20220203 17194700 1.0 FALSE ACS_Specific_Asian_Groups_Booundaries_Clip 002 0.000 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_Web_Mercator_Auxiliary_Sphere <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/2.9.0'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> 20220617 15105400 20220617 15105400 Esri ArcGIS 12.9.3.32739 ACS_Specific_Asian_Groups_Booundaries_Clip BlueVault Chinese Population File Geodatabase Feature Class 0.000 dataset EPSG 6.18.3(9.3.1.2) 0 Simple FALSE 0 TRUE FALSE ACS_Specific_Asian_Groups_Booundaries_Clip Feature Class 0 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. NAME Name String 254 0 0 GEOID Geographic Identifier - FIPS Code String 11 0 0 ALAND Area of Land (Square Meters) Double 8 0 0 AWATER Area of Water (Square Meters) Double 8 0 0 State State String 254 0 0 County County String 254 0 0 B02001_001 Total Population Integer 4 0 0 B02001_002 Total Population Double 8 0 0 B02001_005 Asian alone population Integer 4 0 0 B02001_006 Asian alone population Double 8 0 0 B02011_001 Asian alone or in combination with one or more other races population Integer 4 0 0 B02011_002 Asian alone or in combination with one or more other races population Double 8 0 0 B02018_001 Total Groups Tallied Integer 4 0 0 B02018_002 Total Groups Tallied - Margin of Error Double 8 0 0 B02018_003 Asian Indian Population Integer 4 0 0 B02018_004 Asian Indian Population - Margin of Error Double 8 0 0 B02018_005 Bangladeshi Population Integer 4 0 0 B02018_006 Bangladeshi Population - Margin of Error Double 8 0 0 B02018_007 Bhutanese Population Integer 4 0 0 B02018_008 Bhutanese Population - Margin of Error Double 8 0 0 B02018_009 Burmese Population Integer 4 0 0 B02018__10 Burmese Population - Margin of Error Double 8 0 0 B02018__11 Cambodian Population Integer 4 0 0 B02018__12 Cambodian Population - Margin of Error Double 8 0 0 B02018__13 Chinese, except Taiwanese Population Integer 4 0 0 B02018__14 Chinese, except Taiwanese Population - Margin of Error Double 8 0 0 B02018__15 Filipino Population Integer 4 0 0 B02018__16 Filipino Population - Margin of Error Double 8 0 0 B02018__17 Hmong