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The recent developments in space exploration have reinstated the Moon as a primary target for near future space missions. The principal reasons include the Moon being the closest test bed and analogue for planetary space missions and the prospect of scientific lunar bases and orbital stations within the next decade. Previous space missions have vastly improved our understanding on hazards of human spaceflights but not fully regarding the threats affecting a prospective lunar base or orbital station. The micrometeorite hazard has been partially addressed as an issue which can potentially impact both astronauts' health and safety as well as create issues for lunar bases and orbital stations, such as degradation or permanent damage of equipment and facilities. The current understanding is based partly on dust and micrometeoroid flux measurements and impact flash observations. However, observations with improved spatial and temporal resolution are imperative for advancing existing hazard models. In this paper, a mission concept of a constellation of nanosatellites is proposed that can both observe larger parts of cis-lunar and trans-lunar space while providing higher temporal resolution. Nanosatellite missions are a cost-effective solution providing data for significant improvement of our current understanding of lunar micrometeoroid flux models, and thus directly the scale of hazards caused by micrometeoroid impacts to future lunar missions. Additionally, such a distributed constellation mission will offer countless opportunities for academia, students and young scientists worldwide. The mission concept (Moon Compact Satellite for Hazard Assessment - MOOCHA) is a result of the Nordic-European Astrobiology Campus Summer School 2018 themed “Microsatellites in Planetary and Atmospheric Research” and was further developed during the 2019 follow-up summer school “Design of Small Satellite Missions for Planetary Studies”, both taking place in Tartu, Estonia and co-organized by the Stockholm University Astrobiology Centre, the University of Tartu, the European Astrobiology Campus and the Nordic Network of Astrobiology and supported by European Union's European Regional Development Fund and Estonia.

The last decade has seen a dramatic increase in small satellite missions for commercial, public, and government intelligence applications. Given the rapid commercialization of constellation-driven services in Earth Observation, situational domain awareness, communications including machine-to-machine interface, exploration etc., small satellites represent an enabling technology for a large growth market generating truly Big Data. Examples of modern sensors that can generate very large amounts of data are optical sensing, hyperspectral, Synthetic Aperture Radar (SAR), and Infrared imaging. Traditional handling and downloading of Big Data from space requires a large onboard mass storage and high bandwidth downlink with a trend towards optical links. Many missions and applications can benefit significantly from onboard cloud computing similarly to Earth-based cloud services. Hence, enabling space systems to provide near real-time data and enable low latency distribution of critical and time sensitive information to users. In addition, the downlink capability can be more effectively utilized by applying more onboard processing to reduce the data and create high value information products. This paper discusses current implementations and roadmap for leveraging high performance computing tools and methods on small satellites with radiation tolerant hardware. This includes runtime analysis with benchmarks of convolutional neural networks and matrix multiplications using industry standard tools (e.g., TensorFlow and PlaidML). In addition, a 1/2 CubeSat volume unit (0.5U) (10 x 10 x 5 cm(3)) cloud computing solution, called SpaceCloud (TM) iX5100 based on AMD 28 nm APU technology is presented as an example of heterogeneous computer solution. An evaluation of the AMD 14 nm Ryzen APU is presented as a candidate for future advanced onboard processing for space vehicles.

Cloud computing is relatively a new technique to host and use the services and applications from the internet. Although it offers a multitude of advantages like scalability, low operating cost, accessibility and maintainability, etc., they are often not utilized to the fullest due to the lack of timeliness property associated with the cloud. Cloud services are mainly designed to maximize throughput and utilization of resources and hence incorporating predictable execution time properties in to the cloud is arduous. Nevertheless, cloud still remains a highly attractive platform for hosting real-Time applications and services owing to features like elasticity, multi-Tenancy, ability to survive hardware failures, virtualization support and abstraction layer support which provides flexibility and portability. In order for real-Time safety-critical applications to exploit the potential of cloud computing, it is essential to ensure the predictable real-Time behavior of cloud services. In this paper, we perform a systematic mapping study on real-Time cloud services to identify the current research directions and potential research gaps. Our study focuses on analyzing the current architectures and software techniques that are available at present to incorporate real-Time property of the cloud services. We also aim at investigating the current challenges involved in realizing a predictable real-Time behavior of cloud services. 

