The article was originally published with attribution and spelling errors tied to two of its authors. The previously incorrect name, Alankritaa Mrigatshi, has been corrected to Alankrita Isha Mrigakshi and should be reflected as such in all versions of the article. The author’s affiliation details and email address have also been updated. In addition, the last name of Ingo Reinaecker has been corrected to Rienäcker, along with the corresponding update to the author’s email address. The original article has been corrected.

The increasing importance of Vision-Based Navigation (VBN) algorithms in space missions raises numerous challenges in ensuring their reliability and operational robustness. Sensor faults can lead to inaccurate outputs from navigation algorithms or even complete data processing faults, potentially compromising mission objectives. Artificial Intelligence (AI) offers a powerful solution for detecting such faults, overcoming many of the limitations associated with traditional fault detection methods. However, the primary obstacle to the adoption of AI in this context is the lack of sufficient and representative datasets containing faulty image data. This study addresses these challenges by focusing on an interplanetary exploration mission scenario. A comprehensive analysis of potential fault cases in camera sensors used within the VBN pipeline is presented. The causes and effects of these faults are systematically characterized, including their impact on image quality and navigation algorithm performance. To support this analysis, a simulation framework is introduced to recreate faulty conditions in synthetically generated images, enabling a systematic and controlled reproduction of faulty data. The resulting dataset of fault-injected images provides a valuable tool for training and testing AI-based fault detection algorithms. The final link to the dataset will be added after an embargo period. For peer-reviewers, this private link¹ is available.

Optical Communication Terminals (OCTs) integrate photonic and electronic technologies to enable high-speed, secure, and interference-free space communication. Deployment across multiple orbital regimes exposes these heterogeneous systems to diverse radiation environments, each presenting distinct failure mechanisms that challenge conventional Radiation Hardness Assurance (RHA) approaches, particularly for cost-constrained Small Satellite (SmallSat)-class missions where economic feasibility must be balanced against radiation hardness requirements. This paper presents a multi-orbit radiation effects investigation for photonic-electronic architectures by characterizing environment-specific vulnerabilities and failure modes across Sun-Synchronous Orbit (SSO), Low Earth Orbit (LEO), and Middle Earth Orbit (MEO). SPENVISbased environment analysis shows that SSO presents a mild radiation environment with Total Ionizing Dose (TID) below 1 krad(Silicon (Si)) per year at 4 mm Aluminium (Al) shielding and low charged particle flux. LEO is dominated by proton-induced Single Event Effects (SEEs) with moderate TID, and MEO exhibits TID exceeding 30 krad(Si) per year at the same shielding level, together with relatively higher heavy-ion fluxes that drive SEE rates. Technology-level analysis reveals fundamental differences between photonic and electronic subsystems. Photonics exhibits predominantly parametric degradation that can be mitigated through margin allocation, whereas electronics remains susceptible to discrete, stochastic failure modes requiring active mitigation. These findings motivate reconfigurable architectures leveraging Field Programmable Gate Arrays (FPGAs), radiation-tolerant photonic subsystems,Wide-Bandgap (WBG) power semiconductors, and emerging Non-Volatile Memories (NVMs) to enable component-level commonality across orbital regimes. Additionally, photonic devices require parametric end-of-life characterization with explicit translation to link budget margins, while electronic systems require mission-tailored mitigation strategies and system-level testing.

Understanding and monitoring the space environment is of great importance for the design and development of space avionics. This is especially critical when employing radiation-sensitive commercial-off-the-shelf (COTS) components in space systems. This work presents the design, validation, and characterization of the SpaceRadMon-NG radiation monitoring payload and its sensors, a modular system built around such components. The study covers the payload configuration and preparations for the radiation effects during the in orbit flight experiment (RADIOX) mission, part of the SYNDEO-1 CubeSat. Unique methodologies were applied for radiation qualification, system-level validation and sensor characterization to ensure reliable operation in space. The payload features enhanced capabilities compared to the previous version, including improved resolution, power efficiency, and system modularity. Mechanical and radiation tests confirmed system robustness, and a cross-section smaller than 8.58ċ 10-12 cm2 was determined at a 95% confidence level. Sensor performance was excellent, with relative errors of 0.65% for the COTS static random access memory and 0.41% for the floating gate dosimeter (FGDOS) compared to reference devices. A novel FGDOS characterization under simultaneous temperature cycling and irradiation confirmed the stability of its temperature coefficient and identified an effective compensation method using a radiation-insensitive reference sensor. This compensation approach can be extended to other radiation-sensitive components in space. With its successful validation in space-representative environments, the payload is ready for in-orbit demonstration, where it will measure the total ionizing dose and high energy hadron fluence in low Earth orbit.

