Transaction
ID: 4e7f71ebde...6732
Inputs (12)
Spent
Address:
Output transaction:
Settlement height:
Value:
1 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.01 ERG
Tokens:
2,100,504.85
Spent
Address:
Output transaction:
Settlement height:
Value:
0.024 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
0.024 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0248 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0222 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0288 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0338 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
0.04 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
0.001086765 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
8.5 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
506 ERG
Tokens:
Loading assets...
Outputs (6)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
1 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.005 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.2 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.00185 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
514.5 ERG
Tokens:
Loading assets...
Transaction Details
Confirmations: 220,684
Total coins transferred: 515.71 ERG
Fees: 0.00185 ERG
Fees per byte: 0.000000146 ERG
Raw Transaction Data
{
"id": "4e7f71ebde1c265c5ae40140fed8289a2e379eb87feb56c9074f7d79401f6732",
"blockId": "5f8748bc8d05133431cc3c77cd0e4f95ba89e7c1b763bb0bee24ca9f5a0fea8a",
"inclusionHeight": 1537944,
"timestamp": 1748820547708,
"index": 4,
"globalIndex": 9037346,
"numConfirmations": 220684,
"inputs": [
{
"boxId": "566ce1bdbed338b1016071a8a15f52c7ca53c57991a0a48659e5a0627cafd3af",
"value": 1000000000,
"index": 0,
"spendingProof": null,
"outputBlockId": "8e78a9af4b77878908b79278999b2f1da6faf5e7540fb6ad3167199bc4e61647",
"outputTransactionId": "d7f7b0b1a99006bc75b2be0e7df563f7874efc3161991de4470bb3554d675a64",
"outputIndex": 0,
"outputGlobalIndex": 47201144,
"outputCreatedAt": 1497233,
"outputSettledAt": 1497236,
"ergoTree": "10040e2098ba6f0cfc7758633d52ea3082ae3b8b38b45dbfe8a7247de966dcb608b9008e0e201b4b8b789fdd4a34c5f1cf73b4d99a5cacb8ccba75265f6edf4950893b162f070e201fd6e032e8476c4aa54c18c1a308dce83940e8f4a28f576440513ed7326ad4890e20a7f6d4ccf1288a2b79e3d8741e1e203828135a12b92f8dc4f2d21e40738e0e01d80cd6017300d602d901023c0c630eb58c720201d9010463aedb63087204d901064d0e938c7206018c720202d603d9010363e4c672030464d6048320020203029d022a020a0221028d027c024802c802fd024602a802d20294023102bd0205026c02840288022a0221026a02560273022602c302c70298024e02c80235d605d9010532b4e4720504020442d606d9010663b2db63087206040000d607d9010763b2db63087207040200d608d9010863b2db63087208040400d609d901093c324159d805d60b8c720902d60c8c720b01d60d8c720b02d60e8c720d01d60f8c720d02e5dc24078c72090101d901100e9593b172100412d801d6127cb4721004020412958f7212720c720c95917212720e720e7212720f720fd60ae4e30002d60bdc0c1aa402a70400d60cd9010c05958f720c0580020402958f720c058080020404958f720c05808080020406958f720c0580808080020408958f720c05808080808002040a958f720c0580808080808002040c958f720c058080808080808002040e958f720c0580808080808080800204100412d197830201dad9010d029593720d020bd818d60fb2a5040000d610db6501fed611e4e30263d612c27211d613e4e3030c63d614e4e3011ad615dc640bda720301b2da72020186027210720104000002b383040e720483200202f00236027d02df023402350298021f024a02a50257021b029102dd024602fe02810236026702f20288028d024f02c602910296028f02de02150287029b02978320020258021e02b0025c02e8021402dc023f0224027902e402b00202027d02b102c50249024f02e40220023d027502840212026502f602c70252029702d502ec02c1cbb3831e020269026d022e0270026102690264026502690261022e0263026f026e027402720261026302740273022e02700272026f0270026f02730261026c022e7212ad7213d9011563cbb3831c020269026d022e0270026102690264026502690261022e0263026f026e027402720261026302740273022e0261026302740269026f026e022ec27215b27214040200d616da720701720fd617da720701a7d6188c721701d6198c721702d61ada720801720fd61bda720801a7d61c8c721b01d61db17213d61eb2a5040200d61fc1721ed6209905feffffffffffffffff017219d621dad9012163b2db6308722104000001721ed622dad9012263b2db6308722204020001721ed623c2721ed624b4a504049a721d0404d625b2da720201860272107303040000d626b27214040400968305019683080193cbc2720fda720501b2721504000092c1720fc1a793da720601720fda720601a7938c7216017218938c7216029972190502938c721a01721c938c721a02998c721b027e721d