{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:77XOV7YO42DCOWBXCC2Y7XFO5G","short_pith_number":"pith:77XOV7YO","canonical_record":{"source":{"id":"2504.08504","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2025-04-11T13:10:38Z","cross_cats_sorted":[],"title_canon_sha256":"68350a6d34e2e67f283b2937886c4b4a5d989f844cf6057a7032473869096653","abstract_canon_sha256":"48d236b8beee7f35bf31e903d7ea8a4f73fda761e83b274471bad10489a5929d"},"schema_version":"1.0"},"canonical_sha256":"ffeeeaff0ee68627583710b58fdcaee99061707d9847762147ee67666b232ad3","source":{"kind":"arxiv","id":"2504.08504","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.08504","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"arxiv_version","alias_value":"2504.08504v1","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.08504","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"pith_short_12","alias_value":"77XOV7YO42DC","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"pith_short_16","alias_value":"77XOV7YO42DCOWBX","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"pith_short_8","alias_value":"77XOV7YO","created_at":"2026-07-05T11:55:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:77XOV7YO42DCOWBXCC2Y7XFO5G","target":"record","payload":{"canonical_record":{"source":{"id":"2504.08504","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2025-04-11T13:10:38Z","cross_cats_sorted":[],"title_canon_sha256":"68350a6d34e2e67f283b2937886c4b4a5d989f844cf6057a7032473869096653","abstract_canon_sha256":"48d236b8beee7f35bf31e903d7ea8a4f73fda761e83b274471bad10489a5929d"},"schema_version":"1.0"},"canonical_sha256":"ffeeeaff0ee68627583710b58fdcaee99061707d9847762147ee67666b232ad3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:55:18.458174Z","signature_b64":"vvTvaoGYk5HLET7k+OqJ+3IPPxKrWxMDgfw+lv4c9WOkl9ArZMrN5/ktItFT+aziIALeKIM+wHuHQ4NPpK+kCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffeeeaff0ee68627583710b58fdcaee99061707d9847762147ee67666b232ad3","last_reissued_at":"2026-07-05T11:55:18.457715Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:55:18.457715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.08504","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:55:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U5ldOokdXNFT5YLasAvoHBzZ41bwok6Ffrj9bJI5KZMUKGi4U+sWOgdgDJdj69E7ZZaxx+UC47QEiwSVXsB9Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T01:33:00.528667Z"},"content_sha256":"74122726c048200c075b66741da4a01cac20f4c46383b2a8b56d9dfb8f2ec1f5","schema_version":"1.0","event_id":"sha256:74122726c048200c075b66741da4a01cac20f4c46383b2a8b56d9dfb8f2ec1f5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:77XOV7YO42DCOWBXCC2Y7XFO5G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"STF-GCN: A Multi-Domain Graph Convolution Network Method for Automatic Modulation Recognition via Adaptive Correlation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Dingzhao Li, Fuqing Zhang, Jie Qi, Lin Cao, Mingyuan Shao, Shaohua Hong, Yilin Cai, Yuan Peng, Zhengqiu Fu","submitted_at":"2025-04-11T13:10:38Z","abstract_excerpt":"Automatic Modulation Recognition (AMR) is an essential part of Intelligent Transportation System (ITS) dynamic spectrum allocation. However, current deep learning-based AMR (DL-AMR) methods are challenged to extract discriminative and robust features at low signal-to-noise ratios (SNRs), where the representation of modulation symbols is highly interfered by noise. Furthermore, current research on GNN methods for AMR tasks generally suffers from issues related to graph structure construction and computational complexity. In this paper, we propose a Spatial-Temporal-Frequency Graph Convolution N"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.08504","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.08504/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:55:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oXvuigxPQNCfRtU1Z0TWu4nPA/HbHfiBukXtolKnPhSn7xjHksktug3goR8NQvT4ZteEslXZIdZAyeEoqlbuCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T01:33:00.529207Z"},"content_sha256":"a01e330f6ad5cfe72faa57760c6b8537d7e3883e98aa525229cc4325400508b3","schema_version":"1.0","event_id":"sha256:a01e330f6ad5cfe72faa57760c6b8537d7e3883e98aa525229cc4325400508b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/77XOV7YO42DCOWBXCC2Y7XFO5G/bundle.json","state_url":"https://pith