Search Results (29)

The digital transformation of manufacturing through OT, IoT, and AI integration has created extensive networked sensor ecosystems, introducing critical cybersecurity vulnerabilities at IT-OT interfaces. This might particularly challenge the detection component of the NIST cybersecurity framework. To address this concern, the authors designed a diverse machine learning-based intrusion detection system framework for industrial control systems (DICS). DICS implements a sophisticated dual-module architecture. The screening analysis module initially categorizes network traffic as either unidentifiable or recognized packets, while the classification analysis module subsequently determines specific attack types for identifiable traffic. When unrecognized zero-day attack traffic accumulates in a buffer and reaches a predetermined threshold, the agile training module incorporates these patterns into the system, which enables continuous adaptation. During experimental validation, the authors rigorously assess dataset industrial relevance and strategically divide the datasets into four distinct groups to accurately simulate diverse network traffic patterns characteristic of real industrial environments. Moreover, the authors highlight the system’s alignment with IEC 62443 requirements for industrial control system security. In conclusion, the comprehensive analysis demonstrates that DICS delivers superior detection capabilities for malicious network traffic in industrial settings. Full article

In the electric locomotive traction transmission system, a four-quadrant rectifier has a high fault rate owing to the complicated control and bad operating conditions, and the fault directly affects the system’s safety and stability. To address such an issue, a rectifier fault diagnosis approach regarding a local tangent space alignment (LTSA) dimensionality reduction to optimize the high-dimensional energy entropy feature is proposed. Firstly, the fault signal is analyzed by using different wavelet functions through wavelet packet multi-resolution decomposition technology so as to extract the frequency band information of the signal. Each wavelet function corresponds to a specific frequency band; the energy–information entropy ratio of each frequency band coefficient is calculated, and then, the wavelet function and optimal frequency band, which are appropriate for the fault signal, are determined. Secondly, the energy entropy of each coefficient in the optimal frequency band is calculated to form the high-dimensional energy entropy feature. The LTSA algorithm is adopted to optimize the high-dimensional feature, through the fault sample number and clustering results, solve the difficulty of selecting the inherent dimension and nearest neighbor number in high-dimensional data, and obtain the simple and effective low-dimensional feature vector to describe the fault features, which reduces the conflict and redundancy between features. Finally, the optimized fault features are used as an input to the classifier support vector machine (SVM), and the fault types are obtained through training and testing. To validate the efficacy of the presented approach, it is tested from the aspects of noise environment, sample proportion and algorithm complexity, and compared with advanced methods. The results indicate that the proposed technique attains an average accuracy of 99.0625% in four-quadrant rectifier fault diagnosis. Under a different signal-to-noise ratio (SNR) and different training and test ratios, the average value after 30 diagnoses is better. Compared with other methods, this method shows a high diagnostic rate and strong robustness in terms of output voltage, noise, training and test ratio. Full article

In the argillic alteration zone of the SinAbad area of the Urumieh–Dokhtar magmatic belt (Iran), Mg-rich, Fe-poor chlorites, which crystallised at temperatures between 160 °C and 260 °C, were affected by extensive alteration to smectite mixed-layering at the micro- and nano-scales during the retrograde evolution of the hydrothermal system. Chlorites retain their usual optical aspect and properties, except for the index of refraction perpendicular to the (001) layers, which becomes lower than those parallel to the layers, producing an increase in birefringence and change in the optic and elongation signs, in comparison to the ordinary ones for Mg chlorites. Scanning electron microscopy (SEM) maps and compositions, and electron microprobe (EMP) analyses indicate minor but ubiquitous Ca (and K) content. X-ray diffraction (XRD) of chloritic concentrates allowed the identification of chlorite and tosudite. High-resolution transmission electron microscopy (HRTEM) images show major 14 Å (chlorite), with the frequent presence of 24 Å (contracted tosudite) individual layers and small packets up to five layers thick. Lateral change from 14 Å to 24 Å individual layers has been visualised. High-resolution chemical maps obtained in high-angle annular dark-field (HAADF) mode confirm the existence of areas preferentially dominated by chlorite or tosudite. The overall chemical compositions obtained by SEM, EMP, and transmission electron microscopy (TEM) align from the chlorite to the tosudite end-members, whose pure compositions could be determined from extreme analytical electron microscopy (AEM) analyses. The described intergrowths and interlayers, under the optical resolution, could provide a clue to explain changes in the normal optic properties of chlorite, which are mentioned, but not explained, in the literature. Full article

