Bibliographic record
Abstract
Through-the-Earth (TTE) radio has been proposed for emergency communications in locations inaccessible by conventional means, such as underground mines.While the technology is viable, it is unclear how the signal propagates in inhomogeneous media; neither modelling or obtaining a conductivity distribution in the context of TTE radio has been previously attempted.With a robust model, many practical questions can be answered, such as what is the maximum range or the optimal frequency to use, or where the transmitter and receiver should be ideally placed.To this end, a finitedifference time-domain (FDTD) code was developed and optimized for the forward modelling of TTE radio transmissions.This method is computationally intensive, and to improve performance, it was run on a graphics processing unit (GPU).The code was validated against analytical solutions for simple geometries.The use of a TTE radio signal to estimate the conductivity structure of an operating environment was demonstrated at an experimental coal mine.A transmitter was set up on the surface, and the signal strength in the underground mine was measured at several locations and frequencies.A four-layer conductivity model, with topography, was fitted to this data, using the FDTD code.TTE radio signal range has been experimentally observed to increase in the presence of elongated conductors.A thin-wire approximation was added to the FDTD code, and conductivity models were fitted to match observed data for propagation This thesis could not have been done without the contributions of many people.First, I would like to thank my supervisor, Prof. Claire
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".