sub-ranges, themselves gzipped netcdf files. We need to download the ones we want (potentially all of them), then double-unwrap them, and then put them into a more performant file format for quick access by time index when performing ML training or long historical verification runs.\n",
+    "\n",
+    "Eventually, we want to present these efficiently as a PyEarthTools data accessor which can be quickly indexed by time. An alternative data accessor based on station ID rather than time could be imagined, but we will focus on access by time in this tutorial series.\n",
+    "\n",
+    "Despite being packed into NetCDF files -- which is often used for lat/lon/level/time gridded data -- this data is better visualised as just one massive long list of report entries in a big logbook. Each report is a slightly more complex version of \"time, station_id, lat, lon, elevation, bunch of obs data\".\n",
+    "\n",
+    "Many underlying issues have been sorted out, like stations reporting twice under two ids, changing ids, station upgrades/replacements, plain old errors, sensor quality control and more. Many stations only report for some of the time period, some only once or for a short time, some for a very long time. What we want to do is get this into a good form for time-series use by an ML algorithm. The files on disk are roughly organised by nominal station number, for all time. So if you know what stations you want to work with, you could just pick those files. But let's face it, who wants to take the time to understand the mysterious workings of station numbers - at least at first?\n",
+    "\n",
+    "Singe station time-series modelling is a totally valid use case - e.g. fetching \"station data for Melbourne from 2020 to 2025\". That's fairly straightforward - manually look up the station number of interest, find it in the files, open that files with xarray and then select the time-frame of interest.\n",
+    "\n",
+    "Doing the same thing for a handful of stations is also not too bad. Each station file is only a few megabytes, so opening 5 of them isn't a big deal. However, opening all of them becomes a bigger deal, and trying to merge them all together using simple merge and concat operations will cause a computational failure on most platforms (including HPC platforms). Some data processing is required in order to prepare the data for the time of query we want to use.\n",
+    "\n",
+    "Translating between the 'gridded world' or global and regional modelling and the 'station world' is often done by performing a site-based forecast based on gridded inputs (e.g. siteboost or model output statistics). The translation of station data to a gridded model is done through data assimilation. These two ways of working with the data have significant implications for the data structures which will be used, and for computational efficiency. It would be really nice to have a simple API which could abstract away the messy choices, implement the tricky bits and make it easy to just 'get what we want'.\n",
+    "\n",
+    "From a PyEarthTools perspective based on wanting to develop model architectures which include both gridded and point data at the same time (rather than having a 'translation step'), this means getting the data into a structure where the primary index is date-and-time, and all relevant stations are loaded into that data structure. However, the data still can't be simply gridded, as it more represents a point cloud at each moment in time. A few decisions need to be make still. We will keep things \"simple\" by representing the data for each time step as a list of observation reports from all stations reporting at that time, with a small time delta allowed for stations reporting a few seconds off the base time due to engineering tolerences or other reasons. The \"list to grid\" step will be handled either the model, or in an observation operator step to be developed at a later time.\n",
+    "\n",
+    "This tutorial series contains the code (and explanation) for how to download the data from the Hadley Centre website, unpack it, and then re-process it on disk to have a structure which is well-suited for efficient access in the manner just described.\n",
+    "\n",
+    "The tutorials are structured in a sequence, each with a specific scope. They are:\n",
+    "\n",
+    "1. Downloading the data in the form distributed by the Hadley centre\n",
+    "2. Manual unpack of the data on disk for efficiency reasons (see instructions at the end of StationDownload)\n",
+    "3. Re-processing of the station data to break it up by decade for file size reasons\n",
+    "4. Grouping of individual stations into large station groupings to reduce the number of files on disk\n",
+    "5. Data visualisation of the global station data to demonstrate what it looks like this way\n",
+    "6. (to be done) Integration of this data into PyEarthTools data accessor\n",
