Digital Twin-Enabled Task-Driven UAV Communications Under Uncertainty
Bibliographic record
Abstract
Efficient task information interaction is the key to unmanned aerial vehicle (UAV) swarm collaboration. However, the driving force of both the spatial-temporal correlation and physical-virtual interaction has not been fully considered in existing works. In this paper, we aim to utilize the advanced digital twin technology to realize the efficient information interaction between the physical and virtual layers for UAV swarm collaboratively performing various tasks. Considering the driving force of both task correlation and transmission timeliness, the physical-virtual interaction link selection problem is formulated under uncertain estimation deviations in the form of interval number. To address this challenging problem, we first utilize the interval optimization for transforming the uncertain utility values to the certain preference orderings in matching theory, and then propose an interval rank-maximal matching algorithm to make predictive link selection based on unilateral preference information. Simulation results confirm that our proposed method can improve the interaction efficiency significantly under uncertainty.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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".