This study focuses on optical twin-core couplers, which facilitate light transmission between two closely aligned optical fibers. These couplers operate based on the principle of coupling, allowing signals in one core to interact with those in the other. The Kerr effect, which describes how a material’s refractive index changes in response to the intensity of light, induces the nonlinear behavior essential for generating solitons—self-sustaining wave packets that preserve their shape and speed. In our research, we employ fractional derivatives to investigate how fractional-order variations influence wave propagation and soliton dynamics. By utilizing the modified extended mapping method (MEMM), we derive solitary wave solutions for the equations governing the behavior of optical twin-core couplers under Kerr nonlinearity. This methodology produces novel fractional traveling wave solutions, including dark, bright, singular, and combined bright–dark solitons, as well as hyperbolic, Jacobi elliptic function (JEF), periodic, and singular periodic solutions. To enhance understanding, we present physical interpretations through contour plots and include both 2D and 3D graphical representations of the results. Full article

Image velocimetry has become an effective method of mapping flow conditions in rivers, but this analysis is typically performed in a post-processing mode after data collection is complete. In this study, we evaluated the potential to infer flow velocities in approximately real time as thermal images are being acquired from an uncrewed aircraft system (UAS). The sensitivity of thermal image velocimetry to environmental conditions was quantified by conducting 20 flights over four days and assessing the accuracy of image-derived velocity estimates via comparison to direct field measurements made with an acoustic Doppler current profiler (ADCP). This analysis indicated that velocity mapping was most reliable when the air was cooler than the water. We also introduced a workflow for River Velocity Measurement in Approximately Real Time (RiVMART) that involved transferring brief image sequences from the UAS to a ground station as distinct data packets. The resulting velocity fields were as accurate as those generated via post-processing. A new particle image velocimetry (PIV) algorithm based on staggered image sequences increased the number of image pairs available for a given image sequence duration and slightly improved accuracy relative to a standard PIV implementation. Direct, automated geo-referencing of image-derived velocity vectors based on information on the position and orientation of the UAS acquired during flight led to poor alignment with vectors that were geo-referenced manually by selecting ground control points from an orthophoto. This initial proof-of-concept investigation suggests that our workflow could enable highly efficient characterization of flow fields in rivers and might help support applications that require rapid response to changing conditions. Full article

Chemical reactions between Be⁺ ions and H₂ molecules have significance in the fields of ultracold chemistry and astrophysics, but the corresponding dynamics studies on the ground-state reaction have not been reported because of the lack of a global potential energy surface (PES). Herein, a globally accurate ground-state BeH₂⁺ PES is constructed using the neural network model based on 18,657 ab initio points calculated by the multi-reference configuration interaction method with the aug-cc-PVQZ basis set. On the newly constructed PES, the state-to-state quantum dynamics calculations of the Be⁺(²S) + H₂(v₀ = 0; j₀ = 0) and Be⁺(²S) + D₂(v₀ = 0; j₀ = 0) reactions are performed using the time-dependent wave packet method. The calculated results suggest that the two reactions are dominated by the complex-forming mechanism and the direct abstraction process at relatively low and high collision energies, respectively, and the isotope substitution has little effect on the reaction dynamics characteristics. The new PES can be used to further study the reaction dynamics of the BeH₂⁺ system, such as the effects of rovibrational excitations and alignment of reactant molecules, and the present dynamics data could provide an important reference for further experimental studies at a finer level. Full article

This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications Full article

The Internet of Things (IoT) is an emerging paradigm, even though it does not receive enough attention in many academic plans. In order to introduce our students to IoT, in this paper we present the overall results of a seminar dedicated to encourage them to design basic IoT projects on a software application called Packet Tracer v8.2.1. Such projects were carried out in groups, whereas the evaluation process was performed by means of filling in a peer review construct after the presentation was made by each team. This construct was previously validated by a panel of five experts by applying the Aiken’s V test, where an overall value of 0.907 was obtained. There were nine items within that construct, which were grouped into three dimensions, such as the quality of the prototypes presented, the communication skills during the presentations of the prototypes, and the alignment of the prototypes with SDGs. The average results obtained for all dimensions were all above 8 out of 10, whilst the medians were all 9 and the coefficient of variations were around 0.2. Those results showed high ratings with moderate variation among them. Furthermore, the reliability of the data collected in the evaluation process was assessed by applying the Cronbach’s alpha, accounting for values above 0.9 for each dimension and overall, which means an excellent internal consistency of such data. Likewise, the level of engagement during the activity was also measured by the means of the ISA engagement scale, resulting in an overall value around 6.5, with a median of 7, which proved a high engagement among students. Full article

