Low-Power Chirp Spread Spectrum Signals for Wireless Communication Within Nuclear Power Plants
Bibliographic record
Abstract
There are two major barriers in deploying wireless communication systems in nuclear power plants (NPPs): (a) the electromagnetic compatibility (EMC) between the wireless devices and the existing plant instrumentation and control systems, and (b) the high levels of electromagnetic noise and interference from high-powered devices and ionizing radiation sources. In a typical NPP there exist strict regulations that limit transmission power levels to avoid interfering with the sensitive safety systems inside the containment such as ion chambers. This will result in performance degradation of wireless communication systems. This paper proposes a wireless communication scheme based on low-power chirp spread spectrum (CSS) signals, which meet with the EMC requirements of NPPs and also are capable of providing interference rejection. The advantage of such a scheme is that satisfactory performance can be obtained using low levels of transmission power. The structure of the optimal receiver for low-power binary CSS signals and a closed-form expression for asymptotic bit error rate of this receiver are derived. The electromagnetic environment within an NPP is modeled as a Gaussian-Gaussian mixture process, which is based on the measurement data published in a U.S. Nuclear Regulatory Commission Regulation (NUREG). The parameters in the model can be adjusted to suit a particular NPP site.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".