{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HG5462FECFZTJIEP5Y6NGNA3XH","short_pith_number":"pith:HG5462FE","schema_version":"1.0","canonical_sha256":"39bbcf68a4117334a08fee3cd3341bb9c690f3cc44bd49f7fb87d1b617901004","source":{"kind":"arxiv","id":"2504.13990","version":1},"attestation_state":"computed","paper":{"title":"PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Kyeongjun Ko, Md. Ali Hasan, Md. Shafiqul Islam, M. Humayun Kabir, Wonjae Shin","submitted_at":"2025-04-18T14:18:02Z","abstract_excerpt":"Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural netw"},"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":"2504.13990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-18T14:18:02Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"b62ecea4b5561211ef071b50fcaa632d21eefb58a3210dcd886d329265bafb56","abstract_canon_sha256":"fd68a6752817226a0000f8495771a533f6a3c86e84e13e98da0f189dcc13f7a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:09.290558Z","signature_b64":"Gra9h9bEBmfe5EJ6jEHnf/0K9Fov4Gott/q+rQzyljrxUVOm7NYO48pQyQgoiR7A4zJ7gNKLxB+ZO9cHH11JBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39bbcf68a4117334a08fee3cd3341bb9c690f3cc44bd49f7fb87d1b617901004","last_reissued_at":"2026-07-05T10:51:09.289971Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:09.289971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Kyeongjun Ko, Md. Ali Hasan, Md. Shafiqul Islam, M. Humayun Kabir, Wonjae Shin","submitted_at":"2025-04-18T14:18:02Z","abstract_excerpt":"Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural netw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13990","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.13990/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":"2504.13990","created_at":"2026-07-05T10:51:09.290034+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.13990v1","created_at":"2026-07-05T10:51:09.290034+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13990","created_at":"2026-07-05T10:51:09.290034+00:00"},{"alias_kind":"pith_short_12","alias_value":"HG5462FECFZT","created_at":"2026-07-05T10:51:09.290034+00:00"},{"alias_kind":"pith_short_16","alias_value":"HG5462FECFZTJIEP","created_at":"2026-07-05T10:51:09.290034+00:00"},{"alias_kind":"pith_short_8","alias_value":"HG5462FE","created_at":"2026-07-05T10:51:09.290034+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/HG5462FECFZTJIEP5Y6NGNA3XH","json":"https://pith.science/pith/HG5462FECFZTJIEP5Y6NGNA3XH.json","graph_json":"https://pith.science/api/pith-number/HG5462FECFZTJIEP5Y6NGNA3XH/graph.json","events_json":"https://pith.science/api/pith-number/HG5462FECFZTJIEP5Y6NGNA3XH/events.json","paper":"https://pith.science/paper/HG5462FE"},"agent_actions":{"view_html":"https://pith.science/pith/HG5462FECFZTJIEP5Y6NGNA3XH","download_json":"https://pith.science/pith/HG5462FECFZTJIEP5Y6NGNA3XH.json","view_paper":"https://pith.science/paper/HG5462FE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.13990&json=true","fetch_graph":"https://pith.science/api/pith-number/HG5462FECFZTJIEP5Y6NGNA3XH/graph.json","fetch_events":"https://pith.science/api/pith-number/HG5462FECFZTJIEP5Y6NGNA3XH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HG5462FECFZTJIEP5Y6NGNA3XH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HG5462FECFZTJIEP5Y6NGNA3XH/action/storage_attestation","attest_author":"https://pith.science/pith/HG5462FECFZTJIEP5Y6NGNA3XH/action/author_attestation","sign_citation":"https://pith.science/pith/HG5462FECFZTJIEP5Y6NGNA3XH/action/citation_signature","submit_replication":"https://pith.science/pith/HG5462FECFZTJIEP5Y6NGNA3XH/action/replication_record"}},"created_at":"2026-07-05T10:51:09.290034+00:00","updated_at":"2026-07-05T10:51:09.290034+00:00"}