On-Board Data Analysis and Realtime Information System - Software Development Concept for New Space

In this talk we want to provide an overview over the software development concept of the "On-board Data Analysis and Real-time Information System" (ODARIS) developed at the DLR. The ODARIS technology demonstration will be performed within the upcoming SeRANIS mission of the University of the Bunderswehr Munich, scheduled for 2025, utilizing a state-of-the-art ARM-based on-board computer architecture and software stack. We will focus on our approach for the software development and adaptions from terrestrial application development to the space proven ODARIS software

On-Board Data Analysis And Realtime Information System - Status & Outlook

In this article an overview of the current development status of the “On-board Data Analysis And Real-Time Information System” (ODARIS) developed at the German Aerospace Center (DLR) is presented. We will focus on an upcoming technology demonstration experiment of the ODARIS concept planned around 2023-2025 and include topics as the utilization of “low-cost real-time communication channels”, on-board data evaluation, the communication concept and the benefits and limitations of our approach

On Board Data Analysis and Realtime Information System

For many information applications like alarming services or surveillance systems, reactivity is a very important criterion. This is also true for many applications based on satellite data, for example in the field of remote sensing or space system operation. These applications are currently bottlenecked by classical satellite operation, where contact to the satellite is only established within a few communication windows, when the satellite is in reach of an assigned ground station. The amount of time between collection of the data on the space system and having it available for ground-users can take from several hours up to days. At our institution, we are working on a cost-efficient method to decrease the latency until information, derived from satellite data, becomes available for usage on ground. The basic approach is trying to utilizing satellite based low-latency real-time telecommunication networks, designed for ground-based satellite phone usage in combination with on board information extraction. Information extraction on the satellite will be necessary as the telecommunication networks only offer a very limited data transfer bandwidth in the range of a few hundred bytes and the transmission of raw sensor data is not possible

A Component-Based Middleware for a Reliable Distributed and Reconfigurable Spacecraft Onboard Computer

Emerging applications for space missions require increasing processing performance from the onboard computers. DLR's project “Onboard Computer - Next Generation” (OBC-NG) develops a distributed, reconfigurable computer architecture to provide increased performance while maintaining the high reliability of classical spacecraft computer architectures. Growing system complexity requires an advanced onboard middleware, handling distributed (realtime) applications and error mitigation by reconfiguration. The OBC-NG middleware follows the Component-Based Software Engineering (CBSE) approach. Using composite components, applications and management tasks can easily be distributed and relocated on the processing nodes of the network. Additionally, reuse of components for future missions is facilitated. This paper presents the flexible middleware architecture, the composite component framework, the middleware services and the model-driven Application Programming Interface (API) design of OBC-NG. Tests are conducted to validate the middleware concept and to investigate the reconfiguration efficiency as well as the reliability of the system. A relevant use case shows the advantages of CBSE for the development of distributed reconfigurable onboard software

Guidance, Navigation and Control for Autonomous Close-Range-Rendezvous

This article presents the Guidance, Navigation and Control system (Gnc-system) developed by GSOCs On Orbit Servicing (OOS)-group. It is used for research on autonomous rendezvous, optical sensors and operational concepts with the European Proximity Operations Simulator 2.0 (EPOS) facility and inside an End-to-End simulation. Its modular design intends to make it capable of deployment in space on a novel on-board computer with less effort. Major design decisions as well as plans for future development are introduced

Parallelizing On-Board Data Analysis Applications for a Distributed Processing Architecture

Satellite-based applications produce ever-increasing quantities of data, challenging the capabilities of existing telemetry and on-board processing systems, especially when results must be transmitted quickly to ground. The Scalable On-Board Computing for Space Avionics (ScOSA) platform contributes the processing capability necessary to perform such computationally intensive analysis on-board. This platform offers a high-performance on-board computer by combining multiple commercial off-the-shelf processors and space-grade processors into a distributed computer. Middleware ensures reliability by detecting and mitigating faults, while allowing applications to effectively use multiple, distributed processors. The current work aims to demonstrate the use and advantages of utilizing the data-flow programming paradigm supported by the ScOSA platform to provide high-throughput on-board analysis. This enables rapid analysis even for applications requiring high frame rates, high resolutions, multi-spectral imaging or in-depth processing. The On-Board Data Analysis and Real-Time Information System (ODARIS) is used to demonstrate this method. ODARIS is a system for providing low-latency access to satellite-based observations, even when large quantities of sensor data are involved. By performing on-board processing of the data from the satellite-borne instruments, the amount of data which must be sent to ground is drastically reduced. This allows the use of low-latency telecommunication-satellite constellations for communicating with ground to achieve query-response times of only a few minutes. The current application combines an Earth-observation camera with AI-based image processing to provide real-time object detection. In the data-flow driven implementation of ODARIS on the ScOSA platform, images are captured by a camera and sent to any of several processors for the computationally intensive image processing. This allows multiple images to be processed in parallel by as many processors as are available, while avoiding the need to divide each image across several processors. The results are transferred to an on-board database from which queries can be served asynchronously. The system will be tested in configurations with one, two and three processors and the resulting image throughput presented. Testing is performed on a ground-based prototype system using pre-recorded images. This paper presents the necessary details of the underlying ScOSA and ODARIS systems as well as the implementation of the objection-detection algorithm using a parallelized, data-flow model. The results of executing the system using a variable number of processors are presented to demonstrate the improvement in image throughput and its potential application to other computationally-intensive tasks

