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In the field of paper conservation and archival research, identifying the raw materials of paper is important to elucidate its history and culture. As the most basic element of the raw materials for paper, fibers have not been sufficiently investigated. In this study, we propose a nondestructive method for estimating paper fibers from macro photographs ( $4000\times3000$ pixels) captured using a digital camera. The proposed method consists of background patch (500 pixels per side) detection (BPD), wherein background regions with no text are identified; patch fiber classification (PFC), wherein background patches obtained after BPD are analyzed for fiber classification; and paper fiber estimation (PFE), wherein macro images obtained after PFC are analyzed for fiber estimation. BPD and PFC are employed to perform patch-based classification on segmented macro images, which are reconstructed during PFE to obtain the final fiber estimation results. We performed experiments using 1337 macro images (64176 patches) to evaluate the fiber estimation accuracy for kozo, mitsumata, and gampi via three-fold cross-validation. The average fiber classification accuracy for patch images was observed to be 79.1%; accordingly, the average fiber estimation accuracy for macro images was 85.8%. Experimental results indicated that PFE can be realized in a nondestructive manner on macro images of paper captured using a digital camera.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

Tree-shaped Nanosheet FETS (NSFET) is the most dependable way to scale down the gate lengths deep. This paper investigates the 12nm gate length (LG) n-type Tree-shaped NSFET with the gate having a stack of high- $k$ dielectric (HfO2) and SiO2 using different spacer materials, which can be done using TCAD simulations. The Tree-shaped NFET device with ${\mathrm {T}}_{\mathrm {(NS)}} =5$ nm, ${\mathrm {W}}_{\mathrm {(NS)}} =25$ nm, ${\mathrm {W}}_{\mathrm {IB}} =5$ nm, and ${\mathrm {H}}_{\mathrm {IB}} =25$ nm has high on-current ( $I_{ON}$ ) and low off-current ( $I_{OFF}$ ). The 3D device with single- $k$ and dual-k spacers are compared and its DC characteristics are shown. It is noted that the dual- $k$ device achieves the maximum $I_{ON}/I_{OFF}$ ratio, which is $10^{9}$ , compared to $10^{7}$ because the fringing fields with spacer dielectric lengthen the effective gate length. Additionally, the impact of work function, interbridge height, width, gate lengths, and temperature, along with the device’s analog/RF and DC metrics, is also investigated in this paper. Even at 12 nm LG, the proposed device exhibits good electrical properties with DIBL =23 mV/V and SS =62 mV/dec and switching ratio ( $I_{ON}/I_{OFF}) = 10^{9}$ . The device’s performance confirms that Moore’s law holds even for lower technology nodes, allowing for further scalability.

When the electro-magnetic rail-gun is fired, the transient strong magnetic field environment in the bore will produce induced current in the radio fuze circuit and affect the normal operation of the fuze. Therefore, reasonable shielding and protective measures need to be taken to ensure the reliable operation of the fuze control circuit. Based on Faraday’s law of electro-magnetic induction, a comprehensive electro-magnetic protection method for radio fuze suitable for electro-magnetic rail-gun is proposed in this paper. This method utilizes a high permeability material shell for magnetic shielding to decrease the magnetic induction intensity of the fuze circuit. Additionally, the eddy current effect of high conductivity metal material is utilized to slow down the change rate of magnetic induction intensity of the fuze circuit. The circuit area of the fuze is minimized through reasonable circuit design, and the layout of the circuit board is optimized to reduce the dot multiplication of the circuit loop area vector and the magnetic field direction. Through the simulation analysis of the finite element model of radio fuze in strong magnetic field, the effectiveness of the above electro-magnetic protection method was proved. The research results of this paper can provide reference for the design of radio fuze of electro-magnetic rail-gun.

In the network structure analysis, we explore an underestimated key metric, the Relative Size of Largest Connected Component (RSLCC) and demonstrate its importance in post-disaster network connectivity assessment. RSLCC was first investigated in the study of complex network structures but remains largely unexplored in terms of analysis within a specific application domain such as scenarios in transportation networks, wireless networks, communication networks, power networks, etc. Through the research presented in this paper, we not only prove that this metric is underestimated, but also design 7 methods to predict the value of this metric, with a Deep Neural Network (DNN) prediction accuracy of more than 99%. This study focuses on the assessment and analysis of post-disaster network connectivity, by exploring how the RSLCC, a key metric of network connectivity, can be used to efficiently predict and assess network connectivity in a disaster scenario, specifically, the approximate network connectivity value can be predicted simply by knowing the number of connected edges in the pre-disaster network and the number of connected edges in the post-disaster network. To achieve this, firstly, a sufficiently large-scale 100,000 datasets containing the values of attributes related to the network structure is prepared. Secondly, based on the preprocessing of the data, principal component analysis and variance contribution analysis are carried out, and the metric with the highest contribution to the principal component is approximated as the network connectivity. The next step is the prediction process, Network Disruption Degree (NDD) is chosen as the independent variable. since it is best to choose an extremely simple metric as the independent variable for prediction, rather than all network structure-related metrics, this paper demonstrates that it is possible to get satisfactory prediction results with this metric. It is found that NDD prediction methods have the highest prediction accuracy but take the longest run time and require training data of a sufficiently large size. If the prediction is done in small-size data, then Random Forest Regression (RFR) is proven to have the highest prediction accuracy. Although the network connectivity metric proposed in this paper is only an approximation, it provides good directions for simplifying the network connectivity analysis and the use of this metric for the study of practical modelling problems is also highly interpretable.

Holographic-type communication (HTC) is on the verge of revolutionizing current communication paradigms. It will seamlessly connect people from distant locations through a fully immersive experience that will engage all five senses: sight, hearing, touch, smell, and taste. However, the practical realization and widespread adoption of HTC impose significant demands on current networks and end-user devices. While recent studies have primarily focused on discussing the HTC challenges and potential research directions, this paper takes a further step by evaluating the capabilities of wireless networks to meet HTC requirements. Specifically, it highlights the limitations of the fifth-generation (5G) networks by identifying HTC-related Key Performance Indicators (KPIs) and explores the potential of the sixth-generation (6G) networks. Moreover, it not only proposes potential research approaches for HTC enhancement but also analyzes their impact on the challenges in HTC implementation. Finally, the paper questions the ubiquitous potential of 6G, suggesting that a coordinated approach leveraging artificial intelligence (AI) for jointly optimizing user sites and communication networks is the most promising strategy, rather than solely relying on the capabilities of specific network generations.

This paper presents a systematic review of K-12 cybersecurity education literature from around the world. 24 academic papers dated from 2013–2023 were eligible for inclusion in the literature established within the research protocol. An additional 19 gray literature sources comprised the total. A range of recurring common topics deemed as aspects of cybersecurity behavior or practice were identified. A variety of cybersecurity competencies and skills are needed for K-12 students to apply their knowledge. As may be expected to be the case with interdisciplinary fields, studies are inherently unclear in the use of their terminology, and this is compounded in this field due to the pervasive nature of cybersecurity, relevant and important to every person using a digital device. Almost all the studies within the data focused on secondary school settings and it appears the primary school years are largely ignored. This review suggests that most aspects of cybersecurity are not being systematically taught at K-12 around the world.

Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. However, detecting moving objects in visual streams presents distinct challenges. This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. Through tailored preprocessing and architectural adjustments, we heighten the model’s sensitivity to object movements. Rigorous testing against KITTI, LASIESTA, PESMOD, and MOCS benchmark datasets revealed that the modified YOLOv8 outperforms the state-of-the-art detection models, especially in environments with significant movement. Specifically, our model achieved an accuracy of 90%, a mean Average Precision (mAP) of 90%, and maintained a processing speed of 30 frames per second (FPS), with an Intersection over Union (IoU) score of 80%. This paper offers a detailed insight into object trajectories, proving invaluable in areas like security, traffic management, and film analysis where motion understanding is critical. As the importance of dynamic scene interpretation grows in artificial intelligence and computer vision, the proposed enhanced YOLOv8 detection model highlights the potential of specialized object detection and underscores the significance of our findings in the evolving field of object detection.

The Virtual Power Plant (VPP) serves as the primary form of participation for distributed power generation resources in electricity markets. Throughout this market engagement, uncertainties stemming from renewable energy sources and market prices present significant revenue risks for VPPs. Addressing this challenge, the paper proposes a VPP bidding risk management model centered on dominance constraints. In contrast to conventional methods reliant on risk metrics like Conditional Value at Risk, the proposed model offers improved control over downside and tail risks while reducing optimization iterations to just one, thus substantially reducing computational cost associated with risk management. The study also contrasts the efficacy and computational costs of first-order and second-order dominance models. For instance, in a three-scenario benchmark, the first-order dominance model exhibits a 16.7% lower overall risk level compared to the second-order model. Similarly, in a nine-scenario benchmark, the computational time of the first-order model increases by 0.96% due to the introduction of binary variables, and this increase scales with the number of benchmark scenarios. The paper additionally incorporates mean deviation of profits and expected excess returns to develop a bi-objective optimization model. This model not only fulfills risk control requisites but also shapes return distributions to align more closely with VPP decision-makers’ profit preferences. The case study is based on a VPP comprising conventional thermal units, wind turbines, energy storage stations, and flexible loads.

The radio frequency spectrum serves as a fundamental resource for wireless communication, encompassing various frequency bands allocated for diverse services and applications. Dynamic spectrum management (DSM) is essential to optimise the utilisation of this limited and valuable natural resource, with the aim of improving performance and adapting to changing wireless communication demands. Traditional static spectrum allocation methods have shown inefficiencies, leading to spectrum scarcity and under-utilisation. To address these challenges, the integration of blockchain and Cognitive Radio (CR) technologies has emerged as a promising approach. Blockchain, with its decentralised and secure attributes, can improve transparency and trust in spectrum allocation processes, while CR enables intelligent spectrum sensing and allocation to maximise utilisation. However, this promising approach comes with its own critical challenges, especially when dealing with the 6th Generation (6G) mobile communication. These challenges are related to the fact that the blockchain ecosystem needs to be interoperable and scalable enough to be compatible with the 6G high-demand and substantial resources. Specifically, integrating blockchain with CR requires efficient interoperability techniques where blockchain can easily and effectively interact with the CR platforms as well as radio spectrum environments. Furthermore, the spectrum management system over 6G networks needs to be designed in a way where massive 6G resources can be accommodated and managed without having any service performance degradation. This paper introduces a novel radio spectrum management model in 6G networks, named as BSM-6G, which integrates blockchain technology with CR where interoperability is preserved and scalability is maximised. Specifically, the proposed BSM-6G model merges blockchain’s transparent record keeping with CR’s intelligent spectrum management capabilities. To overcome the interoperability issue, BSM-6G provides an interoperable blockchain Oracle approach which facilitates the real-time interaction among the blockchain platform, the CR, and any data sources off-chain. This paper details all the technical and procedural challenges when implementing the proposed interoperability Oracle approach. To address the scalability challenge, BSM-6G utilizes the Proof-of-History (PoH) consensus protocol to align with the requirements of DSM in advanced networks like Beyond 5th Generation (B5G) and 6G. Evaluation results indicate that BSM-6G offers viable and less complex blockchain Oracle integration architecture measured by the technical implementation of BSM-6G, as well as low interoperability cost measured by transaction response time and transaction fee cost. Compared to state-of-the-art spectrum-based blockchain systems, BSM-6G shows a high scalable DSM-based blockchain in 6G networks measured by transactions per second (TPS).

Digitalization has begun as a transformative force within the energy sector, reforming traditional practices and paving the way for enhanced operational efficiency and sustainability. Enabled by key technologies such as smart meters, digitalization embodies a paradigm shift in energy management. Nonetheless, it is crucial to recognize that these enabling technologies are only the catalysts and not the end goal. This paper presents a comprehensive overview of digital services and products in the energy sector, with a specific focus on emerging technologies like AI and Connected Data Spaces. The objective of this review paper is to assess the maturity and adoption levels of these digital solutions, seeking to draw insights into the factors influencing their varying levels of success. This maturity and adoption assessment was carried out by applying a Fuzzy logic approach which allowed us to compensate for the lack of detailed information in current literature. By analyzing the reasons behind high maturity-low adoption and vice-versa, this study seeks to cast light on the dynamics shaping the digital transformation of the energy sector.

Over the past decade, an abundance of research has been conducted in the area of agricultural technology and innovations. The Internet of Things (IoT) has demonstrated its ability to connect numerous agricultural equipment, sensors, and specialists, boosting agricultural procedures in off-the-grid regions. Agriculture has experienced considerable improvements in production, cost reductions, service accessibility, and operational efficiency. With an emphasis on security, developments and trends in the sector, and technological implementation, this research paper offers an up-to-date analysis of existing and projected IoT applications in agriculture. In this article, enabling technologies, agricultural applications based on cutting-edge machine learning models, and services are all examined in relation to the development of IoT deployment in tackling diverse agricultural concerns. In the IoT-based agriculture system, potential challenges and limitations are also addressed. In its conclusion, this research provides an extensive review of the various aspects of IoT in agriculture, with the goal of empowering future researchers enthusiastic to make contributions to and advancement in their quest for a more in-depth comprehension of this field of study. A total of 96 papers were chosen for the selection from 2018 to 2023, and each was categorized using predetermined standards. The research’s results have been thoroughly examined, providing an overview of IoT in agriculture.

