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An analytical model for diffusion-limited detector sensitivity under low-irradiance conditions is derived from carrier continuity equations and verified with Silvaco TCAD drift-diffusion software. The model is used to determine the optimal design parameters for a mid-wave infrared InAs/InAsSb type-II superlattice nBn photodetector for maximum sensitivity under both topside- and backside-illumination conditions. A minimum attainable noise-equivalent irradiance of 4.5 × 1010 photons/cm2 s is found for InAs/InAsSb nBn at 130 K, roughly 2.4× higher than a detector exhibiting Rule 07 dark current density and unity quantum efficiency. A design heuristic, offering a simple and practical approach to designing a high-sensitivity detector, is then developed and performance is found to be comparable to the optimally designed structures. Finally, an evaluation of the impact of each material parameter on noise-equivalent irradiance is performed, revealing that the intrinsic carrier concentration, effective minority carrier lifetime, and absorption coefficient exhibit the largest impacts on sensitivity for diffusion-limited detectors. [ABSTRACT FROM AUTHOR]

The lateral interdigital array of the graphene microribbons (GMRs) on the h-BN substrate connected by narrow graphene nanoribbon (GNR) bridges serves as an efficient detector of terahertz (THz) radiation. The detection is enabled by the nonlinear GNR elements providing the rectification of the THz signals. The excitation of plasmonic waves along the GMRs (transverse plasmonic oscillations) by impinging THz radiation can lead to a strong resonant amplification of the rectified signal current and substantial enhancement of the detector response. The GMR arrays with the GNR bridges can be formed by the perforation of uniform graphene layers. [ABSTRACT FROM AUTHOR]

This work describes a new type of sensor for growth process monitoring named broadband cavity-enhanced optical flux monitoring sensor (BBCE-OFM). Like existing optical flux monitoring (OFM) solutions, it relies on absorption spectroscopy. However, the implementation of an optical cavity reduces the measurement uncertainty, enabling efficient operation even at very low growth rates. Using the BBCE-OFM sensor mounted in our solid-source oxide molecular beam epitaxy reactor, we achieved an uncertainty of ±2% on the measurement of Sr and Ti growth rates in SrTiO3 at around 1 Ml/min, to be compared to the ±16% obtained in the same conditions using a conventional OFM setup. Furthermore, our sensor architecture, based on an echelle monochromator and LEDs replacing the hollow cathode lamps used in standard OFM sensors, is more robust against drift. [ABSTRACT FROM AUTHOR]

We report on the construction of the SiRO—SiPM ReadOut muon detector, a detection system based on plastic scintillator bars designed for muography applications. Using six 1 m 2 layers of active medium, grouped two by two into three rectangular matrices of pixels, each separated by a variable distance, the spatial coordinates of the muon's impact point on every matrice are obtained and used for trajectory reconstruction. Validation studies have been performed using Monte Carlo simulations and later confirmed by preliminary measurements in our laboratory and in underground, in the Slănic Prahova salt mine, in Romania. [ABSTRACT FROM AUTHOR]

Transition-Edge Sensors (TESs) constitute highly sensitive particle and radiation detectors, widely used in many applications. Each of these requires optimization of TES performances and designs, including sizes and geometries. These may have implications on the superconducting transition mechanisms and, therefore, on TESs performances and stability, through the specific shape of the resistance vs temperature and current R(T,I) and the nature of noise. In this study, we investigate the dependence of the superconducting transition, characterized by R(T,I), on TES size and bias current density. Through analyses of R(T,I) in bare Mo/Au TESs with Tc tuned for this study, we observe how the weak link behavior induced by the superconducting leads weakens and disappears as TES length or driving current increase, being substituted by another dominant transition mechanism, which might be related to a Berezinskii–Kosterlitz–Thouless transition. We also observe a significant broadening of the transition's upper part, attributed to the longitudinal proximity effect induced by the pads; for the shorter devices, this effect is observed for R > 70% Rn and results in TES resistances considerably lower than Rn up to temperatures well above the TES transition: R < Rn up to 3 K for a 8 μm-long device. [ABSTRACT FROM AUTHOR]

Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring values may fail, leading to a biased detection result – false alarm. In such cases, replacing the sensor is necessary instead of performing equipment maintenance. This study proposes an effective framework embedded with autoencoder-based control limits that can dynamically distinguish sensor anomaly from process anomaly in real-time. We conduct a simulation numerical study and an empirical study of semiconductor assembling manufacturers to validate the proposed framework. The results show that the proposed model outperforms other benchmark methods and can successfully identify sensor failures, even under conditions of (1) large variations in process values or sensor values and (2) heteroscedasticity effect. This is particularly beneficial in various practical applications where sensors are used for numerical measurements and support equipment maintenance. [ABSTRACT FROM AUTHOR]

A highly sensitive terahertz stereo metasurface sensor, characterized by a high quality factor (Q-factor) and based on dual toroidal dipole (TD) resonance, has been proposed. The optimal structural parameters are ascertained by comparing the pertinent parameters of the stereo and planar structures in relation to TD modal excitation. The effective excitation of the TD mode is demonstrated using the calculations of multipole scattered power, reflection spectra, surface currents, electric fields, and magnetic field distributions. It is crucial that the stereo metasurface exhibits simplicity and that the dual TD resonance can be readily excited through simple adjustments in the distance and height of the intermediate gap. It also demonstrates exceptionally high sensitivity and Q-factor, both of which are essential for sensing applications. Moreover, the proposed stereo terahertz metasurface sensor still shows excellent sensing performance in a wide range of incidence angles (±40°), which is of great significance for practical applications. In conclusion, this structure offers a novel design framework for high-performance terahertz sensors based on the TD mode. [ABSTRACT FROM AUTHOR]

This article presents the possibility of controlling/managing the frequency selection ability of membrane-based broadband frequency sensors (MB2FS), exploiting systematic selection of membrane stiffness. MB2FS is a bio-inspired system mimicking the geometry and functionality of the basilar membrane (BM) in the mammalian cochlea. The actual BM is tapered in geometry (both length wise and thickness wise), which makes the stiffness of the membrane uniformly varied over its length. Because of varied stiffness, different locations of the BM show resonance deflection at different frequency inputs, which allow the BM to select/sense the entire sonic frequency band within its 35 mm length. While actual BM and conventional MB2FS possess homogeneous stiffness over the domain length, in this article, a comprehensive insight is provided of how the frequency selection ability of the sensors can be manipulated and controlled, predictively, using functionally graded structural stiffness. Therefore, this work is not intended to develop an artificial BM, rather is focused on developing frequency sensors inspired by the membrane stiffness of BM, which plays a vital role in the spatial selection of acoustic frequencies. The study is performed using a numerically validated predictive model developed in a semi-analytical interface to explain the effects of MB2FS stiffness variations. Based on biological occurrence of stiffness in natural BM, three functions (logarithmic, linear, and exponential) are assumed to predict the FSP. While a random sonic frequency band of 10–12 kHz is targeted in this study to demonstrate the stiffness grading principle of MB2FS, a similar process (e.g., choosing the appropriate stiffness distribution of the beam) can be used to develop both sonic and ultrasonic frequency sensors. This study presents a detailed framework of how the sensing parameters of a specific frequency band (e.g., sensing location of band start and end frequencies and membrane segment width necessary to sense the entire frequency band) are dependent on the function coefficients. Finally, a comprehensive guideline is provided to predictively determine the function coefficients for user-defined frequency selection parameters. While existing state-of-the-art only allows designing MB2FS for a specific frequency band, the work presented in this study will open the opportunity to select multiple frequency bands of an MB2FS without altering its geometric configuration. [ABSTRACT FROM AUTHOR]

