{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BSXE4N33WZVLDDE32PSSRBSGEQ","short_pith_number":"pith:BSXE4N33","canonical_record":{"source":{"id":"2504.21354","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-04-30T06:19:38Z","cross_cats_sorted":[],"title_canon_sha256":"491e22d10c9cb1b08b6ca0d1a07b55cf0d77cce0022af7e7327ded1c7e1d3a90","abstract_canon_sha256":"e764d9079c98b582a8c1f54b9e9a46b245de9f4e88685537d64269ec4ed0ed70"},"schema_version":"1.0"},"canonical_sha256":"0cae4e377bb66ab18c9bd3e52886462428a0e3b3346c1d38546f1f19bfa334de","source":{"kind":"arxiv","id":"2504.21354","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.21354","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"arxiv_version","alias_value":"2504.21354v1","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.21354","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"pith_short_12","alias_value":"BSXE4N33WZVL","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"pith_short_16","alias_value":"BSXE4N33WZVLDDE3","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"pith_short_8","alias_value":"BSXE4N33","created_at":"2026-07-05T10:56:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BSXE4N33WZVLDDE32PSSRBSGEQ","target":"record","payload":{"canonical_record":{"source":{"id":"2504.21354","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-04-30T06:19:38Z","cross_cats_sorted":[],"title_canon_sha256":"491e22d10c9cb1b08b6ca0d1a07b55cf0d77cce0022af7e7327ded1c7e1d3a90","abstract_canon_sha256":"e764d9079c98b582a8c1f54b9e9a46b245de9f4e88685537d64269ec4ed0ed70"},"schema_version":"1.0"},"canonical_sha256":"0cae4e377bb66ab18c9bd3e52886462428a0e3b3346c1d38546f1f19bfa334de","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:56:22.712886Z","signature_b64":"79BWXF4PjqhOuFO8SOIPekzAEdieUNHQVFVHM4vlSZgywQhxbrlY2NZmzL4eOaNY+s5OlwXMgxeqNcGUEqgqCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cae4e377bb66ab18c9bd3e52886462428a0e3b3346c1d38546f1f19bfa334de","last_reissued_at":"2026-07-05T10:56:22.712272Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:56:22.712272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.21354","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":"2026-07-05T10:56:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PeridCGPPYSPknK41jccJ0W5V+TwALZOYOopXkiz/2XmdbP0MSCSjR9IlZu53Kf/f5dweOa+QrYwCVwUCkchBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:47:54.422550Z"},"content_sha256":"cb09d5ea0c5b5ee2d07d2c46975121fad00d5444043a301719f9a9202bd0395f","schema_version":"1.0","event_id":"sha256:cb09d5ea0c5b5ee2d07d2c46975121fad00d5444043a301719f9a9202bd0395f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BSXE4N33WZVLDDE32PSSRBSGEQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Three-Stage Composite Outlier Identification of Wind Power Data: Integrating Physical Rules with Regression Learning and Mathematical Morphology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Cong Zhang, Jiayong Li, Limengqian Zheng, Lipeng Zhu, Weijia Wen","submitted_at":"2025-04-30T06:19:38Z","abstract_excerpt":"Existing studies on identifying outliers in wind speed-power datasets are often challenged by the complicated and irregular distributions of outliers, especially those being densely stacked yet staying close to normal data. This could degrade their identification reliability and robustness in practice. To address this defect, this paper develops a three-stage composite outlier identification method by systematically integrating three complementary techniques, i.e., physical rule-based preprocessing, regression learning-enabled detection, and mathematical morphology-based refinement. Firstly, t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.21354","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/2504.21354/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":"2026-07-05T10:56:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A8CGQTPfH333AAH2NIdHPuMqbgQvBsbdOJ8t7DTOYwAuZgk1OzB9WdCgFaiPpG9NF82G3kGmIQ6G73D6FvxNBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:47:54.423472Z"},"content_sha256":"3a7a9252f37eefca6d327231147fc71875338faf3d88834d2c105a7d5226f93d","schema_version":"1.0","event_id":"sha256:3a7a9252f37eefca6d327231147fc71875338faf3d88834d2c105a7d5226f93d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BSXE4N33WZVLDDE32PSSRBSGEQ/bundle.json","state_url":"https://pith