A Self-Powered Environmental Data Sensor Node Based on Efficient RF Power Transfer
Bibliographic record
Abstract
This paper introduces a self-powered environmental data sensor node that relies on the collection of radio frequency (RF) energy transmitted by an unmanned aerial vehicle (UAV). A meticulously designed rectifier circuit not only captures RF energy at 433 MHz but also provides feedback of the second harmonic signal at 866 MHz to the UAV. This feedback mechanism enables antenna alignment, thereby improving energy conversion efficiency. The proposed rectifier circuit exhibits a measured RF-DC conversion efficiency of 22.8% and generates a second harmonic power of -39 dBm at an input power of -20 dBm. It is employed to power a microcontroller which coordinates the sensor to periodically collect environmental data, including temperature, humidity, and carbon dioxide concentration. Then the sensing information is transmitted to a mobile terminal using Bluetooth. An antenna calibration experiment was conducted, during which the transmitter successfully received a second harmonic signal of -59.5dBm at a distance of 12 meters from the rectifier. This experiment serves as verification that the proposed rectifier can effectively assist UAVs in achieving efficient wireless power transfer to sensing nodes located at considerable distances.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".