In this paper, a comprehensive review of the evolving engineering and testing methodologies for radiation hardness assurance (RHA) in commercial-off-the-shelf (COTS) based space avionics, with a focus on recent trends and future directions is provided. The increasing reliance of space engineering on COTS has prompted a shift in RHA strategies, reflecting both technological advances and the complex radiation environments faced by modern space systems. Emphasis is placed on the interplay between traditional RHA frameworks and the integration of state-of-the-art design principles, fault-tolerant architectures and testing approaches. The review highlights how evolving system requirements and accelerated development cycles have influenced radiation testing practices and risk mitigation techniques. Examples are presented of enhancing reliability under radiation exposure, including reconfigurable systems, agile engineering processes and system-level validation. Emerging applications, including intelligent onboard systems and distributed satellite networks, are discussed with attention to their unique challenges and opportunities in RHA. The review concludes with a perspective on the critical gaps and future needs to advance RHA practices in support of increasingly complex and resource-constrained space missions.

Traditional anomaly detection techniques onboard satellites are based on reliable, yet limited, thresholding mechanisms which are designed to monitor univariate signals and trigger recovery actions according to specific European Cooperation for Space Standardization (ECSS) standards. However, Artificial Intelligence-based Fault Detection, Isolation and Recovery (FDIR) solutions have recently raised with the prospect to overcome the limitations of these standard methods, expanding the range of detectable failures and improving response times. This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft for the exploration of Small Solar System Bodies (SSSB), leveraging a multi-channel Convolutional Neural Network (CNN) to perform multi-target classification and independently detect faults in the sensors. Significant attention has been dedicated to ensuring the compatibility of the algorithm within the onboard FDIR system, representing a step forward to the in-orbit validation of a technology that remains experimental until its robustness is thoroughly proven. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level. The detection performances and the capability of the algorithm in reaction triggering are evaluated employing a set of custom-defined detection and system metrics, showing the outstanding performances of the algorithm in performing its FDIR task.

RISC-V-based fault-tolerant system-on-chip (SoC) designs are critical for the new generation of automotive and space SoC architectures. However, reliability assessment requires characterization under controlled radiation doses to accurately quantify the fault tolerance of the fabricated designs. This work analyzes the Trikarenos design, an SoC implemented in TSMC 28 nm, for single event upset (SEU) vulnerability under atmospheric neutron and 200-MeV proton radiation, comparing these results to simulation-based fault injection. All faults in error correction codes (ECCs) protected memory are corrected by a scrubber, showing an estimated cross section per bit of up to 1.09 × 10^-14 cm²bit⁻¹. Furthermore, the triple-core lockstep (TCLS) mechanism implemented in Trikarenos is validated and is shown to correct errors affecting a cross section up to 3.23 × 10^-11 cm², with the remaining uncorrectable vulnerability below 5.36 × 10^-12 cm². When augmenting the experimental analysis of fabricated chips with gate-level fault injection in simulation, 99.10% of injections into the SoC produced correct results, while 100% of injections in the TCLS-protected cores were handled correctly. With 12.28% of all injected faults leading to a TCLS recovery, this indicates an approximate effective flip-flop (FF) cross section of up to 1.28 ×10^-14 cm²/FF.

The radiation environment in space can pose a serious risk to both humans and space systems. Widespread and continuous monitoring of this environment is essential to mitigate risks associated with radiation exposure. Miniaturization and use of commercial-off-the-shelf components have enabled significant advances in space technology. These trends can be leveraged to develop innovative radiation sensing and monitoring technologies. However, dosimeters that can effectively measure radiation levels while minimizing their impact on size, power, mass, and cost are required. Floating gate dosimeters (FGDOSs) possess these characteristics, but rigorous testing is needed to ensure their accuracy in spacecraft applications. In this study, we conducted an extensive characterization campaign for an FGDOS chip using a proton beam, increasing the available information on the sensor. The behavior of the dosimeter with respect to resolution, dose rate, beam energy, total ionizing dose (TID), power consumption, annealing, temperature, and single-event effects (SEEs) was experimentally studied. Notably, we observed a previously unseen phenomenon, which we termed 'frequency surge' (FS). This phenomenon is likely to have implications for the dosimeter's performance under real spacecraft conditions. Our findings show that the dosimeter is able to combine small power consumption with high dose resolution but also highlight the need for testing against other radiation source types and intensities.

