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CRaTeBo: a high-speed, radiation-tolerant and versatile testing platform for FPGA radiation qualification for high-energy particle accelerator applications

2024· article· en· W4390887070 on OpenAlexfundno aff
Antonio Scialdone, P. Gkountoumis, Rudy Ferraro, Manoel Barros Marin, Luigi Dilillo, Frédéric Saigné, J. Boch, Salvatore Danzeca, A. Masi

Bibliographic record

VenueJournal of Instrumentation · 2024
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsnot available
FundersEuropean CommissionTRIUMF
KeywordsField-programmable gate arrayLarge Hadron ColliderRadiationElectronicsInterface (matter)Particle acceleratorComputer scienceEmbedded systemPhysicsElectrical engineeringOperating systemNuclear physicsEngineeringBeam (structure)Optics

Abstract

fetched live from OpenAlex

Abstract The CHARM Radiation Tolerant FPGA Tester Board (CRaTeBo) is a FPGA testing platform for the CERN High-energy Accelerator (CHARM) irradiation facility. It is meant to ease the radiation testing of FPGA-based systems by providing users with a radiation-tolerant carrier card featuring an FPGA interface, a high-speed communication interface, a flexible power supply, and an HPC-FMC connector for additional front-end electronics. It is foreseen to be a permanent installation in the CHARM facility at CERN, giving users the possibility to carry out radiation tests of their system with minimum effort on the test setup development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.260
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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