This paper proposes a parallel implementation of the Bron-Kerbosch algorithm, which finds all maximal cliques in large, complex graphs using CPU and thread-level parallelism and distributed memory with multiple cores. With the growing size and complexity of modern graphs, sequential algorithms can become impractical, making parallel computation a promising solution for reducing duration and enhancing scalability. Decomposing the graph into smaller components and distributing the workload across multiple threads using task parallelism is the proposed method. To optimise the algorithm’s efficacy across processes, load-balancing techniques are also investigated. The modified Bron-Kerbosch algorithm is implemented and evaluated on a variety of large graphs, exhibiting significant runtime and scalability enhancements over the sequential version. The conclusion of the paper discusses the prospective applications of the parallel Bron-Kerbosch algorithm in a variety of domains, including social network analysis, bio informatics, and network security. Overall, this research contributes to the expanding body of work on parallel computing and graph analysis by emphasising the advantages of CPU and thread-level parallelism for efficiently solving complex computational problems.

This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems’ sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) Algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm’s superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more efficient and sustainable power systems in the face of evolving energy landscapes.

Radar echo image sequence prediction is a spatial-temporal forecasting problem, which is one of the main challenges in precipitation nowcasting. In response to issues such as poor extraction of spatiotemporal features and blurred prediction of image sequence in previous models, this paper proposes a spatial-temporal dual discriminator precipitation nowcasting method using SNGAN with InceptionGRU (STD-SNGAN-InceptionGRU). The design of temporal and spatial dual discriminators constrains the generator’s prediction samples, effectively enhancing the spatiotemporal prediction capability of the generator through adversarial training. The generator utilizes multiscale convolution (Inception) and multiscale gate recurrent unit (InceptionGRU) to enhance its ability to extract spatiotemporal features, thereby improving its learning capability for the evolution of radar echo sequences. The comparative experimental results demonstrate that the proposed STD-SNGAN-InceptionGRU model outperforms other algorithms in terms of critical success index (CSI) and probability of detection (POD) at different echo intensities, especially in areas with long time series and high echo intensity. Furthermore, through ablation experiments, this paper proves that the temporal discriminator, spatial discriminator, and InceptionGRU all effectively improve the model’s prediction capability for radar echo images.

Energy hubs (EHs) enable all types of energy customers to participate in demand response programs (DRPs), such as inelastic loads, by combining electricity, heat, natural gas, and other types of energy. Integrated demand response (IDR) is the result of this new vision. From a global warming perspective, environmental emissions are a significant issue to be considered. Furthermore, hydrogen has been recognized as an attractive fuel for decarbonizing sectors that contribute to global warming. Thus, this paper provides a solution to global environmental problems through the utilization of renewable energy sources (RESs) and green hydrogen. In addition, electric vehicles (EVs) are expected to contribute significantly to this scenario due to their rapid expansion. Considering the uncertainty of electricity prices, this paper focuses on coordinating EV parking with hydrogen storage systems (HSS) and IDR with the aim of increasing flexibility, where a robust optimization (RO) method has been implemented to solve the problem. The results demonstrate that in the case of a deterministic solution to the problem and where uncertainty is at the highest level, the proposed scheme reduces the total operating costs by 13.89% and 8.67%, respectively. This indicates that the proposed scheme could avoid overinvestment and cost-effectively achieve the given carbon emission target.

This paper presents a comprehensive review of the advancements and challenges in scalable modular antenna arrays for 5G Massive MIMO (Multiple Input Multiple Output) networks, a pivotal technology for the next-generation wireless communication. Tracing the evolution of wireless technologies from 1G to 4G, the paper contextualizes the paradigm shift brought by 5G, characterized by enhanced spectral efficiency, massive device connectivity, and higher frequency bands. Focusing on Massive MIMO, the paper explores its role in augmenting network capacity and signal quality via advanced techniques like beamforming and spatial multiplexing. It delves into the intricacies of designing and implementing scalable modular antenna arrays, essential for the flexibility and optimization of rapidly evolving 5G networks. The review also covers mathematical modeling, advantages of Massive MIMO, and the challenges in integration, performance under diverse conditions, and cost-complexity balance. Concluding with insights into the commercialization trajectory of MIMO technology and its integration into modern telecommunications, the paper highlights the ongoing research directions and future potential of scalable modular antenna arrays in meeting the ambitious demands of 5G and beyond.

The progressive retirement of conventional power plants is decreasing the total system inertia and making the frequency control more delicate. In this context, voltage-sensitive loads will have a more pronounced impact on frequency. Therefore, there is an interest in understanding and quantifying how voltage dynamics impact the frequency response. For this purpose, a linearization of the system and the analysis of the obtained transfer functions are in focus. The paper aims to complement the traditional eigenvalue analysis, capture a system’s response to large disturbances, and estimate the frequency overshoots. The study also shows that controllers affecting the load voltage, such as Power System Stabilizers (PSSs), can impact frequency response during a large disturbance. The impact of PSS on system responses and its transfer functions is carried out by applying a general control configuration. Both conventional PSS1A and multi-band PSS designs are analyzed and compared. Thereby, the paper explicitly interlinks a PSS design, and its parameters with properties of the system’s frequency response reflected in the system transfer function. Illustrating enhancements facilitated by the presented approach, the explicitly derived PSS designs demonstrate the potential to reduce frequency overshoot. As an example, this reduction is achieved by improving a common mode damping or changing the position of the dominant zero of the closed-loop system.

In this paper, we present a 6.78 MHz Pulsed Wireless Power Transfer (PWPT) system specifically designed for powering the electronics placed on a suction roll of a paper mill machinery, thus enabling rotating equipment monitoring. The system utilizes a pair of different resonant coils to maintain a stable coupling coefficient for a fraction of the roll rotation period while guaranteeing compactness and ease of installation. Designed using both Commercial-off-the-Shelf (COTS) and custom components, the developed WPT system achieves a DC-DC efficiency of 62% in the specific working conditions. The transferred power is used to supply an Internet-of-Things (IoT) node that continuously measures and transmits data about the operation status. A fully operational system for battery and angular position monitoring has been developed and tested in a laboratory environment. This research presents a comprehensive analysis of the system and explores the interaction of different coils to maintain a stable coupling coefficient in dynamic applications. The same approach can be employed for rotating machinery with similar geometrical constraints or more general dynamic applications characterized by periodic movements and limited space for the coupling system.

In the process of frequent start-stop actions of the slewing platform in existing electric shovels, there is a substantial waste of kinetic energy. Furthermore, the installed power of the drive motor is often configured according to the maximum load, which makes it frequently operate in a low-efficiency region further exacerbating energy losses. To address these issues, this paper proposes an electro-hydraulic active-passive hybrid drive system for the shovel’s slewing system. The aim of this system is to efficiently store and utilize the kinetic energy generated by the shovel’s slewing system, reduce the installed power of the system’s drive motor, and consequently enhance the overall energy efficiency. To achieve high-efficiency energy recovery during the swing process of the slewing system, the proposed system introduces an additional hydraulic pump/motor and hydraulic accumulator. The pump/motor is situated at the original drive motor’s drive shaft. It serves to recover kinetic energy during the deceleration of the shovel’s slewing system and assists the motor in driving the load during the acceleration. This approach not only allows efficient storage of kinetic energy but also significantly reduces the installed power of the drive motor. The paper begins by analyzing the operational principles of the proposed electric-hydraulic active-passive hybrid drive slewing system of electric shovel. Subsequently, a test platform was constructed using a 36-ton excavator to preliminarily validate the proposed system feasibility. Experimental results indicate that, compared with the original system, the peck power and decrease energy consumption of the swing motor can be reduced by 28% and 49% respectively. Ultimately, an electromechanical-hydraulic coupling model was established for the electric-hydraulic active-passive hybrid drive electric shovel’s slewing system. Simulation results reveal that, compared with the original system, the peak power and energy consumption of the slewing motor can be reduced by 300 kW and 59%, respectively. The proposed system offers a significant improvement in the efficient storage of kinetic energy in large inertia slewing platforms.

Fuzzy PID control is a control method with good adaptability and stability in complex environments. It is used to achieve precise regulation and stable control of the system. In this paper, a fish scale evolution GSOM is proposed to improve the control method of fuzzy PID. Firstly, the fish scale regulation system is established and the differential evolution theory is introduced to realize the evolutionary upgrading of the system. Secondly, the GSOM module is introduced. The system is optimized by self-organized mapping neural network to achieve dynamic regulation of polymorphic inputs. Meanwhile, the fuzzy rule base and parameter regulation mechanism in fuzzy PID control are dynamically optimized. Improve the performance of the control system. Finally, the control method of improved fuzzy PID for fish scale evolution GSOM is simulated using MATLAB. The simulation experiments also compare several traditional PID control methods. The comparison indexes include stability, robustness, control accuracy and feedback output effect. The results show that the method in this paper is more stable and has fewer iterations when facing the dynamic input environment. The tracking error and control output of the controller system are significantly improved. It has good feedback output effect, solves the saturation problem and has higher control accuracy.

This paper proposes a stretchable-substrate-impregnated fabric for deployable high-gain antenna for small satellites. Constellations of small satellites offer worldwide coverage of wireless communication and earth observation; high-gain antennas are required to realize high-speed communication and high-resolution radar. This paper aims to develop a CubeSat onboard high-gain reflectarray antenna that can be folded compactly and deployed in space. The novelty of this paper is the stretchable-substrate-impregnated fabric, which can realize low stowage volume with high fabrication tolerance and low resistivity with single-piece reflectarray to avoid gain degradation. The origami-folding-pattern compatible reflection elements can avoid creases on the elements, avoiding gain reduction after folding. A $17\times 17$ -element reflectarray using the stretchable-substrate-impregnated fabric and the origami-folding-pattern compatible reflection elements is designed, fabricated, and characterized. The folded volume of the reflectarray is 27 mm in height and 58 mm in diameter, and the measured fabrication tolerance of the reflection element was $20 ~\mu \text{m}$ at most. Electrical antenna measurements confirmed low resistivity of 1.20 $\text{m}\Omega /\Box $ and a high realized gain of 23.6 dBi, comparable with conventional flexible printed circuit reflectarray antennas. The simulated and measured antenna efficiency is 25.3% and 25.1%, respectively. The simulated and measured 1-dB gain down bandwidth is 1.6% and 1.5%, respectively.

To ensure the reliability of autonomous driving, the system must be capable of potential hazard identification and appropriate response to prevent accidents. This involves the prediction of possible developments in traffic situations and an evaluation of the potential danger of future scenarios. Precise Collision Risk Assessment (CRA) faces complex challenges due to uncertainties inherent in vehicle and road environmental conditions. This paper introduces a new CRA approach, the Multi-Dimensional Uncertainties-CRA (MDU-CRA), which integrates uncertainties related to driver behavior, sensor perception, motion prediction models, and road infrastructure into a comprehensive risk evaluation framework. The estimation of vehicle state is initiated using Extended Kalman Filtering (EKF) to capture uncertainties in sensor perception. Concurrently, a probabilistic motion prediction model based on Gaussian distributions has been developed, which considers the uncertainty in driver behavior. Subsequently, the uncertainty of the road structure is modeled using a truncated Gaussian distribution. Finally, collision risk is quantified as the future probability of collision through heuristic Monte Carlo (MC) sampling. This paper presents the results of two experiments Firstly, our proposed method is demonstrated to outperform the reference neural network-based method in terms of short-term motion prediction accuracy. Secondly, two driving scenarios are extracted and reconstructed from the Next Generation Simulation (NGSIM) dataset for validation and evaluation, i.e., an active lane-change scenario and an emergency braking scenario. In the domain of collision risk assessment, our approach consistently outperforms other evaluation methods. It exhibits the capability to perceive collision risks 2 to 5 seconds in advance, significantly reducing the probability of imminent collision incidents.

