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This paper studies interval coordination problems for multiagent systems with antagonistic interactions. For strongly connected signed networks, it is shown that when the intersection of intervals imposed by agents is nonempty: (1) the multiagent system achieves bipartite consensus with structurally balanced network; (2) all agents’ states must converge to 0, if the signed network is structurally unbalanced. We establish the consensus conditions for bipartite consensus and zero-value consensus by employing the Gauge Transformation and robust analysis of signed networks. When the signed networks are strongly connected and the intersection of intervals is empty, the system reaches an asymptotically stable and unique equilibrium. Moreover, the equilibrium states are only decided by the network structure and interval constraints, and are not related to initial agents’ states. Associating the equilibrium of dynamics with the solution of a system of nonlinear equations, we obtain the uniqueness, stability and continuity of equilibria. Finally, numerical simulations are presented to illustrate the theoretical results.

The deployment of renewable energy in power systems may raise serious voltage instabilities. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services including active and reactive power. However, the distributed control of EVs under such scenarios is a complex decision-making problem with enormous dynamics and uncertainties. Most existing literature employs model-based approaches to formulate the active and reactive power control problems, which require full models and are time-consuming. This paper proposes a multi-agent reinforcement learning method featuring actor-critic networks and a parameter sharing framework to solve the EVs coordinated active and reactive power control problem towards both demand-side response and voltage regulations. The proposed method can further enhance the learning stability and scalability with privacy perseverance via the location marginal prices. Simulation results based on a modified IEEE 15-bus network are developed to validate its effectiveness in providing system charging and voltage regulation services.

The conventional hard-switching converters suffer from the limitations like the upper limit on switching frequency, high electromagnetic interference (EMI), more switching losses, large size, increased weight and low efficiency. To overcome these limitations, resonant converters are popularly used in chargers of electric vehicles (EVs). However, the detailed classification of resonant converters used in EVs is not sufficiently discussed in the literature. The guideline to select a resonant converter based topology required to charge an EV on the basis of its rating is not mentioned. To fill this gap, this paper presents a state-of-art literature survey of various resonant converter based topologies used in chargers of EVs. This paper focuses on a detailed classification of resonant converters used in the second stage of EV chargers. Further, it provides a guideline to designers to choose a converter topology used in the first stage and the second stage of EV charger required based on wattage, unidirectional and bidirectional power flow. Depending on the number of reactive elements present in a given resonant converter topology, these are classified as two-element, three-element, and multi-element resonant converters. Depending upon the connection of inductive (L) and capacitive (C) elements with respect to transformer winding, these converter topologies are further categorized as series, parallel (two-elements), inductor–inductor–capacitor (LLC) (three-element) and capacitor–inductor–inductor–capacitor (CLLC) (Multi-elements). However, the LLC type resonant converters offer high efficiency, zero-voltage switching (ZVS turn-on, turn-off) and low voltage stress on switches and high power density. Therefore, this paper mainly focuses on LLC type resonant converter topology. In addition, various modulation schemes and control schemes for LLC, CLLC resonant converter along with control of active power and reactive power are discussed for vehicle-2-grid (V2G) mode of operation.

The transportation sector is one of the most prevalent fossil fuel users worldwide. Therefore, to mitigate the impacts of carbon-dioxide emissions and reduce the use of non-environmentally friendly traditional energy resources, the electrification of the transportation system, such as the development of electric vehicles (EV), has become crucial. For impeccable EVs deployment, a well-developed charging infrastructure is required. However, the optimal placement of fast charging stations (FCSs) is a critical concern. Therefore, this article provides a functional approach for identifying the optimal location of FCSs using the east delta network (EDN). In addition, the electrical distribution network's infrastructure is susceptible to changes in electrifying the transportation sector. Therefore, actual power loss, reactive power loss, and investment cost are three areas of consideration in deploying FCSs. Furthermore, including FCSs in the electricity distribution network increases the energy demand from the electrical grid. Therefore, this research paper recommends integrating solar-based distributed generations (SDGs) at selected locations in the distribution network, to mitigate the burden of FCSs on the system. Hence, making the system self-sustaining and reliable. In addition, the reliability of the distribution system is also analyzed after deploying the FCSs and SDGs. Furthermore, six case studies (CS) have been proposed to deploy FCSs with or without DG integration. Consequently, the active power loss went from 1014.48 kW to 829.68 kW for the CS-6. 2022 The Author(s) This publication was made possible by NPRP grant # [ NPRP-13S-0108-20008 ] from the Qatar National Research Fund (a member of Qatar Foundation) . The statements made herein are solely the responsibility of the authors. The APC is funded by Qatar National Library, Doha, Qatar . Scopus

The continuous deployment of distributed energy sources and increase in the adoption of Electric Vehicles (EVs) require smart charging algorithms. Existing EV chargers offer limited flexibility and controllability, and do not fully consider factors (such as, EV user waiting time and length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions and charging cost. In this paper, a Neuro-Fuzzy-PSO based novel and advanced smart charge controller is proposed which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (PV) output and battery state of health. A rule based Fuzzy controller becomes complex as the number of inputs to the controller increases. Also, it becomes difficult to achieve an optimum operation due to conflicting nature of control requirements. To optimize the controller response, Particle Swarm Optimization (PSO) technique is proposed to provide a global optimum solution based on a pre-defined cost function and to address the implementation complexity PSO is combined with neural network. The proposed Neuro-Fuzzy-PSO control algorithm meets EV user requirements, work within technical constraints and is simple to implement in real-time (and requires less processing time). Simulation using MATLAB and experimental results using dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

