Electronics, Volume 12, Issue 6 (March-2 2023) – 236 articles

Age of information (AoI) is an emerging network metric that measures information freshness from an application layer perspective. It can evaluate the timeliness of information in industrial wireless sensor networks (IWSNs). Previous research has primarily focused on minimizing the long-term average AoI of the entire system. However, in practical industrial applications, optimizing the average AoI does not guarantee that the peak AoI of each data packet is within a bounded interval. If the AoI of certain packets exceeds the predetermined threshold, it can have a significant impact on the stability of the industrial control system. Therefore, this paper studies the scheduling problem subject to a hard AoI performance requirement in IWSNs. First, we propose a low-complexity AoI-bounded scheduling algorithm for IWSNs that guarantees that the AoI of each packet is within a bounded interval. Then, we analyze the schedulability conditions of the algorithm and propose a method to decrease the peak AoI of nodes with higher AoI requirements. Finally, we present a numerical example that illustrates the proposed algorithm step by step. The results demonstrate the effectiveness of our algorithm, which can guarantee bounded AoI intervals (BAIs) for all nodes. Full article

The spread of informatics and electronic systems capable of the real-time monitoring of multiple psychophysiological signals has continuously grown in the last few years. In this study, we propose a novel open-source wearable monitoring platform (WMP) to synchronously acquire and process multiple physiological signals in a real-time fashion. Specifically, we developed an IoT-like modular and fully open-source platform composed of two main blocks that on the one hand connect multiple devices (the sensor fusion unit) and on the other hand process and store the sensors’ data through the internet (the remote storing and processing unit). To test the proposed platform and its computational performance, 15 subjects underwent an experimental protocol, in which they were exposed to rest and stressful sessions implementing the Stroop Color and Word Test (SCWT). Statistical analysis was performed to verify whether the WMP could monitor the expected variations in the subjects’ psychophysiological state induced by the SCWT. The WMP showed very good computational performance for data streaming, remote storing, and real-time processing. Moreover, the experimental results showed that the platform was reliable when capturing physiological changes coherently with the emotional salience of the SCWT. Full article

In the present, many organizations grow on a daily basis, using many assets to perform their activities and generate profit. In large organizations, all of these assets must be managed, occasionally leading to challenges depending on the organization’s size. For this reason, the role of asset custodian is needed. This role entails assigning the fixed assets to one person for their care, maintenance, and safekeeping. In this process, it is necessary to update information in the central system, leading to further administrative processes, which, in the majority of cases, are carried out through traditional methods. This involves time to obtain wet signatures, a great deal of paperwork, and time for the person or people in charge to update the information. Due to these reasons, the process can be updated partially or entirely to use digital means in order to solve the mentioned inconveniences. This paper presents a proof-of-concept system to offer a modernized and practical solution to this problem using the advantages of blockchain technology, and speeding up the process by using assets identified with UHF RFID technology to permit the reading of many tags that can be embedded and hidden with no need for line-of-sight, allowing fast ownership transfer, using smart contracts in the Ethereum private blockchain. Full article

This paper proposes an analytical formulation-based minimization of DC link current ripples for interleaved parallel inverter systems. Parallel inverter systems find applications in multiple fields. The interleaved superposition of the DC link currents in these systems can potentially be adjusted to mitigate the overall harmonics consequently reducing the DC link capacitor size. To this end, a widely used approach in the literature is the Fourier analysis based on interleaving focusing on dominant harmonic mitigation. However, it leaves room for a generic analytical mechanism to provide time shifts leading to an optimal reduction in DC-link ripples. The goal of this work is to target this optimal reduction by utilizing an analytical mechanism. The paper presents an alternate way of DC-link formulation in terms of the piece-wise sinusoids of inverter output currents for space vector modulation-based systems. The formulation is then used to numerically optimize the interleaved shifts for minimum ripples. Moreover, in addition to the traditional concept of fixed time interleaving, a contemporary concept of sequence-based interleaving is utilized, which is anticipated to have more flexibility in the implementation and additional switching synchronism with PWM rectifiers for back–back converters. Therefore, the sequence interleaving has also been utilized in conjunction with the proposed ripple reduction methodology. Further, an underexplored area of using the combined impact of sequence and time interleaving has also been applied in this work. These interleaving methods are shown to provide significantly improved DC-link ripple mitigation, as compared to existing methods, using numerical assessment followed by simulations and experimental evaluation. Full article