Population Integer 4 0 0 B02018__18 Hmong Population - Margin of Error Double 8 0 0 B02018_010 Indonesian Population Integer 4 0 0 B02018__19 Indonesian Population - Margin of Error Double 8 0 0 B02018_011 Japanese Population Integer 4 0 0 B02018__20 Japanese Population - Margin of Error Double 8 0 0 B02018_012 Korean Population Integer 4 0 0 B02018__21 Korean Population - Margin of Error Double 8 0 0 B02018_013 Laotian Population Integer 4 0 0 B02018__22 Laotian Population - Margin of Error Double 8 0 0 B02018_014 Malaysian Population Integer 4 0 0 B02018__23 Malaysian Population - Margin of Error Double 8 0 0 B02018_015 Mongolian Population Integer 4 0 0 B02018__24 Mongolian Population - Margin of Error Double 8 0 0 B02018_016 Nepalese Population Integer 4 0 0 B02018__25 Nepalese Population - Margin of Error Double 8 0 0 B02018_017 Okinawan Population Integer 4 0 0 B02018__26 Okinawan Population - Margin of Error Double 8 0 0 B02018_018 Pakistani Population Integer 4 0 0 B02018__27 Pakistani Population - Margin of Error Double 8 0 0 B02018_019 Sri Lankan Population Integer 4 0 0 B02018__28 Sri Lankan Population - Margin of Error Double 8 0 0 B02018_020 Taiwanese Population Integer 4 0 0 B02018__29 Taiwanese Populaiton - Margin of Error Double 8 0 0 B02018_021 Thai Population Integer 4 0 0 B02018__30 Thai Population - Margin of Error Double 8 0 0 B02018_022 Vietnamese Population Integer 4 0 0 B02018__31 Vietnamese Population - Margin of Error Double 8 0 0 B02018_023 Other Asian Population, specified Integer 4 0 0 B02018__32 Other Asian Population, specified - Margin of Error Double 8 0 0 B02018_024 Other Asian Population, not specified Integer 4 0 0 B02018__33 Other Asian Population, not specified - Margin of Error Double 8 0 0 B02001_cal Percentage of population that is Asian alone Double 8 0 0 B02001_c_1 Percentage of population that it Asian alone - Margin of Error Double 8 0 0 B02001_c_2 Percent of population that is Asian alone or in combination with one or more races Double 8 0 0 B02001_c_3 Percent of population that is Asian alone or in combination with one or more races - Margin of Error Double 8 0 0 Shape__Are Shape__Are Double 8 0 0 Shape__Len Shape__Len Double 8 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. Shape_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. 