Empirical Verification of a Heuristic Radio Propagation Model in a Non-Uniform Subway Tunnel Environment
Bibliographic record
Abstract
Abstract Modern rail and particularly automated train systems utilize train control schemes which rely on continuous onboard-wayside wireless communication in the UHF/SHF frequency bands. The knowledge of radio propagation process and propagation environment are essential for specification, design, installation, and optimization of the cited wireless communication systems. To this end, radio propagation prediction models are applied which are normally characterized by the radio environment as a function of frequency and distance between transmitter and receiver along with electromagnetic characteristics of the propagation environment. These radio propagation models typically predict received power level or path loss profile for specific transmitter and receiver location. A railway tunnel offers a common and complex radio propagation scenario for automated train control applications with strict radio-based data communication subsystem requirements. In this research, special attention has been given to the case of theoretical modeling of the UHF/SHF radio propagation process inside curved multi-section inhomogeneous tunnels. The theoretical results are compared to the measurement data collected through an extensive field validation campaign conducted in the Toronto Transit Commission (TTC) subway tunnels.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".