Empirical Calibration Method for a Multistatic Microwave Sensing System
Bibliographic record
Abstract
A benchtop multistatic microwave sensing system was created as a step toward developing a portable and lowcost diagnostic breast cancer detection device. The system uses a fixed 24-antenna array to eliminate the need for mechanical rotation and is intended to support both reflection and transmission measurements. Accurate calibration is crucial, particularly when costly multiport VNAs or autocalibration hardware are unavailable. This study evaluated the accuracy of a full twoport SOLR calibration method using an estimated reciprocal THRU standard. Calibration quality was assessed by comparing measured and expected S-parameters for a$\mathbf{1 0 ~ d B}$attenuator and a SHORT standard. For the$\mathbf{1 0 ~ d B}$attenuator, the results$\mathbf{4}$weeks after calibration gave a magnitude error of$\mathbf{1 \%} \boldsymbol{\pm} \mathbf{2 \%}$and a phase error of$\mathbf{- 0. 3}$to$\mathbf{- 0. 2}$radians, decreasing linearly as a function of frequency. For the SHORT, the magnitude error remained at$0 \% \mathbf{\pm 3 \%}$for measurements up to$\mathbf{1 2}$weeks following calibration, with phase errors varying between$\mathbf{- 0. 5}$and$\mathbf{0}$radians as a function of frequency. The estimated THRU standard yielded negligible differences compared to an actual THRU. These findings support the use of empirical SOLR calibration as a practical and accurate method for portable multistatic systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".