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In wireless sensor network (WSN) applications, the Received Signal Strength Indicator (RSSI) value from the original signal is determined for computing the distance between the unidentified and beacon nodes in the WSN. However, several factors including noise, diffraction, scattering, and some obstructions affect the precision of the localization techniques. This paper aims to implement a smart node localization scheme in WSNs by estimating the shortest distance between beacon nodes and unknown nodes using the RSSI factor. Initially, the beacon node is positioned at a known position, and the exact location of the unknown node is computed by the hybrid optimization concept. The objective of the proposed node localization method is to reduce the average localization error, and it is derived for assigning the unknown nodes to each beacon nodes. Optimization plays a vital role in providing clear communication among sensor nodes without any hindrance. The hybridized algorithm named as Fitness‐aware Hybrid One‐to‐One with Archery Optimizer (FHOOAO) is used for the positioning of the unknown nodes to each beacon node. After assigning unknown nodes, their best positions are identified by considering the maximum number of hops. Finally, the experimentation is done in three different forms of node positioning in WSN such as S‐shape, H‐shape, and. C‐shape. The simulation experiments demonstrate superior outcomes of the proposed model compared to alternative methods, and it also enhances communication efficiency among sensor nodes. [ABSTRACT FROM AUTHOR]

In this article, the problem of finite‐horizon state estimation is studied for a class of time‐varying complex networks with sensor faults. The phenomenon of measurement quantization is considered such that the measurements are quantized probabilistically before transmitted to the state estimator. To deal with the unknown sensor fault, a neural network is introduced to appropriate the sensor fault whose weights are updated based on estimation error and the gradient descent method. Our aim is to design state estimators so that the state estimation errors are finite‐time bounded. First, sufficient conditions are established to ensure the existence of the desired state estimators. Then, the gains of the state estimators are derived in terms of the solutions to a set of recursive matrix inequalities. Finally, the usefulness of our estimation approach is confirmed by an illustrative example. [ABSTRACT FROM AUTHOR]

Next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary coalescence signal from epochs before the first stars began to form, increasing the number of detectable binaries to hundreds of thousands annually. This will enable us to observe compact objects through cosmic time, probe extreme matter phenomena, do precision cosmology, study gravity in strong field dynamical regimes and potentially allow observation of fundamental physics beyond the standard model. However, the richer data sets produced by these detectors will pose new computational, physical and astrophysical challenges, necessitating the development of novel algorithms and data analysis strategies. To aid in these efforts, this paper introduces gwforge, a user-friendly, lightweight Python package, to generate mock data for next-generation detectors. gwforge allows users to seamlessly simulate data while abstracting away technical complexities, enabling more efficient testing and development of analysis pipelines. Additionally, the package's data generation process is optimized using high-throughput systems like HTCondor, significantly speeding up the simulation of large populations of gravitational-wave events. We demonstrate the package's capabilities through data simulation examples and highlight a few potential applications: performance loss due to foreground noise, bright-siren cosmology and impact of waveform systematics on binary parameter estimation. [ABSTRACT FROM AUTHOR]

Predicting stratified ground consolidation effectively remains a challenge in geotechnical engineering, especially when it comes to quickly and dependably determining the coefficient of consolidation (cv${c}_{\mathrm{v}}$) for each soil layer. This difficulty primarily stems from the time‐intensive nature of the consolidation process and the challenges in efficiently simulating this process in laboratory settings and using numerical methods. Nevertheless, the consolidation of stratified ground is crucial because it governs ground settlement, affecting the safety and serviceability of structures situated on or in such ground. In this study, an innovative method utilizing a physics‐informed neural network (PINN) is introduced to predict stratified ground consolidation, relying solely on short‐term excess pore water pressure (PWP) data collected by monitoring sensors. The proposed PINN framework identifies cv${c}_{\mathrm{v}}$ from the limited PWP data set and subsequently utilizes the identified cv${c}_{\mathrm{v}}$ to predict the long‐term consolidation process of stratified ground. The efficacy of the method is demonstrated through its application to a case study involving two‐layer ground consolidation, with comparisons made to an existing PINN method and a laboratory consolidation test. The results of the case study demonstrate the applicability of the proposed PINN method to both forward and inverse consolidation problems. Specifically, the method accurately predicts the long‐term dissipation of excess PWP when cv${c}_{\mathrm{v}}$ is known (i.e., the forward problem). It successfully identifies the unknown cv${c}_{\mathrm{v}}$ with only 0.05‐year monitoring data comprising 10 data points and predicts the dissipation of excess PWP at 1‐year, 10‐year, 15‐year, and even up to 30‐year intervals using the identified cv${c}_{\mathrm{v}}$ (i.e., the inverse problem). Moreover, the investigation into optimal PWP monitoring sensor layouts reveals that installing sensors in areas with significant variations in excess PWP enhances the prediction accuracy of the proposed PINN method. The results underscore the potential of leveraging PINNs in conjunction with PWP monitoring sensors to effectively predict stratified ground consolidation. [ABSTRACT FROM AUTHOR]

