Aerial BS location optimization for monitoring multiple forest areas with uplink UAV throughput requirements
Bibliographic record
Abstract
The increasing frequency and intensity of forest fires demand innovative technologies to support firefighting and mitigate their impact on ecosystems and communities. This paper explores the application of untethered and tethered uncrewed aerial vehicles (UAVs) as aerial base station (ABS) in the context of forest fire management. We propose an ABS placement algorithm to serve UAV-user equipment (UE) in forest environments. The novelty of the proposed approach is to optimize the strategic ABS placement based on throughput requirements while serving multiple drone forest monitoring areas. We analyze and compare the performances of two aerial base stations (ABSs) such as an untethered ABS (UTABS) and a tethered ABS (TABS) for forest surveillance. We evaluate UTABS and TABS performances across single and multiple UAV-UE monitoring zones in forest environments under different network throughputs, wind effects, payload configurations, monitoring zone areas, communication frequencies, operating costs, etc. Our results indicate that while a UTABS offers superior flexibility, a TABS provides a cost-effective (78.2% reduction) and reliable solution to ensure long-term continuous forest fire surveillance 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 | 0.000 |
| 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) | 0.001 | 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".