To further preserve the data privacy of federated learning (FL), we propose a differentially private FL (DPFL) scheme with misaligned power allocation (MPA-DPFL). Unlike most existing over-the-air FL studies, in MPA-DPFL, the gradients are aggregated through over-the-air computation (Aircomp) but do not need to be aligned in the transmission. Therefore, MPA-DPFL can avoid the problem that the signal-to-noise ratio (SNR) of the system is limited by the device with the worst channel condition. We formulate an optimization problem to minimize the optimality gap of MPA-DPFL while guaranteeing a certain degree of privacy protection. Additionally, we demonstrate that the MPA-DPFL is more suitable than the DPFL with aligned power allocation (APA-DPFL) when the channel condition of a device in the system is lower than a threshold. The analytical results are validated through simulation.

The escalating demand on Electric Vehicles (EVs) has enhanced the necessity of adequate charging infrastructure, especially in residential areas. This paper proposes a smart charging approach for off-board Electric Vehicles (EVs) chargers in Home-Energy-Hub (HEH) applications along with DC sources such as Photovoltaic (PV) and Battery Storage (BS). The proposed method facilitates smart charging and discharging of EVs to obtain both Vehicle-to-X (V2X) and X-to-Vehicle (X2V) operations focusing on the domestic applications integrated with renewable and storage elements. Furthermore, the optimal State-of-Charge (SOC) profiles for BS and EV in the HEHs system is defined by the extended Bellman-Ford-Moor Algorithm (BFMA). This modified BFMA utilizes the forecasted data such as solar irradiation, electricity tariff, and power consumption to gain economic benefits in HEHs with respect to user and EV requirements. Moreover, the plugging time, duration and initial/final SOC are fluctuating at each connection due to the stochastic nature of EV conditions and user settings. This study presents a laboratory implementation of two-level Hierarchical Energy Management System (HEMS) for HEHs with plug-in electric vehicles. In fact, the primary level includes power converters controller, while the proposed algorithm is implemented in the secondary level. Finally, the simulation and experimental results confirm the effectiveness of the proposed analysis regarding the interaction of HEHs and power grid with EVs behavior.

This letter investigates a virtual reality (VR) delivery system, where the original VR contents requested by all users are stored at the macro base station (MBS). To reduce latency, MBS can either transmit the original VR data or the computed VR data to multiple users aided by an intelligent reflecting surface (IRS) to prevent attacks from an eavesdropper with imperfect channel state information (CSI). We jointly optimize the transmission policies, MBS transmit power, IRS phase shift and computing frequency to minimize the latency over all users subject to security constraint. Numerical results validate the robustness of our proposed algorithm.

This paper presents an excitation controller design process for multimachine power systems with synchronous generators using an adaptive backstepping approach to ensure robust performance against parametric uncertainties as well as to improve the transient stability during large disturbances. In this work, electrical dynamics of synchronous generators are described using two-axis models while excitations systems are represented as the IEEE Type II exciter which mainly captures electrical dynamics while swing equations are used to capture mechanical dynamics. The proposed adaptive backstepping control approach is then employed to derive the excitation control law. The proposed adaptive backstepping scheme uses all nonlinearities, which are mainly due to the interconnections and rotations of rotors in synchronous generators, within the dynamical models to improve the transient stability during severe disturbances. Furthermore, this excitation control scheme uses estimated values of parameters appearing in the dynamical models of power system which are estimated using adaptation laws based on real-time measurements and hence, it provides robustness against parametric uncertainties. The theoretical stability of the proposed scheme is assessed using the Lyapunov stability theory, that is, by checking the negative definiteness or semi-definiteness of the derivative of control Lyapunov functions (CLFs). Rigorous simulations are conducted on an IEEE 39-bus 10-machine test power system for evaluating the performance of the proposed scheme under different operating conditions. Simulation results clearly demonstrate the superiority of the adaptive backstepping excitation controller over existing nonlinear controllers including an existing adaptive backstepping excitation controller that is designed using the classical model of synchronous generators.

This paper presents a nonlinear integral backstepping controller (I‐BSC) design scheme for a T‐type residual current compensation (RCC) inverter used in compensated distribution networks with rapid earth fault current limiters (REFCLs). The major control task for the proposed scheme is to reduce the fault current to a value suitable for the mitigation of powerline bushfires due to single phase‐to‐ground faults in compensated distribution networks. The key distinct feature of the proposed I‐BSC over the traditional backstepping controller (T‐BSC) is that it introduces an integral action for analyzing the dynamic of the tracking error which minimizes its steady‐state value and ensures better dynamic performance. In order to prove the global asymptotic stability of the RCC inverter with the proposed integral backstepping controller (I‐BSC), the Lyapunov function‐based theory is used. Finally, the performance of the I‐BSC is analyzed on the MATLAB/Simulink environment and compared with a T‐BSC. The performance of both I‐ and T‐BSCs is assessed in terms of transient behaviors of the injected current to the neutral, fault current, and line‐to‐ground voltage of the faulty phase to ensure the standard operational criteria for self‐extinguishing powerline bushfires. Simulation results clearly demonstrate that both controllers fulfill operational standards for REFCL‐compensated networks though the I‐BSC archives better transient behaviors while comparing with the T‐BSC. Results from the processor‐in‐loop (PIL) validations are also included to further justify the applicability of the newly proposed scheme in the real‐time environment.

