{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6RUMEWEYQLVT6BM67IBN3R7266","short_pith_number":"pith:6RUMEWEY","schema_version":"1.0","canonical_sha256":"f468c2589882eb3f059efa02ddc7faf7b409c8e42a3378265dd265e44bdd71f8","source":{"kind":"arxiv","id":"2302.03749","version":1},"attestation_state":"computed","paper":{"title":"Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Benjamin H. Kirk, R. Michael Buehrer, Samuel R. Shebert","submitted_at":"2023-02-07T20:54:18Z","abstract_excerpt":"In shared spectrum with multiple radio access technologies, wireless standard classification is vital for applications such as dynamic spectrum access (DSA) and wideband spectrum monitoring. However, interfering signals and the presence of unknown classes of signals can diminish classification accuracy. To reduce interference, signals can be isolated in time, frequency, and space, but the isolation process adds distortion that reduces the accuracy of deep learning classifiers. We find that the distortion can be partially mitigated by augmenting the classifier training data with the signal isol"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2302.03749","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2023-02-07T20:54:18Z","cross_cats_sorted":[],"title_canon_sha256":"fac10406d590df304677b9265fbc19a60c125bd04ea7605f5a04cd2767d7c5bf","abstract_canon_sha256":"6745435525447824d54460d2f7fcb04c7cd95faebf4c1e37c47dcf8e27a4b676"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:57.271419Z","signature_b64":"OMrICTFo721NUHMw7op7FCJwg2k/RZEfvM/yQZogNDOzs0tcPFmfcsU+UEfWeRjxzan4rvnZ/Vs/ZCVa1HljCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f468c2589882eb3f059efa02ddc7faf7b409c8e42a3378265dd265e44bdd71f8","last_reissued_at":"2026-07-05T05:39:57.271066Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:57.271066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Benjamin H. Kirk, R. Michael Buehrer, Samuel R. Shebert","submitted_at":"2023-02-07T20:54:18Z","abstract_excerpt":"In shared spectrum with multiple radio access technologies, wireless standard classification is vital for applications such as dynamic spectrum access (DSA) and wideband spectrum monitoring. However, interfering signals and the presence of unknown classes of signals can diminish classification accuracy. To reduce interference, signals can be isolated in time, frequency, and space, but the isolation process adds distortion that reduces the accuracy of deep learning classifiers. We find that the distortion can be partially mitigated by augmenting the classifier training data with the signal isol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03749","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.03749/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2302.03749","created_at":"2026-07-05T05:39:57.271127+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.03749v1","created_at":"2026-07-05T05:39:57.271127+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03749","created_at":"2026-07-05T05:39:57.271127+00:00"},{"alias_kind":"pith_short_12","alias_value":"6RUMEWEYQLVT","created_at":"2026-07-05T05:39:57.271127+00:00"},{"alias_kind":"pith_short_16","alias_value":"6RUMEWEYQLVT6BM6","created_at":"2026-07-05T05:39:57.271127+00:00"},{"alias_kind":"pith_short_8","alias_value":"6RUMEWEY","created_at":"2026-07-05T05:39:57.271127+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.00796","citing_title":"An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266","json":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266.json","graph_json":"https://pith.science/api/pith-number/6RUMEWEYQLVT6BM67IBN3R7266/graph.json","events_json":"https://pith.science/api/pith-number/6RUMEWEYQLVT6BM67IBN3R7266/events.json","paper":"https://pith.science/paper/6RUMEWEY"},"agent_actions":{"view_html":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266","download_json":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266.json","view_paper":"https://pith.science/paper/6RUMEWEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.03749&json=true","fetch_graph":"https://pith.science/api/pith-number/6RUMEWEYQLVT6BM67IBN3R7266/graph.json","fetch_events":"https://pith.science/api/pith-number/6RUMEWEYQLVT6BM67IBN3R7266/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266/action/storage_attestation","attest_author":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266/action/author_attestation","sign_citation":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266/action/citation_signature","submit_replication":"https://pith.science/pith/6RUMEWEYQLVT6BM67IBN3R7266/action/replication_record"}},"created_at":"2026-07-05T05:39:57.271127+00:00","updated_at":"2026-07-05T05:39:57.271127+00:00"}