On-board Processing Research Articles

Vision‐audio fusion SLAM in dynamic environments

AbstractMoving humans, agents, and subjects bring many challenges to robot self‐localisation and environment perception. To adapt to dynamic environments, SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles. However, these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots. In the current industrial environment, mobile robot collaboration scenario, the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms, but the distance estimation of sound sources is difficult. However, in the field of visual perception, the 3D structure information of the scene is relatively easy to obtain, but the recognition and segmentation of moving objects is more difficult. To address these problems, a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed, thereby eliminating the effect of dynamic obstacles on multi‐agent systems. Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Integration, qualification, and launch of the Mini-EUSO telescope on board the ISS

Mini-EUSO is a high-sensitivity imaging telescope that observes the Earth from the ISS in the near ultraviolet band (290 $$\div$$ 430 nm), through the nadir-facing, UV-transparent window in the Russian Zvezda module. The instrument, launched in 2019, has a field of view of 44 $$^{\circ }$$ , a spatial resolution on the Earth’s surface of 6.3 km and a temporal sampling rate of 2.5 microseconds. Thanks to its triggering and on-board processing, the telescope is capable of detecting UV emissions of cosmic, atmospheric, and terrestrial origin on different time scales, from a few microseconds up to tens of milliseconds. The optics is composed of two Fresnel lenses focusing light onto an array of 36 Hamamatsu Multi-Anode PhotoMultiplier Tubes, for a total of 2304 pixels. The telescope also contains two cameras in the near-infrared and visible, an 8-by-8 array of Silicon-PhotoMultipliers and a series of UV sensors to manage night-day transitions. The scientific objectives range from the observation of atmospheric phenomena [lightning, Transient Luminous Events (TLEs), ELVES], the study of meteoroids, the search of interstellar meteoroids and strange quark matter, mapping of the Earth’s nocturnal emissions in the ultraviolet range, and the search of cosmic rays with energy above 10 $$^{21}$$ eV. The instrument has been integrated and qualified in 2019, with the final tests in Baikonur prior to its launch. Operations involve periodic installation in the Zvezda module of the station with observations during the crew night time, with periodic downlink of data samples, with the full data being sent to the ground via pouches containing the data disks. Mission planning involves the selection of the optimal orbits to maximize the scientific return of the instrument. In this work, we will describe the various phases of construction, testing, and qualification prior to the launch and the in-flight operations of the instrument on board the ISS.

Air-to-air Micro Air Vehicle interceptor with an embedded mechanism and deep learning

Illegal flight of small and portable aerial vehicles, or the so-called Micro Air Vehicles (MAVs), has become a serious safety issue in most cities around the world. Most existing intercepting systems hardly detect them due to their size, or these systems are too expensive to install everywhere. This paper presents a novel solution to tackle this problem using another aerial vehicle that is capable of flying in close proximity to intruding MAVs, detecting, and intercepting them. This vehicle is embedded with an intercepting mechanism and a vision-based detector for autonomously tracking and intercepting the target. The tilted arm design installed with inverted motors and nets maximizes the chance of intercepting or even capturing intruding MAVs. A deep learning-based detector integrated with a Kalman filter tracker is proposed to enable real-time MAV detection and tracking using an onboard processing unit and a single camera. In this study, 9332 images of flying MAVs were used for training the pre-trained model. YOLOv4-tiny showed the best balance of accuracy and inference speed among the models chosen for the analysis, which obtained an average precision of 90.04% and 14 FPS in the evaluation. In addition, a control strategy that adapts the detected MAV size or its relative distance to the MAV interceptor in the velocity controller is developed to approach the intruding MAV steadily. The performance of the proposed system for aerial target tracking was validated in several flight tests. The results show that our vehicle can successfully detect, track, and intercept the moving target.

