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"cell_type": "markdown",
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"source": [
"# Getting Started with Acoular – Part 3\n"
]
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"source": [
"---\n",
"title: \"Getting started with Acoular - Part 3\"\n",
"subtitle: \"How to use Acoular - simple example with 64 microphone array and three sources, time domain beamforming\"\n",
"author: \"Ennes Sarradj\"\n",
"date: \"2021-04-03\"\n",
"categories:\n",
" - getting started\n",
" - time domain\n",
"image: thumb_getstart.png\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"This is the third and final in a series of three blog posts about the basic use of Acoular. It assumes that you already have read the first two posts and continues by explaining additional concepts to be used with time domain methods.\n",
"\n",
"Acoular is a Python library that processes multichannel data (up to a few hundred channels) from acoustic measurements with a microphone array. The focus of the processing is on the construction of a map of acoustic sources. This is somewhat similar to taking an acoustic photograph of some sound sources.\n",
"\n",
"To continue, we do the same set up as in [Part 1](getstart1.ipynb). However, as we are setting out to do some signal processing in time domain, we define only `TimeSamples`, `MicGeom`, `RectGrid` and `SteeringVector` objects but no `PowerSpectra` or `BeamformerBase`.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import acoular\n",
"ts = acoular.TimeSamples( file=\"three_sources.h5\" )\n",
"mg = acoular.MicGeom( file=\"array_64.xml\" )\n",
"rg = acoular.RectGrid( x_min=-0.2, x_max=0.2,\n",
" y_min=-0.2, y_max=0.2,\n",
" z=0.3, increment=0.01 )\n",
"st = acoular.SteeringVector( grid=rg, mics=mg )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time domain processing\n",
"\n",
"For processing in time domain in Acoular, one may set up \"chains\" of processing blocks. This is very flexible and allows for easy implementation of new algorithms or algorithmic steps. Each of the blocks acts on all channels at once. Input and output may have different numbers of channels. \n",
"\n",
"For our task we set up the following processing chain:\n",
"1. __data intake__ from file (`TimeSamples`, same as before)\n",
"2. __beamforming__. In the time domain this amounts to different delays that have to be applied to all channels and for all grid points, and a sum for each of the grid points. This is also known as delay-and-sum.\n",
"3. __band pass filtering__ (the time history for each point in the map is filtered). We could skip that step in principle, but it is nice to compare the result to what we got in Parts 1 and 2 from frequency domain processing, where we did a similar approach to band pass filtering.\n",
"4. __power estimation__ (just the square, nothing else), so that we can compute levels\n",
"5. __linear average__ over consecutive blocks in time which makes it possible to have not one image for every sample in time, which is huge amount of (mostly useless) data, but just enough data for some images\n",
"\n",
"Each object in the processing chain is connected to its predecessor via the `source` parameter:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"bt = acoular.BeamformerTime( source=ts, steer=st )\n",
"ft = acoular.FiltOctave( source=bt, band=8000, fraction='Third octave' )\n",
"pt = acoular.TimePower( source=ft )\n",
"avgt = acoular.Average( source=pt, num_per_average=6400 )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And again: lazy evaluation, nothing is computed yet.\n",
"\n",
"Only asking for the result will initiate computing. Although this is not used in this example, it should be mentioned that the architecture allows for endless data processing from a stream of input data. To this end it is possible to replace the `TimeSamples` object that reads the data not from a file, but from hardware.\n",
"\n",
"Different to the frequency domain processing, the result is not computed in one go, but in _blocks_ of data. These blocks have a variable length that can be defined as argument of the `result` function each of the processing blocks have. Note that this function is actually a Python [generator](https://wiki.python.org/moin/Generators), that yields a number of results we have to iterate over. This helps if one wants to process large amounts of data that do not fit into the memory at once. Iteration means we can use it in `for` loop and get a new data block each time we run through the loop.\n",
"\n",
"In our example we use the loop in a Python list comprehension statement. That means we collect all blocks into a list. In our case, the blocks have length 1, i.e. one map per block."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"res = [r.copy() for r in avgt.result(1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The list `res` contains all maps each of which averages over 6400 samples. Now we can plot all of these maps. Because time domain processing sees the map as (number of gridpoints) channels, we have to reshape the maps so that they fit the shape of the grid."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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LF2vb33vvvfj9998xZ84czJkzB/v378e5555bYw7Jo48+iquuugqnnnoq/vOf/+DNN99EQUEBBg4ciG3btlW77datW5GcnIzmzZtL63v06OF/vCqTJk3CrbfeCgBYvHgx1q1bh3Xr1uH0008HANx44424++67MXToUCxbtgx/+9vfsGLFCgwYMEA7iKu++OILJCQk4JdffkHPnj3hcDjQrFkzTJkyBfn5+abbeL1elJWV4ZdffsGkSZPQrFkzaYJSXl4Ol8ulbVe57ocffvC3O3692ra4uBi//fZbtf2nmjEGj/riiy8AHP38nn322QgLC0N8fDyuuuoq7N+/X2v/6aef4o033sCcOXNgt9tN91kZt5VxXMnpdKJz585SXG/duhVdunSRvsw6ftvqjgH3338/Lr/8cgDwx/+6deuQmpoKIQRGjRqFf/zjHxg3bhw+/PBDTJs2Da+//jr+9Kc/oaysrNr35WStWrUKANC1a9cq2/h8PsybNw/t27fHoEGD/OtrOgbs2LHDn0NXXl4Om82mnXSpx5VQxBiuH59//jm8Xq/0WU1PT8fIkSPx9NNPY/Xq1SgsLMQvv/yC2267zV9npVJtY/i3335DSUmJdlyobLtz504tN/R469atQ3h4OC644AJ/rD///PMAgO3bt2PAgAH46aef8Nxzz2Hx4sU49dRTMXHiRG3SdTJqO47XdZ+1jeGanv/HH3+s8/NbDeO6buoyXlRl1apVcDgc6Nixo39dqMZ1dWPzY489hrCwMERERODss882rS1RqXv37rDb7UhJScH48eORkZEhPW6puD7p3+CbkKioKDF16tRat/f5fKKiokLs2bNHABDvv/++/7HKyy3UPOaePXsKAGLx4sX+dRUVFSI5OVlceuml/nWrV68WAMTpp5+uXVLtdDrFpEmTtOeqlJGRIRwOh5ZPUVBQIJo3by7GjBlT7esaOnSo6NSpk+ljYWFh4vrrr692+6oucf/5558FAHHTTTdJ6zds2KBd8mOmU6dOwu12i+joaPHoo4+K1atXi9mzZ4vw8HBx1llnmV4K7HK5BAABQHTs2FFs27ZNenzq1KnCZrOJPXv2SOvHjRsnAPhfq9frFQkJCeK8886T2uXk5Ijo6GgBgLlm9YAxeNTw4cMFABEXFyfuuusuf754YmKiaN++vXTJVUFBgWjTpo245557/OvMLnF/5JFHBACRmZmpPd+wYcNEx44d/csdOnQQw4cP19rt379fANDSX1RVXY66YsUKAUDMnj1bWr9w4UIBQLz88svV7vd41V3ibub7778X4eHh4pJLLqm23UcffSQAiFmzZknrs7KyhM1mE9ddd520fufOnf5jzP79+4UQQjzzzDOml9Lff//9AoCWZhBKGMPm6nK5aH5+vv/y7IKCAumx8vJyMXnyZP9nDoDo0aOHNt7WNoa//vprAUAsWLBAa/voo49Kn+u6vrYrr7xSuFwu6RJdIYQYMWKEiIiIELm5udXu93jVXeI+atQoERcXp71XAwcOrPZ4Vd0l7nWJ4Z49e4qOHTtKlz5XVFSIdu3amV6OHIwY1+aq+uzXZbww8/HHHwubzabVnAm1uBai6rF5//79YvLkyeI///mP+PLLL8X8+fNFv379BADxyiuvSG3feOMN8cgjj4jly5eLVatWiccee0wkJCSIlJQUKfXFSnHNX9DroE+fPpg3bx7+/ve/Y/369abf+B06dAhTpkxBq1at4HA44HQ6kZ6eDgD4+eeftfZqJeUuXbpol1c4HA60b98ee/bs0bYfO3asdkn1gAEDsHr16ipfx8cffwyPx4Px48fD4/H4/7ndbgwaNKjKiqXHq+6y2hOtiFnZZ7XiZZ8+fdClSxd89tln1W7v8/lQWlqKe++9F/fccw/OPfdc/N///R9mzZqFr7/+2nT7tWvXYt26dXjrrbcQHR2NwYMHS9Vur7/+ejidTlx99dX46aefkJWVhX//+99YuHAhgGOXMtlsNtx888347LPP8Le//Q2HDh3Czp07cc0116C4uFhqSyeOMXiUz+cDAFxxxRV4/PHHMXjwYNxwww2YO3cudu7cibffftvfdvr06XA6nXjggQeq3WelquJXXd8Qx4DKb8nVY8Do0aMRGRlZ4zHgRO3evRsXXXQRWrVqhTlz5lTbdu7cuXA4HFofExIScPXVV+ONN97ASy+9hOzsbPzwww+4+uqr/VctVB4Drr76aiQkJOD666/Hhg0bkJubiwULFuC5556T2oUixvDJKS0txaWXXoo9e/bg3XffRVRUlPT4jTfeiEWLFuHpp5/G559/joULFyIsLAx/+tOftNdelxhuqHg/77zz0KpVK2n9xIkTUVxcjHXr1p3QflW33HIL8vLyMH78ePz+++84ePAg7r//fqxduxbAicVbXWL41ltvxa+//opbbrkFf/zxB/bu3YspU6b4/x6hEO+M67qpy3ih+u677zBmzBj069cPs2bN0h4PpbiubmxOTU3Fyy+/jNGjR+Pss8/G2LFj8cUXX6BXr16YPn06PB6Pv+24ceNw7733YsSIERg8eDDuvvtufPTRRzh8+LD0q76V4jr4jwoBtHDhQkyYMAFz5szx54mMHz8eBw4cAHD0pHnYsGFYvHgx7rrrLnz22WfYuHEj1q9fDwAoKSnR9pmQkCAtV16moZb+DwsLM73cRL3MvHLd8ZcVqQ4ePAgAOPPMM+F0OqV/CxcurPFS8