Geometrically, a complex dynamical network (CDN) can be regarded as the interconnected system composed of the node subsystem (NS) and the link subsystem (LS) coupled with each other. Guided by this idea, in order to achieve the goal of each node following asymptotically its own reference target in a CDN, this paper investigates the model following adaptive control (MFAC) problem of NS via the dynamics of links, which implies that the LS plays the important dynamic auxiliary role in the MFAC realization of nodes. Meanwhile, we focus on the condition that the links state information is unavailable, due to sensor practical application and measurement cost constraints. To obviate this restriction, we construct the asymptotical state observer for the LS. Next, to achieve the control goal of this paper, an appropriate Lyapunov candidate function is selected, by which the MFAC scheme for NS is synthesized based on the state observer of LS. Finally, the simulation example is performed to demonstrate the theoretical results in this paper. [ABSTRACT FROM AUTHOR]

Structural dynamic response corrupts frequently due to the sensor malfunction. The loss of dynamic response will hinder the structural condition assessment. In recent years, significant efforts have been devoted to recovering the dynamic response during the linear elastic stage of the structure. However, relevant researches on the response recovery of damaged structures are rarely reported due to its strong nonlinearity. With the growing significance of post-disaster structural maintenance, it is critical to develop effective methods for recovering missing data in damaged structures. To this end, this paper proposes a dynamic response recovery method for damaged structures using residual learning enhanced fully convolutional networks (FCN), which can provide a baseline for the recovery of monitoring data in operational civil infrastructure. Specifically, a FCN incorporating residual learning and skip connections is designed to capture high-dimensional nonlinear relationships between input and output channels, thereby achieving the data recovery for any concerned channel. Then, a time–frequency domain evaluation mode is constructed, in which L2 norm is used to measure the difference of recovery results in the time domain, while instantaneous frequency is employed to evaluate the integrity of the spectral information of recovery results. Finally, a destruction test of an experimental arch was conducted, and the acceleration data under different damaged state were collected to investigate the feasibility of the proposed method. Besides, the recovery effects concerning input channel location and quantity, multi-channel response and cross-state recovery are examined. The results show that even in a severely damaged state, the proposed method effectively recovers the missing data. In addition, improving the correlation between input and output channels and increasing the number of input channels can further enhance the recovery accuracy. [ABSTRACT FROM AUTHOR]

In recent days, underwater exploration has emerged as one of the most predominant technologies for enhancing surveillance and early warning systems. Finding the location of the nodes placed in underwater is a difficult task owing to its harsh underwater environment. In large underwater wireless sensor networks (UWSNs), pinpointing the exact coordinates of sensor nodes may not be feasible or be incredibly expensive. In most of the applications, the coarse coordinate of the node is adequate. The primary technique used in UWSNs to determine the location of sensor nodes, based on the average distance between hops, is referred to as distance vector‐hop (DV‐Hop) localization. Nevertheless, the positioning accuracy in the classic DV‐Hop technique is influenced by the average hop distance. To reduce the localization error, it is possible to create a distinct and optimized DV‐Hop approach. To improve the effectiveness of the localization process, the average distance between hops is primarily used as an objective function. The optimization of this objective function is achieved by employing the Pelican Optimization Algorithm (POA). There is a noticeable decrease in the localization discrepancy if the optimized average hop distance is used to precisely determine the unidentified node to the anchor node distance among them. The factors used to evaluate the ability of the proposed methodology are the ratio of anchor, transmission range, and the density of the node. Compared to other localization procedures, the obtained outcomes demonstrate that the optimized approach that has been suggested achieves a low localization error of 0.3. [ABSTRACT FROM AUTHOR]

Summary: In this article, we exploit the huge advantages of full‐duplex (FD) transmission, nonorthogonal multiple access (NOMA) technique, unmanned aerial vehicle (UAV), and millimeter‐wave (mmWave) communications in an Internet of things (IoT) sensor network. Specifically, the UAV operates as a FD amplify‐and‐forward (AF) relay to aid communications between an IoT sensor and multiple NOMA users. Moreover, the FD‐UAV (FDU) adopts both fixed and variable gains. We obtain exact expressions of outage probability (OP) and throughput of IoT‐FDU‐NOMA‐mmWave network with fixed and variable gains at FDU over Nakagami‐m$$ m $$ fading channels. Numerical illustrations clearly show that the OP and throughput performance of the IoT‐FDU‐NOMA‐mmWave network with variable gain are considerably higher than those with fixed gain. Since mmWave bands are used, the OP and throughput of the IoT‐FDU‐NOMA‐mmWave network are greatly affected by carrier frequency and communication distances. Consequently, when the carrier frequency or/and the distances between the IoT sensor and the users increase, the transmit power of the IoT sensor and the FDU should be increased to maintain the network performance. On the other hand, the effects of residual loop interference (LI) induced by FD transmission are remarkable. Thus, more efforts to reduce the residual LI power have to be continuously performed. Moreover, the specific impacts of the key parameters such as fading order m$$ m $$, the positions of the FDU, and data rates are clarified. More importantly, in practice, according to the fixed gain or the variable gain that is used, we can locate the position of FDU to achieve the best performance for the IoT‐FDU‐NOMA‐mmWave network. [ABSTRACT FROM AUTHOR]