.science/pith/BSXE4N33WZVLDDE32PSSRBSGEQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BSXE4N33WZVLDDE32PSSRBSGEQ/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":"2026-08-06T03:47:54Z","links":{"resolver":"https://pith.science/pith/BSXE4N33WZVLDDE32PSSRBSGEQ","bundle":"https://pith.science/pith/BSXE4N33WZVLDDE32PSSRBSGEQ/bundle.json","state":"https://pith.science/pith/BSXE4N33WZVLDDE32PSSRBSGEQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BSXE4N33WZVLDDE32PSSRBSGEQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BSXE4N33WZVLDDE32PSSRBSGEQ","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":"e764d9079c98b582a8c1f54b9e9a46b245de9f4e88685537d64269ec4ed0ed70","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-04-30T06:19:38Z","title_canon_sha256":"491e22d10c9cb1b08b6ca0d1a07b55cf0d77cce0022af7e7327ded1c7e1d3a90"},"schema_version":"1.0","source":{"id":"2504.21354","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.21354","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"arxiv_version","alias_value":"2504.21354v1","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.21354","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"pith_short_12","alias_value":"BSXE4N33WZVL","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"pith_short_16","alias_value":"BSXE4N33WZVLDDE3","created_at":"2026-07-05T10:56:22Z"},{"alias_kind":"pith_short_8","alias_value":"BSXE4N33","created_at":"2026-07-05T10:56:22Z"}],"graph_snapshots":[{"event_id":"sha256:3a7a9252f37eefca6d327231147fc71875338faf3d88834d2c105a7d5226f93d","target":"graph","created_at":"2026-07-05T10:56:22Z","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/2504.21354/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing studies on identifying outliers in wind speed-power datasets are often challenged by the complicated and irregular distributions of outliers, especially those being densely stacked yet staying close to normal data. This could degrade their identification reliability and robustness in practice. To address this defect, this paper develops a three-stage composite outlier identification method by systematically integrating three complementary techniques, i.e., physical rule-based preprocessing, regression learning-enabled detection, and mathematical morphology-based refinement. Firstly, t","authors_text":"Cong Zhang, Jiayong Li, Limengqian Zheng, Lipeng Zhu, Weijia Wen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-04-30T06:19:38Z","title":"Three-Stage Composite Outlier Identification of Wind Power Data: Integrating Physical Rules with Regression Learning and Mathematical Morphology"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.21354","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:cb09d5ea0c5b5ee2d07d2c46975121fad00d5444043a301719f9a9202bd0395f","target":"record","created_at":"2026-07-05T10:56:22Z","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":"e764d9079c98b582a8c1f54b9e9a46b245de9f4e88685537d64269ec4ed0ed70","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-04-30T06:19:38Z","title_canon_sha256":"491e22d10c9cb1b08b6ca0d1a07b55cf0d77cce0022af7e7327ded1c7e1d3a90"},"schema_version":"1.0","source":{"id":"2504.21354","kind":"arxiv","version":1}},"canonical_sha256":"0cae4e377bb66ab18c9bd3e52886462428a0e3b3346c1d38546f1f19bfa334de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0cae4e377bb66ab18c9bd3e52886462428a0e3b3346c1d38546f1f19bfa334de","first_computed_at":"2026-07-05T10:56:22.712272Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:56:22.712272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"79BWXF4PjqhOuFO8SOIPekzAEdieUNHQVFVHM4vlSZgywQhxbrlY2NZmzL4eOaNY+s5OlwXMgxeqNcGUEqgqCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:56:22.712886Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.21354","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb09d5ea0c5b5ee2d07d2c46975121fad00d5444043a301719f9a9202bd0395f","sha256:3a7a9252f37eefca6d327231147fc71875338faf3d88834d2c105a7d5226f93d"],"state_sha256":"0d7be2c185f7aae14129e30c31beced6e513cca9f4aeb838a50ee72cbddac5f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ljUAvO5XzQiwfxDTJnAg2H3N8ywVE6I0AQ6zLDaGF3mGBLvWHSEg0ztqmtJ7i3AiLfCQ7wgJWgrpPqi1db9iBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T03:47:54.429144Z","bundle_sha256":"a1673a86079b2651c524f1b47f252d2607243d9ea10ec9ca45a5021bd60c87da"}}