Instrumentation for Ionized Space Environments: New High Time Resolution Instrumental Modes of Mutual Impedance Experiments

AbstractMutual impedance experiments are in situ plasma diagnostic techniques for the identification of the plasma density and the electron temperature. Different versions of mutual impedance instruments were included in past and present space missions (e.g., Rosetta, BepiColombo, JUICE and Comet Interceptor). New versions are currently being devised to fit the strong mass, volume and power constraints on nanosatellite platforms for future multi‐point space missions. In this study, our goal is to define and validate two new instrumental modes (i.e., chirp and multi‐spectral modes) to improve the time resolution of the experiment with respect to typical mutual impedance instrumental modes (i.e., frequency sweep). Higher time resolution measurements are expected to simplify the integration of mutual impedance experiments onboard nanosatellite platforms by facilitating antenna sharing between different experiments. The investigation is performed both (a) numerically, using a 1D‐1V electrostatic full kinetic Vlasov‐Poisson model and, (b) experimentally, with laboratory tests using a vacuum chamber and a plasma source. From a plasma diagnostic point of view, we find that both the chirp and multi‐spectral modes provide measurements identical to the (reference) frequency sweep mode. From an instrumental point of view, multi‐spectral measurements are faster than frequency sweep measurements but they require larger amounts of onboard computing resources (i.e., larger power consumption). Chirp measurements, instead, outperform frequency sweep measurements both in terms of measurement duration (20 times faster) and onboard processor usage (20% less).

Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses.

Onboard Processing in Satellite Communications Using AI Accelerators

Satellite communication (SatCom) systems operations centers currently require high human intervention, which leads to increased operational expenditure (OPEX) and implicit latency in human action that causes degradation in the quality of service (QoS). Consequently, new SatCom systems leverage artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomy and control. Onboard processing for advanced AI/ML algorithms, especially deep learning algorithms, requires an improvement of several magnitudes in computing power compared to what is available with legacy, radiation-tolerant, space-grade processors in space vehicles today. The next generation of onboard AI/ML space processors will likely include a diverse landscape of heterogeneous systems. This manuscript identifies the key requirements for onboard AI/ML processing, defines a reference architecture, evaluates different use case scenarios, and assesses the hardware landscape for current and next-generation space AI processors.

Integration of a Real-Time CCSDS 410.0-B-32 Error-Correction Decoder on FPGA-Based RISC-V SoCs Using RISC-V Vector Extension

The Consultative Committee for Space Data Systems (CCSDS) recommends the use of short-block length Bose-Chaudhuri-Hocquenghem (BCH) and binary low-density parity-check (LDPC) codes. Despite the high error-correction capacity of non-binary low-density parity-check (NB-LDPC) codes, they have not yet been considered due to their high decoding complexity. In this paper, the feasibility of NB-LDPC coding for space telecommand link applications using a RISC-V soft-core processor plus a vector co-processor is demonstrated. The purpose of this work is to avoid the need for a dedicated decoder hardware, and thus the customized general-purpose processor which performs decoding can be reconfigured to perform other important on-board tasks. In this way, the logic utilization and power consumption can be reduced since more functionalities can be assumed by the on-board processor. The method of acceleration of a NB-LDPC decoder over GF(16) using the RISC-V vector extension is demonstrated, and a throughput of 8.48 kbps is achieved for the Forward-Backward implementation of the min-max decoding algorithm, which is compatible with the low-rate and mid-rate telecommand systems recommended by CCSDS.