Selecting the correct file system is critical for space applications where risks are present. This study systematically maps and tests Ext4 versus ZFS for onboard data processing on the iX10-100 and iX5-100 payload processors. The test sets are presented along with results on several performance metrics. The conclusion is that both ZFS and Ext4 are useful, but based on certain considerations of onboard data processing, Ext4 is better than the other.

This thesis investigates the efficacy of heterogeneous computing architectures in real-time systems.The goals of the thesis are twofold. First, to investigate various characteristics of the Heterogeneous System Architectures (HSA) compliant reference platforms focusing on computing performance and power consumption. The investigation is focused on the new technologies that could boost on-board data processing systems in satellites and spacecraft. Second, to enhance the usage of the heterogeneous processing units by introducing a technique for static allocation of parallel segments of tasks.

The investigation and experimental evaluation show that our method of GPU allocation for the parallel segments of tasks is more energy efficient compared to any other studied allocation. The investigation is conducted under different types of environments, such as process-level isolated environment, different software stacks, including kernels, and various task set scenarios. The evaluation results indicate that a balanced use of heterogeneous processing units (CPU and GPU) could improve schedulability of task sets up to 90% with the proposed allocation technique.

Autonomous driving is one of the main challenges of modern cars. Computer visions and intelligent on-board decision making are crucial in autonomous driving and require heterogeneous processors with high computing capability under low power consumption constraints. The progress of parallel computing using heterogeneous processing units is further supported by software frameworks like OpenCL, OpenMP, CUDA, and C++AMP. These frameworks allow the allocation of parallel computation on different compute resources. This, however, creates a difficulty in allocating the right computation segments to the right processing units in such a way that the complete system meets all its timing requirements. In this paper, we consider pre-runtime static allocations of parallel tasks to perform their execution either sequentially on CPU or in parallel using a GPU. This allows for improving any unbalanced use of GPU accelerators in a heterogeneous environment. By performing several heuristic algorithms, we show that the overuse of accelerators results in a bottle-neck of the entire system execution. The experimental results show that our allocation schemes that target a balanced use of GPU improve the system schedulability up to 90%.

In recent years, commercial exploitation of small satellites and CubeSats has rapidly increased. Time to market of processed customer data products is becoming an important differentiator between solution providers and satellite constellation operators. Timely and accurate data dissemination is the key to success in the commercial usage of small satellite constellations which is ultimately dependent on a high degree of autonomous fleet management and automated decision support. The traditional way for disseminating data is limited by on the communication capability of the satellite and the ground terminal availability. Even though cloud computing solutions on the ground offer high analytical performance, getting the data from the space infrastructure to the ground servers poses a bottleneck of data analysis and distribution. On the other hand, adopting advanced and intelligent algorithms onboard offers the ability of autonomy, tasking of operations, and fast customer generation of low latency conclusions, or even real-time communication with assets on the ground or other sensors in a multi-sensor configuration. In this paper, the advantages of intelligent onboard processing using advanced algorithms for Heterogeneous System Architecture (HSA) compliant onboard data processing systems are explored. The onboard data processing architecture is designed to handle a large amount of high-speed streaming data and provides hardware redundancy to be qualified for the space mission application domain. We conduct an experimental study to evaluate the performance analysis by using image recognition algorithms based on an open source intelligent machine library 'MIOpen' and an open standard 'OpenVX'. OpenVX is a cross-platform computer vision library.

The contemporary processors are unable to meet the increasing data-intensive and computation-demanding requirements in autonomous vehicle software applications. Recently, the new Heterogeneous System Architecture (HSA) has emerged as a promising solution to meet these requirements. The HSA reduces the latency of data exchange between the compute units and cache-coherent shared memory, which is not supported by the non-HSA compliant heterogeneous platforms with acceleration support. The main goal of the paper is to investigate the performance gain by the HSA and conduct a comparative evaluation of the HSA and non-HSA compliant heterogeneous platforms. The paper aims at evaluating these platforms by using two computation-intensive software functions in autonomous vehicles, namely the object detection and vehicle movement. In order to achieve this goal, the CUDA-accelerated source code of the functions is ported from a non-HSA compliant heterogeneous platform to the HSA platform. In this regard, the paper presents the architecture of a proof-of-concept prototype and provides evaluation using the prototype.

