Impact of Near-Field Probe Failure in Antenna Far-Field Pattern Estimation
Bibliographic record
Abstract
Accurate pattern measurement requires a near field (NF) facility which is expensive to both acquire and also maintain in ideal working condition. For fast NF measurement, multiple NF probes are often used on the NF aperture with their sampling time-multiplexed. This comes at the cost of the extra electronics and probes but simplifies the precision robotics required for sampling the NF surface. Another cost is an increased failure probability, and so a practical problem with NF facilities is the failure of a NF probe. The impact on the FF pattern estimate may not be obvious depending on the FF pattern shape and the relative location of the failed probe. There seems to be no treatment of this in the literature, so this paper discusses examples for a typical range (Satimo SG64, with 5 m cubed chamber) and a typical antenna where the pattern detail is important, viz., a small C-band fixed-beam slot array used for synthetic aperture radar.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".