0593b1db6308720f040696830c0192721fc1721192721f0580ade204937edad9012763b2e4c67227041004000001721e057220938c7221017218938c7221020502938c7222017302938c722202da7209018602b2dc640bda720301b2da7202018602721073010400000283010e83200202f50291028e02b402b00228023c0266029b02dd028a0219025602400276026c021902e4020a0269023a0266029702b7027502b0028e020902050225022302d4b272140400000400008602050086020580a0b787e90505d00f937223721292dad9012763b2e4c67227051104000001721e9adb6903db6503feda7209018602b2721504020086020580b899298602058080f1c8130580f0b25293cb7223da720501b2721504060093dad9012763b2e4c67227051104020001721e0500afdad9012763d801d629e4c672270511b472290404b1722901721ed90127059372270500afdb0c0e7224d9012704d803d629b27224722700d62ac17229d62bdad9012b63b2db6308722b0400000172299683070192722ac1b2721372270092722a058092f401938c722b01721c938c722b02050293dad9012c63b2e4c6722c0411040000017229722091dad9012c63b2e4c6722c0411040200017229050093cbc27229b2adb472150408b17215d9012c32da720501722c72270092dad901270e7cb472270410042001e4dc640adad9012763b2e4c67227040c64040000017225027226b27214040600da7209018602b272150404008602050086029ddad9012763b2e4c67227051104020001722505040500dad901273c0c630eae8c722701d9012963aedb63087229d9012b4d0e938c722b018c722702018602a57226010001720adad9010d029593720d0207dad9010f3c0e639592720bb1a50100d809d611b2a5720b00d612c17211d613c1a7d614c27211d615c4a7d616c2a7d6178cc7a701d618c47211d6198cc772110196830401927212997213058092f40193cb7214da720501b2dc640bda7203018c720f020283010e8c720f01e5e3010e83000204000093b472159a9ada720c017213b17216da720c017e721705b17215b472189a9ada720c017212b17214da720c017e721905b17218978302019299721972170480c33d94721672140186027204b2da7202018602db6501fe7201040000010001720a",
"ergoTreeConstants": "0: Coll(-104,-70,111,12,-4,119,88,99,61,82,-22,48,-126,-82,59,-117,56,-76,93,-65,-24,-89,36,125,-23,102,-36,-74,8,-71,0,-114)\n1: Coll(27,75,-117,120,-97,-35,74,52,-59,-15,-49,115,-76,-39,-102,92,-84,-72,-52,-70,117,38,95,110,-33,73,80,-119,59,22,47,7)\n2: Coll(31,-42,-32,50,-24,71,108,74,-91,76,24,-63,-93,8,-36,-24,57,64,-24,-12,-94,-113,87,100,64,81,62,-41,50,106,-44,-119)\n3: Coll(-89,-10,-44,-52,-15,40,-118,43,121,-29,-40,116,30,30,32,56,40,19,90,18,-71,47,-115,-60,-14,-46,30,64,115,-114,14,1)",
"ergoTreeScript": "{\n val coll1 = placeholder[Coll[Byte]](0)\n val func2 = {(tuple2: (Coll[Box], Coll[Byte])) =>\n tuple2._1.filter({(box4: Box) => box4.tokens.exists({(tuple6: (Coll[Byte], Long)) => tuple6._1 == tuple2._2 }) })\n }\n val func3 = {(box3: Box) => box3.R4[AvlTree].get }\n val coll4 = Coll[Byte](\n 3.toByte, -99.toByte, 42.toByte, 10.toByte, 33.toByte, -115.toByte, 124.toByte, 72.toByte, -56.toByte, -3.toByte, 70.toByte, -88.toByte, -46.toByte, -108.toByte, 49.toByte, -67.toByte, 5.toByte, 108.toByte, -124.toByte, -120.toByte, 42.toByte, 33.toByte, 106.toByte, 86.toByte, 115.toByte, 38.toByte, -61.toByte, -57.toByte, -104.toByte, 78.toByte, -56.toByte, 53.toByte\n )\n val func5 = {(opt5: Option[Coll[Byte]]) => opt5.get.slice(1, 33) }\n val func6 = {(box6: Box) => box6.tokens(0) }\n val func7 = {(box7: Box) => box7.tokens(1) }\n val func8 = {(box8: Box) => box8.tokens(2) }\n val func9 = {(tuple9: (Option[Coll[Byte]], (Long, (Long, Long)))) =>\n val tuple11 = tuple9._2\n val l12 = tuple11._1\n val tuple13 = tuple11._2\n val l14 = tuple13._1\n val l15 = tuple13._2\n tuple9._1.map({(coll16: Coll[Byte]) => if (coll16.size == 9) {(\n val l18 = byteArrayToLong(coll16.slice(1, 9))\n if (l18 < l12) { l12 } else { if (l18 > l14) { l14 } else { l18 } }\n )} else { l15 } }).getOrElse(l15)\n }\n val b10 = getVar[Byte](0.toByte).get\n val i11 = INPUTS.indexOf(SELF, 0)\n val func12 = {(l12: Long) =>\n if (l12 < 128L) { 1 } else {\n if (l12 < 16384L) { 2 } else {\n if (l12 < 2097152L) { 3 } else {\n if (l12 < 268435456L) { 4 } else {\n if (l12 < 34359738368L) { 5 } else {\n if (l12 < 4398046511104L) { 6 } else { if (l12 < 562949953421312L) { 7 } else { if (l12 < 72057594037927936L) { 8 } else { 9 } } }\n }\n }\n }\n }\n }\n }\n sigmaProp(anyOf(Coll[Boolean]({(b13: Byte) => if (b13 == 11.toByte) {(\n val box15 = OUTPUTS(0)\n val coll16 = CONTEXT.dataInputs\n val box17 = getVar[Box](2.toByte).get\n val coll18 = box17.propositionBytes\n val coll19 = getVar[Coll[Box]](3.toByte).get\n val coll20 = getVar[Coll[Coll[Byte]]](1.toByte).get\n val coll21 = func3(func2((coll16, coll1))(0)).getMany(Coll[Coll[Byte]](coll4, Coll[Byte](-16.toByte, 54.toByte, 125.toByte, -33.toByte, 52.toByte, 53.toByte, -104.toByte, 31.toByte, 74.toByte, -91.toByte, 87.toByte, 27.toByte, -111.toByte, -35.toByte, 70.toByte, -2.toByte, -127.toByte, 54.toByte, 103.toByte, -14.toByte, -120.toByte, -115.toByte, 79.toByte, -58.toByte, -111.toByte, -106.toByte, -113.toByte, -34.toByte, 21.toByte, -121.toByte, -101.toByte, -105.toByte), Coll[Byte](88.toByte, 30.toByte, -80.toByte, 92.toByte, -24.toByte, 20.toByte, -36.toByte, 63.toByte, 36.toByte, 121.toByte, -28.toByte, -80.toByte, 2.toByte, 125.toByte, -79.toByte, -59.toByte, 73.toByte, 79.toByte, -28.toByte, 32.toByte, 61.toByte, 117.toByte, -124.toByte, 18.toByte, 101.toByte, -10.toByte, -57.toByte, 82.toByte, -105.toByte, -43.toByte, -20.toByte, -63.toByte), blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 112.toByte, 114.toByte, 111.toByte, 112.toByte, 111.toByte, 115.toByte, 97.toByte, 108.toByte, 46.toByte).append(coll18))).append(coll19.map({(box21: Box) => blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 97.toByte, 99.toByte, 116.toByte, 105.toByte, 111.toByte, 110.toByte, 46.toByte).append(box21.propositionBytes)) })), coll20(1))\n val tuple22 = func7(box15)\n val tuple23 = func7(SELF)\n val coll24 = tuple23._1\n val l25 = tuple23._2\n val tuple26 = func8(box15)\n val tuple27 = func8(SELF)\n val coll28 = tuple27._1\n val i29 = coll19.size\n val box30 = OUTPUTS(1)\n val l31 = box30.value\n val l32 = 9223372036854775807L - l25\n val tuple33 = {(box33: Box) => box33.tokens(0) }(box30)\n val tuple34 = {(box34: Box) => box34.tokens(1) }(box30)\n val coll35 = box30.propositionBytes\n val coll36 = OUTPUTS.slice(2, i29 + 2)\n val box37 = func2((coll16, placeholder[Coll[Byte]](3)))(0)\n val coll38 = coll20(2)\n allOf(Coll[Boolean](allOf(Coll[Boolean](blake2b256(box15.propositionBytes) == func5(coll21(0)), box15.value >= SELF.value, func6(box15) == func6(SELF), tuple22._1 == coll24, tuple22._2 == l25 - 1L, tuple26._1 == coll28, tuple26._2 == tuple27._2 - i29.toLong, box15.tokens.size == 3)), allOf(Coll[Boolean](l31 >= box17.value, l31 >= 5000000L, {(box39: Box) => box39.R4[Coll[Int]].get(0) }(box30).toLong == l32, tuple33._1 == coll24, tuple33._2 == 1L, tuple34._1 == placeholder[Coll[Byte]](2), tuple34._2 == func9((func3(func2((coll16, placeholder[Coll[Byte]](1)))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-11.toByte, -111.toByte, -114.toByte, -76.toByte, -80.toByte, 40.toByte, 60.toByte, 102.toByte, -101.toByte, -35.toByte, -118.toByte, 25.toByte, 86.toByte, 64.toByte, 118.toByte, 108.toByte, 25.toByte, -28.toByte, 10.toByte, 105.toByte, 58.toByte, 102.toByte, -105.toByte, -73.toByte, 117.toByte, -80.toByte, -114.toByte, 9.toByte, 5.toByte, 37.toByte, 35.toByte, -44.toByte)), coll20(0))(0), (0L, (100000000000L, 1000L)))), coll35 == coll18, {(box39: Box) => box39.R5[Coll[Long]].get(0) }(box30) >= CONTEXT.preHeader.timestamp + func9((coll21(1), (43200000L, (2626560000L, 86400000L)))), blake2b256(coll35) == func5(coll21(3)), {(box39: Box) => box39.R5[Coll[Long]].get(1) }(box30) == 0L, {(box39: Box) =>\n val coll41 = box39.R5[Coll[Long]].get\n coll41.slice(2, coll41.size)\n }(box30).forall({(l39: Long) => l39 == 0L }))), coll36.indices.forall({(i39: Int) =>\n val box41 = coll36(i39)\n val l42 = box41.value\n val tuple43 = {(box43: Box) => box43.tokens(0) }(box41)\n allOf(Coll[Boolean](l42 >= coll19(i39).value, l42 >= 2000000L, tuple43._1 == coll28, tuple43._2 == 1L, {(box44: Box) => box44.R4[Coll[Long]].get(0) }(box41) == l32, {(box44: Box) => box44.R4[Coll[Long]].get(1) }(box41) > 0L, blake2b256(box41.propositionBytes) == coll21.slice(4, coll21.size).map({(opt44: Option[Coll[Byte]]) => func5(opt44) })(i39)))\n }), {(coll39: Coll[Byte]) => byteArrayToLong(coll39.slice(8, 16)) }({(box39: Box) => box39.R4[Coll[AvlTree]].get(0) }(box37).get(coll38, coll20(3)).get) >= func9((coll21(2), (0L, ({(box39: Box) => box39.R5[Coll[Long]].get(1) }(box37) / 2L, 0L)))), {(tuple39: (Coll[Box], Coll[Byte])) => tuple39._1.exists({(box41: Box) => box41.tokens.exists({(tuple43: (Coll[Byte], Long)) => tuple43._1 == tuple39._2 }) }) }((OUTPUTS, coll38))))\n )} else { false } }(b10), {(b13: Byte) => if (b13 == 7.toByte) { {(tuple15: (Coll[Byte], Box)) => if (i11 >= OUTPUTS.size) { false } else {(\n val box17 = OUTPUTS(i11)\n val l18 = box17.value\n val l19 = SELF.value\n val coll20 = box17.propositionBytes\n val coll21 = SELF.bytesWithoutRef\n val coll22 = SELF.propositionBytes\n val i23 = SELF.creationInfo._1\n val coll24 = box17.bytesWithoutRef\n val i25 = box17.creationInfo._1\n allOf(Coll[Boolean](l18 >= l19 - 2000000L, blake2b256(coll20) == func5(func3(tuple15._2).getMany(Coll[Coll[Byte]](tuple15._1), getVar[Coll[Byte]](1.toByte).getOrElse(Coll[Byte]()))(0)), coll21.slice(func12(l19) + coll22.size + func12(i23.toLong), coll21.size) == coll24.slice(func12(l18) + coll20.size + func12(i25.toLong), coll24.size), anyOf(Coll[Boolean](i25 - i23 >= 504000, coll22 != coll20))))\n )} }((coll4, func2((CONTEXT.dataInputs, coll1))(0))) } else { false } }(b10))))\n}",
"address": "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",
"assets": [
{
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},
{
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},
{
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}
],
"additionalRegisters": {}
},
{
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"ergoTree": "0008cd03b775aacf3f06a2d0835af6163add1355cbb8830dbcab9c903b35a103fc6f595c",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(b775aa,5105d7,...)))}",
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{
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{
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{
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{
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"address": "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",
"assets": [],
"additionalRegisters": {
"R5": {
"serializedValue": "1107ca918cdd846594a20a100ef0a6380000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1735801275493,84106,8,7,461240,0,0]"
},
"R6": {
"serializedValue": "1d05020000020000020000020000020000",
"sigmaType": "Coll[Coll[SLong]]",
"renderedValue": "[[0,0],[0,0],[0,0],[0,0],[0,0]]"
},
"R8": {
"serializedValue": "11020000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[0,0]"
},
"R7": {
"serializedValue": "0c3c6464024ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e1609000720004ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e1609000720004ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e1609000720004ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e160900072000",
"sigmaType": null,
"renderedValue": null
},
"R4": {
"serializedValue": "0c6402e35554f493561b7dcda03950bb5e1f85549c5ed171d2a202e7c184d2c9d03deb04072000aeb1dd89146b1cc523d13499b378d93e29abc2fdb7dcd458baf9f8d1e712f9ca03072000",
"sigmaType": null,
"renderedValue": null
}
}
}
],
"outputs": [
{
"boxId": "1ebb799f7045d00bbfab314f6893ea80f766bc735c71ab918db279133b91b2e0",
"transactionId": "4e7f71ebde1c265c5ae40140fed8289a2e379eb87feb56c9074f7d79401f6732",
"blockId": "5f8748bc8d05133431cc3c77cd0e4f95ba89e7c1b763bb0bee24ca9f5a0fea8a",
"value": 1000000000,
"index": 0,
"globalIndex": 48170804,
"creationHeight": 1537940,
"settlementHeight": 1537944,
"ergoTree": "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",
"ergoTreeConstants": "0: Coll(-104,-70,111,12,-4,119,88,99,61,82,-22,48,-126,-82,59,-117,56,-76,93,-65,-24,-89,36,125,-23,102,-36,-74,8,-71,0,-114)\n1: Coll(27,75,-117,120,-97,-35,74,52,-59,-15,-49,115,-76,-39,-102,92,-84,-72,-52,-70,117,38,95,110,-33,73,80,-119,59,22,47,7)\n2: Coll(31,-42,-32,50,-24,71,108,74,-91,76,24,-63,-93,8,-36,-24,57,64,-24,-12,-94,-113,87,100,64,81,62,-41,50,106,-44,-119)\n3: Coll(-89,-10,-44,-52,-15,40,-118,43,121,-29,-40,116,30,30,32,56,40,19,90,18,-71,47,-115,-60,-14,-46,30,64,115,-114,14,1)",
