Updating Near Field Antenna Ranges for Improved Performance and Extended Lifetime
Bibliographic record
Abstract
Near-Field (NF) antenna measurement ranges have evolved as an alternative to Far-Field (FF) ranges to be the prominent method for pattern estimation from measurement. This is because NF ranges are more convenient to use and require much less space in laboratories [1]. The Microwave Vision Group (MVG) StarGate series, such as the legacy Satimo StarGate 64 (SG64), employs an array of multiplexed probes to perform measurements, sampling across a synthetic aperture created by moving the test antenna with respect to the probe array. This example of a commercial system does not use a Vector Network Analyzer (VNA), and was not designed to be updated. This means that it can not benefit from improvements made in RF measurement equipment, requiring instead that a new system be purchased, which is unrealistic for many users. This work describes the initial process of updating a legacy SG64 to use a VNA. It includes characterization of the control signals, the transmit and receive paths, as well as the potential improvements to performance and lifetime that upgrading the legacy system to a VNA-based configuration offers.
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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 | 0.002 | 0.004 |
| 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) | 0.006 | 0.005 |
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".