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Paper Citation Record · LEDGER

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry

As of 8 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 0 inbound Pith citation observations for arXiv:2507.17881.

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

pith.paper-citation-record.v1
2507.17881 v1

Coverage vector

measured 88 of 88 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:43:10.206604Z

measured 88 of 88 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

88 of 88 outbound references displayed

  • verified exact43
  • verified fuzzy8
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch13

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e7146c4a-0d20-4847-8086-e381c0aad9c2 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6ebbeb25-ea4e-4d9d-b1ee-556e655d2f3f · outbound

This paper cites The pipeline begins with PIC simulations in CST Particle Studio to generate a multipactor susceptibility dataset for six materials.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry The pipeline begins with PIC simulations in CST Particle Studio to generate a multipactor susceptibility dataset for six materials

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T14:43:14.354654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 5f2221e6-9b6c-44de-8f96-0d5b0759e200 · outbound

This paper cites #. Scores are normalized by the total mutual information across all features such that the bars sum to 1, highlighting each feature’s relative importance in predicting δ!.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry #. Scores are normalized by the total mutual information across all features such that the bars sum to 1, highlighting each feature’s relative importance in predicting δ!

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 74e5e186-bce4-4cca-9b7b-9c8f3b0d66c8 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.717378Z digest=sha256:e1063ad1c16ba96f56224aa04e45c2f2c9d8b3afa3fd2a91ce93ab8fca8c17c2

Observation 215eb4ed-565d-442e-9984-a97e5930a2e3 · outbound

This paper cites # in the (V$%, f×d) plane (Fig. 1), following the similarity scaling law of Vaughan (1988), where f is the excitation frequency. Regions where δ!.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry # in the (V$%, f×d) plane (Fig. 1), following the similarity scaling law of Vaughan (1988), where f is the excitation frequency. Regions where δ!

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.737721Z digest=sha256:94f7bf729773f5060425b1a2249a05e7e85a148a2d9b5ab752a9a4ec1d7dc322

Observation 6299cace-1115-4a2f-9cb1-f694ff9735ef · outbound

This paper cites Entropy, Relative Entropy, and Mutual Information,.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Entropy, Relative Entropy, and Mutual Information,

Reference 12

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raw_fallback, observed 2026-08-06T14:43:14.330424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 790619d4-d236-4ca0-abf0-68d2bc5d564b · outbound

This paper cites # is shown in the color bar. Regions with δ!.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry # is shown in the color bar. Regions with δ!

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.780041Z digest=sha256:ee310abd786b708f17dd28245a9afc539e2f15bc0bb3095f63ac1585c313843b

Observation ff683c28-820a-4d67-8bae-28564ebd4053 · outbound

This paper cites Under the leave-one-material-out cross-validation strategy, the held-out material occupies a region in feature space that is entirely disjoint from the training data.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Under the leave-one-material-out cross-validation strategy, the held-out material occupies a region in feature space that is entirely disjoint from the training data

Reference 17

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 5ab686ae-0848-4c0d-92cd-3d2a0bd0e8d9 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 18

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Observation c8b6251e-bac2-4fe7-bff9-a15c1d4040df · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 23

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Reference 24

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Observation 2955f30a-c043-4df4-97cf-5e191455e541 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 31

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doi, observed 2026-08-06T14:43:11.415943Z

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Reference 33

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Observation 0ac4d429-f761-405c-845d-d898a7296acf · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 36

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verified exact
doi, observed 2026-08-06T14:43:11.274277Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.884056Z digest=sha256:13919358539d363fb2b0e7153664a11c77d9c77b4b02896706f3e8e0f8724009

Observation bc89910f-a605-4ca1-bae8-e116e3c1a9e1 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 37

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Observation ae6ea67c-9dab-4349-ae07-2740e58626f6 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 38

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Observation 29ba04e4-5fbe-4967-9722-03d0d602ae5b · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 39

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doi, observed 2026-08-06T14:43:11.203082Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9aea05af-5b21-4e99-8b65-02712d45724c · outbound

This paper cites Michigan State University.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Michigan State University

Reference 40

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Observation 956dc63c-9244-4ae3-93fa-a32b7ada0f4c · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 41

