Optimal Receiver Placement for <i>K</i> -barrier Coverage in Passive Bistatic Radar Sensor Networks
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Bibliographic record
Abstract
The improvement of coverage quality in the construction of multiple-barrier coverage is a critical problem in a wireless sensor network. In this article, we investigate the K -barrier coverage construction problem in passive bistatic radar sensor networks. In contrast to traditional bistatic radar networks, the transmitters in a passive bistatic radar network are predeployed and noncooperative. To construct K barriers, we need to deploy receivers that couple with predeployed transmitters to build continuous barriers. In this work, we focus on the minimum number of receivers problem of constructing K -barrier coverage, where the minimum number of receivers is based on the predeployed transmitters. To handle this problem, we first investigate the optimal placement of receivers between adjacent transmitters for a sub-barrier formation and then determine the optimal placement of receivers for the one-barrier construction. For multiple-barrier coverage construction, we introduce a weighted transmitter graph (WTG) to describe the relation among different transmitters, where the weight in the graph is the minimum number of receivers needed for these two transmitters for a sub-barrier formation. Based on WTG, the minimum receivers problem changes to a problem of how to find K -disjoint paths with the minimum total weight in the graph. For large-scale networks, we also propose two efficient heuristic algorithms to solve the corresponding problem. Finally, we conduct extensive experiments to validate the correctness and the efficiency of the proposed algorithms.
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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 | 0.000 |
| 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