{"as_of":"2026-08-09T19:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:be04361a5723bf6de908bd5af508d8ba3cc68385b8e665d1f3b8da950f93da23","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:21:58.514921Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2506.10851/citation-record","integrity":"/paper/2506.10851/integrity","json":"/paper/2506.10851/citation-record.json","paper":"/paper/2506.10851"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:02.523150Z","title":"A novel traffic classification ap- proach by employing deep learning on software-defined networking,","venue":null,"work_id":"3b5b7708-c110-4363-a37a-baeff955611a","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.082388Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:fa010f44dc0fff301fd14b6b398524b678ce03da7a0496f4b48d5d1a468d278e","observation_id":"4019a303-0b89-4c67-8b9e-80a3bd893c95","resolution":{"observed_at":"2026-08-07T04:22:02.601839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:02.385694Z","title":"Glads: A global-local attention data selection model for multimodal multitask encrypted traffic classification of iot,","venue":null,"work_id":"918a17c4-ebaf-4ca4-8abf-85c52b5091c9","year":2023},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.112363Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:9ae7e777ef1423837e5b72c59fa806cf39edfb57bf7b0391f2f97dc8e6c527d4","observation_id":"84722f3b-712d-4fd5-985d-0ffe64614670","resolution":{"observed_at":"2026-08-07T04:22:02.465498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:02.185840Z","title":"Cbs: A deep learning approach for encrypted traffic classification with mixed spatio- temporal and statistical features,","venue":null,"work_id":"f7de4cf8-e5ba-4c60-929a-e788c3c56c20","year":2023},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.146562Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:ea990b7af2450ca54613f81b5a428c2e76cf5e2ffe104613999757578c76abc9","observation_id":"e29cba0e-b037-4922-82dd-5b9c8a62ed2f","resolution":{"observed_at":"2026-08-07T04:22:02.291876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:02.013079Z","title":"Deep learning and pre- training technology for encrypted traffic classification: A comprehensive review,","venue":null,"work_id":"e3f3e42b-7088-4f59-8bbe-16e0f3aaf5bf","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.168280Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:596d22ede6ff91cf3114e651f24fc739d778762a82fdf8f442fa4f9595eaad1e","observation_id":"9226f3e1-ead6-4afe-af06-e15592c6119f","resolution":{"observed_at":"2026-08-07T04:22:02.071724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:01.850331Z","title":"Advancing network security in industrial iot: A deep dive into ai-enabled intrusion detection systems,","venue":null,"work_id":"0ca3cf47-895b-4dc2-b6f2-efb00de787d1","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.180419Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:f9c4992c795b583a139d832ecc6a23c0b6b156f63c083884be5824acd3c394dd","observation_id":"f68fd35e-0c38-4c0a-98b8-b7c198473fc5","resolution":{"observed_at":"2026-08-07T04:22:01.903457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:01.714603Z","title":"Machine learning for encrypted malicious traffic detection: Approaches, datasets and comparative study,","venue":null,"work_id":"97ea1ad0-e69f-4152-b81d-6842341cd02d","year":2022},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.191669Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:e777b627a48617297d4c6eaf6238e2ab862a09ed8b04ed3d73df115def2e4864","observation_id":"66a52c43-8340-4aad-90a3-62926d311643","resolution":{"observed_at":"2026-08-07T04:22:01.798731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:01.534171Z","title":"A novel and effective encrypted traffic classification method based on channel attention and deformable convolution,","venue":null,"work_id":"42c58631-af8a-494f-aa70-a16417e291ff","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.205148Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:89ebc267ec8ef7de742081f4e1f1aab54f4983ee032c9c5dabafd6241bd13e04","observation_id":"4a1ee625-7b36-4f73-a6ad-240a7cd36d8b","resolution":{"observed_at":"2026-08-07T04:22:01.623918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:01.386708Z","title":"End-to-end encrypted traffic classification with one-dimensional convolution neural