+    "7. (to be done) Integration of station data into a PyEarthTools pipeline\n",
+    "8. (to be done) Presentation of gridded data and station data to a neural network for training and prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e2c2235-5acf-4d42-95e1-96b247d91269",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/scorecard/Three-SmallChunksByDecade.ipynb b/notebooks/scorecard/Three-SmallChunksByDecade.ipynb
new file mode 100644
index 00000000..4fff3699
--- /dev/null
+++ b/notebooks/scorecard/Three-SmallChunksByDecade.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "adacf9f5-fe40-4f67-b987-0b14f9845e7c",
+   "metadata": {},
+   "source": [
+    "# Chunking the data into small groups\n",
+    "\n",
+    "The ideal end goal would be to have one huge netcdf file with efficient random access to all the data within it. Unfortunately, practical limitations with merging large files make this somewhat difficult. In principle all the various merging algorithms should be happy working on disk, but in practise the merging component of the algorithm seems to happen in memory in the libraries we use. It's more common to use smaller files on disk and then abstract that behind a multi-file lazy-load interface. This is acceptable for small and medium data sets, but eventually the sheer number of files starts to prove a problem, particularly in supercomputing environments, which are typically optimised for large file transfers. \n",
+    "\n",
+    "As a result, we must jump through a few hoops to re-structure our data for efficient indexing by time rather than primarily by station number (or location).\n",
+    "\n",
+    "Step one is to take our per-station files, and then in small groups re-arrange them and write out files by decade. This doesn't immediately reduce the number of files much, but then we will proceed to joining those decadal files into much larger files, and then delete the various intermediate files. Instead of over a thousand small files, we will instead have around about 50 larger ones, organised in a way that we can work with more easily, particularly if we only want a decade or two.\n",
+    "\n",
+    "This notebook does the small-group rechunking; the next one does the recombining. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "00e4c340-5fdc-400f-a527-4d724166576e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tarfile\n",
+    "import gzip\n",
+    "import shutil\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "import os\n",
+    "\n",
+    "from dask.distributed import Client\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1ce7e96e-b49a-4a83-8269-bdb47064bc24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "UNPACKED_DIR = Path('/g/data/kd24/data/') / 'hadisd' / 'unpacked'       # We need a place on disk to unpack the archives\n",
+    "PROCESSING_DIR = Path('/g/data/kd24/data/') / 'hadisd' / 'processing'   # We need to cache some data on disk during reprocessing\n",
+    "PROCESSING_DIR.mkdir()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1f34c861-e4d6-434a-8873-592e53a369f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2089"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "files = list(UNPACKED_DIR.glob('*.nc'))\n",
+    "len(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3d779c04-71de-4e90-8d88-fb4eecc91fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simplify(ds):\n",
+    "    '''\n",
+    "    Here we move the latitude, longitude and elevation from a coordinate to a data variable\n",
+    "    \n",
+    "    This fits the structure of the data more efficiently, allowing simpler concatenation.\n",
+    "    '''\n",
+    "    lats = xr.DataArray(data=[ds.latitude.values[0]] * len(ds.time), coords={'time': ds.time})\n",
+    "    lons = xr.DataArray(data=[ds.longitude.values[0]] * len(ds.time), coords={'time': ds.time})\n",
+    "    elev = xr.DataArray(data=[ds.elevation.values[0]] * len(ds.time), coords={'time': ds.time})\n",
+    "    ds = ds.reset_coords(names=('latitude', 'longitude', 'elevation'), drop=True)\n",
+    "    ds['lat'] = lats\n",
+    "    ds['lon'] = lons\n",
+    "    ds['elev'] = elev\n",
+    "\n",
+    "    ds = ds.drop_attrs()\n",
+    "    return ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ebf5d389-d345-4f31-a46b-105cd2ef21a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "209\n"
+     ]
+    }
+   ],
+   "source": [
+    "filegroups = [files[i:i + 10] for i in range(0, len(files), 10)]\n",
+    "print(len(filegroups))  # We come up with 134 such file groupings from the test data or 1040 for the full dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c3cb82db-d97e-46c1-b92e-d9545af7811e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decades = [('1800', '1930'), # Just in case there is undocumented early data\n",
+    "           ('1930', '1940'), ('1940', '1950'), # Dataset begins in 1930, start by decade here\n",
