An Adaptive Data Acquisition and Clustering Technique to Enhance the Speed of Spherical Near-Field Antenna Measurements
Bibliographic record
Abstract
This letter presents a new approach for adaptive spherical near-field (NF) antenna measurement. The proposed method begins with a number of initial points and sequentially focuses on the areas with a highly dynamic NF pattern. Thereafter, the source reconstruction method is utilized to calculate the equivalent magnetic and electric currents on the surface of an ellipsoid that encompasses an antenna under test. The equivalent sources are used to compute the far-field pattern of the antenna. The comparison of the adaptive algorithm with the uniform sampling indicates that the number of the required samples is decreased significantly using the adaptive method. The adaptive data acquisition can also be used in case of uniform sampling to remove the redundant samples and accelerate the source reconstruction method. Since the newly added point is not necessarily laid on the measurement points, the cubic spline interpolation technique is employed to compute the value of the field. Besides, a machine learning algorithm based on k-means clustering is applied to the uniformly sampled data to determine different clusters of data. Thus, for every new point, the cluster to which the data point belongs can be determined, and only the values of that cluster are used to calculate the value of the new point.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".