Today’s communication networks, especially those involving massive Machine-Type Communications (mMTC), encounter challenges like channel congestion and high power consumption. This paper introduces the Dynamic Transmission and Delay Optimization Random Access (DTDO-RA) scheme to address these issues. The presented approach adjusts the backoff indicator (BI) value based on the number of transmissions to optimize the success rate of the RA procedure. This is achieved while concurrently reducing channel congestion and power consumption. The strategy employs reinforcement learning algorithms, notably Q-learning and Deep Deterministic Policy Gradient (DDPG). Methods based on these algorithms play a critical role in fine-tuning the Backoff Indicator (BI) value and the maximum number of preamble transmissions (Max TX), thereby enabling efficient and responsive channel access management. This paper highlights the critical influence of the BI value on preamble transmission delay and overall access delay. The DTDO-RA method effectively manages the number of preamble transmissions during the RA procedure, expanding the BI range in response to heavy RA traffic from UEs. Reducing channel congestion and power consumption is crucial for enhancing the success rate of the RA procedure. The simulation results in this paper evaluate the power consumption and channel congestion in relation to the number of UEs and the number of Random Access Opportunities (RAOs), providing a comparative perspective on the variations in the BI values achieved by the DTDO-RA scheme. The results of the paper provide insights into the achievement of higher network efficiency, reduction of network congestion and lower power consumption.

The Sixth Generation (6G) Wireless Communication Network (WCN) is the successive provision to ameliorate the gain with ultra-low latency, and e xtremely high energy efficiency. The 6G WCN enables the specifications of artificial intelligence to optimize the services and capabilities. The vision of the 6G era is expected to address a seamless fusion of communication between the human, physical world, and digital world. The latest 6G WCN standard is a fundamental foundation and requires immense attention in the field of research. This paper presents the framework of 6G WCN with an illustration of its key technologies. The different technologies involved in 6G are well explained with the demonstration of the communication scenario such that the key performance indicators are improved with major differences. The primary contribution of this paper is the explanation of the 6G with the technologies that have a drastic impact on the characteristic aspects of a wireless communication network such as data rate, spectrum efficiency, energy efficiency, connection density, and reliability. All these technologies have the capability to revolutionize the subsequent WCN.

Recent advances in low Earth orbit (LEO) satellites have made it possible to achieve zero blind spots on Earth. Considering the give locations of these devices, this makes satellite-ground links between quantum devices a practical possibility. This paper proposes the first quantum federated learning (QFL) application in satellite-ground communication. To improve communication and computing performance, this paper adopts slimmable quantum federated learning (SQFL) and slimmable quantum neural networks (sQNN), which allow for two different configurations in quantum neural networks: the angle and pole configurations. This paper also employs superposition coding and successive decoding to increase communication opportunities. Through extensive experiments, the proposed satellite-ground SQFL framework performs well and is both computationally and communicationally efficient compared to classical federated learning and QFL.

The speed regulation technology for energy conservation of pump and fan systems occupies an important position in industrial field. The paper focuses on the rapid development and research significance of speed regulation technology for energy conservation based on different permanent magnet adjustable speed drives (PMASDs). The development history and speed regulation scheme for energy conservation are introduced step by step. The features of theoretical model based on magnetic vector potential and equivalent magnetic circuit methods are presented and studied in the paper, which is benefit to select a suitable method for a specific drive. Several optimization methods of design parameters of PMASDs are given in the paper. The structure and operating principle of some new speed regulation scheme are described, and their advantages, disadvantages and feasibility of engineering application are analyzed. Finally, the proposed speed regulation technologies for energy conservation are summarized and classified from the aspects of magnetic circuit structure, permanent magnet rotor structure, conductor rotor structure, and the slip power conversion scheme. In a conclusion, the power-fed PMASD will be the future of energy conservation technology for the high-efficiency and low-cost large pump and fan systems.

The fatigue driving detection process faces issues such as a large number of parameters, low accuracy and insufficient continuous detection. To address these, this paper proposes a method combining enhanced YOLOv5s and DeepSort for fatigue driving detection. First, the improved Mobilenet_ECA lightweight backbone is introduced to reconstruct the YOLOv5s backbone part. Then, the CSPDarknet53(C3) module in the neck network is integrated with the Triplet Attention Module (TAM) to enhance the fusion of contextual information and improve the network’s facial feature extraction capability. In addition, FocalEIOU Loss is used to optimize the problem of large errors in Complete Intersection over Union (CIoU) Loss prediction box regression calculations. Next, the DeepSort facial feature tracking algorithm is combined for continuous classification tracking to optimize the problem of driver facial information loss. Finally, the facial feature detection results are combined with the number of consecutive eyeclosed frames, the number of consecutive yawning frames, and the Percentage of Eyelid Closure over the Pupil over Time (PERCLOS) score to build a fatigue driving detection model. The experimental results show that compared with the baseline model YOLOv5s, the algorithm proposed in this article has improved mAP@0.5 and P by 1% and 1.8%, Params decreased by 56.3%, FLOPs decreased by 63.2%, and the model size is only 6.4 MB. The final recognition rate of fatigue and nonfatigue driving was as high as 97.4%. It is verified that the algorithm in this paper can maintain high detection accuracy while being lightweight, and can effectively identify driver states, providing strong support for the deployment of vehicle edge devices.

Currently, the use of fiber-optic Bragg gratings in biomedical applications, especially in the field of magnetic resonance imaging (MRI), is becoming popular. In these applications, the fiber Bragg grating (FBG) encapsulation plays a crucial role in terms of the accuracy and reproducibility of the measurements. This paper describes in detail the fabrication method of a prototype FBG sensor, which is realized by encapsulating a Bragg grating between two layers of the MR-compatible material Acrylonitrile Butadiene Styrene (ABS) by 3D printing. The sensor thus created, implemented, for example, on the chest of a human body, enables monitoring of the vital functions of the human body. The paper describes the complete procedure for the creation of the prototype sensor, including strain and temperature dependence, as well as results of long-term experimental measurements against the conventional electrocardiography (ECG) standard. Results based on the objective Bland-Altman (B-A) method confirm that the implemented sensor can be used for reliable monitoring of cardiac activity (>95% based on B-A). Taking into account the single fiber optic cable, its simple implementation, its small size and weight < 5g, the presented sensor represents an interesting alternative to conventional ECG.

A finite-time observer-based attitude controller is designed for a flexible satellite in the paper titled “Finite-Time Control Algorithm Based on Modal State Observer for Flexible Satellite Attitude Tracking” which is referred to as the “original paper” hereafter. Through a comprehensive review of the article, it has become evident that the design of the observer in the original paper contains critical errors, rendering the results presented therein invalid. In this corrigendum, we meticulously discuss the identified flaws in the original paper and present the necessary amendments to rectify them. Furthermore, we enhance the finite-time observer proposed in the original paper to not only estimate the effects caused by modal variables but also account for the effects resulting from external disturbances and the inherent uncertainty associated with the moment of inertia of the flexible satellite. The finite-time convergence property of the proposed observer, presented within this corrigendum, is assured through the utilization of the Lyapunov approach. By employing the proposed observer outlined in this corrigendum, the validity of the remaining components highlighted in the original paper is preserved, providing an accurate framework for further research and practical applications.

This paper investigates the $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ exponential consensus control problem of leader-follower multi-agent systems based on an event-triggered strategy. It begins by establishing an error system and provides a sufficient condition guaranteeing exponential stability of the error system while satisfying the $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ performance index. Subsequently, utilizing the condition, a design method for the $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ controller is presented. Finally, through a numerical example, this paper discusses the relationship between optimal $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ performance index and the maximum sampling period under different topological structures. The effectiveness of the proposed theoretical framework is validated through the numerical example.

This paper proposes a novel curriculum learning approach for collision avoidance using feedback from the deep reinforcement learning (DRL) training process. Previous research on DRL-based collision avoidance algorithms has encountered challenges such as long training times and difficulty in convergence due to sparse rewards. To address these issues, curriculum learning has been used to divide the target task into multiple subtasks for training. However, manual or random curriculum design often generates unnecessary subtasks that do not improve performance. Furthermore, a standardized curriculum design method for collision avoidance has not yet been presented. Therefore, this paper introduces a curriculum-based collision avoidance learning method that utilizes feedback during the training phase. The proposed method differs from traditional curriculum learning in that the subtask is not predetermined before training. Instead, the curriculum is modified during training based on feedback obtained from validation environments. If a robot demonstrates high collision avoidance performance in a validation environment, it is then validated in more challenging environments for rigorous evaluation. Conversely, if collision avoidance performance is low in the validation environment, the robot is trained in a new environment to overcome frequent collision situations. Simulations and practical experiments were conducted for the proposed method, which showed better performance compared to the non-curriculum method.

Missions across a variety of disciplines require the interception of multiple targets. In defence scenarios, targets may pose a threat to sites, while in agriculture the targets may be invasive pests or fruit ready to harvest. This paper focuses on the cooperative control of a robot swarm for interception missions of multiple static and dynamic targets while avoiding collisions. We formulate two modifications of the classical Navigation-Function for a swarm interception mission which are suitable for deterministic and stochastic scenarios: the Swarm Navigation Function (S-NF) for the deterministic case, and the Swarm Probabilistic Navigation Function (S-PNF) for the stochastic case. Both functions provide a simultaneous solution for the problems of target assignment and motion-planning as opposed to the classical approaches that solve each problem independently. We demonstrate the effectiveness of these functions through extensive simulations and real-world experiments, comparing their performance with optimal solutions and human decision-making in similar scenarios. We show analytically that by following the Swarm-Navigation-Function gradient, the swarm will intercept all static targets while avoiding agent-agent and agent-obstacle collisions and similarly following the gradient of the Probabilistic-Navigation-Function will almost surely converge to a target in finite time, while the probability for agent-agent and agent-obstacles collisions is limited to a predefined value. The complexity of both schemes is linear with the number of targets and robots, and therefore it is scalable. Although not optimal, these solutions are simple and efficient, making them suitable for an extended set of real-time and real-life applications. We compare the resulting Swarm-Navigation-Function trajectories to that of a human in a catch game and an interception virtual game, the comparison indicates that as the trajectories are similar, human decision-making performs better. We conclude the paper with a set of simulated experiments and real-world experiments demonstrating the efficiency of the proposed scheme for dynamic targets.

Software Defined Networking (SDN) has emerged as a new paradigm for managing heterogeneous networks ranging from enterprises to home network via decoupling the control plane from the data plane. In the traditional networking landscape, these two planes are tightly bound together inside a single appliance. The logically centralized and distributed control plane and programmability offer a great opportunity to improve network security, such as by implementing new mechanisms to detect and mitigate various threats, and also enable security as a service in an SDN paradigm. Due to the ever increasing and fast development of SDN, this paper provides an extensive survey of SDN controllers, SDN-related security threats, and solutions to mitigate the security threats. This study provides a comprehensive survey of 53 SDN controllers from different aspects, including language, architecture, organization, open source, scalability, consistency, reliability, API used, library, and their description. We have also provided a detailed security analysis of SDN architecture with an extensive classification of security threats endangering its different architectural components and the solutions to effectively mitigate them. This paper also identifies challenges and promising future directions on SDN deployment, standardization, implementation, and security issues that should be addressed in this field.

Software maintenance takes up a disproportionately large amount of time in the modern software life cycle. One of the common problems is understanding the original code that is being restructured and improved and this is especially true with low-level code. This paper investigates the results and properties of an automated process that can raise the abstraction level of code from low-level operations to high-level structures. The process is made of independent components and can be adapted to different scenarios. The automated improvements implementation relies on the program transformation system FermaT and its catalogue of semantics-preserving transformations. The process uses hill climbing and a metric for the fitness function of the programs. This component was made to work on general inputs, without explicit knowledge of the type of origin of the program. The paper explores how different inputs are actually handled by the system, what are the properties and how these can be used for further improvements. Two main types of inputs are shown, x86 assembly and MicroJava bytecode. These two have many operational differences, and the translator tools introduce some more, but nonetheless, the same process can handle all of these and, on average, improve the Structure metric (a good approximation of the complexity of the code) by around 85%.

The real-time analysis of gas flares is one of the most challenging problems in the operation of various combustion-involving industries, such as oil and gas refineries. Despite the crucial role of gas flares in securing safe plant operation and lowering environmental pollution, they are among the least monitored components of petrochemical plants due to their harsh working environments. Remote sensing techniques are emerging as potential alternatives for conventional sampling-based techniques in visual inspection and performance analysis. This paper presents an in-depth review of significant achievements in Gas flares monitoring over past two decades and highlights ongoing challenges in Artificial Intelligence (AI)-enhanced remote inspection. By reading the content, both industry professionals and academic researchers can gain a comprehensive knowledge of improvements from the integration of AI and remote sensing, understanding the trend of Gas flares monitoring. The paper commences with an analysis of RGB camera-based methods, focusing on how their combination with cutting-edge machine learning and deep learning algorithms can significantly improve the detection, segmentation, and measurement of gas flare systems. It then explores the use of hyper-spectral imaging techniques, including infrared cameras and space-borne satellite sensors, underscoring their potential in remote monitoring and performance analysis. Additionally, the effectiveness of multi-view inspection methods is assessed, highlighting how these approaches enhance the monitoring capabilities. Finally, the paper identifies key research areas that require further attention. It also presents a clearer direction for future progress, emphasizing the importance of continuous research to foster advancements and facilitate broader commercial adoption.

