Bibliographic record
Abstract
In Wireless Sensor Networks (WSNs) localization -the assigning of sufficiently accurate positions to nodes -is frequently required to meaningfully identify the data collected.In Wireless Rechargeable Sensor Networks (WRSNs) -in which nodes are recharged by some external energy source -high accuracy (e.g.smaller than the mean internode distance) is often required to enable charging in a reasonably efficient manner with practical charger power.Localization in Wireless Sensor Networks (WSNs) has attracted research among various strategies, some using Received Signal Strength Indication (RSSI) at nodes.Nodes with known position, called anchors, may be fixed or mobile.Position may be computed by global optimization, or locally between anchors and nodes.For Wireless Rechargeable Sensor Networks (WRSNs), a charger may act as a mobile anchor enabling high-accuracy localization for efficient RF charging.This study describes a hybrid scheme for localization and charging of WRSNs using an Unmanned Aerial Vehicle (UAV) carrying a node and an RF charger.It works reliably under log-normal fading of RSSI due to shadowing.RSSI localization brings the UAV close enough to the node to elicit a response from the RF charger.The time to charge the node by a given amount is inversely proportional to the power received and thus serves as an indication of proximity of the charger.This metric (ToC) is then used to position the UAV accurately above the node, to allow for charging with maximum efficiency.v 3.7 ToC Refinement .......
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".