Methodology and Practical Considerations for the Implementation of the Three-Antenna Method in a Spherical Near-Field Range
Bibliographic record
Abstract
The three-antenna method is a way of calculating antenna gain without the need for a gain standard. Unlike the comparison or direct methods, the three-antenna method calculates antenna gain solely from measured data and does not require the gain of any of the antennas to be known in advance. As a result, it’s the most favored method in applications where accuracy is of chief concern, like in calibration measurements. However, implementing this method presents additional challenges related to the equipment required, test procedures, and analysis of the resulting data. In this paper, these challenges are addressed with a new methodology used to create a custom script and user interface within the NSI2000 software environment. The script itself is described with the aid of a flow chart. The validation method involving two test campaigns, using both calibrated and non-calibrated standard gain antennas, is also given. Following these efforts, the three-antenna method was successfully implemented for the first time in a facility that traditionally only used the gain comparison method. The lessons learned from this project could also prove valuable in understanding the practical considerations concerning the implementation and use of the three-antenna method in any other near-field test range.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".