The novel approach to the Low Energy Bluetooth RSSI (Received Signal Strength Indicator) examination for a personal location during the evacuation process is presented in this paper. The presented system is based on stationary locating localization nodes installed inside the facility and portable wristbands worn by people. A method based on the propagation model and preliminary determination of its characteristics is used to calculate the wristband-locator distance. The accuracy of the distance estimations is increased by assuming Gaussian model of RSSI measurements and using the estimator of RSSI mean value. A modified multilateration approach is used to estimate the person’s position in the 2D Cartesian coordinate system. The paper also includes the outcomes of experiments conducted on the proposed approach as it was applied to the prototype evacuation supervision system. The paper presents a comparison of the position estimation error for the proposed method based on the mean RSSI value estimator with the results obtained when raw RSSI values were used. Analysis and discussion of wristband position estimation error are also included.

Due to the relatively low operational reliability of the MMC valve, it is necessary to establish corresponding test methods to examine the tolerance of the valve to electrical and thermal stresses under engineering requirements through operational tests, to verify its operational reliability. To meet the operational testing requirements for higher voltage and larger current converter valves, an appropriate test topology is required. The paper first analyzes the voltage and current stress characteristics of capacitor components and MMC converter valves in ±800 kV/8 GW Flexible DC transmission system, providing a theoretical basis for the stress of operational testing valve, requiring that to be equivalent to actual operating conditions. Addressing the operational testing demands for high voltage and large current converter valves in the ±800 kV/8 GW flexible DC transmission MMC, a test topology is proposed. This topology can meet the operating conditions of higher voltage and larger current and simultaneously conduct tests on valve segments and modules, significantly improving operational testing efficiency and reducing testing costs. Additionally, a control method applicable to this test topology is proposed, enabling flexible adjustment of DC and AC in the test circuit, thereby enhancing the equivalency of operational tests. In this paper, the proposed topology and control method have enhanced testing efficiency, reduced testing costs, improved testing equivalence, and enhanced the precision of current and capacitor voltage control. Finally, simulations and experiments are constructed to validate the feasibility and effectiveness of the proposed operational test topology and its control method through simulations and experiments.

The demand for personalized learning experiences and effective analytics in education has significantly increased. The integration of technology in education has brought about significant changes in teaching and learning practices. In the era of digital technology, the integration of education technology in the classroom has led to a change in teaching methods and learning strategies. In this paper, we introduce the KNIGHT (AI in Education at Hochschule für Technik (HFT) Stuttgart) framework, which is a holistic solution designed to tackle the complex issue of personalized education in a digital era. The paper explores the application of multimodal data integration, the novel application of deep reinforcement learning to education analytics, and the ethical consideration of privacy-preserving personalized feedback. The proposed framework’s efficacy is substantiated through a case study, demonstrating its potential to revolutionize personalized education. This paper provides a comprehensive overview of the current discourse, providing valuable insights for educators, policymakers, and researchers into the multifaceted landscape of modern education, contributing to ongoing discussions and advancements in educational technology.

Currently, weakly supervised semantic segmentation methods based on image-level annotation often rely on pseudo pixel masks generated by seed regions. However, the growth of seed regions is stochastic, and in cases where targets are occluded or overlapped in the image without additional reference information, the segmentation network may encounter issues of missed or incorrect segmentation. To address this problem, this paper proposes a polarized attention mechanism for weakly supervised semantic segmentation networks. The attention mechanism consists of a semantic perception branch and a boundary detection branch. The semantic perception branch allows the network to better distinguish the category of each pixel in the image. Subsequently, the boundary detection branch enables the seed region to naturally expand towards the target boundary. The pseudo pixel mask generated by this method provides better coverage of the target area and improves the performance of the segmentation network. The test set and validation set mean Intersection over Union (mIoU) of the PASCAL VOC 2012 dataset achieved 72.1% and 73.2%. The results of the experiments demonstrated the effectiveness of the proposed method. The experimental results indicated that the attention mechanism, as proposed in this paper, can effectively enhance the segmentation performance in situations where objects in the image are occluded or overlapped.

A broadband multimode circularly polarized (CP) stacked antenna with pattern diversity is proposed in this paper. The antenna consists of three layers, two substrates with annular-ring patches and a parasitic metal patch. The antenna can radiate different patterns by exciting different modes of annular-ring micro-strip antenna (ARMSA). When port 1 is excited, the antenna is excited at the second-order mode(TM21) to generate the conical radiation pattern, whereas the antenna is excited at the fundamental mode(TM11) to generate the broad beam radiation pattern when port 2 is excited. In order to generate circular polarization, ARMSAs are fed by two feeding points and both of the annular-ring patches are fed with the network in the same layer. The two-port antenna achieves the broadband property by parasitic patch. The parasitic top metal layer can broaden the impedance bandwidth of TM11 mode while the ground plane of TM11 mode can act as the parasitic patch to broaden the impedance bandwidth of TM21 mode. To verify the design, the antenna is fabricated and measured. The measured results show that the antenna proposed in this paper has an impedance bandwidth of 0.22GHz (1.92-2.14GHz) and 3dB axial ratio (AR) bandwidth of about 0.25GHz (1.90-2.15GHz).

This paper proposes scalable and foldable origami-inspired supernumerary robotic limbs that incorporate a continuous robotic mechanism to support activities of daily living and presents an analysis of the fabrication process, evaluation experiments, and potential applications of the proposed devices. These origami-inspired supernumerary robotic limbs can augment the wearer’s capabilities by adjusting the scale of the devices to various applications and attaching them to parts of the body, such as the hands, wrists, arms, and torso. The device’s foldable structure can be unfolded for use and folded when not in use to prevent physical contact with the wearer and the surrounding environment so that the user’s movements are not restricted. In addition, the robotic limb is made of lightweight and soft material, so it remains safe to use and fatigue-free even after prolonged use. To verify the durability of the origami module, we conducted an experiment in which the module underwent 1,000 compressions using different materials and layer counts based on the folding pattern outlined in the manufacturing diagram. The experimental results showed that polypropylene was a suitable material for reducing fatigue due to folding, compared to polypropylene synthetic paper or plain paper. We also observed decreased stiffness with an increase in the number of layers. We believe that the proposed origami-inspired supernumerary robotic limbs can enlighten novel approaches to combining soft robotic and wearable devices.

This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.

Identity systems are fundamental to any network, just as a language is shaped by a consistent set of symbols and their interpretation. A network is defined by a consistent set of identities and their usages. However, as pressures mount to customize and adapt to these identity spaces, one ponders how a network sustains its utility through coherence. This study investigates evolving Internet identifiers and their supporting systems. Owing to the multi-disciplinary nature of the topic, this paper draws perspectives from a wide array of sources that contribute to Internet systems and digital library systems. Initially, this paper highlights the dynamism and role of namespaces, focusing on the critical need to maintain coherence in the public domain. It also discusses the impact of mobility and digital cloning on identifiers and explores their influence on identity and location. In addition, key Internet identifiers are analyzed, contrasting them with methodologies adopted by digital library systems to provide deeper insights into various identity models. Furthermore, this study explores the evolution of the Domain Name System (DNS) as an identity system, examining the tensions and adaptations driven by customization demands. Finally, this paper explores alternative namespaces beyond the DNS, considering potential responses to these evolutionary pressures and future implications.

In modern cities, consumers with distributed energy resources (DERs) can trade energy by managing their consumption and supply. Blockchain is considered to provide technical support for establishing a distributed energy market, while the current mainstream blockchain technology cannot meet the requirement of efficiency and scalability under such a transaction scenario. In addition, the potential defaults of market players also hinder market implementation. Therefore, a reputation-based distributed energy trading mechanism and the corresponding blockchain consensus method considering reputation management are proposed in this paper. Firstly, a distributed reputation management mechanism is designed to systematically evaluate the user’s behavior in the market. Secondly, to address the limitations of traditional consensus methods in the distributed energy market distributed reputation control is introduced into the node model of blockchain, and a new energy blockchain consensus method named DPoR is proposed to solve the technical bottleneck under the distributed energy trading scenario. We also present a corresponding transaction mechanism to encourage market participants to maintain their reputations. Finally, the simulation results verify the effectiveness of the proposed transaction mechanism in this paper, and the presented blockchain consensus mechanism can meet the performance requirements under distributed energy trading scenarios in terms of high efficiency, low consumption, and openness.

This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core objective of this paper is to identify existing security threats to graphical password schemes and the corresponding countermeasures developed to mitigate these attacks. The study process begins by identifying the usable databases and search engines to identify all the relevant resources. The inclusion and exclusion criteria were carefully selected to prioritize the study, focusing mostly on attacks and countermeasures related to graphical password schemes between 2009 and 2023. After thorough identification and selection progress, 59 studies met all the criteria. Among these studies, 47 mentioned shoulder surfing as a threat to graphical password schemes, while 20 discussed brute force attacks. Additionally, there were 21 papers on dictionary attacks, 13 on smudge attacks, spyware attacks, and social engineering, and 19 that discussed guessing attacks as threats to graphical password schemes. Furthermore, the papers identified several other attacks, including frequency of occurrence analysis attacks, video recording, eavesdropping, computer vision, sonar, and image gallery attacks, with the corresponding numbers of papers being 9, 17, 5, 2, 2, and 1, respectively. The results also highlight the countermeasures proposed in the study papers to mitigate the aforementioned attacks. Among the various countermeasures identified, most revolve around randomization, obfuscation, and password space complexity as the most commonly used techniques for enhancing the security of graphical password schemes.

Real-time systems are becoming pervasive with the growing global connectivity and rising consumer demands. The need for real-time processing has become a crucial part of many business applications worldwide. A key factor that determines the time taken for an application to give out the result hinges on its ability to prioritize, manage, and execute real-time workloads. However, there are several difficulties and constraints connected with implementing tasks in a real-time context. This research study primarily focuses on load balancing for mixed real-time tasks on a multi-core system, one of the major challenges for executing real-time workloads. The purpose of load balancing is to distribute the load evenly among the processor(s) and maximize their utility while minimizing overall execution time. The goal of this paper is to present a critical analysis of existing load balancing techniques for both periodic and aperiodic tasks. The paper explores several factors including throughput, performance, migration time, response time, overhead, resource utilization, scalability, fault tolerance, power efficiency, and other variables that play a crucial role in assessing the efficacy of load balancing in real-time systems. The proposed has contributed in four folds. Firstly, the state-of-the-art of various load balancing algorithms are discussed followed by the architecture involved in real-time multi-core systems. Later, different load balancing based scheduling algorithms were compared on the basis of different schemas and metrics for algorithm evaluation is also provided. Finally, the paper also identifies areas that warrant further exploration or investigation, suggesting potential avenues for future research, and highlighting emerging trends or developments that may shape the field.

The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter the camera scene, or face away from the camera. We formulate the problem of assessing student participation into two subproblems: (i) student group detection against strong background interference from other groups, and (ii) dynamic participant tracking within the group. A massive independent testing dataset of 12,518,250 student label instances, of total duration of 21 hours and 22 minutes of real-life videos, is used for evaluating the performance of our proposed method for student group detection. The proposed method of using multiple image representations is shown to perform equally or better than YOLO on all video instances. Over the entire dataset, the proposed method achieved an F1 score of 0.85 compared to 0.80 for YOLO. Following student group detection, the paper presents the development of a dynamic participant tracking system for assessing student group participation through long video sessions. The proposed dynamic participant tracking system is shown to perform exceptionally well, missing a student in just one out of 35 testing videos. In comparison, a state-of-the-art method fails to track students in 14 out of the 35 testing videos. The proposed method achieves 82.3% accuracy on an independent set of long, real-life collaborative videos.

This paper describes an Intrusion Detection System (IDS) which uses several existing known IDS algorithms and Triple Modular Redundancy (TMR) algorithm to make decision about eventual existing attack by majority voting in one constructed ensemble model which solves practically the problem of binary classification. Proposed novel model belongs to so called stacking ensemble methods of machine learning algorithms which uses exactly four algorithms from the group of best binary classification algorithms: Decision trees, Naive Bayes, Support Vector Machine, k-nearest neighbors, logistic regression and AdaBoost and is applicable for any similar problem. Using proposed method, we get a more precisely determined threshold than it is case using whatever of in method individual applied algorithm as well as those algorithms that are the state of the art in the field of binary classification. Besides that, one of the main disadvantages of classic TMR used for classification network traffic, which is the problem of bad over-voting was successfully avoided by improving classic TMR with a new algorithm proposed by the authors. Today a denial of service attacks (DoS) and distributed denial of service (DDoS) are one of most present type on Internet and that is why the authors in this paper paid special attention to them and because of that the authors of proposed method chose to use known KAGGLE dataset which contains data of these type of attacks for the examination of quality of proposed IDS method implemented in suitable software. The dataset itself consists of a wide range of simulated intrusions in a military network environment in United States. Obtained results showed that IDS software with implemented proposed method worked precise and timely, which means alarms was trigger properly and efficiently with better results of quality of classification in most important measures than the individual included algorithms who are the state of the art in binary classification.

