Bibliographic record
Abstract
Establishing reliable communication is a difficult task in underground mines due to the extreme environmental conditions. This work proposes novel approaches and architectures to optimize the reliability, scalability and power efficiency of the communication networks in the environment of underground mines. Our research considers an integrated network architecture with three main elements that represent the common path of information flow from the surface of the mine to remote points deep in the underground. These three main elements are as follows: the backbone networks, the wireless channel of the confined spaces and the Wireless Sensors Networks (WSN). The objectives of enhancing the network reliability, scalability and power efficiency are globally considered for the entire integrated network. As a first step, the backbone network of the mine is optimized with intelligent algorithms for maximum stability, scalability and power efficiency. These objectives were achieved by introducing our novel Prediction-based Adaptive Equalization Algorithm (PAEA) and The Power-aware Adaptive Charging Schedule Algorithm (PACSA). Furthermore, this research introduced a novel wireless channel model to characterize the performance of the wireless systems in underground mines. The new proposed model, called “Mine Segmenting Wireless Channels Model”, is utilized by the wireless network as an added layer of intelligent network capacity. Lastly, with power efficiency and conservation in mind, the performance of the WSN in the underground is optimized for power efficiency, scalability and rapid development of applications. These objectives are achieved by introducing our novel Resources-Aware Sleep Scheduling Algorithm (RASSA) for the wireless sensor networks to provide an integrated platform, where new applications in mines can be rapidly developed to suit the operational requirements of the mine.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".