Optimal Communication Handover of SAR Drone Based on Digital Twin Strategy
Bibliographic record
Abstract
With various potential UAV applications, the deployment of drones in groups will increase, some of which will be ad hoc. The drone group's communications network is known as the Flying Ad-hoc Network (FANET). A reliable FANET is essential to ensuring the success of any particular operation, for example, during a disaster where the existing infrastructure is not available. FANET was formed to enable communication for rescuers (human and machine, including Search and Rescue UAVs), as well as victims, in calling for help. In order to collect data more efficiently, UAVs connected to the IoT (Internet of Things) are needed, where data is automatically collected from various sensors and directly sent through the cloud or data storage systems. However, FANET has major challenges in dynamic topology caused by the high mobility of UAVs, so this dynamic results in frequent communication handovers. To overcome this challenge, it is necessary to integrate the UAV swarm network in real time with the help of digital twin technology to find out the improved handover strategy in order to improve service quality.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".