A systematic approach to the Planck LFI end-to-end test and its application to the DPC Level 1 pipeline

The Level 1 of the Planck LFI Data Processing Centre (DPC) is devoted to the handling of the scientific and housekeeping telemetry. It is a critical component of the Planck ground segment which has to strictly commit to the project schedule to be ready for the launch and flight operations. In order to guarantee the quality necessary to achieve the objectives of the Planck mission, the design and development of the Level 1 software has followed the ESA Software Engineering Standards. A fundamental step in the software life cycle is the Verification and Validation of the software. The purpose of this work is to show an example of procedures, test development and analysis successfully applied to a key software project of an ESA mission. We present the end-to-end validation tests performed on the Level 1 of the LFI-DPC, by detailing the methods used and the results obtained. Different approaches have been used to test the scientific and housekeeping data processing. Scientific data processing has been tested by injecting signals with known properties directly into the acquisition electronics, in order to generate a test dataset of real telemetry data and reproduce as much as possible nominal conditions. For the HK telemetry processing, validation software have been developed to inject known parameter values into a set of real housekeeping packets and perform a comparison with the corresponding timelines generated by the Level 1. With the proposed validation and verification procedure, where the on-board and ground processing are viewed as a single pipeline, we demonstrated that the scientific and housekeeping processing of the Planck-LFI raw data is correct and meets the project requirements.Comment: 20 pages, 7 figures; this paper is part of the Prelaunch status LFI papers published on JINST: http://www.iop.org/EJ/journal/-page=extra.proc5/jins

Space mission design ontology : extraction of domain-specific entities and concepts similarity analysis

Expert Systems, computer programs able to capture human expertise and mimic experts’ reasoning, can support the design of future space missions by assimilating and facilitating access to accumulated knowledge. To organise these data, the virtual assistant needs to understand the concepts characterising space systems engineering. In other words, it needs an ontology of space systems. Unfortunately, there is currently no official European space systems ontology. Developing an ontology is a lengthy and tedious process, involving several human domain experts, and therefore prone to human error and subjectivity. Could the foundations of an ontology be instead semi-automatically extracted from unstructured data related to space systems engineering? This paper presents an implementation of the first layers of the Ontology Learning Layer Cake, an approach to semi-automatically generate an ontology. Candidate entities and synonyms are extracted from three corpora: a set of 56 feasibility reports provided by the European Space Agency, 40 books on space mission design publicly available and a collection of 273 Wikipedia pages. Lexica of relevant space systems entities are semi-automatically generated based on three different methods: a frequency analysis, a term frequency-inverse document frequency analysis, and a Weirdness Index filtering. The frequency-based lexicon of the combined corpora is then fed to a word embedding method, word2vec, to learn the context of each entity. With a cosine similarity analysis, concepts with similar contexts are matched

Development of Space Weather Reasonable Worst-Case Scenarios for the UK National Risk Assessment

Severe space weather was identified as a risk to the UK in 2010 as part of a wider review of natural hazards triggered by the societal disruption caused by the eruption of the Eyjafjallajökull volcano in April of that year. To support further risk assessment by government officials, and at their request, we developed a set of reasonable worst-casescenarios and first published them as a technical report in 2012(current version published in 2020). Each scenario focused on a space weather environment that could disrupt a particular national infrastructure such as electric power or satellites, thus enabling officials to explore the resilience of that infrastructure against severe space weather through discussions with relevant experts from other parts of government and with the operators of that infrastructure. This approach also encouraged us to focus on the environmental features that are key to generating adverse impacts. In this paper,we outline the scientific evidence that we have used to develop these scenarios,and therefinements made to them as new evidence emerged. We show how these scenarios are also considered as an ensemble so that government officials can prepare for a severe space weather event, during which many or all of the different scenarios will materialise. Finally,we note that this ensemble also needs to include insights into how public behaviour will play out during a severe space weather event and hence the importance of providing robust, evidence-basedinformation on space weather and its adverse impacts