20220617 /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD2KOYh /KmAWTsez/T/AAqamyRrIu1xkVEsjQEJMcr0WT1+voaAJ6KKTHNAAetLSCloAKKKDQAVXuy3lhAQ FkOxmIzjNWKrXLYdBIdkIwzNjuDwCewoAsjpRQCCMg5FGaACiiigBM0vaiigBtLigDmlNACYpaSg mgAozSUuKAAUtFFABSE5paKAG4p1FFACGkpSM0EUAJRRRQAUUUUAFFFFABTCZQflCY980+jI59qA HUhAYEEAg9QaWigCv89r0y8P5lP8RU6srKGUgg9CKWoGjeJi8AyCctH0B9x6GgCeimRyrKm5T9QR gg+hp9ABmkNBFAFAAKhvJzBASozI52Rr6sf85/Cp6phhd6tHEoYrb5Z2xxuI4A9eM/mKBruPg0GL yQWeUyHkuHKn9MYHsKc2kyWx3WUnlg/eQjKn3x6/Q1sAYAFMnlEMEkh/hUnmq5ULnfc5f+2Z4ZWj msy+1iN8TdfwP+NS2Gry3d8beS2EQKF0O/JwCByMe9VCdxJPU1Yh0ueQC8iyksf+rz/F7H2rSdJR V0Y06zlKzRsUVVjv4i/lzAwSAZ2ycA/Q9DVrrWJu00FFFFAgpMUtFACUtIetGaAFooooAKKQ5oAN ABmjNL3pDQAmaM0UUAFFFFABRTJXKRlgpYjsBmqE17GrExyuGBztI496pRb2E5JGlUU0rQ/OULRj qR1H4UsVxFMMxuG7U52CLlv/ANdLZ6h0GZW4h3RscHoRWfPDdPJltrY4yGAqYIltqKgHZG6kkE8Z qeeaCKTDCPJGef8A9VWtHoS9dy1RSGkrMsdRSA0tAEMkJLeZEQsvr2b2NOimEhKkbZF6qf8APIqS o5YVlwclXX7rDqKAJKKhjmYP5UwCv2I6N9P8KmoAZNKsMTyOcKgLGl0mCTElxKoV5m3lR24AA/IC qOpyRA2sUrYV5QWHqF5/ntrbjnhVYlEi/P8AdwetNJ7g2krE9Z+sF/sWxFYlm5wOwrQoq07O5Eld WOcs9OundZAoQDByw6j6V0WOMUtFOUnImEFHYgmtYpgd6KfqKxVBsL5LSMh4ZCdqDrFwT/3z29ia 07zUY7UhCGZ26Kqkk/lVCyjcyXFzLEY3mYYDYLBQOAce+T+NZs2jonfYt0UUUhBSHrS0hoAWkFJm igB1FIDS5oAKM80UhoACaM0lFABRRRQAUUUUAFQyLDDG7mMHPUAcmpqiuD8m0LliRj0601uJmH5j 287PCGQHpuHatO0lN3MZSOEUKPr61NfR77STAywGaxI7iWJCqNgE56d62XvrzMvgZtSpBdlon++p x7isphbxOyNJJIQcZXgVGLmQbm6yMQd/cYqGqjBrqTKVzqTSUp60cVzHQAFLRRQAGiiigBrosi7X UEe9QiRrchJjmPosn9D/AI1YqteNJ5Qih2+bL8q7uQPU0AlcxLiU3mqTSEnZCTEgz0x1P4n+Qq9p 0Zlv4uGIU5OO1XbDw9BbwqJWLHqQvArStLJLQyeX0Y5x6e1dCkoxsjnlBynzPYs0UUVkbBSMcKTS 00lWyoIJHUZ6UAYkA87ULq4bJ2kRLnsAMn88j8hVuqN55+lySzrGJYJGBZQcMDjHHY9OnFT2l3Fe QCWLOM4IYYIPoaixb1JicUtFFAgPSm06kNACUUVHNJ5UTSYyF5P07/pQNK7sSUUopKBCilptAoAX FJS5pKACiijtQAUUUUAFJgFgT2pcUUARyqz4VSVHc8VUbT0lmLsgRMYAHf3q/RVKTWwnFPc524tp LdsOvHZh0NQ1u3sIaGR8kkKSF7fWsKumEuZGE42Z1OKWiiuQ6AooooAKKKRnVFLOwVR1JoASSRYo 2dzhVGTTbSCWa4W4mQKAuEUHOM9f5D8qZDDJfOrOpSAYIU9W9z/hWuqhVAFNIbdtB1FFFUQFFFFA