Beampattern random behavior in wireless sensor networks with Gaussian distributed sensor nodes
Why this work is in the frame
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Bibliographic record
Abstract
Collaborative beamforming (CB) has been introduced in wireless sensor networks (WSNs) to increase the transmission range of sensor nodes. CB improves the power efficiency of the transmission. However, the CB beampattern is random in the sidelobe region. Therefore, it is important to characterize the power level in the sidelobe region to predict the interference to neighboring sensor node clusters. In this paper, we assume that sensor nodes in a cluster of WSN are Gaussian distributed and study the random behavior of the beampattern. To characterize the beampattern in the sidelobe region, we first model the array factor as a complex random variable and find the corresponding mean and variance. The distribution function of beampattern level and the outage probability of sidelobes is derived and compared with the corresponding characteristics resulting from uniform distributed sensor nodes.
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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 | 0.001 |
| 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 it