Using the Three-Antenna Gain Method to Improve Measurement Accuracy for VHF Satellite and Space Applications
Bibliographic record
Abstract
Antenna measurements in the VHF band are challenging because of the sensitivity to surroundings in both outdoor and indoor ranges. The large size of the antennas involved makes them difficult to manipulate and therefore more susceptible to damage. In addition, the gain tables for standard gain antennas at these low frequencies is often sparse, especially for older models. This paper proposes to use the three-antenna gain method to mitigate some of these problems by calculating the gains more accurately than other gain calculation methods or the original manufacturer's datasheets. To this end, a new custom NSI2000 script was written and trialed with a trio of antennas commonly used to test new devices for satellite and space related applications. Using our newly refurbished large anechoic chamber with a near-field system, gain data calculated in the 200 - 325 MHz frequency range shows notable differences relative to the datasheets. As compared to other methods of gain calculation, the results for the three-antenna method displayed smaller mean values and standard deviations - indicating a reduction in the influence of any single error on the overall outcome. The lessons learned from this experiment can help improve measurement accuracy at these frequencies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".