{"as_of":"2026-08-19T03:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6c5cca60e7de42e0e9d5c6ec8a3f70cef98e791cecd2f4439e86e9b24fe60d52","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T06:00:15.066541Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2504.19279/citation-record","integrity":"/paper/2504.19279/integrity","json":"/paper/2504.19279/citation-record.json","paper":"/paper/2504.19279"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.968072Z","title":"Mixchannel: Advanced augmentation for multispectral satellite images,","venue":null,"work_id":"a01af3d5-89d7-4bed-9722-8a56fae9a055","year":2021},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.867405Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:b226a2c2b5e79993ac7dd277badabecf049f02b7c5b8e63dd9faa90c39929861","observation_id":"7d7eec6b-155d-454a-aff7-73cebe9bdb6c","resolution":{"observed_at":"2026-08-16T06:00:15.972940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1117/12.2690227","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.141968Z","title":"Evaluation of the hyperspectral monitoring method capabilities for forests areas,","venue":null,"work_id":"61a6dd4c-3d7f-42e1-b50f-1afe7dfd4d82","year":2023},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.872238Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:1d87c38baeb3c14f141d835637baad8ab9df98c2fd14cd4101b84f3b1fd7d6fb","observation_id":"6dc03f23-0761-435d-918e-77fe92bd23bb","resolution":{"observed_at":"2026-08-16T06:00:15.146300Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.952357Z","title":"The method of repre- senting grayscale images in pseudo color using equal-contrast color space,","venue":null,"work_id":"58e3b8fd-2204-4ab8-999c-dd699500e7c8","year":2020},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.876796Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:dbbb31e538bc99ef9e0e2cb62e70464aba9b8b7234221508e4016cac587d2106","observation_id":"1d4a5288-fa7a-49f7-a2c6-e80c5cca72f6","resolution":{"observed_at":"2026-08-16T06:00:15.956894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.938587Z","title":"Video stabilization quality assessment method based on k-means in hsv color space,","venue":null,"work_id":"98fca00f-43f1-42d4-be52-3cfaf21738a5","year":2025},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.881360Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:06fb5606bf2da42b6495083d5241c5b36f287013747dab26cf956bdf5922e94b","observation_id":"525466d9-e0fb-45c8-8eb9-dccfb29f2efe","resolution":{"observed_at":"2026-08-16T06:00:15.943073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/rs16061073","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.127776Z","title":"Evaluation of leaf chlorophyll content from acousto-optic hyperspectral data: A multi-crop study,","venue":null,"work_id":"e4c5786c-bc02-41e3-80b5-e2200128d01b","year":2024},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.886520Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:867127e3ccc5f97d93516b40ceecf33ba25e34c43a4c142884826fdf6a451988","observation_id":"2d0af39a-0ecf-41c9-b920-8ddf107da8ea","resolution":{"observed_at":"2026-08-16T06:00:15.132599Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.924250Z","title":"Mivar’s approach to detailed description of knowledge for the academic subject “rocket and space manufacturing technolo- gies","venue":null,"work_id":"549e9df0-6289-4c05-80b4-c50453e8a2d6","year":2022},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.891171Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:1340315ceae524451fae74250cb053b201ee232c3356ea7bd83bbc11241e6504","observation_id":"6fd1962f-d75b-4509-ba13-3dca3eb35c1a","resolution":{"observed_at":"2026-08-16T06:00:15.929313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.910286Z","title":"Hyperspectral image super-resolution meets deep learning: A survey and perspective,","venue":null,"work_id":"eb322d2e-9c72-48f5-a907-3b5b46b5a20b","year":2023},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.896047Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:9ec92802d5a409ff47a7779867c76a9e4c63b4a2d22273ca8f13761e2b7b3bed","observation_id":"23ee4dad-eb14-438e-a9b5-3840f2b61ae5","resolution":{"observed_at":"2026-08-16T06:00:15.914886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.895883Z","title":"Bands