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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".