This paper evaluates the Single Event Upset (SEU) susceptibility of the NOEL-V processor, a novel and highly modular Intellectual Property (IP) Core by Cobham Gaisler on the Xilinx Kintex Ultrascale SRAM FPGA. The processor is based on the promising RISC-V architecture, an open source Instruction Set Architecture (ISA) that is quickly rising in popularity. In order to characterize the performance of the NOEL-V IP Core in the space radiation environment, the KCU105 development board is used as Device Under Test (DUT) and irradiated with medium and high energy protons. Thanks to the NOEL-V configurability, several versions of the NOEL-V were tested and microarchitectural differences could be exposed. The biggest influence on user logic upsets is observed to be related to the use of an operating system. For a single-core, high-performance configuration, a foreseen in-orbit failure rate of one failure every 395 days is found for for a 51.6° circular orbit at 420 km altitude. Findings indicate that the NOEL-V processor, with the implementation of targeted fault tolerant measures, can be a viable choice for space missions even as soft-core in SRAM FPGA. Due to its modularity, the processor can be used for a multitude of mission types ranging from high performance general-purpose to low-end microcontroller applications. Error Detection And Correction, which is not available in open source versions, will be needed to protect user memory and make sure upsets in caches and Configuration RAM (CRAM) do not lead to a failure of the processor.

This paper describes the work carried out to extend the NOEL-V platform to include data-level parallelism (DLP) by implementing an integer subset of the RISC-V Vector Extension. The performance and resource utilization efficiency of the resulting vector processor for different levels of DLP (i.e., number of lanes) have been compared to the baseline scalar processor on a Xilinx Kintex Ultrascale field-programmable gate array, employing typical kernels for compute-intensive applications. The role of the memory subsystem has also been investigated, comparing the results obtained with a low-latency and a high-latency main memory. The results show that the speed-up due to the use of the vector pipeline increases with the number of lanes in the vector processor, achieving up to 23.0× the performance of the scalar processor with only 4.3× the resources of the baseline scalar processor. Using an implementation with 32 lanes increases performance even for problem sizes larger than the number of lanes, achieving up to more than 11.7× the performance of the scalar processor with just 1.9× its resource utilization for 128 × 128 matrix multiplications. This work proves that implementations of the selected subset are easily scalable and fit for small-processor implementations in highly constrained space embedded systems.

This chapter provides an overview of the command and data handling system (CDHS) in small satellites and CubeSats. The chapter presents first analysis of radiation effects, specifically targeted at this subsystem, to justify components and architecture choices. Improvements in radiation testing strategies are also presented, specifically for small satellites. State-of-the-art components are then presented, providing an overview of the current market and the most common architectures. An overview of past and current missions is also presented, providing a clear mapping of the presented state-of-the-art components and architectures to guide future designs. High-level design considerations are also presented to help the reader follow some of the current trends in the sector. This chapter, overall, aims at presenting the most common approaches for the CDHS system and comparing this with traditional satellites, showing where the main differences lay with component selection and testing strategies being the fundamental points driving the architecture choices.

This paper introduces an adaptive Convolutional Neural Network (CNN)-based Unscented Kalman Filter for the pose estimation of uncooperative spacecraft. The validation is carried out at Stanford's robotic Testbed for Rendezvous and Optical Navigation on the Satellite Hardware-In-the-loop Rendezvous Trajectories (SHIRT) dataset, which simulates vision-based rendezvous trajectories of a servicer spacecraft to PRISMA's Tango spacecraft. The proposed navigation system is stress-tested on synthetic as well as realistic lab imagery by simulating space-like illumination conditions on-ground. The validation is performed at different levels of the navigation system by first training and testing the adopted CNN on SPEED+, Stanford's spacecraft pose estimation dataset with specific emphasis on domain shift between a synthetic domain and an Hardware-In-the-Loop domain. A novel data augmentation scheme based on light randomization is proposed to improve the CNN robustness under adverse viewing conditions, reaching centimeter-level and 10 degree-level pose errors in 80% of the SPEED+ lab images. Next, the entire navigation system is tested on the SHIRT dataset. Results indicate that the inclusion of a new scheme to adaptively scale the heatmaps-based measurement error covariance based on filter innovations improves filter robustness by returning centimeter-level position errors and moderate attitude accuracies, suggesting that a proper representation of the measurements uncertainty combined with an adaptive measurement error covariance is key in improving the navigation robustness.

