Substrate Integrated Waveguide-Based Liquid Sensor With Self-Calibration Capability
Bibliographic record
Abstract
This article presents a microwave sensor for liquid permittivity measurement, using substrate integrated waveguide (SIW) resonators. The proposed design consists of two SIW cavities linked via an iris window, with two microstrip ports connected to the upper SIW cavity through a grounded coplanar waveguide (GCPW) transition. To enable sensitivity to permittivity variation, seven rectangular holes are etched into the iris between the cavities. The sensor exhibits a dual-band response: the first band is sensitive to changes in hole permittivity, making it suitable for liquid permittivity detection, while the second band remains fixed and serves as a reference to compensate for environmental variations. The self-calibration mechanism is achieved by comparing the two bands, which ensures that any environmental changes are compensated. The sensor was designed for the ultrahigh-frequency (UHF) industrial, scientific, and medical (ISM) band and validated using ethanol-water mixtures as test liquids. The measured results show that the first resonant frequency shifts from 903.3 to 910.5 MHz as the ethanol volume fraction increases from 0% to 100%. The proposed sensor is highly promising for direct radio frequency (RF) sensing applications because of its environmental interference resistance, ease of integration with other planar circuits, simple design, low-cost fabrication, and enclosed structure, making it ideal for direct liquid contact applications.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".