Demand-Aware Onboard Payload Processor Management for High Throughput NGSO Satellite Systems

High-Throughput Satellite (HTS) systems with digital payload technology have been identified as a key enabler to support 5G/6G high-data connectivity with wider coverage area. The satellite community has extensively explored resource allocation methods to achieve this target. Typically, these methods do not consider the intrinsic architecture of the flexible satellite digital payload, which consists of multiple processors responsible for receiving, processing, and transmitting the signals. This paper presents a demand-aware onboard processor management scheme for broadband Non-Geostationary (NGSO) satellites. In this context, we formulate an optimization problem to minimize the number of active on-board processors while meeting the system constraints and user requirements. As the problem is non-convex, we solve it in two steps. First, we transform the problem into demand-driven bandwidth allocation while fixing the number of processors. Second, using the bandwidth allocation solution, we determine the required number of processors with two methods: 1) sequential optimization with the Branch & Bound method and 2) Bin Packing with Next Fit, First Fit, and Best Fit methods. Finally, we demonstrate the proposed methods with extensive numerical results. It is shown that the Branch & Bound, Best Fit, and First Fit methods manage the processors better than the Next Fit method. Furthermore, Branch & Bound requires fewer processors than the above methods.

Sensor and actuator integrated tooling systems

The advances in electronics, sensors, actuators, smart materials, computers, and communication technologies led to new and improved mechatronics-based smart tooling systems. This paper reviews the sensors and actuators used in tooling systems. The sensor principles to measure force, displacement, and temperature are explained. Actuators based on piezo, magnetorheological fluid, shape memory alloy, and magnetostrictive principles are discussed. Wireless and wire-based power supply and signal transmission techniques to stationary and rotating tools are described. The integration of sensors, actuators, power supply, and microcontrollers to various tooling systems used in boring, milling, turning, and grinding operations are presented along with their process monitoring and control applications in the industry. It is shown that the sensor-actuator tooling systems improve the accuracy, robustness, and productivity of machining which adds intelligence to the machine tools. The current challenges include widening the operating bandwidth of tooling systems, wireless power and high-speed data transmission, miniaturization of actuators and sensors, and onboard processing using embedded micro-controllers.

Determination of the parameters of the curvilinear movement of vehicles

Today, the creation of new vehicles is impossible without computer modeling. Modern vehicles have numerous electronic systems that increase its traffic safety, comfort, and route planning management. Reliable operation of all systems is achieved by using an accurate dynamic model of the vehicle to assess safety and the possibility of movement along specified trajectories. Creating an accurate dynamic vehicle model requires correct initial parameters. When modeling the curvilinear movement of motor vehicles, such parameters as the coefficients of resistance to lateral sliding of tires and the angles of sliding of axles, which are mainly determined on test benches in laboratory conditions, are important to obtain the adequacy of the object under study. However, quite often it is impossible to obtain reliable information from tire manufacturers or testing laboratories, which ultimately affects the adequacy of modeling the parameters of vehicle movement. A method is proposed for determining the design parameters of tires using primary information sensors, which are sensors of angular and linear velocities, linear accelerations, rotation angles of controlled wheels and an on-board information processing device. The advantages of this method are that it allows real-time measurement of the angles of the axles and the coefficients of resistance to the lateral withdrawal of the axles when the vehicle is moving at an arbitrary speed along a trajectory of variable curvature. This method can be used to create an accurate dynamic model in the study of the controllability and stability of the vehicle, as well as an integral part of the vehicle dynamics control system.

Sidcof - inspection and surveillance of penstocks

The SIDCOF (Innovative Forced Pipe Diagnostic System) project, carried out within the framework of FUI 22, aims to develop innovative NDT methods and tools in three batches allowing:  In situ monitoring of penstocks  A robotic inspection of the inside of the pipes to avoid human intervention in a complex environment  A software platform to process data and help the operator to make decisions This paper will focus on the 1st batch. The work has enabled the development of sensor networks permanently installed directly on the pipe for monitoring installations and monitoring in real time the damage caused by rockfall, landslides and water leaks. Two dedicated systems have been developed and validated under real operating conditions. The first is based on a network of connected autonomous extensometers, each module of which operates wirelessly and is energy independent. It is able to follow the deformations of pipes in risk areas following landslides. This tool greatly simplifies the implementation of sensors and automatically routes the data produced by these sensors to the cloud. A totally IIOT product. The second uses a network of acoustic emission sensors to detect and locate (with an accuracy of the order of a few meters) in real time falling rocks, and the appearance of leaks on the pipe. The tool developed is a dedicated multi-channel system for on-board electronics and processing. The data processed is transferred automatically to the client cloud via the 4G network. The SIDCOF project is led by SETEC with CETIM, SOREA, ETE and Sub C Marine as partners, funded by BPI France, TENERRDIS, INDURA, Région Occitanie, Région Auvergne Rhône Alpes.

