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Commissioned in 2017 by the Italian Space Agency (ASI), the Sardinia Deep Space Antenna (SDSA) is currently able to operate in the X-band, supporting deep space and near Earth telemetry missions and scientific experiments. While sharing its infrastructure with the Sardinia Radio Telescope (SRT), the SDSA boasts its own dedicated control center and specific equipment. This paper outlines a new optical design and the radio frequency upgrades to enhance the SDSA’s capabilities for current and future tracking of deep space and near Earth missions. The proposed design aims to establish the SDSA as a state-of-the-art antenna in the field of space activities by capitalizing on its exceptional versatility. This includes its ability to accommodate new equipment and leverage the inherent characteristics of the SRT antenna, such as its active surface and frequency agility. A collaborative effort with ESA has resulted in a newly designed optical configuration enabling reception and transmission in the X, K, and Ka frequency bands. Specifically, the X and K frequency bands are allocated to near Earth radio communication (within a 2 million kilometer range). Additionally, the X- and Ka- bands configurations are designed for deep space operations, including the well-known triple-link technique for radio science missions beyond 2 million kilometers. To characterize the performance of the upgraded antenna, we present the electromagnetic simulations of the new optical design. We analyze the antenna radiation pattern across various downlink and uplink frequency bands and evaluate key parameters like gain-to-noise system temperature ( $G/T_{sys}$ ) and effective isotropic radiated power (EIRP). The simulations were conducted using the dedicated commercial software GRASP and CHAMP 3D by TICRA. The results of this study provide valuable information on the capability of the upgraded SDSA to enable a wide range of space communication and space science applications, making the SRT in its SDSA configuration one of the most equipped and technologically advanced ground segment facility.

Fixed-quality image compression is a coding paradigm where the tolerated introduced distortion is set by the user. This article proposes a novel fixed-quality compression method for remote sensing images. It is based on a neural architecture we have recently proposed for multirate satellite image compression. In this article, we show how to efficiently estimate the reconstruction quality using an appropriate statistical model. The performance of our approach is assessed and compared against recent fixed-quality coding techniques and standards in terms of accuracy and rate-distortion, as well as with recent machine learning compression methods in rate-distortion, showing competitive results. In particular, the proposed method does not introduce artifacts even when coding neighboring areas at different qualities.

This research presents a multi-objective dispatch (MOD) for energy storage systems (ESS) utilizing batteries and renewable energy resources (RES) in distribution network applications. This proposal employs a convex weighting-based approach. The exact MOD nonlinear programming model, which is non-convex due to the power balance constraint, is approximated using a second-order cone equivalent in order to ensure a global optimum, leveraging the convex properties of the objective functions and the conic equivalent in the complex-variable domain. The MOD, based on the linear combination of the objective functions via the weighting-based method, enables the construction of the optimal Pareto front, as each combination of the weighting factors generates a convex optimization sub-problem. The MOD analysis simultaneously considers the minimization of the expected grid operating costs with regard to energy purchasing at the substation terminals, the reduction of the operating costs associated with the RES and the ESS, and the minimization of the expected daily energy losses. This research makes two contributions: 1) the incorporation of the active and reactive power capabilities of the power electronic converters that interface with the ESS and the RES and 2) a robust analysis via convex optimization to address the uncertainties related to the expected daily generation and demand profiles. Numerical results obtained in the IEEE 33- and 85-bus test system confirm the effectiveness and robustness of the proposed MOD in comparison with the continuous genetic algorithm, the particle swarm optimizer, and the vortex search algorithm. In addition, the best metah-euristic technique was employed for constructing the Pareto front and comparing its performance against the proposed convex approach for the 33-bus grid. In the case of the 85-bus grid, battery power losses were included to demonstrate the effectiveness of this solution methodology in medium-scale distribution grids.INDEX TERMS Energy losses minimization, grid operating cost minimization, multi-objective dispatch, second-order conic equivalent, robust convex optimization, variable power factor operation, weighting-based multi-objective methodology.

The complexity of modern software systems, the integration of several software components, and the increasing exposure to public networks make systems more susceptible to cyber-attacks, especially those targeting memory. Memory error exploitation received worldwide attention thanks to the Morris worm in 1988 and has been around for over 30 years. As a matter of fact, attacks that involve memory safety, such as buffer overflows, are still a plague in modern software. The research in countering those kinds of attacks has gone in several directions. This work surveys memory integrity techniques developed during the last quarter century for embedded or general-purpose open-source operating systems, ranging from older mechanisms to state-of-the-art solutions. A comparison of various memory integrity techniques is presented to examine their effectiveness and technical significance. Insights into ongoing trends and developments are also provided to assess their potential impact in the foreseeable future.