sTERNP9FxUVoby8XHtNtVW5z9TUVO2xtLS0al9TZb8AYPjw4dL6yoP5d999p21z+umno1+/frj66quxevVqCCFw7733+h/v0qULlixZgj179qBbt25ISkrC448/jieffBIApJy2Bx54AHfccQf+/ve/IyUlBR06dAAA/yXzx7elE8MYPKqqz/rw4cNhGIb/s75x40Y8//zzmD17NkpLS5Gbm4vc3Fz4fD54PB7k5ub6Lxuv3KdZv7Ozs6X3qapjQHZ2NgD9Pa2trKwsOBwOfz2ISoZh1Pienqg9e/Zg8ODBcDgc+Oyzz6rt+5EjR7Bs2TJceOGFpn/3F154AVdccQVuuukmJCYmolevXujcuTMuvPBCuFwu/3uclJTkr1/Rr18/xMfH49Zbb8VTTz0FILSPFYzhE1dWVoZLLrkEX331FZYtW4a+fftKj69YsQJz587FSy+9hKlTp+Kcc87BmDFjsHLlSmRnZ0u3NqxtDNd0XDAMw5/jXldZWVlVjvdVPeeJOO+88/Daa6/hiy++wCmnnILmzZtj8eLF/tuvnki81SWGr732Wjz22GN488030bJlS7Ru3Rrbtm3DX//61xN+fqthXNddbceL423evBlDhw5Fhw4dsHz5cu0S61CK67qMzZWcTieuuOIKZGVlYceOHdW27dOnDzp27Oj/DALWimtHzU2oUlJSEp555hk888wzyMjIwLJlyzB9+nQcOnQIK1aswNatW/H9999j3rx5mDBhgn+7nTt3NlifKg9+6jqzwK6UlJQEAHjvvff8317WRffu3fHOO+/gwIED0gGwMt+iW7dudd4ncOyAkZmZiZYtW0qP7d+/39/vqvTo0UMKtEpCCAA1f5sVHR2Nzp0749dff5XWjxgxAnv27MHOnTvh8XjQsWNH/Oc//wEAnHPOOf52DocDTz31FB5++GHs2rULSUlJSE1NxfDhw9G2bVvtNVHdMQaP6tGjR7X3HK/8rG/btg1CCFxyySVam7179yI+Ph5PP/00pk6diu7duwM4Gsennnqqv53H48Evv/yCq666yr+ue/fuWLBgATwej5TrVh/HAI/Hg8OHD0uTdCEEDhw4gDPPPPOE9luVPXv24Nxzz4UQAmvWrKkxRt98802Ul5dLxeGOFxkZiTfffBPPPfcc9u7di7S0NCQlJaFz584YMGCA9F6deeaZ2LZtG3bv3o2ioiJ06NAB3377LQD5uBJqGMMnpqysDKNGjcLq1avx/vvv47zzztPabNmyBQC0OImLi0P79u2l2hC1jeFTTjkF4eHhpvmUP/74I9q3b296L+HaSExMNC2kVFlHo6Yxvy4mTJiAq6++Gjt27IDT6UT79u0xa9YsGIaBgQMHntA+6xLDd999N6ZOnYodO3YgOjoa6enpuOGGGxAZGakVoQ1GjOu6q8t4ARydnA8ZMgTp6en45JNPtGKQQOjEdV3H5uPV9py/sq3azipxzV/QT1Dr1q1xyy23YOjQof5fqyovB1G/0XrppZcarB8LFizwfxiBox/qtWvXStUUVcOHD4fD4cBvv/2GM844w/RfdUaOHAnDMLSKjPPmzUN4eDjOP//8arevfH/Ub0z/9Kc/AQDeeustaf0333yDn3/+2fSE5HiXXXYZAOCjjz6S1i9fvhzA0W/DqnPkyBH/gUllGAY6dOiALl26wOv14tlnn0XPnj1NT6SjoqLQvXt3pKam4rvvvsNnn32G22+/vdrnprpryjF4ySWXwDAM7bP+0UcfQQjh/6yff/75WL16tfYvJSUF/fr1w+rVq/0F2/r27YvU1FTMmzdP2ud7772HwsJCXHrppdLzFxYWSpVmAeD1119HWlqa9sueqqpjQGWMq8eARYsWoaioqMZjQF1kZGTg3HPPhdfrxapVq2p1MjZ37lykpaVpFV5V8fHx6NGjB5KSkrBs2TJs3769ymNAmzZt0LVrVzidTjz55JNIS0vTKvCGqqYcw3VR+cv5qlWrsGjRIu3KmUqVv1CpX1RnZWXh119/lU5yaxvDDocDF198MRYvXoyCggJ/u4yMDKxevVo6LlTF5XKZ/kJ63nnnYdWqVVphyzfeeAMRERE1jtl15XA40KVLF7Rv3x55eXl4+eWXMXLkyJOeiNU2hl0uF7p164b09HRkZGRg4cKFmDx5MsLDw0/q+a2GcV03tRkvtmzZgiFDhqBly5ZYuXIl4uPjTfcVCnF9ImNzpYqKCixcuBBJSUmm5/LHW79+PXbs2FFlfxo7rvkLei3l5eVh8ODBGDt2LDp37ozo6Gh88803WLFihf+D3LlzZ5xyyimYPn06hBBISEjAf//7X6xcubLB+nXo0CFccsklmDx5MvLy8jBz5ky43W7cc889VW7Tpk0bPPzww5gxYwZ+//13nH/++YiPj8fBgwexceNGREZG4qGHHqpy+65du+K6667DzJkzYbfbceaZZ+KTTz7Byy+/jL///e81XoZS+Uvds88+iwkTJsDpdKJTp07o1KkTrr/+evzzn/+EzWbDiBEjsHv3btx///1o1aoV7rjjjmr3O2zYMFx88cV4+OGH4fP50K9fP2zatAkPPfQQLrroIpx99tkAjv4thw4dirFjx6JDhw4IDw/Hr7/+imeffRZlZWWYOXOmtN9bb70V5557LhITE/H777/jueeew759+/D5559L7dasWYNvvvkGPXr0gBACGzduxOOPP47zzz8ft9xyS7V9p5oxBo/p3Lkzbr75Zjz//POIjo7GiBEj8Ouvv+K+++5Dr169MGbMGABHL+czu8zP7XYjMTFROlGx2+2YPXs2xo0bhxtuuAFXXXUVduzYgbvuugtDhw6VvngbMWIEhg4dihtvvBH5+flo3749FixYgBUrVuCtt96qslJ8pcpjwOOPP44RI0bAbrejR48eGDp0KIYPH467774b+fn5OOuss/DDDz9g5syZ6NWrF8aNG1ftfoGjXygA8Ffq3bRpkz9Pt/LLiEOHDmHw4MHIzMzE3LlzcejQIRw6dMi/j5YtW2rf2G/YsAE//fQT7r333ipf36JFi7B//3506dIFpaWlWLNmDZ599llMmTIFI0eOlNrOmDHD/0VeRkYGXn31VWzYsAEffvhhyJ2wV2IMyz7//HMcPnwYwNE7MuzZs8f/+R00aJD/KpLLL78cH330EWbMmIHExERpAh4TE+O/4uXSSy/FAw88gBtvvBH79u3D6aefjszMTDzxxBMoLi6WTvrrEsMPPfQQzjzzTFx00UWYPn06SktL8cADDyApKQl33nlnje9v9+7dsWbNGvz3v/9FamoqoqOj0alTJ8ycORMffPABBg8ejAceeAAJCQmYP38+PvzwQ8yePdv0F8Lj7dmzB9988w0A+O+SUvn+tWnTxj+ZOnToEJ588kmcddZZiI6Oxi+//ILZs2fDZrP5b/lYqbi42P+lfuX7/Pnnn+PIkSOIjIyUvpyrbQxv3boVixYtwhlnnAGXy4Xvv/8ejz32GDp06OC/zD6YMa5ltY3r2o4X27dvx5AhQwAAjzzyCHbs2CFdvn3KKaf49xnscV2XsXnatGmoqKjAWWedhebNm2Pv3r345z//iS1btuC1116TXutpp52Ga665Bl26dIHb7cbGjRvxxBNPoHnz5rjrrrukPlgmrk+qxFwTUlpaKqZMmSJ69OghYmJiRHh4uOjUqZOYOXOmVDF527ZtYujQoSI6OlrEx8eL0aNHi4yMDAFAzJw509+usnLk4cOHpeeZMGGCiIyM1J5/0KBBomvXrv7lyiqVb775prjttttEcnKycLlcYuDAgWLTpk3StmqVykpLly4VgwcPFjExMcLlcon09HRx+eWXi08//bTG96O8vFzMnDlTtG7dWoSFhYmOHTuK5557rsbtKt1zzz0iLS1N2Gw2qUqq1+sVjz/+uOjYsaNwOp0iKSlJXHPNNWLv3r212m9xcbG4++67RatWrYTD4RCtW7cW99xzjygtLfW3KS0tFZMmTRJdunQRUVFRwuFwiJYtW4prrrlG/PTTT9o+R44cKVJTU4XT6RTNmzcXEydOFLt379baff3116Jv377+97Nbt27iH//4hygvL6/1+0JVYwzKPB6PeOyxx0T79u2F0+kUqamp4sYbbxQ5OTk1bmtWxb3S22+/LXr06CHCwsJE8+bNxW233aZVPxbiaFXb2267TTRv3lyEhYWJHj16mFaENVNWViYmTZokkpOThWEY0l0dSkpKxN133y3S09Pr/LqEEFL1avVfpcq/XVX/jv+cVJo8ebIwDEP89ttvVT73kiVLRM+ePUVkZKQIDw8XZ5xxhpg7d67pHSRuvPFG//EzKSlJXHbZZeKHH36o1WsMVoxhvT9VfQaPrxxe3Wd10KBB0j4zMzPFLbfcItq3by/cbrdIS0sTF154oVi3bp32/HWJ4U2bNonzzjtPREREiJiYGDFq1Cixc+fOGl+jEEJs2bJFnHXWWSIiIkLr848//iguvvhiERsbK8LCwsRpp51W6zsvvPbaa1W+L8dXl87KyhLDhg0TycnJwul0itatW4tbb71V+9wIIcSuXbuq3Gd6errUtrYxvH37dnHOOeeIhIQEERYWJtq3by/uu+8+UVhYWKvXaXWMa70/tYnr2o4X1X3OYXKnkmCO67qMzXPnzhV9+vQRCQkJwuFwiPj4eDF8+HDx8ccfa/u98sorRfv27UVkZKRwOp0iPT1dTJkyxbRSvVXi2hDiuOs/KGisWbMGgwcPxrvvvuv/VYiIAocxSBTcGMNEoYdxTaGAOehEREREREREFsAJOhEREREREZEF8BJ3IiIiIiIiIgtotF/Qn3/+ebRt2xZutxu9e/fGl19+WWXbxYsXY+jQoUhOTkZMTAz69++Pjz/+OIC9JaLaYFwThR7GNVHoYVwTWVejTNAXLlyIqVOnYsaMGdi8eTMGDhyIESNGICMjw7T9F198gaFDh2L58uX49ttvMXjwYFx88cXYvHlzgHtORFVhXBOFHsY1UehhXBNZW6Nc4t63b1+cfvrpeOGFF/zrunTpglGjRmHWrFm12kfXrl1xxRVX4IEHHmiobhJRHTCuiUIP45oo9DCuiazNEegnLC8vx7fffovp06dL64cNG4a1a9fWah8+nw8FBQVISEgwfbysrAxlZWVS++zsbCQmJsIwjBPvPFEjEUKgoKAAaWlpsNmsV9sxEHENMLYptDCuj2JcU6ixcmwzrolOTCDjOuAT9CNHjsDr9SIlJUVan5KSggMHDtRqH08++SSKioowZswY08dnzZqFhx566KT7SmQ1e/fuRcuWLRu7G5pAxDXA2KbQxLhmXFNosmJsM66JTk4g4jrgE/RK6rdnQohafaO2YMECPPjgg3j//ffRrFkz0zb33HMPpk2b5l/Oy8tD69atcTYugAPOk+s4nTybXV8VGS4t+4pK5AY+b0P2yPI8qMBXWI7o6OjG7kq1GjKugSCIbeWzbY+KUB5X3p8Kj7YLX1FxvXcrUGxul7bOCJdjW5TIse0rLUNTxbg+yvJx3dSZ/K3t0VHSsq9EjmNRUd6gXbK6YIhtxnXTZjj0KaAtQjkXZ1xLAhnXAZ+gJyUlwW63a9/SHTp0SPs2T7Vw4UJcd911ePfddzFkyJAq27lcLrhc+omiA044DB4UGp1hMkE3wqRln6FMXAxrXSIWcP+rFGHVy8ICEddAEMS28tm2K59r9URXmHyufUZFvXcrUNQ4BgBDWScM+cs2n+Fr0D5ZGuMaQBDEdVNnNkHXxmw5joXRxO/ga+HYZlwTABiGyQSdcV29AMZ1wGc9YWFh6N27N1auXCmtX7lyJQYMGFDldgsWLMDEiRPx9ttv48ILL2zobhJRHTCuiUIP45oo9DCuiayvUS5xnzZtGsaNG4czzjgD/fv3x8svv4yMjAxMmTIFwNHLYv744w+88cYbAI4eFMaPH49nn30W/fr183/rFx4ejtjY2MZ4CVQHhlP+Rs6WEKe3CXfLK3zyt3a+oqL67hbVM8Y1tFQM4ZGvBFEvaTfs+nektshIeZsy5RIzr0m6R+BvxgEAsCmXedli9Mu+RLj8C4qRpTQoLa3vblE9Ylw3QcqvQ/ZEvRCYESUfp4ysHGm5qV8Ka3WM66bH5pbPsw2Ty7SNCKWNcr7BuA6cRpmgX3HFFcjKysLDDz+MzMxMdOvWDcuXL0d6ejoAIDMzU7oX40svvQSPx4Obb74ZN998s3/9hAkTMG/evEB3n4hMMK6JQg/jmij0MK6JrK1R7oMeaPn5+YiNjcW5GMm8l0ZwIr+g+w7LP7M19V/QPaICa/A+8vLyEBMT09jdsQyrx7YtQi4SV5tf0GGX89hD7Rd0ZOVKi94c+Ze3poRxbc7qcR3yTuAXdJ/yC7qvoKD++xVEGNs6xnXjOpFf0H1HsuVlnosHLK6beOUtIiIiIiIiImvgBJ2IiIiIiIjIAhrtPuhUv9RLV3xlJvcWbqzLYNVL2hP0giLCJ/fNcPKjScHPV1z9Pc2FR79Vh025b7gtTo4XUaYXaQnU5aTqJfu2xHi5H7HyZa8AtOOOrUi/7Q5RU2Mot59SU1kakz0pSV5hNmYry2b3VCZqatS0L1Tot031BagwqjovsCUlSssiWh7PAUB45QLNhl2/LTIFBn9BJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiILYNJQiFDvlmc3uX2CNz8/MJ2xyTkrhlO+nYbPJKfF8Mh5tertqIhCkkldCDVv3R4mx4+h5JUBgFGq3IqtQs9Trw9a3qzSNxGmDylGuRLLoX9nT6IaiXI5Rm2Rev2GQN3SSL0VqnarJZdJXJfKubXCwzGbCMptUNXbEQKAoYyBDVV/Qn1u9ZanwqXf6s4okvPjGdeNh7+gExEREREREVkAJ+hEREREREREFsAJOhEREREREZEFMAc9RBiGfD9loeTBAHqeWUPlqdpjouTncSv5bT75PosAYHjk/noDlHtHZHXafUkNPSfMcCu5ZfUQ22b3NVZz2nwRyvPqt3WHuspXVP294YmapEYcs20JcfLzRIbXvJGSR+srKKjHHhGFCK9+vmtTarl46yEH3ayGhREtn4v7IvX6NRo1ros5XjcW/oJOREREREREZAGcoBMRERERERFZACfoRERERERERBbACToRERERERGRBbBIXIjwlZZKy6YFI+zy9zGioub92qKj5X2EOeVlp7wMAFCKSwn18XL9iX2HjtTcGaImSC2+ZI+JMWmkFKJRikaqRWkAwJaUKK9QYlu49Nj22e1yG+V5DLOCOLmF0rKHxaSItGJMsOm/l6gFFrUx29CrMtoTE+QmauyH6XEtHHJcq/s1vNooDmTn6uuImji1qJrdZOyFTYkvtSCroR8L1EKORrhc8E1E6AXgfE5lvLYrz1uhF6ZEVq6+jhoFf0EnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgtgDnqI8hUV1Xkbe3y8tk60SJGWy5Mi5OcJM8mb88j5ao5iOXHOfkTPQT2R/hI1Rd78/Brb2NxyPpotJVlrU94mSVouSQqTln1OPb9VzUV1Fso55+4jci0MABDF+joikqm5q6Zsck6pXclLBQBfq+bScnlyuLTsNRuzfeqYLeemhh3Wx2dffqG2johk3rxajNdKXQhbXKy+n1bNpOXSZHmM97pNfm9VSkeoce0+qB9zRElJdV2lAOIv6EREREREREQWwAk6ERERERERkQVwgk5ERERERERkAcxBb8qUfDbRKkVrUtBBzoUpTpa/0/G69DxVm3Kv1vBsOb8mxqPfK5mI6o8RKdeKKGun56Dntpfvz1qcIsey16Xf+9hWIce/+4h8DDFMbpcc/rvJvVZDiOEMq/ZxUVEeoJ5QqDPsypjd0mzMjpaWi1KUMVu/XTJsHnnZnSWfGsZW6GO2Eeqfa+X8CL7QPo5RA6nF58YIi5SWPa2baW3y28ttipW49tQirl3Z8jb2Mn0je3mFti