networks,","venue":null,"work_id":"906aad13-918a-481d-a940-905eea0d2586","year":2017},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.219646Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:054b79681af567260ea905afa1c079d24027f555913167b3025f6afe53d21811","observation_id":"10b27dbf-b94a-40fa-87af-fad55bbcdc0c","resolution":{"observed_at":"2026-08-07T04:22:01.456111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:01.254635Z","title":"Deep packet: A novel approach for encrypted traffic classification using deep learning,","venue":null,"work_id":"4b39c23b-34ef-490f-8bc9-808aec029fc5","year":1999},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.233870Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:6119f3de41195c05a5c07a085778d162395451ea79ca325e155f4e45fa6d0ef3","observation_id":"f2bf6230-cc64-441e-820e-4d0e57228a5b","resolution":{"observed_at":"2026-08-07T04:22:01.328052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.08727","last_updated":"2023-01-25T08:01:55Z","snapshot_observed_at":"2026-07-06T14:43:16.371886Z","submitted_at":"2023-01-20T18:47:24Z","title":"Neural Architecture Search: Insights from 1000 Papers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.08727","snapshot_observed_at":"2026-08-07T04:21:57.248302Z","title":"Neural architecture search: Insights from 1000 papers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.248302Z"},"links":{"cited_paper":"/paper/2301.08727","citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:1831803a3f085de249726a4f4b0a4e35f0b07ba235c5729631f2c46da034dc64","observation_id":"219dd8b3-06e0-4567-a42c-6f1217f7e20f","resolution":{"observed_at":"2026-08-07T04:21:57.248302Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:01.144749Z","title":"Neural architecture search benchmarks: Insights and survey,","venue":null,"work_id":"93c6a216-39fe-42ce-960d-43d9f4013a69","year":2023},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.268369Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:c972f19c9d877fa47f1b87b8ae3bc8e0ba3b8a7f2e5e16ba7b874036feb4ead5","observation_id":"455f6c15-ca21-4210-8bd9-95dcf8cd6c97","resolution":{"observed_at":"2026-08-07T04:22:01.203542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:57.282683Z","title":"Characterization of encrypted and vpn traffic using time-related,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.282683Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:ff1adef6dce0f4f605dfdb84b86950aff5dded389ca0f736f71b4202b0396e09","observation_id":"86c4fdda-7006-4ba4-b0bd-8016df9682fa","resolution":{"observed_at":"2026-08-07T04:21:57.282683Z","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":"p/5728232","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:58.756232Z","title":"(2022) Hongke sharing — what is deep packet inspection (dpi)? (chinese)","venue":null,"work_id":"a71cbf58-8090-4e3d-be2a-81ab89b1a9e7","year":2022},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.296918Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:5c9f9ef5fbac6cc731a81d3275c6dfad298ea33057d7b66dcfeaf3de2e74f3f5","observation_id":"8ed650bf-0a5d-4b1c-884b-d16f11d03fdb","resolution":{"observed_at":"2026-08-07T04:21:58.814328Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:00.951458Z","title":"Retracted: Flow online identification method for the encrypted skype,","venue":null,"work_id":"bc9350f9-f04a-40a6-a6e0-a8a7c881d933","year":2019},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.309644Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:99b07f98c9f398db6ab160d9166cdb8f2be64e8966210baf13f019f42ba33d32","observation_id":"321cc761-0098-40ae-acc5-ed24c8cf7649","resolution":{"observed_at":"2026-08-07T04:22:01.070753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:00.829949Z","title":"Random forest based traffic classification method in sdn,","venue":null,"work_id":"db6f1d4e-1874-4163-b190-3d832ab03121","year":2018},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.319312Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:a39d5fb4e8c7ac3eed08b2c900ed43f51ec536129ba0892dace967b5d0c0e340","observation_id":"14511afb-bf4f-430c-ab74-019ff4c68b43","resolution":{"observed_at":"2026-08-07T04:22:00.904663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:00.691915Z","title":"Iclstm: encrypted traffic service identification based on inception-lstm neural network,","venue":null,"work_id":"dd7fb01f-7269-48d0-9528-1ce9b5260a77","year":2021},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.392769Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:c017abfde12d5a0b71ab9d6131fe9d67a9c8597385d9b9b86c606c1aa3fb0564","observation_id":"fd1a2d97-e267-429d-8b53-e0505623b13e","resolution":{"observed_at":"2026-08-07T04:22:00.754576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:00.553872Z","title":"A