Technique for Antenna Gain Correction Based on Return Loss Compensation
Bibliographic record
Abstract
Antenna measurements are subject to many sources of error and uncertainty. One of the most common and significant is impedance mismatch error. This error is a result of a difference in impedance looking into the antenna terminals and the circuitry that directly precedes it. Furthermore, this error is difficult to quantify and compensate for when the antenna is embedded in a dense, three-dimensional integrated package. This paper proposes a theoretical methodology to correct this error if the return loss of the antenna is known. Its usefulness is demonstrated by implementing it as a custom NSI2000 script to process data from recent antenna measurements. The results from two test cases show the expected behavior and prove that even in well matched cases, errors can be identified and compensated for. The simplicity and ease of implementation of this method make it attractive for use with any type of antenna measurement for the purpose of increasing gain measurement accuracy.
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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 | 0.001 | 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".