A Radiation-Tolerant Wireless Monitoring System Using a Redundant Architecture and Diversified Commercial Off-the-Shelf Components
Bibliographic record
Abstract
This paper presents a radiation-tolerant design of a wireless system which allows commercial off-the-shelf electronic components to be used in high-level radiation fields, such as those found in a nuclear power plant (NPP) after a severe accident. This paper starts with the analysis of the characteristics of the radiation environment after a severe accident, with the focus on radiation effects on electronics. Based on such analysis, two approaches have been taken to mitigate the impact of radiation and to prolong the life of the electronics system: radiation shielding and radiation-tolerant design. To assess the effectiveness of this design approach, an evaluation method for radiation protection and system reliability evaluation has been developed. Even though such an assessment technique cannot replace physical radiation tests, it provides an effective way to select suitable components and estimate the radiation-tolerance at the system design stage. The results of the assessment for the current design have concluded that under the radiation conditions similar to those in an NPP under a severe accident, the designed system can be comparable to those made of radiation-hardened components. The developed redundant wireless system can be deployed in environments with a radiation up to 1 M · Rad (Si).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".