{"as_of":"2026-08-07T05:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c37d555e0c2da53bae06fa2b6dae1a49e3425efe2f4439e7af26326ff8a768f6","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T13:26:42.535586Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.22795/citation-record","integrity":"/paper/2509.22795/integrity","json":"/paper/2509.22795/citation-record.json","paper":"/paper/2509.22795"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Statistical feature extraction is used to classify events from PMU data stream [8]","venue":null,"work_id":"edf50458-c2f5-472f-98d5-bcd99a49aa68","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:f864afb14a7153f08ad0289692d5448425e469f4493168f5fb0b8461b2ec2cd7","observation_id":"a953194e-a7aa-46bc-afc9-e2c438eec6bc","resolution":{"observed_at":"2026-05-22T13:36:36.714304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Such constraints often result in poor generalization of event classifiers when applied to unseen conditions","venue":null,"work_id":"da3bfb89-c641-46cf-9869-abcb8c33a3c4","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:471b2b8920aca483ad23b0a46a32cc446ff95778fa65ebe7bf67fadd082ef5f6","observation_id":"f2c69072-3a10-4709-9aa5-0c22ce24074f","resolution":{"observed_at":"2026-05-22T13:36:36.746826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The encoder network receives normalized PMU measurements at a 5 -second length 𝑴","venue":null,"work_id":"562cf20d-dc6d-4759-9cf2-07fba6a16f5b","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:8b0ee17df9964bcca162ce0f042d796dedc97ea7a66f76f4471e1b2701f4899a","observation_id":"bcd347ba-4373-4211-9828-f32bd9b572f9","resolution":{"observed_at":"2026-05-22T13:36:36.761451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a94441c8-d547-445a-9d8e-d1057a333b04","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:a942eb744f23a9d1d6a2742111f6d5e419c9c586c27b647f693f94a6489dc4af","observation_id":"57219501-40f3-4560-8399-f97de3ee9781","resolution":{"observed_at":"2026-05-22T13:34:54.963248Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"We develop two complementary decision-making strategies: Fig","venue":null,"work_id":"19600e32-b766-4ad7-938a-3cf673f87ec0","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:e57750db84512a0cc9aa0ebcd3f698f3f3f4496c2c10ed09e30a2226b6a61504","observation_id":"b1168585-461a-4b0e-8518-b99fa9331338","resolution":{"observed_at":"2026-05-22T13:34:54.970726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a4202a8b-a100-4832-a54b-62dac8a62c94","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:e3d933d094c3275291156a248cd5bac2da2bb2950334ec3847ddecdf1f22fcfa","observation_id":"cb93ad1b-c990-413d-ae5d-ac8620191ea1","resolution":{"observed_at":"2026-05-22T13:36:36.753578Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a7fe6547-08ba-4e5f-873c-15bbfdee22af","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:f887257abcbca0b766a21779d01872b59fd70091e6c756bc3949576e6d49b49c","observation_id":"83e0e046-38f4-4956-8baf-be308d174afe","resolution":{"observed_at":"2026-05-22T13:36:36.718166Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The scalability of the algorithm is tested on a laptop PC","venue":null,"work_id":"e4fec5d8-f74c-4fa5-b7e5-80d1cdb9c18c","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:e3e692642254659832d58a152f3f9fdfb6698c3d83c3909a5e96a6d72beb02af","observation_id":"4b150c5f-5c29-44da-971a-cb8dae7479c2","resolution":{"observed_at":"2026-05-22T13:36:36.729075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Figure 7 illustrates the distribution of individual PMU data segments in the 2D feature space defined by the reconstruction error and discriminator error","venue":null,"work_id":"fd54a69c-a28f-4d6b-8a66-679086b2f37f","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:4fac256838114994db0928cbc9c00852d4651bc34003ac610d241a492c2df05e","observation_id":"92801b51-5f0f-4677-84e4-f4491b1ee3e0","resolution":{"observed_at":"2026-05-22T13:36:36.721702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The left subplot shows normalized voltage magnitude signals from multiple PMU channels over a 60 -second interval","venue":null,"work_id":"c60e060c-1694-46c7-91b8-f0ab6b4c7744","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:28f9d02d8728bd61afc4f96ea5ac00c2d5af1a6b7786bd6eeb3a09afe6567771","observation_id":"47a0ef7e-afd5-4541-b289-d691bb773778","resolution":{"observed_at":"2026-05-22T13:36:36.757520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The left panel shows the voltage magnitude measurement, where a sharp deviation indicates the presence of an event","venue":null,"work_id":"a48c26aa-c441-49a9-a83b-38f36f727e90","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:cf4d2b0caf862ad6f916e60d8ab8f30f16d8ce86c57fffee26ebc873a62de853","observation_id":"63be3eb4-8795-4ca0-9036-eb4ea0086fc8","resolution":{"observed_at":"2026-05-22T13:34:54.982189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ee8276fa-6ad4-407b-9e01-467fddca8e51","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:c433140f0863c37bc6560a4d2008ade11568b16393d7d3dce0699ab7d7c1069a","observation_id":"4acdcd2c-542d-4e30-be06-60ae31c532b3","resolution":{"observed_at":"2026-05-22T13:34:54.963544Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Without