The aim of this paper is to analyze statistical characteristics of the new differential communication scheme based on chaotic radio pulses in the presence of additive white noise (Gaussian) and using various distributions of instantaneous values of the chaotic signal. The characteristic feature of the presented scheme is the usage of significantly shorter time delays compared to the classical differential chaotic shift keying (DCSK) scheme. In order to investigate noise immunity of the direct chaotic differential communication (DC²) scheme, numerical statistical simulation is performed in terms of the bit error probability (BER) of the transmitted information. Then, the results of this simulation are compared to the results of analytical research. It is shown that due to the inherent internal noises of the scheme, the bit error probability (BER) for arbitrarily large values of the ratio of the signal energy to the Gaussian noise spectral density (E_b/N₀) is higher than 10⁻³ for the values of processing gain K < 30 for any distribution of instantaneous values of the chaotic signal. With the increase of the K values, there is a rapid decrease in BER in a system with a channel without white noise. Numeric simulation is performed, which verifies and clarifies the analytical estimates obtained earlier regarding the bit error probabilities as functions of processing gain and ratio of the signal energy to the Gaussian noise spectral density. The minimum values of E_b/N₀ are obtained, which provide necessary error probabilities with the processing gain set. It is shown that with a high processing gain (K > 30), the communication scheme considered here operates effectively both in a channel without fluctuation noises and in a channel with additive white Gaussian noise. The statistical characteristics of the proposed scheme do not depend on the choice of a particular distribution of instantaneous values of the chaotic signal. Taking into account that the scheme uses short delays, which do not depend on the processing gain of the used signal and are easily implemented, for example, on fragments of a high-frequency cable, the results obtained show good prospects for its implementation in a physical experiment. Full article

Task-oriented dialogue systems depend on dialogue state tracking to keep track of the intentions of users in the course of conversations. Although recent models in dialogue state tracking exhibit good performance, the errors in predicting the value of each slot at the current dialogue turn of these models are easily carried over to the next turn, and unlikely to be revised in the next turn, resulting in error propagation. In this paper, we propose a revisable state prediction for dialogue state tracking, which constructs a two-stage slot value prediction process composed of an original prediction and a revising prediction. The original prediction process jointly models the previous dialogue state and dialogue context to predict the original dialogue state of the current dialogue turn. Then, in order to avoid the errors existing in the original dialogue state continuing to the next dialogue turn, a revising prediction process utilizes the dialogue context to revise errors, alleviating the error propagation. Experiments are conducted on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.4 and results indicate that our model outperforms previous state-of-the-art works, achieving new state-of-the-art performances with 56.35, 58.09, and 75.65% joint goal accuracy, respectively, which has a significant improvement (2.15, 1.73, and 2.03%) over the previous best results. Full article

Compared with the widely used four-pole magnetic bearings, three-pole magnetic bearings are driven by a three-phase power inverter and have advantages pertaining to their small volume, low costs, and low power losses. However, the asymmetric structure of the three-pole bearings presents disadvantages in terms of their strong nonlinearity and couplings among the suspension forces of the control currents and displacements. The radial–axial hybrid magnetic bearing (RAHMB) with six-pole bearings is proposed to solve this problem. Firstly, the structure and working principle of the RAHMB are introduced. Secondly, the mathematical models of the RAHMB are established, and in order to obtain the radial capacity, the maximum suspension forces of the three-pole and six-pole RAHMBs are theoretically analyzed. Thirdly, the nonlinearity and couplings of the suspension forces with the control currents and displacements are analyzed. The radial capacity of the three-pole and six-pole RAHMB is 74.6 N and 83.6 N, respectively, which is an increase of 12.0%. Finally, the experiment results prove that the nonlinearity and couplings of the six-pole RAHMB are smaller than the nonlinearity and couplings of the three-pole RAHMB, and the maximum radial capacity of the three-pole and six-pole RAHMB is 84.1 N and 94.8 N, respectively, which is an increase of 12.7%. The simulation results are basically consistent with the experimental results, indicating the correctness of the theoretical analysis. Full article

