Near Field Measurement: Characterizing System SNR Against Frequency
Bibliographic record
Abstract
For estimating a far-field (FF) pattern, near-field (NF) measurement system manufacturers normally specify an expected accuracy for the peak gain, but seldom address the accuracy of the rest of the pattern. Many factors affect measurement accuracy, including the primary measurement parameter, signal-to-noise-ratio (SNR). To help understand the measurement system accuracy, a test procedure to quantify the SNR against the measurement frequency is discussed here. In a NF system, an automated vector network analyzer (VNA) is commonly used to estimate the path gain between the testing probe antenna and the antenna-under-test (AUT), for each NF spatial sample point and measurement frequency. While path gain measurement is founded on the deterministic Friis equation, for NF measurements, the FF gains require correction for good accuracy, and this is normally part of the calibration process. The SNR, and therefore the estimation accuracy, decreases for increasing frequencies, with cable attenuation dominating the losses as the frequencies reach the popular mmWave bands. Characterizing a system's SNR involves understanding the role of all the losses including the often-cryptic actions of the automated VNA.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".