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Record W2112012844 · doi:10.1109/spacomm.2009.43

The Generic Nanosatellite Bus: From Space Astronomy to Formation Flying Demo to Responsive Space

2009· article· en· W2112012844 on OpenAlexaffabout
Freddy Pranajaya, Robert Zee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPayload (computing)SpacecraftAerospaceAeronauticsSystems engineeringAerospace engineeringPropulsionAvionicsSpace explorationComputer scienceSatelliteSpace technologyIn-space propulsion technologiesEngineeringSpacecraft propulsion

Abstract

fetched live from OpenAlex

The Space Flight Laboratory (SFL) at the University of Toronto Institute for Aerospace Studies develops missions using spacecraft measuring 20 by 20 cm in its cross section and up to 40 cm in length. Each spacecraft can weigh up to 15 kg with up to 9 kg of payload. One of the three SFL operational missions uses the Generic Nanosatellite Bus (GNB) form factor and was conceived, built, and delivered into orbit within seven months from project inception. This nanosatellite precedes an operational 75 kg microsatellite mission by demonstrating the payload technology. Other technologies currently in orbit include reaction wheels and propulsion system, which will be used in follow up missions. Of the five nanosatellites currently under construction at SFL, two are intended for performing astrophysics investigation, two are intended for carrying out formation flying technology demonstration, and one is intended for performing preoperational duties as a way to fast track the readiness of new technologies that are slated for larger, operational missions; the latter is currently slated for launch in Q3 2009. In addition, SFL is also providing a number of critical subsystems for an operational microsatellite mission. These spacecraft build upon a set of common components and technologies that are shared across multiple missions and implement an architecture that is directly expandable to larger, operational missions. The development of these missions follows the microspace approach for managing risks and ensuring rapid development, which maintains cost-effectiveness and responsiveness to new missions. Typically each spacecraft implements multiple on-board computers, high data rate radios, sensors and actuators. The system implements a number of redundancies to mitigate failures. The subsystem complement and the complexity of the spacecraft can be tailored to meet various mission needs, from a passively stabilized spacecraft using permanent magnets to a three-axis stabilized platform with reaction wheels with optional propulsion system. The spacecraft can also accommodate fixed appendages such as booms, antennas, and additional solar panels. SFL also builds its own separation systems called ldquoXPODsrdquo and arranges, on a regular basis, shared launches for nanosatellite developers worldwide through its Nanosatellite Launch Service (NLS) program.

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)0.001
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 score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.005

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 head0.201
Teacher spread0.192 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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