The Consultative Committee for Space Data Systems (CCSDS) recently published a lossless compression standard for housekeeping and telemetry. These data are critical for the safe and productive operation of virtually all remote sensing missions, including Earth observation. The new standard, CCSDS 124.0-B-1 “Robust Compression of Fixed-Length Housekeeping Data,” allows lossless, real-time telemetry packet transmission with low energy and memory requirements. Furthermore, it offers flexible resiliency mechanisms to resume decompression after losing one or more data packets. This work provides an accessible introduction to CCSDS 124.0-B-1, paying special attention to its resiliency features. Two novel and complementary decoder resynchronization strategies are also presented, which maximize the amount of correctly decoded data. Comprehensive experiments with real and simulated mission data were conducted for this work to assess the standard’s performance and the impact of user-defined parameters. Results indicate competitive coding performance, with average compression ratios between 4 and 16 depending on the dataset. Smooth trade-offs are also observed between compression performance and the degree of protection against data loss events, which enhances the usability of CCSDS 124.0-B-1 in multiple mission types and operational conditions.

iquaflow is a framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated and a set of unsupervised methods is offered by default. Furthermore, iquaflow measures quality by using the performance of AI models trained on the images as a proxy. This also helps to easily make studies of performance degradation of several modifications of the original dataset, for instance, with images reconstructed after different levels of lossy compression; satellite images would be a use case example, since they are commonly compressed before downloading to the ground. In this situation, the optimization problem involves finding images that, while being compressed to their smallest possible file size, still maintain sufficient quality to meet the required performance of the deep learning algorithms. Thus, a study with iquaflow is suitable for such case. All this development is wrapped in Mlflow: an interactive tool used to visualize and summarize the results. This document describes different use cases and provides links to their respective repositories. To ease the creation of new studies, we include a cookiecutter repository. The source code, issue tracker and aforementioned repositories are all hosted on GitHub.

The detection of contaminants in several environments (e.g., air, water, sewage systems) is of paramount importance to protect people and predict possible dangerous circumstances. Most works do this using classical Machine Learning tools that act on the acquired measurement data. This paper introduces two main elements: a low-cost platform to acquire, pre-process, and transmit data to classify contaminants in wastewater; and a novel classification approach to classify contaminants in wastewater, based on deep learning and the transformation of raw sensor data into natural language metadata. The proposed solution presents clear advantages against state-of-the-art systems in terms of higher effectiveness and reasonable efficiency. The main disadvantage of the proposed approach is that it relies on knowing the injection time, i.e., the instant in time when the contaminant is injected into the wastewater. For this reason, the developed system also includes a finite state machine tool able to infer the exact time instant when the substance is injected. The entire system is presented and discussed in detail. Furthermore, several variants of the proposed processing technique are also presented to assess the sensitivity to the number of used samples and the corresponding promptness/computational burden of the system. The lowest accuracy obtained by our technique is 91.4%, which is significantly higher than the 81.0% accuracy reached by the best baseline method.

Embedded computing systems are becoming increasingly relevant in the Internet of Things (IoT) and edge computing domains, where they are often employed as the control entity of a cyber-physical system. When operating in such interconnected domains, a software system is susceptible to cyber-attacks from external agents, which can compromise the correct behavior of the system. In addition, the software executing in these systems is typically characterized by stringent timing constraints, which must be satisfied during system execution. Enabling software protections to enhance the security level of the embedded software comes at the cost of increasing the computation times of the tasks, introducing the risk of deadline misses that could also jeopardize the system behavior. This paper presents a methodology to optimize the security level of real-time software while preserving system-wide schedulability by leveraging timing analysis. The proposed approach is based on a mixed-integer linear programming (MILP) formulation that maximizes the security level of the tasks and integrates a response-time analysis technique to assess the schedulability of the system whenever additional protections are activated to shield the software against cyber-attacks targeting specific classes of vulnerabilities. An experimental evaluation is presented to assess the performance of the proposed approach on a representative set of tasks included in standard benchmarking suites for embedded software.