A Modular COsmic Ray Detector (MCORD) was prepared for use in various physics experiments. MCORD detectors can be used in laboratory measurements or can become a part of large measurement sets. MCORD can be used as a muon detector, a veto system, or a tool supporting the testing and calibration of other detectors. MCORD can also work as a stand-alone device for scientific and commercial purposes. The basic element of MCORD is one section consisting of eight oblong scintillators with a double-sided light reading performed by silicon photomultipliers (SiPMs). This work presents a practical description of testing, calibration, and programming of analogue and digital electronics modules. The characterisation and calibration methods of the analogue front-end electronic modules, the obtained results, and their implementation into an operating system are presented. In addition, we describe the development environment and the procedures used to prepare our kit for practical use. The architecture of the FPGAs is also presented with a description of their programming as a data-collecting system in a simple coincidence circuit. We also present the possibilities of extending the data analysis system for large experiments. Full article

The Korean government is facing growing concern over the increasing levels of fine dust. A significant contribution to this problem comes from coastal vessels. To mitigate this, an electric ship propulsion system has been proposed as a solution to reduce air pollution. The application of a fully electric propulsion system in a ship is challenging due to size, capacity limitations, and the cost investment of the battery system. To address the challenges of battery limitation and initial investment costs, the development and supply of removable battery supply systems (RBSSs) for fully electrified battery powered (F-EBP) car ferries are studied. A permanent magnet synchronous motor (PMSM) for the F-EBP car ferry using a roll-on/roll-off-type RBSS is developed in this work. Firstly, the concept of the F-EBP car ferry is discussed, and the specifications of the electric car ferry propulsion system are provided. Secondly, motor design and electromagnetic analysis are performed using finite-element analysis (FEA), where the heat sources including copper loss, core loss, and PM loss are calculated. Mechanical loss is also considered. Finally, a thermal network of the motor is built considering the lumped-parameter model. The results of the thermal analysis indicate that the motor operates within the safe region and can perform well in rated working conditions. Full article

Sharpness is an important factor for image inpainting in future Internet, but the massive model parameters involved may produce insufficient edge consistency and reduce image quality. In this paper, we propose a two-stage transformer-based high-resolution image inpainting method to address this issue. This model consists of a coarse and a fine generator network. A self-attention mechanism is introduced to guide the transformation of higher-order semantics across the network layers, accelerate the forward propagation and reduce the computational cost. An adaptive multi-head attention mechanism is applied to the fine network to control the input of the features in order to reduce the redundant computations during training. The pyramid and perception are fused as the loss function of the generator network to improve the efficiency of the model. The comparison with Pennet, GapNet and Partial show the significance of the proposed method in reducing parameter scale and improving the resolution and texture details of the inpainted image. Full article

In recent years, cloud computing has attracted extensive attention from industry and academia due to its convenience and ubiquity. As a new Internet-based IT service model, cloud computing has brought revolutionary changes to traditional computing and storage services. More and more individual users and enterprises are willing to deploy their own data and applications on the cloud platform, but the accompanying security issues have also become an obstacle to the development of cloud computing. Multi-tenancy and virtualization technologies are the main reasons why cloud computing faces many security problems. Through the virtualization of storage resources, multi-tenant data are generally stored as shared physical storage resources. To distinguish the data of different tenants, labels are generally used to distinguish them. However, this simple label cannot resist the attack of a potential malicious tenant, and data still has the risk of leakage. Based on this, this paper proposed a data partitioning method in a multi-tenant scenario to prevent privacy leakage of user data. We demonstrate the use of the proposed approach in protecting patient data in medical records in health informatics. Experiments show that the proposed algorithm can partition the attributes more fine-grained and effectively protect the sensitive information in the data. Full article

This paper evaluates the ${\mathcal{L}}_1$ adaptive model predictive control (AMPC-${\mathcal{L}}_1$) method in terms of its control performance and computational load. The control performance is assessed on the basis of the nonlinear simulation of a fly-back booster conducting stage separation and re-entry, and compared to baseline nonadaptive MPC and as a pole placement controller in both longitudinal and lateral control tasks. Simulation results show that AMPC-${\mathcal{L}}_1$ exhibits superior control performance under nominal conditions, and aerodynamic and guidance law uncertainties. The computational load of AMPC-${\mathcal{L}}_1$ is also evaluated on an embedded platform to demonstrate that AMPC-${\mathcal{L}}_1$ preserves the efficiency properties of AMPC while improving its performance. Full article