Smad7 in T cells drives T helper 1 responses in multiple sclerosis and experimental autoimmune encephalomyelitis

Autoreactive CD4+ T lymphocytes play a vital role in the pathogenesis of multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis. Since the discovery of T helper 17 cells, there is an ongoing debate whether T helper 1, T helper 17 or both subtypes of T lymphocytes are important for the initiation of autoimmune neuroinflammation. We examined peripheral blood CD4+ cells from patients with active and stable relapsing–remitting multiple sclerosis, and used mice with conditional deletion or over-expression of the transforming growth factor-β inhibitor Smad7, to delineate the role of Smad7 in T cell differentiation and autoimmune neuroinflammation. We found that Smad7 is up-regulated in peripheral CD4+ cells from patients with multiple sclerosis during relapse but not remission, and that expression of Smad7 strongly correlates with T-bet, a transcription factor defining T helper 1 responses. Concordantly, mice with transgenic over-expression of Smad7 in T cells developed an enhanced disease course during experimental autoimmune encephalomyelitis, accompanied by elevated infiltration of inflammatory cells and T helper 1 responses in the central nervous system. On the contrary, mice with a T cell-specific deletion of Smad7 had reduced disease and central nervous system inflammation. Lack of Smad7 in T cells blunted T cell proliferation and T helper 1 responses in the periphery but left T helper 17 responses unaltered. Furthermore, frequencies of regulatory T cells were increased in the central nervous system of mice with a T cell-specific deletion and reduced in mice with a T cell-specific over-expression of Smad7. Downstream effects of transforming growth factor-β on in vitro differentiation of naïve T cells to T helper 1, T helper 17 and regulatory T cell phenotypes were enhanced in T cells lacking Smad7. Finally, Smad7 was induced during T helper 1 differentiation and inhibited during T helper 17 differentiation. Taken together, the level of Smad7 in T cells determines T helper 1 polarization and regulates inflammatory cellular responses. Since a Smad7 deletion in T cells leads to immunosuppression, Smad7 may be a potential new therapeutic target in multiple sclerosis

Connected Component Labeling algorithm for very complex and high-resolution images on an FPGA platform

Connected Component Labeling (CCL) is a basic algorithm in image processing and an essential step in nearly every application dealing with object detection. It groups together pixels belonging to the same connected component (e.g. object). Special architectures such as ASICs, FPGAs and GPUs were utilised for achieving high data throughput, primarily for video processing. In this article, the FPGA implementation of a CCL method is presented, which was specially designed to process high resolution images with complex structure at high speed, generating a label mask. In general, CCL is a dynamic task and therefore not well suited for parallelisation, which is needed to achieve high processing speed with an FPGA. Facing this issue, most of the FPGA CCL implementations are restricted to low or medium resolution images (≤ 2048 ∗ 2048 pixels) with lower complexity, where the fastest implementations do not create a label mask. Instead, they extract object features like size and position directly, which can be realized with high performance and perfectly suits the need for many video applications. Since these restrictions are incompatible with the requirements to label high resolution images with highly complex structures and the need for generating a label mask, a new approach was required. The CCL method presented in this work is based on a two-pass CCL algorithm, which was modified with respect to low memory consumption and suitability for an FPGA implementation. Nevertheless, since not all parts of CCL can be parallelised, a stop-and-go high-performance pipeline processing CCL module was designed. The algorithm, the performance and the hardware requirements of a prototype implementation are presented. Furthermore, a clock-accurate runtime analysis is shown, which illustrates the dependency between processing speed and image complexity in detail. Finally, the performance of the FPGA implementation is compared with that of a software implementation on modern embedded platforms

VIMOS - Modular Commanding and Execution Framework for Onboard Remote Sensing Applications

For decades, field help in case of disasters on the Earth’s surface - like floods, fires or earthquakes - is supported by the analysis of remotely sensed data. In recent years, the monitoring of vehicles, buildings or areas fraught with risk has become another major task for satellite-based crisis intervention. Since these scenarios are unforeseen and time-critical, they require a fast and well coordinated reaction. If useful information is extracted out of image data in realtime directly on board a spacecraft, the timespan between image acquisition and an appropriate reaction can be shortened significantly. Furthermore, on board image analysis allows data of minor interest, e.g. cloud-contaminated scenes, to be discarded and/or treated with lower priority, which leads to an optimized usage of storage and downlink capacity. This paper describes the modular application framework of VIMOS, an on board image processing experiment for remote sensing applications. Special focus will be on resource management, safety and modular commandability