Tunable Microwave Waveguide Probe for Noncontact Ice Detection on Metallic Surfaces
Bibliographic record
Abstract
Ice accretion on the surface of infrastructures can lead to significant challenges and hazards highlighting the crucial role of reliable sensors to detect ice and address preventive measures. This article presents a noncontact microwave waveguide resonator sensor to detect ice formation on metallic surfaces. The sensor is comprised of a WR-62 waveguide with engineered slits to provide mechanical tunability of the sensor, for enhanced sensitivity and resolution. The proposed sensor can be located at a distance from metallic surfaces, where ice accumulation occurs. The sensor was designed to operate at the resonant frequency of 15.6 GHz and the resonant amplitude of$\sim -40$dB. The experimental results showed a 170 MHz downshift in the resonant frequency of the reflection coefficient (${S}_{{11}}\text {)}$for a$130~\mu $L frozen droplet of ice with good repeatability and reproducibility of the results. The designed sensor was also investigated to characterize different volumes of ice ranging from 100 to$400~\mu $L. A maximum variation of ~788 MHz in resonant frequency was observed and recorded for this range of ice volumes. In addition, the sensor was investigated to identify saline ice formations within the concentration range of 2%–4% of salt in$100~\mu $L ice samples. According to the authors’ knowledge, the proposed slit-based resonant system is unique for sensing thin ice on existing structures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".