An Adaptive Data Acquisition Technique to Enhance the Speed of Near-Field Antenna Measurement
Bibliographic record
Abstract
In this article, a new approach is proposed to improve the speed of near-field measurement of antennas. The near-field measurements are mainly performed using a planar near-field scanner, named RFX2. In RFX2, which is an electronically switched probe array, the probes in the$x$and$y$directions cannot be fabricated at the same place. At each point, only one tangential component of the magnetic field is measured, and the other component is estimated numerically using an interpolation technique. Here, an adaptive data acquisition technique is proposed that sequentially collects samples from the field in the areas with highly dynamic behavior and skips the regions that have smooth near-field variations. Since the newly introduced points at each iteration of the algorithm are not necessarily laid on the probe locations of the RFX2, the values of the data at these points are calculated using an interpolation method. The proposed adaptive algorithm in this work requires remarkably fewer samples to reach the same accuracy as uniform sampling. This method is also utilized in spherical NF measurement over$\theta $and$\phi $plane. The validity of the approach is verified using various numerical and measurement results.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| 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".