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Record W2619058020 · doi:10.1155/2017/9834247

Conceptual Design of a Small Hybrid Unmanned Aircraft System

2017· article· en· W2619058020 on OpenAlexvenueno aff
Umberto Papa, Salvatore Ponte, Giuseppe Del Core

Bibliographic record

VenueJournal of Advanced Transportation · 2017
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsnot available
FundersUniversità degli Studi di Napoli Federico II
KeywordsAeronauticsObstacleEngineeringPropulsionCruiseAutomotive engineeringComputer scienceAerospace engineeringSimulationSystems engineering

Abstract

fetched live from OpenAlex

UAS (Unmanned Aircraft System) technologies are today extremely required in various fields of interest, from military to civil (search and rescue, environmental surveillance and monitoring, and entertainment). Besides safety and legislative issues, the main obstacle to civilian applications of UAS systems is the short time of flight (endurance), which depends on the equipped power system (battery pack) and the flight mission (low/high speed or altitude). Long flight duration is fundamental, especially with tasks that require hovering capability (e.g., river flow monitoring, earthquakes, devastated areas, city traffic monitoring, and archeological sites inspection). This work presents the conceptual design of a Hybrid Unmanned Aircraft System (HUAS), merging a commercial off-the-shelf quadrotor and a balloon in order to obtain a good compromise between endurance and weight. The mathematical models for weights estimation and balloon static performance analysis are presented, together with experimental results in different testing scenarios and complex environments, which show 50% improvement of the flight duration.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.014
GPT teacher head0.206
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2017
Admission routes1
Has abstractyes

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