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Observation 7ed35484-27a6-4d1c-9a2c-8b3a4ce388c8 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 43

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Observation ef9f4c08-daa5-4905-9919-5e682bf80be9 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 48

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Observation 47307638-573f-461c-ba66-5ab3d3a5b3dc · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 49

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Observation 0a846569-5106-4a40-9444-814ee5315bbb · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 51

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Observation 55142659-dc7d-4585-8d7f-574a04fb0172 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 52

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Observation f2dcb31b-07fe-4588-b4d3-19bfb28cd16d · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 54

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Observation 5ec8f0ef-b82f-4932-8405-fc0f7d04945c · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 55

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Observation c40c9016-c7db-4dc7-8a0c-f7d1482c35af · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 56

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Observation d0cfdd3c-e1ba-46c8-9c57-d40d3a898980 · outbound

This paper cites Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Reference 57

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local_arxiv, observed 2026-08-06T14:43:10.791145Z

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Reference 58

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local_arxiv, observed 2026-08-06T14:43:10.759870Z

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Observation 6e686349-1fa2-4aa3-b5c5-f2085869403d · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 59

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Observation 644e9b06-ad12-4424-8605-25cd4ff3e074 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 60

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doi, observed 2026-08-06T14:43:10.703890Z

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Reference 61

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Reference 62

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source=pdf_text observed=2026-08-06T14:43:10.015175Z digest=sha256:a94a9c0adfaddff8bf247af80b8727c781b94768b2085304c24ecbc2c73f99d5

Observation 1acee3cf-d8dd-4e00-bd55-40f893803934 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 64

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doi, observed 2026-08-06T14:43:10.645080Z

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source=pdf_text observed=2026-08-06T14:43:10.025197Z digest=sha256:b41d200f791858bbb6614931b6f950c9a450a101cfbcf9c60e6adc55ab81a123

Observation f5ff767a-2413-4845-8de4-a45ac7f41283 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 66

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no resolver link, observed 2026-08-06T14:43:10.039190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:10.039190Z digest=sha256:faba91e6bb7c2bbea63ae87b1bc431f17787a2451793545e696bb610edc16c5d

Observation edc41f9d-523c-4c47-8158-bbcd31f7672a · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:10.048040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:10.048040Z digest=sha256:3edca7006d534042f351d877bf6984963ccf2a6db690de00d484accb50f301ac

Observation c2c6b154-df53-41b5-91cf-b23f859c4710 · outbound

This paper cites https://doi.org/10.18429/JACOW-IPAC2024-WEPC60 Space Engineering: Multipacting Design and Test,.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry https://doi.org/10.18429/JACOW-IPAC2024-WEPC60 Space Engineering: Multipacting Design and Test,

Reference 68

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.570138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.056914Z digest=sha256:f5532036cf642ec0922eae829e307ec505491042172d53f33618109ad7d3a302

Reference 69

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.544609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.062736Z digest=sha256:d6befca1ce1a2d07f35173a486206e80415c865b98c6af9ae859025011e2a9bf

Reference 70

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T14:43:12.981642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.067699Z digest=sha256:e840b28a21aaac0b4db74be01e514b840e95a436395f81dbae24d57ba0550b58

Observation 812b9d26-1cdf-4633-94a3-94a4b8b0c05f · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 71

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T14:43:12.844869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.073319Z digest=sha256:dfb6db86af0c7f10933f18ee1c4f5d48cf6ac17edbe28da1ee15f21a92790ebc

Observation f3c4ecd6-58e4-4775-8eb0-9baf332b7c83 · outbound

This paper cites JACoW IPAC2023, WEPA127.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry JACoW IPAC2023, WEPA127

Reference 72

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.963937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.078499Z digest=sha256:4a25d8c9419a2ddd40f59561573815ad35dfb47763b4567de3084e6af0fc6a26

Observation 54493e9d-466d-46e0-a6a4-0686f9e627ed · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 73

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.519835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.083967Z digest=sha256:2643c25d084fd47620b843ccfe0fad2c86954a1725a459b47da6eddcc30b1c7b

Reference 75

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.490520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.095655Z digest=sha256:8cc49cdeea639f35635c7e4438112e9a3ec658c455235ee9d3fc6a81652b4a36