In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes. 10.13039/501100001809-Natural Science Foundation of China (Grant Number: 62031017, 61971220 and 61971221); Open Research Fund of State Key Laboratory of MillimeterWaves of Southeast University (Grant Number: K202215).

An indoor visible light positioning system using the received signal strength intensity method and the trilateration technique with user mobility support is proposed for applications, e.g. healthcare. Accurate position information of medical staffs and patients is required for data transmission, tracking, navigation, etc. Seamless data transmission while user is on the move has been conceptualized considering the optical network formed by photosensor (light emitting diode)-based transmitters. Efficiency of these services depends on the position accuracy. To investigate factors contributing to higher position errors in a practical environment, the proposed system is evaluated. Evaluation is done through simulation and experiment for different channel conditions, and the receiver rotation due to user mobility. Simulation results show an average two-dimensional position error of 0.32 m in a channel with a strong background light illumination level of − 60 dBm. Experimental results show that the mean position error is 0.7 m in a normal daytime indoor light condition.

The DC power system, due to its convenience of conversion, integration, and use, is getting immense attention in the field of power transmission and distribution. It is superior to traditional AC systems in terms of efficiency, reliability, and control simplicity as well. A DC circuit breaker is one of the important elements of any DC power system. It is a sophisticated technology designed to break DC current only. The breaking of a DC current is always challenging compared to the breaking of an AC current, as DC current does not have natural zero crossing points like AC current has. Moreover, DC current breaking becomes more critical when the current is inductive as energy stored in the network inductance opposes instantaneous current breaking. Hence, this energy needs to be absorbed and dissipated as heat during the current breaking operation, which is exactly what is done in the traditional DC circuit breaker topologies. This paper introduces a new topology for DC circuit breakers with a mechanism to reuse this stored energy instead of dissipating it. The mechanism is analogous to regenerative braking in electric drive systems and can enhance the overall system efficiency. The proposed scheme was analyzed through rigorous computer simulation and was experimentally validated.

With the rapidly developing wireless communication technology, electromagnetic pollution problems have become more prominent. Electromagnetic pollution has caused great harm to wireless equipment, precision instruments, military safety, etc., which urgently requires the development of lightweight, high-efficiency, broadband electromagnetic waves (EMW) absorbing materials. MXene is an emerging two-dimensional (2D) material with the advantages of lamellar structure, excellent conductivity, and abundant surface groups. At the same time, conducting polymers (CPs) have excellent performance in terms of conductivity, surface activity, quality, and electromagnetic loss, making them have excellent potential in EMW absorbing direction. This article examines the preparation, structure, and performance of MXene and CPs-based radar absorbing materials (RAM). A comprehensive summary and objective analysis of the nowaday study progress on the EMW absorbing performances of MXene and CPs, and a comprehension of the absorbing mechanism are reviewed. Finally, the research direction of absorbing materials has been prospected.

A fiber ring laser based on a side-polish fiber Mach-Zehnder interferometer (MZI) for effective improving refractive index (RI) and torsion sensing is proposed and investigated. The side-polished fiber MZI sensor can not only enhance the evanescent wave but also break circular symmetry of optical fiber face section, so it can be used for the surrounding RI and torsion sensing. A spectrum 3 dB bandwidth of less than 0.15 nm has been achieved which makes the fiber ring laser torsion sensing system to have higher measurement resolution. Experiment results show that the RI and torsion sensitivity of proposed sensor is dependent on the polish depth: the thicker the polish depth, the higher the sensitivity. For a sensor with side polish depth of 47 μ m, the measured RI sensitivity reaches-81.36 nm/RIU, the torsion sensitivity is as high as-0.019nm/O, the sensitivity converted to torsional rate is-0.267 nm/(rad. m-1).

Wireless networks have been widely utilized in industries, where wireless links are challenged by the severe nonstationary Rician fading channel, which requires online link quality estimation to support high-quality wireless services. However, most traditional Rician estimation approaches are designed for channel measurements and work only with nonmodulated symbols. Then, the online Rician estimation usually requires a priori aiding pilots or known modulation order to cancel the modulation interference. This article proposes a nondata-Aided method with redundant Gaussian mixture model (GMM). The convergence paradigm of GMM with redundant subcomponents has been analyzed, guided by which the redundant subcomponents can be iteratively discriminated to approach the global optimization. By further adopting the constellation constraint, the probability to identify the redundant subcomponent is significantly increased. As a result, accurate estimation of the Rician parameters can be achieved without additional overhead. Experiments illustrate not only the feasibility but also the near-optimal accuracy.

MnCo2O4 is regarded as a good electrode material for supercapacitor due to its high specific capacity and good structural stability. However, its poor electrical conductivity limits its wide-range applications. To solve this issue, we integrated the MnCo2O4 with Ni3S4, which has a good electrical conductivity, and synthesized a MnCo2O4/Ni3S4 nanocomposite using a two-step hydrothermal process. Comparing with individual MnCo2O4 and Ni3S4, the MnCo2O4/Ni3S4 nanocomposite showed a higher specific capacity and a better cycling stability as the electrode for the supercapacitor. The specific capacity value of the MnCo2O4/Ni3S4 electrode was 904.7 C g−1 at 1 A g−1 with a potential window of 0–0.55 V. A hybrid supercapacitor (HSC), assembled using MnCo2O4/Ni3S4 and active carbon as the cathode and anode, respectively, showed a capacitance of 116.4 F g−1 at 1 A g−1, and a high energy density of 50.7 Wh kg−1 at 405.8 W kg−1. Long-term electrochemical stability tests showed an obvious increase of the HSC’s capacitance after 5500 charge/discharge cycles, reached a maximum value of ∼162.7% of its initial value after 25,000 cycles, and then remained a stable value up to 64,000 cycles. Simultaneously, its energy density was increased to 54.2 Wh kg−1 at 380.3 W kg−1 after 64,000 cycles.