Radiation effects research is crucial as it defines risk to both human bodies and spacecraft. Employing radiation-hardened products is one way to mitigate radiation effects on in-orbit systems. However, radiation effects prohibit most of the state-of-the-art commercial off-the-shelf (COTS) technologies from use in space. Furthermore, radiation effects on software components are less studied compared to hardware components. In this work, we introduce a simulation tool that analyzes the impact of radiation effects on schedulability of task sets executing on COTS system-on-chip (SoC) platforms in the in-orbit systems. In order to provide a meaningful verification environment, single-event effects (SEEs) are introduced as aleatory disturbances characterized by probability distribution of occurrence using their predefined models. The tool supports interoperability with several other tools as it uses the extensible markup language (XML) model files for input and output, i.e., for importing input task sets and radiation effects and exporting the simulation results.

Autonomous vehicular systems require computer vision and intelligent on-board decision making functionalities that include a mix of sequential and parallel workloads. The execution times of the workloads and power consumption in these functionalities can be lowered by utilizing the accelerators (e.g., GPU) instead of running the workloads entirely on the host processing units (CPU). However, allocating all the parallelizable workload to accelerators can create a computation bottleneck in the accelerators that, in turn, can have an adverse effect on schedulability of the systems. This paper presents a novel framework that can allocate the accelerate-intensive workloads to the accelerators as well as to the non-accelerated host processing units. Within the context of this framework, the paper introduces five offloading techniques to mitigate the accelerator-intensive workloads by utilizing excess capacity of non-accelerated processing units under dynamic scheduling in CPU-GPU heterogeneous processors. The proposed techniques are evaluated using simulation experiments. The evaluation results indicate that one of the proposed techniques can achieve up to 16% improvement in schedulability of the task sets compared to the traditional non-offloading technique.

On-board data processing is one of the prior on-orbit activities that improves the performance capability of in-orbit space systems such as deep-space exploration, earth and atmospheric observation satellites, and CubeSat constellations. However, on-board data processing encounters higher energy consumption compared to traditional on-board space systems. This is because the traditional space systems employ simple processing units such as single-core microprocessors as the systems do not require heavy data processing. Moreover, solving the radiation hardness problem is crucial in space, and adopting a new processing unit is challenging.

In this paper, we consider a Graphics Processing Unit (GPU) accelerated in-orbit space system for on-board data processing. According to prior works, there exist radiation-tolerant GPU, and the computing capability of systems is improved by using heterogeneous computing method. We conduct experimental observations of energy consumption and computing potential using this heterogeneous computing method in our GPU accelerated in-orbit space systems.The results show that the proper use of GPU increases computing potential with 10-140 times and consumes between 8-130 times less energy. Furthermore, the entire task system consumes 10-65% of less energy compared to the traditional use of processing units.

The technological advancements make the intelligent on-board data processing possible on a small scale of satellites and deep-space exploration spacecraft such as CubeSats. However, the operation of satellites may fall into critical conditions when the on-board data processing interferes strongly to the basic operation functionalities of satellites. In order to avoid these issues, there exist techniques such as isolation, partitioning, and virtualization. In this paper, we present an experimental study of isolation of on-board payload data processing from the basic operations of satellites using Docker. Docker is a leading technology in process level isolation as well as continuous integration and continuous deployment (CI/CD) method. This study continues with the prior study on heterogeneous computing method, which improves the schedulability of the entire system up to 90%. Based on this heterogeneous computing method, the comparison study has been conducted between the non-isolated and isolated environments.

On-board data processing is one of the prior on-orbit activities that it improves the performance capability of in-orbit space systems such as deep-space exploration, earth and atmospheric observation satellites, and CubeSat constellations. However, on-board data processing encounters with higher energy consumption compared to traditional space systems. Because traditional space systems employ simple processing units such as micro-controllers or a single-core processor as the systems require no heavy data processing on orbit. Moreover, solving the radiation hardness problem is crucial in space and adopting a new processing unit is challenging.

In this paper, we consider a GPU accelerated real-time system for on-board data processing. According to prior works, there exist radiation-tolerant GPU, and the computing capability of systems is improved by using heterogeneous computing method. We conduct experimental observations of power consumption and computing potential using this heterogeneous computing method in our GPU accelerated real-time system.The results show that the proper use of GPU increases computing potential with 10-140 times and consumes between 8-130 times less energy. Furthermore, the entire task system consumes 10-65% of less energy compared to the traditional use of processing units.