Self-Sustainability of Energy Harvesting Systems: Concept, Analysis, and\n Design
Bibliographic record
Abstract
Ambient energy harvesting is touted as a low cost solution to prolong the\nlife of low-powered devices, reduce the carbon footprint, and make the system\nself-sustainable. Most research to date have focused either on the physical\naspects of energy conversion process or on optimal consumption policy of the\nharvested energy at the system level. However, although intuitively understood,\nto the best of our knowledge, the idea of self-sustainability is yet to be made\nprecise and studied as a performance metric. In this paper, we provide a\nmathematical definition of the concept of self-sustainability of an energy\nharvesting system, based on the complementary idea of eventual outage. In\nparticular, we analyze the harvest-store-consume system with infinite battery\ncapacity, stochastic energy arrivals, and fixed energy consumption rate. Using\nthe random walk theory, we identify the necessary condition for the system to\nbe self-sustainable. General formulas are given for the self-sustainability\nprobability in the form of integral equations. Since these integral equations\nare difficult to solve analytically, an exponential upper bound for eventual\noutage probability is given using martingales. This bound guarantees that the\neventual outage probability can be made arbitrarily small simply by increasing\nthe initial battery energy. We also give an asymptotic formula for eventual\noutage. For the special case when the energy arrival follows a Poisson process,\nwe are able to find the exact formulas for the eventual outage probability. We\nalso show that the harvest-store-consume system is mathematically equivalent to\na $GI/G/1$ queueing system, which allows us to easily find the outage\nprobability, in case the necessary condition for self-sustainability is\nviolated. Monte-Carlo simulations verify our analysis.\n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".