NanoSheet-Gate-All-Around-FETs (NS-GAAFETs) are commonly recognized as the future technology to push the digital node scaling into the sub-3 nm range. NS-GAAFETs are expected to replace FinFETs in a few years, as they provide highly electrostatic gate control thanks to the GAA structure, with four sides of the NS channel entirely enveloped by the gate. At the same time, the NS rectangular cross-section is demonstrated to be effective in its driving strength thanks to its high saturation current, tunable through the NS width used as a design parameter. In this work, we develop a NS-GAAFET compact model and we use it to link peculiar single-device parameters to digital circuit performance. In particular, we use the well-known BSIM-CMG core solver for multigate transistors as a starting point and develop an ad hocresistive and capacitive network to model the NS-GAAFET geometrical and physical structure. Then, we employ the developed model to design and optimize a digital inverter and a five-stage ring oscillator, which we use as a performance benchmark for the NS-GAAFET technology. Through Cadence Virtuoso SPICE simulations, we investigate the digital NS-GAAFET performance for both high-performance and low-power nodes, according to the average future node present in the International Roadmap for Devices and Systems. We focus our analysis on the main different technological parameters with regard to FinFET, i.e., the inner and outer spacers. Our results highlight that in future technological nodes, the choice of alternative low-K dielectric materials for the NS spacers will assume increasing importance, being as relevant, or even more relevant, than photolithographic alignment and resolution at the sub-nm scale. Full article

Recently, prompt-based learning has shown impressive performance on various natural language processing tasks in few-shot scenarios. The previous study of knowledge probing showed that the success of prompt learning contributes to the implicit knowledge stored in pre-trained language models. However, how this implicit knowledge helps solve downstream tasks remains unclear. In this work, we propose a knowledge-guided prompt learning method that can reveal relevant knowledge for text classification. Specifically, a knowledge prompting template and two multi-task frameworks were designed, respectively. The experiments demonstrated the superiority of combining knowledge and prompt learning in few-shot text classification. Full article

Farmers face an increasing administrative burden as agricultural policies and certification systems of trade partners ask for more sustainability reporting. Several indicator frameworks have been developed to measure sustainability, but they often lack empirical operationalization and are not always measured at the farm level. The research gap we address in this paper is the empirical link between the data needs for sustainability reporting and the developments in data management at the farm level. Family farms do not collect much data for internal management, but external demand for sustainability data can partly be fulfilled by reorganizing data management in the farm office. The Farm Financial Accounts (FFAs) and Farm Management Information Systems (FMISs) are the main data sources in the farm office. They originate from the same source of note-taking by farmers but became separated when formalized and computerized. Nearly all European farms have a bank account and must keep financial accounts (e.g., for Value-Added Tax or income tax) that can be audited. Financial accounts are not designed for environmental accounting or calculating sustainability metrics but provide a wealth of information to make assessments on these subjects. FMISs are much less frequently used but collect more technical and fine-grained data at crop or enterprise level for different fields. FMISs are also strong in integrating sensor and satellite data. Integrating data availability and workflows of FFAs and FMISs makes sustainability reporting less cumbersome regarding data entry and adds valuable data to environmental accounts. This paper applies a design science approach to design an artifact, a dashboard for sustainability reporting based on the integration of information flows from farm financial accounting systems and farm management information systems. The design developed in this paper illustrates that if invoices were digitized, most data-gathering needed for external sustainability reporting would automatically be done when the invoices is paid by a bank transfer. Data on the use of inputs and production could be added with procedures as in current FMISs, but with less data entry, fewer risks of differences in outcomes, and possibilities of cross-checking the results. Full article

Network traffic analysis can raise privacy concerns due to its ability to reveal sensitive information about individuals and organizations. This paper proposes a privacy-preserving Block-chained AutoML Network Traffic Analyzer (BANTA). The system securely stores network traffic logs in a decentralized manner, providing transparency and security. Differential privacy algorithms protect sensitive information in the network flow logs while allowing administrators to analyze network traffic without the risk of leakages. The BANTA uses blockchain technology, where smart contracts automate the process of network traffic analysis, and a multi-signature system ensures the system’s security, safety, and reliability. The proposed approach was evaluated using a real-world network traffic dataset. The results demonstrate the system’s high accuracy and real-time anomaly detection capabilities, which makes it well-suited for scalable cybersecurity operations. The system’s privacy protection, decentralized storage, automation, multi-signature system, and real-world effectiveness ensure that the organization’s data is private, secure, and effectively protected from cyber threats, which are the most vexing issue of modern cyber-physical systems. Full article

