Multiplicatively regularized source reconstruction method for phaseless near-field antenna measurements
Bibliographic record
Abstract
The source reconstruction method (SRM), as an antenna measurement technique, often operates on data collected in the near-field of the antenna under test (AUT) to reconstruct an equivalent current distribution of the AUT. In general, the SRM computes equivalent electric and magnetic currents on a virtual surface enclosing the AUT that radiate the same electromagnetic fields as the AUT. These currents can be used to compute the far-field pattern of the AUT, as well as provide valuable antenna diagnostic information. Most SRM research considers measured near-field data that has both amplitude and phase information, but an increasing trend towards antenna operation at higher frequencies makes collecting accurate phase information more challenging and expensive (R. G. Yaccarino and Y. Rahmat-Samii, IEEE Int. Sym. Antennas Propag., 4, 416–419, 2001). To this end, we investigate the application of the SRM to phaseless (amplitude-only) near-field measurement data.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".