In this article, we propose a data‐driven networked control architecture for unknown and constrained cyber‐physical systems capable of detecting networked false‐data‐injection attacks and ensuring plant's safety. In particular, on the controller's side, we design a novel robust anomaly detector that can discover the presence of network attacks using a data‐driven outer approximation of the expected robust one‐step reachable set. On the other hand, on the plant's side, we design a data‐driven safety verification module, which resorts to worst‐case arguments to determine if the received control input is safe for the plant's evolution. Whenever necessary, the same module is in charge of replacing the networked controller with a local data‐driven set‐theoretic model predictive controller, whose objective is to keep the plant's trajectory in a pre‐established safe configuration until an attack‐free condition is recovered. Numerical simulations involving a two‐tank water system illustrate the features and capabilities of the proposed control architecture. [ABSTRACT FROM AUTHOR]

DNA aptamers have emerged as a promising class of probes for the development of biosensors. However, the only viable strategy thus far for adjustment of probe densities is tuning DNA concentrations. Herein, we constructed a class of Supra-EAB sensors to introduce localized high probe densities and achieved significantly improved stability against enzymes. [ABSTRACT FROM AUTHOR]

Smart polymers represent a class of advanced materials that undergo reversible changes in their physical or chemical form and are known as responsive polymers. These polymers show transitions when external stimuli, such as temperature and pH, come into play. Smart polymers are being increasingly applied in various fields, such as drug delivery to a targeted site and gene therapy. They also play a pivotal role in tissue engineering, environmental sensors, and the development of shape memory polymers. Despite their major challenges, they remain effective in overcoming significant barriers. It can be said that these polymers have the potential to revolutionize various fields. This review highlights the underlying types and applications of smart polymers, emphasizing their roles in the future. [ABSTRACT FROM AUTHOR]

In terms of safety and emergency response, identifying hazardous gaseous acid chemicals is crucial for ensuring effective evacuation and administering proper first aid. However, current studies struggle to distinguish between different acid vapors and remain in the early stages of development. In this study, we propose an on-site monitorable acid vapor decoder, MOF-808-EDTA-Cu, integrating the robust MOF-808 with Cu-EDTA, functioning as a proton-triggered colorimetric decoder that translates the anionic components of corrosive acids into visible colors. The sensor exhibits a cyan-to-yellow shift when exposed to HCl vapor and can visually differentiate various acidic vapors (HF, HBr, and HI) through unique color changes. Furthermore, the compatibility of the MOF-based sensor with multiple metal ions having atomic-level dispersion broadens its discrimination range, enabling the identification of six different colorless acid vapors within a single sensor domain. Additionally, by incorporating a flexible polymer, the MOF-808-EDTA-Cu has been successfully processed into a portable miniaturized acid sensor, exhibiting distinct color changes that can be easily monitored by the naked eye and camera sensors. This provides experimental validation as a practical sensor capable of on-site 24-hour monitoring in the real world. Identifying hazardous acid vapors is crucial for safety and emergency response. This study reports on-site monitorable MOF-based acid vapor sensors capable of translating diverse anionic components of corrosive acids into distinct visible color changes. [ABSTRACT FROM AUTHOR]

Background: Light‐sharing detector designs for positron emission tomography (PET) systems have sparked interest in the scientific community. Particularly, (semi‐)monoliths show generally good performance characteristics regarding 2D positioning, energy‐, and timing resolution, as well as readout area. This is combined with intrinsic depth‐of‐interaction (DOI) capability to ensure a homogeneous spatial resolution across the entire field of view (FoV). However, complex positioning calibration processes limit their use in PET systems, especially in large‐scale clinical systems. Purpose: This work proposes a new 3D positioning in‐system calibration method for fast and convenient (re‐)calibration and quality control of assembled PET scanners. The method targets all kinds of PET detectors that achieve the best performance with individual calibration, including complex segmented detector designs. The in‐system calibration method is evaluated and empirically compared to a state‐of‐the‐art fan‐beam calibration for a small‐diameter proof of concept (PoC) scanner. A simulation study evaluates the method's applicability to different scanner geometries. Methods: A PoC scanner geometry of 120 mm inner diameter and 150 mm axial extent was set up consisting of five identical finely segmented slab detectors (one detector under test and four collimation detectors). A 22Na point source was moved in a circular path inside the FoV. Utilizing virtual collimation and by selecting gamma rays incident approximately perpendicular to the detector normal of the detector under test, training data was created for the training of a 2D positioning model with the machine‐learning technique gradient tree boosting (GTB). Data with oblique ray angles was acquired in the same measurement for subsequent angular DOI calibration. For this, a 2D position estimate in the detector under test was calculated first. On this basis, the DOI label was calculated geometrically from the ray path within the detector to finally establish up to 3D training data. Results: With a mean absolute error (MAE) of 0.8 and 1.19 mm full‐width at half maximum (FWHM) along the planar‐monolithic slab dimension, the in‐system methods performed similarly within 1% to the fan‐beam collimator results. The DOI performance was at ∼90% with 1.13 mm MAE and 2.47 mm FWHM to the fan‐beam collimator. Analytical calculations suggest an improved performance for larger scanner geometries. Conclusion: The functionality of the 3D in‐system positioning calibration method was successfully demonstrated with the measurements within a PoC scanner configuration with similar positioning performance as the bench‐top fan‐beam setup. The in‐system calibration method can be used to calibrate and test fully assembled PET systems to enable more complex light‐sharing detector architectures in, for example, large PET systems with many detectors. The acquired data can further be used for more complex energy and time calibrations. [ABSTRACT FROM AUTHOR]

