Design of Landing Gear for Medium Altitude Long Endurance (Male) Unmanned Aerial Vehicle
Bibliographic record
Abstract
This paper examines the procedure adopted for the design of UAV landing gear.Aircraft landing gear serves as a mechanism to support weight of the aircraft during landing, take-off , taxiing and also provide a shock absorbing function.The design of landing gear for the Medium Altitude Long endurance unmanned Aerial Vehicle is simple and it is base on safe life and fail safe concept and at the same time, make optimum selection and used of high strength materials for the design.This design considerations for this landing gear are significantly different, but past design procedures were used as guide to this design .Various landing gear configurations and types are in used today.The most common landing gear use for UAV is the fixed tricycle arrangement with one nose wheel (NLG) and two main wheels (MLG) at the rear.The retractable tricycle type was adopted for this design.The most attractive feature for this design is the improve stability during braking and ground maneuvering.The result obtain from this study indicate that the landing gear stability of the UAV could be improve with longer wheel axle, by increasing the wheel track.The approach used for the design, of the landing gear for this Medium Altitude Long Endurance UAV, follows the recommendations from previous designs of UAV landing gear and federal Aviation Regulation (FAR).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".