Recently, the thermal management of power electronic converters has gained significant attention due to the continuous trend of developing very compact power electronic converters with high power density. With the evolution of power semiconductor devices, high operating temperatures and large thermal cycles have become possible, necessitating a significant improvement in thermal system designs. Researchers have made significant efforts to develop effective thermal management systems to improve the reliability and lifetime of power electronic converters. This article intends to present a thorough review of thermal management systems employed in power electronics cooling. The applied thermal management techniques have been reviewed from the perspective of electrical parameter regulation and heat dissipation control. Regulation of electrical parameters involves active thermal control, which is a method for controlling junction temperature and thermal cycling of power semiconductor devices. The active thermal control implementation processes reviewed in this article consist of increasing overload capacity, manipulating switching and conduction losses, employing modified modulation processes, balancing thermal stress at the converter level, and controlling thermal stress at the system level. Control of heat dissipation can be achieved through direct and indirect cooling of power electronic converters with air or liquid as the coolant. The effectiveness and implementation methods of these cooling techniques, such as channel cooling, phase change material-based cooling, immersion cooling, jet impingement and spray cooling, are reviewed in this paper. Moreover, performance-enhancing ideas and challenges for these techniques are discussed. The primary objective of this review paper is to bridge the existing gap in the literature by offering a comprehensive comparison of commonly employed thermal management techniques.

Airborne 3D imaging using ultrasound is a promising sensing modality for robotic applications in harsh environments. Over the last decade, several high-performance systems have been proposed in the literature. Most of these sensors use a reduced aperture microphone array, leading to artifacts in the resulting acoustic images. This paper presents a novel in-air ultrasound sensor that incorporates 1024 microphones, in a 32-by-32 uniform rectangular array, in combination with a distributed embedded hardware design to perform the data acquisition. Using a broadband Minimum Variance Distortionless Response (MVDR) beamformer with Forward-Backward Spatial Smoothing (FB-SS), the sensor is able to create both 2D and 3D ultrasound images of the full-frontal hemisphere with high angular accuracy with up to 70 $\mathrm { \text {dB}}$ main lobe to side lobe ratioin the array response in a single-source scenario. This paper describes both the hardware infrastructure needed to obtain such highly detailed acoustical images, as well as the signal processing chain needed to convert the raw acoustic data into said images. Utilizing this novel high-resolution ultrasound imaging sensor, we wish to investigate the limits of both passive and active airborne ultrasound sensing by utilizing this virtually artifact-free imaging modality.

Recently, there is rapid integration of power electronic converter (PECs) into the power grid. Most of these PECs are grid-following inverters, where weak grid operation becomes an issue. Research is now shifting focus to grid-forming (GFM) inverters, resembling synchronous generators. The shift towards converter-based generation necessitates accurate PEC models for assessing system dynamics that were previously ignored in conventional power systems. Data-driven modeling (DDM) techniques are becoming valuable tools for capturing the dynamic behavior of advanced control strategies for PECs. This paper proposes using power hardware-in-the-loop experiments to capture dynamic GFM data in the application of DDM techniques. Furthermore, the paper derives an analytical approach to obtaining a mathematical model of GFM inverter dynamics and compares it with the DDM. A square-chirp probing signal was employed to perturb the active and reactive power of the load inside an Opal-RT model. The dynamic response of the GFM inverter, including changes in frequency and voltage, was recorded. This data was then used in a system identification algorithm to derive the GFM DDMs. The effectiveness of DDM is cross-validated with an analytical approach through experimental simulation studies, and the goodness-of-fit for both approaches is compared. Both approaches show more than 85% accuracy in capturing the dynamic response of GFM inverters under different loading conditions.

The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out cross-validation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pre-trained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification.

Fog computing is a promising and challenging paradigm that enhances cloud computing by enabling efficient data processing and storage closer to data sources and users. This paper introduces a game-theoretic approach called GTRADPMFC (Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing) to address resource allocation and dynamic pricing challenges in fog computing environments with limited resources. The proposed model features non-cooperative competition among fog nodes for resources and dynamic pricing mechanisms to encourage efficient resource utilization. Theoretical analysis and simulations demonstrate that GTRADPMFC improves resource efficiency and overall fog computing system performance. Additionally, the paper discusses how to handle situations with insufficient samples and provide flexibility for users unable to meet completion time requirements. GTRADPMFC effectively manages resource allocation by establishing pricing in fog computing, considering potential delays in completion time. This is achieved through research, simulations, convergence analysis, complexity evaluation, and optimization guarantees.

Traffic collisions pose a significant global concern, necessitating innovative solutions to enhance safety. Post-collision, it becomes crucial for the police to determine the responsibilities of the involved parties, distinguishing between criminal acts and non-criminal incidents. Insurance companies also rely on such investigations to compensate victims. Assessing responsibility after a crash is a complex task requiring advanced knowledge of road rules. For straightforward scenarios like crashes with traffic lights, decisions are fast and easy. However, in situations such as crashes without any traffic signs, expert knowledge is essential. Automating such tasks demands innovative approaches, representing a necessity for the future of the automobile and insurance industries. Despite these critical needs, there has been limited research in the domain. This paper introduces a system that is capable of detecting vehicle collision and implements an original responsibility assessment process to assess drivers responsibilities. This paper is an extended version of our previous work. In the previous work, the system supported only three types of head-on/angle crash scenarios without traffic lights within three weather conditions, in addition to those with traffic lights. This study presents the improved system which now accommodates six different types of head-on/angle crash scenarios without traffic lights within six harsh weather conditions, such as foggy and snowy days. Additionally, extensive experiments are conducted with results showing that the system performs better than its previous version, mainly during nighttime without traffic lights (up to 93% accuracy against up to 82.5% obtained previously). Moreover, through case studies and comparisons with existing research, the effectiveness and superiority of the system are demonstrated.

The most recent ultra-lightweight mutual authentication protocol (UMAP) adopts a mechanism for key updating to keep the security forward and backward. The back-end server updates the secret keys immediately after sending the final message. Certainly, an adversary can intercept the final messages so that the back-end server updates the secret keys while the RFID tag does not. The replay attack can send the last messages by continuing to render the back-end server and RFID tags unauthorized. The major problem faced by existing protocols, when using high cryptographic operations on low-cost RFID tags, requires more memory, and computational power makes them unusable for these types of tags. In addition, there are weaknesses in the design of the existing protocols that lead to the emergence of desynchronization, secret disclosure, and replay attacks. Therefore, this paper proposes the design and development of an ultra-lightweight mutual authentication protocol that overcomes the weaknesses in earlier protocols and builds by implementing the rotation operator, mechanism of the secret keys, T-function, and timestamp technique. The informal and formal analysis results using security protocol verification official tools (Scythe and AVISPA) show that the proposed protocol has the ability to prevent attacks, especially replay attacks. Experimental analyses of our protocol to measure the performance shows that the proposed protocol involves a lower overhead than previous protocols. The total execution time in milliseconds of the processes of the proposed protocol with the existing protocols is 0.031ms, and it achieves an average of 78.51% over other protocols. In addition, this paper provides a comparison with the most recent protocols on performance, privacy and security requirements. Our proposed protocol storage space requirement is lowest than the existing protocols, the cryptographic requirement is ultra-lightweight and efficient and shows that the communication messages for authentication in our protocol are lowest than existing protocols and our protocol fulfills the privacy and security requirements.

Path planning is one of the research hotspots for outdoor mobile robots. This paper addresses the issues of slow convergence and low accuracy in the Double Deep Q Network (DDQN) method in environments with many obstacles in the context of deep reinforcement learning. A new algorithm, Improve Double Deep Q Network (IDDQN), is proposed, which utilizes second-order temporal difference methods and a binary tree data structure to improve the DDQN method. The improved method evaluates the actions of the current robot using second-order temporal difference methods and employs a binary tree structure to store the results obtained from these methods, replacing the traditional experience pool structure. The environment is constructed using a grid method, programmed in the Python language, with two two-dimensional grid maps created for simple and complex environments. DDQN and four related deep reinforcement learning methods, such as Multi-step updates and Experience Classification Double Deep Q Network (ECMS-DDQN), are compared through simulation experiments with the IDDQN method. Simulation results indicate that the IDDQN method improves various path planning metrics compared to the DDQN method and other relevant reinforcement learning methods. In the simple environment, IDDQN method exhibits a 26.89% improvement in step convergence time, a 22.58% improvement in reward convergence time, and a 10.30% improvement in average reward value after convergence compared to the original DDQN algorithm. It also outperforms other simulated methods in the simple environment, although the difference is not significant. In the complex environment, the IDDQN method avoids falling into local optima compared to other methods, demonstrating the accuracy of its strategy in complex environments. Other methods show artificially high average reward values after converging in local optima, lacking reference value. In the complex environment, IDDQN method exhibits a 33.22% improvement in step convergence time and a 25.47% improvement in reward convergence time compared to the original DDQN algorithm, clearly surpassing other participating simulated methods. The data above indicate that the IDDQN method improves both convergence speed and accuracy compared to the DDQN method and the relevant improvement methods simulated in this paper. Particularly in environments with many obstacles, the performance improvement is evident, allowing for effective path planning in such environments.

We investigated low Earth orbit satellite (LEO) communications applying Multiple-Input/Multiple-Output (MIMO) technology to the feeder link transmission between a satellite and gateway antennas to improve the channel capacity. This paper calls LEO satellites using MIMO technology “LEO-MIMO” and considers satellite and gateway antenna configurations to improve the feeder link capacity. LEO-MIMO operates with a single LEO satellite equipped with multiple antenna elements and a gateway with multiple remotely located antennas. The conditions of the antenna configuration, such as the distances of satellite/gateway antenna elements and the number of antenna elements in the satellite and gateway, affect the channel capacity of LEO-MIMO. This paper reveals antenna configurations that improve the channel capacity for LEO-MIMO systems. Computer simulations show that the channel capacity of LEO-MIMO can achieve 4 bps/Hz at maximum higher than that of the conventional multi-beam system.

To address the challenges of road extraction in high-resolution remote sensing images, this paper presents an enhanced UNet++ road extraction method that incorporates CBAM. The original UNet++ network is referenced, and the loss function is improved by introducing a new joint loss function. The enhanced UNet++ network utilizes an attention mechanism to enhance the network’s ability to identify road features, thereby improving the accuracy of road extraction. Additionally, a new joint loss function is employed to enhance the network’s stability and further improve its road extraction capability. Experimental validation is performed on the Massachusetts roads dataset and DeepGlobal road dataset. The experimental results demonstrate that this method outperforms U-Net, SegNet, and UNet++ networks in terms of IoU, Recall, OA, and Kappa. Specifically, on the Massachusetts road dataset, the OA and Kappa values are 94.92% and 0.9202, respectively. On the DeepGlobal road dataset, the OA and Kappa values for this algorithm are 98.12% and 0.9515, respectively. The ablation experiment confirms the effectiveness of the proposed enhancements. In conclusion, this paper presents a method that effectively extracts roads from high-resolution remote sensing images, exhibits a certain level of generalization ability, and can provide valuable support for road protection and planning.

The breath rate can now be monitored remotely due to the advancements in digital stethoscope sensor technology, signal processing, and machine learning. Automatic breathing rate classification, on the other hand, provides additional benefits in medical diagnostics. In this paper, a lightweight convolutional neural network is proposed for automatic breathing rate classification utilizing the piezoresistive sensor data. In the proposed work, the raw signals from the piezoresistive sensor are pre-processed using a continuous wavelet transform to generate the corresponding images. These images are then fed into a lightweight convolutional neural network, which efficiently classifies the breathing rate into six classes based on the number of breaths per minute. Through extensive results, we show that the proposed model results in a classification accuracy of 96.40% which is higher than all the benchmark models considered in this paper. We also evaluate the performance of the proposed model using edge computing devices such as Raspberry Pi, Nvidia AGX Xavier, and Nvidia Jetson Nano.

Speech and voice disorders are highly prevalent and a significant concern, particularly among children. These disorders have a notable impact on individual’s personalities, academic performance and overall development. Early detection and intervention of these disorders surely help out and generate an effective outcome. Automatic detection of speech and voice disorders at an early stage plays a crucial role in identifying and addressing these communication challenges because of their efficiency over traditional diagnostic methods. Over time, numerous techniques were developed in the past, but some of them have become less efficient in the present scenario. Deep Learning has surfaced as a recent and significant advancement in detecting speech and voice disorders. This paper provides a systematic review of the utilization of deep learning techniques for the detection of speech and voice disorders. The review encompasses studies published from 2018 to 2023 exploring various architectures of deep learning models for capturing complex patterns in speech data. Each deep learning based speech sound disorder detection technique is discussed with critical appraisal and relevant benchmarks available for evaluation of results. This review holds significance for new researchers who are interested in exploring the field of automatic speech disorder detection as the paper concludes by discussing future directions and potential areas of improvement in automatic speech sound disorder detection using deep learning.