sensitive convolutional network for hyperspec- tral image classification,","venue":null,"work_id":"0a60a79d-f61f-4096-aa06-808bdad19dbc","year":2016},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.900153Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:8ba8ba09e3fa8f51af2d8b8ee8ee212d6eefe026c97fd5f8d606288c83de1b42","observation_id":"74f494e9-fa85-41a1-9f92-2fd1c6809f5a","resolution":{"observed_at":"2026-08-16T06:00:15.900503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:14.904398Z","title":"Ikeuchi, Computer vision: A reference guide","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.904398Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:5079b70ed8d56de9115ddfd28c964364059e6903a8c0f8ba8e8c271c03e49875","observation_id":"8899a6fc-409b-4895-8ca9-0e443c35df11","resolution":{"observed_at":"2026-08-16T06:00:14.904398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.873181Z","title":"The role of hyperspectral imaging: A literature review,","venue":null,"work_id":"d6282a58-f91e-4ed3-a193-195c611958ba","year":2018},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.908619Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:0f2e0f65a02aa86b2e6b0fab3f978ecd4f37b113a535bb351c0e942bdf7673d9","observation_id":"3b8ccbbc-21f5-4b14-bff4-ac708ac3a564","resolution":{"observed_at":"2026-08-16T06:00:15.877808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:14.912602Z","title":"A review on the combination of deep learning techniques with proximal hyperspectral images in agriculture,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.912602Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:e319c51faba867358b821411309b19374f241e8aba250328a91be3e9c28ee114","observation_id":"fdb79024-6b3b-4dd6-8772-44b0b9a2cea1","resolution":{"observed_at":"2026-08-16T06:00:14.912602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.850180Z","title":"Automatic apple recognition based on the fusion of color and 3D feature for robotic fruit picking,","venue":null,"work_id":"083e2d2c-cb04-4b9a-bdc5-ef22e5ed5b04","year":2017},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.916785Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:2dbaaf5b7bd6bf92743ccf467f5a783e33173d45cee7389c41255e81fdb41815","observation_id":"b26e6aeb-821a-4f33-822c-1b53388f1ce1","resolution":{"observed_at":"2026-08-16T06:00:15.854856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:14.920961Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.920961Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:72c3aa31babc89a98a0fe2492a8390f93444212389959e045eadaf6662c4e959","observation_id":"c3beed0c-0fb7-4957-b14f-c49981a39f01","resolution":{"observed_at":"2026-08-16T06:00:14.920961Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.825380Z","title":"Land use and land cover classification with hyper- spectral data: A comprehensive review of methods, challenges and future directions,","venue":null,"work_id":"c6ece7f7-f390-4d87-b0e3-c2b4fa9fcb55","year":2023},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.926015Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:1b7da9f81d38b53d4118ffb923564e87317fbc7138c5a671a81fe8f52f88688c","observation_id":"f11056e9-16ff-4adf-ad98-c020219b3de1","resolution":{"observed_at":"2026-08-16T06:00:15.830339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.811625Z","title":"Bs-nets: An end-to-end framework for band selection of hyper- spectral image,","venue":null,"work_id":"5504d0f0-97f8-473e-a325-f74a6ac19953","year":1969},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.930250Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:d356c963585471741406775a9d213ac0b8fef1374c9e32e38083540772607fb3","observation_id":"ea5023b7-4f0c-43b9-9afb-e28a9149639c","resolution":{"observed_at":"2026-08-16T06:00:15.816112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.797391Z","title":"Attend in bands: Hyperspectral band weighting and selection for image classification,","venue":null,"work_id":"522db5be-d119-4da8-84de-e839c45f70ce","year":2019},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.934903Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:ed0d7b27e0436fa63c1ebb09ba951d505a99af7f95d43de21f406556bc2282bd","observation_id":"d2497aad-0669-4a55-bc12-c4bb44a64ac4","resolution":{"observed_at":"2026-08-16T06:00:15.802050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.782895Z","title":"Lidar-guided