Metamaterial Based Sensor Using Fractal Hilbert Structure for Liquid Characterization
Bibliographic record
Abstract
In this work, a simple and efficient approach is presented to design a metamaterial based sensitive sensor for liquid characterization. The proposed sensor based on the Hilbert structure has a compacted size of $40\times 60\times 1.6 \mathrm{mm}^{3}$. The Hilbert curve is used to enhance the sensitivity of the sensor by increasing the interaction area with the sample tested. The simulation studies are carried out by using the Computer Simulation Technology (CST) Microwave Studio. The resonant frequency of the proposed sensor is about 0.46 GHz. The resonant frequency has shifted approximately 30 MHz after the receptacle is printed on the sensor surface. The proposed sensor has successfully detected different samples of liquids. The variations of the resonant frequency, scattering parameters, bandwidth and quality factor of different samples are discussed.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".