Simulation of Wearable Microstrip Patch Antenna by Using Textile Material for Ambient RF Energy Harvesting
Bibliographic record
Abstract
Future enhancements in radio frequency (RF) energy harvesting technology will make easy the creation of a network with no need of dedicated transmitters, as a reliable source of wireless energy power. Recently, the development and employment of wearable antennas has increased rapidly for application in the miniaturization of wireless communication devices. The principal feature of wearable antennas is that they are designed as garment elements able to transmit or receive wireless signals. In this context, we propose a design of a wearable textile microstrip patch antenna operating for wireless body area network (WBAN)at the resonance frequency =2.40GHz. The microstrip patch antenna with an edge feeding technique is designed and simulated by using the Keysight Advanced Design System (ADS) software. Textile materials have a low dielectric constant that reduces the surface wave losses and increases the impedance bandwidth of the antenna. The chosen dielectric substrate in this work is the jeans fabric with relative permittivity of 1.70, thickness of 1.00 mm and dissipation factor of 0.025. The antenna performance parameters are obtained from ADS Momentum. Simulation results show that antenna has the gain of 3.22 dBi and directivity of 8.10 dBi, showing that the wearable textile microstrip antenna has a good performance.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".