In view of the low detection accuracy of magnetostrictive displacement sensor, a calculation model of output voltage of magnetostrictive displacement sensor considering skin effect is proposed in this study. On the baisis of explaining the working principle of the magnetostrictive displacement sensor, the influence of skin depth is analyzed, and the Wiedemann effect and piezomagnetic effect are considered for theoretical analysis. The relationship between the excitation pulse current and the output voltage of the magnetostrictive displacement sensor is simulated by the skin depth generated by the excitation pulse signal acting on the waveguide wire at different frequencies. The optimal parameter value of excitation pulse current frequency is obtained using the experimental platform. Simulation experiments verify that the correctness of the output voltage model of the magnetostrictive displacement sensor considering the skin effect, indicating that the proposed model further improves the detection accuracy of the magnetostrictive displacement sensor and enriches the mathematical model. [ABSTRACT FROM AUTHOR]

Recent advancements in wireless communication have markedly increased data throughput and decreased latency in progressing toward sixth‐generation (6G) networks, wherein the terahertz (THz) waveband offers significant potential. However, conventional THz sensors often suffer from high noise, low responsivity, and low spectrum efficiency, limiting their effectiveness for THz applications. Here, to address these challenges, a magnetic‐enhanced frequency‐selective is developed THz sensor by depositing an Au‐enhanced active layer of MoTe2 on a designed 3D printing high‐depth micro‐helical stepped structure. This sensor, featuring four characteristic frequency points and an effective area of 107.12 mm2, demonstrated noticeable improvements under a 0.145 mT magnetic field at 0.1 THz: optical responsivity improved by 413.23% (from 3.32 to 17.03 MV W−1), noise equivalent power decreased by 80.51% (from 49.16 to 9.58 pW Hz−1/2), and the detectivity reached 3.30 × 1010 cm·Hz1/2·W−1. This work highlights the potential of integrating 3D microstructures with novel topological materials for practical 6G communication. [ABSTRACT FROM AUTHOR]

This study tackles the problem of fault‐tolerant attitude estimation for small satellites. A probabilistic adaptive technique is presented for the multiplicative extended Kalman filter (MEKF) algorithm that is used in attitude estimation. The presented method is based on tracking the normalized measurement innovations in the filter and calculating the probability of the normal operation of the estimation system. Using this probability, the filter gain is corrected to maintain the tracking performance of the filter despite faulty measurements. In order to evaluate the performance of this method, several simulations are performed where different types of faults are introduced to the synthetic attitude sensor measurements (magnetometer and sun sensor) at different times. Simulation results are compared not only with a conventional EKF but also with another popular adaptive Kalman filter, an adaptive Kalman filter with multiple scaling factors (MSFs). [ABSTRACT FROM AUTHOR]

Halide perovskites hold great potential in developing next‐generation X‐ray detectors. However, preparing high‐quality and thick perovskite films in a way compatible with a thin‐film transistor (TFT)‐integrated X‐ray flat‐panel detectors (XFPDs) remains challenging. Here, by engineering ink with effective printability and shape fidelity, direct ink writing (DIW) is developed as a new approach to printing a unique single‐crystal‐assembled perovskite (SCAP) thick film. In contrast to polycrystalline grains consisting of randomly orientated crystal domains, the SCAP is made of tightly packed crystals with well‐defined crystal facets, showing 3–4 orders of magnitude lower trap density (4.48 × 1012 cm−3). Consequently, the SCAP X‐ray detectors offers the state‐of‐the‐art detection performance (sensitivity‐to‐dark current ratio: 1.26 × 1011 µC Gyair−1 A−1), a low detection limit (114.2 nGyair s−1), and negligible baseline drift (0.27 fA cm−1 s−1 V−1). Furthermore, the XFPD based on a 64 × 64 pixelated TFT array realizes high‐resolution digital radiography, opening a new avenue for further development of perovskite X‐ray detectors. [ABSTRACT FROM AUTHOR]