The state of Mars' present day atmosphere is integral to forming a complete understanding of its climate, including the possible emergence and evolution of biological life. Investigating atmospheric characteristics at various scales is essential for enabling an effective, holistic understanding of the Martian climate, surface environment and habitability. Additionally, the ionizing radiation environment is one of the main factors impacting surface habitability and atmospheric loss. Long-term exposure to ionizing radiation poses concerns for future human exploration. However, given the sparse and incomplete nature of present environmental datasets, creating sufficiently accurate, detailed and complete models of atmospheric processes and interactions is not feasible. Till now, most investigations of Mars' atmosphere have been limited to orbiters and solitary landers or rovers, which leaves a considerable gap in the ability to acquire datasets with satisfactory surface coverage and spatio-temporal resolution. This paper describes various aspects of a novel science & exploration mission equipped with payloads aimed at the acquisition of these much-needed datasets. The proposed Ultimate Tumbleweed Mission will consist of a swarm of wind-driven mobile impactors with the ability to morph into measurement stations, so as to explore the Martian surface and collect measurements of near-surface meteorological parameters. Following a brief description of the notional mission concept and spacecraft design, we describe the scientific value that can be returned during the mobile and stationary phases of said mission. Datasets from a networked set of Tumbleweed Measurement Stations would enable the refinement of Martian climate and weather models. Through various in-situ measurements, micro- and meso-scale atmospheric phenomena and processes involving interaction between water, dust, and carbon dioxide can be constrained and studied in order to fill existing knowledge gaps. The swarm would help in investigating the ionizing radiation environment on Mars by acquiring direct measurements of flux, absorbed dose, spectral distribution, and angular distribution of various high-energy particles and their secondaries. Atmospheric modulation of incident cosmic rays and solar energetic particles, the nature and abundance of secondary particles, exposures and hazards for electronic and biological systems, and the shielding properties of the Martian regolith as well as natural landscape can be understood further, in order to prepare for future human and robotic exploration missions to the Red Planet. Preliminary formulation of the science case - including a set of candidate instruments - indicates that a network of Tumbleweed Measurement Stations can deliver holistic in-situ characterization of various near-surface atmospheric phenomena as well as the ionizing radiation environment on Mars.

The Earth-Moon system is constantly bombarded by meteoroids of different size and impact speed. Observation of the impacts on the Moon can enable thorough characterization of the Lunar meteoroid flux, which is similar to that of the Earth. While Earth-based Lunar observations are restricted by weather, geometric and illumination conditions, a Lunar-based observation campaign can improve the detection rate and, when observing the Lunar far side, complement in both space and time the observations taken from Earth. The Lunar Meteoroid Impact Observer (LUMIO), one of the two winning concepts of the ESA SysNova Lunar CubeSats for Exploration challenge, is a mission designed to observe, quantify, and characterize the micro-meteoroid impacts on the Lunar far side. It is based on a 12U CubeSat that carries the LUMIO-Cam, a custom-designed optical instrument capable of detecting light flashes in the visible spectrum. The spacecraft is placed on a halo orbit about the Earth–Moon L2 point, where permanent full-disk observation of the Lunar far side can be performed with excellent quality, given the absence of Earth background noise. After passing Phase 0 and an independent feasibility study in the ESA Concurrent Design Facility, the mission has successfully completed its Phase A in March 2021. Although the Phase 0 design of the LUMIO spacecraft was assessed as feasible by the ESA CDF study, a number of critical issues were identified, which have been tackled by the Phase A design. The paper presents the outcome of this Phase A design effort for the LUMIO spacecraft. Particularly relevant changes or updates in the spacecraft design include: a consolidated design of the LUMIO-Cam, with longer baffle for straylight protection; a set of ADCS sensors and actuators with increased redundancy; a combination of Direct-to-Earth communication and inter-satellite link with a mothership in Lunar orbit; use of Earth ranging to complement and validate the current innovative autonomous navigation strategy based on optical observations of the Moon by means of the LUMIO-Cam; re-assessment of the COTS components selection for the power and propulsion systems.

