SpacePDP: Open and modular payload data processing framework

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SpacePDP: Open and modular payload data processing framework

Technology abstract

An Italian SME proposes a smart way to meet increased telecommunication needs both in data management (available mass memory) and in data transmission (bandwidth) thanks to ad-hoc data processing capabilities looking at automatic data selection (based on features) and autonomous tasking. The proposed system is composed of independent hardware and software modules and completed by a specifically designed IDE (Integrated Development Environment), including a user GUI (Graphical User Interface).

Technology Description

An Italian SME has developed a system able to meet the increased telecommunication needs both in data management (available mass memory) and in data transmission (bandwidth) thanks to ad-hoc data processing capabilities enhanced by automatic data selection (based on features) and autonomous tasking. The proposed system is composed of independent hardware and software modules and completed by a specifically designed IDE (Integrated Development Environment), including a user GUI (Graphical User Interface). The SW architecture is characterised by flexible and customisable building blocks to be easily integrated in customised applications.
New development tasks are facilitated thanks to the integrated development framework, able to access HW resources and transfer the compiled objects to the target HW, manage the tool chain and execute/debug the SW from a single interface (with a GUI based on Eclipse Rich Client Platform).
The open and modular data processing framework herein proposed was originally developed within a national co-funded space project that had the aim of transferring satellite data processing from the Ground to the Space Segment. SpacePDP (Payload Data Processing), the name of both the project and its outcome, was used in different operational conditions, both for SW (Real Time Executive Multiprocessor Systems and VxWorks) and HW (LEON2 and 3, Digital Signal Processor, Advanced RISC Machine) and both on satellite platforms and on planetary exploration rovers.
SpacePDP is currently used for the scientific data processing and compression in the Solar Wind Analyser instrument suite on-board of Solar Orbiter and in the development of OP3C, a novel hyperspectral data cubes compression module. OP3C compression technique achieves high compression ratios, low data distortion, still keeping a limited computation complexity. The performances of the OP3C compressor have been demonstrated on two different sensors: AVIRIS (airborne) and Hyperion (spaceborne) on the standard dataset (Aaron et al. 2009) adopted by NASA and CCSDS for hyper-spectral compressor benchmarking.
Since SpacePDP was developed to provide mission standard tasks (e.g. TeleMetry/TeleCommand, sensors control, mass memory management, uplink and downlink), it makes possible a straightforward implementation of specific tasks as scientific data processing or optimised telecommunication processes or Big Data Handling for security and encryption applications.

Innovations & Advantages

The Italian data processing framework herein described, spacePDP, was developed to be open and modular and offers the following features:
• HW and SW modularity and scalability for data processing
• Complex processing capabilities available on-board
• Reduced effort in missions SW design, implementation, verification and validation tasks
• HW/SW abstraction level comparable to multitasking Unix-like systems allowing SW and algorithms re-use
• Development tools & GUI (integrated in Eclipse Rich Client Platform)

Furthermore the technology is in line with relevant standards: the Consultative Committee for Space Data Systems defines a suite of pay load data compression techniques, both lossless and lossy, applicable to any kind of scientific data (from imaging or non-imaging instruments). SpacePDP already implements all the recommended standards:
• 123.0-B-1 Lossless Multispectral & Hyperspectral Image compression
• 122.0-B-1 Lossless Image Data compression
• 121.0-B-2 Lossless Data compression

Further Information

ANN (Artificial Neural Network) based algorithms for data compression and features extraction have been tested and validated on the spacePDP framework, implementing computational models in a multi-layer perceptron configuration.
An ANN consists of multiple computing nodes each corresponding to a simple mathematical model of a biological neuron that performs a linear combination of its inputs followed by a non-linear logistic sigmoid function. Nodes are organized in a few layers and each node is connected to all the others in the next layer with a weight representing its relevance in the model and cut-in threshold. Weights and thresholds are automatically “learned” using training data sets in an iterative process.

Current and Potential Domains of Application

The open and modular data processing framework herein proposed was originally developed within the national co-funded space project SpacePDP that had the aim of transferring satellite data processing from the Ground to the Space Segment. SpacePDP is currently used for the scientific data processing and compression in the Solar Wind Analyser instrument suite on-board of Solar Orbiter ESA mission and in the development of OP3C, a novel hyperspectral data cubes compression module, which have been on the Hyperion (spaceborne) sensors on the standard dataset (Aaron et al. 2009) adopted by NASA and CCSDS (Consultative Committee for Space Data Systems) for hyper-spectral compressor benchmarking. Since SpacePDP has been developed to provide tasks as TeleMetry/TeleCommand, sensors control, mass memory management, uplink and downlink, a straightforward implementation of specific tasks is possible, such as:
• Scientific data processing
• Optimised telecommunication processes
• Big Data Handling for security applications
• Big Data Handling for encryption applications