Modified Microwave Sensor with a Patterned Ground Heater for Detection and Prevention of Ice Accumulation
Bibliographic record
Abstract
Ice accumulation on aircraft is known to negatively impact the aerodynamic and mechanical operation, sometimes resulting in catastrophic failure. Recently, microwave resonators have gained interest as durable and reliable frost and ice detectors. Here, a microwave resonator sensor with built-in heating capability patterned into the ground plane was designed, fabricated, and tested to investigate real-time ice and frost growth. Sensing was performed on surfaces with anti-icing coatings to quantitatively analyze the effectiveness of these materials. The sensor was also tested to determine its ability to evaluate different deicing methods. The sensor itself was a split-ring resonator (SRR) operating at 5.82 GHz, which could effectively distinguish between water and ice by detecting changes in the dielectric properties on or around its surface. This application was particularly suited for an SRR due to the extreme difference between the relative permittivity of water (ε = 90) and ice (ε = 3.2) at 5 GHz and 0 °C. The results from this sensor can be used to determine the holdover time of various coatings to resist ice formation. This study validates the use of SRRs as ice detection sensors for applications where ice and frost are of great interest, such as on aircraft, roads, or walkways.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".