APPROACHES TO DESIGN AND PRACTICE OF UNMANNED AERIAL VEHICLES OF THE AIRPLANE TYPE
Bibliographic record
Abstract
Nowadays unmanned aviation has found wide application in many fields of human activity. Over the last two decades, such technology has moved from the category of military or experimental exotics to something applied and ubiquitous. Occupying more and more new spheres, unmanned aerial vehicles (UAVs) get all the new functions. For their implementation the designers often take quite bold decisions, which are rare in the «big» manned aviation. The article examines the current state of the civilian airplane-type UAVs industry in terms of their design features, as well as the specifics of their application in various sectors of the economy. The authors analyse the principles underlying the choice of this or that aerodynamic scheme of a UAVs on the process of its design. In the context of possible UAVs application scenarios the advantages and disadvantages as well as limitations of a particular UAVs airframe layout, applied engine unit and construction materials are under consideration. Based on a summary of the parameters analysed, it stands out a number of classification features, which can be used as a basis for a comprehensive classification of a wide range of unmanned civil aviation.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".