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Record W4316464557 · doi:10.52912/jsta.2022.2.4.287

Introduction of Artemis 1 Nanosatellite Missions and Technology Trends

2022· article· en· W4316464557 on OpenAlexaboutno aff
Bogyeong Kim, Ik‐Seon Hong

Bibliographic record

VenueJournal of Space Technology and Applications · 2022
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsnot available
FundersEuropean CommissionJapan Aerospace Exploration AgencyNational Research Foundation of KoreaNational Aeronautics and Space Administration
KeywordsAeronauticsSpacecraftPlan (archaeology)Aerospace engineeringEngineeringGeography

Abstract

fetched live from OpenAlex

The Artemis program is a manned lunar exploration plan being pursued by NASA in the United States. It is of great significance that manned lunar exploration has been resumed since Apollo 17. The main partner countries are the European Union (ESA), Japan (JAXA), and Canada (CSA). A total of 22 countries, including Korea, have signed in the Artemis Accords to participate in this program. The first mission of the Artemis program, Artemis 1, is primarily aimed at flight test launch vehicles and spacecraft before full-scale exploration missions. As a secondary mission, 10 nanosatellites mounted on Artemis 1 are deployed and carry out their respective missions. Artemis 1, which is introduced in Korea, consists of only the main mission, and it is very rare to deal with the 10 nanosatellites, making it difficult to understand the scientific and technological mission goals of each nanosatellite. In this paper, the nanosatellite missions of Artemis 1, which have not been introduced in detail in Korea, and the recent trend of nanosatellite development.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · 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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.004
GPT teacher head0.203
Teacher spread0.199 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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