"ergoTreeScript": "{\n val coll1 = placeholder[Coll[Byte]](0)\n val func2 = {(tuple2: (Coll[Box], Coll[Byte])) =>\n tuple2._1.filter({(box4: Box) => box4.tokens.exists({(tuple6: (Coll[Byte], Long)) => tuple6._1 == tuple2._2 }) })\n }\n val func3 = {(box3: Box) => box3.R4[AvlTree].get }\n val coll4 = Coll[Byte](\n 3.toByte, -99.toByte, 42.toByte, 10.toByte, 33.toByte, -115.toByte, 124.toByte, 72.toByte, -56.toByte, -3.toByte, 70.toByte, -88.toByte, -46.toByte, -108.toByte, 49.toByte, -67.toByte, 5.toByte, 108.toByte, -124.toByte, -120.toByte, 42.toByte, 33.toByte, 106.toByte, 86.toByte, 115.toByte, 38.toByte, -61.toByte, -57.toByte, -104.toByte, 78.toByte, -56.toByte, 53.toByte\n )\n val func5 = {(opt5: Option[Coll[Byte]]) => opt5.get.slice(1, 33) }\n val func6 = {(box6: Box) => box6.tokens(0) }\n val func7 = {(box7: Box) => box7.tokens(1) }\n val func8 = {(box8: Box) => box8.tokens(2) }\n val func9 = {(tuple9: (Option[Coll[Byte]], (Long, (Long, Long)))) =>\n val tuple11 = tuple9._2\n val l12 = tuple11._1\n val tuple13 = tuple11._2\n val l14 = tuple13._1\n val l15 = tuple13._2\n tuple9._1.map({(coll16: Coll[Byte]) => if (coll16.size == 9) {(\n val l18 = byteArrayToLong(coll16.slice(1, 9))\n if (l18 < l12) { l12 } else { if (l18 > l14) { l14 } else { l18 } }\n )} else { l15 } }).getOrElse(l15)\n }\n val b10 = getVar[Byte](0.toByte).get\n val i11 = INPUTS.indexOf(SELF, 0)\n val func12 = {(l12: Long) =>\n if (l12 < 128L) { 1 } else {\n if (l12 < 16384L) { 2 } else {\n if (l12 < 2097152L) { 3 } else {\n if (l12 < 268435456L) { 4 } else {\n if (l12 < 34359738368L) { 5 } else {\n if (l12 < 4398046511104L) { 6 } else { if (l12 < 562949953421312L) { 7 } else { if (l12 < 72057594037927936L) { 8 } else { 9 } } }\n }\n }\n }\n }\n }\n }\n sigmaProp(anyOf(Coll[Boolean]({(b13: Byte) => if (b13 == 11.toByte) {(\n val box15 = OUTPUTS(0)\n val coll16 = CONTEXT.dataInputs\n val box17 = getVar[Box](2.toByte).get\n val coll18 = box17.propositionBytes\n val coll19 = getVar[Coll[Box]](3.toByte).get\n val coll20 = getVar[Coll[Coll[Byte]]](1.toByte).get\n val coll21 = func3(func2((coll16, coll1))(0)).getMany(Coll[Coll[Byte]](coll4, Coll[Byte](-16.toByte, 54.toByte, 125.toByte, -33.toByte, 52.toByte, 53.toByte, -104.toByte, 31.toByte, 74.toByte, -91.toByte, 87.toByte, 27.toByte, -111.toByte, -35.toByte, 70.toByte, -2.toByte, -127.toByte, 54.toByte, 103.toByte, -14.toByte, -120.toByte, -115.toByte, 79.toByte, -58.toByte, -111.toByte, -106.toByte, -113.toByte, -34.toByte, 21.toByte, -121.toByte, -101.toByte, -105.toByte), Coll[Byte](88.toByte, 30.toByte, -80.toByte, 92.toByte, -24.toByte, 20.toByte, -36.toByte, 63.toByte, 36.toByte, 121.toByte, -28.toByte, -80.toByte, 2.toByte, 125.toByte, -79.toByte, -59.toByte, 73.toByte, 79.toByte, -28.toByte, 32.toByte, 61.toByte, 117.toByte, -124.toByte, 18.toByte, 101.toByte, -10.toByte, -57.toByte, 82.toByte, -105.toByte, -43.toByte, -20.toByte, -63.toByte), blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 112.toByte, 114.toByte, 111.toByte, 112.toByte, 111.toByte, 115.toByte, 97.toByte, 108.toByte, 46.toByte).append(coll18))).append(coll19.map({(box21: Box) => blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 97.toByte, 99.toByte, 116.toByte, 105.toByte, 111.toByte, 110.toByte, 46.toByte).append(box21.propositionBytes)) })), coll20(1))\n val tuple22 = func7(box15)\n val tuple23 = func7(SELF)\n val coll24 = tuple23._1\n val l25 = tuple23._2\n val tuple26 = func8(box15)\n val tuple27 = func8(SELF)\n val coll28 = tuple27._1\n val i29 = coll19.size\n val box30 = OUTPUTS(1)\n val l31 = box30.value\n val l32 = 9223372036854775807L - l25\n val tuple33 = {(box33: Box) => box33.tokens(0) }(box30)\n val tuple34 = {(box34: Box) => box34.tokens(1) }(box30)\n val coll35 = box30.propositionBytes\n val coll36 = OUTPUTS.slice(2, i29 + 2)\n val box37 = func2((coll16, placeholder[Coll[Byte]](3)))(0)\n val coll38 = coll20(2)\n allOf(Coll[Boolean](allOf(Coll[Boolean](blake2b256(box15.propositionBytes) == func5(coll21(0)), box15.value >= SELF.value, func6(box15) == func6(SELF), tuple22._1 == coll24, tuple22._2 == l25 - 1L, tuple26._1 == coll28, tuple26._2 == tuple27._2 - i29.toLong, box15.tokens.size == 3)), allOf(Coll[Boolean](l31 >= box17.value, l31 >= 5000000L, {(box39: Box) => box39.R4[Coll[Int]].get(0) }(box30).toLong == l32, tuple33._1 == coll24, tuple33._2 == 1L, tuple34._1 == placeholder[Coll[Byte]](2), tuple34._2 == func9((func3(func2((coll16, placeholder[Coll[Byte]](1)))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-11.toByte, -111.toByte, -114.toByte, -76.toByte, -80.toByte, 40.toByte, 60.toByte, 102.toByte, -101.toByte, -35.toByte, -118.toByte, 25.toByte, 86.toByte, 64.toByte, 118.toByte, 108.toByte, 25.toByte, -28.toByte, 10.toByte, 105.toByte, 58.toByte, 102.toByte, -105.toByte, -73.toByte, 117.toByte, -80.toByte, -114.toByte, 9.toByte, 5.toByte, 37.toByte, 35.toByte, -44.toByte)), coll20(0))(0), (0L, (100000000000L, 