cross-attention fusion for hyperspectral band selection and image classification,","venue":null,"work_id":"111ba6bc-a536-481e-95ca-ddd321babf1d","year":2024},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.939073Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:042e23d1450760161d316cd7ecfe7e3c65c0954821b787a4bb6c01f1d9d9a749","observation_id":"55679420-e3c9-49c9-a34e-7442ebf13c5f","resolution":{"observed_at":"2026-08-16T06:00:15.787610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.768833Z","title":"Hyperspectral band selection: A review,","venue":null,"work_id":"de3bec17-0260-42d9-86a2-9f68701aeb84","year":2019},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.943225Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:59095590ba11f90c835f3c7f07f25ecd6888fce1cbd7af2d30dedc45537fed04","observation_id":"de48cb95-3508-407d-ad66-87b86bee1e0c","resolution":{"observed_at":"2026-08-16T06:00:15.773359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:14.947412Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.947412Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:9680d0d7598ca00d6aa04c2dc2955db059cf9a487b1ddfbb13e7d086d70acfe4","observation_id":"86b030d6-d245-4633-8a91-ff06f17c12c9","resolution":{"observed_at":"2026-08-16T06:00:14.947412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:14.951953Z","title":"Transformers in remote sensing: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.951953Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:86a823a9f2760df9888eea739ee8dbfa58aae80013384dd1d0d6dfb43611b1c9","observation_id":"90b27c4f-e7a4-468f-bcf9-0caaa4a0763b","resolution":{"observed_at":"2026-08-16T06:00:14.951953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.735976Z","title":"Spectralformer: Rethinking hyperspectral image classification with transformers,","venue":null,"work_id":"a3517c88-963a-4dc7-8abf-bd624c610ff1","year":2021},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.956008Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:ccf4af8ab37e59d48d1af5db9c1d0e61fbb62a5fb5cf9558d5eebce3cab000e5","observation_id":"7db75a92-e565-40b6-b576-f32a2a6948cb","resolution":{"observed_at":"2026-08-16T06:00:15.740810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.721702Z","title":"Hsi-bert: Hyperspectral image classifica- tion using the bidirectional encoder representation from transformers,","venue":null,"work_id":"fed8bd3c-5ce2-41da-8e96-762883fdbcb8","year":2019},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.960100Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:9a06d27dff130595b5b3bd0999b65c4923f2ecac435c70dbc6500d1d407345e4","observation_id":"08050321-4617-4645-b2c5-5c0e95573354","resolution":{"observed_at":"2026-08-16T06:00:15.726462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-08-17T20:47:46.242385Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-16T06:00:14.964287Z","title":"Mamba: Linear-time sequence modeling with selective state spaces,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.964287Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:2605af394f70acdd98599b66b2ceccfb385e0bbcf1cdc0270edd460be089d2df","observation_id":"f21df8c3-c5d6-4550-b84c-77993ef81d42","resolution":{"observed_at":"2026-08-16T06:00:14.964287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-08-14T11:12:31.002605Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-16T06:00:14.968704Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.968704Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:1a74dc6102fa3da43a803a5e8b6ea80d224a031d34d657cb264217c6ae8e8462","observation_id":"fab579ad-d9f4-4fe8-acc4-4bce316231cf","resolution":{"observed_at":"2026-08-16T06:00:14.968704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.707232Z","title":"Optimal mri undersampling patterns for ultimate benefit of medical vision tasks,","venue":null,"work_id":"02c17a8c-6601-4d7f-9b57-b0d767319482","year":2023},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.972983Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:350f7e40fdcb68f02709be4eb9454dc32e44d79e1ebe566ef6c057d29775e1a5","observation_id":"a9e5cd2b-cdcc-4df7-b622-939db2e63aea","resolution":{"observed_at":"2026-08-16T06:00:15.712007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.693213Z","title":"Learning