GR4i1WfSdKa4tYVmnz8qH07n8K5z4e3Ul/c6xeXDF7iV0LMfT5uB/n0ra1t2a8CkfKq8e9ZNlBFp 94bq1iWKVhhiowCPcdK6oW9k49WcdRv2ylfRHQa7jyIs5zu49K5VNYXQdTf7ZDOlrOoKyBcqx9f5 g/QV1Buhqdm0IVftHXaeh+lBsDPawW8lukttIgEqS9j3PtWaS5eWRreXNzQIU1Szb70wiPpL8n5Z 6/hU/wBogxnzo8f7wqMPZachsjG5WEAJk5JHpn2qF7Sys4JdWvRHHCI87AvQdvqf8ax9mzb2kS8r q67kYMPUHNJXlcvibUBqc11ZTfZ45DxCACuB0yPX3rq9H8aWd1Cseouttc5wTg7G989vxrSphqlN XaIp4inVdos6mkZQ6lWGQRgihWDKGUggjII70tYGxXgcxkW8h+ZR8rH+Mev19asVDdA+SXC7mjIc AdTjsPwqUMHQMpyCMg0l2Klr7wvWiobQg2sYHZdp9iODU1NCas7BRRSgUCEFOpMUtACYFJTutNoA XrURkZAxkXCjuvPFSgUj52HABOOAaEA3cJEOxx06iqEt7dQqA8HJ7jpio7dZrS63yxlI34OOQP8A CtXGat2i+5Gsl2Me3nNzPIZZSny8YPHv+lZ9b89jDPkkbWxgEdqyZ7KaGTbtLjqCoraEoszlFnQ0 UZozXMbhRRRQAVWntPNlEyviQDA3KGA/D/69WaKBptbEa3N3ABuiWRR/zzPP5Gpo9VhJCuSjHs4w f1ptIyq6lWUMp6gjINF2Gj6F9Jo36MKkyKxTaRj/AFTPEe2w8D8DxTle8hHDpKPf5T/WncOXszYo rMTVNg/fxPH67hwPxHFXIruGYAo4IPTBp3Jaa3MvXSPNhHfB5rJRWkcIoyT0rQ1sk3w542DH61St 7eS5l8uMAtjPJxXVDSJxT1mxl3riaJdNZxaXPcXir8sq/cJI9fTmunsr37XbxvJEYJWGTE7Alfyr HmhuVs0t5LfAR8hwc9atW8X2eIIDznJNZ1HHl8zWkpcz7Fe5tmu9Ylj3KvOSSe2K5jxy97dpDBaQ TNp0AyzKpKlvU/5711lxaPKzXPmhXOBGD1Y+lc/Fc6xdarcJLZGH9ysCJkhXYMTuHHTnritqMtVL sYV46OD6nnlzaz2cvlzoUbGR7ioILi3mufJaXBRwJAOoHr+VeyS+FbHU7ULfRvv2/KQcMh7kYr5q 1pf7J8Vahb293JOLe4eNZ24LYOPU12wrqqnFbnL9WdOSk9j6ctY4orSGOD/UrGoTn+HHH6VNXjXh Lx5qUdsYsidYlVSspJAHONpB4rvdL8VS6qoaGK0Yk4MaT7mX68cV5U8PUhuj1o16c3ZPU6iobX/j 1Qf3Rt/LimWd2t3G3y7JUO2RM52n/CoYQZpp4g37jdvGP4gRyPpnNYdTe2jQ+1ni+0TQK3Vi6cdQ euPXnP51cqtdwM8IaEhJY+Yzjp6j6EcU1L5DbGRxh1UEoCMnPTH1oWmgpPTmJ55PKhZ8ZI6D1Pan puCKHILY5IGATVbbNctG0ieVGrbthILEjpnHHXn8BVkkDGTjPFMzV27js0ZpKKChQaO9JThQAUUV A8jREsylk65H8NAD5EWRGRuhGDVdHkt3Ecp3RnhZD/I1Ms8TnCupPpnmllTzImT1FUtNGLzRFPdL Bwx+YkYGOoqpNqpSUrEiso7nvWdK8jHbIxJXjmo63jSXUxdR9DqKUUtFcxuJmloooAKKKKACiiig AooooAKrLCjakgRQpC7mKjGc+v5GpppPKiZ8Zx0Hqe1TafbMm6WUDzHOTj+VG7KTsrjrrTYbol2L B8ABgen4VnzQS6Tpt1Pu+dPmR064963aQgEEEAg9Qa1U2tGYSpp6rc47TvEt3dRxx6jbxRzsu6PY /wDrB649f8a27eUXSFo1b5euR0qK+8NW9xqMV9C5iljQpsx8hB9uxrTs1t0tgkDqyLwSD3q6nJLW KIpe0i7TZ5Frl+5+IWrWEuqSW8X2aOSCDeQrShQcj0OAfzrp7G91J57RNNngvbiGESSLNJzKnTg/ 57VzvinR4pvFJurKVrue6BZZNqgAAbdqsDzgDH+Nc5Z63/ZOttGWa3uYDhkl+TeCOQM9q9CFJSpr