The Multi-Swarm approach allows the use of multiple configurations between two or more populations of particles, where each one can present different approaches (e.g. lbest, gbest, Unified, Guaranteed-Convergence) directed towards improving the optimization process. This article presents a proposal for local/global stochastic interconnection applied to the context of the Multi-Swarm algorithm, as well as for incrementing a local search method for refining previously obtained solutions. Two proposals are introduced for this new Multi-Swarm PSO (MSO). The first one is the inclusion of “counterpart particles”, which establishes a sub-topology between inter-swarm particles, accessed by migration and evaluability rules. The other involves using customized crossover operators and is based on the BLX scheme (Blend Crossover) with direction information used as a reference for establish a subspace search around the particles. Performance and robustness of the new approaches were assessed by ten constrained engineering design optimization problems (COPs), as is compared to other solutions already published in the scientific literature. Results indicate significant performance improvements for all 10 COPs when compared to concurrent-based MSOs. By making available new references from other swarms, the counterpart particles approach tends to improve the optimization process in the search space, while an intermediate layer of local search based on a modified directed BLX crossover should provide an extra search around the particle, and thus, refining previously obtained solutions.

The Sardinia deep space antenna (SDSA), managed by the Italian Space Agency (ASI) has started its operations in 2017 aiming to provide tracking and communication services for deep space, near earth, and lunar missions, and to support new and challenging radio science experiments. The SDSA shares with the Sardinia Radio Telescope (SRT) a part of the system and infrastructure, but has its own specific equipment and a dedicated control center. The current SDSA capabilities involve the X-band (8.4 GHz–8.5 GHz) reception of telemetry from deep space probes within interplanetary missions. In this work we describe the development and performance of the X-band receiving system. It was designed and assembled with the cooperation of both the NASA-Jet Propulsion Laboratory (JPL) and the European Space Agency (ESA). Specifically, NASA-JPL provided the X-band feed and the cryogenic receiver installed in a suitable focus of the SRT devoted to space applications, and ESA provided the intermediate frequency modem system (IFMS) for signal processing. The coupling of the X-band feed with the parabolic reflector of the SRT and the radiating features of the SDSA have been evaluated with simulations performed using CST Studio Suite and GRASP by Ticra. The telecommunication performance of the system has been assessed by measurements and experiments showing a good agreement between estimates and simulations.

This paper compares four sliding-mode control strategies for a dual active bridge. The proposed control algorithms define different switching functions resulting in first-and second-order controllers, and using both discontinuous and continuous sliding-mode algorithms. The paper details the design stage and includes simulation and experimental tests to better compare the performance for each control scheme.

The amount of accessible computational devices over the Internet offers an enormous but latent computational power. Nonetheless, the complexity of orchestrating and managing such devices requires dedicated architectures and tools and hinders the exploitation of this vast processing capacity. Over the last years, the paradigm of (Browser-based) Volunteer Computing emerged as a unique approach to harnessing such computational capabilities, leveraging the idea of voluntarily offering resources. This article proposes VFuse, a groundbreaking architecture to exploit the Browser-based Volunteer Computing paradigm via a ready-to-access volunteer network. VFuse offers a modern multi-language programming environment for developing scientific workflows using WebAssembly technology without requiring the user any local installation or configuration. We equipped our architecture with a secure and transparent rewarding mechanism based on blockchain technology (Ethereum) and distributed P2P file system (IPFS). Further, the use of Non-Fungible Tokens provides a unique, secure, and transparent methodology for recognizing the users’ participation in the network. We developed a prototype of the proposed architecture and four example applications implemented with our system. All code and examples are publicly available on GitHub.

Wireless communication systems are being considered for medical applications to facilitate the doctors’ operation and the quality of the medical procedures. A demonstrative example of this is the catheterization laboratory (CathLab), where it is desirable to replace the existent wired connections by wireless alternatives. However, there are some challenging requirements that need to be fulfilled by the wireless link, especially for intra-vascular ultra-sound (IVUS) systems, since the images acquired by the catheter should be transmitted with very high data rate and low latency, together with the highest possible amplification efficiency, to increase the battery life. The communication requirements can be achieved with latest the Wi-Fi standard IEEE 802.11ax (Wi-Fi 6). However, since Wi-Fi is based on orthogonal frequency division multiplexing (OFDM) waveforms, the transmitted signals present high envelope fluctuations, leading to amplification difficulties due to the nonlinear distortion effects and low energy efficiency. In this paper, we present an innovative amplification scheme named quantized digital amplification (QDA). It is shown that the QDA allows a quasi-linear amplification of IEEE 802.11ax signals while maintaining a very high energy efficiency. To demonstrate this, a QDA prototype and a set of performance results, regarding both the linearity of the transmitted signals and the energy efficiency, are presented.

