Design and Analysis of Collaborative Unmanned Surface-Aerial Vehicle Cruise Systems
Why this work is in the frame
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Bibliographic record
Abstract
With the increasing application of unmanned surface vehicle-unmanned aerial vehicles (USV-UAVs) in maritime supervision, research on their deployment and control is becoming vitally important. We investigate the application of USV-UAVs for synergistic cruising and evaluate the effectiveness of the proposed collaborative model. First, we build a collaborative model consisting of the cruise vehicles and communication, detection, and command-and-control networks for the USV-UAV. Second, based on an analysis of the problems faced by collaborative USV-UAV systems, we establish a model to evaluate the effectiveness of such synergistic cruises. Third, we propose a weighting method for each evaluation factor. Finally, a model consisting of one UAV and four USVs is employed to validate our synergistic cruise model.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 it