An intelligent reflecting surface (IRS) is proposed to enhance the physical layer security in the Rician fading channel wherein the angular direction of the eavesdropper (ED) is aligned with a legitimate user. A two-phase communication system under active attacks and passive eavesdropping is considered in this scenario. The base station avoids direct transmission to the attacked user in the first phase, whereas other users cooperate in forwarding signals to the attacked user in the second phase, with the help of IRS and energy harvesting technology. Under the occurrence of active attacks, an outage-constrained beamforming design problem is investigated under the statistical cascaded channel error model, which is solved by using the Bernstein-type inequality. An average secrecy rate maximization problem for the passive eavesdropping is formulated, which is then addressed by a low-complexity algorithm. The numerical results of this study reveal that the negative effect of the ED's channel error is larger than that of the legitimate user.

In this paper, we study the transmission design for reconfigurable intelligent surface (RIS)-aided multiuser communication networks. Different from most of the existing contributions, we consider long-term CSI-based transmission design, where both the beamforming vectors at the base station (BS) and the phase shifts at the RIS are designed based on long-term CSI, which can significantly reduce the channel estimation overhead. Due to the lack of explicit ergodic data rate expression, we propose a novel deep deterministic policy gradient (DDPG) based algorithm to solve the optimization problem, which was trained by using the channel vectors generated in an offline manner. Simulation results demonstrate that the achievable net throughput is higher than that achieved by the conventional instantaneous-CSI based scheme when taking the channel estimation overhead into account.

Long Range Wide Area Network (LoRaWAN) is one of the most promising IoT technologies that are widely adopted in low-power wide-area networks (LPWAN). LoRaWAN faces scalability issues due to a large number of nodes connected to the same gateway and sharing the same channel. Therefore, LoRa networks seek to achieve two main objectives: successful delivery rate and efficient energy consumption. This paper proposes a novel game theoretic framework for LoRaWAN named Best Equal LoRa (BE-LoRa), to jointly optimize the packet delivery ratio and the energy efficiency (bit/Joule). The utility function of LoRa node is defined as the ratio of the throughput to the transmit power. LoRa nodes act as rational users (players) which seek to maximize their utility. The aim of the BE-LoRa algorithm is to maximize the utility of LoRa nodes while maintaining the same signal-to-interference-and-noise-ratio (SINR) for each SF. The power allocation algorithm is implemented at the network server, which leads to an optimum SINR, spreading factors (SFs) and transmission power settings of all nodes. Numerical and simulation results show that the proposed BE-LoRa power allocation algorithm has a significant improvement in packet delivery ratio and energy efficiency as compared to the Adaptive Data Rate (ADR) algorithm of legacy LoRaWAN. For instance, in very dense networks (624 nodes), BE-LoRa can improve the delivery ratio by 17.44% and reduce power consumed by 46% compared with LoRaWAN ADR.

Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.

International audience; Use of unmanned aerial vehicles (UAVs) to provide on-demand communications has been receiving growing interest, especially for use in remote and hard-to-reach areas. Also, the use of light-emitting diode-based lighting in UAVs has opened opportunities for data transmission through visible-light communications. To manage multiuser interference while avoiding complex handover procedures, we consider the use of zero forcing (ZF) pre-coding. Since the performance of ZF pre-coding depends on the correlation between channel gains of users, we propose in this paper to reduce it by means of location optimization of UAVs. More specifically, we use particle swarm optimization with the objective of maximizing the overall achievable network throughput. Furthermore, to relax the optimization requirements at UAVs, we investigate the case when the optimization is performed at a specific rate under different mobility conditions.

The detection of ultralow heavy metal ion concentration is highly significant for protecting human health and maintaining the stability of the ecological environment. Herein, a microfiber interferometer chemical sensor for the detection of Ni2+ ions was proposed and experimentally demonstrated. The microfiber sensor was coated with an ion-imprinted chitosan polymer using Ni2+ as the template ion. Experimental results demonstrated a high sensitivity of 0.0454 nm nM−M for detect-ing Ni2+ in the range of 10 nM to 100 nM, and a limit of detection as low as 6.5 nM was achieved. The microfiber sensor was verified using two different non-template heavy ions, Cu2+ and Cr3+, and was determined to be highly selective to Ni2+. Furthermore, the regeneration characteristics of the sensor were experimentally assessed by three repeated adsorption–desorption cycles, and the results showed that the microfiber sensor achieved good stability without a significant loss in sensitivity. Besides, the detecting tests of Ni2+ in lake water and industrial sewage samples demonstrated the sensor's practical application. This proposed sensor has the advantages of simple configuration, high selectivity and sensitivity, fast response, and the ability to serve as a platform for water safety monitoring and remote sensing.

