A WiFi-Based System for Ice Monitoring in Harsh Environments Using 2.7 GHz Microwave Sensors
Bibliographic record
Abstract
The integration of microwave sensors within wireless sensor nodes enables remote real-time monitoring and detection across diverse sectors. However, the conventional use of a bulky vector network analyzer (VNA) to stimulate the microwave sensor poses limitations for practical applications. This paper presents a WiFi-based ice sensing system utilizing a microwave sensor integrated with a readout circuit that inherently excites the sensor eliminating the need for a VNA. The microwave sensor is designed to monitor ice accumulation in harsh environments and is composed of three split-ring resonator tags coupled to the transmission line. The data collected from the microwave sensor is processed using the integrated readout circuit and transmitted to the server via a WiFi module. The WiFi-based ice sensing system has been successfully tested at a low temperature of -40°C and a frequency range of 1.75 GHz to 2.8 GHz. Measured results show that the WiFi-based ice sensing system can differentiate between ice, water, and air with a reduced number of frequency points, across a broad frequency spectrum, compared to the VNA.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".