Investigation of a Circularly Polarized Metasurface Antenna for Hybrid Wireless Applications
Bibliographic record
Abstract
The increasing prevalence of the Internet of Things (IoT) as the primary networking infrastructure in a future society, driven by a strong focus on sustainability and data, is noteworthy. A significant concern associated with the widespread use of Internet of Things (IoT) devices is the insufficient availability of viable strategies for effectively sustaining their power supply and ensuring their uninterrupted functionality. The ability of RF energy-harvesting systems to externally replenish batteries serves as a primary driver for the development of these technologies. To effectively mitigate concerns related to wireless technology, it is imperative to adhere strictly to the mandated limitations on electromagnetic field emissions. A TA broadband polarization-reconfigurable Y-shaped monopole antenna that is improved with a SADEA-tuned smart metasurface is one technique that has been proposed in order to accomplish this goal. A Y-shaped printed monopole antenna is first taken into consideration. To comprehend the process of polarization reconfigurability transitioning from linear to circular polarization (CP), a BAR 50-02 V RF PIN Diode is employed to shorten one of the parasitic conducting strips to the ground plane. A SADEA-driven metasurface, which utilizes the artificial intelligence-driven surrogate model-assisted differential evolution for antenna synthesis, is devised and positioned beneath the radiator to optimize performance trade-offs while increasing the antenna's gain and bandwidth. The ultimate prototype achieves the following: an impedance bandwidth of 2.58 GHz (3.27-5.85 GHz, 48.45%); an axial bandwidth of 1.25 GHz (4.19-5.44 GHz, 25.96%); a peak gain exceeding 8.45 dBic; and when a highly efficient rectifier is integrated, the maximum RF-DC conversion efficiency of 73.82% and DC output of 5.44 V are obtained. Based on the results mentioned earlier, it is considered appropriate to supply power to intelligent sensors and reduce reliance on batteries via RF energy-harvesting mechanisms implemented in hybrid wireless applications.
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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".