Aspects of antenna pattern estimation from planar near-fields
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Bibliographic record
Abstract
There are established procedures for determining the measurement uncertainty for certain pattern types (such as high or low directivity) for near-field measurement configurations [1-2-3]. This measurement uncertainly refers to the peak gain, rather than to the low directivity regions of a pattern which are seldom addressed. A very convenient configuration for pattern estimation is planar near-field sampling. The sampling density is governed by avoiding spatial aliasing of radiating waves. This paper discusses an experimental study of pattern estimation using planar near-field samples, including the effect of the sampling density on the far-fields. We use a standard professional-grade planar near-field system (NSI-200 V-5×5) to test a high-gain linearly polarized reflector antenna (10GHz 1.2m or 40 wavelength diameter) with an offset primary feed horn, and gain of about 40dB. Our account is from a typical user's viewpoint rather than from a manufacturer's viewpoint. We demonstrate that increasing the sampling density above the manufacturer's recommendation gives different far-field results for the pattern. Because the pattern is a transform of the near-field aperture, this suggests that the default sampling density of the near-field aperture is under-sampled or that the sampling is inaccurate. This highlights a grey area in the sampling requirements for the near-field region. We also demonstrate that although the accuracy of the peak gain is robust, the accuracy in low directivity regions of the main lobe is suspect.
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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