{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KGK4WUXSD5ZCLUMH7AXYYGVFE7","short_pith_number":"pith:KGK4WUXS","schema_version":"1.0","canonical_sha256":"5195cb52f21f7225d187f82f8c1aa527fa95fce6dbdc5d4048e93e81d74005d4","source":{"kind":"arxiv","id":"2608.00796","version":1},"attestation_state":"computed","paper":{"title":"An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Alperen Marasli, Durdu Can Yerdeyatar, Muhammet Sefa Demirel, Nurettin Safak, Ozgun Ersoy, Taha Eren Atmaca","submitted_at":"2026-08-01T17:57:36Z","abstract_excerpt":"Automatic RF modulation recognition is of critical importance in spectrum monitoring, electronic warfare, and cognitive radio applications, where low signal-to-noise ratio (SNR) conditions and the growing diversity of modulation schemes limit the performance of existing methods. This paper proposes an uncertainty-driven hybrid deep learning architecture for recognizing RF signals over a broad modulation space. The proposed approach carries out a multi-stage classification process by combining spectral information obtained through low-cost FFT-based preprocessing with time-frequency features ex"},"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":"2608.00796","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-08-01T17:57:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1003f82ef5707908c16a7ef9c5258cde89ef652d7c0b40e85e25cde020dc3064","abstract_canon_sha256":"e61869954e38105d560aa08abd4abec57158f19523b3aa1f96693a7396385f0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T01:55:09.868393Z","signature_b64":"MoPxfrYjwAF4ENt9yI4XE1w6x1Vrh6E5IP/MeN4M18RTr0xfsTGNgOAmgw556cpr4SyiB/agd0DgeM7Osy0NCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5195cb52f21f7225d187f82f8c1aa527fa95fce6dbdc5d4048e93e81d74005d4","last_reissued_at":"2026-08-04T01:55:09.866635Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T01:55:09.866635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Alperen Marasli, Durdu Can Yerdeyatar, Muhammet Sefa Demirel, Nurettin Safak, Ozgun Ersoy, Taha Eren Atmaca","submitted_at":"2026-08-01T17:57:36Z","abstract_excerpt":"Automatic RF modulation recognition is of critical importance in spectrum monitoring, electronic warfare, and cognitive radio applications, where low signal-to-noise ratio (SNR) conditions and the growing diversity of modulation schemes limit the performance of existing methods. This paper proposes an uncertainty-driven hybrid deep learning architecture for recognizing RF signals over a broad modulation space. The proposed approach carries out a multi-stage classification process by combining spectral information obtained through low-cost FFT-based preprocessing with time-frequency features ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.00796","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/2608.00796/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":"2608.00796","created_at":"2026-08-04T01:55:09.868091+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.00796v1","created_at":"2026-08-04T01:55:09.868091+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.00796","created_at":"2026-08-04T01:55:09.868091+00:00"},{"alias_kind":"pith_short_12","alias_value":"KGK4WUXSD5ZC","created_at":"2026-08-04T01:55:09.868091+00:00"},{"alias_kind":"pith_short_16","alias_value":"KGK4WUXSD5ZCLUMH","created_at":"2026-08-04T01:55:09.868091+00:00"},{"alias_kind":"pith_short_8","alias_value":"KGK4WUXS","created_at":"2026-08-04T01:55:09.868091+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7","json":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7.json","graph_json":"https://pith.science/api/pith-number/KGK4WUXSD5ZCLUMH7AXYYGVFE7/graph.json","events_json":"https://pith.science/api/pith-number/KGK4WUXSD5ZCLUMH7AXYYGVFE7/events.json","paper":"https://pith.science/paper/KGK4WUXS"},"agent_actions":{"view_html":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7","download_json":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7.json","view_paper":"https://pith.science/paper/KGK4WUXS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.00796&json=true","fetch_graph":"https://pith.science/api/pith-number/KGK4WUXSD5ZCLUMH7AXYYGVFE7/graph.json","fetch_events":"https://pith.science/api/pith-number/KGK4WUXSD5ZCLUMH7AXYYGVFE7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7/action/storage_attestation","attest_author":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7/action/author_attestation","sign_citation":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7/action/citation_signature","submit_replication":"https://pith.science/pith/KGK4WUXSD5ZCLUMH7AXYYGVFE7/action/replication_record"}},"created_at":"2026-08-04T01:55:09.868091+00:00","updated_at":"2026-08-04T01:55:09.868091+00:00"}