A Multi-angle Method For Deriving The Temperature Distribution In Biological Structures With Microwave Radiometry
Bibliographic record
Abstract
A new approach for deriving the temperature distribution in biological tissues by microwave radiometry is proposed here. It consists in the measurement of the ther- mal radiation of the body, in a given frequency band, as a function of the observation angle, for two mutually orthog- onal polarizations (multi-angle method). Theoretically, this method yields results comparable to those obtained with the well known multi-spectral method. Microwave radiometry can be used to evaluate the subcuta- neous temperature distribution in biological tissues. Different configurations of tissues with different temperature distributions may produce the same radiometric signal. One solution to this problem is to perform measurements at several frequency bands (multi-spectral method); the data thus obtained have to be pro- cessed by means of inversion techniques, in order to retrieve the correct temperature profile. We propose, instead, that the mea- surement of the thermal signal of the body should be carried out in one frequency band, only, but at several observation an- gles, for two mutually orthogonal polarizations. The proposed method consists in the following three steps: a) modelling of the biological body; b) computation of the thermal signal emitted as a function of the tissue parameters; c) retrieval of the thermal field in the tissues by the inversion of the radiometric data. The biological tissues are simulated by a structure of three planar layers, with known permittivities and thicknesses, the temperature being constant within each layer (figure 1). The brightness temperature TB measured by the antenna is given in this case by:
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".