Effects of failed elements on sidelobes of array beampatterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is common for arrays to degrade as elements fail, resulting in high sidelobes. The sensitivity of sidelobe levels to element failures is examined for an arbitrarily shaded array. Using the difference of complex beampatterns, it is found that the beampattern, which can be associated with just the failed elements, controls the degraded array response in the deep sidelobe region. Using results for addition of weighted random phasers, expressions are presented for an upper bound, the mean and standard deviation of the power sidelobes of the degraded array in terms of the number and shading weights of the failed elements. The upper bound depends on the percent of elements that fail and is independent of array size. The average sidelobe level depends on both the failed-to-good ratio and the number of remaining good elements, making large arrays more robust for the same percentage of failed elements. The standard deviation of sidelobe levels is approximately equal to the mean. The ratio of failed to remaining good elements is analogous to the combined amplitude and phase variance for uncorrelated tolerance errors. Combined effects of element failures and random amplitude and phase errors are then presented.
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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