New Type of Thermal Moisture Sensor for in‐Textile Measurements
Bibliographic record
Abstract
The measurement of moisture in textile materials worn on or near the skin can be performed for a variety of reasons, for example, to analyze the amount of perspiration in clothing, wound fluid in bandages or even urine in diapers or bed sheets. Conventional moisture measurement methods, such as electrical resistance or capacitance measurement, can be susceptible to cross sensitivities to electrical fields or ionic impurities, often occurring in measurements close to the human body. The very reliable gravimetric methods are too bulky and difficult to be integrated in portable and online measurements. In this paper, the authors present a “transient heat moisture sensor” (THMS) which is small and comparatively easy to integrate into textiles. The authors describe the measurement principle and present a sensor element manufactured with thin film technologies. The analytical description of the sensor fits to both, experimental data and the result of first numerical analysis (COMSOL Multiphysics). The authors demonstrate how to limit the sensors spatial sensitivity to a thin layer of textile without being influenced by the adjacent environment by proper timing of the signal readout.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".