A Generalizable Physics-Informed Long-Range Attentive Network for Radio Wave Propagation Modeling in Tunnels
Bibliographic record
Abstract
Accurate and efficient modeling of radio wave propagation in tunnel environments is critical for the deployment and optimization of modern intelligent transportation systems (ITSs). Traditional physics-based methods, such as the parabolic wave equation (PWE) methods, face significant challenges due to their high computational cost. Recently, the adoption of machine learning (ML) methods has enabled rapid modeling of radio wave propagation in tunnels. However, these approaches face significant challenges in accurately predicting long-range propagation in tunnel environments, limiting their practical applicability. This article proposes a physics-informed long-range attentive propagation network (PLAPN) that combines convolutional feature encoding, a dual-path attention translator, and a sliding window inference strategy. The model is designed to directly learn received signal strength (RSS) distributions from short-range simulations in tunnels, enabling long-range sequence prediction while capturing both spatial and temporal dependencies. Meanwhile, we introduce a sliding window strategy that substantially enhances prediction accuracy by focusing attention on local temporal contexts and reducing error accumulation over long sequences. Both measurement data obtained from the Massif Central tunnel and simulation results across diverse tunnel geometries and frequency settings confirm that the windowed version of the model (PLAPN-Win) delivers superior reconstruction accuracy compared to its nonwindowed variant (PLAPN-Nowin) and a conventional convolutional neural network (CNN) baseline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".