This research paper studies and highlights the features of the most popular finite control set model predictive control (FCS-MPC) strategies available in the state of the art, which are the optimal switching vector (OSV-MPC), modulated model predictive control (M2PC), and optimal switching sequence (OSS-MPC) methods. Thus, these strategies are studied experimentally by analyzing the transient and steady state performance using a grid tie conventional three-phase two-level voltage source inverter (VSI) with inductive output filter in a Typhoon HIL real-time simulator (RTS) with a Texas Instruments F28379D digital signal processor (DSP). Hence, quantitative indicators, such as the maximum tracking error, the mean absolute error, the settling time, the total harmonic distortion, the switching frequency spectrum, the switching pattern, and the computational burden are compared with the aim to deduce the best strategy for each criteria. Full article

The Voltage Source Converter (VSC) is the basis of STATCOMs and other power systems. It is composed of a three-phase inverter in which the activation of the switching devices must be controlled to generate the intended signals. The control technique used to switch the power devices affects the performance of the converter in terms of harmonic distortion mainly. Although some complex modulation techniques have been proposed in the related literature, local controllers opt for simpler methods as they provide robustness and they ease the implementation. In this paper, we propose a simple but effective technique to switch the transistors of a three-phase inverter with a Space Vector Modulation (SVM) supported by a Finite-State Machine (FSM). With this model, the switching technique can be easily implemented in low-cost microcontrollers with reduced memory and computational resources if code optimisation is performed. With an electrical analysis, we have designed a low-pass band filter adequate for the proposed switching technique. In a laboratory prototype, the performance of this proposal is evaluated under static and dynamic conditions. When compared with other control techniques (classical SVM and PWM), we conclude that a similar harmonic distortion is achieved. Full article

Light Field (LF) cameras can capture angular and spatial information simultaneously, making them suitable for a wide range of applications such as refocusing, disparity estimation, and virtual reality. However, the limited spatial resolution of the LF images hinders their applicability. In order to address this issue, we propose an end-to-end learning-based light field super-resolution (LFSR) model called MFSR, which integrates multiple features, including spatial, angular, epipolar plane images (EPI), and global features. These features are extracted separately from the LF image and then fused together to obtain a comprehensive feature using the Feature Extract Block (FE Block) iteratively. Gradient loss is added into the loss function to ensure that the MFSR has good performance for LF images with rich texture. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods, with a peak signal-to-noise ratio (PSNR) improvement of 0.208 dB and 0.274 dB on average for the 2× and 4× super-resolution tasks, and structural similarity (SSIM) of both improvements of 0.01 on average. Full article

The role of blockchain in new business model development requires greater focus because the technology is still in its infancy. Thus, there has been little research on the effects of the various blockchain networks (such as public, private, and consortium). This finding prompted a thorough investigation of new blockchain-based business models created between 2012 and 2022 to close this gap. This review’s focus is on journals, and duplicate articles have been removed. Works based on interviews, articles in press, non-English articles, reviews, conferences, book chapters, dissertations, and monographs are also not included. Seventy-five papers from the past ten years are included in this evaluation. This study examines the current state of new blockchain-based business models. Additionally, the implications and applications in the related literature have been investigated. These findings highlight numerous open research questions and promising new directions for investigation, which will likely be helpful to academics and professionals. The business strategies built on blockchain are currently on a path with a rapid upward trajectory. Blockchain technology offers businesses numerous chances to modify and develop new company models. By changing the conventional framework, blockchain innovation leads to the development of new methods for developing company models. The supportive potential of blockchain technologies such as NFT and P2E is increasingly being coupled with the development of new corporate projects and the modification of current business models. Since this field of study is still fairly new, researchers will have fresh opportunities to analyze its characteristics. Full article

Lithium-ion batteries are crucial building stones in many applications. Therefore, modeling their behavior has become necessary in numerous fields, including heavyweight ones such as electric vehicles and plug-in hybrid electric vehicles, as well as lightweight ones like sensors and actuators. Generic models are in great demand for modeling the current change over time in real-time applications. This paper proposes seven dynamic models to simulate the behavior of lithium-ion batteries discharging. This was achieved using NASA room temperature random walk discharging datasets. The efficacy of these models in fitting different time-domain responses was tested through parameter identification with the Marine Predator Algorithm (MPA). In addition, each model’s term’s impact was analyzed to understand its effect on the fitted curve. The proposed models show an average absolute normalized error as low as $0.0057$. Full article