6U2GNi5BXKPelFaZm2ja+0cero8Bd0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIBF4powteBMWbNIrU1BS7lNSapcBcrr0ovH2EuVYlPh8vdAziK5gBUARMbJxei8uXkmPQ4O9mS9IJcRJhfKE0rRCV9hkbaNKNOLVRDVhhEhx1hRql7IrKC1vFyeKhd9srn14ja+EnnI8EbIy44SfUgJT0qQV2Rla20sSykU5WiRqrdxKq9ZKbIjioq1Tbw5OSfdNWp6DLs8lpam6GNpQUu5TXGaPEb7wk2KP5bIY7bHLe8jrFAuKAkA0YlyXHuDKa4N+fU60kzi2qEUiSuTj4++/AJtE1+xHutENVLOD0tSw7UmBa1sShslrt36ubitVC3qLC+7CvTzgtjkRHmbg4dMOmxNasFWe0s9roVbec0VciU9W4l+3i0OHTn2HMIAAlRHj7+gExEREREREVkAJ+hEREREREREFsAJOhEREREREZEFMAe9DmwRer5XMOcc2VPkXOnSJKfWpjRJzlerSFTyNcLlZQCoKFNytwwlvyZLeRxAZGozeUUQ5aA7mqdIyyIhVmvjs8nfhdkK5dAzSk3yXpiDHjChFtveZvJnsCRR/y62PFmO3egkuQ5CtFv//BWXyflbuUaUtFyapee0VTSPlpZt2006bFGOVmnSsjchxqSRGttyfQnDox8jKTBCLa5tyUnSckmSfgpXmiyP2b4kOWHSGa4nUHrKlXo0hpxzXnJEP35EN1dqrQRRDrqjdUtp2RcfrbURdjlP3ZZfIi0bSh0ZAEDwfrSCii1Sr5fkK9Lr+ASNZLmeQ0mCfo6snosjUR6f3W6Tc/Fy+fhQJpS4TpA/4wAQ01zOQUcQ5aDbWzSXlr2Jelz7nPJ7ay+S30ejTD8+iopj9SeECFACOhrxF/Tnn38ebdu2hdvtRu/evfHll19W2TYzMxNjx45Fp06dYLPZMHXq1MB1lIhqjXFNFHoY10Shh3FNZF2NMkFfuHAhpk6dihkzZmDz5s0YOHAgRowYgYyMDNP2ZWVlSE5OxowZM3DaaacFuLdEVBuMa6LQw7gmCj2MayJra5QJ+lNPPYXrrrsOkyZNQpcuXfDMM8+gVatWeOGFF0zbt2nTBs8++yzGjx+P2Fj98mEianyMa6LQw7gmCj2MayJrC3gOenl5Ob799ltMnz5dWj9s2DCsXbu2Xp6jrKwMZcfl7+bn59fLfn0lJdo6e3y8tCyUNj6zPCWrUO6p6nXq+Sg+JaVUzTl3hpnkvSjLXpf8MfOEmzxPhJ67alVq/pNIjJOWvdH6PWNthfI9VKHknAd1/hQCE9dAA8a2SV6qPU4+CRHq38zCsS2UPCs1jgEATvm+qZEu+TMa5VQ+sybyXfK90r36Rx8et9wXl0M+HggL5Wjb1Xs7Kznn3hj9jbSVKP1X7qvqPZJVP51rBEEf17UYs32F8rH3+HxDy1Huze0N08dSr0vOVXW45RHZ5ao5h7LCLe/DE67/luONDJ4x254s58t7k5S4Njn/sBfLnwOjVF72BFGdHFXQx3WIjdewqfcr15uo9zl3K7Uk3GE1x3WpW64H5XXrU0BvlPzk+hHGOtS/uVepJeGJMhmvy+RzFqNMHq99h4/AKgL+C/qRI0fg9XqRkiIX1kpJScGBAwfq5TlmzZqF2NhY/79WrVrVy36JyFwg4hpgbBMFEuOaKPQwromsr9GKxBmG/L2MEEJbd6Luuece5OXl+f/t3bu3XvZLRNVryLgGGNtEjYFxTRR6GNdE1hXwS9yTkpJgt9u1b+kOHTqkfZt3olwuF1wuk2tEiKhBBCKuAcY2USAxrolCD+OayPoCPkEPCwtD7969sXLlSlxyySX+9StXrsTIkSMD3Z26EUJfpeRPGuHhcgMr572UyTlV9nL99dnL5G9TPco9zj02fRtfhXxhhlPZh80sVcZnss6ibNHyvZ99yjfO9jw979HIl/McPQcO1n/HGlFQx3UVRLn8QTXClHwms/vUmxwjGoNNyZ+0mxyGjFI5lgtL5RMpm6G/FrWNr1geQuwmb4m9TA5uq+ScGw59+NOO38o1Zo48/QUahXK8i4NyDptVXu+JCPq4rsWYbQt3S8teK+egK8ekWo3Zyr2Qy03GbE+FfCywlShjtsnzGD55nTWOfIBhMiE0oiKUFfLrc+TrB0hbnjxmezPr79LvxhaSce2VxxnDrXwOLDxe63GtN7GVyoNRuRLXhsl4rce1vA/Tc/EgYij1oGozXtuKlFg/nC0tWqlWQcAn6AAwbdo0jBs3DmeccQb69++Pl19+GRkZGZgyZQqAo5fF/PHHH3jjjTf822zZsgUAUFhYiMOHD2PLli0ICwvDqaee2hgvgYgUjGui0MO4Jgo9jGsia2uUCfoVV1yBrKwsPPzww8jMzES3bt2wfPlypKenAwAyMzO1ezH26tXL///ffvst3n77baSnp2P37t2B7DoRVYFxTRR6GNdEoYdxTWRtjTJBB4CbbroJN910k+lj8+bN09YJq1yKQkRVYlwThR7GNVHoYVwTWVejVXEnIiIiIiIiomMa7Rf0UOErKJCWbRFyMRLDqRSWAiAsUoRGLVQWuT9Na1PUXC6a5HU55eVwuQgFANjL5O99XFlyQRZXvl4RzlYuF+7xmvTXKoRSXM84cLjaxwHAq3xOyPp8xcXSss0tF5PSisYBEGaFaBqBsVeO7aj9sVqb0iSlSJxDLrhSpBTPAgChFIkMOyQPIe4s/RcWR5FcicYyv8HY9WOXGrv2P2ou+OY9klW//aIGpY7ZalExs+KBVin05/ljv7QcmZmqtSlKVQo5KsepsnD99dmUMdt9RFnO1cdso1R+T6wS14ZJXGtFuA7kSMvCpDCUh3EdVHxFyngdKZ+Lq+fmR7cp0tY1Bt/ufdJyZIdErU1xinzuXaIct4rd8uMAYKhxnSUvh+XrUWsrl8++1UJsjfae2UzGazWu98sxK3z6cct7SD5ft0yhQBP8BZ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIA56PVMzVsNJs7vf9PWxUd1ktsUyR8Zj0kOuq1CzukIz5bzQCL26/leOHhEX2dR3pycmhtRyPGZ5ClalfoZjd56WGvjCW8mLRcVyjlsHpOcNruc8gX3ETnWozP0+gv2zGx5v3p3G4VZvQDvYf19otBmlboRJ8L1015tXXxMG2nZWSiP0V63SQ66EtfhWfKYHfmH/h4ZB62Zo212DhbM52VUSz45d1qtNWGWw2wVal2qyB/2a20qIltKy1pcm52LK8OxO1seryMzTcbrI/nSssciefrq3xcI/fGav6ATERERERERWQAn6EREREREREQWwAk6ERERERERkQUwB538vPn52jr3ys3SckS7dGlZhOv3goZXznMxSuX8NaOoRNuE9xwlajjeHb9r6xIK5NyymDYp8jZm90sul3NT7YVybNvy9VxP9d7NRFQ/vAcPaesiP5PH16jWadKyzyQH3ahQ7n1crIzZJXoOuifE8z8pxJjkMFuVZ98f2rrYT+WxNbqdHNfecJP7oCvn4vYiJa6L9bj2ZR6sdT+pYfEXdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAReKoWsLjkZa9v/7WSD2xDsPlkpftdnk5IrzGffhy85Sd6t+ViYryuneOQp9h6OuE0NfVwHNAKQajLMufanM+pS8+s0Yn0LfGYouIkJYNhzxEGtFR+kbqe5CnF9tU+Yr+V/BH+Kp404hOjK+gQF7x0/YatwmeCG0YtuhobZ1hl8dkrxrXJmN2MBUio+DizcmRV3wrL9fm11afrRajehB9hm2RkdKy4VbOzWP0uFaJnFx52asPyNoxNUD4CzoRERERERGRBXCCTkRERERERGQBnKATERERERERWQBz0KlpUfJF7XFx0rJokSItlzWXc1wAoDTJKS173EpOsElCn7NYzmtxZ8u5/a79et6qUVZxbMFXBuzS90tNjy1Kz4P2FRbKKwKV9x2o56lF7pwtTI5LQ8krFWlJ0nJpqv4+liTKz+MJr01syyvdOXIOn3u/nr9mLzlaX0J4ywCW9aCmSq2nUYvjieEMk5btafKYXZGWoG1TmiLnplZE1PzblKNE7osrp0JaDvsjV+9buTJmZ9T4NESBE6j8cmW8Nmx63RytllOUfK4tmidKy2bjdXGyPIX1yCVkIEzK9TiL5eXwrGby8h9F2jb24rJj+/SWATv1/TYE/oJOREREREREZAGcoBMRERERERFZACfoRERERERERBbAHPQmRL3Hr6+4uIqWoUHNcQEAe6qSY95GzkvNPUXepqiFnsRSHi/nk/tccl6P4dO3CcuWvwsLPyQ/T3R0vLZN+IFS//97PKXMQScA5vfktCfJ+VrerGy5QRDdi9yMYZdz2mzhbr1RM/k9KGst56LmnSLnrha20nehxrYIU3L2TJLanDly38IPyrnw0VGx2jYRmUdz2jyeUuagU5XUMUyUlVXRMkgpxyXTMVs5tnlayMvZ7eXc1YLW+u9OZfHy83jdyvOaHB7DspW4PiTvNyZS7gcAuPcfO6fyepmDTubUOgqioryRetIw1JxzW7TJ/ciV8bq0lTxOauN1S30XFQnKeO2qebx2ZMvTXvdhdbyO0baJ3H/suOvxlDIHnYiIiIiIiKgp4QSdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAReKaEFuiXDTJFiMXbvAcOBjI7jQ4tSgeAFSkysXY8tvIRWny28vtfa1LtH3ExRRJy5FhFdJySYVcdAIAsuPkQjZet1LkSuihaC891jdPRXAX+aKGZUTJny9HeLi07Nm7L5DdqXfCI8cYwuO0Np4UuchMfhu5yEyeEtu2NnIcA0BqbKG0HB0mF+UqLNeLWB2Oj5LbhCvHHSEXmwIAe9nRvnkqfNpjRJXsyXIRU1EmF5PyHj4cyO40OCMsTFvnbSaP2QXt5GNdbkf5d6bSdL2QXlScPI5HuuT3saRcH7ML4uQ49obLfTN8JmN2ybFx3ePRHiYCANibqXEtf2a9R7IC2Z16J5QPv+HS47q8mTz/qGm8dqTLYzMAtFTG6xhXqbRsNl4fjJOft0gZrw2fPl47yo4dHzwVXu3xhsJf0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIApiDbgH2uNhqH/fm5tV5n4ZD/9OKGDl3yyiW8zVsal40AF9pqbbOqtT+GxHhWpuKWDnPpaSZIT+eLOempSfnaPtIi5T/HglhxdJydrme+77LLuetHCiT81xKi/QcnYoDx74/81Twu7SAM+TPBkTd6wDYlboPKm9Wdp33aRbbviglj6pUzmmzx8Toz52fX+fnbiz2WLn/Zrmq5TFyHmlxivz38zaXj2WnJOt5fulR8t8j0Snnqed69Nje7mgmLe8qk/8+pUV6Hlz5oaPx7GVcByVbdHS1j/sKCuq8T9MxO1aub2AUFmttQonZe+CJkWO9KEXJOU+R811TUvTzpbQoeV2iS34fc8r1c4VddvnYnV0aJy2XFOp9jco8Nq57KvRcVrI2e7xc7wA2ZQw5gfHa7LxaKHnQRr4yDqjnHsAJnX80Fu01m+SgV8TI8aOei/tS5PG6ncl43UYZr5PD5ONuVrl8/AQAl10+XvxWro7X+t+r/OCxv4+3PHBjNs8OiIiIiIiIiCyg0Sbozz//PNq2bQu3243evXvjyy+/rLb9559/jt69e8PtdqNdu3Z48cUXA9RTIqotxjVR6GFcE4UexjWRdTXKBH3hwoWYOnUqZsyYgc2bN2PgwIEYMWIEMjIyTNvv2rULF1xwAQYOHIjNmzfj3nvvxW233YZFixYFuOdEVBXGNVHoYVwThR7GNZG1NUoO+lNPPYXrrrsOkyZNAgA888wz+Pjjj/HCCy9g1qxZWvsXX3wRrVu3xjPPPAMA6NKlCzZt2oR//OMfuOyyywLZdY2aM6XmRhrReg6EKFTuv+uUt/HVQ26oEa7nVMEn33NXOOQcqWDKNzdjRMr5oWrOPQB43UpOkXL7U5tbzhVX81UAPec8xiHfY7VC6N97RYXJue32cHm/wq7fh9XrOtZXn80kJ8liQimuAcCwy/GhxXak/vkSSgwZTvnv6ivQ7+VZ536ZxLbhVe7NqfTdewI5sVZiKDltZrHtiZDjzqekvTlccsxFOOSYBIDkMPnvE+uQY91r8p12tFPO93eFy/ds99r1HPTK2Paa5RpaTMjFtTpmq8sm+eWiSB6z1fomIu/kx2xbhF7fQB2z1bgOdoZLjg0jRj9f8oTLfx81ruGS3yO3Qx+z1ZxzdcwuM7n3cWSYHMc5Ecp+bSb58sedX3js1o7tkItr5TzacMufLVuUyXhdIn8OtHPI/JMfN2tzLg6n8lkKonxzM+p7LyL198ATXv147azVeC3/fWLtyrm4wySu1fHaLe+33NBz0I8/Fw/kmB3wX9DLy8vx7bffYtiwYdL6YcOGYe3atabbrFu3Tms/fPhwbNq0CRUVFVr7srIy5OfnS/+IqOEEIq4BxjZRIDGuiUIP45rI+gI+QT9y5Ai8Xi9SUlKk9SkpKThw4IDpNgcOHDBt7/F4cOTIEa39rFmzEBsb6//XqlWr+nsBRKQJRFwDjG2iQGJcE4UexjWR9TVakThDuUxACKGtq6m92XoAuOeee5CXl+f/t3fv3nroMRHVpCHjGmBsEzUGxjVR6GFcE1lXwHPQk5KSYLfbtW/pDh06pH07V6l58+am7R0OBxITE7X2LpcLLpee90dEDSMQcQ0wtokCiXFNFHoY10TWF/AJelhYGHr37o2VK1fikksu8a9fuXIlRo4cabpN//798d///lda98knn+CMM86A06kX1wok4VEKhxjyRQmGTy/2oBafUovCiTK5iMGJ8BXqxagc+XKhG++RrJN+HktRiv3A5L23VcjrHEpdvJJC+fOUX64PLrkVJkU/jpNTrhf7KSyXK2B4i+W+Oj0m30AfXyjE4jVDQi2uAT22hfJ5skeYFGtTPoNqUTifUmzqRJjGdoFcBMmXkys3CLaiMzaluIvyvgqTX2xsXuXvoxxGS4vlGMwr1/9+uRUmhbqOk12uFxoq8sj7LS+V+2pSs+pYPFv8z9IU4lobs022UYuZCTWu66HAqlctHgt9zPZkHjzp57ESWy0mb7YKuaCWXXmrjWL5WJFfqu8zL0Iu/OQT8l8512TMLiqXP6tCGbMNk7g2fOb/bzUhGdcVcrEvQy28ZjO5YFiN6yKlKGg95Mz7TOLaXiB/3rwHQiuutWKWJucfNo+8zqaN1/Jnymy8zqlhvM6q0Mfrggr5WFBaIo/fDqXeLgAYXvP/b2iNcon7tGnTMGfOHLz66qv4+eefcccddyAjIwNTpkwBcPSymPHjx/vbT5kyBXv27MG0adPw888/49VXX8XcuXPx17/+tTG6T0QmGNdEoYdxTRR6GNdE1tYot1m74oorkJWVhYcffhiZmZno1q0bli9fjvT0dABAZmamdC/Gtm3bYvny5bjjjjvw73//G2lpaXjuuecscWsHIjqKcU0UehjXRKGHcU1kbY0yQQeAm266CTfddJPpY/PmzdPWDRo0CN99910D94qITgbjmij0MK6JQg/jmsi6Gm2CHqrUPBjv4cON1BE958N78JDcRM3FC3JqXqDNJOfIfUDOOYqMjZaWy2PkkDgYEavto1TJTYt0yX/zkgo9rHLz5FwYe57cxpWrbQJH2XF/wwqLJ6s2BT45+ajRajiYxbaSwxb0sa2812puoM2h5LgBcB+UYzsiVm5THiPHbYY7QdtHcYXcJtZVqjwu56sBwMFc+RgicuU2Ybl6VrOj9H8JqozrRmeZMdunJzdqOecmbYKZr1iJazVvGIArs0BajoqX25QrcZ4TIccjAPzslduEh8n37S41