1000L)))), coll35 == coll18, {(box39: Box) => box39.R5[Coll[Long]].get(0) }(box30) >= CONTEXT.preHeader.timestamp + func9((coll21(1), (43200000L, (2626560000L, 86400000L)))), blake2b256(coll35) == func5(coll21(3)), {(box39: Box) => box39.R5[Coll[Long]].get(1) }(box30) == 0L, {(box39: Box) =>\n val coll41 = box39.R5[Coll[Long]].get\n coll41.slice(2, coll41.size)\n }(box30).forall({(l39: Long) => l39 == 0L }))), coll36.indices.forall({(i39: Int) =>\n val box41 = coll36(i39)\n val l42 = box41.value\n val tuple43 = {(box43: Box) => box43.tokens(0) }(box41)\n allOf(Coll[Boolean](l42 >= coll19(i39).value, l42 >= 2000000L, tuple43._1 == coll28, tuple43._2 == 1L, {(box44: Box) => box44.R4[Coll[Long]].get(0) }(box41) == l32, {(box44: Box) => box44.R4[Coll[Long]].get(1) }(box41) > 0L, blake2b256(box41.propositionBytes) == coll21.slice(4, coll21.size).map({(opt44: Option[Coll[Byte]]) => func5(opt44) })(i39)))\n }), {(coll39: Coll[Byte]) => byteArrayToLong(coll39.slice(8, 16)) }({(box39: Box) => box39.R4[Coll[AvlTree]].get(0) }(box37).get(coll38, coll20(3)).get) >= func9((coll21(2), (0L, ({(box39: Box) => box39.R5[Coll[Long]].get(1) }(box37) / 2L, 0L)))), {(tuple39: (Coll[Box], Coll[Byte])) => tuple39._1.exists({(box41: Box) => box41.tokens.exists({(tuple43: (Coll[Byte], Long)) => tuple43._1 == tuple39._2 }) }) }((OUTPUTS, coll38))))\n )} else { false } }(b10), {(b13: Byte) => if (b13 == 7.toByte) { {(tuple15: (Coll[Byte], Box)) => if (i11 >= OUTPUTS.size) { false } else {(\n val box17 = OUTPUTS(i11)\n val l18 = box17.value\n val l19 = SELF.value\n val coll20 = box17.propositionBytes\n val coll21 = SELF.bytesWithoutRef\n val coll22 = SELF.propositionBytes\n val i23 = SELF.creationInfo._1\n val coll24 = box17.bytesWithoutRef\n val i25 = box17.creationInfo._1\n allOf(Coll[Boolean](l18 >= l19 - 2000000L, blake2b256(coll20) == func5(func3(tuple15._2).getMany(Coll[Coll[Byte]](tuple15._1), getVar[Coll[Byte]](1.toByte).getOrElse(Coll[Byte]()))(0)), coll21.slice(func12(l19) + coll22.size + func12(i23.toLong), coll21.size) == coll24.slice(func12(l18) + coll20.size + func12(i25.toLong), coll24.size), anyOf(Coll[Boolean](i25 - i23 >= 504000, coll22 != coll20))))\n )} }((coll4, func2((CONTEXT.dataInputs, coll1))(0))) } else { false } }(b10))))\n}",
"address": "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",
"assets": [
{
"tokenId": "171c56d1aa54a6709bdadcc0f053e7a786411224a8f40111a6878549a3fae842",
"index": 0,
"amount": 1,
"name": "Paideia DAO",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "4afba4014c298e92a214caea22a3d9bd57240449c03f5a136267998cf8e5bd89",
"index": 1,
"amount": 9223372036854776000,
"name": "Walrus DAO Proposal",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "97f262a9dde3bb81a680e9716e79c0b4e6e4e88b13f785acc917604fc02425b8",
"index": 2,
"amount": 9223372036854776000,
"name": "Walrus DAO Action",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "1bae7121fe05c45423a85f4d9cb73eb2df8df81f8dc41dc7f86cb5069886516d",
"mainChain": true
},
{
"boxId": "92e99745b0df810a70be1276acfba7619a899fd2216836ce6263326a925f3e84",
"transactionId": "4e7f71ebde1c265c5ae40140fed8289a2e379eb87feb56c9074f7d79401f6732",
"blockId": "5f8748bc8d05133431cc3c77cd0e4f95ba89e7c1b763bb0bee24ca9f5a0fea8a",
"value": 5000000,
"index": 1,
"globalIndex": 48170805,
"creationHeight": 1537940,
"settlementHeight": 1537944,
"ergoTree": "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",
"ergoTreeConstants": "0: Coll(-104,-70,111,12,-4,119,88,99,61,82,-22,48,-126,-82,59,-117,56,-76,93,-65,-24,-89,36,125,-23,102,-36,-74,8,-71,0,-114)\n1: Coll(27,75,-117,120,-97,-35,74,52,-59,-15,-49,115,-76,-39,-102,92,-84,-72,-52,-70,117,38,95,110,-33,73,80,-119,59,22,47,7)\n2: Coll(31,-42,-32,50,-24,71,108,74,-91,76,24,-63,-93,8,-36,-24,57,64,-24,-12,-94,-113,87,100,64,81,62,-41,50,106,-44,-119)\n3: Coll(-89,-10,-44,-52,-15,40,-118,43,121,-29,-40,116,30,30,32,56,40,19,90,18,-71,47,-115,-60,-14,-46,30,64,115,-114,14,1)",