Flexible electromechanical sensors frequently suffer from unexpected impact loadings caused by slipping, collisions and falling objects, to name a few. Without sufficient protection, these undesired impacts would lead to critical mechanical instability even damage to flexible sensors, resulting in restricted measurement range and imprecise sensing. Thus, it is of significance, but still is a fresh challenge to enhance the mechanical stability and energy‐absorption capacity of flexible sensors under impacts. Here, a multi‐design strategy is proposed to construct an interpenetrating‐phase cellulose‐acetate composite (IPC2) architecture for flexible sensors in impact‐intensive sensing applications. The external structure mimics bellows‐morphology of beverage‐straws that deform in programmed loading direction to enhance the mechanical stability, while the internal conductive core has a co‐continuous interpenetrating‐phase architecture that can efficiently absorb impact energy. Systematic numerical analysis and experimental tests demonstrate that IPC2 architecture presents excellent structural stability, cyclic performance and a unique combination of exceptional specific energy absorption (SEA = 2.66±1.2 kJ kg−1), low density (ρ = 720±10 kg m−3), electromechanical properties (GF≈39.6). Remarkably, the recovery behaviors in terms of shape and electrical signals show good repeatability and reliability. This study offers a new composite framework to exploit the potentialities of flexible sensors with protective functions and commercial values. [ABSTRACT FROM AUTHOR]

Flexible sensing offers real‐time force monitoring, presenting a versatile and effective solution for dexterous manipulation, healthcare, environmental exploration, and perception of physical properties. Nonetheless, a limitation of many existing flexible force sensors stems from their isotropic structure or material properties, preventing them from simultaneously detecting both the direction and magnitude of the applied force. Herein, a high‐performance 3D force sensor based on orthogonal multimodal sensing, the cancellation principle, and the strain effect is proposed. Finite element analysis further reveals the decoupling and anti‐interference mechanisms of the innovative capacitor‐resistance dual‐mode sensing based on a solid mechanics and electrostatic multiphysics model. The sensor demonstrates the ability to measure both the magnitude and direction of normal and shear forces in any combination using the proposed decoupling and reconstruction algorithms, showing the potential for accurately reconstructing the posture of objects. [ABSTRACT FROM AUTHOR]

In discrete-event systems, to save sensor resources, the agent continuously adjusts sensor activation decisions according to a sensor activation policy based on the changing observations. However, new challenges arise for sensor activation in networked discrete-event systems, where observation delays and control delays exist between the sensor systems and the agent. In this paper, a new framework for activating sensors in networked discrete-event systems is established. In this framework, we construct a communication automaton that explicitly expresses the interaction process between the agent and the sensor systems over the observation channel and the control channel. Based on the communication automaton, we can define dynamic observations of a communicated string. To guarantee that a sensor activation policy is physically implementable and insensitive to non-deterministic control delays and observation delays, we further introduce the definition of delay feasibility. We show that a delay feasible sensor activation policy can be used to dynamically activate sensors even if control delays and observation delays exist. A set of algorithms are developed to minimise sensor activation in a transition-based domain while ensuring a given specification condition is satisfied. A practical example is also provided to show the application of the proposed framework. Finally, we briefly discuss how to extend the proposed framework to a decentralised observation setting. [ABSTRACT FROM AUTHOR]

A forensic investigator performing source identification on a questioned image from a crime aims to identify the unknown camera that acquired the image. On the camera sensor, minute spatial variations in intensities between pixels, called photo response non‐uniformity (PRNU), provide a unique and persistent artifact appearing in every image acquired by the digital camera. This camera fingerprint is used to produce a score between the questioned image and an unknown camera using a court‐approved camera identification algorithm. The score is compared to a fixed threshold to determine a match or no match. Error rates for the court‐approved camera‐identification PRNU algorithm were established on a very large set of image data, making no distinction between images with different brightness levels. Camera exposure settings and in‐camera processing strive to produce a visually pleasing image, but images that are too dark or too bright are not uncommon. While prior work has shown that exposure settings can impact the accuracy of the court‐approved algorithm, these settings are often unreliable in the image metadata. In this work, we apply the court‐approved PRNU algorithm to a large data set where images are assigned a brightness level as a proxy for exposure settings using a novel classification method and then analyze error rates. We find statistically significant differences between error rates for nominal images and for images labeled dark or bright. Our result suggests that in court, the error rate of the PRNU algorithm for a questioned image may be more accurately characterized when considering the image brightness. [ABSTRACT FROM AUTHOR]

X-ray cone beam computed tomography (CBCT) is a common imaging tool in medical and industrial fields due to its non-invasive and efficient approach to 3D internal imaging. However, obtaining clear cross-section image with CBCT requires precise geometry alignment. Previous alignment methods have mostly relied on specially designed phantoms and/or the iterative optimisation of reconstructed images, both of which are costly and inefficient. This paper presented a Swin-Transformer-enhanced detector z-axis virtual geometry alignment method, which considered the tilt angle (around the central column of the detector) and the horizontal deviation of the centre of projection. The method took advantage of both cross-section and projection domains to virtually restore the geometric misalignments with the help of mathematical models. The maximum prediction error of the tilt angle was 0.0696°, and the single prediction time was about 0.0395 s. To the best of our knowledge, this work was the first study to detect the multiple geometric errors from the slice using deep learning method. It provided a new geometric alignment method for the CBCT system. [ABSTRACT FROM AUTHOR]

