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Record W7071828794

Space Missions Key Management Concept . Green Book. Issue 1. November 2011. CCSDS 350.6-G-1

2011· article· en· W7071828794 on OpenAlexfundno aff

Bibliographic record

VenueBiblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · 2011
Typearticle
Languageen
FieldComputer Science
TopicSecurity in Wireless Sensor Networks
Canadian institutionsnot available
FundersNuclear PhysicsNational Institute of Information and Communications TechnologyJapan Aerospace Exploration AgencyCanadian Space AgencyCenter for Long-Term Cybersecurity, University of California BerkeleyGeo-Informatics and Space Technology Development AgencyIran Telecommunication Research CenterEuropean Space AgencyAgenzia Spaziale ItalianaEuropean Organization for the Exploitation of Meteorological SatellitesKorea Aerospace Research InstituteNational Commission for Science and TechnologyTürkiye Bilimsel ve Teknolojik Araştırma KurumuNational Oceanic and Atmospheric AdministrationNational Space OrganizationChina National Space AdministrationBelgian Federal Science Policy OfficeChinese Academy of SciencesU.S. Geological SurveyIndian Space Research OrganisationCentre National d’Etudes SpatialesCommonwealth Scientific and Industrial Research OrganisationNational Aeronautics and Space Administration
KeywordsStandardizationKey (lock)Context (archaeology)Key managementSpace explorationSpace (punctuation)Core (optical fiber)
DOInot available

Abstract

fetched live from OpenAlex

This Informational Report provides the core concepts of cryptographic key management in the context of space missions. The concepts described are the baseline for the CCSDS standardization activities with respect to security services and, more concretely, key management schemes for space missions. .

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.006
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0680.079

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 head0.085
GPT teacher head0.331
Teacher spread0.247 · 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
GenreMethods

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
Published2011
Admission routes1
Has abstractyes

Explore more

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