+    "           ('1950', '1960'), ('1960', '1970'), ('1970', '1980'), \n",
+    "           ('1980', '1990'), ('1990', '2000'), ('2000', '2010'), # 1980 is a common time to start from\n",
+    "           ('2010', '2020'), ('2020', '2030')\n",
+    "          ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dedfafc9-e785-4209-9b69-6992e234e1f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing group 0 of 209\n",
+      "00:28:28.093766\n",
+      "Processing group 1 of 209\n",
+      "00:31:28.043623\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This takes around 20-60 seconds per grouping. If you just want to get the hang of it, limit it to three groupings\n",
+    "# Otherwise, the test set have 67 groupings, so will take around half an hour to run\n",
+    "# The full set of stations will take several hours!\n",
+    "\n",
+    "# For testing, just try three file groups\n",
+    "\n",
+    "for i, fg in enumerate(filegroups):       # Use me to process all downloaded data\n",
+    "    print(f\"Processing group {i} of {len(filegroups)}\")\n",
+    "    print(datetime.now().time())\n",
+    "    loaded = [xr.open_dataset(f, engine='h5netcdf') for f in fg]\n",
+    "    simplified = [simplify(_ds) for _ds in loaded]\n",
+    "    merged = xr.concat(simplified, dim='report')\n",
+    "\n",
+    "    for d in decades:\n",
+    "        decadal = merged.sel(time=slice(*d))\n",
+    "        if len(decadal.time):\n",
+    "            filename = PROCESSING_DIR / f'{d[0]}-{d[1]}-sg{i}.nc'\n",
+    "            if not os.path.exists(filename):\n",
+    "                decadal.to_netcdf(filename)\n",
+    "            else:\n",
+    "                print(f\"{filename} exists, skipping\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bce54e4-ed2c-4400-bac8-fb7cc484f555",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('done')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4da1b95-c11b-4101-b75b-f754907c3398",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/scorecard/Two-DataDownload.ipynb b/notebooks/scorecard/Two-DataDownload.ipynb
new file mode 100644
index 00000000..edddbd28
--- /dev/null
+++ b/notebooks/scorecard/Two-DataDownload.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "70473e3c-69ff-422d-b516-165b6d397b74",
+   "metadata": {},
+   "source": [
+    "## Downloading The Hadley ISD Dataset\n",
+    "\n",
+    "Note - if you are at NCI, you may be able to use an already-downloaded version if you are in project kd24.\n",
+    "\n",
+    "If you are working at another facility, it is highly recommended to start with just the test data, and run the entire sequence of tutorials from that data to get the hang of working with the data. This is just because of the download volume and processing time required. \n",
+    "\n",
+    "That said, this data set is entirely reasonable to work with on many laptops, workstations or general computing environments, it just requires a little patience to get up and running smoothly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10f05488-d4f2-40cf-8edc-34e00815a3a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A spot to put the data on disk. We keep both the data as-downloaded and the reprocessed version, so you might need up to 50GB free in order to make this work.\n",
+    "\n",
+    "import requests\n",
+    "from pathlib import Path\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "DOWNLOAD_DIR = Path('/g/data/kd24/data/') / 'hadisd' / 'as_downloaded'  # We will download data here and keep a copy\n",
+    "DOWNLOAD_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "# For testing, we download just under 4GB data\n",
+    "testing_download = [\n",
+    "    \"000000-029999\", \"500000-549999\", \"722000-722999\", \"800000-849999\",\n",
+    "]\n",
+    "\n",
+    "# Download list for all files - these approximately map to station IDs\n",
+    "full_download = [\n",
+    "    \"000000-029999\", \"030000-049999\", \"050000-079999\", \"080000-099999\",\n",
+    "    \"100000-149999\", \"150000-199999\", \"200000-249999\", \"250000-299999\",\n",
+    "    \"300000-349999\", \"350000-399999\", \"400000-449999\", \"450000-499999\",\n",
+    "    \"500000-549999\", \"550000-599999\", \"600000-649999\", \"650000-699999\", \n",
+    "    \"700000-709999\", \"710000-714999\", \"715000-719999\", \"720000-721999\",\n",
+    "    \"722000-722999\", \"723000-723999\", \"724000-724999\", \"725000-725999\", \n",
+    "    \"726000-726999\", \"727000-729999\", \"730000-799999\", \"800000-849999\",\n",
+    "    \"850000-899999\", \"900000-949999\", \"950000-999999\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0971f5fb-46b4-409b-8d41-fdfc0c5f5ae4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def download_wmo_range(wmo_id_range, download_dir):\n",
+    "    wmo_str = f\"WMO_{wmo_id_range}\"\n",