Information characteristics of an orbital side-scan synthetic aperture radar

Ratios are given for estimating the amount of information in the channels of sensing and data transmission of the orbital synthetic aperture radars (SAR) based on information-theoretic and statistical methods. They are presented in terms of the indicators characterizing the amount of information received about the state of the object of observation. It is shown that the sensing channel should use significantly redundant information in relation to the final result of radar observation. On the example of the SAR of the “Venera-15” and “Venera-16” spacecrafts, the specific conditions of an orbital radar imaging are illustrated, and its informational characteristics are given for both methods of transmitting observation data. The qualitative consideration carried out in the paper makes it possible to estimate the potentially achievable reduction in the amount of information in the data transmission channel when using on-board SAR signal processing under specific radar imaging conditions.

Digital Prototype Filter Alternatives for Processing Frequency-Stacked Mobile Sub-Bands Deploying a Single ADC for Beamforming Satellites

This paper presents a two stage approach for the processing of frequency-stacked mobile sub-bands. The frequency stacking is performed in the analogue domain to enable the use of a wideband ADC, instead of employing multiple narrowband ADCs, to support multiple antenna elements for digital satellite beamforming. This analogue front end provides a common broadband digital interface to the on-board processor and can be configured to support multiple satellite missions, reducing the cost of commissioning a digital processor for individual satellite missions. This paper proposes a framework on the specification of digital prototype filter for the Analysis of frequency-stacked mobile sub-bands. The computational complexity of the Analysis operation, with two digital filter alternatives, are evaluated. A series of results, taken from our European Space Agency sponsored project, are presented here to demonstrate the applicability of the proposed two stage approach, reporting on the savings in power consumption when an Nth-band all-pass based recursive filter having an Infinite Impulse Response is used as the digital prototype filter.

Autonomous Satellite Rendezvous and Proximity Operations with Time-Constrained Sub-Optimal Model Predictive Control

This paper presents a time-constrained model predictive control strategy for the 6 degree-of-freedom (6DOF) autonomous rendezvous and docking problem between a controllable “deputy” spacecraft and an uncontrollable “chief” spacecraft. The control strategy accounts for computational time constraints due to limited onboard processing speed. The translational dynamics model is derived from the Clohessy-Wiltshire equations and the angular dynamics are modeled on gas jet actuation about the deputy's center of mass. Simulation results are shown to achieve the docking configuration under computational time constraints by limiting the number of allowed algorithm iterations when computing each input. Specifically, we show that upwards of 90% of computations can be eliminated from a model predictive control implementation without significantly harming control performance.

Practical use of on-board fuel metering systems of diesel locomotives

Introduction. On-board fuel metering systems for diesel locomotives created by different manufacturers differ both in the composition of the sensors used and in the software for processing the registration data. This indicates that the pursuit of better efficiency for these systems is still ongoing and is an urgent problem.Materials and methods. The article discusses the problems of determining the fuel consumption according to the data of on-board fuel metering systems for diesel locomotives, associated with various methods for determining the moments of measuring the fuel amount in the tank. The possibility of estimating the influence of the number of position switches of the locomotive throttle on the indicators of absolute and specific fuel consumption has been studied.Results. In the absence of distance and speed sensors in the on-board system, it is proposed to determine the moments of measurement of the fuel amount based on the analysis of changes in the signals of the fuel level sensors over time. The authors present an example of the practical implementation of this solution. The results of on-board systems data processing are also presented, showing that the effect of the spread in the number of throttle position switches observed in ordinary operation of diesel shunter on fuel consumption is comparable to the error in determining this value according to the on-board systems data. The implementation of algorithms in specialised software designed to process data from onboard systems that allow identifying the indicated influence is relevant only for cases of extreme (by an order of magnitude) difference in the number of switching throttle positions in work shifts.Discussion and conclusion. The efficiency of the practical use of information from the on-board systems for metering fuel consumption of a diesel locomotive is largely determined by the quality of specialised software. When developing and improving software, it is recommended to use the approaches discussed in the article.