For future healthcare in the terahertz (THz) band, a triple-band microstrip planar antenna integrated with metamaterial (MTM) based on a polyimide substrate is presented. The frequencies of operation are 500, 600, and 880 GHz. The triple-band capability is accomplished by etching metamaterial on the patch without affecting the overall antenna size. Instead of a partial ground plane, a full ground plane is used as a buffer to shield the body from back radiation emitted by the antenna. The overall dimension of the proposed antenna is 484×484 μm2. The antenna's performance is investigated based on different crucial factors, and excellent results are demonstrated. The gain for the frequencies 500, 600, 880GHz is 6.41, 6.77, 10.1 dB, respectively while the efficiency for the same frequencies is 90%, 95%, 96%, respectively. Further research has been conducted by mounting the presented antenna on a single phantom layer with varying dielectric constants. The results show that the design works equally well with and without the phantom model, in contrast to a partially ground antenna, whose performance is influenced by the presence of the phantom model. As a result, the presented antenna could be helpful for future healthcare applications in the THz band.

This paper presents a cloud energy storage (CES) architecture for reducing energy costs for residential microgrid users. The former of this article concentrates on identifying an appropriate battery technology from various battery technologies with the aid of a simulation study. The later part addresses the economic feasibility of the storage architecture with three different scenarios namely grid connected energy storage, distributed energy storage (DES) and CES. The performance of the proposed architecture has been evaluated by considering five residential users with suitable battery technology identified from the former part of the study. For the purpose of the analysis, PV and load profiles including seasonal effects and grid price were taken from IIT Mumbai, India and IEX portal, respectively. In addition, this article also examines the impact of increased number of users with CES. The value of this study is that the proposed CES architecture is capable of reducing the cost of electricity experienced by the user by 11.37% as compared to DES. With this, CES operator’s revenue can be increased by 6.70% in summer and 16.97% in winter in the case of fixed number of users. Finally, based on the analysis and simulation results, this paper recommends CES with Li-ion battery technology for residential application.

A novel model reference adaptive system for the rotor position/speed estimation and sensorless operation of a brushless doubly fed reluctance generator with maximum power point tracking is presented. Its main advantage over the existing designs are the inherent robustness and stability afforded by the complete parameter independence of the reference model using only the measured currents of the converter-fed (secondary) winding. In addition, the respective stationary frame current components are estimated by the adaptive model using the voltage and current measurements of the grid-connected (primary) winding at line frequency, offering higher accuracy. The simulation and experimental results have demonstrated the excellent real and reactive power controller performance for typical operating characteristics of wind turbines.

Precise timing among wireless sensor nodes is a key enabling technology for time-sensitive industrial wireless sensor networks (WSNs). However, the accuracy of timing is degraded by manufacturing tolerance, aging of crystal oscillators, and communication delays. This article develops a framework of packet-coupled oscillators (PkCOs) to characterize the dynamics of communication and time synchronization of clocks in WSNs. The nonidentical clock is derived to describe the embedded clock's behavior accurately. A proportional–integral (PI) packet coupling scheme is proposed for synchronizing networked embedded clocks, while, scheduling wireless Sync packets to different slots for transmission. It also possesses the feature of automatically eliminating the effects of unknown processing delay, which further improves the synchronization performance. The rigorous theoretical analysis of PI-based PkCOs is presented via studying a closed-loop time synchronization system. The performance of PI-based PkCOs is evaluated on a hardware testbed of IEEE 802.15.4 WSN. The experimental results show that the precision of the proportional-integral PkCOs protocol is as high as 60 $\mu s$ (i.e., 2 ticks) for 32.768 kHz crystal oscillator-based clocks.

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders.

This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K.

A new U-shape panda polarization-maintaining fiber (PPMF) based microfiber interferometer coated with graphene oxide (GO) film was proposed and experimentally demonstrated for relative humidity (RH) sensing. Experimental results show that the U-shape sensor has refractive index (RI) sensitivity of 1692.5 nm/RIU in the RI range of 1.33 when the diameter of the taper waist is 10.08 μm. The surface of the U-shape sensor was then modified chemically and coated with a thin layer of GO film (59.64 nm) for RH detection and the sensitivity is proportional to RH: as RH increases from 30% to 98%, the sensitivity increases from 0.111 to 0.361 nm/%RH and the response time is 0.28 s. In addition, the cross sensitivity to temperature, stability, reproducibility, and response/recovery time of the RH sensor were studied in detail. The proposed U-shape fiber RH sensor has advantage of high sensitivity, good reproducibility and fast response (0.28 s), which has potential application in areas requiring dynamic measurement of RH variations such as industrial product fabrication process control and breath state monitoring.

At present, the limited existence of fossil fuels and the environmental issues over greenhouse gas emissions have been directly affected to the transition from conventional vehicles to electric vehicles (EVs). In fact, the electrification of transportation system and the growing demand of EVs have prompted recent researchers to investigate the optimal location of electric vehicle charging stations (EVCSs). However, there are numerous challenges would face when implementing EVs at large scale. For instance, underdeveloped EVCSs infrastructure, optimal EVCS locations, and charge scheduling in EVCSs. In addition, the most fundamental EV questions, such as EV cost and range, could be partly answered only by a well-developed EVCS infrastructure. According to the literature, the researchers have been followed different types of approaches, objective functions, constraints for problem formulation. Moreover, according to the approaches, objective functions, constraints, EV load modeling, uncertainty, vehicle to grid strategy, integration of distributed generation, charging types, optimization techniques, and sensitivity analysis are reviewed for the recent research articles. Furthermore, optimization techniques for optimal solution are also reviewed in this article. In addition, the EV load impact on the distribution network, environmental impacts and economic impact are discussed.

