Optimization and Decision-Making Framework for Small Unmanned Aircraft Systems Fleet Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many unmanned aircraft applications require or benefit from deploying a fleet to perform a mission. Technology innovations such as rapid prototyping and microelectronics enable the use of unmanned aircraft systems (UAS) at lower costs, so the ability to design a bespoke UAS for a specific mission is an additional advantage. The problem of designing the new UAS and allocating the fleet of these aircraft is a challenging optimization problem. Using constrained multi-objective design optimization with an interactive multicriteria decision-making method, which involves the mission customer, the approach in this paper generates UAS designs and fleet allocations and determines preferential solutions for the customer. The paper presents a case study involving a UAS weather profiling mission for the aircraft sizing and fleet allocation and uses inputs from meteorological subject-matter experts acting as customers to demonstrate the functionality of the approach.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it