28, July 2020

ESA Open Invitation to Tender AO10291
Open Date: 24/07/2020
Closing Date: 07/09/2020 13:00:00

Status: ISSUED
Reference Nr.: 20.117.01
Prog. Ref.: Technology Developme
Budget Ref.: E/0901-01 – Technology Developme
Tender Type: C
Price Range: 200-500 KEURO
Technology Domains: Space System Software / Advanced Software Technologies / Advanced Software Development Methods and Tools
Establishment: ESOC
Directorate: Directorate of Operations
Department: Mission Operations Department
Division: Advance Concepts and Mgmt Support Office
Contract Officer: Hurtz, Anne Maria
Industrial Policy Measure: C1 – Activities in open competition limited to the non-Larg…
Last Update Date: 24/07/2020
Update Reason: Tender issue

ObjectivesImprove the development process of space missions, from design to AIT and operations, by extracting knowledge out of scattered data thanks to artificial intelligence and machine learning.DescriptionCurrently, data from past space missions, from design, analysis, test and operations phases, are scattered in inhomogeneous files, formats and content which makes the retrieval of information cumbersome and time consuming. This information is not analysed nor accessible to build knowledge. Targeting a systematic and efficient knowledge transfer from past missions, Artificial Intelligence (AI), in particular text and data mining can bring significant value out of the existing data. This can substantially improve the development as well as the Assembly Integration and Test (AIT) process of complex space and ground systems.While adoption of machine learning (ML) and AI is critical for success in the era of rapid digital transformation, it is even more important how organizations structure data to make it usable for driving insights. AI can help extract data structure and bring information out of what looks like noise.There is no machine intelligence without knowledge representation. Ontologies provide machines powerful tools to make sense of data. By adding Ontologies to a computers representations, machines can process the content of information instead of just presenting the information to humans.This technology push can increase the level of efficiency of the spacecraft design, test and operations, facilitating the extraction of knowledge from already available data, such as lessons learnt from previous missions, log files, telemetry data, etc.This activity encompasses the following tasks:- Identify use cases of text/data mining, and the corresponding data sources.- Define ontologies to structure data coming from heterogeneous sources (e.g. Non-Conformity Reports, System log/event files, test reports).- Validate applicability and potential expansion of current standards supporting the sustainable process of structuring future data- Envisage the functionality of sharing data across distributed teams belonging to diverse entities- Assess ML and other data analytics approaches suitable to support the analysis tasks on design data and log event files- Identify a pilot case- Provide an example of ML/AI application usingstructured data from CDF Study Folders and from log/event files while highlighting benefits- Define guidelines to extend the approach across ground and space segment analysis tasksSoftware shall be delivered under an ESA Software Community Licence, so that any individuals or entities within ESA Member States can access to it and can provide update to the community of users.Procurement Policy: C(1) = Activity restricted to non-prime contractors incl. SMEs). For additional information please go to EMITS news “IndustrialPolicy measures for non-primes, SMEs and RD entities in ESA programmes”.

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