Reference 76

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.456599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.102604Z digest=sha256:0ee06cbb8ed97740eabfa3e5ce64d36bf1c7a05cd46df3e03c270c66b71d1459

Reference 77

Resolution
verified exact
raw_fallback, observed 2026-08-06T14:43:12.591865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.108244Z digest=sha256:9b32d100512b95838fefa4f3a5f0718184458c502079fa779dfd8d44dbec1ad4

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:10.114140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:10.114140Z digest=sha256:99166dc42683276510b01d11b827482bde924ec9c6e15748a25181fca8b589b8

Observation 77dbff5c-f1da-491d-9153-586afaf0b152 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 80

Resolution
verified exact
raw_fallback, observed 2026-08-06T14:43:12.323439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.131323Z digest=sha256:839aa1397683b8d00b8f58784402dff9aff4b4c645b4e6928bc4d61a5e1c1ed3

Observation 7d4e9399-d4e6-4802-9983-e2aa0a2c4330 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 81

Resolution
verified exact
raw_fallback, observed 2026-08-06T14:43:12.148197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.139131Z digest=sha256:27c35ce1f6c50e7b6b0a643af55c95e7614f6aff15a1b6543b4ad9dcf04ed7dc

Observation 01a89fb7-6af1-406d-bec4-8d5af93127c5 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 82

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T14:43:12.032224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.145204Z digest=sha256:33ec59719fdea87029228f08508001b9d7abe3371109e3d2608ca0881e129784

Observation c9341ef6-eb05-43c6-8b03-06cc288c60b9 · outbound

This paper cites https://doi.org/10.18429/JACOW-IPAC2024-THPG04 Yang, S., Nam, J., Dietschreit, J.C.B., Gómez-Bombarelli, R.,.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry https://doi.org/10.18429/JACOW-IPAC2024-THPG04 Yang, S., Nam, J., Dietschreit, J.C.B., Gómez-Bombarelli, R.,

Reference 83

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.414766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.158158Z digest=sha256:5cb490cea15275b1aa935bc8443afb74a5556d16e5526ce070cb4ba1847f0f48

Observation 473440ec-2710-4f8a-b212-1af119466638 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 84

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.381895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.166077Z digest=sha256:f16aebade292f51b343497967024857492a7de7ec24f1a0086fd1ada6d10e05a

Observation 967a287f-27cc-438d-8151-7b7928b7e867 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 86

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T14:43:11.839548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.175950Z digest=sha256:1ceedc90db697944f77d1ec283776f9136baeb40f39d4275b7d708388f16e93d

Observation ce7af68f-9a30-4d7a-8d9c-54760cb2e507 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 87

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.312790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.182834Z digest=sha256:fbb82489a686489f6e67f0e415a88affdfff6903dd917a151b07869c7a5725eb

Reference 88

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T14:43:10.278754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.196229Z digest=sha256:03f2c2d35be85ca89ca8b94781c829b7648c87238f0cfc55da54251239c84efe

Observation 76817116-8a88-46b0-b115-31eda7e6c486 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 89

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:10.206604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:10.206604Z digest=sha256:d1d0888b888823ed86d4439c7381844c752f08f5fac0c0aa09c0e2ca4a7ad655

Observation 4bd317b6-516d-437f-9410-fecd7d1339a0 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 99

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.660772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.020696Z digest=sha256:4a3800978430e75e2ac421b1a94a343d0a468fac52212a88a7282e50ab9b2f19

Observation ea06a817-a322-45f6-85e1-e6b29313668e · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1627

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.892463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.953293Z digest=sha256:c47bf63f8c837e45d5f5baa1265be6661c0dcdb13f1d79206b1431a50f80e3d0

Observation cbaaea00-c4ff-4919-86a0-01e3964aa1aa · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1954

Resolution
verified exact
doi, observed 2026-08-06T14:43:11.301712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.879902Z digest=sha256:b77ba1657473c2b77d09055e96a94792e50a01d47752a0411f28efde9cff3e52

Observation 22247be8-6d87-4184-86d1-ab6f4f380203 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1958