"ergoTreeScript": "{\n val func1 = {(box1: Box) => box1.tokens(0) }\n val func2 = {(tuple2: (Coll[Box], Coll[Byte])) =>\n tuple2._1.exists({(box4: Box) => box4.tokens.exists({(tuple6: (Coll[Byte], Long)) => tuple6._1 == tuple2._2 }) })\n }\n val preHeader3 = CONTEXT.preHeader\n val b4 = getVar[Byte](0.toByte).get\n val func5 = {(box5: Box) =>\n val coll7 = box5.R5[Coll[Long]].get\n coll7.slice(2, coll7.size)\n }\n val opt6 = getVar[(Int, Long)](10.toByte)\n val tuple7 = (-2, 0L)\n val tuple8 = tuple7\n val tuple9 = opt6.getOrElse(tuple8)\n val i10 = tuple9._1\n val l11 = tuple9._2\n val func12 = {(tuple12: (Coll[Box], Coll[Byte])) => tuple12._1.filter({(box14: Box) => blake2b256(box14.propositionBytes) == tuple12._2 }) }\n val func13 = {(box13: Box) => box13.R4[Coll[Int]].get(0) }\n val func14 = {(box14: Box) => box14.R5[Coll[Long]].get(1) }\n val func15 = {(box15: Box) => box15.R6[AvlTree].get }\n val func16 = {(box16: Box) => box16.R4[Coll[Int]].get(1) }\n val coll17 = placeholder[Coll[Byte]](3)\n val func18 = {(tuple18: (Coll[Box], Coll[Byte])) =>\n tuple18._1.filter({(box20: Box) => box20.tokens.exists({(tuple22: (Coll[Byte], Long)) => tuple22._1 == tuple18._2 }) })\n }\n val func19 = {(box19: Box) => box19.R4[AvlTree].get }\n val func20 = {(tuple20: (Option[Coll[Byte]], (Long, (Long, Long)))) =>\n val tuple22 = tuple20._2\n val l23 = tuple22._1\n val tuple24 = tuple22._2\n val l25 = tuple24._1\n val l26 = tuple24._2\n tuple20._1.map({(coll27: Coll[Byte]) => if (coll27.size == 9) {(\n val l29 = byteArrayToLong(coll27.slice(1, 9))\n if (l29 < l23) { l23 } else { if (l29 > l25) { l25 } else { l29 } }\n )} else { l26 } }).getOrElse(l26)\n }\n val func21 = {(box21: Box) => box21.R5[Coll[Long]].get(0) }\n sigmaProp(\n anyOf(\n Coll[Boolean](\n {(b22: Byte) =>\n if (b22 == 12.toByte) {\n {(coll24: Coll[Coll[Byte]]) =>\n allOf(\n Coll[Boolean](\n preHeader3.height - SELF.creationInfo._1 > 788400, coll24.forall({(coll26: Coll[Byte]) => !func2((OUTPUTS, coll26)) }), INPUTS.size == 1\n )\n )\n }(Coll[Coll[Byte]](func1(SELF)._1))\n } else { false }\n }(b4), {(b22: Byte) => if (b22 == 13.toByte) {(\n val coll24 = func5(SELF)\n val coll25 = SELF.propositionBytes\n val box26 = func12((OUTPUTS, blake2b256(coll25)))(0)\n val l27 = box26.value\n val l28 = func14(SELF)\n val coll29 = CONTEXT.dataInputs\n val coll30 = Coll[Byte]()\n val coll31 = func19(func18((coll29, placeholder[Coll[Byte]](0)))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-10.toByte, -1.toByte, -117.toByte, 114.toByte, 16.toByte, 1.toByte, 85.toByte, 69.toByte, -44.toByte, -77.toByte, -84.toByte, 95.toByte, -58.toByte, 12.toByte, -112.toByte, -128.toByte, -110.toByte, -48.toByte, 53.toByte, -95.toByte, -95.toByte, 97.toByte, 85.toByte, -64.toByte, 41.toByte, -24.toByte, -43.toByte, 17.toByte, 98.toByte, 124.toByte, 122.toByte, 44.toByte), Coll[Byte](-81.toByte, 120.toByte, 91.toByte, 10.toByte, -35.toByte, -128.toByte, 92.toByte, 92.toByte, 49.toByte, -15.toByte, -52.toByte, 58.toByte, 62.toByte, -106.toByte, -56.toByte, -112.toByte, 8.toByte, -3.toByte, 113.toByte, 39.toByte, 50.toByte, 1.toByte, 7.toByte, -93.toByte, 120.toByte, 75.toByte, 127.toByte, 73.toByte, -23.toByte, 86.toByte, 72.toByte, 66.toByte)), getVar[Coll[Byte]](1.toByte).getOrElse(coll30))\n val tuple32 = (1L, (999L, 500L))\n val opt33 = opt6\n val tuple34 = tuple7\n val tuple35 = tuple8\n val tuple36 = tuple9\n val coll37 = func19(func18((coll29, placeholder[Coll[Byte]](1)))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-11.toByte, -111.toByte, -114.toByte, -76.toByte, -80.toByte, 40.toByte, 60.toByte, 102.toByte, -101.toByte, -35.toByte, -118.toByte, 25.toByte, 86.toByte, 64.toByte, 118.toByte, 108.toByte, 25.toByte, -28.toByte, 10.toByte, 105.toByte, 58.toByte, 102.toByte, -105.toByte, -73.toByte, 117.toByte, -80.toByte, -114.toByte, 9.toByte, 5.toByte, 37.toByte, 35.toByte, -44.toByte), Coll[Byte](118.toByte, 124.toByte, -86.toByte, -128.toByte, -71.toByte, -114.toByte, 73.toByte, 106.toByte, -40.toByte, -87.toByte, -10.toByte, -119.toByte, -60.toByte, 65.toByte, 10.toByte, -28.toByte, 83.toByte, 50.toByte, 127.toByte, 15.toByte, -107.toByte, -23.toByte, 80.toByte, -124.toByte, -64.toByte, -82.toByte, 32.toByte, 99.toByte, 80.toByte, 121.toByte, 59.toByte, 119.toByte)), getVar[Coll[Byte]](2.toByte).getOrElse(coll30))\n val box38 = func12((OUTPUTS, {(opt38: Option[Coll[Byte]]) => opt38.get.slice(1, 33) }(coll37(1))))(0)\n allOf(Coll[Boolean]((coll24.indices.forall({(i39: Int) => if (i39 == i10) { coll24(i39) == l11 } else { coll24(i39) <= l11 } }) && (coll24.size > i10)) && (i10 >= 0), allOf(Coll[Boolean](box26.propositionBytes == coll25, l27 >= SELF.value - 3000000L, l27 >= 2000000L, func1(box26) == func1(SELF), func13(box26) == func13(SELF), func14(box26) == l28, func5(box26) == coll24, func15(box26) == func15(SELF), func16(box26) == if ((l28 >= {(box39: Box) => box39.R5[Coll[Long]].get(1) }(func18((coll29, coll17))(0)) * func20((coll31(0), tuple32)) / 1000L) && \n val l39 = l11\n l39 >= l28 * func20((coll31(1), tuple32)) / 1000L\n ) { i10 } else { -2 })), preHeader3.timestamp > func21(SELF), allOf(Coll[Boolean](box38.value >= 1000000L, {(tuple39: (Coll[Box], Coll[Byte])) => tuple39._1.flatMap({(box41: Box) => box41.tokens }).fold(0L, {(tuple41: (Long, (Coll[Byte], Long))) =>\n val tuple43 = tuple41._2\n tuple41._1 + if (tuple43._1 == tuple39._2) { tuple43._2 } else { 0L }\n }) }((Coll[Box](box38), placeholder[Coll[Byte]](2))) >= min(SELF.tokens(1)._2, byteArrayToLong(coll37(0).get.slice(1, 9))))), func16(SELF) == -1))\n )} else { false } }(b4), {(b22: Byte) => if (b22 == 6.toByte) {(\n val coll24 = getVar[Coll[Coll[Byte]]](1.toByte).get\n val coll25 = coll24(4)\n val coll26 = coll24(3)\n val coll27 = coll26.indices.slice(0, coll26.size / 8).map({(i27: Int) => byteArrayToLong(coll26.slice(i27 * 8, i27 + 1 * 8)) })\n val l28 = coll27.fold(0L, {(tuple28: (Long, Long)) => tuple28._1 + tuple28._2 })\n val coll29 = SELF.propositionBytes\n val box30 = func12((OUTPUTS, blake2b256(coll29)))(0)\n val l31 = func21(SELF)\n val avlTree32 = func15(SELF)\n val opt33 = avlTree32.get(coll25, coll24(0))\n val bool34 = opt33.isDefined\n val coll35 = coll24(1)\n val coll36 = func5(SELF)\n val coll37 = func5(box30)\n allOf(Coll[Boolean](byteArrayToLong({(box38: Box) => box38.R4[Coll[AvlTree]].get(0) }(func18((INPUTS, coll17))(0)).get(coll25, coll24(2)).get.slice(8, 16)) >= l28, box30.propositionBytes == coll29, box30.value >= SELF.value, box30.tokens == SELF.tokens, func13(box30) == func13(SELF), func16(box30) == func16(SELF), func21(box30) == l31, func15(box30).digest == if (bool34) { avlTree32.update(Coll[(Coll[Byte], Coll[Byte])]((coll25, coll26)), coll35).get } else { avlTree32.insert(Coll[(Coll[Byte], Coll[Byte])]((coll25, coll26)), coll35).get }.digest, preHeader3.timestamp < l31, if (bool34) {(\n val coll38 = opt33.get\n val coll39 = coll38.indices.slice(0, coll38.size / 8).map({(i39: Int) => byteArrayToLong(coll38.slice(i39 * 8, i39 + 1 * 8)) })\n allOf(Coll[Boolean](func14(box30) == func14(SELF) - coll39.fold(0L, {(tuple40: (Long, Long)) => tuple40._1 + tuple40._2 }) + l28, coll37 == coll36.zip(coll39.zip(coll27).map({(tuple40: (Long, Long)) => tuple40._2 - tuple40._1 })).map({(tuple40: (Long, Long)) => tuple40._1 + tuple40._2 })))\n )} else { allOf(Coll[Boolean](func14(box30) == func14(SELF) + l28, coll37 == coll36.zip(coll27).map({(tuple38: (Long, Long)) => tuple38._1 + tuple38._2 }))) }, func2((INPUTS, coll25)), coll37 != coll36))\n )} else { false } }(b4)\n )\n )\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "4afba4014c298e92a214caea22a3d9bd57240449c03f5a136267998cf8e5bd89",
"index": 0,
"amount": 1,
"name": "Walrus DAO Proposal",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "1fd6e032e8476c4aa54c18c1a308dce83940e8f4a28f576440513ed7326ad489",
"index": 1,
"amount": 5000000,
"name": "Paideia",
"decimals": 4,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "10020601",
"sigmaType": "Coll[SInt]",
"renderedValue": "[3,-1]"
},
"R5": {
"serializedValue": "1104cef0be83e965000000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1749263309863,0,0,0]"
},
"R6": {
"serializedValue": "644ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e160900072000",
"sigmaType": null,
"renderedValue": null
},
"R7": {
"serializedValue": "0e0953656c6c2050414c4d",
"sigmaType": "Coll[SByte]",
"renderedValue": "53656c6c2050414c4d"
}
},
"spentTransactionId": "da3b1e58c58e689ec32d0ad8d69e0d94363cebf44eba5837976170e1c5442ead",
"mainChain": true
},
{
"boxId": "2ce74f0198e62749e1178431c5853f696d58ff8542c97b78263672876450701f",
"transactionId": "4e7f71ebde1c265c5ae40140fed8289a2e379eb87feb56c9074f7d79401f6732",
"blockId": "5f8748bc8d05133431cc3c77cd0e4f95ba89e7c1b763bb0bee24ca9f5a0fea8a",
"value": 1000000,
"index": 2,
"globalIndex": 48170806,
"creationHeight": 1537940,
"settlementHeight": 1537944,
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