Interpolation or coupling matrices in the calibration of antenna arrays
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Bibliographic record
Abstract
Various approaches are available for correcting errors in antenna arrays through calibration. In this context, the concept of coupling matrices is a popular approach since it allows the prediction of the array port voltages for an arbitrary excitation and, for some array geometries, the estimation of the incident electromagnetic fields through the inversion of the matrices. Another popular approach consists in measuring the array port voltages for signals arriving from a set of incident angles, and interpolating between the data points to predict the voltages for intervening angles. Unfortunately, it is not possible to estimate the incident electromagnetic fields using this approach, but in many applications this is not required. Currently, the advantages of coupling matrices over interpolation in these applications are unclear. This paper compares the accuracy of array calibration performed using coupling matrices and interpolation in typical applications and array geometries, and determines the conditions where one approach should be preferred over the other.
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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