Battery-Less Icing Detection Using a Self-Calibrated SIW Wireless Sensor
Why this work is in the frame
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Bibliographic record
Abstract
Deployment of microwave-based sensors for ice detection faces significant challenges, including the lack of a wireless communication link, high costs and maintenance due to internal power sources, performance degradation when mounted on metallic surfaces, and susceptibility to environmental variations causing calibration difficulties. Addressing these issues, this article introduces a battery-free wireless ice detection sensor based on substrate-integrated waveguide (SIW) resonators. All features of wireless connection, self-calibration, and noise resistance in one standard UHF RFID (Ultra High Frequency Radio Frequency Identification) band are realized simultaneously in a low-cost and robust architecture, utilizing a dual-band SIW configuration. This configuration includes two connected SIW cavities coupled through a window, an RFID chip connected and matched to the SIW cavity, an etched slot antenna on the SIW surface, and a sensing hole in the coupling window. One of the bands changes with the relative permittivity difference between ice and water within the sensing hole, while the other remains fixed to serve as a reference, allowing compensation for environmental changes and distance ambiguity. To validate the concept, the proposed wireless sensor was fabricated, and practical measurements were made by placing the sensor at a distance from an RFID reader. The sensor operates without a battery by harvesting energy from signals transmitted by the reader. The measured results at 906 MHz indicate significant phase changes between the air, water, and ice conditions. In contrast, no observable phase alteration occurred in the upper part of the band at 917 MHz. The proposed wireless sensor holds great promise for RFID applications and provides a highly reliable solution for ice detection on surfaces such as aircraft, wind turbines, and pipelines, as well as for other applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it