Bibliographic record
Abstract
Large companies such as Amazon and Google are currently testing deliveries using unmanned drones, intending to use these drones on the market. Topics tackled in this field of research include object collision routing optimization, delivery routing optimization, battery management optimization, and the addition of solar panels on the drones. Little publicly available research has been found to have been conducted on developing the methods to optimize the powertrain of the drones to maximize their delivery radii through modifying their electric motor and propeller parameters. In working towards that goal, this paper puts forth a delivery drone driving cycle simulation written in MATLAB with which to monitor their performance and fine-tune their properties. The driving cycle has been written to accommodate unmanned drones that use any number of propellers and perform vertical take-off and landing maneuvers. This driving cycle algorithm iteratively runs through multiple driving profiles to find the one which produces the maximal delivery radius for the drone. A data processing tool for polynomial interpolation, which is also written in MATLAB, is developed to manipulate the electric motor and propeller data into usable states for the simulation. For the tested drone configurations, no discernible pattern was noticed in the ideal power throttle needed to reach cruise altitude most efficiently. During cruise, an ideal pitch between 27 to 47 degrees which allowed them to displace horizontally while spending the least amount of energy per meter was found for all configurations.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".