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Record W7106015984 · doi:10.2322/astj.24.s60

The Evaluation of Two-Step Landing Characteristics for SLIM

2025· article· en· W7106015984 on OpenAlexaff

Bibliographic record

VenueAEROSPACE TECHNOLOGY JAPAN THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES · 2025
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsThe Audio Recording Academy
Fundersnot available
KeywordsTouchdownSoft landingTerrainSpacecraftMoon landingShock (circulatory)

Abstract

fetched live from OpenAlex

The Small Lunar Landing Demonstration spacecraft, SLIM, planned to land using a new method called the two-step landing. On January 20, 2024 (JST), SLIM successfully achieved a soft landing while maintaining functionality. However, the attitude and speed before touchdown deviated from expectations, leading to the postponement of the two-step landing method demonstration to a future mission. This method is particularly effective for landing on gravitational bodies using elongated spacecraft that fully utilize the rocket's fairing envelope, especially on sloped terrain or for small exploratory spacecraft with strict mass reduction requirements. This paper presents an analysis based on the landing dynamics simulations conducted for SLIM, focusing on factors such as the forces acting on the shock absorbers and the spacecraft during landing, as well as trends in attitude and terrain that contribute to stabilizing the two-step landing.

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)0.002
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.285
Teacher spread0.271 · 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
Published2025
Admission routes1
Has abstractyes

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