+    "    url = f\"https://www.metoffice.gov.uk/hadobs/hadisd/v343_2025f/data/{wmo_str}.tar.gz\"\n",
+    "    tar_name = f\"{wmo_str}.tar.gz\"\n",
+    "    filename = download_dir / tar_name    \n",
+    "\n",
+    "    head = requests.head(url, allow_redirects=True)\n",
+    "    remote_size = int(head.headers.get('content-length', 0))\n",
+    "    local_size = filename.stat().st_size if filename.exists() else 0\n",
+    "  \n",
+    "    if filename.exists() and local_size == remote_size:\n",
+    "        print(f\"File already fully downloaded: {filename} ({local_size/1024**2:.2f} MB)\")\n",
+    "        return filename, tar_name\n",
+    "\n",
+    "    if filename.exists() and local_size != remote_size:\n",
+    "        # Users may have done this deliberately, so just print a message\n",
+    "        print(f\"Local filesize of {filename} does not match. Attempting to resume. You may need delete it and re-download it\")\n",
+    "            \n",
+    "    headers = {}\n",
+    "    mode = 'wb'\n",
+    "    initial_pos = 0\n",
+    "    if filename.exists() and local_size < remote_size:\n",
+    "        headers['Range'] = f'bytes={local_size}-'\n",
+    "        mode = 'ab'\n",
+    "        initial_pos = local_size\n",
+    "        print(f\"Resuming download for {filename.name} at {local_size/1024**2:.2f} MB...\")\n",
+    "    else:\n",
+    "        print(f\"Starting download for {filename.name}...\")\n",
+    "\n",
+    "    response = requests.get(url, stream=True, headers=headers)\n",
+    "    total = remote_size\n",
+    "    with open(filename, mode) as f, tqdm(\n",
+    "        desc=f\"Downloading {filename.name}\",\n",
+    "        total=total,\n",
+    "        initial=initial_pos,\n",
+    "        unit='B', unit_scale=True, unit_divisor=1024\n",
+    "    ) as bar:\n",
+    "        for chunk in response.iter_content(chunk_size=8192):\n",
+    "            if chunk:\n",
+    "                f.write(chunk)\n",
+    "                bar.update(len(chunk))\n",
+    "\n",
+    "    final_size = filename.stat().st_size\n",
+    "    if final_size == remote_size:\n",
+    "        print(f\"Download complete: {filename} ({final_size/1024**2:.2f} MB)\")\n",
+    "    else:\n",
+    "        print(f\"Warning: Download incomplete. Local size: {final_size}, Remote size: {remote_size}\")\n",
+    "\n",
+    "    return filename, tar_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20e1e2b6-4225-40f8-8fa8-bd4e8185e147",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for wrange in testing_download:\n",
+    "#     download_wmo_range(wrange, DOWNLOAD_DIR)\n",
+    "\n",
+    "# FOR FULL STATION DOWNLOAD\n",
+    "# Note, if at NCI doing the hackathon, use the pre-downloaded data\n",
+    "\n",
+    "# Note - need to make the DOWNLOAD_DIR directory\n",
+    "\n",
+    "for wrange in full_download:\n",
+    "    try:\n",
+    "        download_wmo_range(wrange, DOWNLOAD_DIR)    \n",
+    "    except:\n",
+    "        # This is a fault-tolerant approach which will print error messages but continue\n",
+    "        # to try to fetch the remaining files\n",
+    "        import traceback\n",
+    "        traceback.print_exc()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2565195-6db7-447d-abea-139ba8521fc7",
+   "metadata": {},
+   "source": [
+    "## Unpacking the Data\n",
+    "\n",
+    "The next step is easiest to do manually, and is a bit awkward to put in a notebook step.\n",
+    "    \n",
+    "First, go to your top-level download directory. Make a new directory called `unpacked`, then run the following command.\n",
+    "\n",
+    "This will result in a lot of individual .nc.gz files on disk. Once tutorial three has been run, it is okay to delete these interim files.\n",
+    "    \n",
+    "Run `for file in *.tar.gz; do tar -xzf \"$file\" --directory ../unpacked; done`\n",
+    "    \n",
+    "Once this is done, change directory into the unpacked directory and run\n",
+    "    \n",
+    "`gunzip *`\n",
+    "    \n",
+    "This is much faster for some reason than trying to use Python to get the job done."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c7db752-56c3-4071-b485-d88d3848fffe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb09f28f-d8ee-4bc8-83bf-6397a10ca257",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a1f4ebf-29ab-45c0-bd02-f74e687fedef",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/packages/data/src/pyearthtools/data/download/weatherbench.py b/packages/data/src/pyearthtools/data/download/weatherbench.py
index a90d2f99..b983141e 100644
--- a/packages/data/src/pyearthtools/data/download/weatherbench.py
+++ b/packages/data/src/pyearthtools/data/download/weatherbench.py
@@ -1,8 +1,8 @@
-import sys
-import logging
-import textwrap
 import hashlib
+import logging
 import shutil
+import sys
+import textwrap
 from pathlib import Path
 from typing import Literal
 