Wireless sensor networks (WSNs) are currently being deployed in everyday objects to collect and transmit information related to humidity, temperature, heartbeat, motion, etc. Such networks are part of the massive machine-type communication (mMTC) scenario within the fifth/sixth generation of wireless networks. In this paper, we consider the optimization and design of an optical WSN composed of multiple battery-powered sensor nodes based on light-emitting diode transmitters. Extending our previous work, we take into account both line-of-sight and diffuse-light propagation, and show that in indoor scenarios, diffuse radiation can improve link availability under shadowing/blocking and extend battery life. In order to optimize the optical wireless link parameters, we use a machine-learning approach based on a genetic algorithm to ascertain the performance limits of the system. The presented results indicate that the proposed system is a viable wireless option for WSNs within the context of mMTC.

International audience; Visible light communications (VLC) (VLC) has been introduced as a complementary wireless technology that can be widely used in industrial indoor environments where automated guided vehicles aim to ease and accelerate logistics. Despite its advantages, there is one significant drawback of using an indoor vehicular VLC (V-VLC) network that is there is a high handover outage duration. In line-of-sight VLC links, such handovers are frequently due to mobility, shadowing, and obstacles. In this paper, we propose a frequency diversity and link aggregation solution, which is a novel technique in Data link layer to tackle handover challenge in indoor V-VLC networks. We have developed a smallscale prototype and experimentally evaluated its performance for a variety of scenarios and compared the results with other handover techniques. We also assessed the configuration options in more detail, in particular focusing on different network traffic types and various address resolution protocol intervals. The measurement results demonstrate the advantages of our approach for lowoutage duration handovers in V-VLC. The proposed idea is able to decrease the handover outage duration in a two-dimensional network to about 0.2 s, which is considerably lower compared to previous solutions.

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

Layered or hybrid optical orthogonal frequency division multiplexing (OFDM) has been proposed for use in optical communications due to its excellent spectral and power efficiencies, especially in visible light communications (VLC). However, most of the current works concentrate on transmitter and receiver design as well as the quality of service in communication networks. In this paper, we propose a spectrum-efficient dimmable triple-layer hybrid optical OFDM (DTH-OFDM) scheme to tackle the illumination requirements, considering different practical indoor VLC scenarios from low illumination to high illumination intensities. In the proposed DTH-OFDM scheme, the required dimming level is achieved by jointly adjusting the dimming factors and direct current bias. We investigate the comprehensive performance analysis of the proposed DTH-OFDM in detail, including probability density function, bit error rate (BER), spectral and energy efficiencies. In addition, a joint dimming control and optimal power allocation problem for DTH-OFDM is formulated and solved using convex optimization under the constraints of light emitting diode (LED) nonlinearity, dimming target and communications reliability. Numerical results show that, the proposed DTH-OFDM can offer continuous and arbitrary dimming target with higher spectral efficiency and lower BER compared with its counterparts, as well as an enhanced tolerance to the LED nonlinearity.

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.

Multifunctional environmental sensing is crucial for various applications in agriculture, pollution monitoring, and disease diagnosis. However, most of these sensing systems consist of multiple sensors, leading to significantly increased dimensions, energy consumption, and structural complexity. They also often suffer from signal interferences among multiple sensing elements. Herein, we report a multifunctional environmental sensor based on one single sensing element. A MoS2 film was deposited on the surface of a piezoelectric microcantilever (300 × 1000 μm2) and used as both a sensing layer and top electrode to make full use of the changes in multiple properties of MoS2 after its exposure to various environments. The proposed sensor has been demonstrated for humidity detection and achieved high resolution (0.3% RH), low hysteresis (5.6%), and fast response (1 s) and recovery (2.8 s). Based on the analysis of the magnitude spectra for transmission using machine learning algorithms, the sensor accurately quantifies temperatures and CO2 concentrations in the interference of humidity with accuracies of 91.9 and 92.1%, respectively. Furthermore, the sensor has been successfully demonstrated for real-time detection of humidity and temperature or CO2 concentrations for various applications, revealing its great potential in human–machine interactions and health monitoring of plants and human beings

In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted wireless communication system with energy harvesting. In the single information user (IU) scenario, we consider the power control of base station (BS) and the random deployment of energy users (EUs). To this end, we first characterize the statistical features of the channel gains over BS-RIS-IU and BS-RIS-EU cascaded links. Then, we derive a closed-form expression of the information outage probability (IOP) of the IU and show an upper bound of the energy outage probability (EOP) of EUs by invoking the Jensen's inequality. Furthermore, we consider two more general extensions, namely, the existence of imperfect phase alignment and multiple IUs. Finally, the correctness of the analysis results is verified by Monte-Carlo simulation.

Apnoea, a major sleep disorder, affects many adults and causes several issues, such as fatigue, high blood pressure, liver conditions, increased risk of type II diabetes, and heart problems. Therefore, advanced monitoring and diagnosing tools of apnoea disorders are needed to facilitate better treatment, with advantages such as accuracy, comfort of use, cost effectiveness, and embedded computation capabilities to recognise, store, process, and transmit time series data. In this work we present an adaptation of our apnoea-Pi open-source surface acoustic wave (SAW) platform (Apnoea-Pi) to monitor and recognise apnoea in patients. The platform is based on a thin-film SAW device using bimorph ZnO and Al structures, including those fabricated as Al foils or plates, to achieve breath tracking based on humidity and temperature changes. We applied open-source electronics and provided embedded computing characteristics for signal processing, data recognition, storage, and transmission of breath signals. We show that the thin-film SAW device out-performed standard and off-the-shelf capacitive electronic sensors in terms of their response and accuracy for human breath-tracking purposes. This in combination with embedded electronics makes a suitable platform for human breath monitoring and sleep disorder recognition.