Resolution and Frequency Effects on UAVs Semi-Direct Visual-Inertial Odometry (SVO) for Warehouse Logistics.

For the commercial sector, warehouses are becoming increasingly vital. Constant efforts are in progress to increase the efficiency of these facilities while reducing costs. The inventory part of the goods is a time-consuming task that impacts the company's revenue. This article presents an analysis of the performance of a state-of-the-art, visual-inertial odometry algorithm, SVO Pro Open, when varying the resolution and frequency of video streaming in an industrial environment. To perform efficiently this task, achieving an optimal system in terms of localization accuracy, robustness, and computational cost is necessary. Different resolutions are selected with a constant aspect ratio, and an accurate calibration for each resolution configuration is performed. A stable operating point in terms of robustness, accuracy of localization, and CPU utilization is found and the trends obtained are studied. To keep the system robust against sudden divergence, the feature loss factor extracted from optical sensors is analyzed. Innovative trends and translation errors on the order of a few tens of centimeters are achieved, allowing the system to navigate safely in the warehouse. The best result is obtained at a resolution of 636 × 600 px, where the localization errors (x, y, and z) are all under 0.25 m. In addition, the CPU (Central Processing Unit) usage of the onboard computer is kept below 60%, remaining usable for other relevant onboard processing tasks.

SegMarsViT: Lightweight Mars Terrain Segmentation Network for Autonomous Driving in Planetary Exploration

Planetary rover systems need to perform terrain segmentation to identify feasible driving areas and surround obstacles, which falls into the research area of semantic segmentation. Recently, deep learning (DL)-based methods were proposed and achieved great performance for semantic segmentation. However, due to the on-board processor platform’s strict comstraints on computational complexity and power consumption, existing DL approaches are almost impossible to be deployed on satellites under the burden of extensive computation and large model size. To fill this gap, this paper targeted studying effective and efficient Martian terrain segmentation solutions that are suitable for on-board satellites. In this article, we propose a lightweight ViT-based terrain segmentation method, namely, SegMarsViT. In the encoder part, the mobile vision transformer (MViT) block in the backbone extracts local–global spatial and captures multiscale contextual information concurrently. In the decoder part, the cross-scale feature fusion modules (CFF) further integrate hierarchical context information and the compact feature aggregation module (CFA) combines multi-level feature representation. Moreover, we evaluate the proposed method on three public datasets: AI4Mars, MSL-Seg, and S5Mars. Extensive experiments demonstrate that the proposed SegMarsViT was able to achieve 68.4%, 78.22%, and 67.28% mIoU on the AI4Mars-MSL, MSL-Seg, and S5Mars, respectively, under the speed of 69.52 FPS.

Exploiting domain transformation and deep learning for hand gesture recognition using a low-cost dataglove

Hand gesture recognition is one of the most widely explored areas under the human–computer interaction domain. Although various modalities of hand gesture recognition have been explored in the last three decades, in recent years, due to the availability of hardware and deep learning algorithms, hand gesture recognition research has attained renewed momentum. In this paper, we evaluate the effectiveness of a low-cost dataglove for classifying hand gestures in the light of deep learning. We have developed a cost-effective dataglove using five flex sensors, an inertial measurement unit, and a powerful microcontroller for onboard processing and wireless connectivity. We have collected data from 25 subjects for 24 static and 16 dynamic American sign language gestures for validating our system. Moreover, we proposed a novel Spatial Projection Image-based technique for dynamic hand gesture recognition. We also explored a parallel-path neural network architecture for handling multimodal data more effectively. Our method produced an F1-score of 82.19% for static gestures and 97.35% for dynamic gestures from a leave-one-out-cross-validation approach. Overall, this study demonstrates the promising performance of a generalized hand gesture recognition technique in hand gesture recognition. The dataset used in this work has been made publicly available.