Data-driven methods have been shown to be suitable for the diagnosis and prognosis of the health of engineering systems. However, the training of data-driven methods usually requires a large amount of data, which is rarely available in industry. In addition, the prediction accuracy often degrades when operating or environmental conditions change or when there are similar systems with different technical characteristics. Transfer learning offers the possibility to transfer knowledge about the degradation behavior between such systems. However, there is a risk that the degradation behavior differs too much, which leads to a so-called negative transfer. Therefore, the authors argue that a similarity assessment of degradation behavior is essential. An assessment based on the operational data of systems seems particularly appropriate. In this paper, the suitability of time series similarity measures for such data-based similarity assessments is investigated. The current state of research is presented. Thereby, no studies on the similarity comparison of degradation curves of engineering systems using time series similarity measures are found. Furthermore, measures for assessing the similarity of degradation curves are identified and categorized. In a case study on filter clogging curves, similarity tests are performed using these measures to find the most similar time series. Various approaches are proposed for evaluation, two of which are used in this paper. The results show that mostly a good selection is made, with some measures performing particularly well. The work presented in this paper represents valuable groundwork for the use of time series similarity measures in transfer learning.

This paper investigates a class of sampled-data control systems in which at least one of the discrete-time zeros remains on the unit circle regardless of the sampling period. This type of zero is referred to as an “uniformly marginally stable” (UMS) zero. Utilizing this novel concept, we present necessary conditions as well as sufficient conditions for the continuous-time plant whose sampled-data model has an UMS zero. The theoretical finding of this paper newly highlights the significance of the symmetry regarding the zeros and poles of the continuous-time plant, as it plays a vital role in enforcing a discrete-time zero of the sampled-data system to be UMS, for both discretization zeros and intrinsic zeros. We develop the theory for sampled-data systems employing a conventional sampler and a zero-order hold together with a single-input single-output continuous-time plant, and then observe that certain results can be extended to the generalized holds. As an application, we construct a discrete-time stable inversion-based control in the sampled-data setting and establish a condition for continuous-time plants that allow for this type of control, in which absence of such an UMS zero is crucial.

The semiconductor industry has adopted heterogeneous integration (HI), incorporating modular intellectual property (IP) blocks (chiplets) into a unified system-in-package (SiP) to overcome the slowdown in Moore’s Law and Dennard scaling and to respond to the increasing demand for advanced integrated circuits (ICs). Despite the manifold benefits of HI, such as enhanced performance, reduced area overhead, and improved yield, this transformation has also led to security vulnerabilities in the SiP supply chain and in-field operations, ranging from chiplet piracy and SiP reverse engineering (RE) to information leakage. Although conventional countermeasures provide the desired robustness for monolithic ICs, they are insufficient for addressing these challenges in the context of HI. To address these concerns, this paper presents a novel root-of-trust architecture, augmenting the process of integration using a centralized chiplet hardware security module (CHSM), aiming to provide comprehensive and robust protection throughout the SiP supply chain and in-field operations. Also, the proposed architecture equipped with the CHSM effectively addresses potential security breaches while providing robust protection against zero-day attacks through its reconfigurable capabilities. Throughout five detailed case studies, this paper performs a comprehensive security analysis to illustrate the resilience of CHSM against contemporary attack scenarios in the HI domain.

In Software-Defined Networks (SDN), the ternary content addressable memory (TCAM) capacity in switches is limited, making them vulnerable to low-rate flow table overflow attacks. Most existing research in this field has not focused on the influence of flow entry eviction mechanisms on the effectiveness of such attacks. This paper proposes an adaptive low-rate flow table overflow attack (ALFO), which can adopt corresponding attack modes under different flow entry eviction mechanisms, significantly degrading network service quality. Due to the different features of ALFO under different attack modes, the existing attack detection methods are ineffective in this attack. Therefore, this paper proposes a detection and mitigation framework, which is called adaptive low-rate flow table overflow attack guard framework (ALFO-Guard). It extracts flow features from flow entry information in the switch and aggregates them into a current-time graph model. Then, combining graph neural networks, it performs graph anomaly detection and flow entry classification to identify attack flow entries. Finally, the attack can be eliminated by deleting the identified attack flow entries and blocking the attack flows. The effectiveness of ALFO and ALFO-Guard is validated through extensive experiments, and the experimental results demonstrate that ALFO-Guard can effectively defend against ALFO.

It is undeniable that the development of robot manipulators has gained significant attention due to its numerous applications in various sectors. In order to address and resolve several issues for robotic manipulators, this paper provides a review of recent control approaches that have been conducted. In the beginning, a succinct comprehensive introduction of the pertinent terminology, configurations, components, advantages/disadvantages, and applications is given. A state-of-the-art review of the various control strategies for robotic manipulator systems is then presented, along with potential solutions when the systems are faced with various obstacles and impediments. An analytical study is discussed showing percentages of the papers discussed in this study in terms of the different control strategies, link types, models, applications, and degrees of freedom of robotic manipulators. For academic, research, medical, and industrial uses, some off-the-shelf developments in robotic manipulators are also presented.

Joint Radar and Communications (JRC) can satisfy the apparent demand for applications based on object detection, tracking, ranging, and positioning. JRC is, therefore, often seen as candidate technology for 6G mobile systems. Implementing JRC will require novel approaches in many research and engineering fields, including protocol design, digital and analog signal processing, and hardware development. The ongoing debates on JRC already include many white papers and research articles ranging in content from very specific technical problems to comprehensive bird’s eye-level reviews. This paper represents the work within the Open6GHub research project in Germany, which aims to investigate and implement potential end-to-end solutions for 6G. In this framework, we propose a consolidated vision for potential JRC architectural approaches. The subsequent discussion on integrating radar sensing with communications highlights this technology’s state-of-the-art and presents relevant opportunities and challenges.

Deep learning has significantly transformed face recognition, enabling the deployment of large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep neural networks (DNNs) and the rise of Machine Learning as a Service emphasize the need for secure DNNs. This paper revisits the face recognition threat model in the context of DNN ubiquity and the common practice of outsourcing their training and hosting to third-parties. Here, we identify backdoor attacks as a significant threat to modern DNN-based face recognition systems (FRS). Backdoor attacks involve an attacker manipulating a DNN’s training or deployment, injecting it with a stealthy and malicious behavior. Once the DNN has entered its inference stage, the attacker may activate the backdoor and compromise the DNN’s intended functionality. Given the critical nature of this threat to DNN-based FRS, our paper comprehensively surveys the literature of backdoor attacks and defenses previously demonstrated on FRS DNNs. As a last point, we highlight potential vulnerabilities and unexplored areas in FRS security.

This paper proposes a design principle for reactance compensation circuit for multiple-input multiple-output (MIMO) inductive power transfer (IPT) where the primary side employs a coil array to send power to a receiving coil array located at the secondary side. Delivering wireless power over multiple streams, MIMO topology is an effective approach to relax strict burdens on system components in high-power IPT applications. An important requirement in MIMO topology is a method to mitigate cross-coupling inside the coil arrays so that load-independent constant-current and constant-voltage can be realized in the same fashion as conventional single-input single-output IPT. Based on transfer matrix analysis, this paper proposes a design principle indicating that passive compensation circuit should implement the negative of the cross-coupling’s reactance matrix to cancel it in the reactance domain. The proposed design is independent of the transmission distance, applicable to MIMO IPT systems at arbitrary scale and does not require the coil arrays to employ any decoupling structure. Full-wave electromagnetic simulations with $3\times 2$ MIMO and experiments with $2\times 2$ MIMO are provided to verify effectiveness and feasibility of the proposed scheme.

Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.

Neural cellular automata have been proven effective in simulating morphogenetic processes. Developing such automata has been applied in 2D and 3D processes related to creating and regenerating complex structures and enabling their behaviors. However, neural cellular automata are inherently uncontrollable after the training process. Starting from a neural cellular automaton trained to generate a given shape from one living cell, this paper aims to gain insight into the behavior of the automaton, and to analyze the influence of the different image characteristics on the training and stabilization process and its shortcomings in different scenarios. For each considered shape, the automaton is trained on one RGB image of size $72 \times 72$ pixels containing the shape on an uniform white background, in which each pixel represents a cell. The evolution of the automaton starts from one living cell, employing a shallow neural network for the update rule, followed by backpropagation after a variable number of evolutionary steps. We studied the behavior of the automaton and the way in which different components like symmetry, orientation and colours of the shape influence its growth and alteration after a number of epochs and discussed this thoroughly in the experimental section of the paper. We further discuss a pooling strategy, used to stabilize the model and illustrate the influence of this pooling on the training process. The benefits of this strategy are compared to the original model and the behavior of the automaton during its evolution is studied in detail. Finally, we compare the results of models using different filters in the first stage of feature selection. The main results of our study are the insights gained into how the neural cellular automaton works, what it is actually learning, and what influence this learning, as there are observable result differences depending on the characteristics of the input images and the filters used in the model.

With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative approach not only streamlines the process of literature review in the IRS field but also has the potential to be adapted and implemented across a variety of research fields. As a result of this methodology, 287 papers related to the IRS were identified from a pool of 6143 studies extracted by the developed web robot based on initial keywords. This highlights its effectiveness in navigating and extracting valuable insights from the extensive body of literature. Furthermore, this research methodology allows the identification of prominent researchers, journals, conferences, and high-quality papers in the field of study.

This paper introduces a first-order method for solving optimal powered descent guidance (PDG) problems, that directly handles the nonconvex constraints associated with the maximum and minimum thrust bounds with varying mass and the pointing angle constraints on thrust vectors. This issue has been conventionally circumvented via lossless convexification (LCvx), which lifts a nonconvex feasible set to a higher-dimensional convex set, and via linear approximation of another nonconvex feasible set defined by exponential functions. However, this approach sometimes results in an infeasible solution when the solution obtained from the higher-dimensional space is projected back to the original space, especially when the problem involves a nonoptimal time of flight. Additionally, the Taylor series approximation introduces an approximation error that grows with both flight time and deviation from the reference trajectory. In this paper, we introduce a first-order approach that makes use of orthogonal projections onto nonconvex sets, allowing expansive projection (ExProj). We show that 1) this approach produces a feasible solution with better performance even for the nonoptimal time of flight cases for which conventional techniques fail to generate achievable trajectories and 2) the proposed method compensates for the linearization error that arises from Taylor series approximation, thus generating a superior guidance solution with less fuel consumption. We provide numerical examples featuring quantitative assessments to elucidate the effectiveness of the proposed methodology, particularly in terms of fuel consumption and flight time. Our analysis substantiates the assertion that the proposed approach affords enhanced flexibility in devising viable trajectories for a diverse array of planetary soft landing scenarios.

The evolution of wireless communication has led to the introduction of IEEE 802.11be, a leading-edge wireless standard also referred to as Extremely High Throughput (EHT) or Wi-Fi 7. Multi-Link Operation (MLO) is the most innovative and disruptive technology proposed in this amendment, which holds the potential to revolutionize wireless networks. MLO aims to increase throughput through link aggregation, enhance reliability through redundant frame transmission over multiple links, and decrease latency by utilizing the earliest available link. However, to the best of our knowledge, there is no specific survey or review paper is dedicated solely to MLO, despite the extensive research conducted on Wi-Fi 7. Thus, this survey paper will bridge the gap by presenting an exhaustive overview of cutting-edge research spanning from 2010 to 2023, exclusively focused on MLO characteristics, current status and future directions. This study has discovered several open issues, including backoff count overflow, channel selection, load balancing and channel access fairness. These findings lay the groundwork for future research that has the potential to impact the current status of wireless communication significantly.

Unmanned Aerial Vehicles (UAVs) are aerial vehicles that can go to a particular position without human control or with remote human control. It is because unmanned control mainly relies on position estimation that a lot of research has been studied on GPS spoofing attacks. Detection methods against GPS spoofing attacks mainly include monitoring RF signals or IMU sensor values inside UAVs. In this work, we analyze GPS attack and detection in an advanced autopilot system, PX4 without excluding RF detection. Recent studies have shown that GPS spoofing is unable to evade the detection using EKF sensor fusion. Therefore, this paper experiment whether the detection could be evaded by strengthening the attacker model. This paper classifies attacker models according to whether an attacker knows the true position of UAVs or the estimated position by UAV and proposed attack methods depending on each model in PX4. We inject GPS value which our attack method intends to UAV during mission flight in simulation environment. By manipulating the GPS driver code of PX4 controller, we inject GPS value the attack method wants into UAV in physical environment. Finally, we observed the innovation test ratio during GPS spoofing and demonstrate that GPS spoofing attack is possible keeping the innovation test ratio under the anomaly detection criterion. After that, we proposed our approach which scales the EKF’s Kalman gain randomly to increase the detection method’s efficiency and experiment with the attack methods. This detection method has little effect on the test ratio without the attack. But during the attack, the innovation test ratio was increased higher than the anomaly detection criterion so that the attack could be detected.

