Selection of Sampling Interval and Size of Random Sample for Radar Detection in the Moments Space
Why this work is in the frame
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Bibliographic record
Abstract
Recently a novel method for radar detection was conceived to process the scattered signal parameters and detect through its statistical moments. Among the advantages of detection in the moments space stands the opportunity of considering the moments like Gaussian random variables, decreasing the uncertainty about the distribution of the variables used by traditional methods. Therefore, it is very important to study the conditions for assuming the above within certain level of confidence. This work uses real radar signals in order to study the influence of two essential variables for detection in the moments space: the sampling interval and the size of the random sample. Average correlation coefficient, hypothesis testing and numerical goodness-of-fit coefficients are used to estimate the values of the previous variables that allow to take the joint distribution of moments as close to the multivariate Gaussian. The guidelines presented should be taken into account for the proper configuration of detectors in the moments space.
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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