Research on developing deep learning techniques for autonomous spacecraft relative navigation challenges is continuously growing in recent years. Adopting those techniques offers enhanced performance. However, such approaches also introduce heightened apprehensions regarding the trustability and security of such deep learning methods through their susceptibility to adversarial attacks. In this work, we propose a novel approach for adversarial attack detection for deep neural network-based relative pose estimation schemes based on the explainability concept. We develop for an orbital rendezvous scenario an innovative relative pose estimation technique adopting our proposed Convolutional Neural Network (CNN), which takes an image from the chaser's onboard camera and outputs accurately the target's relative position and rotation. We perturb seamlessly the input images using adversarial attacks that are generated by the Fast Gradient Sign Method (FGSM). The adversarial attack detector is then built based on a Long Short Term Memory (LSTM) network which takes the explainability measure namely SHapley Value from the CNN-based pose estimator and flags the detection of adversarial attacks when acting. Simulation results show that the proposed adversarial attack detector achieves a detection accuracy of 99.21%. Both the deep relative pose estimator and adversarial attack detector are then tested on real data captured from our laboratory-designed setup. The experimental results from our laboratory-designed setup demonstrate that the proposed adversarial attack detector achieves an average detection accuracy of 96.29%. [ABSTRACT FROM AUTHOR]

Virtual coupling of automated trains has emerged as a promising technology to enhance the operational efficiency and line capacity of railway transportation systems. However, the vulnerability of such systems to malicious cyber attacks poses a significant threat to their stability, reliability and applicability. This article addresses the challenge of ensuring secure virtual coupling control of connected train platoons subject to various cyber attacks, including false data injection attacks, denial-of-service attacks, and replay attacks. First, based on the low-dimensional and noisy sensor measurements, novel set-valued observers are designed for each follower train to estimate their inaccessible full longitudinal motion states despite uncertainties and unknown attacks and noises. Then, distributed secure attack-compensation-based virtual coupling control laws are developed to ensure the platoon's stability and resilience against cyber attacks. Additionally, formal stability analysis of the resultant estimation error system and platoon tracking error system is provided. Furthermore, two algorithms are presented to elaborate the offline observer and controller design process as well as their online implementation. Finally, based on the data of a realistic urban rail transit line, simulation experiments are conducted to validate the efficacy of the proposed theoretical findings. [ABSTRACT FROM AUTHOR]

Orbital angular momentum (OAM) beam's spatial information captured using a camera, employs millions of single-pixel detectors arranged in a two-dimensional matrix. Poses challenges in OAM detection due to significant storage requirements, reduced speed due to low frame rate, and increased post-processing time. To address this bottleneck, a novel OAM detection technique utilizing only a diffuser and a single-pixel fast photodiode (FPD) is reported. The sixteen OAM beams, l = [ − 8 + 8 ] interact with the rotating diffuser resulting in a temporally varying speckle field. The 1D temporal speckle information (TSI) has been segregated from the temporally varying 2D speckle pattern images for each beam. The segregated 1D TSI has been classified by employing a machine learning model and achieved a classification accuracy of 97.1% and 97.3% on simulated and experimental data respectively. To further validate the proposed single-pixel OAM detection method, the 1D TSI is captured directly using a FPD. The experimentally captured 1D TSI captured via photodiode has been classified by a lightweight custom-designed 1D convolutional neural network and achieved an increased classification accuracy of 96%. This approach redefines OAM detection, operating in the temporal domain with a single-pixel FPD, thereby significantly reducing storage and computational costs while promising high-speed OAM detection. [ABSTRACT FROM AUTHOR]

We investigate refractive index (RI) sensors using no-core fibers with diameters of 250 µm, 125 µm, and 62.5 µm. Experiments show that RI sensors with sizes of 62.5 µm and 250 µm are more sensitive than those with a diameter of 125 µm. The maximum RI sensitivity was 4886 nm/RIU. The RI sensor has a high sensitivity, a wide measurement range, and is insensitive to temperature, making it well-suited for a variety of practical applications. [ABSTRACT FROM AUTHOR]

In recent years, the use of mobile sensors for human activity recognition has become an intriguing research area due to the proliferation of wearable and mobile sensors. In Muslim life, the prayer (Salah) is an activity that believers are obligated to perform five times a day. In this study, a new dataset, including Salah, is presented for use in human activity recognition. Named HAR-P (Human Activity Recognition for Praying), the dataset comprises linear acceleration, acceleration, magnetic field, and gyroscope sensor data for eight activities: walking, running, typing, downstairs, upstairs, sitting, standing, and praying. Data were collected from 50 male volunteers aged 15-60 using a smartwatch for the HAR-P dataset. The classification performance of LSTM, ConvLSTM, and CNN-LSTM models was compared for the HAR-P dataset. The highest average classification accuracy of 91% was achieved with the LSTM method using linear acceleration sensor data and the ConvLSTM model using acceleration sensor data, while the lowest average accuracy of 83.6% was attained with the gyroscope sensor data and the ConvLSTM method. [ABSTRACT FROM AUTHOR]