session- packets-based encrypted traffic classification using capsule neural net- works,","venue":null,"work_id":"519d05a0-cd1f-4427-81e3-d0e0b8fbc7a0","year":2019},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.460585Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:17747e529832a92f49b04cd6c6233e2b10e1469c7af9434cd6a367939729ac54","observation_id":"f89c62b9-4bd6-4143-8527-8f962ecac671","resolution":{"observed_at":"2026-08-07T04:22:00.611102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:00.383545Z","title":"Network traffic classification model based on attention mechanism and spatiotemporal features,","venue":null,"work_id":"5b394817-90ee-442b-a675-7a5b7b158f32","year":2023},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.550515Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:15cff02b44e64e871296d785ed41d671e92c743010b96aa7407b4ebae9fd5b27","observation_id":"1bc76577-be79-457e-ba88-d66fdd960f0c","resolution":{"observed_at":"2026-08-07T04:22:00.450838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01578","last_updated":"2017-02-15T05:28:05Z","snapshot_observed_at":"2026-07-06T05:17:29.499249Z","submitted_at":"2016-11-05T00:41:37Z","title":"Neural Architecture Search with Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01578","snapshot_observed_at":"2026-08-07T04:21:57.611326Z","title":"Neural architecture search with reinforcement learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.611326Z"},"links":{"cited_paper":"/paper/1611.01578","citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:d768534ef81de685450631e5ff60c0d53eda00022a38e4b3ac14036c99d929d8","observation_id":"cd6d1503-73ff-4e57-9937-7af17fffee2e","resolution":{"observed_at":"2026-08-07T04:21:57.611326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.09055","last_updated":"2019-04-23T06:29:32Z","snapshot_observed_at":"2026-08-09T13:23:20.015254Z","submitted_at":"2018-06-24T00:06:13Z","title":"DARTS: Differentiable Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.09055","snapshot_observed_at":"2026-08-07T04:21:57.683045Z","title":"Darts: Differentiable architecture search,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.683045Z"},"links":{"cited_paper":"/paper/1806.09055","citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:83250c02ff7ff2a601ff64c01a7ef47f74646f5d0c286b5a4cf2649421a5304d","observation_id":"dfaba9a7-2178-4409-a995-310a079a61f8","resolution":{"observed_at":"2026-08-07T04:21:57.683045Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.09336","last_updated":"2021-01-22T21:13:46Z","snapshot_observed_at":"2026-08-07T00:40:56.156835Z","submitted_at":"2021-01-22T21:13:46Z","title":"A Comprehensive Survey on Hardware-Aware Neural Architecture Search","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.09336","snapshot_observed_at":"2026-08-07T04:21:57.771370Z","title":"A comprehensive survey on hardware-aware neural architecture search,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.771370Z"},"links":{"cited_paper":"/paper/2101.09336","citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:7d08739035d795ff4e1c7756a89ffd44709c601e9e096730f3e6683dd245b8eb","observation_id":"d4db01de-5b37-44b8-a779-c7acd1501e44","resolution":{"observed_at":"2026-08-07T04:21:57.771370Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:00.253759Z","title":"An afford- able hardware-aware neural architecture search for deploying convolu- tional neural networks on ultra-low-power computing platforms,","venue":null,"work_id":"d0e70976-dede-4482-b6f8-845a4a630cee","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.840945Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:e80aed6ff1be1b57f8b6eebcf8e290b917dde617d3425bd3956b9b40369417c8","observation_id":"696b6999-d746-42c4-bae7-11c57d260b41","resolution":{"observed_at":"2026-08-07T04:22:00.297432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:22:00.100236Z","title":"Multi-objective hardware-aware neural architecture search using hardware cost diversity,","venue":null,"work_id":"74962c54-5210-4594-9e32-fa68c07e3f25","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.910209Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:2141e4620adad1ad96de3563afa98d836bc2e1bb653496a0c56fd7e26c10102b","observation_id":"cf4dac5d-fdf0-46c1-8100-df4866c2d24c","resolution":{"observed_at":"2026-08-07T04:22:00.160142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:59.928665Z","title":"Combining