Paper Citation Record · LEDGER

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning

As of 12 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2606.20976.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.20976 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T17:25:19.273780Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact3
verified fuzzy0
unresolved33
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1de329f6-0266-436f-9cb8-edf0d5a4f9d9 · outbound

This paper cites Orbital debris requires prevention and mitigation across the satellite life cycle,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Orbital debris requires prevention and mitigation across the satellite life cycle,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 81d9d6b0-1758-446d-ad61-b2d5b5ad51b2 · outbound

This paper cites You only look once: Unified, real-time object detection,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning You only look once: Unified, real-time object detection,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 964093dc-50fd-42f7-aa81-9b30689dbe88 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 206594738.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Available: https://api.semanticscholar.org/CorpusID: 206594738

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation eae717fe-1c77-4e72-a362-126783c3af9a · outbound

This paper cites Cross-domain object detection with hierarchical multi-scale domain adaptive yolo,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Cross-domain object detection with hierarchical multi-scale domain adaptive yolo,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 68194adb-aecc-4a49-a834-368947a16020 · outbound

This paper cites Tensor Extraction of Latent Features (T-ELF),.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Tensor Extraction of Latent Features (T-ELF),

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 33c2a5b5-6044-4e1c-b2c8-06a4b6abee1a · outbound

This paper cites Semi-supervised classification of malware families under extreme class imbalance via hierarchical non-negative matrix factorization with automatic model selection,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Semi-supervised classification of malware families under extreme class imbalance via hierarchical non-negative matrix factorization with automatic model selection,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 096d299d-0489-4003-b2ef-37fffdc6bdcf · outbound

This paper cites Source identification by non-negative matrix factorization combined with semi-supervised clustering,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Source identification by non-negative matrix factorization combined with semi-supervised clustering,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation e612d002-7d2c-41c7-9b88-d9ca023f790b · outbound

This paper cites Chen and C.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Chen and C

Reference 8

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Observation ef1ef33a-7172-4590-9e2e-d1dfa57cc6a6 · outbound

This paper cites an unresolved cited work.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Unresolved cited work

Reference 9

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Observation 42dcd2c7-c174-4488-a647-5c39e1254b94 · outbound

This paper cites Optical surveys for space debris,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Optical surveys for space debris,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 0383f831-c356-4f1b-a89c-37b822c1d4b2 · outbound

This paper cites A fast algorithm for the detection of faint orbital debris tracks in optical images,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning A fast algorithm for the detection of faint orbital debris tracks in optical images,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation c527924d-e138-4840-babd-20cddeaf58be · outbound

This paper cites Detection of small radar cross-section orbital debris with the haystack radar,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Detection of small radar cross-section orbital debris with the haystack radar,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation c34c37e5-df6c-4b75-9173-63dee298b9cf · outbound

This paper cites Detecting, tracking and imaging space debris,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Detecting, tracking and imaging space debris,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 160e3c87-d086-4388-9621-aafac72742ce · outbound

This paper cites The development of non-coherent passive radar techniques for space situational awareness with the murchison widefield array,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning The development of non-coherent passive radar techniques for space situational awareness with the murchison widefield array,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation cd9f1fb0-b17a-45bb-b3e7-68a1edf974d5 · outbound

This paper cites Deep learning- based space debris detection for space situational awareness: A fea- sibility study applied to the radar processing,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Deep learning- based space debris detection for space situational awareness: A fea- sibility study applied to the radar processing,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 36bbf6de-b9c8-4943-b985-d09bf9c6f22f · outbound

This paper cites A semi-supervised object detection method for close range detection of spacecraft and space debris,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning A semi-supervised object detection method for close range detection of spacecraft and space debris,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation fab47294-2912-4dfb-8779-5172678e8dc2 · outbound

This paper cites Radars for the detection and tracking of ballistic missiles, satellites, and planets.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Radars for the detection and tracking of ballistic missiles, satellites, and planets

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 1741bfe2-a6d5-4788-9008-5eaa9d348e8e · outbound

This paper cites Sintra space debris identification and tracking,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Sintra space debris identification and tracking,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 89353103-7e14-48be-a1b3-ea7a4b0f99e1 · outbound

This paper cites A theory of incoherent scattering of radio waves by a plasma,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning A theory of incoherent scattering of radio waves by a plasma,

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-26T17:29:37.687502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c9f313db-20b8-4a86-846a-21f1a3faef74 · outbound

This paper cites Radar investigation of postsunset equatorial ionospheric instability over kwajalein during project windy,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Radar investigation of postsunset equatorial ionospheric instability over kwajalein during project windy,