Chromosome karyotyping is an important yet labor-intensive procedure for diagnosing genetic diseases. Automating such a procedure drastically reduces the manual work of cytologists and increases congenital disease diagnosing precision. Researchers have contributed to chromosome segmentation and classification for decades. However, very few studies integrate the two tasks as a unified, fully automatic procedure or achieved a promising performance. This paper addresses the gap by presenting: 1) A novel chromosome segmentation module named ChrRender, with the idea of rendering the chromosome instances by combining rich global features from the backbone and coarse mask prediction from Mask R-CNN; 2) A devised chromosome classification module named ChrNet4 that pays more attention to channel-wise dependencies from aggregated informative features and calibrating the channel interdependence; 3) An integrated Render-Attention-Architecture to accomplish fully automatic karyotyping with segmentation and classification modules; 4) A strategy for eliminating differences between training data and segmentation output data to be classified. These proposed methods are implemented in three ways on the public Q-band BioImLab dataset and a G-band private dataset. The results indicate promising performance: 1) on the joint karyotyping task, which predicts a karyotype image by first segmenting an original microscopical image, then classifying each segmentation output with a precision of 89.75% and 94.22% on the BioImLab and private dataset, respectively; 2) On the separate task with two datasets, ChrRender obtained AP50 of 96.652% and 96.809% for segmentation, ChrNet4 achieved 95.24% and 94.07% for classification, respectively. The COCO format annotation files of BioImLab used in this paper are available at https://github.com/Alex17swim/BioImLab The study introduces an integrated workflow to predict a karyotyping image from a Microscopical Chromosome Image. With state-of-the-art performance on a public dataset, the proposed Render-Attention-Architecture has accomplished fully automatic chromosome karyotyping.

Metasurfaces can achieve multifunctional wavefront control, but most co-polarized metasurfaces can only achieve a 360° phase coverage at specific frequency points. In this paper, we propose a metasurface with efficient co-polarized Terahertz (THz) wave transmission and full 360° phase coverage over a wide band. The proposed metasurface consists of five metal layers and four dielectric layers, which can be equivalent to two cascade polarization converters to form a Fabry-Perot resonator. The forward incident y-polarized THz wave can transform into the same polarized transmission wave after transmitting the proposed metasurface. Electromagnetic waves interfere repeatedly inside the multilayer metasurface, resulting a high transmission efficiency; furthermore, by changing the structure parameters of the middle two metal layers, a full 360° phase coverage in the broadband is achieved. Wideband THz focus metalens, orbital angular momentum generator and holographic imaging are realized by properly arranging the metasurface unit cells. Simulation results show that the designed multifunctional metasurface can achieve good wavefront control over the band 0.7-1.0 THz. This paper provides a new idea for the design of broadband co-polarized metasurfaces, and has great potential value in communication, imaging, remote sensing and other fields.

Frequency-modulated continuous-wave (FMCW) radars are becoming increasingly important in industrial applications due to their low cost, simple signal processing, and high resolution. The research is driven by the growing trend of radar applications in industrial settings, including tasks like autonomous robot navigation, radar-based monitoring of humans, and enhancing safety across various scenarios, such as smart factories, traffic control, or environmental monitoring. The network of distributed sensor nodes is a feature to be implemented by the sixth generation of wireless mobile networks. As a result, 6G will become an enabler technology that will develop even further use cases and advance the expansion of these technologies. Therefore, the mutual interference of these sensor systems is becoming highly relevant since they can be avoided by scheduling the individual resources of the sensor nodes by a network structure. This paper investigates the interference behavior of FMCW radar sensors in a dense indoor scenario. In these environments, in particular, the reliability of the sensor system plays an important role, and disruption can have critical consequences like not recognizing a target, triggering a false alarm, or the complete failure of the sensing. To correctly classify the expected interference in the overall context, the paper investigates the physical layer effects on the performance, such as multipath propagation, channel spread, delay, Doppler shift, and attenuation. By evaluating these effects using state-of-the-art signal processing, it was revealed that a high channel spread in particular and the dynamics of the radar channel can significantly reduce the detection quality, as the interference power can exceed the received power by up to 20 dB, depending on the scenario. The simulation results given in this paper show that a potential communication between the radars and compliance with a synchronization delay, the interference problems can be reliably prevented with an appropriately designed anti-aliasing filter. Our simulations have quantified the detection performance under interference through the entire signal chain by evaluating the physical layer, the radar signal processing with non-idealities, and the hardware. The paper has investigated interference probability in indoor environments by examining radars with uncoordinated transmissions. Integrating findings from the physical propagation characteristics, the deviations in chirp durations, and the frequency of interference cases offers insights into the potential risk of interference.

Improving operational efficiency and reducing energy consumption are common objectives for all ports. In previous research by authors, a new high operation platform mode was proposed. It had been proven to reduce energy consumption and be economically feasible. On this basis, this paper studied the influence of high operation platform mode on automated container terminal (ACT) efficiency. This paper took U-shaped ACT as the research object, put forward the operation characteristics of U-shaped ACT under high operation platform mode, and constructed the operation time model of high and low operation platform mode respectively. This paper took 30 containers at the same bay as an example to calculate and verify. The computational results shows that when $V_{v}^{1}/V_{h}^{1}$ is above the $V_{v}^{1}/V_{h}^{1}-\alpha $ curve, the operation time of high platform mode is shorter, and the operation efficiency of ACT is higher ( $V_{v}^{1}$ represents the speed of spreader with container vertically moving; $V_{h}^{1}$ represents the speed of spreader with container horizontally moving.); when $V_{v}^{1}/V_{h}^{1}$ is under the $V_{v}^{1}/V_{h}^{1}-\alpha $ curve, the shortening time of quay cranes operation is greater than the increasing time of yard cranes operation, so the overall operational efficiency of ACT is still improved. Therefore, high operation platform mode can improve efficiency of ACT.

The Smart Grid is a modern power grid that relies on advanced technologies to provide reliable and sustainable electricity. However, its integration with various communication technologies and IoT devices makes it vulnerable to cyber-attacks. Such attacks can lead to significant damage, economic losses, and public safety hazards. To ensure the security of the smart grid, increasingly strong security solutions are needed. This paper provides a comprehensive analysis of the vulnerabilities of the smart grid and the different approaches for detecting cyber-attacks. It examines the different vulnerabilities of the smart grid, including system vulnerabilities and cyber-attacks, and discusses the vulnerabilities of all its elements. The paper also investigates various approaches for detecting cyber-attacks, including rule-based, signature-based, anomaly detection, and ma-chine learning-based methods, with a focus on their effectiveness and related research. Finally, prospective cybersecurity approaches for the smart grid, such as AI approaches and blockchain, are discussed along with the challenges and future prospects of cyberattacks on the smart grid. The paper’s findings can help policymakers and stakeholders make informed decisions about the security of the smart grid and develop effective strategies to protect it from cyber-attacks.

To address the limitation of traditional surprise boxes that only support single or fixed quantity customization, this paper proposed a new method: utilizing comparators to detect cascaded quantities for specific customization. This innovative approach aims to simplify the customization process of surprise boxes while avoiding the complexity of using micro-controllers for quantity determination. A dual comparator is employed to compare the results that are greater or smaller than the set quantity. Then the output of the dual comparator is connected to an AND gate circuit, triggering the hidden function of the central surprise box when the AND gate generates high levels. The system design involves setting the number of surprise boxes and connecting upper and lower limit comparators to the current surprise box connection signal. By selecting the size of the cascade resistor, the number of surprise boxes can be determined. This paper provides step-by-step instructions for calculating the cascade resistor. Furthermore, the proposed method’s reliability in determining the number of surprise boxes is demonstrated by setting the number of secondary surprise boxes to seven and four, with the allowed resistance error 6.4% and 11%, respectively. The results indicate that a higher number of connected surprise boxes leads to a smaller range of cascaded resistors and higher accuracy requirements. Through these case studies, it has been verified that the design using dual comparators can achieve the desired quantity detection, and the detection circuit exhibits a high degree of redundancy in resistance value error, resulting in increased reliability of the detection results.

This paper introduces the perpendicular parking algorithm of car-like robots, such that the generated path consists of a cusp. This path planning is based on the car’s turning radii, which can be determined by the car’s geometry and its maximum steering angle. As far as we know, this paper is novel in developing autonomous perpendicular parking based on circular arc and straight line segments, such that a cusp on the generated path is allowed. Since a cusp is allowed, the proposed parking approach is suitable for parking in a small space. The simulation results show the validity of the proposed approach.

The technology development greatly increases the amount of digital visual information. Existing devices cannot efficiently process such huge amounts of data. The technical characteristics of digital image processing (DIP) devices and systems are being actively improved to resolve this contradiction in science and technology. The state-of-the-art methodology includes a huge number of very diverse approaches at the mathematical, software, and hardware implementation levels. We have analyzed all modern trends to improve the technical characteristics of DIP devices and systems. The main distinguishing feature of this review is that we are not limited to considering various aspects of neural network image processing, to which the vast majority of both review and research papers on the designated topic are devoted. Review papers on the subject under consideration are analyzed. Various mathematical and arithmetic-logical methods for improving the characteristics of image processing devices are described in detail. Original and significant architectural and structural solutions are analyzed. Promising neural network models of visual data processing are characterized. Hardware platforms for the design and operation of DIP systems that are efficient in terms of resource costs are considered. The most significant improvements achieved through the hardware implementation of models and methods on field-programmable gate arrays and application-specific integrated circuits are noted.

This paper presents an advanced vertical control system using active suspension to stabilize the vertical motion of an in-wheel motor-driven electric vehicle. The design of the control system and conceptualization were carried out by mathematical modeling with integration with the quarter car model. A novel active suspension is developed through the model-based design to control the vehicles to move smoothly and continuously. Even though experimental validation is an effective and more accurate method to evaluate the control system, it is important to reduce the dependency on physical testing. Thus, a reliable simulation framework would be beneficial in the vehicle development process. In this paper, the hydraulic system is designed and simulated by using the Simcenter Amesim. For the dynamic vibration absorber, we are seeking to design a skyhook control to control the damping force by an electric damper, whereas the spring stiffness can be switched by opening and closing the fluid passage to the gas chamber. In the spring stiffness control, the stiff suspension can reach a ratio of 1.3 compared to the soft suspension. The objective of this research work was realized with the proposed simulation framework to test the effectiveness of suspension control system to improve ride and handling in a four in-wheel motor-driven electric vehicle.

Air formation is a common style of air combat, which demonstrates a high degree of flexibility and strategic value in complex battlefield environments. The activity state of air formation is the result of the intertwining of time domain and air domain, which requires accurate execution of tactical processes in the time axis and skillful deployment of forces in three-dimensional space. Therefore, air formation target combat intention recognition is a complex and challenging task that requires an in-depth understanding of the dynamically changing behavioral patterns of the formation. To address this problem, this paper proposes the STIRNet (Spatio-Temporal Network for Intention Recognition) model, which abstracts the air formation as a spatial graph structure composed of vehicle nodes and combines its temporal data evolving over time. The model autonomously adjusts its attention to different moments and spatial locations through the spatio-temporal attention mechanism, focusing on the important spatio-temporal features that are crucial for recognizing the combat intention of the air formation; and simultaneously captures and integrates the feature information of the air formation in both the temporal and spatial dimensions through the spatio-temporal convolutional operation, which effectively solves the deficiencies of the traditional methods in dealing with the complex spatio-temporal dependency relationships. The experimental results show that the model proposed in this paper effectively improves the accuracy of the combat intention recognition of air formation targets, which is of great value for command decision-making and air battlefield situation assessment.

The automobile industry is regarded as one of the primary producers of greenhouse gas emissions. Hence, one major approach for promoting global sustainability is vehicle electrification. Energy source hybridization is imperative in vehicle electrification because no alternative clean energy source can match the performance of vehicles with internal combustion engines. For a compact energy source system with minimal source degradation and low operational costs, it is crucial to determine the optimal sizing of multiple sources in a hybrid electric vehicle (HEV). The objective of this article is to conduct a thorough assessment of the optimal energy source sizing methodology for HEV alongside an analysis of energy management systems. The importance of dynamic energy source characteristics in the optimal source design is explained and the dynamic modelling of these sources is investigated in detail. Moreover, this paper discusses the dynamic source degradation models used in the literature. This analysis on HEV component sizing provides a comprehensive picture of the current state-of-the-art and highlights several shortcomings, difficulties, and knowledge gaps. The findings indicate that the establishment of an energy management system and energy source sizing are interdependent activities that can prolong the usable life of the energy sources and reduce energy consumption. The aging, temperature, depth of discharge, and charging state are found to be essential characteristics of vehicle energy sources, yet these dynamic aspects have largely been neglected in previous papers. Additionally, if the source deterioration and associated factors are considered throughout the design phase, the HEV system can be built with long-life characteristics. The assessment conclusions highlight the significance of continued analysis to overcome obstacles and improve the hybrid automobile sector.