@@ -11,11 +11,11 @@
 from numcodecs.blosc import Blosc
 from tqdm.dask import TqdmCallback
 
-from pyearthtools.data.time import Petdt
 from pyearthtools.data.indexes import AdvancedTimeDataIndex, decorators
 from pyearthtools.data.indexes.utilities import spellcheck
-from pyearthtools.data.transforms.transform import Transform, TransformCollection
+from pyearthtools.data.time import Petdt
 from pyearthtools.data.transforms.coordinates import Select
+from pyearthtools.data.transforms.transform import Transform, TransformCollection
 
 
 def _extract_dataset_infos(url: str) -> tuple[dict[str, str | None], list[int]]:
@@ -106,6 +106,8 @@ def _save_variable(darr: xr.DataArray, path: Path):
         logger.info(f"Incomplete download of {varname} found, removing folder {zarrpath}.")
         shutil.rmtree(zarrpath)
 
+    zarrpath = zarrpath.expanduser()
+
     compressor = {"compressor": Blosc(cname="zstd", clevel=6)}
     zarr_kwargs = {"encoding": {darr.name: compressor}, "consolidated": False}
 
@@ -113,9 +115,11 @@ def _save_variable(darr: xr.DataArray, path: Path):
     logger.info(f"Saving {varname} under {zarrpath}, it will take at most {dsarr_size:.2f} {unit} of storage space.")
 
     disable_bar = logger.getEffectiveLevel() > logging.INFO
+
     with TqdmCallback(desc="Writing", disable=disable_bar):
         darr.to_zarr(zarrpath, **zarr_kwargs)
 
+    canary_file = zarrpath / ".completed"
     canary_file.touch()
     logger.info(f"Saving {varname} finished.")
 
@@ -155,6 +159,8 @@ def open_local_dataset(path: Path, variables: list[str], level: list[int]) -> xr
     """Open a locally saved dataset made of 1 zarr folder per variable and level"""
     logger = logging.getLogger(__name__)
 
+    path = path.expanduser()
+
     dsets = []
     for varname in variables:
         filepath = path / f"{varname}.zarr"
@@ -164,7 +170,7 @@ def open_local_dataset(path: Path, variables: list[str], level: list[int]) -> xr
         else:
             filelist = [path / f"{varname}_level-{lvl}.zarr" for lvl in level]
             if any(not (fpath / ".completed").is_file() for fpath in filelist):
-                raise MissingVariableFile("Missing .zarr folder for some variables")
+                raise MissingVariableFile(f"Missing .zarr folder for some variables - see {filelist}")
             logger.debug(f"Loading {varname} variable from folders {[str(p) for p in filelist]}.")
             dset = xr.open_mfdataset(filelist, concat_dim="level", combine="nested", consolidated=False)
         dsets.append(dset)
diff --git a/packages/tutorial/pyproject.toml b/packages/tutorial/pyproject.toml
index a955285d..68561149 100644
--- a/packages/tutorial/pyproject.toml
+++ b/packages/tutorial/pyproject.toml
@@ -17,6 +17,7 @@ classifiers = [
 dependencies = [
   "rich",
   "ipywidgets",
+  "basemap",
   "hydra-core",
   "scores",
   "dask",