GbMLcuT8VkeuvA9Xjh67/rgGgAoLJ6E3Aepny1dSojdS6k80RHypxxcA8B6UjzFBP14rRIk8btry9Tz88ANyTnlEvHyOXB4rj6MZEfHaPso8ctxGhcmJ7Gbj9QF1vM6R27iy9fHaWXIslo0AjtmNdps1IiIiIiIiIjqGE3QiIiIiIiIiC+AEnYiIiIiIiMgCmIPehIRanotKyzlSlgHAyM6RlmNtHaVlrzNKWi70yPdMBID8eDlnJTdczjUzyvXvvZwFcl5L+EF5OfKAnvvkzjqWu+Tx6HlMRJVCPba9OXLcIk+/P60jO1dajkNbaVnY5JzSApN7qB5MlPPYD7iVuKzQY9tRIOemRimxHXFAz0V1Zx3NgfV4KrTHiPxCLOdcpR63vFnZeiNlXbSjs7xNWJy0bHj1vNOSBLm2RJFbjklbuZ536i6QYz0iU849NRuzXVnHcmDtnjLtcWpEJrVbIBonvszy0kNJbc7FbflybYk4xynyNmFyjnqhV7+n+d5E5fzcpfw9Tc7F7flK3voB5Vw8Ux+vww83zrk4f0EnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAlgkzgIMl1yYyHDIfxbTAlA+ueCFLUopoODVi1948/XCSpZhKEVazAp61ANRJhduMTb/LC0nlneQlsOzo7V9lCTKRaF8TnnZMPlzufLlwhOuXLmRo0AvPGEvKD22Ty8LzgQFm/xZsMfHyo+rn/NyvUiYGu9GhFwsBSbHA29uXu372JDU1wcAhvI9cH0UvjLZh3p8s2/eLi0nlspFaNy5ckFIAChJlPvqDZP/njavflwKy5fXufLkv4+zQP97OQqOxrOdcR2UbBFKgUG7/DlBhUlce5XCZME0ZpvEtaG85kAVqvT9KMd1nK+TtOzKMxuz5XMqn0OOc5vZmK3EsStb/ptWxrC0n/zjimH5GNvBxp6YIK9QzsVNx+uSEmnZcMvn82rcA4CvoEBb1xjUuQdgMv9QzpnrK87V98CxeYe0nOhtLy278pTzIJjEtVNetpnUYHXlyn8Pd478esJy9XNxR+6xv7EtgGM2f0EnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgtoWjnoNjtgHM2bsoU5a2yu5o6IirrfoF7N5wAAo1tHabmkhZyLVh6t5LGW63mP9lIlp/lIqfx4npwXAwB2m5xHFrC8VTUvNzZGa+IrLpaW1byXhqLm04jv5Zz0yD1KDjGAqDh9nbSPwiJ9XYn891HzDdW8paPr3Mf+31f3z16TUtfYVmocnMjnzdE8RVtX3j5VWi5uJv9dyyPl70QdZSaxXabkSB2W++bIkWMFAOxKnrc3J8ekxw2gFrGt5uj5SushB70WfKVKzG3ZJi1GZ8Rr20SrNQNs8vtqFOnHVaHmJQr573d8HPv9L1fOxjzVWjMbS4VSi6Ve6hson2kAsPWQ85xrGrNtFXpcO9Qx+7AyZufqcW1TjlMBy109gbgOVA66Wp/Gt/UXaTniDz2uI+OU/qvH/zz9fdVen7KNWf6uOG6d4JhdK2ZxrdYtEWp9hhOIc61uBADRVa5LUthCznMuq8W5uLNYHa+VuDYbrw8q43WAzsVtylhkMzmXFUr9jECdi/uK5PNm2yb5XDx+v36+FZOgxLXyk7MtXx+vjRIlp145TzD9PB63zgjgmM1f0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAppWDvpxbPFx+spwJVewWM5f8GaZ5HUq+Yb2Vi3kXXRM1jbJ6homLZcmy3ktnmglh9Gj34PUmS//6SL2y/f0jdmj5z1GKDnotgo5Z0zNAakvak6wKNdzswzlPqsNcxf0ujPNDWqIfCE1XxYAcOx5PMLkho5kyiy2RaScW2ZT85AKCvUdKffUFK2aS8v5bfX77eZ0knPWyhLU2FZqD5jFdp68j4hMOd81drd8/AAANbvOpuSDNlTuam1iW7tHdGNRcki9Wdl6G7N1DaAyF9AnmKdaWzaTPGhDyStV63/UphaDo6U8Zpd2aq61OdJDzjcuUcfsmJMfs2N362O2eudfm5KLq9ZuqS+GeuwrMam9IKwySstM/+YNUJPDW0Nurpdjdq3Y4vWaAYYyXgul9ofP5O+p1qNwtFbOxTs107Y50l05F09S4jr2BMZrLa71WgURDiW3PVA1ZJSxWJSZjddK/r9a5yNA1Jpfnj179UZ7qt9HfVW7Ob7eRCDjmr+gExEREREREVkAJ+hEREREREREFsAJOhEREREREZEFcIJOREREREREZAFNqkicLdwFm3G0KITw+bTHDaVoGpxyASRbulx0AgA8zeTCNYWpcqGXrG56gaSyU+SCYG3TjkjL8S658EupV+4HAPyRFyst58bGyQ0M/U/rLJSLV4SVJknLvj0mhcp8J19mwacWU7FocRkKXrbIcH9sm32+DI/yOXYphdbiUrVtyprL8VLUXI7DnC56wRhvW7mYzSnND0vL8W45tos9esG3fTXFNvTjgRbbZYnSsijVCxqpRVhOhBbbZhjvGt//ikL6WEiqeoZx9B8AqOMzTD7XDnncUwvAAYA3JU5aLmgpF2E80l0fO0vby8+T3kIesxPdcnE6szF7b678vHkxcpwbwmTMLpALUTqV90Ds/UPbRnj096muhBLXjGCqT4YzDIbxvxgxO8dUipcZSjFSe8s0bRNPMzme8lvIBSSPdDc5F28vn/Oe0kIer9W4PqHx2tCPBY4S+ZgT5pHHa5tJsdX6KOLsK1HO8dVloF7O+UPN8cdDwSJxRERERERERE0LJ+hEREREREREFsAJOhEREREREZEFNKkcdF9RMXzG0fwBm1fPsxA2+fsKIy1F3j5OzhsBgNJkl7Sc00nOc/F21vNGzm6zS16O2yktt3BmS8tFPvk5AOCbmHbS8qfoJG+TH69tE7VfzoVxZsn7tbn15/EVF2vr6ow5qNTAfAWF8JnkelUylJw2pCZLixXx4do2pYny/vJOUb7P7FCobTMo/XdpuW+svFyr2I6WY3uV0UFaLsyX89UAoHS/nBvnyJJrYdjC5WUA8NZDDjpjmxqUEKjMgPbm5+uPG3IdCDXn3Bcr12YAgNIUOTc1u7N8GlR+qj7mDWizW1o+O26HtNwqLEtaLvDqx5NvottKy9qYnaeP2dH75GOQI1uOcyNcfx5RUKCtI7ISUVEOYRyNa1+eSVzb5fNoe1pzadkXa3Iu3kyOBfVc3NNZj+uz28rj8wmdiyvj9WfoKG+Tn6BtE5WpnIvnqHGtj9eohxx05pcHF/6CTkRERERERGQBnKATERERERERWQAn6EREREREREQW0KRy0I9XeR/a6jhK5HuBelPjtDZFzeU8l5IWco5HxxT5fqmAnudyboScz5am5N8c9tZ8X9Pf4uV7mm9OjNHalEcrOfbq66uPfHOiRuYzycE0lPsj2+Ple5d63fL9hgGgOFmOl9I0OQ67psj3TAWAAbFybA+M+E1aPqHYjlViOyFOa1MWI+83Wsm5N83fJQp2Sg0EoYxh3pZ6vYbCVPlYUKyM2R1Mxuxz4n+VltUxO8UuHyuyvfo+VDvim0nLWxPMxmw5riOL5fMWL/PNKcgJj8kYqKwTxSXScm3OxWsT1w1xLr4jVo7r75VzDQAoj5L3a5Qo4/URuaYFVUGpQRJqNXH4CzoRERERERGRBXCCTkRERERERGQBAZ+g5+TkYNy4cYiNjUVsbCzGjRuH3NzcardZvHgxhg8fjqSkJBiGgS1btgSkr0RUO4xrotDDuCYKPYxrIusL+AR97Nix2LJlC1asWIEVK1Zgy5YtGDduXLXbFBUV4ayzzsJjjz0WoF4SUV0wrolCD+OaKPQwromsL6BF4n7++WesWLEC69evR9++fQEAr7zyCvr374/t27ejU6dOpttVHjh2794dqK6aEg5DW+eJkNeJCLmIRIuIPG2bNk65uJRaiCLK5paWvZALZABAjE0uFuO2K8Ur9K5CKAUVfIdqLmRDVJOgiGslxrTiUnaz2JaXDSW2U8P1wmutnHJxlxOJ7TibXOiqNrGtrcvRjztWZTjDtHWiotykJQVSUMS1yibHm3Dov0FURCljdpRcTCot8uTHbJjEdbRdXue2V8gNzOJa4d1/sOZGRNUIxrg21HPXMLvWRo9redxsGZmrbdMg5+KOWsS1WsvsoF5wlmohxIrCqQL6C/q6desQGxvrPygAQL9+/RAbG4u1a9cGsitEVE8Y10Shh3FNFHoY10TBIaC/oB84cADNmjXT1jdr1gwHDhyot+cpKytDWdmxW4jl8xZDRA0mUHENMLaJAoVxTRR6GNdEwaFefkF/8MEHYRhGtf82bdoEQL9UBQCEEKbrT9SsWbP8xS9iY2PRqlWrets3UVNhtbgGGNtEJ4txTRR6GNdEoaVefkG/5ZZbcOWVV1bbpk2bNvjhhx9w8KCeQ3X48GGkpKTUR1cAAPfccw+mTZvmX87Pz6/VgUHNhRRRchKqz6l/nyGUVTanT1ou8Tq1bQp84dLyYW+O0kLOadnv0fMsdpcnSctZpZHSsr1I76urQM61C/X8DTo5Votr4CRiO0yObV+0HINmOehq7pjNIcd2mU/PgysWLmk521f32P69XP51I7tMPg7ZivXYDiuU+xZMsc1888AKpbjWxEZJi6ZjthK2Nqc8LpZ59dOiuo7Z+5SyEQCwuzxZ3keJ3FdHocmYnSvvSHgqtDZEQIjFtVpLIkaJa5N6UHpcq+N1w8T1b8p4rcV1gX6e4M6VjzminHFNunqZoCclJSEpKanGdv3790deXh42btyIPn36AAA2bNiAvLw8DBgwoD66AgBwuVxwuVw1NySiKlktrgHGNtHJYlwThR7GNVFoCWiRuC5duuD888/H5MmTsX79eqxfvx6TJ0/GRRddJFWO7Ny5M5YsWeJfzs7OxpYtW7Bt2zYAwPbt27Fly5Z6z5chorpjXBOFHsY1UehhXBMFh4DfB33+/Pno3r07hg0bhmHDhqFHjx548803pTbbt29HXt6xW50sW7YMvXr1woUXXggAuPLKK9GrVy+8+OKLAe07EZljXBOFHsY1UehhXBNZX0CruANAQkIC3nrrrWrbCCV/cuLEiZg4cWK99sMWEaGvayZfHuRzy/njZnkvhpL66SuQt8ksjtG2+aFEzsHxKsmu6n0V/6iI1/axIb+dtLxzv5zfFpmpf/fiPijfw9FXXKy1qSvDUfNHSHhMEncopFglrgHAHq/HC5Lkdb4w+XNrmoOupHF7tdiO1Tb5vrh1tX1zG3KumVlsf5PfVlremSnnuEWYxHb4YSW28wur7YcZNZbVvP2jK5X70RYV1fl5KHhYKa4Nk0tl7c3kcc/nltv4wvRY0cbswrqP2Sq3Ta6j8EdFgtZmXe4p0vKeP+Tzjdh9+jHInSnHse9Eakso+by2ML0ujvo3FMdV3qbQY6W4trnd+roUNa6Vc3GTuFbH68aK611KXEf/ocd1+IF6OBdX4tpwmpyL+5S4Zr2XoBLwX9CJiIiIiIiISMcJOhEREREREZEFcIJOREREREREZAEBz0FvTPa4GNiNo3mVRnyc9rgvUr4noidOzo3xhem5JPYSOccjLFvOC9l3WM8x/cqQc1Z+i5DzbVw2OWd7X5He152Z8jZhO+S+J2zT76vo+HWvtOzVWujUvD97gvJ6THLifAVy3hxz0Kmh2ZOSYLf9L2c6Xs81ExHy57giVl72msS2Q0kLc+bIh8s9JrFtg1wbYndkorxPJQH2D5M89l+VnHOnGtu/6PEUtlO+r63HK0e3Wa0II1zery0hTm7g02NbFCo558xBpwZki4yA7X9jti1Jz/8U6pidKC973SZjdmn9j9lOmxxvZmP2b/vkbcK3y8egpB/lvFQAED/t0NbVRM3ptSUrt97yKUn4AHy5edIyc9CpIdmio47FdaIeb2pcVyTINaO8Lv23RYdyLu5U4nqvSVyvNeTxeleEPF47lfE6o0jfx29/qHEtx1/Sj3osGdt+l5ZrU1lCrZtlS1SOh2ZxnV8gPw9z0IMKf0EnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAppUkTgYtqP/AMBjUiLNIX9fYVTIRRcMk1pnYYVyeQdPlryPMiEXuwCA3/ObS8u7IpQiLkrFCCMnTNtHxH75eZK3yMUfXF9t07bxFhdr62piC5cLXogKuficL08uQnG0DQtRUGAZdhsM29GYECbFUoQS27ZyuY3Nq5dpcSqx7cqWC06VIVLb5tcCufCTWkxKKF0TuSax/Ydc3CbpBznmIjbKBWYAwHMkS1tX3fMCgM2lPHe5EttqQTgAvgI93okCwmTMVuP6hMbs7BMYs8PVMVs+NhjZTm0fUfvk50nZKI/Htq+2aNvUpniUSi3+KMrl8Vjk5Wvb+EpLT+CZiE6MYbfBqDwX95qM1055DLSVy7Fv8+hTF6dcm9hkvJaLrAHAznx5DPwtXB6v4VPiOsckrvfKcd3sOzmW7Gu+07YxGY5rZEQoxyWl+DLH69DDX9CJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAKaVA66NycXhnE0h8RuM7THDbecj2JT82A8ekZYWKGS86akydkq9O9A3Iflt93rlpftSjqYmgsLAHG/KXkun2+Wln3iRLLXTPjk/RhOJQfH5H0kCjRv9nGxbeifSVuY/Ln1hSmxXWES20VKbGeqeez687gPy8/jVZ7XXia3N4vt2N/lRq51v8j7LNJzzU6IeoxwNKnhgIKAr6gYPuNobQTDbtceV+soCJccb7Uas5WEUFt5zWO2z6WM2SXKcxSYjNm/ynFrrP1ea1MvvPJJiOFQctIb5lmJas2bm39svDaJa/Vc3FDGa6NWcS2Pz3aTuBaHlPFaOX44lLJNZuN1/K9yI+PrLVqbeqGO18q5hWFy3kPBjb+gExEREREREVkAJ+hEREREREREFsAJOhEREREREZEFNNmkQ29WtrbOVirnftrCWkrLYXY9x6MsQb7vsbtEvjehK88kL0RZJZTcEaGk5IRnKgluAOy7DkjL3vrKOVd48+V7ptrUe6qWKUm1RI1AVJRDGEdjwJN5QHvcrnxuba5Uadlp17+r9DnkHC93qZzbGVZYc86XocSlGuvhh/T4ce45LC176ivnXOFT7odsqPdB5z1UyULUsQjQxyMjwi0tO/NM8k7tcn6rK0eJ6/ya71JcY1wfUJJXAdh27ZeW9bu614+axmze85ysxOxc3F4hn0fbwuWYDTOJ6zK7ci6eLcexK+8E7j6uDPHhB/Rzcdtu5Vy87s9SK96cPGlZfY/Mjo8U3PgLOhEREREREZEFcIJOREREREREZAGcoBMRERERERFZACfoRERERERERBbQZIvEmfEpxZgc2XJRBpszQdvGfUguPOENlwtLOQr0IlBCKTZnlCllJZSvTYxSuXjT0Z2cQMGLesACMxSM1EI0jtgYadnn1g+Friy5uJLXLVdvdB/R41LY5Ni2KUUj1di2lSqPA0ADFXzUnsYjP7dgUTgKMup45MiRCyUJtz5mu7LkMdmrxL7ZmA2bHLhGiRL7tRmzjZqLSjYEjtkUbNSCZ468WGnZ55bPswE9rn3KeO0okMdzwGS8VuNWjfsyPa4Nh11b1yB88jyBReFCH39BJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiILYA56NXw5udKyER2pNwqTc2GcxXKei5Ev57Wb7dcWEy03cIXV3DmH/KcznPI2okLPtyGio0RRsbRsK9VjW7jk3DJ7rpyzbSswye3MlfPCjHC3vE9nLQ65Sk6bPUbOl2fuGZE5UVAoLdviorU2vjA5BtUx25YvHxsAQGTnSstGdJTcoFZxrYzZLpf8HGUmue9EpI/XZVF6G6cybqpxXVCib