compact and discriminative stacked autoencoder for hyperspectral image classification,","venue":null,"work_id":"561d6d25-d20e-42c1-a309-22140221dc2f","year":2019},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.976830Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:9451b27c0484cc6e62d99c2a4dabccd6947632861b22b1310bd6a766fdc1dd2a","observation_id":"5ec65434-e1f6-4283-89e2-8ff89532b721","resolution":{"observed_at":"2026-08-16T06:00:15.697875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:14.981129Z","title":"Convolutional neural networks for hyperspectral image classification,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.981129Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:d2389748216f106e41a8442ab4c50eed857cf53f27748fdea847d4c2c33e1eea","observation_id":"bf8d528f-fcfe-41bd-a6ca-7abfe3187f18","resolution":{"observed_at":"2026-08-16T06:00:14.981129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.669712Z","title":"Deep recurrent neural networks for hyperspectral image classification,","venue":null,"work_id":"748234f4-9c83-4d56-9985-0fdfca65eb2b","year":2017},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.985296Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:16543e7620a49d9466d8b7d3277621ecb4c3cd8ea7a668df07efbc393ba1971a","observation_id":"1d1e861d-7d0a-4cb7-b42b-ea7063b8cde5","resolution":{"observed_at":"2026-08-16T06:00:15.674186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.656000Z","title":"Generative adversarial networks for hyperspectral image classification,","venue":null,"work_id":"227f81e4-73c7-4781-8029-a29a3c5fb206","year":2018},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.989331Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:abb5e3fcfe5127df7c6cd149a517ab043bf9dd1726f7690836121e85da6d8022","observation_id":"05375072-6f5b-4732-a409-98b76eaa50e5","resolution":{"observed_at":"2026-08-16T06:00:15.660456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.641810Z","title":"Capsule networks for hyperspectral image classification,","venue":null,"work_id":"0f01db83-0807-4115-b356-a964c194dec5","year":2018},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.993468Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:c0f41a3cde67244b0a5bb920efc413841cbde3b478121d527910f216968fb342","observation_id":"ba2ffcc4-d0d8-4ba8-9745-f03750b7fbdb","resolution":{"observed_at":"2026-08-16T06:00:15.646115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:14.997628Z","title":"Recognition of forest damage from sentinel-2 satellite images using u-net, randomforest and xgboost,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:14.997628Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:772ef36b8c720beb6d281d85216ed27bf40839e022a74fc05d661b9d8d07d9e9","observation_id":"48924a34-1f0b-4d57-b1b5-2315aa35f116","resolution":{"observed_at":"2026-08-16T06:00:14.997628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.001874Z","title":"Using neural networks and machine learning methods to detect clearcut regions in sentinel–2 satellite imagery,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.001874Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:6c1ceeb3f8ca1ff97b983ec01cf94d30b7080db084be155bbad41556fdb11e66","observation_id":"353c634c-9539-4cbd-819d-8c34665b2682","resolution":{"observed_at":"2026-08-16T06:00:15.001874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1134/s0010952523700569","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.113551Z","title":"Identification of logged and windthrow areas from sentinel-2 satellite images using the u-net convolutional neural network and factors affecting its accuracy,","venue":null,"work_id":"87015887-3129-409f-9069-7b8f428a86bb","year":2023},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.005984Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:9cbb8fee50698da0bd58ae92a8ab2787e8accd42882005bffeef68a2db0b8f28","observation_id":"c4e5379b-b2c9-4db2-acba-dade0f8bc1c6","resolution":{"observed_at":"2026-08-16T06:00:15.118709Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.626486Z","title":"Hyperspectral