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Record W3095676401 · doi:10.2514/6.2020-4250

A Novel Concept of a Parallel Partial Space Elevator With Multiple Carts

2020· article· en· W3095676401 on OpenAlexaff
Zheng Zhu, Gangqiang Li

Bibliographic record

VenueASCEND 2020 · 2020
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsYork University
Fundersnot available
KeywordsPayload (computing)Position (finance)Computer scienceLibration (molecule)TrajectoryControllabilitySimulationAerospace engineeringControl theory (sociology)EngineeringPoint (geometry)PhysicsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a novel concept of tether transportation system with high-efficiency payload transferring, two tether transportation systems with multiple carts are bundled together. A high-fidelity and high-accuracy model is built up based on the nodal position finite element method in the arbitrary Lagrangian-Eulerian description. First, the dynamic behavior is investigated by comparing the result of tether transportation system with single tether. Next, the dynamic characteristic of looped tether transportation system is investigated. The results show the interaction effect between two tether transportation systems is significant when the trajectories of moving carts are different. It suggests the trajectories of moving carts should be well designed to minimize the interaction effect between two tether transportation meanwhile suppress the libration motion of tether transportation system.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.185
Teacher spread0.177 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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