Range Extension in Partial Spherical Near-Field Measurement Using Machine Learning Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Often due to physical limitations, there is a gap in the very-near-field and near-field (NF) measurements of antennas. However, to compute the complete far-field (FF) pattern, near-field data over the whole measurement sphere are required. In this letter, an iterative extrapolation-based machine learning algorithm is presented to expand the region over which the calculated far-field is more accurate. In each iteration, the well-known analysis of variance test is used to check the overall feasibility of the regression model and derive the coefficients of the extrapolation function. To validate the method, three examples with a folded dipole antenna at 1 GHz, a vivaldi antenna at 5 GHz and a dual-frequency planar antenna are presented using both simulated and measured full and truncated near-field data.
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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