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Hyperspectral Lightcurve Inversion for Attitude Determination

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Simão da Graça Marto, Massimiliano Vasile, Andrew Campbell, Paul Murray, Stephen Marshall, Vasili Savitski

Signal Processing · Submitted 19 Dec 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Results are shown based on synthetic data.

how it is stated · asserted 0.98 read the record →

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arXiv ID2401.05397 (version 1)
Pith IDRIY44UFK, Pith's permanent record of this paper and its review
CitationAndrew Campbell, Massimiliano Vasile, Paul Murray, Sim\~ao da Gra\c{c}a Marto, Stephen Marshall, Vasili Savitski. "Hyperspectral Lightcurve Inversion for Attitude Determination." arXiv:2401.05397 (2023). https://pith.science/paper/RIY44UFK
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,146 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.61 · human participants 0.02 · controlled comparison 0.09 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.33 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Spectral lightcurves consisting of time series single-pixel spectral measurements of spacecraft are used to infer the spacecraft's attitude and rotation. Two methods are used. One based on numerical optimisation of a regularised least squares cost function, and another based on machine learning with a neural network model. The aim is to work with minimal information, thus no prior is available on the attitude nor on the inertia tensor. The theoretical and practical aspects of this task are investigated, and the methodology is tested on synthetic data. Results are shown based on synthetic data.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Spectral lightcurves consisting of time series single-pixel spectral measurements of spacecraft are used to infer the spacecraft's attitude and rotation. Two methods are used. background 0.40 0.49 0.04 asserted 1.00
s1 One based on numerical optimisation of a regularised least squares cost function, and another based on machine learning with a neural network model. method 0.99 0.68 0.04 asserted 0.94
s2 The aim is to work with minimal information, thus no prior is available on the attitude nor on the inertia tensor. background 0.51 0.35 0.04 asserted 0.98
s3 The theoretical and practical aspects of this task are investigated, and the methodology is tested on synthetic data. contribution 0.74 0.85 0.04 asserted 0.98
s4 Results are shown based on synthetic data. result 0.89 0.89 0.09 asserted 0.98

What kind of paper the classifier reports this is: method or tool 0.98 · evidence from simulation or computation 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Spectral lightcurves consisting of time series single-pixel spectral measurements of spacecraft are used to infer the spacecraft's attitude and rotation. Two methods are used. One based on numerical optimisation of a regularised least squares cost function, and another based on machine learning with a neural network model. The aim is to work with minimal information, thus no prior is available on the attitude nor on the inertia tensor. The theoretical and practical aspects of this task are investigated, and the methodology is tested on synthetic data. Results are shown based on synthetic data.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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