5OnjNcREfLjLj3XXaPEtT1Ozpf35sq1rIhqi7+gExEREREREVkAJ+hEREREREREFsAJOhEREREREZEFMAe9Gto9VvML9UZRcs6KUSTnuXgyD9T8PMr919WcUyNSfo6jK5X7N0aGS8veXOagE1VFFMtxais0yU/zynUdjBIlpg5naduo+WZqbQibkp9mKHF7dKVyv2Qljx3MQScy5SuWc1XthXo+uS1CjidDiX2vyZgtPHL9CTUGT2jMVuLayxx0IlOiRD4XN80nD5drOhjF8ja+g4e1bdTjhaHkpOvjtUlcK/dSh1JbguhE8Rd0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrKAgE/Qc3JyMG7cOMTGxiI2Nhbjxo1Dbm5ule0rKipw9913o3v37oiMjERaWhrGjx+P/fv3B67TRFQtxjVR6GFcE4UexjWR9QW8SNzYsWOxb98+rFixAgBw/fXXY9y4cfjvf/9r2r64uBjfffcd7r//fpx22mnIycnB1KlT8ec//xmbNm1q0L7aIpSCEK4wrY1RIhd2qU1RuJpohSuE0PumFqtQClMYJoUqBIvQUAMJprgG9PgRTv1QaJRWyG0OyEVmfAUFNT6PqJALywmlICSET39epW+G0ykt26KjtW1q0xeiugq6uFZiwzSu1TF73x9yA5PxtibamO31mvQtSl6hFJBUC0oC+vGDqD4EW1wbUZHSsnA59UZlcqx498vn4rU5/1WLQYpCZbxWi0UCMKLkuDYc8jGH4zWdqIBO0H/++WesWLEC69evR9++fQEAr7zyCvr374/t27ejU6dO2jaxsbFYuXKltO6f//wn+vTpg4yMDLRu3TogfScic4xrotDDuCYKPYxrouAQ0Evc161bh9jYWP9BAQD69euH2NhYrF27ttb7ycvLg2EYiIuLa4BeElFdMK6JQg/jmij0MK6JgkNAf0E/cOAAmjVrpq1v1qwZDhyo3aXhpaWlmD59OsaOHYsY5d6jlcrKylB23OUseXlH703sQQVQh6vXbEK+ZMbmM7lExidfyuYRFXqbOlIvaTeEXe+bT/7TCWUbn0k/RD30jRqHB0f/durf2QoCFddA/cU2fMql516T2FYuUxXK8cAsxmqiHlMMnx7b2jqffBm8T+iXvZ5IX6jxMa6Pqq+4VmMUJnFt1DRmn8DfQh+zDa2NTT3maHHNMTuUWDW2gzKufTXHNbzy5edqPJ1ILNmE/BumWVwb6rxAOxfneB1KAhnX9TJBf/DBB/HQQw9V2+abb74BABiG/gEXQpiuV1VUVODKK6+Ez+fD888/X2W7WbNmmfbnKyyv8TkkxcpyRt02P2FqmotZugpTWJqkrKwsxMbGBuS5rBbXQD3G9sEalhuKekxRlwEgKxAdISthXNdTXOfUsNxQ1DFbT1UFikzWUcgLVGyHdFwfqmG5oZTWsAwAuQHoB1lOIOLaEPXwNcCRI0dw5MiRatu0adMGb7/9NqZNm6ZVi4yLi8PTTz+Nv/zlL1VuX1FRgTFjxuD333/HqlWrkJiYWGVb9Vu73NxcpKenIyMjI2AnQScrPz8frVq1wt69e6v9htIq2N+GlZeXh9atWyMnJydgl5RZLa6B4I/tYPvcsb8Ni3F9FOM6sIKtv0Dw9TnQsc24rn/B9pkDgq/PwdbfQMZ1vfyCnpSUhKSkpBrb9e/fH3l5edi4cSP69OkDANiwYQPy8vIwYMCAKrerPCjs2LEDq1evrvGg4HK54DKpYh4bGxsUH4DjxcTEBFWf2d+GZbMFrmyE1eIaCJ3YDrbPHfvbsBjXjOvGEGz9BYKvz4GKbcZ1wwm2zxwQfH0Otv4GIq4DWiSuS5cuOP/88zF58mSsX78e69evx+TJk3HRRRdJlSM7d+6MJUuWAAA8Hg8uv/xybNq0CfPnz4fX68WBAwdw4MABlJfzFiREjY1xTRR6GNdEoYdxTRQcAjpBB4D58+eje/fuGDZsGIYNG4YePXrgzTfflNps377dX0xi3759WLZsGfbt24eePXsiNTXV/68uFSeJqOEwrolCD+OaKPQwromsL6BV3AEgISEBb731VrVtjk+Lb9OmzUlXy3O5XJg5c6bppTZWFWx9Zn8bltX72xhxDVj/fVGxvw2L/a1fjOvaYX8bXrD12cr9ZVzXTrD1Fwi+PrO/VauXInFEREREREREdHICfok7EREREREREek4QSciIiIiIiKyAE7QiYiIiIiIiCwgZCfoOTk5GDduHGJjYxEbG4tx48YhNze3yvYVFRW4++670b17d0RGRiItLQ3jx4/H/v37LdlfAFi8eDGGDx+OpKQkGIaBLVu2NGgfn3/+ebRt2xZutxu9e/fGl19+WW37zz//HL1794bb7Ua7du3w4osvNmj/VHXpb2ZmJsaOHYtOnTrBZrNh6tSpgevo/9Slv4sXL8bQoUORnJyMmJgY9O/fHx9//HEAe9s4gi2uT6TPQGBjm3HdsBjXtRNssc24rl/BFtcAY7s2GNf1K9jiGgi+2LZMXIsQdf7554tu3bqJtWvXirVr14pu3bqJiy66qMr2ubm5YsiQIWLhwoXil19+EevWrRN9+/YVvXv3tmR/hRDijTfeEA899JB45ZVXBACxefPmBuvfO++8I5xOp3jllVfEtm3bxO233y4iIyPFnj17TNv//vvvIiIiQtx+++1i27Zt4pVXXhFOp1O89957DdbHk+nvrl27xG233SZef/110bNnT3H77bcHpJ8n2t/bb79dPP7442Ljxo3i119/Fffcc49wOp3iu+++C2i/Ay3Y4vpE+ixE4GKbcW2t/jbVuBYi+GKbcd14/W3suBaCsV1bjOv6E2xxfSJ9buzYtlJch+QEfdu2bQKAWL9+vX/dunXrBADxyy+/1Ho/GzduFACq/MPUl5Pt765duxp8gt6nTx8xZcoUaV3nzp3F9OnTTdvfddddonPnztK6G264QfTr16/B+ni8uvb3eIMGDQr4QeFk+lvp1FNPFQ899FB9d80ygi2uhbB+bDOuGxbjunaCLbYZ1/Ur2OJaCMZ2bTCu61ewxbUQwRfbVorrkLzEfd26dYiNjUXfvn396/r164fY2FisXbu21vvJy8uDYRiIi4trgF4eU1/9bSjl5eX49ttvMWzYMGn9sGHDquzfunXrtPbDhw/Hpk2bUFFR0WB9BU6sv42pPvrr8/lQUFCAhISEhuiiJQRbXAPWjm3GdcNiXNdesMU247r+BFtcA4zt2mJc159gi2sg+GLbanEdkhP0AwcOoFmzZtr6Zs2a4cCBA7XaR2lpKaZPn46xY8ciJiamvrsoqY/+NqQjR47A6/UiJSVFWp+SklJl/w4cOGDa3uPx4MiRIw3WV+DE+tuY6qO/Tz75JIqKijBmzJiG6KIlBFtcA9aObcZ1w2Jc116wxTbjuv4EW1wDjO3aYlzXn2CLayD4YttqcR1UE/QHH3wQhmFU+2/Tpk0AAMMwtO2FEKbrVRUVFbjyyivh8/nw/PPPW76/gaL2pab+mbU3W99Q6trfxnai/V2wYAEefPBBLFy40HRwsbpgi+tA9jkQGNcNq6nGNRB8sc24ltubrW8owRbXQNONbcZ14wm2uK6qD1Z5P81YJa4dJ72HALrllltw5ZVXVtumTZs2+OGHH3Dw4EHtscOHD2vfjKgqKiowZswY7Nq1C6tWrTqpb+wC0d9ASEpKgt1u175BOnToUJX9a968uWl7h8OBxMTEBusrcGL9bUwn09+FCxfiuuuuw7vvvoshQ4Y0ZDcbTLDFdaD63NAY1w2rqcc1EHyxzbiW2zOuzTX12GZcB16wxTUQfLFtubg+6Sx2C6os9LBhwwb/uvXr19dY6KG8vFyMGjVKdO3aVRw6dCgQXRVCnHh/KwWqSNyNN94orevSpUu1xSm6dOkirZsyZUpAi87Upb/Ha6zCFHXt79tvvy3cbrdYsmRJA/fOGoItroWwfmwzrhsW47p2gi22Gdf1K9jiWgjGdm0wrutXsMW1EMEX21aK65CcoAtx9FYJPXr0EOvWrRPr1q0T3bt3126V0KlTJ7F48WIhhBAVFRXiz3/+s2jZsqXYsmWLyMzM9P8rKyuzXH+FECIrK0ts3rxZfPjhhwKAeOedd8TmzZtFZmZmvfev8tYDc+fOFdu2bRNTp04VkZGRYvfu3UIIIaZPny7GjRvnb195e4c77rhDbNu2TcydO7dRbttS2/4KIcTmzZvF5s2bRe/evcXYsWPF5s2bxU8//WTJ/r799tvC4XCIf//739JnNTc3NyD9bSzBFtcn0mchAhfbjGtr9bepxrUQwRfbjOvG668QjRvXJ9LnphrbjOv6E2xxfSJ9FoJjdqWQnaBnZWWJq6++WkRHR4vo6Ghx9dVXi5ycHKkNAPHaa68JIY5982X2b/Xq1ZbrrxBCvPbaa6b9nTlzZoP08d///rdIT08XYWFh4vTTTxeff/65/7EJEyaIQYMGSe3XrFkjevXqJcLCwkSbNm3ECy+80CD9qq/+mr2X6enpluzvoEGDTPs7YcKEgPW3MQRbXJ9In4UIbGwzrq3T36Ya10IEX2wzrhu3v40d13Xtc1ONbcZ1/Qq2uBYi+GLbKnFtCPG/igFERERERERE1GiCqoo7ERERERERUajiBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgBN0IiIiIiIiIgvgBJ2IiIiIiIjIAjhBJyIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiIL4ASdiIiIiIiIyAI4QSciIiIiIiKyAE7QiYiIiIiIiCyAE3QiIiIiIiIiC+AEvZE8+OCDMAyj3vb37bff4uabb0b37t0RHR2NlJQUDBkyBKtWraryudV/brfbdN9HjhzB7bffjjZt2sDlciElJQUjRoxAdna21K6wsBBTp05FWloa3G43evbsiXfeecd0n9999x2GDBmCqKgoxMXF4dJLL8Xvv/9eq9f66KOPYunSpbVqWxczZsxAr169kJCQALfbjXbt2uH666/Hnj17tLb33XcfLrroIrRo0QKGYWDixIlV7nf+/Pno1asX3G43kpKSMHbsWOzdu1drV1BQgNtuuw0tWrSAy+VCx44dMXv2bHi9Xq3txo0bMXz4cERHRyMqKgqDBw/G119/fVKvn05efcf13r17cckll6Bdu3aIjIxEbGwsevXqhX/961/weDxS2zlz5mDUqFFo06YNwsPD0b59e9x4443IzMw03fc777yDnj17wu12Iy0tDVOnTkVhYaHWrinF9c6dOzFu3Di0bt0a4eHhOOWUUzBt2jRkZWVJ7RYsWIBzzjkHKSkpcLlcSEtLw8UXX4y1a9dq+2Rch4bGjG0AWLRoEc466ywkJCQgLi4Offr0wZtvvlntcxw8eBCJiYkwDAPvvfee9niwx/bxSkpK0LFjRxiGgX/84x/Vtv3000/95z1HjhyRHmvTpo3p+ZHZORJjm4gajKBGMXPmTFGfb/+dd94pzjjjDPHUU0+Jzz77TCxbtkxccMEFAoB4/fXXTZ97xYoVYt26df5/GzZs0Pb7xx9/iHbt2omOHTuKOXPmiM8//1wsWrRI3HLLLSIzM1NqO3ToUBEXFydefPFFsWrVKjFp0iQBQMyfP19q9/PPP4vo6GgxcOBA8eGHH4pFixaJrl27irS0NHHo0KEaX2tkZKSYMGFC3d+kGtx0003i8ccfF8uWLROrV68W//73v0VqaqpISUkRR44ckdpGRESIfv36iSlTpoiwsLAq+/Pcc88JAGLSpElixYoVYs6cOSI1NVWkp6eL7Oxsf7uKigrRt29fER8fL/71r3+JTz75REybNk0YhiFuvfVWaZ8bN24ULpdLDBw4UCxZskQsXrxY9OvXT7hcLrF27dp6f1+o9uo7rn/++Wcxfvx48eqrr4pPP/1ULF++XNxyyy0CgLjuuuuktmlpaeLqq68W8+fPF2vWrBEvvfSSaNmypUhNTRUHDhyQ2r711lv+z+WqVavEiy++KGJjY8XQoUO1PjSVuD506JBITEwUbdu2FfPmzROrVq0STz75pIiKihI9e/YUXq/X3/af//ynmD59unjvvffEmjVrxIIFC8SZZ54p7Ha7WLNmjb8d4zp0NGZsz507VwAQl112mVi+fLn46KOPxJVXXikAiKeeeqrK57jssstEWlqaACDeffdd7fFgj+3j3Xnnnf7X+sQTT1TZrqCgQLRp08bf9vDhw9Lj3333nXRetG7dOrFw4UIBQFx55ZX+doxtImpInKA3kvoe7A8ePKit83g8okePHuKUU04xfW51YDIzcuRI0aJFC2kyaebDDz8UAMTbb78trR86dKhIS0sTHo/Hv2706NEiKSlJ5OXl+dft3r1bOJ1Ocdddd9XYp0AM9pWWL18uAIi5c+dK648/Wa+qP6WlpSI2NlZcfPHF0vq1a9cKAOLee+/1r1uwYIEAIBYtWiS1vf7664XNZhO//PKLf93w4cNFSkqKKCoq8q/Lz88XSUlJYsCAASf0Oql+1HdcV2XMmDHC4XCI0tJS/zqzY8A333wjAIi//e1v/nUej0ekpqaKYcOGSW3nz58vAIjly5f71zWluH7llVcEAPHpp59KbR999FEBQHz33XfV7jM3N1c4nU4xbtw4/zrGdehozNg+66yzRHp6ujTu+Hw+0blzZ9GjRw/T/bz33nsiKipKvP7666YT9FCK7Q0bNoiwsDDx7rvv1jhBv/nmm0WvXr3EfffdV+vzoAcffFA7NjC2iaghBf0l7ocPH8b111+PVq1aweVyITk5GWeddRY+/fRTf5uVK1di5MiRaNmyJdxuN9q3b48bbrhBu7Sp8hK2H374AaNHj0ZsbCwSEhIwbdo0eDwebN++Heeffz6io6PRpk0bzJ49W9p+zZo1MAwDb731FqZNm4bmzZsjPDwcgwYNwubNm2v1ehYuXIj+/fsjMjISUVFRGD58eK22bdasmbbObrejd+/eppdT18bu3buxbNkyTJ48GfHx8dW2XbJkCaKiojB69Ghp/V/+8hfs378fGzZsAAB4PB588MEHuOyyyxATE+Nvl56ejsGDB2PJkiXVPo9hGCgqKsLrr7/uv+zs3HPP9T++detWjBw5EvHx8f5L9l5//fU6vvJjkpOTAQAOh0Nab7PVHDpbt25FXl4eLrjgAml9//79kZCQgEWLFvnXff311zAMAyNGjJDaXnTRRfD5fNL78vXXX+Pcc89FRESEf110dDTOOeccrF27tspLmoMJ47p6ycnJsNlssNvt/nVmx4DevXvDbrdLx4D169cjMzMTf/nLX6S2o0ePRlRUlPRZa0px7XQ6AQCxsbFS27i4OACoMgWoUnR0NNxut7RPxrWOsV09s9h2Op2IioqSxh3DMBATE2P6uczOzsbNN9+MRx55BK1btzZ9nlCJ7fLyclx77bW4+eabccYZZ1Tb9ssvv8TLL7+MOXPmSO9vdYQQeO2119CuXTv86U9/8q9nbBNRQwr6Cfq4ceOwdOlSPPDAA/jkk08wZ84cDBkyRMoZ/O2339C/f3+88MIL+OSTT/DAAw9gw4YNOPvss1FRUaHtc8yYMTjttNOwaNEiTJ48GU8//TTuuOMOjBo1ChdeeCGWLFmCP/3pT7j77ruxePFibft7770Xv//+O+bMmYM5c+Zg//79OPfcc2vM13r00Udx1VVX4dRTT8V//vMfvPnmmygoKMDAgQOxbdu2Or83Ho8HX375Jbp27Wr6ePfu3WG325GSkoLx48cjIyNDevzLL7+EEAJpaWm46qqrEBUVBbfbjXPPPRfr1q2T2m7duhVdunTRJrI9evTwPw4c/VuUlJT416ttd+7cidLS0ipf07p16xAeHo4LLrgA69atw7p16/D8888DALZv344BAwbgp59+wnPPPYfFixfj1FNPxcSJE7UTs+p4PB6UlJRg8+bNmDp1Kjp27IhLL7201