the sliding window (top row), the classification accuracy is 81.89%","venue":null,"work_id":"542fc31e-6116-4b64-a049-3a599e8b7280","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:5ab93fdc5ed3836d01301998fddb435a4217f8fbb11bf49fdfd08fb8807304d9","observation_id":"e718e5ed-cd19-4397-aaf3-028f542fcdd5","resolution":{"observed_at":"2026-05-22T13:36:36.736255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f081786d-b1f1-4ee7-a273-64b285fc48bf","year":null},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:39722ef51d34e34fba9f037e0be47e4ceea501f9170060d3b992b5360d939c8b","observation_id":"510eb86b-e315-493e-9d48-277577a632b4","resolution":{"observed_at":"2026-05-22T13:36:36.742965Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The 2003 blackout: Solutions that won’t cost a fortune","venue":null,"work_id":"3b849f0c-9b02-4bac-ac97-3eddd2fe7094","year":2003},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:49fa8eb4d82d914a4a14f2db90459e082494f94c9755b493290a5b533374ef18","observation_id":"87530a43-0647-47d6-93f4-30d7a0c3a164","resolution":{"observed_at":"2026-05-22T13:36:36.750159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ice Storm Makes Christmas a Dark Day for Tens of Thousands","venue":null,"work_id":"8e43743e-f18a-4f9a-966d-b124072ced62","year":2025},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:7c5633829155d1882aa072257cf361b12a44474496a7a371ca5ec8336c80bec8","observation_id":"9ac2cf85-5949-4ead-9445-78569e153868","resolution":{"observed_at":"2026-05-22T13:36:36.768053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Wavelet - based event detection method using PMU data","venue":null,"work_id":"0d56328d-a9a7-4613-b9c0-4eca0f0e8165","year":2015},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:c7cbf86ff56046bd5ed346a1d77de46d28df2891fa530f52ffbee2617b5313ff","observation_id":"923f6c70-9348-4f63-93d3-c70af8463a12","resolution":{"observed_at":"2026-05-22T13:36:36.710655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Event detection method for the PMUs synchrophasor data","venue":null,"work_id":"eddee7fb-b646-495a-9e99-d81a4b73c816","year":2012},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:106d3948c51dc109027b3d7c304319e4fb93d582aba0e614c42956cfbe5c4a11","observation_id":"856bf378-1281-4738-82ae-abd8ad4415b5","resolution":{"observed_at":"2026-05-22T13:34:54.954853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dimensionality reduction of synchrophasor data for early event detection: Linearized analysis","venue":null,"work_id":"2ab0a5c6-0f04-471f-afab-e29ebe350114","year":2014},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:28c52d4fa03bb8efa5ec2a733200273c26424eb473419d79a47acab3e9506cef","observation_id":"8b410d32-9741-4bc6-94f4-4a0ae43a5e81","resolution":{"observed_at":"2026-05-22T13:36:36.703032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Real time anomaly detection in wide area monitoring of smart grids","venue":null,"work_id":"8b6c15f3-0e66-4a3e-bfd8-105598463e39","year":2014},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:024812580df3876dcb3b6af780dc07940e7968cb0d37f65a64f850b479590b0c","observation_id":"6b42a468-d5f8-4c79-80e9-ca68e12e3a1e","resolution":{"observed_at":"2026-05-22T13:36:36.699295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Real -time event identification through low-dimensional subspace characterization of high -dimensional synchrophasor data","venue":null,"work_id":"35fa1a80-6d3c-4efb-b5f0-62c916413802","year":2018},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:74d65e8d6c8e1f6593688e06bce136f8d70ab566ee675b63d37d9e2ffa2830f4","observation_id":"712ad516-93c2-457d-b121-1b2a8c79690c","resolution":{"observed_at":"2026-05-22T13:36:36.732615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Event detection and its signal characterization in PMU data stream","venue":null,"work_id":"811583f9-4282-439c-9507-83d1d56e747c","year":2017},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:b120d7b7af5dd8096897c15eea269f8a4e61471280d81f75158664a3341ad8d5","observation_id":"8ab23271-c662-4662-9268-478953b21de9","resolution":{"observed_at":"2026-05-22T13:36:36.739748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A novel event detection method using PMU data with high precision","venue":null,"work_id":"bf43d890-24b6-4fad-b1d7-2aeee108cbb0","year":2019},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:a953523bbbe4ce33ad39c13446f7abf2a1edfcff1d8c9748696ca8d362e09232","observation_id":"63e04773-6ce3-46cc-aaed-8b05408d6f67","resolution":{"observed_at":"2026-05-22T13:34:54.959763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Preliminary work to classify the disturbance events recorded by phasor measurement units","venue":null,"work_id":"5d15bf93-88a9-4485-973e-2169c6377edb","year":2012},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:8eaf25caffa1ddc1ff22835abc85ff7b044f01ad19b88d241eaccb85c213e2e9","observation_id":"df03ce76-8f3c-475d-a685-855937a75c6f","resolution":{"observed_at":"2026-05-22T13:34:54.966701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Frequency disturbance event detection based on