Artificial neural network models for radiowave propagation in tunnels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The authors present a machine learning approach for the extraction of radiowave propagation models in tunnels. To that end, they discuss three challenges related to the application of machine learning to general wireless propagation problems: how to efficiently specify the input to the model, which learning method to use and what output functions to seek. The input that any propagation modelling tool (be it a ray‐tracer, a full‐wave method or a parabolic equation solver) uses, can be considered as visual, in the form of an image or a point cloud of the environment under consideration. Therefore, they propose an artificial neural network structure that generalises well to various geometries. The desired output can be values of the electromagnetic field components across the channel or just a path loss model. They apply these ideas to the case of arched tunnels for the first time. They consider cases where the geometric parameters of the tunnel, the position of the receiver and the frequency of operation are parts of a model trained by a vector parabolic equation solver. The model is evaluated using solver‐generated as well as measured data. The numerical results demonstrate that this approach combines computational efficiency with high accuracy.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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