Novel wireless channels characterization model for underground mines
Bibliographic record
Abstract
The propagation characteristics of electromagnetic waves in underground mines are different from those in free space because of the harsh underground environment . Physical phenomena like severe reflection, scattering, and diffraction along the mines’ rough walls will affect the propagation of electromagnetic waves. Channel predictions are crucial for reliable and optimal wireless communication in an underground environment. Although there are several channel prediction techniques, most of them are very difficult and time consuming. This work presents a new approach in wireless channel modeling in underground mines. The model is generated by adopting a performance-based approach rather than classical coverage-based approach. This new model, called “Mine Segmenting Wireless Channel Model”, divides the mine area into three main segments: (1) Line-of-Sight (LOS), (2) Partial-Line-Of-Sight (PLOS) and (3) Non-Line-Of-Sight (NLOS). We examine the impact of topology on performance of 802.11b system with Rician/Rayleigh fading. The model is statistically verified using simulations and is applied to fading wireless local area networks channel for IEEE 802.11 applications. Finally, the communication performance of a realistic IEEE 802.11b signal is evaluated in a real underground mine gallery (NORCAT Mine, Sudbury, Ontario, Canada). The results of the actual experiment were very similar to that of the model simulation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".