vY8+pVaqt20PRtbt9S1cweaE09VYl4o5y7N7kDjrXI+JfBtpq9xFbWOlyi7Zg0lxCmAc8c9vxrrf 7VshpiajJcRxWrqHEjsAMH3qpZeIri71+Cy0ieKM48xxKMmRP93rj3/WsotxW2xo/ee+rPMoLJ9C uJLWxntbpoZf38cnLKQcYO0nI4x+dSGTToNVub0Wi6dPEQWVZdyjPIZOBjNekS+G9Ok8T3n9lBDc SlnukljCxqW569+T0/WoB8O7W+u7e/kg+0PbdR91Xx0GO/0rT6zFWbJ9hJtxRq6fazWWlW+pPcSX EEyqZSOfMQ859eM10UkEVxCCoUHb+7kA5X0IrLllijiiTdO8ZGJYFXlT61lTWmqW1w88V3dQWqNh rYEEhc9Rwecc8V5lRczbR6dGTilHsdBPL9ojt0cEQyPtlI/vA42n6nj/APXRb2VuYy0MSIVlYxOE GRz/AC6/hWNLHNugmsJ7m7hc7pF3qxDBlOccdga0Ir24uxDbWkM1rtyJHljB2gdAO2TXNFPmdzR8 t2aSXIzskGJR1Qc59x7U9ELN5kn3uy/3f/r1kzJqkMyogtrhycrKWKOPXIwRj8as+dqs5VFtYrb+ 9I8m/H0A6/jirs+o7roaNFVrS1e3aV5Z2mlkIJYjAAHQAdqs1RIU6kApaACkKgjmlooAq3VsZwCh CuOQ2Oagj1AJIsMy4cHaWB4qzcTmLG1GbHLYHQVn6kYDEhTAdjux7GtIq+jM5O2qK1/L5t0xG3A4 BFVqM560V0pWVjFu7udVRRRXEdQUUUUAFFFFABRRRQAUUUUAVbl/MnitlBLFg78dF5xz9QK0Ib2N VaOdlikTqCeCPUVRmJgnS5Ucfcf6dj+f86m1L7I8Ebzlg38OzrVwSbsxVW0k0aakMoYHIIyKWoYJ YTboY5FKAAA5qUEHkEGhiTuBAZSD3GKwpoEh0zUbVWKXBiZfMxxg8A/rW9TGVTuOxSxGDnv7VUZW JlHmPEGtLnTdUjhbfKyn5TbvncD/AHSP89aWfwrqesmT7eS8DEGNJ3yYh7HrnnmvZLaxjMC/aYYm cMXC7AQhPp+f61Kthao5ZYEz7jNdssb5anDHBNddDyLSbnTLAPZ30MUnksQkjp5i+nAI4+tO1j+z b1I7jSoo3uS3zPDHh9vvxnGa7rxPpOm3ZjWWyi8w/M0ijaxHpkVy9nosVlqMUWhCPzFQhrd2JG3g khucVpCrGXv7P8DGdKcfcvdE+j6vZ2l9b6W1nLPG4WPzF4PmE+n/ANeuxsls7BpLrz5itxgeUzbl jx1AHaucljuZ7dI8iAuyiZkOSqg5O0+vFV9Y8b29n4itdE0rTGfUJXjLzSDKRxE/MxAOc4BH45rG oud+78zoovl3+R2fk2S2kl0CrKAX8xgCR+dUL20kvrWbaZ0ULjeH+c5HYdBimXGr6fp1sZTeRrDI +WiwGwO4/wDrUsXiaC8jeWzzPHgZjEbZB+uMfma52pJc3Q35ot2MDwsZWjVN2Mr8+BjBGQeOxrek eW2uGit4A7TEMpzgLxgk9+341Q0yIWV3OiAPJJ8/HQM3J59K2oovLBJO52+8x7//AFq59kkdEld/ 12K8ZktHJuNriQ/65QRj0BHYe+cVcpGUOpVgCpGCD3oVQqhRnAGBk5oBJocKSilxQMAaWkxQM0AL RRRQBUvWkISOJwrOcZrKubOW3UPIwIJxwa3iisckDPrWNqk6yTLGpyE6/Wtqbd7IzqJWuyhRRRW5 gdVRRRXEdYUUUUAFFFFABRRRQAUUhNGc0AIyrIjIwyrDBFZghubqY2zY/cnaCT94Hoa0+RUM+YZE uQfu8P8A7vr+H+NOMuV3QOKmuVmfJZTLcrbhPnIz9amtDLbxTyh2R4iuUPRs9iK6CMrIBIAM4xmq uoWIuImaMYl4zz97HY1uql9GczpW1RJZ30V3GuGAkxymeasO4jXc2cDqfSsvSLExZuJVIfooI6e9 acyGSF0U4LKQD6VEkk9DSDk43YeZiYR45Kls1BfXi2cO7GXPCj1rFn+2WE4eR8uwO0g5FWrqGe9t 4CykSq+xs9PrVciTV9iPaNppLUq/2rK12k7xoSo24A7VheJ7aKTUNLlguZIre53I8aE5JGCRx2PF