Due to the space–time coupling access, we find that anti-eavesdropping opportunities exist in underwater acoustic networks (UANs), where packets can be successfully received only by the intended receiver, but collide at the unintended receivers. These opportunities are highly spatially dependent, and this paper studies the case that linearly deployed sensor nodes directly report data toward a single collector. We found an eavesdropping ring centered around these linearly deployed sensor nodes, where the eavesdropper could steal all the reported data. Since the typical receiving-alignment-based scheduling MAC (TRAS-MAC) will expose the relative spatial information among the sensor nodes with the collector, the eavesdropper can locate the eavesdropping ring. Although moving the collector into the one-dimensional sensor node chain can degrade the eavesdropping ring to a point that constrains the eavesdropping risk, the collector’s location will be subsequently exposed to the eavesdropper. To efficiently protect the reported data and prevent the exposure of the collector’s location, we designed a slotted- and receiving-alignment-based scheduling MAC (SRAS-MAC). The NS-3 simulation results showed the effectiveness of the SRAS-MAC and the TRAS-MAC in protecting data from eavesdropping, which protect 90% of the data from eavesdropping in the one-eavesdropper case and up to 80% of data from eavesdropping in ten-eavesdropper cases. Moreover, unlike TRAS-MAC, which will expose the collector’s location, SRAS-MAC provides multiple positions for the collector to hide, and the eavesdropper cannot distinguish where it is. Full article

The proposed hierarchical secondary voltage control is a spatially distributed control system using communication networks which are disturbed by both a time delays and packet data dropouts. A state feedback integral control is adopted to eliminate the effect of non-zero disturbance and provide exact tracking of the references of the pilot points and alignment of the reactive powers of the generators that participate in the control. The system is modeled as a discrete-time switched system, and the control gains are synthesized by solving LMIs for a stability condition based on a state-dependent Lyapunov function. For that, the cone complementarity linearization (CCL) algorithm is used. The effectiveness of the proposed control strategy in preventing time delays and packet losses is simulated, considering the model of a realistic electric power grid under typical operational conditions using MATLAB. Full article

In this paper, we study via simulation the performance of irregular repetition slotted ALOHA under multi-packet detection and different patterns of the load process. On the one hand, we model the arrival process with a version of the M/G/∞ process able to exhibit a correlation structure decaying slowly in time. Given the independence among frames in frame-synchronous coded-slotted ALOHA (CSA), this variation should only take effect on frame-asynchronous CSA. On the other hand, we vary the marginal distribution of the arrival process using discrete versions of the Lognormal and Pareto distributions, with the objective of investigating the influence of the right tail. In this case, both techniques should be affected by the change, albeit to a different degree. Our results confirm these hypotheses and show that these factors must be taken into account when designing and analyzing these systems. In frameless operations, both the shape of the packet arrivals tail distribution and the existence of short-range and long-range correlations strongly impact the packet loss ratio and the average delay. Nevertheless, these effects emerge only weakly in the case of frame-aligned operations, because this enforces the system to introduce a delay in the newly arrived packets (until the beginning of the next frame), and implies that the backlog of accumulated packets is the key quantity for calculating the performance. Full article

To achieve high-frequency and effective inter-vehicle communication between harvesters and transport vehicles during cooperative harvesting, a protocol for wireless communication was designed by analyzing actual communication requirements. Two different wireless communication modes (radio and 4G) were selected for the hardware design; then, a Kalman Filter was designed based on real-time Dead-reckoning and inter-vehicle Communication data after delay Compensation (KFDCC). Finally, the relative longitudinal deviation between two vehicles was obtained and updated steadily at a 10 Hz frequency. By using the relative longitudinal deviation of two vehicles, calculated after aligning the UTC stamp with the local GNSS data from the harvester and transport vehicle as a comparative metric, accuracy evaluation experiments were conducted regarding radio and 4G. The maximum absolute errors of the KFDCC output value were 0.03783 and 0.07381 m, respectively, and the mean square errors were 0.00392 and 0.01317 m, respectively. Compared with systems without the KFDCC method, the mean square errors were reduced by 88.76% and 90.60%, respectively. The KFDCC method can also effectively solve the problems of data delay, packet loss, blockage, error, and so on, in wireless communication, and has short-time breakpoint endurance capabilities. Field experiments showed that the proposed method can provide accurate data support for the dynamic alignment and unloading processes of harvesters and transport vehicles, and it can also provide algorithmic support for real-time communication data fusion between different wireless communication modes. Overall, the inter-vehicle communication mode and data-processing method designed in this paper have good effects and adaptability, and they can guarantee that the whole process of autonomous harvesting operates properly. Full article