The Lunar Meteoroid Impacts Observer (LUMIO) is a CubeSat mission to observe, quantify, and characterize the meteoroid impacts on the lunar farside by detecting their flashes. This complements the knowledge gathered by Earth-based observations of the lunar nearside, thus synthesizing global information on the lunar meteoroid environment and contributing to the lunar situational awareness. The goal of LUMIO is to advance our current knowledge of meteoroid models in the solar system. In this work, we present the methodology devised to predict the scientific contribution of LUMIO. Our approach relies on combined modeling and simulation of payload, orbit, and environment. The analyses carried out have been used to drive the design of the LUMIO mission and its payload, the LUMIO-Cam. A payload radiometric model is derived and exploited to assess the quality of the scientific measurements. A dedicated study about straylight rejection is carried out to assess how straylight noise affects LUMIO-Cam measurements. Our results indicate that a 150 mm baffle grants good performance when the Sun angle is between 20° and 90°. Furthermore, the present-day LUMIO mission has the potential to detect more than 6000 impact flashes during the activity peak of the Geminids in 2024 in the range of the equivalent impact kinetic energy at Earth of [10 ⁻⁶,10 ⁻¹]kton TNT Equivalent. Compared to previous programmes, LUMIO could refine information and fill the knowledge gap about the meteoroid population in the ranges of the equivalent impact kinetic energy at Earth of [10 ⁻⁶,10 ⁻⁴]kton TNT Equivalent and [10 ⁻⁴,10 ⁻¹]kton TNT Equivalent, respectively.

The estimation of the relative pose of an inactive spacecraft by an active servicer spacecraft is a critical task for close-proximity operations, such as In-Orbit Servicing and Active Debris Removal. Among all the challenges, the lack of available space images of the inactive satellite makes the on-ground validation of current monocular camera-based navigation systems a challenging task, mostly due to the fact that standard Image Processing (IP) algorithms, which are usually tested on synthetic images, tend to fail when implemented in orbit. In response to this need to guarantee a reliable validation of pose estimation systems, this paper presents the on-ground validation of a Convolutional Neural Network (CNN)-based monocular pose estimation system on representative rendezvous scenarios recreated in ESA's GNC Rendezvous, Approach and Landing Simulator (GRALS) testbed. Special focus is given on solving the domain shift problem which characterizes CNNs trained on synthetic datasets when tested on more realistic imagery. The validation of the proposed system is ensured by the introduction of a calibration framework, which returns an accurate reference relative pose between the target spacecraft and the camera for each lab-generated image, allowing a comparative assessment at a pose estimation level. The VICON Tracker System is used together with two KUKA robotic arms to respectively track and control the trajectory of the monocular camera around a scaled 1:25 mockup of the Envisat spacecraft. After an overview of the facility, this work describes a novel data augmentation technique focused on texture randomization, aimed at improving the CNN robustness against previously unseen target textures. Despite the feature detection challenges under extreme brightness and illumination conditions, the results on the high exposure scenario show that the proposed system is capable of bridging the domain shift from synthetic to lab-generated images, returning accurate pose estimates for more than 50% of the rendezvous trajectory images despite the large domain gaps in target textures and illumination conditions.

Integrated circuits employed in space applications generally have very low-volume production and high performance requirements. Therefore, the adoption of Commercial-Off-The-Shelf (COTS) components and Third Party Intellectual Property cores (3PIPs) is of extreme interest to make system design, implementation and deployment cost-effective and viable w.r.t. performance. On the other hand, this design paradigm exposes the system to a number of security threats both at design-time and at runtime. In this paper, we discuss the security issues related to space applications mainly focusing on threats that come from the adoption of the well-known RISCV microprocessor. We highlight how Hardware Trojan horses (HTHs) and Microarchitectural Side-Channel Attacks (MSCAs) may compromise the overall system operation by either altering its nominal behavior or by stealing secret information. We discuss the security extensions provided by the RISC-V architecture as well as their limitations. The paper is concluded by an overview of the issues that are still open regarding the security of such microprocessor in the space domain.