XAI-based Robust Train Communication Framework to Analyze Vehicle Path Loss for Public Safety
Bibliographic record
Abstract
In the dynamic setting of train communication systems, ensuring consistent and reliable communication, especially during emergency situations, is of utmost for public safety. The framework predicts Vehicle Path Loss (VPL), a critical metric that serves as an indicator of signal propagation characteristics and communication quality. The prediction of VPL is influenced by multiple features, including ’RSRP_mean_interpolated_outdoor’, ’RSRP_mean_interpolated_indoor’, and ’BS_distance. The proposed framework leverages Artificial Neural Networks (ANN) to predict VPL within train communication systems. Furthermore, the interpretability of the predictions is provided by Explainable Artificial Intelligence (XAI) models like SHAP and LIME. The use of XAI fosters trust and transparency in decision-making processes. The results obtained showcase the effectiveness of the proposed framework, achieving a mean squared error of 0.0431 and a mean absolute error of 0.02678. The proposed framework prioritizes interpretability along with dependability in order to increase safety and efficacy in communication during emergency situations onboard trains.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".