Asynchronous Resilient Wireless Sensor Network for Train Integrity Monitoring
Why this work is in the frame
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Bibliographic record
Abstract
To increase railway use efficiency, the European Railway Traffic Management System (ERTMS) Level 3 requires all trains to constantly and reliably self-monitor and report their integrity and track position without infrastructure support. Timely train separation detection is challenging, especially for long freight trains without electrical power on cars. Data fusion of multiple monitoring techniques is currently investigated, including distributed integrity sensing of all train couplings. We propose a wireless sensor network (WSN) topology, communication protocol, application, and sensor nodes prototypes designed for low-power timely train integrity (TI) reporting in unreliable conditions, like intermittent node operation and network association (e.g., in low environmental energy harvesting conditions) and unreliable radio links. Each train coupling is redundantly monitored by four sensors, which can help to satisfy the train collision avoidance system (TCAS) and European Committee for Electrotechnical Standardization (CENELEC) software integrity level (SIL) 4 requirements and contribute to the reliability of the asynchronous network with low rejoin overhead. A control center on the locomotive controls the WSN and receives the reports, helping the integration in railway or Internet-of-Things (IoT) applications. Software simulations of the embedded application code virtually unchanged show that the energy-optimized configurations check a 50-car TI (about 1-km long) in 3.6-s average with 0.1-s standard deviation and that more than 95% of the reports are delivered successfully with up to one-third of communications or up to 15% of the nodes failed. We also report qualitative test results for a 20-node network in different experimental conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it