Network enabled sensing for unmanned urban combats
Bibliographic record
Abstract
Summary form only given. The routing and munitions management of multiple formations of unmanned combat aerial vehicles (UCAVs) through network enabled sensing will be discussed. The ability of the UCAVs to group into large formations and to divide into smaller formations on their way to high-value, tactical targets will be cast in the optimization. To solve such problem a sensory information management (SIM) network will handle data from a set of mobile sensors. SIM will determine whether sensor redundancy should and can be used, before it estimates an information state vector on the locations of the adversarial ground units and decoys. The networked UCAVs will calculate the worst-case minimization policy for an effective routing and munitions management relying on the SIM-net supplied information state vector. As in any time-critical control problem, closing the loop requires that sensor management, estimation, and high-level decision be accomplished within hard real-time constraints. Heuristic algorithms aiming at achieving real-time performance will be discussed. The effectiveness of UCAV routing and munitions management will be illustrated by means of examples featuring trajectory planning of multiple formations evolving in a dynamic urban theatre.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".