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DEEPCUBE

The goal of the project was to develop a service as a product to simplify fitting DNN (Deep Learning Neural Networks) in on board HW to make better use of AI on space missions The role of the service is to support data processing engineers in reducing HW resources requirement of powerful DNN for image analysis to be executed onboard. 

France

GT27-048ED

Agenium Space
MBSE Deployment for EnVision Science Mission Study
  • Enforce the use of the physical engineering model that TAS-F already has by sub-system architects in order to have the model as a central source of truth
  • Produce an interactive representation of the model allowing its consultation without having to install dedicated modelling tools
  • Produce trend graphs on system budgets
  • Produce document generation capability to export graphs and tables to the system and subsystem budget reports
France

20-P-M-SCI-02

Thales Alenia Space
Innovative security concepts, mechanisms and architectures for future space missions

This study aimed to achieve the following objectives:

  • To anticipate the security needs and requirements of future space missions (Earth observation, communication, navigation, science, exploration, etc.) with respect to architectures considered for future missions.

  • To build an advanced security architecture portfolio embedding state-of-the-art security concepts, technics, technologies, components and solutions.

France

15/018

Thales Alenia Space

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