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.

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

Spacecraft operations are highly critical, demanding impeccable reliability and safety. Ensuring the optimal performance of a spacecraft requires the early detection and mitigation of anomalies, which could otherwise result in unit or mission failures. With the advent of deep learning, a surge of interest has been seen in leveraging these sophisticated algorithms for anomaly detection in space operations. Our study aims to compare the efficacy of various deep learning architectures in detecting anomalies in spacecraft data. The deep learning models under investigation include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based architectures. Each of these models was trained and validated using a comprehensive dataset sourced from multiple spacecraft missions, encompassing diverse operational scenarios and anomaly types. We also present a novel approach to the rapid assignment of spacecraft telemetry data sets to discrete clusters, based on the statistical characteristics of the signal. This clustering allows us to compare different deep learning architectures to different types of data signal behaviour. Initial results indicate that while CNNs excel in identifying spatial patterns and may be effective for some classes of spacecraft data, LSTMs and RNNs show a marked proficiency in capturing temporal anomalies seen in time-series spacecraft telemetry. The Transformer-based architectures, given their ability to focus on both local and global contexts, have showcased promising results, especially in scenarios where anomalies are subtle and span over longer durations. Additionally, considerations such as computational efficiency, ease of deployment, and real-time processing capabilities were evaluated. While CNNs and LSTMs demonstrated a balance between accuracy and computational demands, Transformer architectures, though highly accurate, require significant computational resources. In conclusion, the choice of deep learning architecture for spacecraft anomaly detection is highly contingent on the nature of the data, the type of anomalies, and operational constraints. This comparative study provides a foundation for space agencies and researchers to make informed decisions in the integration of deep learning techniques for ensuring spacecraft safety and reliability. 

A Comparison of Deep Learning Architectures for Spacecraft Anomaly
  Detection

With the increase of the potentiality of the astronomical research system through the use of new and expensive tools on multi-wavelength, both in the space and ground structures and through the availability of simulated data too, scientists are now fully aware that this amount of investments require to maximize the exploitation of such tools and their scientific return. A typical theme to face in this attempt is the need to preserve the patrimony inherent in the acquired data. The data must be acquired, described, processed and preserved in order to make possible their reuse from the future generations of researchers and scientific programs whose purposes can be different from those that have originally pushed the acquisition of

/pdf/the-virtual-observatory-in-italy-status-and-prospect-4hducbumlf.pdf

The Virtual Observatory in Italy: status and prospect.

The Prototype TNG Long-Term Archive and its Interactions with the Italian GRID Project

With more and more data created by grid computing, large file distribution is a key issue in data grid Current file distribution in data grid is usually a style site-to-site and this mechanism is not efficient and wastes bandwidth In this paper, we propose a novel algorithm to distribute large file efficiently in data grid We split the file into n subfiles and transfer each subfile to a different node And then the node which receives a subfile transfers the subfile to other nodes The size of each subfile is different in different network environment We must compute the size of the subfile using our simple but efficient algorithm before file splitting To verify the performance of our algorithm, we build a simulation system and compare with Multiunicast and FastReplica [1] The result shows our algorithm significantly reduce the file distribution time

Fast large file distribution in data grid

HouseKeeping and Science Telemetry: the case of Planck/LFI

The pilot project aiming at the definition of a long-term archive for the national Galileo telescope (TNG) is described. The scope of the project, its development plan and the current status are reported, with particular reference to the requirements for interoperability with other archives, aimed at the inclusion of the TNG long-term archive in the phase-B of the European Astronomical Virtual Observatory (AVO) project.

/pdf/the-pilot-project-for-the-tng-long-term-archive-n1piit83cl.pdf

The pilot project for the TNG Long-Term Archive

Dynamic data distribution is a key factor in Grid computing. The DMC project, aiming at improving collaborative research by allow- ing data to be shared more easily across applications cooperating within a federated environment, is described. DMC is the data management system chosen by the PlanckSatellite Survey Community, and specifi- cally by the two Data Processing Centers, as a common infrastructure for the data handling applications being developed. Particular reference is here made to the design of the model, the data structures and to the portability of the Planckexperience to other pipeline-oriented distributed environments, with particular reference to Grid-enabled systems.

/pdf/grid-data-distribution-strategy-design-and-implementation-of-31ym566ch3.pdf

Grid Data Distribution strategy: Design and Implementation of a Pipeline Oriented Data Management System

The Grid paradigm for accessing and exploiting network-distributed facilities has been extended to the Data Grid concept, based on storage and processing systems, and metadata and communications management services. Data Grid and the International Virtual Observatory are the two environments the Italian astrophysical community will have to deal with in the future. This paper describes how archives will interface with these new realities, and the pilot Long-Term Archive for the TNG is brought as an example.

http://sait.oat.ts.astro.it/MSAIS/3/PDF/368.pdf

INAF Archives in the Framework of the Astronomical Data Grid

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HouseKeeping and Science Telemetry: the case of Planck/LFI

The pilot project for the TNG Long-Term Archive

Grid Data Distribution strategy: Design and Implementation of a Pipeline Oriented Data Management System

INAF Archives in the Framework of the Astronomical Data Grid