.science/pith/77XOV7YO42DCOWBXCC2Y7XFO5G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/77XOV7YO42DCOWBXCC2Y7XFO5G/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-01T01:33:00Z","links":{"resolver":"https://pith.science/pith/77XOV7YO42DCOWBXCC2Y7XFO5G","bundle":"https://pith.science/pith/77XOV7YO42DCOWBXCC2Y7XFO5G/bundle.json","state":"https://pith.science/pith/77XOV7YO42DCOWBXCC2Y7XFO5G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/77XOV7YO42DCOWBXCC2Y7XFO5G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:77XOV7YO42DCOWBXCC2Y7XFO5G","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"48d236b8beee7f35bf31e903d7ea8a4f73fda761e83b274471bad10489a5929d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2025-04-11T13:10:38Z","title_canon_sha256":"68350a6d34e2e67f283b2937886c4b4a5d989f844cf6057a7032473869096653"},"schema_version":"1.0","source":{"id":"2504.08504","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.08504","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"arxiv_version","alias_value":"2504.08504v1","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.08504","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"pith_short_12","alias_value":"77XOV7YO42DC","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"pith_short_16","alias_value":"77XOV7YO42DCOWBX","created_at":"2026-07-05T11:55:18Z"},{"alias_kind":"pith_short_8","alias_value":"77XOV7YO","created_at":"2026-07-05T11:55:18Z"}],"graph_snapshots":[{"event_id":"sha256:a01e330f6ad5cfe72faa57760c6b8537d7e3883e98aa525229cc4325400508b3","target":"graph","created_at":"2026-07-05T11:55:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.08504/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatic Modulation Recognition (AMR) is an essential part of Intelligent Transportation System (ITS) dynamic spectrum allocation. However, current deep learning-based AMR (DL-AMR) methods are challenged to extract discriminative and robust features at low signal-to-noise ratios (SNRs), where the representation of modulation symbols is highly interfered by noise. Furthermore, current research on GNN methods for AMR tasks generally suffers from issues related to graph structure construction and computational complexity. In this paper, we propose a Spatial-Temporal-Frequency Graph Convolution N","authors_text":"Dingzhao Li, Fuqing Zhang, Jie Qi, Lin Cao, Mingyuan Shao, Shaohua Hong, Yilin Cai, Yuan Peng, Zhengqiu Fu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2025-04-11T13:10:38Z","title":"STF-GCN: A Multi-Domain Graph Convolution Network Method for Automatic Modulation Recognition via Adaptive Correlation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.08504","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:74122726c048200c075b66741da4a01cac20f4c46383b2a8b56d9dfb8f2ec1f5","target":"record","created_at":"2026-07-05T11:55:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"48d236b8beee7f35bf31e903d7ea8a4f73fda761e83b274471bad10489a5929d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2025-04-11T13:10:38Z","title_canon_sha256":"68350a6d34e2e67f283b2937886c4b4a5d989f844cf6057a7032473869096653"},"schema_version":"1.0","source":{"id":"2504.08504","kind":"arxiv","version":1}},"canonical_sha256":"ffeeeaff0ee68627583710b58fdcaee99061707d9847762147ee67666b232ad3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ffeeeaff0ee68627583710b58fdcaee99061707d9847762147ee67666b232ad3","first_computed_at":"2026-07-05T11:55:18.457715Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:55:18.457715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vvTvaoGYk5HLET7k+OqJ+3IPPxKrWxMDgfw+lv4c9WOkl9ArZMrN5/ktItFT+aziIALeKIM+wHuHQ4NPpK+kCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:55:18.458174Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.08504","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:74122726c048200c075b66741da4a01cac20f4c46383b2a8b56d9dfb8f2ec1f5","sha256:a01e330f6ad5cfe72faa57760c6b8537d7e3883e98aa525229cc4325400508b3"],"state_sha256":"474a5e9a407aa15ceed4f641bee44ae808946085f180a151bbc60e5831176771"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t05IsKQrE08Qrtg9YwNoiwlnAyQGjDZvwsmZshRegE8pY0GijwxzqVHsitLESW2ur+sU9yGG6opHwKLnaJffDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T01:33:00.534170Z","bundle_sha256":"daf2e7e5030b3f261fc31072d400a2b37843e3506249a0c68dffd8597f90d589"}}