image classification with deep learning models,","venue":null,"work_id":"82b8bbe1-b775-4769-b99f-5aca56ae1954","year":2018},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.011016Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:c99288cd43e69369c8c0dd362db21815f86b5d8f8979d79537bd4f8838e2e50a","observation_id":"3fed7972-bc0d-4897-a449-0c2189eda09d","resolution":{"observed_at":"2026-08-16T06:00:15.631291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.611888Z","title":"Multi-scale 3d deep convolutional neural network for hyperspec- tral image classification,","venue":null,"work_id":"6223f2d7-748c-4ef1-bf25-8330e189f9d9","year":2017},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.015274Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:edd736d85e9184bf60e99367bc648d544cd6c6a7a94e10da700ee68b40d47a8b","observation_id":"eb851f62-18e4-47a8-bcd3-c6103d281339","resolution":{"observed_at":"2026-08-16T06:00:15.616801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.11886","last_updated":"2021-04-19T07:06:02Z","snapshot_observed_at":"2026-08-16T18:37:16.726000Z","submitted_at":"2021-03-22T14:32:07Z","title":"DeepViT: Towards Deeper Vision Transformer","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.11886","snapshot_observed_at":"2026-08-16T06:00:15.019699Z","title":"Deepvit: Towards deeper vision transformer,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.019699Z"},"links":{"cited_paper":"/paper/2103.11886","citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:36b301b7b3504f304879795380e53c17a7296ba2ce51e86ac44489d4306dd84b","observation_id":"080aa46e-aabf-445f-a86a-556570b7f286","resolution":{"observed_at":"2026-08-16T06:00:15.019699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.596619Z","title":"Tokens-to-token vit: Training vision transformers from scratch on imagenet,","venue":null,"work_id":"60117e71-e0a2-45cc-bf4b-6afd729802a3","year":2021},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.024382Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:d9a251a6d6a020271dc4d66ff77d83ca1b1fad3bdce8b8eed8da06d411390415","observation_id":"a414deae-7ff0-4137-a0cd-8e263d1ef349","resolution":{"observed_at":"2026-08-16T06:00:15.601881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.581147Z","title":"Levit: a vision transformer in convnet’s clothing for faster inference,","venue":null,"work_id":"5e6978e1-fafd-4097-9742-eea6e709cffe","year":2021},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.028534Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:fa2043a1e8ba0904af8419a2e894893104605bb2d0ef550a92930d39db7ca616","observation_id":"edea477e-1a4b-4bfb-bfe2-36059e209cd8","resolution":{"observed_at":"2026-08-16T06:00:15.585844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.566430Z","title":"Hyperspectral image transformer classification net- works,","venue":null,"work_id":"db170ef4-6eae-4b35-88d0-ce936bbc9907","year":2022},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.032750Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:31fd91d9ddb539c1788ebd5e92775258abe0614f8d4999778d1238bc089370ef","observation_id":"8b39772d-76b5-4d96-828d-9aa038621078","resolution":{"observed_at":"2026-08-16T06:00:15.571143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04771","last_updated":"2022-03-21T03:02:37Z","snapshot_observed_at":"2026-08-16T17:15:52.131547Z","submitted_at":"2022-03-09T14:42:26Z","title":"Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04771","snapshot_observed_at":"2026-08-16T06:00:15.036747Z","title":"Multiscale convolutional transformer with center mask pretraining for hyperspectral image classification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.036747Z"},"links":{"cited_paper":"/paper/2203.04771","citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:0bad2ffab9ed03602c128a93a34f08d3044f7f08f5296987b5918a66431df3a7","observation_id":"79f386e0-6c37-4c0e-bdbb-156df3336a8e","resolution":{"observed_at":"2026-08-16T06:00:15.036747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.552577Z","title":"Spectral–spatial feature tokenization transformer for hyperspectral image