Resolution
verified exact
doi, observed 2026-08-06T14:43:11.331836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.874921Z digest=sha256:0f3712412d44b5165f2b333d897a8b3dba1a8147ba04a8641628b334eed1f216

Observation a5b159e3-b92d-48d1-97c4-373217ebf3fa · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1961

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.706020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.706020Z digest=sha256:9a775f5e666b8e79da3b33d00715d9acc14a2b2c5afba5454a7daeeff471d278

Observation 03beca7b-12e7-42dd-b525-048620fa0cef · outbound

This paper cites This highly nonlinear phenomenon is triggered by secondary electron emission (SEE) (Furman and Pivi, 2002; Iqbal et al., 2019; Ludwick et al., 2020; Vaughan, 1989,.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry This highly nonlinear phenomenon is triggered by secondary electron emission (SEE) (Furman and Pivi, 2002; Iqbal et al., 2019; Ludwick et al., 2020; Vaughan, 1989,

Reference 1988

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.687440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.687440Z digest=sha256:6ab00eb1bc80c63b0ff1b91e89994b9aaabba372106dfe8ebf68ac0c67cf5588

Observation e6b2ea68-56b7-47f6-a48b-e8580bf366a9 · outbound

This paper cites 5 Table 1: Secondary electron yield (SEY) parameters for the six material property sets used in this study.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry 5 Table 1: Secondary electron yield (SEY) parameters for the six material property sets used in this study

Reference 1989

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:14.376152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.745088Z digest=sha256:9210a10c072075b5f1e4ecca9b008fdba8d14a54468c713cad61c98aaaa036cd

Observation 531a83e3-3d28-4b8e-b374-710b1be7c7e0 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1993

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.693966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.693966Z digest=sha256:c5733c4dde05b6fe4e97f6579fc336416a371158df38973d0cb7760628b69e6d

Observation a9f84599-69aa-4ede-a795-35dc31a3abf5 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1996

Resolution
unresolved
raw_fallback, observed 2026-08-06T14:43:14.272606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.768690Z digest=sha256:f7608b0ffc61029aa945fde1bd887b8bda1ed029c7ade52d90fd907b2e0fdcbd

Observation 4f107248-0c52-49bb-9756-95cd46920ad1 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1997

Resolution
verified exact
raw_fallback, observed 2026-08-06T14:43:12.704361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.088894Z digest=sha256:bc301e354da1c8e69def050af9f44a2486c8be18c7adde70da7949fc3a39d428

Observation 7012c55e-81b7-4cb0-a2dd-f4f97ff2f44c · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 1998

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.946410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.939571Z digest=sha256:61a07f4cd334c5d4fa4e1697fe94e7fdd82a1b6af2962dcb6d9158226b4b6fcc

Observation 0f8e1404-52f1-40b2-b475-fa8c9884edf7 · outbound

This paper cites XGBoost is a scalable, regularized boosting algorithm that sequentially fits new decision trees to correct the residual errors of previous trees.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry XGBoost is a scalable, regularized boosting algorithm that sequentially fits new decision trees to correct the residual errors of previous trees

Reference 1999

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:14.242307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.773103Z digest=sha256:b4bd2017c49a959545d5efff91c03d53660edf9413e9fa1f78812d4d6aa237fd

Observation f9b1f908-d79c-4315-a064-11e72e61f8c9 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2001

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.816686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.816686Z digest=sha256:e9c0c3447121be858c949271816d9429ad1e52248514cf7bcda62984de3f0d8b

Observation aedb839d-cbf3-427a-8677-36797dc64e52 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2002

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.852618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.852618Z digest=sha256:7d6de43504b3d6ec074ba4db2e570660a78d1c74ab22dbfa90310dfaa677595d

Reference 2003

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T14:43:13.270009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.967027Z digest=sha256:89e0c46b3b01b382a78a0887b4d7ebda616b3d141f868eab147fea01278c556e

Reference 2004

Resolution
verified exact
raw_fallback, observed 2026-08-06T14:43:12.461814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.121861Z digest=sha256:9bf63e8640ca42ea43451c988f9dc1c4cbb82c954dc8950bec2fc46b97587381