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Background and Objectives: In this paper, a novel and efficient unsupervised machine learning algorithm named EiForestASD is proposed for distinguishing anomalies from normal data in data streams. The proposed algorithm leverages a forest of isolation trees to detect anomaly data instances. Methods: The proposed method EiForestASD incorporates an isolation forest as an adaptable detector model that adjusts to new data over time. To handle concept drifts in the data stream, a window-based concept drift detection is employed that discards only those isolation trees that are incompatible with the new concept. The proposed method is implemented using the Python programming language and the Scikit-Multiflow library. Results: Experimental evaluations were conducted on six real-world and two synthetic data streams. Results reveal that the proposed method EiForestASD reduces computation time by 19% and enhances anomaly detection rate by 9% compared to the baseline method iForestASD. These results highlight the efficacy and efficiency of the EiForestASD in the context of anomaly detection in data streams. Conclusion: The EiForestASD method handles concept change using an intelligent strategy where only those trees from the detector model incompatible with the new concept are removed and reconstructed. This modification of the concept drift handling mechanism in the EiForestASD significantly reduces computation time and improves anomaly detection accuracy. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have multitudinous Sensor Nodes (SN) with low-power, low-cost, and miniature characteristics. These nodes are data-centric and responsible for gathering appropriate data in the target area and they transmit to the control centre or Base Station (BS) in a single-hop or multi-hop manner. However, WSN faces certain difficulties like security enhancement and energy optimization due to their limited resources, working environments, and communication characteristics to provide network security. This research proposes a Multi-objective-Trust Aware Improved Pelican Optimization Approach (M-TAIPOA) to achieve a secure and energy-efficient clustering and routing process. POA is enhanced using sine chaos mapping, the fusion of Sine Cosine Optimization (CSO) and levy flight strategy for enhancing search diversity, convergence accuracy, and to jump out of local optima. M-TAIPOA selects Secure Cluster Head (SCH) based on node degree, distance among the neighbour nodes, location factor, distance between BS and CH, and trust. Then, distance and energy are used for the secure routing process. M-TAIPOA achieves a lower energy consumption of 4.5J for 10 rounds for scenario 3 when compared to the existing technique, Energy Optimization Routing by utilizing an improved Artificial Bee Colony (EOR-iABC). [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a methodology to monitor the health of an engine by detecting any failures in the fuel injectors or in-cylinder pressure sensors using an accelerometer that is non-intrusively mounted on the engine block. A multi-cylinder engine with each cylinder having its own pressure sensor and injector is considered. First, a model relating the combustion component of the measured acceleration signal to the combustion component of in-cylinder pressure is proposed. Then, gains of the model are tuned to reduce the cycle-to-cycle estimation error by analyzing cycle-to-cycle variations with respect to the combustion pressure peak and engine vibration peak. Using the developed model, cylinder combustion pressures are estimated from engine vibration signals with small cycle-to-cycle estimation errors. Subsequently, a health monitoring system that can detect faults in pressure sensors, fuel injectors, and the accelerometers is proposed based on residues obtained from the difference between estimated combustion pressure and measured pressure signals. The source of the failed component can be identified uniquely by analyzing the pattern of residues. The proposed combustion pressure estimation algorithms are validated by extensive evaluation with experimental data obtained by operating a four-cylinder compression-ignition direct-injection engine with a range of experimental data. Finally, the developed health monitoring system is evaluated with various failure scenarios involving faults in the in-cylinder pressure sensor, fuel injector, and accelerometer. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of New Chemistry is the property of International Journal of New Chemistry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of New Chemistry is the property of International Journal of New Chemistry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Piezocomposites with both flexibility and electromechanical conversion characteristics have been widely applied in various fields, including sensors, energy harvesting, catalysis, and biomedical treatment. In the composition of piezocomposites or their preparation process, a category of materials is commonly employed that do not possess piezoelectric properties themselves but play a crucial role in performance enhancement. In this review, the concept of auxiliary phase is first proposed to define these materials, aiming to provide a new perspective for designing high‐performance piezocomposites. Three different categories of modulation forms of auxiliary phase in piezocomposites are systematically summarized, including the modification of piezo‐matrix, the modification of piezo‐fillers, and the construction of special structures. Each category emphasizes the role of the auxiliary phase and systematically discusses the latest advancements and the physical mechanisms of the auxiliary phase enhanced flexible piezocomposites. Finally, a summary and future outlook of piezocomposites based on the auxiliary phase are provided. [ABSTRACT FROM AUTHOR]