Mechanical and Electrical (M&E) services account for 10-70 of the total cost of projects, depending on the project type. Sadly, the complex nature of the design of M&E services among other peculiar attributes has led to an enormous challenge in the cost management of M&E services in the construction industry. This study examined the knowledge areas required to effectively cost-manage M&E services in construction projects. Data for the study were collected by administering copies of a carefully designed questionnaire among experts in M&E services after a pilot study with a team of experts. The variables outlined in the questionnaire were extracted from relevant literature. In selecting the M&E services experts that participated in the survey, purposive and snowballing sampling techniques were used. The professionals involved include Electrical, Mechanical engineers, and Quantity Surveyors. Data analysis was done using Mean Item Score, Gap analysis, Spearman rank correlation, and Kendall coefficient of concordance. Findings revealed that knowledge in Bill of Quantities (BOQ) preparation, valuation, measurement of M&E works, estimating, and quality management were ranked as the most important knowledge areas and a statistically significant agreement among the experts was recorded. Gap analysis revealed that the ability of cost managers/QSs on the knowledge areas is currently below expectation while a moderate relationship was observed between the importance and performance of QSs/cost managers in the knowledge areas. The study concludes that there is a need for cost managers/Quantity Surveyors to enhance their knowledge in the identified knowledge areas and strive hard to close the gap between importance and their performance on the knowledge areas.

In this paper, a dual hollow-core negative curvature fiber is proposed for the polarization beam splitter. The effects of the structure parameters of the dual hollow-core negative curvature fiber on the coupling length and coupling length ratio of the x -polarized and y -polarized core modes and the higher-order mode extinction ratio are analyzed by the finite element method. Moreover, the normalized output powers of the x -polarized and y -polarized modes in the cores A and B and the corresponding extinction ratio are also investigated by the mode coupling theory. The simulation results show that a dual hollow-core negative curvature fiber polarization beam splitter with the length of 6.45 cm can achieve a broad bandwidth of 400 nm (1.23–1.63 µm), covering the O + E + S + C + L communication band. In addition, the higher-order mode extinction ratio is greater than 100 in the considered wavelength range, which means that it has good single-mode characteristics. It is believed that the proposed dual hollow-core negative curvature fiber polarization beam splitter will have significant applications in the optical communication system.

In this study, polyacrylic acid (PAA) films were deposited onto a quartz surface acoustic wave (SAW) resonator using a spin-coating technique for ammonia sensing operated at room temperature, and the sensing mechanisms and performance were systematically studied. The oxygen-containing functional groups on the surfaces of the PAA film make it sensitive and selective to ammonia molecules, even when tested at room temperature. The ammonia molecules adsorbed by the oxygen-containing functional groups of PAA (e.g., hydroxyl and epoxy groups) increase the membrane’s stiffness, which was identified as the primary mechanism leading to the positive frequency shifts. However, mass loading due to adsorption of ammonia molecules is not a major reason as it will result in a negative frequency shifts. When the PAA coated SAW sensor was exposed to ammonia with a low concentration of 500 ppb, it showed a positive frequency shift of 225 Hz, with both good repeatability and stability, as well as a good selectivity to ammonia compared with those to C2H5OH, H2, HCl, H2S, CO, NO2, NO, and CH3COCH3.

Due to the widespread deployment of distributed energy resources, renewable energies and battery energy storage, the peer to peer (P2P) energy trading schematic has gained the staple attention for improving the energy efficiency and energy flexibility of power grids. This is while, smart demand response programming (DRP) is considered as the bridge between these two indicators of smart grid. Moreover, the subtle point of proliferating P2P schematics is the regulation towards the maximization of social welfare leading to economic profitability of customers and owner's of microgrid and, eventually, reduction of pollutant emission of fossil fuels. Also, uncertainty, aroused by electrical consumption and renewable energy resources, is the core of every considerations, which has to be dealt with intelligent algorithms for strengthening the stability of transactions. On the other hand, compatibility with upper grid's regulations, i.e. power loss and voltage deviation, along with determining fair price of energy trading are the subjects of P2P-based tactics. Therefore, this paper proposes a P2P-based transactive energy sharing architecture, as two stage mixed integer non-linear programming, using smart DRP integrated with machine learning approach, i.e. radial basis neural network.Firstly, the uncertainty of electrical demand and renewable energies are relaxed through short term forecasting. Doing so, the day-ahead transactions of peers are obtained based on their energy management objective, targeting the energy reliability of customers, which energy not supplied criterion has to be equal to zero. Then, participation of customers in DRP, cost of customers, revenue of microgrid's owner and transactions of real time programming are optimally acquired based on Pareto front technique. Also, the simulations are conducted on IEEE 85 bus test system to realize the considerations. The results convey that the profitability of customers and owners is tied with the implementation of smart DRP and accurate forecasting of uncertain variables. In addition, the maximum improvements towards maximizing the revenue of owners and minimizing the cost of customers take place at hours which the electrical consumption is shifted from peaks to off-peaks and mid-peaks, certifying the performance of proposed methodology.

Both MXene and conducting polymers are hot research topics on electrode materials for supercapacitors (SCs). The combination of these two different types of materials can solve the defects that exist when they are used as electrode materials alone. Based on theoretical capacity, specific surface area, mass load, flexibility and excellent mechanical properties, MXene/conducting polymers composites demonstrate their potential to become advanced electrode materials. In order to further illustrate the changes brought about by these composites, a large number of examples of MXene/conducting polymers as electrodes are described in details. In general, this review covers the latest developments in the study of SCs based on MXene/conducting polymers composites, including materials preparation, electrode materials, symmetrical supercapacitors (SSCs) and asymmetrical supercapacitors (ASCs). This article aims to understand the application of MXene/conducting polymers composites in the research of SCs, and provides a guideline for further research of these promising materials.