Display-to-camera (D2C) communications has emerged as a key method for next-generation videos that offer side information to camera-equipped devices during normal viewing. This paper presents Deep learning-driven Complementary Color Barcode-based Optical Camera Communications (DeepCCB-OCC), a D2C system using multiple deep neural networks built for imperceptible transmission and reliable communication in a D2C link. DeepCCB-OCC takes advantage of a the You Only Look Once (YOLO) model to provide seamless detection of a color barcode area in electronic displays. To identify transmitted color barcode symbols in the received image, we define various color barcode patterns caused by the synchronization jitter between the camera and the display. Then, DeepCCB-OCC incorporates convolutional neural network (CNN) models to accurately detect the pilot and data symbols in the transmission packets, regardless of the various D2C environments. Experiments with a commercial monitor and a smartphone demonstrate that DeepCCB-OCC outperforms the conventional CCB-OCC system from various distances and angles of a D2C link. The experiment results prove that, when the alignment angle was 20 degrees at a distance of 90 cm between the display and the camera, the proposed scheme achieved approximately 79.1 bps, which showed a performance improvement of 14.1% compared to the existing technique. Full article

In this paper, we revisit a two-user space-time line coded uplink non-orthogonal multiple access (STLC-NOMA) system for Internet-of-things (IoT) networks and propose a novel low-complexity STLC-NOMA system. The basic idea is that both IoT devices (stations: STAs) employ amplitude-shift keying (ASK) modulators and align their modulated symbols to in-phase and quadrature axes, respectively, before the STLC encoding. The phase distortion caused by wireless channels becomes compensated at the receiver side with the STLC, and thus each STA’s signals are still aligned on their axes at the access point (AP) in the proposed uplink STLC-NOMA system. Then, the AP can decode the signals transmitted from STAs via a single-user maximum-likelihood (ML) detector with low-complexity, while the conventional uplink STLC-NOMA system exploits a multi-user joint ML detector with relatively high-complexity. We mathematically analyze the exact BER performance of the proposed uplink STLC-NOMA system. Furthermore, we propose a novel expectation-maximization (EM)-based blind energy estimation (BEE) algorithm to jointly estimate both transmit power and effective channel gain of each STA without the help of pilot signals at the AP. Somewhat interestingly, the proposed BEE algorithm works well even in short-packet transmission scenarios. It is worth noting that the proposed uplink STLC-NOMA architecture outperforms the conventional STLC-NOMA technique in terms of bit-error-rate (BER), especially with high-order modulation schemes, even though it requires lower computation complexity than the conventional technique at the receiver. Full article

In the simulation of dilute gas-solid flows such as those seen in many industrial applications, the Lagrangian Particle Tracking method is used to track packets of individual particles through a converged fluid field. In the tracking of these particles, the most dominant forces acting upon the particles are those of gravity and drag. In order to accurately predict particle motion, the determination of the aforementioned forces become of the upmost importance, and hence an improved drag force formula was developed to incorporate the effects of particle concentration and particle Reynolds number. The present CFD study examines the individual effects of particles located both perpendicular and parallel to the flow direction, as well as the effect of a particle entrain within an infinite matrix of evenly distributed particles. Results show that neighbouring particles perpendicular to the flow (Model 2) have an effect of increasing the drag force at close separation distances, but this becomes negligible between 5–10 particle diameters depending on particle Reynolds number (Re_p). When entrained in an infinite line of particles co-aligned with the flow (Model 1), the drag force is remarkably reduced at close separation distances and increases as the distance increases. The results of the infinite matrix of particles (Model 3) show that, although not apparent in the individual model, the effect of side particles is experienced many particle diameters downstream. Full article