LoRa Wireless Link Performance in Multipath Underground Mines
Bibliographic record
Abstract
It has become a challenge to effectively maintain higher levels of safety and productivity in mining operations due to the present-day smart IoT technology advancements in mining equipment and gadgets. Regardless of the complexity of IoT schemes, all the systems rely on one common factor - an effective and reliable transportation mechanism for data and control information from/to the smart devices. Therefore, the communication infrastructure in confined spaces is the most critical element in the smart system operation. This is especially true due to the limitations and complications of physical phenomena affecting the wireless system and networks in the mines and tunnels. Strong multipath nature of the wireless channel affects the smart wireless communication significantly in underground mine. In this paper, we use LoRa technology to provide better connectivity in harsh environment such as in mines. With its long range, deep penetration, and ultralow power consumption and single hop wireless communication technology; LoRa provides reliable connectivity to previously infeasible underground mining environment. This paper presents simulation study that provide LoRa performance in mining area with strong multipath conditions for different spread factors (SF).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".