A novel diagnostic system for adding reliability to communication networks in underground mines
Bibliographic record
Abstract
A series of unfortunate worldwide mine disasters took place recently and shock the world, the disasters ranged from collapsed mines causing death to trapping miners underground which caused many life losses and injuries. The importance of robust and reliable underground communication networks has become evident once again. A reliable underground communication network in mines will not only facilitate the day- to-day operations but will also help save many lives. However, designing reliable and robust underground network has always been a challenge due to the special nature of mines. Until now, no single communication system exists that can solve all of the problems underground. As these extreme and harsh mine conditions require special communication systems like leaky feeders, Through The Earth (TTE), and wireless mesh networks. While TTE is used in case of disaster recovery to locate trapped miners and wireless mesh is used for sensing the mine conditions, the leaky feeder system can be considered the most popular communication system used for day-to-day operations. It is good for carrying voice, video, and data; however, the leaky feeder system is not reliable and problems are hard to diagnose. This paper describes a novel diagnostic system that enhances leaky feeders; this system will not only allow monitoring of all underground nodes connected to the leaky feeder. It will also allow remote programming from the control room above ground and monitoring the losses in the leaky feeder in the sense that the infrastructure will be able to monitor itself. The system is described here along with simulation results proving its performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".