Designing small and efficient mobile neural networks is difficult because the challenge is to determine the architecture that achieves the best performance under a given limited computational scenario. Previous lightweight neural networks rely on a cell module that is repeated in all stacked layers across the network. These approaches do not permit layer diversity, which is critical for achieving strong performance. This paper presents an experimental study to develop an efficient mobile network using a hierarchical architecture. Our proposed mobile network, called Diversity Network (DivNet), has been shown to perform better than the basic architecture generally employed by the best high-efficiency models–with simply stacked layers–, regarding complexity cost and performance. A set of architectural design decisions are described that reduce the proposed model size while yielding a significant performance improvement. Our experiments on image classification show that compared to, respectively, MobileNetV2, SqueezeNet, and ShuffleNetV2, our proposal DivNet can improve accuracy by 2.09%, 0.76%, and 0.66% on the CIFAR100 dataset, and by 0.05%, 4.96%, and 1.13% on the CIFAR10 dataset. On more complex datasets, e.g., ImageNet, our proposal DivNet achieves 70.65% Top-1 accuracy and 90.23% Top-5 accuracy, still better than other small models like MobilNet, SqueezeNet, ShuffleNet.

Surgical planning is crucial to Stereoelectroencephalography (SEEG), a minimally invasive procedure that requires clinicians to operate with no direct view of the brain. Decisions making involves different clinical specialties and requires analysis of multiple multimodal datasets. We present a DepthMap tool designed to localize, measure, and visualize surgical risk, and an AlternativeFinder tool, designed to search for alternative trajectories maintaining adherence to the initial trajectory with three different re-planning strategies: similar entry, similar target, or parallel trajectory. The two tools transform the 3D problem into the 2D domain using projective geometry and distance mapping. Both use the graphics processing unit (GPU) to create a 2D depth image used by DepthMap for measurement and visualization, and by AlternativeFinder to find alternative trajectories. Tools were tested with 12 SEEG cases using digital subtraction angiography. DepthMap was used to measure vessel distance. AlternativeFinder was then used to search for alternatives. Computation time and displacements of the entry and target points for each trajectory and adherence strategy were recorded. The DepthMap tool found vessels in 118 initial trajectories (out of 145). Ninety alternative trajectories were found to meet the required avascular constraints (average 820K alternatives evaluated per initial trajectory). The average computation time was 449 ms per initial trajectory (77 ms when alternatives were found). The tools presented helped clinicians examine and re-plan SEEG trajectories to avoid vascular risks using three adherence strategies. Quantitative measurement of the adherence shows the potential of this tool for clinical use.

Efficient high-throughput (HT) compression algorithms are paramount to meet the stringent constraints of present and upcoming data storage, processing, and transmission systems. In particular, latency, bandwidth and energy requirements are critical for those systems. Most HT codecs are designed to maximize compression speed, and secondarily to minimize compressed lengths. On the other hand, decompression speed is often equally or more critical than compression speed, especially in scenarios where decompression is performed multiple times and/or at critical parts of a system. In this work, an algorithm to design variable-to-fixed (VF) codes is proposed that prioritizes decompression speed. Stationary Markov analysis is employed to generate multiple, jointly optimized codes (denoted code forests). Their average compression efficiency is on par with the state of the art in VF codes, e.g., within 1% of Yamamoto et al.'s algorithm. The proposed code forest structure enables the implementation of highly efficient codecs, with decompression speeds 3.8 times faster than other state-of-the-art HT entropy codecs with equal or better compression ratios for natural data sources. Compared to these HT codecs, the proposed forests yields similar compression efficiency and speeds.

Wearable electrochemical sensors represent a point of convergence between lab-on-a-chip technologies, advanced microelectronics and connected intelligence. These three pillars establish data flow from analytes present in body fluids, to the Cloud infrastructures towards next-generation personal healthcare and wellness. The design of electrode-embedded interfacing instrumentation in advanced CMOS technology nodes offer a number of challenges spanning from ultra-low power operation, small footprint, sufficient general purpose operability, and compatibility with advanced CMOS technology nodes. This paper presents a low-power frontend with extended amperometric dynamic range and wide potentiostatic range for electrochemical transducers with Delta-Sigma (ΔΣ) digital output. The second-order single-bit continuous-time ΔΣ modulator architecture reuses the electrochemical cell dynamic characteristics for quantization noise shaping, while the differential potentiostat enables 1.8 Vpp of control range under single 1.2-V supply. The proposed frontend has been integrated in TSMC 65-nm CMOS technology occupying 0.07 mm2. From electrical and electrochemical tests, the micro potentiostat achieves a Signal-to-Distortion-and-Noise of 80 dB with 15-μW power consumption and a combined multi-scale dynamic range of 105 dB.

