Advanced diagnostic system with ventilation on demand for underground mines
Bibliographic record
Abstract
After a series of recent world wide mine tragedies, ranging from collapsing mines in China and Colombia to trapped miners in Chile, 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. 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 and locating 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 daily operations. It carries voice, video, and data; however, the leaky feeder system is not reliable and problems are hard to diagnose. This paper describes a novel advanced diagnostic system with a ventilation on demand capability 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. Ventilation on demand is another advantage of this system as it will operate the underground mine ventilation fans depending on data collected from sensors attached to the leaky feeder. This will provide huge power savings since ventilation fans are responsible for about 60% of power consumption in mines. The system is described here along with simulation results proving its performance.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".