classification,","venue":null,"work_id":"bff88d18-ec87-417d-b618-7c3b48db42b5","year":2022},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.041295Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:450a0bf67ddc975552f1915de7d7217aca22743af63dea4f6f90c51185c04968","observation_id":"cad11823-9be5-40fd-9ede-0b3f1abd0658","resolution":{"observed_at":"2026-08-16T06:00:15.557093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.14052","last_updated":"2023-04-29T03:18:40Z","snapshot_observed_at":"2026-08-18T19:17:21.619873Z","submitted_at":"2022-12-28T17:56:03Z","title":"Hungry Hungry Hippos: Towards Language Modeling with State Space Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.14052","snapshot_observed_at":"2026-08-16T06:00:15.045426Z","title":"Hungry hungry hippos: Towards language modeling with state space models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.045426Z"},"links":{"cited_paper":"/paper/2212.14052","citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:8c0e1a842ae5f4df5a5199d6fcb241f077e9ca00ac3fe598534f26d63fef8c95","observation_id":"fbf8e74f-ddee-4d6a-ba58-b73f2c4aa048","resolution":{"observed_at":"2026-08-16T06:00:15.045426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v39i17.33974","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.096653Z","title":"Certification of speaker recognition models to additive perturbations,","venue":null,"work_id":"afe29b97-5c76-4fe1-905b-5fd46069124e","year":2025},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.049383Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:1116e91be336a6d696dd7764ce196ed184b4b5f05ea7fe89ae7bfe9f18279283","observation_id":"1f839d6b-0fa4-4125-879b-7b09ac872e8f","resolution":{"observed_at":"2026-08-16T06:00:15.103954Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T06:00:15.537122Z","title":"Noise-robust hyperspectral image classification via multi-scale total variation,","venue":null,"work_id":"9690f4fe-d430-4dc0-9736-a54f3a0132c1","year":1948},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.053792Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:8e2ab204b60568d7eec98293417d5b5fafc48dd4903f197a38b158d722a0fcd2","observation_id":"541a5c11-c3f9-4cfa-97b0-0d4b2c82db38","resolution":{"observed_at":"2026-08-16T06:00:15.542880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.06083","last_updated":"2019-09-04T18:53:10Z","snapshot_observed_at":"2026-08-07T14:27:46.872660Z","submitted_at":"2017-06-19T17:53:11Z","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.06083","snapshot_observed_at":"2026-08-16T06:00:15.057972Z","title":"Towards deep learning models resistant to adversarial attacks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.057972Z"},"links":{"cited_paper":"/paper/1706.06083","citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:8fd8320c968a93600a9b8432f9d5e6ae4ca5738f919d9be2a8a2c526ed83e771","observation_id":"8c5499a9-df08-494b-995b-50f08261b498","resolution":{"observed_at":"2026-08-16T06:00:15.057972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.062432Z","title":"The airborne visible/infrared imaging spectrometer (aviris),","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.062432Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:9742f308e1ca53908a6b3fff73a6b8c8e5d5beea910c5965cc4c0cfaf6a4d84d","observation_id":"dea4e688-fa42-4d60-98da-3183edaba5bd","resolution":{"observed_at":"2026-08-16T06:00:15.062432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T06:00:15.522660Z","title":"Multispec: A freeware multispectral image data analysis system,","venue":null,"work_id":"c8eb2261-dba6-41c9-9fc2-cf94e99782e4","year":1999},"citing_paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T06:00:15.066541Z"},"links":{"citing_paper":"/paper/2504.19279"},"observation_digest":"sha256:185f1f790bc54ff09992cf8ef73423d41406376cca7112f1879a9cefa70c8f14","observation_id":"76456adb-3de0-459a-a4ce-777f90c5fa27","resolution":{"observed_at":"2026-08-16T06:00:15.527221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.19279","last_updated":"2025-04-27T15:33:33Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T05:54:12.393212Z","submitted_at":"2025-04-27T15:33:33Z","title":"Optimal Hyperspectral Undersampling Strategy for Satellite Imaging"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":4,"verified_fuzzy":28},"total_outbound_references":47},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2504.19279."}