Measurement of Air-Ground link over Virtual Forces Algorithm For Autonomous Aerial Drone Systems, VBCA
Bibliographic record
Abstract
With recent technological advances, the capabilities of UAVs have rapidly increased and their use and role in society have also changed from the military to the civilian domain. Many studies have been made for the collaboration of several UVAS to accomplish a mission. But most of the studies done on the subject of FANET do not propose any overall approach to the network itself and remain fragmented, generally based on the final mission. The solutions proposed are not reproducible on other missions. This aspect pushes us to propose a solution independent of the mission and easily reproducible. Otherwise the positioning of nodes in FANETs can provide a mission-independent response. Reason why the solutions for positioning network nodes are numerous. One in particular offers a comprehensive and interesting approach. It is VBCA. This method has the particularity of positioning the nodes in 3-D. In general, the 3-D positioning becomes an NP-Hard problem. But with VBCA positioning is relatively simple. In this paper we present an optimization of communication in a topology based on the VBCA (Virtual forces Based Clustering Algorithm). The topology is optimized by the VBCA algorithm for a better coverage area. This method is 40% more efficient than existing approaches in terms of area coverage. In the first versions of VBCA, the performances in terms of communication between nodes have not been tested. The resulting network performances are very encouraging, in terms of throughput, delay, packets loss and signal strength. Therefore, this work provides a first answer to the performances in terms of network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".