{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GPAFEK4VCEMJCGI52N5J76BMWJ","short_pith_number":"pith:GPAFEK4V","schema_version":"1.0","canonical_sha256":"33c0522b95111891191dd37a9ff82cb27b8263428c8f3da9ab294b2e8abd8c49","source":{"kind":"arxiv","id":"1810.13273","version":2},"attestation_state":"computed","paper":{"title":"Ionospheric activity prediction using convolutional recurrent neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexandre Boulch, No\\\"elie Cherrier, Thibaut Castaings","submitted_at":"2018-10-31T13:25:17Z","abstract_excerpt":"The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications. Being able to forecast globally the Total Electron Content (TEC) would enable a better anticipation of potential performance degradations. A few studies have proposed models able to predict the TEC locally, but not worldwide for most of them. Thanks to a large record of past TEC maps publicly available, we propose a method based on Deep Neural Networks (DNN) to forecast a sequence of global TEC maps consecutive "},"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":"1810.13273","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-31T13:25:17Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"119f2a70d6c324edb6d504c0e6b5fbd9827ad4069bde8675cb4716299e3caea7","abstract_canon_sha256":"530be0b60cefd5fdd0cdb427b36c2c262ec24fe07e9e26d860c277636ef958c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:25.279414Z","signature_b64":"kgG1NsxwzUXesKfLRsPpeSB6D37lUl7Sk6qjnGEojhq9XYSq0TFIkiDRC333bbsy7BbdEfGPXc7jgHd3UVrgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33c0522b95111891191dd37a9ff82cb27b8263428c8f3da9ab294b2e8abd8c49","last_reissued_at":"2026-05-18T00:01:25.278990Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:25.278990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ionospheric activity prediction using convolutional recurrent neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexandre Boulch, No\\\"elie Cherrier, Thibaut Castaings","submitted_at":"2018-10-31T13:25:17Z","abstract_excerpt":"The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications. Being able to forecast globally the Total Electron Content (TEC) would enable a better anticipation of potential performance degradations. A few studies have proposed models able to predict the TEC locally, but not worldwide for most of them. Thanks to a large record of past TEC maps publicly available, we propose a method based on Deep Neural Networks (DNN) to forecast a sequence of global TEC maps consecutive "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13273","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1810.13273","created_at":"2026-05-18T00:01:25.279058+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.13273v2","created_at":"2026-05-18T00:01:25.279058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13273","created_at":"2026-05-18T00:01:25.279058+00:00"},{"alias_kind":"pith_short_12","alias_value":"GPAFEK4VCEMJ","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GPAFEK4VCEMJCGI5","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GPAFEK4V","created_at":"2026-05-18T12:32:25.280505+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/GPAFEK4VCEMJCGI52N5J76BMWJ","json":"https://pith.science/pith/GPAFEK4VCEMJCGI52N5J76BMWJ.json","graph_json":"https://pith.science/api/pith-number/GPAFEK4VCEMJCGI52N5J76BMWJ/graph.json","events_json":"https://pith.science/api/pith-number/GPAFEK4VCEMJCGI52N5J76BMWJ/events.json","paper":"https://pith.science/paper/GPAFEK4V"},"agent_actions":{"view_html":"https://pith.science/pith/GPAFEK4VCEMJCGI52N5J76BMWJ","download_json":"https://pith.science/pith/GPAFEK4VCEMJCGI52N5J76BMWJ.json","view_paper":"https://pith.science/paper/GPAFEK4V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.13273&json=true","fetch_graph":"https://pith.science/api/pith-number/GPAFEK4VCEMJCGI52N5J76BMWJ/graph.json","fetch_events":"https://pith.science/api/pith-number/GPAFEK4VCEMJCGI52N5J76BMWJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GPAFEK4VCEMJCGI52N5J76BMWJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GPAFEK4VCEMJCGI52N5J76BMWJ/action/storage_attestation","attest_author":"https://pith.science/pith/GPAFEK4VCEMJCGI52N5J76BMWJ/action/author_attestation","sign_citation":"https://pith.science/pith/GPAFEK4VCEMJCGI52N5J76BMWJ/action/citation_signature","submit_replication":"https://pith.science/pith/GPAFEK4VCEMJCGI52N5J76BMWJ/action/replication_record"}},"created_at":"2026-05-18T00:01:25.279058+00:00","updated_at":"2026-05-18T00:01:25.279058+00:00"}