ttXKi8vBwC4XC7tMZfLhR07dvhfa3l5OWw2m3+ScHw7APjhhx+k/Va1TwD48ccf69xXq2Fcy4QQ8Hg8yMnJwcKFCzFv3jzceeedWrypPv/8c3i9XukYUBmLagw6nU507tzZ/3hl26YS16NGjULr1q1x55134qeffkJhYSG++OILPPbYY7j44ovRpUsXbZ9erxcVFRXYvXs3brzxRgghcPPNN/sfZ1zrGNuy2sT2rbfeip9//hmPPPIIDh8+jCNHjuAf//gHvv32W/z1r3/V9nnbbbehbdu2uOWWW6p83lCJ7YcffhhFRUX429/+Vm27kpISXHfddZg6dSpOP/30Wu0bOJqvvmfPHlx77bVSDQLGNhE1qEb9/b4eREVFialTp9a6vc/nExUVFWLPnj0CgHj//ff9j1Vewvbkk09K2/Ts2VMAEIsXL/avq6ioEMnJyeLSSy/1r1u9erUAIE4//XTh8/n86ysvBZs0aZL2XJUyMjKEw+HQcpcKCgpE8+bNxZgxY2r9GivNmDFDABBLly6V1r/xxhvikUceEcuXLxerVq0Sjz32mEhISBApKSli3759/nazZs0SAERMTIwYOXKkWLFihVi0aJHo0aOHcLvd4vvvv/e37dChgxg+fLjWh/379wsA4tFHHxVCCPH1118LAGLBggVa28pLSffv31/t66rqcrkrr7xSuFwukZGRIa0fMWKEiIiIELm5udXuVwghMjMzBQD/v759+4o//vjjhPqTlZUlbDablk+4c+dO//4rX+szzzwjAIgvv/xSanv//fcLANLlyD179hQdO3aULnesqKgQ7dq1M71kMRgxrmWVsQhAGIYhZsyYUeM2+fn5okuXLqJVq1aioKDAv/6RRx4RALQaEkIIMWzYMNGxY0f/clOL6/3794v+/ftLbUePHi1dbny8Tp06+dulpqaKr776Snqcca1jbMtqG9tLly4VsbGx/rbh4eHirbfe0tp98MEHwul0ih9//FF6jeol7qEQ25s3bxZOp1OsWLFCCCHErl27qrzE/c477xTt2rUTxcXFQojap/pdccUVwm63S+dGQjC2iahhBf0v6H369MG8efPw97//HevXrzf9dv3QoUOYMmUKWrVqBYfDAafTifT0dADAzz//rLW/6KKLpOUuXbpolzI5HA60b9/etBLw2LFjpW9a09PTMWDAAKxevbrK1/Hxxx/D4/Fg/Pjx8Hg8/n9utxuDBg3CmjVranwvjjdnzhw88sgjuPPOOzFy5EjpsXHjxuHee+/FiBEjMHjwYNx999346KOPcPjwYelba5/PBwBo2bIlFi1ahOHDh+PSSy/FihUrYLPZtG+4q6twqz5Wl7a1tWrVKpx33nlo1aqVtH7ixIkoLi7WfvU3k5SUhG+++QZfffUVXnnlFWRnZ2Pw4MEndAlaQkICrr76arzxxht46aWXkJ2djR9++AFXX321//K6yksWr776aiQkJOD666/Hhg0bkJubiwULFuC5556T2gFHf0359ddfccstt+CPP/7A3r17MWXKFP9nsTaX31sd41o2ceJEfPPNN/j4449x11134YknnsCtt95aZfvS0lJceuml2LNnD959911ERUVpbaqKs5OJ1WCO65ycHIwcORL5+fmYP38+vvjiCzz//PP46quv8Oc//7nKytobNmzAu+++i1NPPRUjRoyQ/qaMax1jW1ab2F6xYgWuueYaXHrppfjoo4+wcuVKTJo0CRMnTsRrr73mb5eXl4cbbrgBd999N7p161bjcwdzbHs8Hlx77bW44oorMHz48GqfZ+PGjXjmmWfw0ksvITw8vNb9y87OxtKlS3H++eejRYsW0mOMbSJqSNVfHxkEFi5ciL///e+YM2cO7r//fkRFReGSSy7B7Nmz0bx5c/h8PgwbNgz79+/H/fffj+7duyMyMhI+nw/9+vVDSUmJts+EhARpOSwsDBEREVquV1hYGPLz87Xtmzdvbrru+++/r/J1HDx4EABw5plnmj5elwP4a6+9hhtuuAHXX389nnjiiVpt06dPH3Ts2BHr16/3r0tMTAQADBkyRMrXSk1NxWmnnYbvvvtOaqveigiA/1Zsle9p5T6ramsYhj/ns66ysrKQmpqqrU9LS6vyOVUOh8Ofx3bWWWfh/PPPR9u2bfHYY4/h2WefrXOfXnjhBQghcNNNN2HKlCmw2WwYN24cUlJS8PHHH/vfj6SkJKxYsQITJkxAv379ABx9r5566ilcd9110snBtddei8OHD+Pvf/87XnjhBQBH89r/+te/4vHHH9dOJIIR41p/nsrnHzZsGOLj4zF9+nRce+216NWrl9S2rKwMl1xyCb766it88MEH6Nu3r/T48TGYkpIiPZadnS29T00prh9//HFs2bIFe/bs8T/fwIED0blzZ/zpT3/C/PnzMWHCBGm/lakDffr0wahRo9CrVy/cfvvt/s8E41rH2Nafp7rYFkLg2muvxTnnnINXX33Vv92QIUOQl5eHW2+9FWPGjEFkZCRmzJgBp9OJW265Bbm5uQDgv3VicXExcnNzERsbC8Mwgj62n3nmGfz+++/4z3/+43+tlX/b0tJS5ObmIjo6Gna7Hddeey0uvfRSnHHGGf62lZfl5+fnw+VyITo6WnuOt956C2VlZZg0aZL2GGObiBpS0E/Qk5KS8Mwzz+CZZ55BRkYGli1bhunTp+PQoUNYsWIFtm7diu+//x7z5s2TTq527tzZYH06cOCA6brKgc5MUlISAOC9997z/1JwIl577TVMmjQJEyZMwIsvvlinb7aFENJJhVnOWVVtu3fvjgULFsDj8Ug5bZW5VZXf5p9yyikIDw83zbn68ccf0b59+xqLMVUlMTHR9Jfu/fv3Azj2HtdFy5YtkZaWhl9//fWE+hQZGYk333wTzz33HPbu3Yu0tDQkJSWhc+fOGDBggPRenXnmmdi2bRt2796NoqIidOjQAd9++y0A4JxzzpH2e/fdd2Pq1KnYsWMHoqOjkZ6ejhtuuAGRkZHo3bv3CfXVShjX1evTpw8A4Ndff5Um6GVlZRg1ahRWr16N999/H+edd562bffu3QEcjbdTTz3Vv97j8eCXX37BVVddJbVtKnG9ZcsWtGjRQpswVE7Ajs/NN+NwOHD66afjP//5j7Y94/oYxnb11Ng+ePAgMjMzccMNN2htzzzzTLzxxhvYvXs3unbtiq1bt2L37t2mXzhUvpc5OTmIi4sL+tiuLMLaoUMH7bH7778f999/PzZv3oyePXvip59+wk8//YR3331Xa3vKKafgtNNOw5YtW7TH5s6di5SUFO0KjUqMbSJqKCF1XU3r1q1xyy23YOjQof5fdysnqGqBjpdeeqnB+rFgwQIIIfzLe/bswdq1a6XKparhw4fD4XDgt99+wxlnnGH6rybz5s3DpEmTcM0112DOnDl1mpyvX78eO3bs8H8TDAB9+/ZFy5Yt8cknn8Dr9frX79+/H99//73U9pJLLkFhYaFUmRwAXn/9daSlpfl/xXM4HLj44ouxePFiFBQU+NtlZGRg9erVtSrG5nK5TH9FOe+887Bq1Sr/4F7pjTfeQEREhNTf2tq5cyf27duH9u3b13nb48XHx6NHjx5ISkrCsmXLsH37dtx+++2mbdu0aYOuXbvC6XTiySefRFpamlZpFzj6PnTr1g3p6enIyMjAwoULMXny5DpdwhcMmnpcm6m89Pb4z2XlL+erVq3yp6SY6du3L1JTUzFv3jxp/XvvvYfCwkIpBptSXKelpWHfvn34448/pLaVl9m2bNmy2n2WlpZi/fr1VR4rGNc6xrZOje3K6ubHX91Wad26dbDZbP4vlZ555hmsXr1a+vf0008DOFrxfvXq1f50l2CP7enTp2uvdcGCBQCAKVOmYPXq1f73UG23evVq/xcWS5cuxZw5c7T9b9q0CT/88AMmTJhQYzFOxjYR1beg/gU9Ly8PgwcPxtixY9G5c2dER0fjm2++wYoVK/yDRufOnXHKKadg+vTpEEIgISEB//3vf7Fy5coG69ehQ4dwySWXYPLkycjLy8PMmTPhdrtxzz33VLlNmzZt8PDDD2PGjBn4/fffcf755yM+Ph4HDx7Exo0bERkZiYceeqjK7d99911cd9116NmzJ2644QZs3LhRerxXr17+E57TTjsN11xzDbp06QK3242NGzfiiSeeQPPmzXHXXXf5t7HZbHj66acxZswYjBw5EjfeeKO/WmpYWJj0ekaMGIGhQ4fixhtvRH5+Ptq3b48FCxZgxYoVeOutt6RL5B966CGceeaZuOiiizB9+nSUlpbigQceQFJSEu68884a39/u3btjzZo1+O9//4vU1FRER0ejU6dOmDlzJj744AMMHjwYDzzwABISEjB//nx8+OGHmD17tnb7pOP98MMPuOOOO3D55ZejXbt2sNls+PHHH/H0008jMTFRq5T7+eef4/DhwwCOVnLes2cP3nvvPQDAoEGD/LdxWrRoEfbv348uXbqgtLQUa9aswbPPPospU6ZotQFmzJiB7t27IzU1FRkZGXj11VexYcMGfPjhh9IAvnXrVixatAhnnHEGXC4Xvv/+ezz22GPo0KFDjZVsgwHj+piZM2fi4MGDOOecc9CiRQvk5uZixYoVeOWVVzB69Gjpl5fLL78cH330EWbMmIHExETphD4mJsb/a7ndbsfs2bMxbtw43HDDDbjqqquwY8cO3HXXXRg6dCjOP/98/3ZNKa5vvvlmzJ8/H0OHDsX06dPRqlUrbN26FX//+9+RkpKCq6++2t92wIAB+POf/4wuXbogNjYWu3fvxgsvvIDffvtNu+0U4/oYxvYxtY1tl8uFm266CU899RTGjx+PK664Ana7HUuXLsXbb7+N6667zn85es+ePat8vq5du0pfOAR7bHfu3BmdO3eW1u3evRvA0V/Fj3+tZl+0VNYIOOuss0x/qZ87dy4A4LrrrquyD4xtImowjVWdrj6UlpaKKVOmiB49eoiYmBgRHh4uOnXqJGbOnCmKior87bZt2yaGDh0qoqOjRXx8vBg9erTIyMgQAMTMmTP97aqq6jlhwgQRGRmpPf+gQYNE165d/cuV1VLffPNNcdttt4nk5GThcrnEwIEDxaZNm6Rt1YqwlZYuXSoGDx4sYmJihMvlEunp6eLyyy8Xn376abXvxYQJE6TKw+q/Xbt2+dteeeWVon379iIyMlI4nU6Rnp4upkyZUmUl1qVLl4ozzzxTuN1uERsbK/785z+Ln376SWtXUFAgbrvtNtG8eXMRFhYmevToYVr5VQghNm3aJM477zwREREhYmJixKhRo8TOnTurfY2VtmzZIs466ywREREhAIhBgwb5H/vxxx/FxRdfLGJjY0VYWJg47bTTxGuvvVbjPg8cOCCuueYaccopp4iIiAgRFhYm2rVrJ6ZMmaJVmBXi6N++qvd69erV/nZLliwRPXv2FJGRkSI8PFycccYZYu7cuVLF4Eo33nijaN26tQgLCxNJSUnisssuEz/88IPWbvv27eKcc84RCQkJIiwsTLRv317cd999orCwsFbvn9Uxro9ZtmyZGDJkiEhJSREOh0NERUWJPn36iOeee05UVFRIbauL/+NjpNLbb78tevToIcLCwkTz5s3FbbfdJlV7r9SU4vq7774Tl1xyiWjZsqVwuVyiXbt2YtKkSVrbO++8U5x22mkiNjZWOBwO0bx5c3HJJZeIr7/+Wtsn4/oYxvYxdYltr9crXnnlFXHGGWeIuLg4ERMTI3r16iX+9a9/ifLy8mqfp6oq7kIEd2ybqa6Ku6q6Ku7FxcUiNjZWnHPOOdXug7FNRA3FEOK467ropKxZswaDBw/Gu+++i8svv7yxu0NE9YBxTRSaGNtERGRFIZWDTkRERERERBSsOEEnIiIiIiIisgBe4k5ERERERERkAY32C/rzzz+Ptm3bwu12o3fv3vjyyy+rbLt48WIMHToUycnJiImJQf/+/fHxxx8HsLdEVBuMa6LQw7gmIiIKnEaZoC9cuBBTp07FjBkzsHnzZgwcOBAjRoxARkaGafsvvvgCQ4cOxfLly/Htt99i8ODBuPjii7F58+YA95yIqsK4Jgo9jGsiIqLAapRL3Pv27YvTTz8dL7zwgn9dly5dMGrUKMyaNatW++jatSuuuOIKPPDAAw3VTSKqA8Y1UehhXBMREQWWI9BPWF5ejm+//RbTp0+X1g8bNgxr166t1T58Ph8KCgqQkJBg+nhZWRnKysqk9tnZ2UhMTIRhGCfeeaJGIoRAQUEB0tLSYLNZr7ZjIOIaYGxTaGFcH8W4plBj9dgmImsL+AT9yJEj8Hq9SElJkdanpKTgwIEDtdrHk08+iaKiIowZM8b08VmzZuGhhx466b4SWc3evXvRsmXLxu6GJhBxDTC2KTQxrhnXFJqsGttEZG0Bn6BXUr8VF0LU6pvyBQsW4MEHH8T777+PZs2amba55557MG3aNP9yXl4eWrdujbNxARxwnlzHG4DhclXfwKdnIYiK8gbqTeOwueX3wFfukRv4vAHsjfV4UIGvsBzR0dGN3ZVqNWRcA8EX202N4QzT1tmiI6VlcdwvpQDgKypu0D5ZGeP6KMZ18FFjPdTOSU5WsMQ2EVlTwCfoSUlJsNvt2rfvhw4d0r6lVy1cuBDXXXcd3n33XQwZMqTKdi6XCy6TSa8DTjgM6w32Rk19Mkwm6CbrgpnNkAd7n3ryZzTxS8T+9+e26uWegYhrIPhiu6kxO5bZbMqJvHLs8hkVDdonS2NcA2BcByM11kPtnOSkWTy2icjaAj7rCQsLQ+/evbFy5Upp/cqVKzFgwIAqt1uwYAEmTpyIt99+GxdeeGFDd5OI6oBxTRR6GNdERESB1yiXuE+bNg3jxo3DGWecgf79++Pll19GRkYGpkyZAuDo5W5//PEH3njjDQBHB/vx48fj2WefRb9+/fzf5oeHhyM2NrYxXkK9EuXKpWFqYX2Tb2Btbre8iden7ENZBiA8Hm1dIBgO+WNmi4rU24SHyytycqVFX2nTvsQ9GDCumx4tthPi9DaREfIKX568XFRUz72i+sS4JntMjLZOjWtvdo60rKayEBFR7TXKBP2KK65AVlYWHn74YWRmZqJbt25Yvnw50tPTAQCZmZnSPVZfeukleDwe3Hzzzbj55pv96ydMmIB58+YFuvtEZIJxTRR6GNdERESB1Sj3QQ+0/Px8xMbG4lyMtGY+m/oLeW1+QVfy9ULtF3Sf9gt6ab33K5h4RAXW4H3k5eUhxuTXjKbK8rEd4rTYTtRvpaX+0iZy5F/QvTnyL29NCePaHOPaWvgLet0xtonoZDTxyltERERERERE1sAJOhEREREREZEFNNp90Ok4NWUZmDyuXtJui1XutVmu37rIm59f566dCFuEfOmbLSFeWhaRSkE4AMInvx6jpERu0MQvcaemS40nUaGnqgTsHsRKuo09KVHuh3ocgv9uQ8dtxO+FiWyRcqqXMBmzG+ve4vbkZHlFnElcK+cltqJiadnbxC9xJyI6GTxTIiIiIiIiIrIATtCJiIiIiIiILIATdCIiIiIiIiILYA56kNJy09RbqIW7tW2MEjmPu6Hy2ww1t07JOfdFybeIAwBbsdIXX8jf/Y+oVtScc5tJDQdvbmByVW3K7RDhCpMWhVMfUgw1t9Yk15aoyfF6pUWz2496c5VYaaC74hrKbVuNCPn8wafEOaDHdRO4Yy8RUcDwF3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgDmoIcKNWdbyW8D9NzV+shbVXPXAACxUXLXYuTnFfKtlI+yyd8V+Up433MiM2b3SzacSi54Q9WXiJZjW0TotS40Sm6qNz+/PrtEFJyUMQ/CpzdRxldfacOMi7aa4trspxwlrn0FBfXcKyKipou/oBMRERERERFZACfoRERERERERBbACToRERERERGRBXCCTkRERERERGQBLBIXInzFxdKyLTyh5o1sdmUnJoXloqOlZSNCLvhmREbofXGbFI47fhthsi6/SFpuqCJXRMFGjQXDaXLYtplVXqyeLTJS3m+4W1mWYx0AhMsprzDk5zV8eqErZOfWuW9EoU4ds+1hsXoju11fV0dqIVe1IBygx7rwynFsenRhXBMRNRj+gk5ERERERERkAZygExEREREREVkAJ+hEREREREREFsAc9BAhPB55ubikxm1sSq64ml8OAKJFM2m5LEnOOfeE15wjZ6uQ89lc2WX685TU3F8i0nNXa0OtJQEARus0abm8mZyT7nHrsa3Wj3CUyMcd5xG9b6Ko7v0lamq8+YX6SpO6MMcznGHaOlt6C2nZkxwjL5vENZS4tpXJz+vI0vvmM+svERHVC/6CTkRERERERGQBnKATERERERERWQAn6EREREREREQWwBz0EOUrKKixjT1Rvle6r22a1ib/FDkvtbiZ/J2OV751MgDAkNNS4cqRE9zs5fq9kp1loX3fczVX0LDL76OvvELfqIb8Q6KqGA7l0N62hdYmv4N83+WiFDk31auXpNBi250tP0+sR0lmBWCYfbZDiJrfbyj3hjerGaDWDCGq1fHeJseorUMbrUlhhzhpubiZvI3Hrd/V3KbErTtbXo7x6GO2qAjtMZuIqDHxF3QiIiIiIiIiC+AEnYiIiIiIiMgCOEEnIiIiIiIisgBO0ImIiIiIiIgsgEXimjDD6ZSWi9P0qlD56fJ3OCXN5WIxPrdePMZWohaSk5edxXLBNACIj5MLVtWmyJ1VOVrqBbkQJr/X8Mnvm620TNvEeyTL//+GEADrSlEtGS6XtFyaFq21KWgtF48qTpULQ3nDTWK7VIntcLnglLNYfl4AiFGKUXoPHzbpsTWp76OtlV5IE05lGK2QA9VeqB9XPQcPVT4DoNfVIzJlc9cc1/mt5c9jcZoS1y49ru2lchx7ItQxW/8MR8bHy/vNyTHpcXBQC+YCgBEmn6cIj1zET5gUf/QVFdVvx4ioyeIv6EREREREREQWwAk6ERERERERkQVwgk5ERERERERkAcxBrwPDqedOi4ryRuhJ/fAlyTlkxcl2rU1pspKvliznSrtdemJ0eZn8sSo15Lw5V7ac7wYA3pQ4ecXefVobq3KkNpeWfYkxWhsRJr8nRkmFtGxT8tsAQHiOvbdCMAG9Idmi9VzOYK6DYEtJlpaLk/VDfUkzOTfVkywfy5xu/TPnKZePEaVQcmKP6N/5RqckyiuCKAddzTn3JkRpbYRTfk/shfIx0iiTY/3oRkL+LzUIm9utrfOVljZCT+qHrVmStFyS7NTaqHFdkSjHsS1cj+uKUuX4YMjLJVn6uUFkqnyMQRDloNuTlb4nxWltfEpc2wqVz43HZExmDjoR1ZNG+wX9+eefR9u2beF2u9G7d298+eWXVbbNzMzE2LFj0alTJ9hsNkydOjVwHSWiWmNcE4UexjUREVHgNMoEfeHChZg6dSpmzJiBzZs3Y+DAgRgxYgQyMjJM25eVlSE5ORkzZszAaaedFuDeElFtMK6JQg/jmoiIKLAaZYL+1FNP4brrrsOkSZPQpUsXPPPMM2jVqhVeeOEF0/Zt2rTBs88+i/HjxyM2Nta0DRE1LsY1UehhXBMREQVWwHPQy8vL8e2332L69OnS+mHDhmHt2rX18hxlZWUoKzuWB5ifn18v+zXLN9dyV71yLrHP5F6ZVqHmTv5/e3cfI1d53n38Omde93V27bUXDH4jL5g0JqVGBpNSVKkxJKEg1IpYjpakTWkRilJjVQ0ERSH5x/mjL1ErCGpKEjUBgto4UlB5nLgKoUTYUOPd8jwxcUiwMca79u56d/Z9duac+/nDYPu672Pvej1z5sz6+5GQuM+cM3N7d35z5p7Z6zpB1q0Nt69zns/resp8NqK+0jKT13VyQZP7tAvyeluSuxd6aT1X06lrzitt7rWg/Yp13fNZ/XMLTzZO/V6UOHItUrtsh5NuTlPWdX7N9LQ+JsG1rMa6Nnfgts+QIKdrVe2a82xEfwlbxXp9CPJurWrY7NbJJpV9PeSwU9ecl9vdH2RqRr/me7P65xYODlVpdvFr+FxHZNTJ9aw+ryf5WtYmPY9ztvUU9fL6+ZnOuv1OykbfT5DTj1PJRzxOc8SLSkI5vQismvOg4F7n3Z+xXv/KetzI130HkHyxr4OGhoYkCALp7u5W27u7u2VgYKAqj7Fjxw4pFAqn/1u5cmVV7hdAtDhyLUK2gTiRawAA4le3Lyo9T38ia4xxti3UQw89JMVi8fR/b7/9dlXuF8D51TLXImQbqAdyDQBAfGL/E/euri5JpVLOp+8nTpxwPqVfqFwuJ7mc+2fGAGojjlyLkG0gTuQaAID4xb5Az2azsmHDBtm9e7fcddddp7fv3r1b7rzzzrinc/HKupbYa9K1TH5oXUdcklO76pV1LVpq1r0mrz+t/8hidta6nrfnHlMpW9cPndH34UeUrXth41wP2G9uPu/t6VH39+vN6Gsjy/CoGia5V8F8NHyuw4jr0Fu1qZKxaqkTkuMontXjIOW2z5BUSX8Dal/j3PPnznbKen3wI15D7GwnOen267ctW4zoQ2JfH3lA15wn5fV+IRo+1xGMdf1qL299MDAd8fuKeH2oB6+kc50uuWlKWefbSsmqJ484Z5tZfUxmWr82RL1+SJDkJGue3SvI1//e1FjEOXtcn5ODgRNVnxcAnEvsC3QRke3bt0tPT49cf/31smnTJvmXf/kXOXLkiNx3330icurP3d555x35t3/7t9PH9PX1iYjIxMSEDA4OSl9fn2SzWfnQhz5Uj38CAAu5BhYfcg0AQLzqskD/1Kc+JcPDw/K1r31N+vv75cMf/rA899xzsnr1ahER6e/vd66xet11153+/1dffVWeeuopWb16tRw+fDjOqQM4B3INLD7kGgCAeNVlgS4icv/998v9998fedt3v/tdZ5sxjfPnVMClilwDiw+5BgAgPkm+3DQAAAAAAJeMun2DvljYDYBSVsMZr7XFOcYLdOM4U47qwFJ73jvH1bi1v83ZZ6ZLN8aazuTVeKrJapwlIp7VcCY/rMfZsYhmdLNWEx6ro68pWU3W6ii05uIf0z9Hz3M/9wpGRmo6J1RfODmpxp71nPQyWeeYemXZVnnrqBq3XtXl7DO9XP97pqx/Tzki2/6Mbh6VG7bGRbcppjejm3Il+btVY7+e95+0dohonNev85+UhmKIFk5MqLHf2qrHdtM4SU4Tz8rbOtct7yxz9plaphsdhln9Ni/I6aZxIiKZ2TlyPeY+p+0ms0nOtdivywOD1u36NUpEpDI2VsMJAcD58Q06AAAAAAAJwAIdAAAAAIAEYIEOAAAAAEACUINeZcFoUY39FrcGXXzP3VYHdl10S987zj5h5ko1Hh/TT5lKs/sZj2+Xk4/o6rTm42XnmNSQrveqJKjm3GbXwwcJniuqJ0l9EOZk1UHn+g45uyzJv0+NM+NWtpsism1Ft2lY15y3vKNruEVEvMGTzrakCoaG6z0F1JrVRyAcH6/TRC5e5n9/62xb0vRBNc6O614SlSb3/YdvlWDnRvXrR9OA+9rnD+n3D273ieSw35cBQNLxDToAAAAAAAnAAh0AAAAAgARggQ4AAAAAQAJQg15j9rWUk6zyzjFnW7NVk9n6/jX6mHZ9XfQo/qwucPMn3Hq28MTQPGYIYCGCYbcOPPd/dF1m8wfWqnHY6l4P2qvoSlNvWl9f2Jtya9Arx0/Me54A5i+IuFZ35qf71HjZ2tVqHLbo66SLiHhWXb59XXCvZF1HXEQqnLMBoGb4Bh0AAAAAgARggQ4AAAAAQAKwQAcAAAAAIAFYoAMAAAAAkAA0iVus/JS7LQwu+G5MSTd0C355UI29edyHl9eN5Izvfi4UTk1d8NzqxtP/ai+lf9Z+od09Jq2jFo7qBl32fYiIhDNn/exNKBI6uwALZ70eBAd/M+chZs49Gptn5VTsbDc3uwcZHczAyjZQT5VDb9V7CokXmWvrPO80/K3SeywAiMI36AAAAAAAJAALdAAAAAAAEoAFOgAAAAAACUAN+iKVam1xtgVjY3WYiUg4M1OXx41i15j6HQU1NpctU+PZ5e7PcXpZRo0rTVYlfkShbnpGb8yPVNQ4NzDhHJOqnKltNUFJ5Ffu/QKXBLvvQzrj7pLV27wm3ftCli9Vw9Jlbc59zHRZ2c7Pne3spK5Bzw/PqnHmnVF3rqXyqf8JSyJH3fsEcA5W7Xd61RVqXF7R6RwysyynxpUm67upiFxnpnSucyetXPePugcFZ9WghyWRI+4uADAffIMOAAAAAEACsEAHAAAAACABWKADAAAAAJAA1KAvUlH15imr3nqxX6/XuaaxiKSuuFyNZ9d0qfHo+3Td6sRK90rvpSW6Ns3krGufRtWpntR1c/kTWTVu7ehwjmk+fuY66JVKxDVXgXf5LbpXgnPN3gbn2dcjb2ly91mia09nr9Djop3tK+eTbT0Waygikhu2s63n1t7u1ss39U+JiEgQzFCDjnPy8/o5m6R+LnGx37eEa69U45PrdC+J8VXu906lTn1SDrN67EWes/X7h+bjOuetBTfX+XdzLSISBNSgA1g4vkEHAAAAACABWKADAAAAAJAALNABAAAAAEgAFugAAAAAACQATeIWqagGaZ7VbCWVy6lxcPxETecUN7+52dkWLO9Q4+Ia3YSn+EHrgNVus63ujgk1bs+W1HiirBvAiYgMdrbqfey5ee5nZanZM01oKuXAuR14j9/Zocdt+vlWGTge42yqz1QqekPGzVily2oWtVq/vhXfb+2/Ztq5j46CzntLtqzG02W3MdTJTt2gr9Kct/ZwX4v98qlGcpWy26gOeI+/dIkaeyV9rgmGhuOcTs1FvW+RKy5Tw/EP6JyPflCfO2dWzzp30dKhs95k53rWzfVkp272GDTZ+7hzTU+fec2pVCI6zwHAPPENOgAAAAAACcACHQAAAACABGCBDgAAAABAAlCDXg+erjtMdXTo2wNdbxyMjV34Q1j15SIiYbuue7Y/nYk6xlg1b0nmWXWpXnOTs89sp/43Tl2mfxflbv3v/eByt8ZvVeuIGi/LjqvxcFnXpIqI/Dq1XI3fKunozUy6P/uWgTO/oUqKz9IaUcrq+2CCUI3Dcf3cmY+oOk1T0DXn3qSuubSzISJiym6tZlL5eV3X7UXkodKq/41Ty/U+s8t13enKZaPOfaxq09lektU16Sdn3WwfSus64WOllBrPTLk/+/KJU3OrlMl1I6pJrqMy6uRaP7e80aJ7jN2vIcms90J+q5uv8hL9vmXicqvm/DL9713W7f5MVrTq91CduSk1Him5/WreSnWq8WipXY2nJ93X4db+M7+fSjnl3A4A88W7AwAAAAAAEqBuC/THHntM1q5dK/l8XjZs2CAvvvjiefd/4YUXZMOGDZLP5+Wqq66Sxx9/PKaZApgvcg0sPuQaAID41GWB/swzz8i2bdvk4Ycflt7eXrn55pvl4x//uBw5ciRy/0OHDsknPvEJufnmm6W3t1e+9KUvyRe+8AX54Q9/GPPMAZwLuQYWH3INAEC86lKD/g//8A/yuc99Tv7iL/5CRES+8Y1vyE9+8hP55je/KTt27HD2f/zxx2XVqlXyjW98Q0RErrnmGtm3b5/83d/9nfzJn/xJnFN32HXbvjX2CrpuSUTETOn6J69J10qHI6MXP6+sW88mFV0nZ7L6up6NVG8exW/RP0dTaHP2KbfourDQ+jGlc7r+vymt61ZF3JrzQlrX+5ZCN1YtGV3vm8nbdYIRPQPOuhsTOjcnzmLKtUhET4OM/r161rXGRUTM+ITeYNWLh2OjFz+vJre3glT081Z8/dlrI9WbR8pYr1Wtbs1o0KT/zYEVKS8/d7btmvPOjH6trhi3rrTVyna6SWfb+O41liu5U7W3gZf866AvulxbmXTO4e3uecNMWz0drAyaKpyz/aa8u9GqbTfWa1BD1ZtH8Jt1jr3ODmefcpv+Nwf2j8nKdXPGzbVdc96WnlHjUuCes5tzOtfFJus11nOPCbJnXoMCjwpSAAsX+yvI7OysvPrqq7J582a1ffPmzfLSSy9FHrNnzx5n/1tvvVX27dsn5bL7YgwgXuQaWHzINQAA8Yv9G/ShoSEJgkC6u7vV9u7ubhkYGIg8ZmBgIHL/SqUiQ0NDcvnll6vbSqWSlM76NnhsAV3QAcxfHLkWIdtAnMg1AADxq9vf4HjWn/YZY5xtc+0ftV1EZMeOHVIoFE7/t3LlyirMGMBcaplrEbIN1AO5BgAgPrEv0Lu6uiSVSjmfvp84ccL51P09l112WeT+6XRali5d6uz/0EMPSbFYPP3f22+/Xb1/AABHHLkWIdtAnMg1AADxi/1P3LPZrGzYsEF2794td9111+ntu3fvljvvvDPymE2bNsmzzz6rtv30pz+V66+/XjIZtwFPLpeTXM5tulULdmM1YzegiTjGy+suJ3YDmnBSNypaiGB01NmWbraa0Q2fvOjHSRTrZy+++9P3QqN3sXpnVab0fYyW3IZcw+UWNQ6sz7kGZ93mYWMl/TufndQNyPJld65+5cxcw8C5OVHiyLVIzNm2G6tZnfq8wP2l2A2owqJuKFiNX2Q4OeVsS4/p5nTB4NBFP06S2N+82jkWEfEqepvVB0qMle2xWfd5NFqOaMB3lpOzbnO6ibLOcmVaP066EpHt4NRcw4Q3f1yUubYaqzkNVf2I7y2suRkrg+GM9WRbgGB83NmWthpRBicWWa6tpncS8RcWXqBznbJ+1J6d6xn3eVS03nMFRj/O6Kyb+wn7fib14/gR/fnOPmf7Ffc1CgDmqy5/4r59+3b513/9V/n2t78tr7/+ujzwwANy5MgRue+++0Tk1Kfp99xzz+n977vvPnnrrbdk+/bt8vrrr8u3v/1teeKJJ+Rv/uZv6jF9ABHINbD4kGsAAOJVl8usfepTn5Lh4WH52te+Jv39/fLhD39YnnvuOVm9erWIiPT396trrK5du1aee+45eeCBB+TRRx+VFStWyD/90z8l4pItAE4h18DiQ64BAIhXXRboIiL333+/3H///ZG3ffe733W23XLLLbJ///4azwrAxSDXwOJDrgEAiE/dFuiLlV0/bte7iYh4qZQ+ZsqtKb1oxq1/qhzrn3Ofhjarr7HrTUw7uzQd17VmLQVdT15u0zWSR5s6nfuYKut9WrK6Vnly1qppFJGTo7qWMHVSRy8fUVqYmThToOqVE16segmwsxwMDddnIhF17JXjg3Pu08js10h/3K0zzR/X9eHNS3XGSh16fKKl3bmPUlnv05qzs+3WUI+O6teQ1IiV7YhWH5nJU3km1/VnP7ciz8d2bXQtzp1R5+z+43rDYsv1pD5H+xn33JkfsM7ZVo7Lbfr91EiuzbmPGSu3TVau7dtFRKZG9ONmR3RFaG7Y/X2lps/8fkxlcf2uAMSrbpdZAwAAAAAAZ7BABwAAAAAgAVigAwAAAACQANSg15h9nXQREWPXs/m6hqpmdWaLrebcEoyN6Q0R15VNWds6ZLUaG0/Xk45HXB91eImufx3MW9fHnnWv5ZoZ05+FNR/T+7T2u7/z/Ikz9XmVysVfZxeL2CKrTbU59f+Dg84+npX/QuaD+pis7gMxHrjZLlrZHsnZ2XY/086M6Sy3HNO3twxEZPv4qWyT6wZRr3PnYs91WdeCR+Z6ZESNC57OtfF1L4lURA+Y0hJdYz6a1b9PP+Kc3TRu5brfWGO3v1Bu6Mw5OxW47/0AYL74Bh0AAAAAgARggQ4AAAAAQAKwQAcAAAAAIAFYoAMAAAAAkAA0iauDVFubGnttunmRhLoxkZmeFls4rZsL+Tnd3MhuqiQiEk5NXcg0a8duiiciXkY/Fc2sbh6zoCY9EccEo0X9uL0H1Xjp1Fo1bhrWDWhERKa79PzDtB777o9e8qO62U9upKzG6aLbUMYfO+v3FdJwphGlOjv1hrT1khuRU/u57zXl5zzGfl7Xjd0AU0S8rG7aFNU