Underground Propagation Rician K-Factor Analysis for Near-Ground Los Scenario at 2.4 Ghz
Bibliographic record
Abstract
This paper analyzes the Rician K-factor for nearground Line-of-Sight (LoS) propagation in underground mines at 2.4 GHz using Power Delay Profile (PDP)-based and Method of Moments (MoM)-based methods. Results show that at$\mathbf{1 0 ~ c m}$height, the K-factor peaks at 9.97 dB (PDP) and 12.95 dB (MoM), reflecting strong LoS dominance. At$\mathbf{1 2 0 ~ c m}$, it decreases to 3.37$\mathbf{d B}$(PDP) and$\mathbf{1. 2 6 ~ d B}$(MoM), influenced by enhanced multipath scattering. The PDP-based method provides more stable and consistent results, while MoM exhibits greater variability, sometimes yielding negative K-factors in high-scattering environments. These findings underscore the height-dependent K-factor behavior, offering valuable insights for designing reliable Wi-Fi and IoT communication systems in underground mining operations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".