Anomalous Hall effect (AHE) sensor has attracted significant attention in recent years due to its simpler structure and better sensitivity as compared to semiconductor-based Hall sensors. However, up to now, most of the work has been focused on its basic functionalities, with very little attention given to optimizing its performance for practical applications. In this work, we systematically investigated how the lateral dimension affects the performance of CoFeB-based AHE sensors. By adjusting the sensor width while keeping the length and other parameters constant, it is found that although the output signal amplitude decreases with increasing the sensor width, the noise and power consumption also decrease simultaneously, leading to an overall enhancement of the sensor performances with a sensitivity, detectivity, and power efficiency of 8960 Ω/T, 205 nT / Hz @ 1 Hz , and 2009 V/WT, respectively. To illustrate the potential applications of AHE sensors, we demonstrate a proof-of-concept experiment to showcase the potential AHE sensor in pulse detection. [ABSTRACT FROM AUTHOR]

Four types of barrier detectors based on a type II InAs/InAsSb superlattice with a wide-gap barrier made of a solid AlInAsSb lattice matched to the GaSb buffer were compared. The tested detectors differed in the type of doping of the active layer and the level and type of doping of the contact layer at the barrier. The epitaxial layers were deposited on GaAs (100) substrates using the molecular beam epitaxy method. The spectral and current–voltage characteristics of the analyzed detectors were compared. The highest current responsivities were observed in the structure with a p-type absorber (p+BpN+). Detectors with an n-type absorber (p+Bnn+, n+Bnn+, and nBnn+) show an increase in the current responsivity with an increase in the reverse bias voltage due to the reduction in the undesirable barrier in the valence band. Arrhenius characteristics for the dark current show that only in nBnn+ detectors, it was possible to limit the generation–recombination current. These detectors at 150 K were characterized by the highest normalized detectivity of approximately 3 × 1011 cm · Hz1/2/W. The obtained results were compared with literature data, showing that the parameters of type II superlattice photodetectors are close to those of HgCdTe photodiodes according to the "Rule 07" and "Rule 22" principles. [ABSTRACT FROM AUTHOR]

Transition-edge sensors (TESs) have the potential to perform electron spectroscopic measurements with far greater measurement rates and efficiencies than can be achieved using existing electron spectrometers. Existing spectrometers filter electrons by energy before detecting a narrow energy band at a time, discarding the vast majority of electrons available for measurement. In contrast, TESs have intrinsic energy sensitivity and so do not require prior filtering to perform energy-resolved measurements. Despite this fundamental advantage, TES electron spectroscopy has not, to our knowledge, previously been reported in the literature. We present the results of a set of proof-of-principle experiments demonstrating TES electron spectroscopy experiments using Mo/Au TESs repurposed for electron calorimetry. Using these detectors, we successfully measured the electron spectrum generated by an electron beam striking a graphite target with energies between 750 and 2000 eV, at a noise-limited energy resolution of 4 eV. Based on the findings of these experiments, we suggest improvements that could be made to TES design to enhance their electron detection capabilities through the use of a dedicated electron absorber in the device with integrated electron optics. [ABSTRACT FROM AUTHOR]

Dual-energy imaging represents a versatile and evolving technology with wide-ranging applications in medicine and beyond. Recent technological developments increased the potential for improved diagnostic accuracy and expanded imaging capabilities across various fields. The purpose of this work is to design and develop an energy-integrating multilayer detector, known as a sandwich detector, aimed at single-shot dual-energy imaging tasks such as material discrimination and contrast cancellation. The sandwich detector uses two complementary metal oxide semiconductor advanced pixel sensors of 50 μm pixel size. The top and bottom sensors detect low-energy (LE) and high-energy (HE) photons, with sensors coupled with 250 and 600 μm scintillators, respectively. For better spectral separation between layers without excessively affecting the detected statistic in the bottom layer, the insertion of a 0.25-mm Cu filter between the layers was found to be the optimal choice, from among the tested 0-, 0.25-, and 0.5-mm filter options. The thickness selection for scintillator and intermediate Cu filter was carried out through a dual-energy simulation model. The experiments confirmed the model's reliability in selecting the optimal thicknesses of the intermediate Cu filter, thereby providing reassurance also on the choice of the top scintillator. [ABSTRACT FROM AUTHOR]