Bimorph mirrors at synchrotron beamlines: from walking to flying

With brighter synchrotron sources, automated sample changers, and faster detectors, there is a strong scientific need for rapid and precise variation of the X-ray beam profile, rather than the “set and forget” operation of years past. Piezoelectric bimorph deformable mirrors already allow quick beam profile changes without the heat generation and wear of mechanical devices. Now, their early technological limitations – excessively constraining holders, progressive “junction effect” distortion, and communication bottlenecks with power supplies – are being overcome by a collaboration of scientists and engineers both in industry and at Diamond Light Source. A new generation of bimorph mirrors maintains a stable figure over extended periods of operation. Improved holders and flexible electrical connectors are greatly reducing the mechanical strain imparted to bimorphs, thereby improving their speed, accuracy, and stability. A more sophisticated high voltage power supply has on-board signal processing capacity, allowing large focusing changes within seconds and providing programmable time-varying voltage profiles to counteract piezoelectric creep. The communication between beamline systems and power supplies is being freed of bottlenecks and now runs stably up to 1 Hz. Early tests have already shown that bimorph mirrors can repeatedly switch the size of an X-ray beam in well under 10 seconds. Bimorph mirrors at synchrotron beamlines are now growing beyond the largely static operation of the past and gaining a new dynamism through development projects that are now well advanced. We report on how these endeavours will make it easier for beamlines to utilise the full potential of bimorph mirrors.

An Efficient Architecture and High-Throughput Implementation of CCSDS-123.0-B-2 Hybrid Entropy Coder Targeting Space-Grade SRAM FPGA Technology

Nowadays, hyperspectral imaging is recognized as cornerstone remote sensing technology. The explosive growth in image data volume and instrument data rates, compete with limited on-board storage resources and downlink bandwidth, making hyperspectral image data compression a mission critical on-board processing task. The Consultative Committee for Space Data Systems (CCSDS) extended the previous issue of the CCSDS-123.0 Recommended Standard for multi- and hyperspectral image compression to provide with Near-Lossless compression functionality. A key feature of the CCSDS-123.0-B-2 is the improved Hybrid Entropy Coder, which at low bit rates, provides substantially better compression performance than the Issue 1 entropy coders. In this paper, we introduce a high-throughput hardware implementation of the CCSDS-123.0-B-2 Hybrid Entropy Coder. The introduced architecture exploits the systolic design pattern to provide modularity and latency insensitivity in a deep and elastic pipeline achieving a constant throughput of 1 sample/cycle with a small FPGA resource footprint. This architecture is described in portable VHDL RTL and is implemented, validated and demonstrated on a commercially available Xilinx KCU105 development board hosting a Xilinx Kintex Ultrascale XCKU040 SRAM FPGA, and thus, is directly transferable to Xilinx Radiation Tolerant Kintex UltraScale XQRKU060 space-grade devices for space deployments. Moreover, state-of-the-art SpaceFibre (ECSS-E-ST-50-11C) serial link interface and test equipment were used in the validation platform to emulate an on-board deployment. The introduced CCSDS-123.0-B-2 Hybrid Entropy Encoder achieves a constant throughput performance of 305 MSamples/s. To the best of our knowledge, this is the first published fully-compliant architecture and high-throughput implementation of the CCSDS-123.0-B-2 Hybrid Entropy Coder, targeting space-grade FPGA technology.