Gaussian regression and power spectral density estimation with missing data: The MICROSCOPE space mission as a case study

Baghi, Quentin; Métris, Gilles; Bergé, Julien; Christophe, Bruno; Touboul, Pierre; Rodrigues, Manuel

doi:10.1103/physrevd.93.122007

Cited by 26 publications

(44 citation statements)

References 43 publications

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“…In this section, we review the case of the Microscope data model. The Microscope space mission aims at testing the universality of free fall, for the first time in space [15]. In the following, we consider an extract of the data model used to parameterize the telemetry processing and especially to combine two telemetries.…”

Section: Case Studiesmentioning

confidence: 99%

Finding conservative schema evolutions by analysing API changes

Oubelli¹,

Aït‐Ameur²,

Bedouet³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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Because verification and validation are important activities in model driven engineering (MDE), verifying interfaces preservation is considered an interesting step to understand the evolution of data models by analyzing their interfaces. The interfaces defined on a data model can be used to define model evolution correctness using observational semantics. In this paper, we propose an approach that supports rigorous analysis, verification and validation of behavioral re-factoring. Our work addresses the problem of data model evolution in a formal modelling and verification setting. We focus on data conservation in the specific context of space engineering, where data models may involve thousands of concepts, relationships and each concept has a number of fields or attributes and each relationship has a number of properties.

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Section: Case Studiesmentioning

confidence: 99%

Finding conservative schema evolutions by analysing API changes

Oubelli¹,

Aït‐Ameur²,

Bedouet³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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show abstract

“…In this section, we show our approach applies to particular evolutions of data models used in the PHARAO [7] and MICROSCOPE [8] space missions supplied by the French space agency (CNES) and at the French Aerospace lab (ONERA).…”

Section: Case Studiesmentioning

confidence: 99%

“…In this section, we study how the proposed approach applies to a particular evolution of a data model used in the MICROSCOPE project [8] . In the following, we detail the comparison, evolution and migration strategies that could be used to co-evolve (migrate) parameters data used during the telemetry processing, following our pipeline architecture.…”

Section: Contextmentioning

confidence: 99%

A scalable model based approach for data model evolution: Application to space missions data models

Oubelli

Aït‐Ameur

Bedouet

et al. 2018

Computer Languages, Systems & Structures

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible a b s t r a c t During the development of a complex system, data models are the key to a successful engineering process, as they contain and organize all the information manipulated by the different functions involved in the design of the system. Moreover, these data models evolve throughout the design, as the development raises issues that have to be solved through a restructuration of data organization. But any such data model evolution has a deep impact on the functions that have already being defined.Recent research tries to deal with this issue by studying how complex industrial data models evolve from one version to another and how their data instances co-evolve. Complexity and scalability issues make this problem a major scientific challenge, leading to huge gains in development efficiency. This problem is of particular interest in the field of aeronautics and space systems. Indeed, the development of these systems produces many complex data models associated to the designed systems and/or to the systems under design, hence on the one hand data models are available. On the other hand, it is well known that these systems are developed in the context of collaborative projects that may last for decades. In such projects, specifications together with the associated data models are bound to evolve and engineering processes shall take into account this evolution.Our work addresses the problem of data model evolution in a model-driven engineering setting. We focus on minimizing the impact of model evolution on the system development processes in the specific context on the space engineering area, where data models may involve thousands of concepts and relationships, and we investigate the performance of the model-based development (MBD) approach we propose for data model evolution over two space missions, namely PHARAO and MICROSCOPE.

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“…In this section, we will see how the previous approach is applied to a particular revision of a data model used in the MICROSCOPE project [16]. This project studies the universality of free fall in space thanks to two differential accelerometers supplied by the French National Aerospace Research Center, ONERA, and embedded in a microsatellite from the CNES.…”

Section: Case Study From the Microscope Projectmentioning

confidence: 99%

Automating the Evolution of Data Models for Space Missions. A Model-Based Approach

Oubelli

Aït‐Ameur

Bedouet

et al. 2017

Model and Data Engineering

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In space industry, model-driven engineering (MDE) is a key technique to model data exchanges with satellites. During the preparation of a space mission, the associated data models are often revised and need to be compared from one version to another. Thus, due to the undeniably growth of changes, it becomes difficult to track them. New methods and techniques to understand and represent the differences, as well as commonalities, between different model's revisions are highly required. Recent research works address the evolution process between the two layers (M2/M1) of the MDE architecture. In this research work, we have explored the use of the layers (M1/M0) of the same architecture in order to define a set of atomic operators and their composition that encapsulate both data model evolution and data migration. The use of these operators improves the quality of data migration, by ensuring full conservation of the information carried by the data.

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Gaussian regression and power spectral density estimation with missing data: The MICROSCOPE space mission as a case study

Cited by 26 publications

References 43 publications

Finding conservative schema evolutions by analysing API changes

Finding conservative schema evolutions by analysing API changes

A scalable model based approach for data model evolution: Application to space missions data models

Automating the Evolution of Data Models for Space Missions. A Model-Based Approach

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