Under conditions of partial shadowing, traditional Maximum Power Point Tracking (MPPT) algorithms face difficulties in precisely locating the maximum power point (MPP) of the system. To address this problem, this paper proposes an optimization algorithm, Ant-Fuzzy Optimization (AFO) algorithm. AFO utilizes the global search capability of the ant colony optimization (ACO) algorithm and the high precision performance of the fuzzy logic (FL) algorithm, mitigating the tendency of the fuzzy algorithm to fall into local optima in shadow conditions. Internally, the AFO algorithm comprises two parallel logics, selecting different strategies for tracking based on varying environmental states, achieving a balance between tracking accuracy and computational efficiency. This intelligent logic selection mechanism allows the algorithm to flexibly adapt to diverse working environments of photovoltaic (PV) arrays, enhancing the robustness and adaptability of the system. The paper establishes corresponding simulation models in MATLAB/SIMULINK and validates AFO through hardware experiments on the dSPACE real-time simulation system. The results demonstrate the feasibility and effectiveness of AFO in practical environments. Both simulation and experimental prototypes indicate that AFO can rapidly and accurately extract the maximum power point with an accuracy of 98.7%. Furthermore, AFO exhibits rapid dynamic response characteristics, reaching steady state within 0.9 seconds, providing a reliable solution for optimizing the output power of photovoltaic arrays.

Steel slit damper (SSD) is specifically engineered to absorb and disperse energy to mitigate the possibility of structural damage and collapse during seismic events. However, the effectiveness of these dampers are compromised due to wear, corrosion and other forms of material degradation and failure mechanisms. For this reason, it is important to consistently monitor its condition and conduct maintenance and repairs to guarantee safety and dependability of the structures it is installed with. Now, vibration-based techniques have emerged as a promising structural health monitoring (SHM) strategy. It involves methodologies for detecting changes in functions and analyzing vibrations to discover potential problems. This study entails a comprehensive examination of the most recent advancements and breakthroughs in vibration-based methodologies used in SHM. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020), we identified several key techniques and technologies that show promise in enhancing the maintenance and monitoring of steel– or steel and metal surfaces- slit dampers. The pros and cons of these methods are presented to be applicable when monitoring an SSD. This paper presents notable figures and their corresponding discussions to achieve enhanced understanding of the advancements in SHM towards sensor technologies, monitoring techniques, and applications in civil infrastructure. Furthermore, this paper reviews the applications and functions framework for real-time SHM of SSDs together with their practical implementations and applications using diverse monitoring technologies to promote innovation. Finally, the optimal sensor selection criteria, data gathering methodologies, experimental settings, innovative approaches with comparative analyses, and a proposed maintenance standard for SSDs are described in this study.

The concept of reconfigurable intelligent surface-assisted (RIS) for assisting non-orthogonal multiple access (NOMA) transmission, where the phases can be adjusted to decrease error rates and increase capacity, has been gaining popularity as a promising candidate for 6G communication and beyond. This paper considers RIS-NOMA downlink transmission under the Rician fading channel. A coordinate reflector interleaving (CRI) is proposed, where two groups of reflector elements are introduced, such as in-phase element and quadrature element groups. Therefore, it can reduce system complexity because of fewer successive interference cancellations (SIC) among users. As a result, a computer simulation shows that CRI-RIS-NOMA preserves a lower BER compared to conventional RIS-NOMA. Moreover, the superiority of CRI-RIS-NOMA over RIS-NOMA is that it can work at any power allocation for two users. This paper comprehensively studies theoretical derivation for 2 and 4 users to verify the computer-simulated BER. An upper-bound analysis of CRI-RIS-NOMA was also studied to observe the impact of RIS elements for high SNR cases.

This paper aims to develop a defect-background separated generative adversarial network (GAN) using deep learning and GAN to enhance the accuracy of battery exterior defect inspection. In actual battery production lines, the occurrence rates of defects vary by defect type, making it challenging to create a large, uniform defect dataset due to the time required for defect acquisition. This leads to a reduction in the accuracy of battery exterior defect inspection. To construct a large, uniform defect dataset, this paper proposes a defect-background separated GAN based on the principles of GANs. The defect-background separated GAN performs effective defect and background separation learning by referring to the segmentation labeling of defects. Through dataset augmentation using the defect-background separated GAN, the performance quality of newly generated synthetic defect images has improved, and accuracy in battery exterior defect inspection can be enhanced through extensive dataset training. Experimental results show the lowest Fréchet inception distance score among various other methods for the battery exterior defect dataset, and it generates clear synthetic defects perceptible to the human eye. By training defect segmentation on this large, uniform defect dataset, an accuracy of 96.1% and an intersection over union value of 0.71 were achieved. Ultimately, applying this defect inspection network to the actual production line demonstrated a 72% improvement in time efficiency. This demonstrates the stability and robustness of the large, uniform defect dataset generated through the defect-background separated GAN.

This paper presents a $3^{rd}$ -order intermodulation transponder designed to achieve high suppression of fundamental two-tone signals simultaneously. In previous papers, a nonlinear response system combining matching and suppression has never been considered. The core innovation lies in the proposed output matching network, which effectively shifts the output impedance of the diode to the load impedance at the target intermodulation frequency of 2.3 GHz and to short at the fundamental frequencies of 2.4 and 2.5 GHz. It achieves a high conversion gain and provides a substantial isolation between a two-tone fundamental input port and an intermodulation output port through a sharp band-pass filtering characteristic. For validation, we fabricate and measure the proposed intermodulation transponder with dimensions of $72.5\times53$ mm2. The measurement results demonstrate a peak conversion gain of −26 dB at an input power of 8 dBm and an intermodulation frequency of 2.3 GHz. Furthermore, the proposed transponder exhibits suppression characteristics of at least −7 and −23 dBc for the two-tone signals at 2.4 and 2.5 GHz, respectively. A conversion gain of over −30 dB is maintained across an input power range of 3–19 dBm at 2.3 GHz.

Partial discharge (PD) is one of the main reasons of insulation deterioration in gas insulated switchgear (GIS). How to efficiently and accurately identify PD signals is an important guarantee for the stable operation of GIS. In this paper, an improved northern goshawk optimization (SCNGO) is proposed, which automatically optimizes parameters of variational mode decomposition (VMD) and support vector machine (SVM) to realize fault identification of GIS PD. Firstly, to overcome the shortcomings that NGO is easy to fall into local optimal solution and slow convergence speed, the opposite learning of refraction strategy and sine cosine algorithm (SCA) are introduced to optimize NGO. By comparing the test functions of various algorithms, the superiority of SCNGO algorithm is proved. Then, GIS PD experiment is designed for fault signal acquisition and algorithm verification. SCNGO-VMD is used for parameter adaptive optimization of PD signals. On this basis, the effective intrinsic mode functions (IMFs) are screened by composite index. Furthermore, time-domain, frequency-domain, and entropy features are constructed as mixed features and t-SNE is used to reduce the dimension. Finally, the feature vectors are input to SCNGO-SVM for fault identification. Through experimental analysis, compared with other algorithms, the proposed algorithm model has good state identification accuracy for GIS PD fault diagnosis. The paper provides a reference for the application of optimization algorithm in GIS PD fault identification.

Industry 5.0 complements the Industry 4.0 approach by enabling the transition of industry digitization to a sustainable, human-centered and resilient paradigm. This paper delves into the exploration of enabling technologies that facilitate both Industry 4.0 and Industry 5.0 in the context of supporting supply chain (SC) logistics. The paper defines the principles of Logistics 5.0, which focuses on smart logistics systems for customized distribution, transportation, inventory management and warehousing by emphasizing interconnectivity, digitization, and optimization across SC operations. The traditional logistics framework requires innovative solutions grounded in emerging Industry 5.0 technologies capable of capturing and processing extensive datasets to empower decision-making based on information and knowledge. A comprehensive research has enabled to critically analyze enabling Industry 5.0 technologies by assessing their application status through real-case scenarios within SC Logistics 5.0. Furthermore, the paper identifies research gaps in the reviewed technologies by outlining promising areas for each Industry 4.0 technology. This guidance aims to direct future studies toward the practical application of technologies in supporting Logistics 5.0.

In the field of face frontalization, the model obtained by training on a particular dataset often underperforms on other datasets. This paper presents the Pre-trained Feature Transformation GAN (PFT-GAN), which is designed to fully utilize diverse facial feature information available from pre-trained face recognition networks. For that purpose, we propose the use of the feature attention transformation (FAT) module that effectively transfers the low-level facial features to the facial generator. On the other hand, in the hope of reducing the pre-trained encoder dependency, we attempt a new FAT module organization that accommodates the features from all pre-trained face recognition networks employed. This paper attempts evaluating the proposed work using the “independent critic” as well as “dependent critic”, which enables objective judgments. Experimental results show that the proposed method significantly improves the face frontalization performance and helps overcome the bias associated with each pre-trained face recognition network employed.

This paper presents a comprehensive survey of machine learning, deep learning, and digital twin technology methods for predicting and managing the battery state of health in electric vehicles. Battery state of health estimation is essential for optimizing the battery usage, performance, safety, and cost-effectiveness of electric vehicles. Estimating the state of health of a battery is a complex undertaking due to its dependency on multiple factors. These factors include battery characteristics such as type, chemistry, size, temperature, current, voltage, impedance, cycle number, and driving pattern. There are drawbacks to traditional methods, such as experimental and model-based approaches, in terms of accuracy, complexity, expense, and viability for real-time applications. By employing a variety of algorithms to discover the nonlinear and dynamic link between the battery parameters and the state of health, data-driven techniques like machine learning, deep learning, and data-driven digital twin technologies can get beyond these restrictions. Data-driven methods can also incorporate physics and domain knowledge to improve the explainability and interpretability of the results. This paper reviews the latest advancements and challenges of using data-driven techniques for battery state of health estimation and management in electric vehicles. The paper also discusses the future directions and opportunities for further research and development in this field. The survey scope spans publications from the year 2021 to 2023.

Many studies on the application of deep reinforcement learning (DRL) in the field of traffic signal control do not fully consider the influence of vehicles approaching the intersection on traffic flow. In this paper, the convolutional block attention module (CBAM) is incorporated on the basis of the Dueling Double Deep Q Network (D3QN) method to improve the sensitivity of the model to the traffic situation, which can help the model to focus more on the distribution and dynamics of vehicles near intersections. To further improve the model performance, this paper introduces the traffic light phase variable time interval based on the original D3QN method, which helps the model to take into account the traffic requirements in all directions of the intersection. In addition, Double Deep Q Network (Double DQN) and Dueling Deep Q Network (Dueling DQN) technologies are used to further improve the performance of the model. The simulation experiments using the urban traffic simulator SUMO show that the proposed method has significant advantages over D3QN, Maximum Pressure algorithm and Fixed Timing Strategy for key indicators such as mean vehicle delay time, mean queue length and average number of stops. This shows that the method proposed in this paper has great potential in practical traffic signal control applications.

Given the recent advances in information technology, speed has become an important requirement in data transmission. In this regard and given that an LED (light emitting diode) can turn on and off for several thousands, even millions, of times per second, then LiFi (light fidelity) technology has strong advantages over WiFi (wireless fidelity) in terms of speed in data transmission. In this research project, a consortium of industrial and academic partners have decided to evaluate the maturity of LiFi technology by designing a demonstrator that showcases an efficient and speedy intra-vehicle data transmission system. The demonstrator implements a two-way LiFi communication between the reading light of a vehicle and a portable device, such as a tablet or smartphone. This nomadic device must display, via an interface, receiving transmitted data in real time. The data is of type RNT (Digital Terrestrial Radio) and TNT (Digital Terrestrial Television) provided by an in-house antenna. LiFi depends on optical technology hence, the devices’ optical characteristics have significant influences on the system. This paper presents various conducted experiments and techniques to improve the system’s throughput, communication range, and reception area. For instance, by replacing the reading light with one of better optical characteristics, the communication range and reception area improve. By using symmetrical power supply, the throughput improves. In the same manner, by using optical filters that eliminate noise, throughput likewise improves. We have indeed demonstrated that LiFi data transmission in the vehicle is feasible; low-definition videos were successfully transmitted. The paper also presents various possible techniques to still improve the system performance.