Basics of Wireless Energy Harvesting and Transfer
Bibliographic record
Abstract
Introduction Energy harvesting is an important aspect of green communication that provides self-sustainable operation of wireless communications systems and networks. Energy harvesting has been adopted in low-power communication devices and sensors. There are different forms of energy harvesting suitable for different applications. Table 1.1 shows the summary of different energy harvesting technologies. • Photovoltaic technology has been developed over decades, and it is one of the most commonly used energy harvesting techniques. A solar panel which is composed of multiple solar cells converts sunlight into a flow of electrons based on the photovoltaic effect. The effect describes the phenomenon that the light excites electrons into a higher state of energy. The electrons then can act as charge carriers for electric current. A solar cell contains a photovoltaic material, e.g., monocrystalline silicon, polycrystalline silicon, amorphous silicon, and copper indium gallium selenide/sulfide. The efficiency of a solar cell can be up to 43.5%, while the average efficiency of a commercial solar cell is 12%–18%. Photovoltaic technology has been adopted in many applications, including rooftop and building integrated systems, power stations, rural electrification, and telecommunication. However, photovoltaic systems need a large area and cannot supply energy during the night. Moreover, their efficiency depends on the orientation of the solar panel, which can be complicated to optimize. Photovoltaic systems are suitable for static data communication units, e.g., a base station and access point, while their applicability to mobile units, e.g., user equipment, is limited. • Thermal energy or heat can be converted to electricity using a thermoelectric generator based on the Seebeck effect or Thomson effect. The effect describes the conversion of temperature difference and electricity in thermoelectric devices. While thermoelectric devices are typically used for measuring temperature, recently they have been developed to serve as energy sources. The devices can produce 20–16 μW/cm 2 with the human body as a heat source at room temperature. The benefit of thermoelectric devices is the capability of generating energy as long as there is a temperature difference or a heat flow. Additionally, since they do not have any moving parts, they have high reliability. However, the devices are bulky and heavy. Also, they can supply only a small amount of energy, with typical efficiencies of approximately 5%–8%.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.022 |
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".