{"as_of":"2026-08-11T16:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e459095d1ca0e1baf2ae4cdc84a215992e49404d9e9f752030d4f9d10a500e54","coverage":[{"denominator":4,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T23:03:14.060642Z","state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2511.07802/citation-record","integrity":"/paper/2511.07802/integrity","json":"/paper/2511.07802/citation-record.json","paper":"/paper/2511.07802"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:03:13.830826Z","title":"Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.07802","last_updated":"2026-06-24T09:05:08Z","snapshot_observed_at":"2026-08-08T23:52:06.545814Z","submitted_at":"2025-11-11T03:39:21Z","title":"Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T23:03:13.830826Z"},"links":{"citing_paper":"/paper/2511.07802"},"observation_digest":"sha256:fb229140a335906a5f97ac2ec2d0e031e8241df6b2a029cc1539e7d0461fe879","observation_id":"b026185a-9630-4083-b545-40df76c76f67","resolution":{"observed_at":"2026-08-03T23:03:13.830826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:03:13.894884Z","title":"A Brief, In - Depth Survey of Deep Learning - Based Image Watermarking,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.07802","last_updated":"2026-06-24T09:05:08Z","snapshot_observed_at":"2026-08-08T23:52:06.545814Z","submitted_at":"2025-11-11T03:39:21Z","title":"Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection","version":2},"reference_index":497,"source":"pdf_text","source_observed_at":"2026-08-03T23:03:13.894884Z"},"links":{"citing_paper":"/paper/2511.07802"},"observation_digest":"sha256:e393d715c53bfc810ba81ddab0aa9af7cfb667c2108d800d2cb04bf65489d50a","observation_id":"5cc44518-4621-43a0-8095-0ccbea175733","resolution":{"observed_at":"2026-08-03T23:03:13.894884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:03:13.779934Z","title":"StegaStamp: Invisible Hyperlinks in Physical Photographs,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.07802","last_updated":"2026-06-24T09:05:08Z","snapshot_observed_at":"2026-08-08T23:52:06.545814Z","submitted_at":"2025-11-11T03:39:21Z","title":"Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection","version":2},"reference_index":697,"source":"pdf_text","source_observed_at":"2026-08-03T23:03:13.779934Z"},"links":{"citing_paper":"/paper/2511.07802"},"observation_digest":"sha256:24dd8c8211535a689751879df501256ef7ec9279e385d9ae6fe9ad070e190d39","observation_id":"85548cda-0d0e-4102-9447-dbb1d510e7c4","resolution":{"observed_at":"2026-08-03T23:03:13.779934Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:03:14.060642Z","title":"Frequency Bias in Neural Networks for Input of Non - Uniform Density,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.07802","last_updated":"2026-06-24T09:05:08Z","snapshot_observed_at":"2026-08-08T23:52:06.545814Z","submitted_at":"2025-11-11T03:39:21Z","title":"Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection","version":2},"reference_index":2410,"source":"pdf_text","source_observed_at":"2026-08-03T23:03:14.060642Z"},"links":{"citing_paper":"/paper/2511.07802"},"observation_digest":"sha256:ed2e16f5eecfa0d5ad9a4ab5600596e15a39b725dde3b4dc3e6849a286554ec8","observation_id":"f983a40b-4eea-49aa-9704-2526dd7c65bc","resolution":{"observed_at":"2026-08-03T23:03:14.060642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.07802","last_updated":"2026-06-24T09:05:08Z","latest_version":2,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-08T23:52:06.545814Z","submitted_at":"2025-11-11T03:39:21Z","title":"Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection"},"reference_resolution":{"displayed":4,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":4},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 4 of 4 outbound references and 0 inbound Pith citation observations for arXiv:2511.07802."}