4sxrs+/X6dLaXhB9Q41zRet0VkZlO/Zm1ne2U9bIkIpIfsbI9rHdKj7mN4Pzi5LsPQK4bkW+fw63nfRjVHNbKtd96/vO+iEg4ObnAGcbAznqNGunZ72VM3wE17px+vxo3DRac+5hZqpvEhdbLsF9x554b1efo3En9O/Un3Fx742fO2T7ZBnAR+AYdAAAAAIAEYIEOAAAAAEACsEAHAAAAACABqEGvsfTKK51tpfctV+PpZbo+qtysPzdJz7j1UZkpXa+WH9T1UKnhCeeY1PEhNQ7GxiJmXH1+c7Met7c5+5iSrs8L7Br0GrHrVs1rv1LjlsNuDXrr0k5nm7qPcfdnb6yeARLoulWvqcm9o9xZtbthPD+PhuWnRLxT9cJ2PwMRt0bUBFafh/KF/3ztOlQRkfCaNWo8sUL/XkttVk1z2c12esbOtlXTPOL2kkh5+jUjGBlxJ1wL1s81tcTNhpnVtZy1qkF3Htf+ne7XtattR5c5x7QVrN+pXRc8POocE07oOmH7cU1G1+CLiIT5Uz1DQkOukybV7r7mB+tWq/FUt871bKvOX2o2ItfT+rmUG7LO2aMRuT5hnbNj6jXhWc9Zv93t12Cf0+rV4yY4+Bs1zv7Wff3P271AjP5dhMVx5xgnx9btodX3R0TEP6tfiCHbAC4C36ADAAAAAJAALNABAAAAAEgAFugAAAAAACTAJVuDbtdFi7i10WbGqrGy64jFvcavXKVrzseucuvZTq7TdailJbq6KWi16pMr7rWFM0V9H83HdI1Y4bBbH9Vs1eamjutxMHzSOaYqUnquUddod64PnRCRdfq1qN2fiXhunVUHGJiyczvOEgYi79Zh+x1LnZu9ZqvG366fHHNrEJ37uPJyNZ56/xJnn+Hf0f0kZrp0tivtVrYDN9vpMZ2XlmP6tapwSD+GiIj9apayaixrVbvqXON8JqK+3E/I58DWdZqD4yfcfaK2XezDRvQ3eG9bSK7nzc/nnW1eQZ9fzdS0GocTbj8QL63z469dqcaRuf6QPqZk57rV6lUQkevMmD7HNffr9xuFt9x/n3POtm6vXa6t15hyxDk7Zc8mGaLeXwSDg9V/nIheGsFZ2zhnA7gYCXnnBAAAAADApY0FOgAAAAAACcACHQAAAACABGCBDgAAAABAAiSzM1ccwtDZZDcX8aymNH6X2zymfHmHGk9eoZuzDf+O2yymslY3snn/5bqBSWd+So2nKlYjOhE5Wiyo8WiHnof4biOp1IxuSmO3kUuFRmzByIiz7ULZjaOiGklFNVK61J39MzE0nDkvL5MVz3v3OR8E7g52k6OczpR3xWXuId26AdXU5ToxJ3/HbZI0+z6d7atWDKnx0vykGs9U3Jy+PaazPdLeqXcw7jHpSd0kMmv9e/1Z9/kTTk052y6U3SgpqnESUA3GuOcnz3pe2+fs9FIrOyJSXqG3TazQxwxH5Lr0Pt1Ucq2V686cztJM4Gb02Jh+PZnXOXta5zo3qxtg+hHn0jCi4eiFsnMcRrw3kDDidRYAUBV8gw4AAAAAQAKwQAcAAAAAIAFYoAMAAAAAkACXbA16ZJ2WVc+WXn2lPqajxTlkpkvXso6+T3/mYT4w4RzzB2sOqfGmwm/V+IrMSTWeDO1qcZFX29eq8X95H1Tj8XFdqyYi0nxc/7ozY3ruqeYm5xipRg069eWoMVOeFeOdqpMMi+PO7d60zru3oluNo7JdsrP9AV2bGlyt68lFRP5g7ZtqbGd7VWZYjaOy/Ur7VWr8X6KzPVF0s932jpXtIavGPuc+jlShBh2IS1R/g8Dall69Ut/eqfuuiIjMLNNZGLFyXbnazcXvW7m+ycr1yuzcuf4fK9c/8z6gxuMTbq5b+nWus8P6fr2miHN2NWrQrX48AIB48Q06AAAAAAAJwAIdAAAAAIAEYIEOAAAAAEACXLI16JHs63paNelBi3ud0slu/RnHzAp9H+u69fVSRUR+v/CGGt/crOvZulP6Pk9GXdfZ8tuOLjV+tbPD2We2TdfaebO6zqxyrH/Ox8Gpa24rJtRD6vfqKqrngb0tPa1rV4MV+trjIiKT3Tov01dY2b5s0DlmrmyvSOn7PBm6z5VAPDV+o7BMjXsjsl2yst1qXR85qEIvCSDpjP28jzhnTy23cm2dsz8Qdc7u+I0a39ykxyvSOrPzOWcf6tA15/M6Z0/r1zFyDQCLE9+gAwAAAACQACzQAQAAAABIgNgX6CMjI9LT0yOFQkEKhYL09PTI6OjoeY/ZuXOn3HrrrdLV1SWe50lfX18scwUwP+QaWHzINQAA8Yt9gb5161bp6+uTXbt2ya5du6Svr096enrOe8zk5KR89KMfla9//esxzRLAhSDXwOJDrgEAiF+sTeJef/112bVrl+zdu1duuOEGERH51re+JZs2bZKDBw/K1VdfHXnce28IDh8+HNdUT/F045cw7X6eUWnR+0irbix3edOYc8zKzLAa242jWv28dcS0cx8d/pQa51NWsynPOMfY/GE9t9DMfUzd+Cl3m93ULyZRTcguZQ2X6whhys122c52i87YFc1F55gLzXYQke12f0aN3Ww7h7hODM+9D3AejZhrL63f0phMRK5bdYBMq87XihY316usXK+03gtU5Zw9H4MnL/wYAEDDifUb9D179kihUDh9shcRufHGG6VQKMhLL70U51QAVAm5BhYfcg0AQH3E+g36wMCALF++3Nm+fPlyGRgYqNrjlEolKZXOXG5lbMz9FhtAdcSVaxGyDcSFXAMAUB9V+Qb9kUceEc/zzvvfvn37RETE89y/zzTGRG5fqB07dpxualMoFGTlypVVu2/gUpG0XIuQbeBikWsAAJKtKt+gf/7zn5ctW7acd581a9bIa6+9JsePH3duGxwclO7u7mpMRUREHnroIdm+ffvp8djY2LxO+H5e15GZQqsahxH1bMba5GdCNS7bO4jIlMmp8WAwYu2ha1CPVdza8Ddn9TcbQzMteh6Tbs12bkzPzVQWUANXL3WqN7+UJS3XIgvPtpfJqrFpadLjdMRCZI5sTwcZ55gLzfbRiAgenu1S42Er26lJ9zUlN67z0VDZRqwWVa5zOm/2OTvILuCcHbrnzsnQzrWdrws/Zw9O67lG5rrYwOdsAMCCVWWB3tXVJV1dXXPut2nTJikWi/LKK6/Ixo0bRUTk5ZdflmKxKDfddFM1piIiIrlcTnLWiRvAhUlarkXINnCxyDUAAMkWa5O4a665Rm677Ta59957Ze/evbJ3716599575fbbb1cdYdetWyc/+tGPTo9PnjwpfX19cuDAAREROXjwoPT19VW9Dg7AhSPXwOJDrgEAqI/Yr4P+5JNPyvr162Xz5s2yefNmufbaa+V73/ue2ufgwYNSLJ651MmPf/xjue666+STn/ykiIhs2bJFrrvuOnn88cdjnTuAaOQaWHzINQAA8Yu1i7uIyJIlS+T73//+efcx1vW4P/vZz8pnP/vZqs4j1d7ubly+VA3DvP7xhJmIxjhWqVkwpmtd+6cKziH/O7VKH2MVxbX4JTV+p9zp3MfLY1ep8RvHdH1byzvuZy9Nx/W1WcPhGl1T1b5mOfXji15Sci0i4re0uNuWWdnOWdmOqEH3dPmnBOO65rx/yn0NqUW2f9O/TN/HMTfbeTvbU1POPnOyGn/58/iT43BmZs590LgSn+tunY0gpzMa1TfGFk7MnevXps9fD7+gXB+zch15zp7Ucx0fP+885sU+P4uIl7GuH18qOfsAAOIT+zfoAAAAAADAxQIdAAAAAIAEYIEOAAAAAEACxF6DXk+pzg5Jee/WiHe5NWKmRV8HfbZTj4OsW6eatko9MyO6vuutQfdxRN6nRodbdH1s2ip+fXuyw7kHu+Y8+2t9Xeelvyw7x2ReP6LGgXVNVS/tPh3s68z67W16B+Ne7zWc1D+UqtTNAedxdra9TrfvQ9iss1zp1HkJI7Otn9uZkzrbb0dk+xeezvZvm/XlrHK+7sewkGwved3NdvrXb6vxfLo++M3NemzV6UvoZttM6JpYoQYdNZTqaD+T6yVu3sJWneuydc5eSK6PnFjiHPMLa3zIOmdnrHP20Yhc270kcs45e9ad64HDajyfXHsZ3QcntTTqPYhmpnQPi4AadACoK75BBwAAAAAgAVigAwAAAACQACzQAQAAAABIABboAAAAAAAkwCXVJE4yGRE/c86bTUY3i0mVdOMXP3CbJmXH9bbKsG5KUzItzjG/GdON195s0o2kjLEa24zopi8iIi3v6M9Wlu3XTV2yL/xf55ig7DahUY9rNY0TEUl1LY3Y8wy7IZwITeFQB54n4r+bmzB0braz7ZX1Pp771JfshM52flhnbkZ0kzURkd+M6ay+2Xzh2W4+ZmW7T+c2/+IB55hgys3hXLwm3VBLZnXzOTOtG0eJiASjxQt+HGDBPP/UfyIigdsizaTsc7bexy/r20Xcc7ad65LRzdtERN4c71bjw836vGhCK9ejEefso/pxllvn7MwL/+scYzdynQ+/Rc/fBPq1zkScn0OaPQJAovANOgAAAAAACcACHQAAAACABGCBDgAAAABAAlxSNejhSFFC71QNeirj1qL7Wb0tzOjPL/ySW4OeE6vmraKPSZXcz0DMCf04QV6PU1bpZ2bSfdyO3+qasfSLr+nHWEDt2rxYdave7Pnr2oE4BCdHxHsv275bd+rndE2oyel9/EpEf4kJq07dKoH1ZxeQbavUMzMxj2z/4v+pcThHL4l5s2t609bpIHTnBsQpGBk9K9eec7t9zjb2Obsc1TfmwnOdH6xCrn+jd0r9fL8aVy1tVm49+2fk870MACQdr9QAAAAAACQAC3QAAAAAABKABToAAAAAAAlwSdWgm/KsGO9UfVYwcNy53S/p2s5UZpkau1c2FZkt6K1NU7r2O1d06+aMXUtnDY1VQpsfdGtOM0eH1bhSo5rzcExfM9Ur62slBydHavK4wEIFg4POtpSVD9+qIc1E1LealM52fkYXq9q1rCLiZNm5T+sj0fyJkrNP5rB+bapUq+bcYl/T3C/rn1E4OVmTxwUWIhg+6WzzZ/X5yO41kY3Idakzp8ZN09Y5e2zuc7adY/v2puPudcUzh6xcO3tURzA2psa+9doXTk3V6JEBANXCN+gAAAAAACQAC3QAAAAAABKABToAAAAAAAnAAh0AAAAAgAS4pJrEnc1ENFULhobUON3arI/JtDvH5Id0M5ggr3+k2YmIBk9p/bmIV9bNp4x9eyWiGZVv7ZPRzXFMlRpLOQ1laDCDBhSM6GaG6UKbGpus+1KYG9YN3Oxs58bdBm8mpXPpl/TrjPHt23WTq3qiKRwaTTium5imR/XYZDqcY/KD02ocNOmGkdmIpqwmY5+zrXNySjeJ82Zq1QLuwtEUDgAaD9+gAwAAAACQACzQAQAAAABIABboAAAAAAAkwCVbgx7JGD2c0LVbXkuTe0gmpcbpEV3f5k/qsYiIGZ/Q99vWqm9vyumx736OYjL6V+cv6VDj4PgJ5xgApzgZtPpNiIiIle3MlK5N9ccjsj2ma2C91hZ9e2YeL7lpvU+qXfe+CMbG5r4P4BJk589vc3Nt95vITOs+EN6U7isjMo9zdlbXsUeycu0367lRKw4AeA/foAMAAAAAkAAs0AEAAAAASAAW6AAAAAAAJAA16OdhZnQtWmQ9uVUvbtevhYPDzjH29Yb9CWvc2aEPaNN1rKceyLruaj7n7gMgkpnU9Z7+hFv/OVe2gwG3z4MpWddGHymqYcrqFeG1RNS++zrb0pTXY2rQgUh2HXdqwj1nS7POk3vOHnLv13ov4Fk16Sn7nG1nVkQkZV1LvdnqaUMNOgDgXXyDDgAAAABAArBABwAAAAAgAVigAwAAAACQALEv0EdGRqSnp0cKhYIUCgXp6emR0dHRc+5fLpfli1/8oqxfv15aWlpkxYoVcs8998ixY8fimzSA8yLXwOJDrgEAiF/sTeK2bt0qR48elV27domIyF/+5V9KT0+PPPvss5H7T01Nyf79++XLX/6yfOQjH5GRkRHZtm2b3HHHHbJv376aztVu4mKyGXefad0UKug/ro+xm0ZFsBvQiNU0zgtD9yC7cZyvP2tJdRScQ4LRorMNqIZGyrWI25zNpFPuPla2K0eO6h2MmfuBwkAfYmVbAjfbXqvOtpfRrzt+i9s00m48CVRDo+Xab2vTGzLuWxyvNKvGlXf69Q5WZqPY5/XQahrnVSru4zbp9xN2rr2c2+h1Pu8fAACLT6wL9Ndff1127dole/fulRtuuEFERL71rW/Jpk2b5ODBg3L11Vc7xxQKBdm9e7fa9s///M+yceNGOXLkiKxatSqWuQOIRq6BxYdcAwBQH7H+ifuePXukUCicPtmLiNx4441SKBTkpZdemvf9FItF8TxPOjo6ajBLABeCXAOLD7kGAKA+Yv0GfWBgQJYvX+5sX758uQwMDMzrPmZmZuTBBx+UrVu3Snt7e+Q+pVJJSmf9aVixeOpPuytSFpnHX6W+x4T6T+EkcP/czAvLehejx8Yaz4dv9ON6ofvntxKkrX30n8oa6z6i5obGUZFTvzsznz+rjllcuRaJO9v6T10rdn4W8LvwjXUtZBPxp/Wh9bJsPU4Yke2QbDckcn1K1XLtnDsj/kTcOle6uZ77T9xt7jnb/e7D2Wa9vkSdnxfy/gHJkORsA0i+qizQH3nkEfnqV7963n3+53/+R0REPM9zbjPGRG63lctl2bJli4RhKI899tg599uxY0fkfH4hz835GMqJOca1Mj7HWCS+uSBRhoeHpVBw+wvUQtJyLVLFbJ+cY1wrM3OMRURG4pgIkoRcVynXdnbiypLdAoKWEHhXnNkGsHh4pgof7w0NDcnQ0NB591mzZo089dRTsn37dqcLbEdHh/zjP/6j/Nmf/dk5jy+Xy3L33XfLm2++KT/72c9k6dKl59zX/jR+dHRUVq9eLUeOHGmYF8qxsTFZuXKlvP322+f95iEpmG9tFYtFWbVqlYyMjMT2p6JJy7VI42e70Z53zLe2yPUp5DpejTZfkcabcz2yDWDxqMo36F1dXdLV1TXnfps2bZJisSivvPKKbNy4UUREXn75ZSkWi3LTTTed87j3TvZvvPGGPP/883Oe7HO5nOQiOqIWCoWGeGE/W3t7e0PNmfnWlu/H1zYiabkWWTzZbrTnHfOtLXJNruuh0eYr0nhzjjPbABaPWF85rrnmGrntttvk3nvvlb1798revXvl3nvvldtvv111hF23bp386Ec/EhGRSqUif/qnfyr79u2TJ598UoIgkIGBARkYGJDZWbcWE0C8yDWw+JBrAADqI/aP9p588klZv369bN68WTZv3izXXnutfO9731P7HDx48HSTmKNHj8qPf/xjOXr0qPzu7/6uXH755af/u5BOsgBqh1wDiw+5BgAgfrF2cRcRWbJkiXz/+98/7z5nl8WvWbPmortg5nI5+cpXvhL5J3RJ1WhzZr61lfT51iPXIsn/udiYb20x3+oi1/PDfGuv0ebcaPMFkCxVaRIHAAAAAAAuDt0rAAAAAABIABboAAAAAAAkAAt0AAAAAAASgAU6AAAAAAAJsGgX6CMjI9LT0yOFQkEKhYL09PTI6OjoOfcvl8vyxS9+UdavXy8tLS2yYsUKueeee+TYsWOJnK+IyM6dO+XWW2+Vrq4u8TxP+vr6ajrHxx57TNauXSv5fF42bNggL7744nn3f+GFF2TDhg2Sz+flqquukscff7ym87NdyHz7+/tl69atcvXVV4vv+7Jt27b4JvquC5nvzp075WMf+5gsW7ZM2tvbZdOmTfKTn/wkxtnWR6PleiFzFok32+S6tsj1/DRatsl1dTVarkXINoDaWbQL9K1bt0pfX5/s2rVLdu3aJX19fdLT03PO/aempmT//v3y5S9/Wfbv3y87d+6UX//613LHHXckcr4iIpO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