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Deeper Dive into Interoperability and Its Implications for LunaNet Communications and Navigation Services

2024· preprint· en· W4394989074 on OpenAlexaff
Angela Peura, James Schier, Coralí Roura, Phillip E. Paulsen, Karl R. Vaden, Jennifer Rock, Charles J. Sheehe, Marc Seibert, Erica Weir

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSystems Engineering Methodologies and Applications
Canadian institutionsTrillium Therapeutics (Canada)
Fundersnot available
KeywordsInteroperabilityComputer scienceInteroperationWorld Wide Web

Abstract

fetched live from OpenAlex

In planning for cislunar exploration and science missions, space agencies have been collaborating to enable communications, networking, and Position, Navigation, and Timing (PNT) systems to exchange information and provide services to cislunar spacecraft and space systems, thus helping each other to achieve their common goals. To achieve commonality and lower cost for mutual benefit, the strategy of interoperability is being adopted to help fit all the pieces together and function smoothly. Interoperability provides cislunar users the ability to operate in a collaborative environment similar to the terrestrial Internet, allowing them to share information, navigate safely despite increasing radio frequency congestion, and follow common processes and procedures for effective joint operations. Unlike prior government-dominated efforts, this ecosystem is expected to include for-profit (commercial) businesses, non-profit organizations, and academic institutions as active stakeholders. Ultimately, the goal is to enable a cislunar ecosystem of service providers and users to contribute to and/or utilize infrastructure and capabilities to achieve mission objectives that span the full range of human endeavors while supporting a variety of business models. This approach enables a Systems-of-Systems (SoS) such as a Network-of-Networks to be sustainable in the context of the LunaNet ecosystem as systems evolve over time in technologies, standards, component and subsystem upgrades, and user applications. This paper reports on the results of an effort to help frame the development of the international LunaNet architecture by providing a canonical definition of interoperability broad enough to meet these needs, examining architectural and operational implications of the definition, and exploring interoperability strategies and tactics to deploy and evolve the services proposed for cislunar exploration and science 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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0050.018
Scholarly communication0.0140.037
Open science0.0010.008
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0040.001

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 head0.060
GPT teacher head0.335
Teacher spread0.274 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations2
Published2024
Admission routes1
Has abstractyes

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