{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4XIA6TEIBFT4J2B5JIKZQXFOKX","short_pith_number":"pith:4XIA6TEI","schema_version":"1.0","canonical_sha256":"e5d00f4c880967c4e83d4a15985cae55d646c2b432fc70ee4d4c6ff7f86d1726","source":{"kind":"arxiv","id":"2504.00317","version":1},"attestation_state":"computed","paper":{"title":"Principal Component Stochastic Subspace Identification for Output-Only Modal Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Biqi Chen, Jun Zhang, Ying Wang","submitted_at":"2025-04-01T00:49:43Z","abstract_excerpt":"Stochastic Subspace Identification (SSI) is widely used in modal analysis of engineering structures, known for its numerical stability and high accuracy in modal parameter identification. SSI methods are generally classified into two types: Data-Driven (SSI-Data) and Covariance-Driven (SSI-Cov), which have been considered to originate from different theoretical foundations and computational principles. In contrast, this study demonstrates that SSI-Cov and SSI-Data converge to the same solution under the condition of infinite observations, by establishing a unified framework incorporating instr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.00317","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2025-04-01T00:49:43Z","cross_cats_sorted":[],"title_canon_sha256":"79adef13c7ffbc09fd1710f522b07c0ed3d9cc3f0683ca27f1ac91de4e48fe01","abstract_canon_sha256":"426e8d652d52986b9fe7b545345c46f26c0fb390d1001716d7066182ef08135c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:42:24.269565Z","signature_b64":"FLYpnOhUfeP+xo8l46Tq2F1iJ7p4xCVis/TbsRbDsp0KTHaf4DZQXWhb2d6il/UIEbnePUJxsaO1kNZ13Jq7AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5d00f4c880967c4e83d4a15985cae55d646c2b432fc70ee4d4c6ff7f86d1726","last_reissued_at":"2026-07-05T10:42:24.269048Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:42:24.269048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Principal Component Stochastic Subspace Identification for Output-Only Modal Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Biqi Chen, Jun Zhang, Ying Wang","submitted_at":"2025-04-01T00:49:43Z","abstract_excerpt":"Stochastic Subspace Identification (SSI) is widely used in modal analysis of engineering structures, known for its numerical stability and high accuracy in modal parameter identification. SSI methods are generally classified into two types: Data-Driven (SSI-Data) and Covariance-Driven (SSI-Cov), which have been considered to originate from different theoretical foundations and computational principles. In contrast, this study demonstrates that SSI-Cov and SSI-Data converge to the same solution under the condition of infinite observations, by establishing a unified framework incorporating instr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.00317","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/2504.00317/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.00317","created_at":"2026-07-05T10:42:24.269099+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.00317v1","created_at":"2026-07-05T10:42:24.269099+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.00317","created_at":"2026-07-05T10:42:24.269099+00:00"},{"alias_kind":"pith_short_12","alias_value":"4XIA6TEIBFT4","created_at":"2026-07-05T10:42:24.269099+00:00"},{"alias_kind":"pith_short_16","alias_value":"4XIA6TEIBFT4J2B5","created_at":"2026-07-05T10:42:24.269099+00:00"},{"alias_kind":"pith_short_8","alias_value":"4XIA6TEI","created_at":"2026-07-05T10:42:24.269099+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.06525","citing_title":"Adaptive Physics-Informed System Modeling with Control for Nonlinear Structural System Estimation","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX","json":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX.json","graph_json":"https://pith.science/api/pith-number/4XIA6TEIBFT4J2B5JIKZQXFOKX/graph.json","events_json":"https://pith.science/api/pith-number/4XIA6TEIBFT4J2B5JIKZQXFOKX/events.json","paper":"https://pith.science/paper/4XIA6TEI"},"agent_actions":{"view_html":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX","download_json":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX.json","view_paper":"https://pith.science/paper/4XIA6TEI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.00317&json=true","fetch_graph":"https://pith.science/api/pith-number/4XIA6TEIBFT4J2B5JIKZQXFOKX/graph.json","fetch_events":"https://pith.science/api/pith-number/4XIA6TEIBFT4J2B5JIKZQXFOKX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX/action/storage_attestation","attest_author":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX/action/author_attestation","sign_citation":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX/action/citation_signature","submit_replication":"https://pith.science/pith/4XIA6TEIBFT4J2B5JIKZQXFOKX/action/replication_record"}},"created_at":"2026-07-05T10:42:24.269099+00:00","updated_at":"2026-07-05T10:42:24.269099+00:00"}