In this letter, we analyze the secrecy performance of a reconfigurable intelligent surface (RIS)-aided communication system with spatially random unmanned aerial vehicles (UAVs) acting as eavesdroppers. We consider the scenarios where the base station (BS) is equipped with single and multiple antennas.The signal-to-noise ratios (SNRs) of the legitimate user and the eavesdroppers are derived analytically and approximated through a computationally effective method. The ergodic secrecy capacity is approximated and derived in closed-form expressions.Simulation results validate the accuracy of the analytical and approximate expressions and show the security-enhanced effect of the deployment of the RIS., Comment: Accepted by IEEE Wireless Communications Letters (WCL)

There are significant challenges in controlling uniformity of crystal inclination angles, growth orientations and film thicknesses to generate dual-mode surface acoustic waves (e.g., Rayleigh ones and shear-horizontal ones) for lab-on-a-chip applications. In this study, we demonstrate large area (up to three inches) and uniformly inclined piezoelectric ZnO films, sputtering-deposited on silicon using a glancing angle deposition method. Characterization using X-ray diffraction showed that the inclined ZnO films have an average crystal inclination angle of 29.0°, apart from the vertical (0002) orientation, at a substrate tilting angle of 30 o. Reflection signals of ZnO/Si surface acoustic wave devices clearly show the generations of both shear horizontal surface acoustic waves and Rayleigh waves. The Rayleigh waves enable efficient acoustofluidic functions including streaming and transportation of sessile droplets. Excitation direction of Rayleigh waves on the acoustofluidics versus the inclined angle direction has apparent influences on the acoustofluidic performance due to the anisotropic microstructures of the inclined films. The same device has been used to demonstrate biosensing of biotin/streptavidin interactions in a liquid environment using the shear-horizontal surface acoustic waves, to demonstrate its potential for integration into a complete lab-on-a-chip device.

It is a great challenge to achieve both high specific capacity and high energy density of supercapacitors by designing and constructing hybrid electrode materials through a simple but effective process. In this paper, we proposed a hierarchically nanostructured hybrid material combining Zn0.76Co0.24S (ZCS) nanoparticles and Co(OH)2 (CH) nanosheets using a two-step hydrothermal synthesis strategy. Synergistic effects between ZCS nanoparticles and CH nanosheets result in efficient ion transports during the charge-discharge process, thus achieving a good electrochemical performance of the supercapacitor. The synthesized ZCS@CH hybrid exhibits a high specific capacity of 1152.0 C g-1 at a current density of 0.5 A g-1 in 2 M KOH electrolyte. Its capacity retention rate is maintained at ∼ 70.0% when the current density is changed from 1 A g-1 to 10 A g-1. A hybrid supercapacitor (HSC) assembled from ZCS@CH as the cathode and active carbon (AC) as the anode displays a capacitance of 155.7 F g-1 at 0.5 A g-1, with a remarkable cycling stability of 91.3% after 12,000cycles. Meanwhile, this HSC shows a high energy density of 62.5 Wh kg-1 at a power density of 425.0 W kg-1, proving that the developed ZCS@CH is a promising electrode material for energy storage applications.

Privacy protection as a major concern of the industrial Big Data enabling entities, makes the massive safety-critical operation data of wind turbine, unable to exert its great value because of the threat of privacy leakage. How to improve the diagnostic accuracy of decentralized machines without data transfer remains an open issue, especially these machines are almost accompanied by skewed class distribution in the real industries. In this study, a class-imbalance privacy-preserving federated learning framework for fault diagnosis of decentralized wind turbine is proposed. Specifically, a biometric authentication technique is first employed to ensure that only legitimate entities can access private data and defend against malicious attacks. Then, the federated learning with two privacy-enhancing techniques enables high potential privacy and security in low-trust systems. After that, a solely gradient based self-monitor scheme is integrated to acknowledge the global imbalance information for class-imbalanced fault diagnosis. We leverage a real-world industrial wind turbine dataset to verify the effectiveness of the proposed framework. By comparison with five state-of-the-art approaches and two non-parametric tests, the superiority of the proposed framework in imbalanced classification is ascertained. An ablation study indicates the proposed framework can maintain high diagnostic performance while enhancing privacy protection.

A simulated design for a temperature-compensated voltage sensor based on photonic crystal fiber (PCF) infiltrated with liquid crystal and ethanol is presented in this paper. The holes distributed across the transverse section of the PCF provide two channels for mode coupling between the liquid crystal or ethanol and the fiber core. The couplings are both calculated accurately and explored theoretically using the finite element method (FEM). The influence of voltage on the alignment of the liquid crystal molecules and confinement loss of the fiber mode are studied. Liquid crystal molecules rotate which changes its properties as the voltage changes. As the characteristics of the liquid crystal will be affected by temperature, therefore, we further fill using ethanol, which is merely sensitive to temperature, into one hole of the PCF to realize temperature compensation. The simulated results show that the sensitivity is up to 1.29977 nm/V with the temperature of 25 °C when the voltage ranges from 365 to 565 V. The standard deviation of the wavelength difference is less than 2 nm within the temperature adjustment from 25 to 50 °C for temperature compensation. The impacts of the construction parameters of the PCF on sensing performances of this voltage sensor are also analyzed in this paper.