com- pressed sensing and neural architecture search for sensor-near vibration diagnostics,","venue":null,"work_id":"de7b276a-0f4a-4f2d-a55e-057d83c5225e","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:57.995726Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:c9fa03398b932612c11198f0f15c66e7366510c8ddc59be6224fd2b059fdebb2","observation_id":"18d2610d-f053-4e6e-8891-7f97cd351aeb","resolution":{"observed_at":"2026-08-07T04:21:59.987879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:59.787413Z","title":"Compression- accuracy co-optimization through hardware-aware neural architecture search for vibration damage detection,","venue":null,"work_id":"35552c8e-e006-4917-8d30-f9d667aeb174","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:58.045983Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:2c85d3e43f6a400fe568dbd2ab6176f6603305c5103b37243ebe8f687bbc34ee","observation_id":"f335bff0-f96d-4907-81ab-64a1a011e3e9","resolution":{"observed_at":"2026-08-07T04:21:59.843610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:59.636703Z","title":"Tiny neural net- works for session-level traffic classification,","venue":null,"work_id":"9cf4e779-346c-439e-83dc-9b5e6e0737dc","year":2024},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:58.116326Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:4cfa793f3a0bd970fffe18e72c3210beea2f45bb1d416d5c477180d763456261","observation_id":"7eac9f17-7d07-48f6-93e7-127f12b3a14e","resolution":{"observed_at":"2026-08-07T04:21:59.699011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:58.203107Z","title":"Malware traffic classification using convolutional neural network for representation learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:58.203107Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:8298be033ee4309d43fbc2eebfa4ccfff233d98903d0700e79dc4ff6f0f92b2d","observation_id":"4a87ffce-e5b7-4f6a-a062-f3cad0741651","resolution":{"observed_at":"2026-08-07T04:21:58.203107Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:59.449838Z","title":"Centime: A direct comprehensive traffic features extraction for en- crypted traffic classification,","venue":null,"work_id":"eb2a2b1c-7872-45ae-8dcf-0d01eb7bcee0","year":2021},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:58.300213Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:0395155ed01eb88fd2ca043b6c894b4fa2725ff10a7cce8bd397d3ddebfcba03","observation_id":"ee3113e5-c3b2-400f-bc35-527471438958","resolution":{"observed_at":"2026-08-07T04:21:59.553183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:59.270227Z","title":"Identification of encrypted traffic through attention mechanism based long short term memory,","venue":null,"work_id":"46e0ec1c-48d0-40c1-a455-6a254180ea3c","year":2019},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:58.365654Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:582db0227e12c65421956e52b9999d9fbca413bda402d89b790ba25a0f2b5c30","observation_id":"12fdb5d2-e884-4773-9fcb-5d20de4b3b1e","resolution":{"observed_at":"2026-08-07T04:21:59.332896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:59.087919Z","title":"Encrypted traffic classification based on text convolution neural networks,","venue":null,"work_id":"d32979ae-079d-4588-a626-0ded30de3081","year":2019},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:58.456106Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:f3fde4450f908d36d478bb3dc7184faa08dba8fc23b767b4b4577cc792acd0c6","observation_id":"54877200-4370-483d-9111-05c57cca219d","resolution":{"observed_at":"2026-08-07T04:21:59.155007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:21:58.881875Z","title":"An encrypted traffic classification framework based on convolutional neural networks and stacked autoencoders,","venue":null,"work_id":"ede4a1ad-c44c-44b7-886e-4e5d770e7b31","year":2020},"citing_paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T04:21:58.514921Z"},"links":{"citing_paper":"/paper/2506.10851"},"observation_digest":"sha256:2c7cf8e37a970c17206a4aa118e1a2e2ce7ae6b7e9d51507a391e2b970d48014","observation_id":"360de41d-ec18-48db-a211-c7318bdd8f5f","resolution":{"observed_at":"2026-08-07T04:21:58.967335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.10851","last_updated":"2025-06-12T16:10:22Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-07T04:14:15.286063Z","submitted_at":"2025-06-12T16:10:22Z","title":"Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":24},"total_outbound_references":31},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.10851."}