Reference 20

Resolution: verified exact; doi, observed 2026-06-26T17:29:37.691551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92fa5ba9-e086-4ea2-8b86-0b3ee4f96692 · outbound

This paper cites Incoherent scatter plasma lines: Observations and applications,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Incoherent scatter plasma lines: Observations and applications,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 66674b85-5001-4f14-9583-1c2723a9f53a · outbound

This paper cites space-track.org.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning space-track.org

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 348e09db-d0c0-44e7-9a7b-bee9a948bef3 · outbound

This paper cites Celestrak.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Celestrak

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 8d30c928-6d91-4275-9771-90729a1db8be · outbound

This paper cites Learning the parts of objects by non- negative matrix factorization,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Learning the parts of objects by non- negative matrix factorization,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 415d5041-e30f-43dc-bdff-2de56bd2747b · outbound

This paper cites Nonnegative matrix factorizations as probabilistic inference in composite models,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Nonnegative matrix factorizations as probabilistic inference in composite models,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 818f3a28-1dfe-4f28-a852-2967f38376da · outbound

This paper cites Bayesian nonlinear modeling for the prediction compe- tition,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Bayesian nonlinear modeling for the prediction compe- tition,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation e1b902c6-21ac-4ea5-bf51-07877bb767ba · outbound

This paper cites Bayesian pca,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Bayesian pca,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 4547fac5-5024-4582-af47-1b105f73018c · outbound

This paper cites Tuning pruning in sparse non-negative matrix factorization,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Tuning pruning in sparse non-negative matrix factorization,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation af948f9b-1320-4784-a920-8b168711e8a3 · outbound

This paper cites Automatic relevance determination in nonnegative matrix factorization with the/spl beta/-divergence,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Automatic relevance determination in nonnegative matrix factorization with the/spl beta/-divergence,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 2d7fc615-6891-46f1-af03-059340082efc · outbound

This paper cites Metagenes and molecular pattern discovery using matrix factorization,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Metagenes and molecular pattern discovery using matrix factorization,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 37f0c8a6-a28a-4457-abb9-f18a046f49a1 · outbound

This paper cites and Nik-Zainal, Serena and Wedge, David C.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning and Nik-Zainal, Serena and Wedge, David C

Reference 31

Resolution: metadata mismatch; doi, observed 2026-06-26T17:29:37.689672Z

Source-reported events for the cited work

correction dated 2013-10-08. Source: crossref record 10.1038/nature12666->10.1038/nature12477:correction, observed 2026-07-11T03:12:19.350704+00:00. This notice travels one citation hop only.

Observation 842400c6-3e9f-4d36-bf7d-2e86ac6fb6ac · outbound

This paper cites The repertoire of mutational signatures in human cancer,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning The repertoire of mutational signatures in human cancer,

Reference 32

Resolution: verified exact; doi, observed 2026-06-26T17:29:37.693288Z

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correction dated 2023-01-25. Source: crossref record 10.1038/s41586-022-05600-5->10.1038/s41586-020-1943-3:correction, observed 2026-07-11T02:55:41.956893+00:00. This notice travels one citation hop only.

Observation 7739e6fb-fe3c-4a92-b9c3-8332afe83970 · outbound

This paper cites Uncovering novel mutational signatures by de novo extraction with sigprofilerextractor,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Uncovering novel mutational signatures by de novo extraction with sigprofilerextractor,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 07623e13-de57-4a87-aa1e-3f285638a60d · outbound

This paper cites A neural network for determination of latent dimensionality in non-negative matrix factorization,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning A neural network for determination of latent dimensionality in non-negative matrix factorization,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation bc2c1acc-269e-4486-ac8c-0ea1e11b3af8 · outbound

This paper cites Semantic nonnegative matrix factorization with automatic model determination for topic modeling,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Semantic nonnegative matrix factorization with automatic model determination for topic modeling,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 41b6201a-7134-43eb-97c1-d1e7125cc9b3 · outbound

This paper cites s-step iterative methods for symmetric linear systems.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning s-step iterative methods for symmetric linear systems

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:59:33.500574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5ba2a480-9d9b-429c-86cf-94f1b328d63d · outbound

This paper cites Finding the number of latent topics with semantic non-negative matrix factorization,.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Finding the number of latent topics with semantic non-negative matrix factorization,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 0d2a9778-33f1-45a7-8015-1c2b53e1cecb · outbound

This paper cites Haynes,Wilcoxon Rank Sum Test.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Haynes,Wilcoxon Rank Sum Test

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Observation 3a783357-54e8-4e76-a4d5-f7279919a2ee · outbound

This paper cites Chapelle, B.

Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning Chapelle, B

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-26T17:25:19.273780Z

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Pith citing papers

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