Assume that a graph G$$ G $$ models a detection system for a facility with a possible "intruder," or a multiprocessor network with a possible malfunctioning processor. We consider the problem of placing detectors at a subset of vertices in G$$ G $$ to determine the location of an intruder if there is any. Many types of detection systems have been defined for different sensor capabilities; in particular, we focus on identifying codes, where each detector can determine whether there is an intruder within its closed neighborhood. In this research we explore a fault‐tolerant variant of identifying codes applicable to real‐world systems. Specifically, error‐detecting identifying codes permit a false‐negative transmission from any single detector. We investigate minimum‐sized error‐detecting identifying codes in several classes of graphs, including cubic graphs and infinite grids, and show that the problem of determining said minimum size in arbitrary graphs is NP‐complete. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Science & Technology (JOCSTEC) is the property of PT. Padang Tekno Corp and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In many applications of cyber-physical systems, a sequence of tasks is necessary to perform a certain functionality. For example, from a sensor to an actuator, the first task reads the sensor value (cause), the second task processes the data, and the third task produces an output for the actuator (an effect is triggered). For such scenarios, the end-to-end timing properties (the so-called end-to-end latency) of the sequence of tasks (the so-called cause-effect chain) are of importance. This tutorial recaps different metrics for the end-to-end latency of cause-effect chains, and summarizes fundamental properties and existing analytical results in a systematic manner. To that end, this tutorial has a special focus on the reaction time (how fast can a reaction be in the worst case) and the data age (how old is the data source of an actuation in the worst case). The goal of this tutorial is to provide a systematic view of the fundamental end-to-end timing properties of cause-effect chains and offer an outlook of possible research directions in the near future. Furthermore, we extend the proof of one fundamental property in the literature to comply with the current state-of-the-art definition of end-to-end latencies. [ABSTRACT FROM AUTHOR]

Ensuring relative consistency in executing temporal queries to access real-time sensor data streams maintained in a database is a challenging problem, particularly when data transmission delays are lengthy and highly variable. Due to the unordered arrivals of sensor data, the databases may contain numerous open data versions (ODVs) with undefined validity intervals. Accessing ODVs may violate the relative consistency requirements of temporal queries, resulting in incorrect results. Although the Re-execution with Every Update (REU) method can resolve this issue, it may introduce heavy re-execution costs and significant delays in query completion. In this article, we study the problem of retrieving data items with temporal consistency requirements in a multi-data-stream database. To balance response time and meet the relative consistency requirements of queries, we introduce an enhanced REU mechanism called Re-Execution with Deadline (RED). Moreover, we propose a novel optional mechanism called Backward Execution Option (BEO) for temporal queries to achieve relative consistency in their execution with quick results by relaxing the data freshness constraints. By combining RED with BEO, we formulate the Repeated BEO (RBEO) to further reduce the query response time. We extend the timestamped key-value store MVLevelDB into MVLevelDB+ to implement the proposed mechanisms. To reduce the query re-execution cost as required in RED and REU, we designed the Query Pool with Execution State (QpES) mechanism to achieve relative consistency in query execution with lower checking overhead and only one re-execution. We conducted extensive evaluation experiments on MVLevelDB+ using benchmark programs to illustrate their performance characteristics on handling temporal queries in the modeled IoT system. [ABSTRACT FROM AUTHOR]

This article investigates the control problem of unmanned surface vessels with sensor measurement sensitivity under deception attacks, and proposes a novel self‐triggered adaptive neural control scheme under the backstepping design framework. To solve the control design problem of unknown time‐varying gains caused by deception attacks and measurement sensitivity in kinematic and kinetic channels, the parameter adaptive and neural network technology are involved. In addition, to decrease actuator wear caused by the high‐frequency wave and sensor measurement sensitivity and reduce the computational burden caused by continuous monitoring of the triggered condition, a self‐triggered mechanism is constructed in the controller–actuator channel. Finally, a self‐triggered adaptive neural control solution is proposed, which can guarantee that all signals in the whole closed‐loop system are bounded by theoretical analysis. The effectiveness and superiority are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

The AMoRE-II experiment will search for the 0 νββ decay of 100Mo nuclei using molybdate crystal scintillators, operating at milli-Kelvin (mK) temperatures, with a total of 80 kg of 100Mo. The background goal for the experiment is 10–4 counts/keV/kg/year in the region of interest around the 0 νββ decay Q-value of 3,034 keV. To achieve this level, the rate of background signals arising from emissions produced by decays of radioactive impurities in the detector and shielding materials must be strictly controlled. To do this, concentrations of such impurities are measured and are controlled through materials selection and purification. In this paper, we describe the design and the construction materials used to build the AMoRE-II detector and shielding system, including active and passive shielding, the cryostat, and the detector holders and instrumentation, and we report on measurements of radioactive impurities within candidate and selected materials. [ABSTRACT FROM AUTHOR]

This paper aims to optimize the testability analysis method of aero-engines by presenting a testability modeling and an improved correlation matrix method. Because of strong coupling in aero-engines, the traditional testabilitsy modeling method based on graph theory is difficult to accurately express the relationship between faults and test points. Simulation technology can simulate actual work process of system. So this paper launches the research based on simulation model. Firstly, the gas path model is established according to the thermodynamic principle of aero-engines and accuracy of the model is verified. Secondly, common faults of gas path are selected. Affected parameters are obtained after injecting faults into the model, so as to obtain the relationship between faults and test points, that is, the correlation matrix. Then, after going through masses of simulations, it is found that the relationship between faults and test points can be divided into three categories: positive correlation, negative correlation and no correlation. The correlation matrix can be improved by diversifying its elements. During simulation, accuracy of the sensors are not considered. The correlation matrix is optimized with the accuracy of sensors in the gas path as a constraint, so that it is more in line with engineering practice. Finally, four testability characteristics and two testability metrics are defined, and the correlation matrix before and after improvement are analyzed and compared. It is found that the improved correlation matrix can isolate more faults on the premise of reducing test points, which proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]