Design of Waveguide Applicators Using a Quarter-wave Transformer Prototype
Bibliographic record
Abstract
In this paper, we propose a design methodology for waveguide applicators to maximize microwave power deposition into human tissues. The optimized applicators can be used in the experimental studies of the biological effects of exposure to electromagnetic radiation in the frequency range from 6 GHz to 100 GHz. The design methodology relies on the provision of reflectionless matching of a dissipative waveguide load, achieved by employing a matching network based on a quarter-wave transformer prototype. The prototype is synthesized by knowledge of the voltage standing wave ratio (VSWR) evaluated in the unmatched loaded waveguide. A key difference from the conventional synthesis procedure is that in our design approach, the characteristic impedance of the first transformer section is given, and we have to not only determine the characteristic impedances of the remaining sections, but also establish the output load. A solution of this synthesis problem and the process of converting the transformer prototype into a waveguide structure are described. The physical structure can be implemented according to provided sample models of waveguide WR137 applicators employing symmetric inductive or capacitive posts. The matched waveguide applicators are easy to manufacture, and according to the results from computational simulations, they demonstrate superior performance compared to the unmatched waveguides. Limitations of our designs (narrow bandwidth, dependence on the type of tissues encountered, limited potential for miniaturization) are discussed.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".