djbaRbwqfMAlY9z2rkvEGhavbazFf2C+far8ojXlowfvZB9fUVvRlHn0djCtCfJqrnVWptxpfnSL HKYkJJYDIwOhJrntHktNasb/AFJLKOC7uXa2jkVQ0gXAP3sdt2fwq6NRt9NliVA0kFw6xymTgIDn k1oaRe2+tbr+3BEMTtEgK4yR/EPqKizim7Giak0r/IwLazV7mw0G4tReW0O6TM7ANGoGBwOvUcV1 MemWFpbtFBaQQxk5IRAuTVsRorFgihjnnHNZF8895cNBDjy4yN2T1PWolNz8i4wVNd2ZOlRM2oXR aUnypCPqPT6Vt1kWUDHUb1lkKYkycc5yAa16xkboKKWkqRhSijFGKAFoooNABRSZpaAMu91ABHhR SH5Bz2rJrR1K2lNy8qrlCMkjtWdXVTStoc8731CiiirIOqoooriOsKKKKACiiigAooooAQijFLRQ AUjAMpVgCCMEHvS0UAJpspjZrZzyn3T6r2rTrFuP3TJcrnMf3vde/wCXX861oZBJGGB600Eu5JSZ paKokjkhjm2+YgbbnGffimzs8cJaKPe44VamoPAzQFipbPcF2SdY1IwcqfX2qW4fZazMeMIeR9Ky /J/tO/Mn7wW4HUjGSPStcoixFMfIFxj2qpJJkRbaZh6TZw3S3HnxLJGRt2sMg561o6fY2+j2f2aG RvIViUV2B2A9geuM+tU7TVrS2jMZjaMAkjHOavTW9vf2+4rywBVwMMKqbd7PYmnGNk1uVtV1YaeY wEMm8lcL1zWDFbajPcyXKvJE0nPHAA/HrSW5mk1ho7ti/luEXd6Yz/hXRVDfKbGba29xZ7v3ay7j uZt2CT3rQFKaOBUN3AUUhozRxmkMUUUUUAFFFFACEUtFFAFS/YrZSY7jFYFdDeoXs5R/s5/Kuero o7GNXcKKKK1MjqqKKK4jrCiiigAooooAKKKKACiiigAooooACMjBqOxnFs7W0h4XlD6r2/w/CpKq X0Z2LOgy8XPHde/+P4UbalLXQ0JZy/A4GeKFuHHfP1rMmuy0aJD80r/dA/nTCt7atnBuFIz8gClf wJ5ouChc6GN96Bqq3zXibWtgCoGWGMk03TbqOeE4b5s8ir1XF9TOS6DY2DRqVGARwMYxSSkCJyT/ AAmn1ma5dC106V3QsuMcHBGaAOX+2pBdpvQTYb7mOtaN1rl1MhW3QRE/xMc4/Af41k29uExtBaRs ZPcmt61jW3RYggaduXGRx9a1n3ZEbLRFKyt7m5uhcyvuIIyxXGcelbmKFGKWsZSuaJdxp60U40mK kYlKKMUYoAWiiigApkhZMMCNoPII60+qt6xCRoOkkgUn0ppXYnsWjQKKKQxCMgiuamTy55EHRWIr pq5mc5nkPqx/nW1HdmVUjooorcxOqoooriOsKKKKACiiigAooooAKKKKACiiigAoopDQBDb2kVtu MYOT3PJx6fSp6M0UDbb1ZUntG8wz2zeXN3HZ/r/jViw1Fp41zy3QqeoPpT6rPZqZzNFI0Uh5bbjD H3pbDumrM1LqcQW0kgIyozXLalqR1H9yBiLALg9Sc9P0q1fSXIjaOe381QPkmRgMfUE1naTab5wJ Dk/ff3PpW0bNXM3zRL1lYJLEJfMkVgeCvHPtWlFAkOdoJY9WJyTUgAUYAAHoKWolJsFFIKKKKkoK TNLx+dJigBRRSCloAKKKKACqGpSYhx8yurAq2OD+NX6p6jMi2roSCzDhe/1qofETLYnt1xChL72x y2c5NSZqGzRY7WNUbcMZzU+KT3GthOtczMczSH/aNdPXP38Pk3bjs3zD8a1ovUzq7FWiiitzE6qi iiuI6wooooATrS0UUAFFFFABRRRQAUUUUAFFFFADaKKKAClFFFAGfq822FYh1Y5P0FV9IbE7rgcr 170UVul+7MW/fNmiiisDYRWVs7TnBwaAQehzRRTEVkfzL+RS3+rA2j69TVqiinIIhRRRUjCiiigB MVHPEJIypLAd9vcUUUIGZ2n3UcG+GR8Lu+UmtNZAzlQQRtByKKK1qRW5nB9B9VL60+0xfLgOvT/C iis07O6LavoYLKUYqwwRwQaSiiuxHMf/2Q==