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Record W7132969177

Geometric and Access Time Utilities for a Payload Operations Planning Software

2023· dissertation· W7132969177 on OpenAlexaff
Lukasz Jagodzinski

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPayload (computing)SoftwareSpacecraftSatelliteScheduleGround segmentSatellite constellationPolygon (computer graphics)Computation
DOInot available

Abstract

fetched live from OpenAlex

The Payload Operations Planning Software is a tool that satellite mission operators use for handling the deterministic aspects of mission operations, which include schedule validation and searching for observation opportunities. The software is applicable to any Earth observation satellite mission. This work presents the design and implementation of two modules within the planning software, namely the Geometric Utilities and the Access Time Utilities. The Geometric Utilities perform calculations involving ellipsoidal polygons representing area regions on Earth’s surface, spacecraft sensor fields, and specular reflections on Earth’s surface. The Access Time Utilities compute the periods of access between a spacecraft and objects of interest, which are ground stations, ground targets, latitude ranges, polygon areas, and specular points. Both modules are integral to finding observation opportunities. A Technology Demonstration Mission with radio frequency sensing, bistatic radar, and imaging capability is used as an example for describing design and 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)0.002
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0740.033

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 head0.045
GPT teacher head0.356
Teacher spread0.311 · 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
Published2023
Admission routes1
Has abstractyes

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