Game Theory (GT) formalizes dispute scenarios between two or more players where each one makes a move following their strategy profiles. The following paper introduces the integration of GT to selection and crossover steps of Genetic Algorithms as an evolutionary model of the representation of population in a similar way to human social evolution. Two ideas are proposed to be incorporated into the GA. First, the Genetic Algorithm with Social Interaction (GASI), a family of GAs that uses GT in selection phase to increase the diversification of the solutions. Second, the (Game-Based Crossover) GBX and GBX2 crossover operators, competition-based tournament selection methods that employ social dispute to generate more diverse offspring. Performance and robustness of the new approaches were assessed by ten continuous and constrained engineering design optimization problems and compared against variants of the canonical GA, as well as well-known heuristics from the literature. Results indicate significant performance relevance in most instances compared to other algorithms and highlight the benefits of combining GT and GA.

This paper proposes a design methodology for a synthesizable, fully digital TDC architecture. The TDC was implemented using a hardware description language (HDL), which improves portability between platforms and technologies and significantly reduces design time. The proposed design flow is fully automated using TCL scripting and standard CAD tools configuration files. The TDC is based on a Tapped Delay Line architecture and explores the use of Structured Data Path (SDP) as a way to improve the TDL linearity by homogenizing the routing and parasitic capacitances across the multiple TDL's steps. The studied approach also secures a stable, temperature independent measurement operation. The proposed TDC architecture was fabricated using TSMC 180nm CMOS process technology, with a 50MHz reference clock and a supply voltage of 1.8V. The fabricated TDC achieved an 111ps RMS resolution and a single-shot precision of 54ps (0.48 LSB) and 279ps (2.51 LSB), with and without post-measurement software calibration, respectively. The DNL across the channel is mostly under 0.3 LSB and a maximum of 8 LSB peak-to-peak INL was achieved, when no calibration is applied.

Collective intelligence projects based on citizen participation are gaining momentum in today's society. Citizen science applies crowdsourcing techniques to produce reliable data, quickly and easily. These projects allow getting new knowledge and help professional scientists to come to real conclusions. This paper proposes that the use of reputation systems improves the results obtained in citizen science projects. To prove this hypothesis, a reputation system is applied to a real experiment and the results are analyzed. The goal of the experiment is to calculate the real-time solar activity, known as the Wolf number, using the infrastructure and user community of the GLORIA project (a set of professional robotic telescopes running since 2013). The sample size of the study are 196 end-users and 2,108 executions of the experiment. The key findings presented in the paper are: 1) the online experiment with volunteers correctly reproduces the traditional method of the year 1848 performed by astronomers or advanced amateurs, 2) the model is contrasted and validated with the values published by the official organization, and 3) the reputation system reduces the error in calculations by more than half, discarding the contributions of the users with lowest karma.

Time-to-Digital Converters (TDC) are popular circuits in many applications, where high resolution time measurements are required, for example, in Positron Emission Tomography (PET). Besides its resolution, the TDC's linearity is also an important performance indicator, therefore calibration circuits usually play an important role on TDCs architectures. This paper presents an all-digital TDC implemented using Structured Datapath to reduce the need for calibration circuitry and cells custom design, without compromising the TDC's linearity. The proposed design is fully implementable using a Hardware Description Language (HDL) and enables a complete design flow automation, reducing both development time and system's complexity. The TDC is based on a Delay Locked Loop (DLL) paired with a coarse counter to increase measurement range. The proposed architecture and the design approach have proven to be efficient in developing a high resolution TDC with high linearity. The proposed TDC was implemented in TSMC 0.18 CMOS technology process achieving a resolution of 180ps, with Differential Non-Linearity (DNL) and Integral Non-Linearity (INL) under 0.6 LSB.

Predictive image coding systems yield a high-compression performance at low computational complexity, and are therefore popular in standards and prominent coding techniques for both lossless and near-lossless compression. However, few prediction-based coding techniques include rate-control approaches because of the difficulty in properly combining the prediction feedback loop and the quantization. None of the rate-control approaches for predictive image coding are focused on delivering homogeneous quality along the whole image. The main objective of this paper is to define a novel rate-control approach based on prediction that homogenises the quality over the entire image. The extensive experimental results, on medical, remote sensing, and natural images, suggest that our proposal attains more regular image quality and lower peak absolute error than the state-of-the-art approaches based on prediction and also transform-based techniques such as JPEG2000 and CCSDS-122.

In this paper, we provide a method to obtain tight lower bounds on the minimum redundancy achievable by a Huffman code when the probability distribution underlying an alphabet is only partially known. In particular, we address the case where the occurrence probabilities are unknown for some of the symbols in an alphabet. Bounds can be obtained for alphabets of a given size, for alphabets of up to a given size, and alphabets of arbitrary size. The method operates on a computer algebra system, yielding closed-form numbers for all results. Finally, we show the potential of the proposed method to shed some light on the structure of the minimum redundancy achievable by the Huffman code.