Collaborative filtering recommendation systems are facing the data sparsity problem associated with interaction data, and social recommendations introduce user social information to alleviate this problem. Existing social recommendation methods cannot express the user interaction interest and social influence deeply, which limits the recommendation performance of the system. To address this problem, in this paper we propose a graph neural network social recommendation algorithm integrating multi-head attention mechanism. First, based on the user-item interaction graph and social network graph, the graph neural network is used to learn the high-order relationship between users and items and deeply extract the latent features of users and items. In the process of learning user embedding vector representation based on the social network graph, the multi-head attention mechanism is introduced to increase the importance of friends with high influence. Then, we make rating predictions for the target users according to the learned user embedding vector representation and item embedding vector. The experimental results on the Epinions dataset show that the proposed method outperforms the existing methods in terms of both Recall and Normalized Discounted Cumulative Gain. Full article

Fiber-optic sensors are highly promising within soft robot sensing applications, but sensing methods based on geometry-based reconstruction limit the sensing capability and range. In this study, a fiber-optic sensor with a different deployment strategy for indirect sensing to monitor the outside posture of a soft manipulator is presented. The internal support structure’s curvature was measured using the FBG sensor, and its mapping to the external pose was then modelled using a modified LSTM network. The error was assumed to follow the Gaussian distribution in the LSTM neural network and was rectified by maximum likelihood estimation to address the issue of noise generated during the deformation transfer and curvature sensing of the soft structure. For the soft manipulator, the network model’s sensing performance was demonstrated. The proposed method’s average absolute error for posture sensing was 63.3% lower than the error before optimization, and the root mean square error was 56.9% lower than the error before optimization. The comparison results between the experiment and the simulation demonstrate the viability of the indirect measurement of the soft structure posture using FBG sensors based on the data-driven method, as well as the significant impact of the error optimization method based on the Gaussian distribution assumption. Full article

Different implantable antenna designs exist to establish communication with implantable devices depending on the domain of use and the implantation space. Owing to their nature and purposes, these antennas have many imposed criteria on various characteristics, such as bandwidth, multiband behavior, radiation pattern, gain, and specific absorption rate (SAR). This presents a challenge when it comes to achieving satisfying results without a major compromise in any of these crucial parameters. Additionally, many of the existing designs do not follow a specific approach to obtain results. Measuring different parameters of such fabricated structures requires special conditions and special environments mimicking the tissues where they are supposed to be placed. For such issues, the use of biological or synthetic phantoms is widely employed to validate what is obtained in simulation, and a multitude of formulas exist for the creation of such phantoms, each with its advantages and drawbacks. In this paper, a miniature dual-band structure derived from the first iteration of the Koch fractal structure is designed to operate 2 mm below the skin in the arm of the human body, with the MICS (Medical Implant Communication System) and ISM (Industrial, Scientific, Medical) 2.4 GHz bands. The purposes of the design are to derive structures from commonly used shapes with certain behavior while maintaining miniaturization, and to easily design dual-band implantable antennas. More than one band is used to diversify uses, since bands such as the MICS band are mainly dedicated to telemetry. The structure is characterized not only by its low profile compared to various structures found in the literature with dimensions of 17.2 × 14.8 × 0.254 mm³, but also its ease of design, independent shifting of resonant frequencies, and the absence of the need for a matching circuit and a shorting pin (via) for miniaturization. It exhibits satisfying performance: bandwidths of 23 MHz in the MICS band and 190 and 70 MHz in the vicinity of the ISM 2.4 GHz band, and measured gain in the latter band of −18.66 and −17 dBi in the azimuth and elevation radiation patterns, respectively. To validate the antenna’s properties in a skin-mimicking environment, two simple phantom formulas found in the literature were explored and compared in order to identify the best option in terms of accuracy and ease of fabrication. Full article

