{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2TZZKM3IG777QGFURNKMUQMDEC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6e027dfcf8267dc02529632d47df438fa9cb7859c970263c48bcc1a7a0090ebe","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2026-04-14T15:37:54Z","title_canon_sha256":"aeaaac4b41058e6cd094075cfd0d3caab27fec2c4ed23990b83277f544cbf5f5"},"schema_version":"1.0","source":{"id":"2604.12888","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.12888","created_at":"2026-06-04T01:08:50Z"},{"alias_kind":"arxiv_version","alias_value":"2604.12888v2","created_at":"2026-06-04T01:08:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.12888","created_at":"2026-06-04T01:08:50Z"},{"alias_kind":"pith_short_12","alias_value":"2TZZKM3IG777","created_at":"2026-06-04T01:08:50Z"},{"alias_kind":"pith_short_16","alias_value":"2TZZKM3IG777QGFU","created_at":"2026-06-04T01:08:50Z"},{"alias_kind":"pith_short_8","alias_value":"2TZZKM3I","created_at":"2026-06-04T01:08:50Z"}],"graph_snapshots":[{"event_id":"sha256:3dbaa06f31c51928193fef7edd2a7227800de83d0859b3a6e9b535514385ac26","target":"graph","created_at":"2026-06-04T01:08:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"we present an open and user-friendly NDT framework that integrates controllable vehicular mobility with the site-specific ray tracer Sionna and the discrete-event ns-3 network simulator, enabling virtualized end-to-end modeling of wireless networks across the radio, network, and application layers."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the combined mobility-ray-tracing-network simulation produces data sufficiently close to real-world measurements to be useful for training and validating machine-learning algorithms without additional calibration or validation against field data."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"An open integration of vehicular mobility, Sionna ray tracing, and ns-3 produces realistic cross-layer datasets for vehicular wireless networks and is released with code and example data."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"An open framework combines vehicle mobility models, site-specific ray tracing, and network simulation to generate realistic wireless datasets for machine learning."}],"snapshot_sha256":"6577a6f4297cab7a94c84acd46b396d8df416f8e7d00eed299dce8604f98eff8"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.12888/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The integration of accurate and reproducible wireless network simulations is a key enabler for research on open, virtualized, and intelligent communication systems. Network Digital Twins (NDTs) provide a scalable alternative to costly and time-consuming measurement campaigns, while enabling controlled experimentation and data generation for data-driven network design. In this paper, we present an open and user-friendly NDT framework that integrates controllable vehicular mobility with the site-specific ray tracer Sionna and the discrete-event ns-3 network simulator, enabling virtualized end-to","authors_text":"Carlo Fischione, G\\'abor Fodor, Oscar Stenhammar, Sundeep Rangan","cross_cats":["eess.SP"],"headline":"An open framework combines vehicle mobility models, site-specific ray tracing, and network simulation to generate realistic wireless datasets for machine learning.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2026-04-14T15:37:54Z","title":"Advancing Network Digital Twin Framework for Generating Realistic Datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.12888","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T14:09:21.012535Z","id":"9cf79127-c04d-466f-b031-46f4461bf1c8","model_set":{"reader":"grok-4.3"},"one_line_summary":"An open integration of vehicular mobility, Sionna ray tracing, and ns-3 produces realistic cross-layer datasets for vehicular wireless networks and is released with code and example data.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"An open framework combines vehicle mobility models, site-specific ray tracing, and network simulation to generate realistic wireless datasets for machine learning.","strongest_claim":"we present an open and user-friendly NDT framework that integrates controllable vehicular mobility with the site-specific ray tracer Sionna and the discrete-event ns-3 network simulator, enabling virtualized end-to-end modeling of wireless networks across the radio, network, and application layers.","weakest_assumption":"That the combined mobility-ray-tracing-network simulation produces data sufficiently close to real-world measurements to be useful for training and validating machine-learning algorithms without additional calibration or validation against field data."}},"verdict_id":"9cf79127-c04d-466f-b031-46f4461bf1c8"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dc2f0c7efa718e0df862098ce3cb21d1e7b7bfafe4b11d61ec1014076bc2560c","target":"record","created_at":"2026-06-04T01:08:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6e027dfcf8267dc02529632d47df438fa9cb7859c970263c48bcc1a7a0090ebe","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2026-04-14T15:37:54Z","title_canon_sha256":"aeaaac4b41058e6cd094075cfd0d3caab27fec2c4ed23990b83277f544cbf5f5"},"schema_version":"1.0","source":{"id":"2604.12888","kind":"arxiv","version":2}},"canonical_sha256":"d4f395336837fff818b48b54ca418320a74ce969928d1c7d0851c6c4d4134f82","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d4f395336837fff818b48b54ca418320a74ce969928d1c7d0851c6c4d4134f82","first_computed_at":"2026-06-04T01:08:50.106957Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:08:50.106957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8wpMsVFrHNWFMvouVNGoirhQSSRcYlsiZ91pgz+URKkRMWnTJofh00oqqrgOg5CAmheOLsprwDjhpRPkBf0YBA==","signature_status":"signed_v1","signed_at":"2026-06-04T01:08:50.107534Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.12888","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dc2f0c7efa718e0df862098ce3cb21d1e7b7bfafe4b11d61ec1014076bc2560c","sha256:3dbaa06f31c51928193fef7edd2a7227800de83d0859b3a6e9b535514385ac26"],"state_sha256":"f4681e7e47f78469230834d5a00d5d88d0ab19e17b1dc4a1051f16cc88686ac5"}