synchrophasors and deep learning","venue":null,"work_id":"b8024d80-cc89-4dc6-8ef3-0d46452dba91","year":2020},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:fe1e33a1429bce89e71e06d157e1a851bbfb5ea10f7a40af6d6fba269ca855ef","observation_id":"0d449140-33f4-455e-aa23-2e8c3095d4ea","resolution":{"observed_at":"2026-05-22T13:36:36.706688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"PMU-data-driven event classification in power transmission grids","venue":null,"work_id":"598ef1e5-df38-4319-8b25-cc661a8e09fe","year":2021},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:4bb5f6518c4d8e68a7968532def9ba64798a6084572a5524dacee91273f0767f","observation_id":"17aa4d78-d243-4574-809e-b1f66c82e364","resolution":{"observed_at":"2026-05-22T13:34:54.958661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hierarchical convolutional neural networks for event classification on PMU measurements","venue":null,"work_id":"b64182d6-a261-499e-821f-5cb36d250f8e","year":2021},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:c6175c65feba5fb681efb6920762a0dc9c7f6934d4f5ab9bcb8acd2cc814ed3d","observation_id":"f2d78647-439b-42a2-9cd7-c035b009ecc8","resolution":{"observed_at":"2026-05-22T13:34:54.978533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learningbased real-time event identification using rich real PMU data","venue":null,"work_id":"f43f1cbf-a794-4af7-bae9-47096e069658","year":2021},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:284d4011d7edbee89c2ad939c295e56ea0bb39c6dbdf576e788a7984a8e4365b","observation_id":"775b0c4c-7978-4dd1-9caa-b8160368513c","resolution":{"observed_at":"2026-05-22T13:34:54.952391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Big data processing for power grid event detection","venue":null,"work_id":"dde6acd6-6ec5-49f7-9127-0fc0a1237ff0","year":2020},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:a295a266115e78809ceb03a855169b72c2359ea7001cf402d2a6a90567ec1b50","observation_id":"56285338-ee85-45bc-ad62-ad58408e1966","resolution":{"observed_at":"2026-05-22T13:34:54.944837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep-Learning based Multiple Class Events Detection and Classification using Micro -PMU Data","venue":null,"work_id":"517d2fc0-6522-470d-a27b-1a44f8a9c87b","year":2024},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:479f20d41ea5ec52584415f6fb3a7c69c271c761f0f7e801faa112f3ac2eeb82","observation_id":"d58ad68f-ab52-4bf2-8995-764cbc5926c4","resolution":{"observed_at":"2026-05-22T13:34:54.951638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Online power system event detection via bidirectional generative adversarial networks","venue":null,"work_id":"44fd879d-3ffa-4716-8c4c-81c405aaaa4e","year":2022},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:6134418d259358b4ccf8f8062dd837af38d7a5a50c4706e80d62d299325d2d78","observation_id":"a65c16b4-1850-4a12-8557-0015f70ec6dd","resolution":{"observed_at":"2026-05-22T13:34:54.937857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Smart grid line event classification using supervised learning over PMU data streams","venue":null,"work_id":"9fdc8518-8565-4b74-a952-87689e5a47ac","year":2015},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:17cb0a7db2e2203852f133633c5fb6317e1e97c01f69747a75e2a26e3580876c","observation_id":"268e04df-0308-4b11-9902-db278da71503","resolution":{"observed_at":"2026-05-22T13:34:54.941393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Data- driven event detection of power systems based on unequal-interval reduction of PMU data and local outlier factor","venue":null,"work_id":"ae07dc5c-bc66-46a6-a49b-e97085f8804e","year":2020},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:89874f50c16489a111dec21157551531cd01c95c66134ec55e6dc17de632a8d6","observation_id":"64640255-4c6d-44c4-8af2-47fcb703cfa8","resolution":{"observed_at":"2026-05-22T13:36:36.764785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Generative adversarial networks-based synthetic PMU data creation for improved event classification","venue":null,"work_id":"7ec9d77a-cbc3-4e5d-9992-53c9e057f0fd","year":2021},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:bfe3c95524ac7e5776e5bb68181b3a373991a1a9e9a2114eb6c396b044599df7","observation_id":"205478f2-0552-4522-a6c1-91f95c3ffab6","resolution":{"observed_at":"2026-05-22T13:34:54.974967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Automated High-Speed Power System Event Detection and Classification Using Synchrophasor Data","venue":null,"work_id":"681173ab-88c8-4dc1-b426-e09f96996923","year":2024},"citing_paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-22T13:26:42.535586Z"},"links":{"citing_paper":"/paper/2509.22795"},"observation_digest":"sha256:45ee4d132d8b53b841b026f307054f9ef894418f669261d80ef8b1a3409337f2","observation_id":"a65a1883-293b-4983-bb1f-bfb56e835f65","resolution":{"observed_at":"2026-05-22T13:36:36.725431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.22795","last_updated":"2025-09-26T18:04:03Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T18:04:03Z","title":"Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":30},"total_outbound_references":35},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2509.22795."}