This work proposes a hybrid drive controlled configuration, using a minimum emissions search algorithm, which ensures the operation of the Internal Combustion Engine (ICE) in its fuel efficiency range, minimizing CO${}_2$ emissions by controlling the power flow direction of the Electric Machine (EM). This action is achieved by means of Power Converters, in this case a bi-directional DC-DC Buck-Boost Converter in the DC-side and a DC-AC T-type Converter as the inverting stage. Power flow is controlled by means of a bi-directional Model Predictive Control (MPC) scheme, based on an emissions optimization algorithm. A novel drivetrain configuration is presented where both, the ICE and the EM are in tandem arrangement. The EM is driven depending on the traction requirements and the emissions of the ICE. The EM is capable of operates in motor and generator mode ensuring the Minimum Emission Operating Point (MEOP) of the ICE regardless of the mechanical demand at the drivetrain. Simulation and validation results using a Hardware in the Loop (HIL) virtual prototype under different operation conditions are presented in order to validate the proposed overall optimization strategy. Full article

AlGaN/GaN high-electron-mobility transistors (HEMTs) with two different gate–drain distances (30 $\mathsf{\mu }$m and 10 $\mathsf{\mu }$m) were exposed to 1 MeV, 0.6 MeV, and 0.4 MeV protons at a fluence of 2.16 × 10¹² cm⁻². The gate–channel electron density and low-field mobility were obtained by measuring the capacitance–voltage characteristics and current–voltage characteristics. After proton irradiation, the gate–channel low-field electron mobility of the AlGaN/GaN HEMT with a 30 $\mathsf{\mu }$m gate–drain distance increases and that with a 10 $\mathsf{\mu }$m gate–drain distance decreases. It is studied and found that the mobility behavior is related to the polarization Coulomb field scattering, and the proton irradiation influences the intensity of the polarization Coulomb field scattering by changing the polarization/strain distribution in the barrier layer. The different gate–drain distances correspond to different variation trends of scattering intensity. The effect of 1 MeV protons on the barrier layer is smaller compared with 0.6 MeV and 0.4 MeV protons, so the mobility variation is smaller. Full article

Sixth-generation communication networks must be highly dependable due to the foreseen connectivity of critical infrastructures through them. Dependability is a compound metric of four well-known concepts—reliability, availability, safety, and security. Each of these concepts have unique consequences for the success of 6G technologies and applications. Using these concepts, we explore the dependability of 6G networks in this article. Due to the vital role of machine learning in 6G, the dependability of federated learning, as a distributed machine learning technique, has been studied. Since mission-critical applications (MCAs) are highly sensitive in nature, needing highly dependable connectivity, the dependability of MCAs in 6G is explored. Henceforth, this article provides important research directions to promote further research in strengthening the dependability of 6G networks. Full article

The conductive bridge random access memory (CBRAM) device has been widely studied as a promising candidate for next-generation nonvolatile memory applications, where Cu as an electrode plays an important role in the resistive switching (RS) process. However, most studies only use Cu as one electrode, either the top electrode (TE) or the bottom electrode (BE); it is rarely reported that Cu is used as both TE and BE at the same time. In this study, we fabricated CBRAM devices by using Cu as both the TE and BE, and studied the RS characteristic of these devices. With Al₂O₃ as the switching layer (5~15 nm), the devices showed good bipolar RS characteristics. The endurance of the device could be as high as 10⁶ cycles and the retention time could be as long as 10⁴ s. The Al₂O₃ thickness influences the bipolar RS characteristic of the devices including the initial resistance, the forming process, endurance, and retention performance. The Cu electrode-based RRAM devices also present negative bias-suppressed complementary resistive switching (CRS) characteristics, which makes it effective to prevent the sneak path current or crosstalk problem in high-density memory array circuits. Full article

The high penetration of inverter-fed renewable energy sources (RESs) in modern energy systems has led to a reduction in the system’s inertial response. This reduction in the rotational inertial response is associated with synchronous generation and might result in a deteriorated frequency response following a power disturbance. This paper investigates the frequency stability of the Kingdom of Saudi Arabia’s (KSA) grid. It includes a description of the changing energy landscape of the KSA’s electricity grid and an investigation of the impact of high penetration levels of inverter-fed RESs on the dynamic behavior of the KSA grid. The impact of RESs has been studied through a simulation of case studies of the future KSA power system using the MATLAB/Simulink simulation software. The frequency stability of the KSA’s power system has been evaluated with various RES levels under peak and base load conditions. The simulation results show that the high penetration levels of RESs dramatically affect the system’s frequency response, especially under off-peak conditions. In addition, the significance of battery energy storage systems (BESSs) for compensating the reduction in the system inertial response has been addressed. The results show the effectiveness of aggregated BESSs for enhancing the system frequency control of the KSA grid. Full article