Observation 67815788-3f8c-4699-8422-56110aa81386 · outbound

This paper cites John Wiley & Sons, Ltd, pp.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry John Wiley & Sons, Ltd, pp

Reference 2005

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.847636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.847636Z digest=sha256:d37f5876ff07b0867b6c27d091493e7c58dedc82ce5415c750e099128ada2036

Observation d8838d42-b094-4e96-8b65-9b304913cfaa · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2006

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.856672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.856672Z digest=sha256:8ed4a284465a3094d3521c7d3867267bc54788568f384b925b91dccded1b80de

Observation a1483092-7127-4ae5-8605-6f0f5c0f4a64 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2010

Resolution
verified exact
doi, observed 2026-08-06T14:43:11.633921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.796692Z digest=sha256:16e0ea82dbc51350b651d62161cc286c5ffa7aa18098a7526ee022c28f53e131

Observation 0e26544e-6765-4a2f-8178-2b12349d06e0 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2011

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.349887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.171332Z digest=sha256:95f7c2b3ec9195a7c9d0808ee6f7bd0e8cf25ec1c571993c5c31749def64e71a

Reference 2012

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T14:43:14.138659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.802662Z digest=sha256:9f9e6549b86e273a68e0d56b6b39de077bddf40ef91ee153d7e7c1e3ff6e16a5

Observation 4344462b-6b92-4876-8213-24581c1601a1 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2013

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.997347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.917881Z digest=sha256:0ad56fda2b8527bff18d47146192c9e0743a818dde3c785a40cfee95716a7a99

Observation 919a8913-d1df-45b3-9933-b0985246d857 · outbound

This paper cites Association for Computing Machinery, New York, NY, USA, pp.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Association for Computing Machinery, New York, NY, USA, pp

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.832312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.832312Z digest=sha256:bfa0f63019bb6b4cd4581fe2c4494f44f40c0bf697e47c02cbe3e6d3d34ecc05

Observation ba7a5d9d-cd2a-486d-8454-88766e0c19ef · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.699650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.699650Z digest=sha256:32ce8c8e07ba733144a02d7d957067dd8895de9da32d39384d33707ba838225d

Reference 2018

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T14:43:11.544386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.842065Z digest=sha256:8c59b9c6ebc3b1d4cb6375464f1b31eaee20212c1b76fb403a4688e65439ef7d

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.726229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.726229Z digest=sha256:58140745834857cbd2423d50edcc85956a080776c333daa499cd486de0cff9e7

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.811041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.811041Z digest=sha256:61b89b07025cf29a316d16cf48001ecdea82d3043ee27bbc7ef37d7c20b0af9b

Reference 2021

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T14:43:13.788631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.837642Z digest=sha256:75db6ac8e742ae38b5b6b41ce1bb6982b2024a0554914f7f5701300d2c2ab2d8

Observation 58d2c046-c59d-414e-8656-9cd6fbbb897e · outbound

This paper cites CST Studio Suite,.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry CST Studio Suite,

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.732215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.732215Z digest=sha256:5170a4f6393a152e61055419563b254787e491383d74256fb082f80e820ce449

Observation c43384d5-5a95-4b23-b4ef-c17d973496e3 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2023

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T14:43:13.464723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.931465Z digest=sha256:770c6196472af36a4be09eaaa0713ea53d8f9307d224f5c4464d71cce47ff8d2

Reference 2024

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T14:43:13.669917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:09.865260Z digest=sha256:2212ece0f2321455426eb3e1820cab629ab0ab103def57da054f1cce888cc149

Observation 638f93ce-fb28-4958-8967-0f3e9dc49d6d · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:09.935580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:09.935580Z digest=sha256:f276d831d5da7acc6082a2a24a4e0e1885541f417878aff9918bda4f7b258c05

Observation 7aa5d706-c62b-43b3-87ae-914d2afab113 · outbound

This paper cites an unresolved cited work.

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry Unresolved cited work

Reference 2752

Resolution
verified exact
doi, observed 2026-08-06T14:43:10.623216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T14:43:10.031965Z digest=sha256:e62139264461ef397b90f85592f843beec6b953ef4b891a11dcaf7a6bf88737a

Pith citing papers

No inbound Pith citation observations are available.