Miniaturized photoionization detectors (PIDs) are used in conjunction with gas chromatography systems to detect volatile compounds in gases by collecting the current from the photoionized gas analytes. PIDs should be inexpensive and compatible with a wide range of analyte species. One such PID is based on the formation of a He plasma in a dielectric barrier discharge (DBD), which generates vacuum UV (VUV) photons from excited states of He to photoionize gas analytes. There are several design parameters that can be leveraged to increase the ionizing photon flux to gas analytes to increase the sensitivity of the PID. To that end, the methods to maximize the photon flux from a pulsed He plasma in a DBD-PID were investigated using a two-dimensional plasma hydrodynamics model. The ionizing photon flux originated from the resonance states of helium, He(3P) and He(21P), and from the dimer excimer He2*. While the photon flux from the resonant states was modulated over the voltage pulse, the photon flux from He2* persisted long after the voltage pulse passed. Several geometrical optimizations were investigated, such as using an array of pointed electrodes. However, increasing the capacitance of the dielectric enclosing the plasma chamber had the largest effect on increasing the VUV photon fluence to gas analytes. [ABSTRACT FROM AUTHOR]

In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance. [ABSTRACT FROM AUTHOR]

A novel mode-localized acceleration sensor employing an electrostatically coupled resonator and integrating a lever with proof mass is micromachined using standard silicon on insulator (SOI) technology. In order to determine the linear dynamic range of the sensor, a reduced order model is developed while assuming that the resonators vibrate below the critical amplitude. Then, open-loop and closed-loop testing platforms are established to measure the performance of the linearly operating accelerometer in a vacuum environment (less than 5 Pa). Moreover, the corresponding amplifier circuit based on the capacitive detection principle is designed in order to extract and amplify the current signal from the resonators. The obtained results show that the accelerometer sensitivity can be increased by three orders of magnitude when using the relative shift of amplitude ratio as the output metric instead of the relative shift of frequency, and the experimental measurements are consistent with the theoretical predictions. Remarkably, the Allan standard deviation of the mode-localized acceleration sensor obtained from the closed-loop testing circuit is around 5.03 μg. [ABSTRACT FROM AUTHOR]

In this work, we studied the free-carrier transport properties and space-charge formation/polarization in a CdZnTe bar-shaped radiation detector using the Laser-Induced Transient Current technique. We found out that a steady-state space charge ranging from 8 × 108 to 2.1 × 109 cm−3 is formed throughout the detector at about 0.5 s following biasing. The measured current waveforms were modeled by Monte Carlo simulations after taking into account the electric and weighting fields inside the detector and shielding box. Modeling of the unpolarized waveforms revealed an exceptionally high electron mobility-lifetime product μ e τ e ≥ 0.095 c m 2 V − 1 . The observed formation of positive space charge in the biased detector and linear scaling of the current waveform shapes on applied bias revealed that the detector polarization is attributed to carrier injection at the anode. Measurements in pulsed and DC bias in modified electrode geometry proved the surface-charge formation. [ABSTRACT FROM AUTHOR]

Millimeter-thick methylammonium lead tribromide (MAPbBr3) single crystal x-ray detectors have recently raised attention due to their high x-ray attenuation efficiency and good charge transport properties. However, an intriguing feature of the photocurrent response of MAPbBr3 detectors has been largely overlooked in the literature. After biasing, transient sensitivity is measured under x rays at short-circuit (bias = 0 V), thus revealing a large remnant electric field that builds up under bias. Here, we exploit the x-ray sensitivity of MAPbBr3 detectors at zero-bias in order to probe the internal built-in field, as well as to investigate the charge transport properties of the perovskite material. Our model derived from the Hecht equation is able to fully rationalize the response of the detectors both at short-circuit and under moderate applied bias. Moreover, we provide a method for the estimation of the internal electric field, and for the sum of the electrons and holes mobility–lifetime products μ e τ e + μ h τ h. This general method could extend to any perovskite-based x-ray detector exhibiting transient sensitivity at zero-bias. [ABSTRACT FROM AUTHOR]

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]

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]