A Statistical Analysis of RF-Energy Harvesting in Wireless Networks
Bibliographic record
Abstract
The ability of wireless terminals to harvest energy from Radio Frequency (RF) signals is gaining tremendous momentum as a mean to prolong the lifetime of these terminals and to recycle the radiated energy. This article gives a much needed statistical analysis for the amount of harvested energy over a period of time T. By studying the input/output characteristics of a typical harvesting circuit, we show that i) the amount of harvested energy over a period of T seconds, denoted by ψout, is a stochastic process, and ii) that it can be written as a scaled version of the amount of incoming energy, ψin, over the same period of time, i.e., ψout= ε̂ψin, where ε̂ ∈ [0, 1] is the harvesting efficiency. We also unveil the dependence of ψouton the sensitivity of the harvesting circuit which is captured using a Signal to Noise Ratio (SNR) threshold, γth.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".