The secondary control is applied in islanded Microgrids (MGs), after the primary control, in order to restore the buses voltage and frequency to specified values. The existing power flow methods can accurately calculate the power flow for droop-controlled islanded MGs, but in many cases, they cannot calculate the steady-state solution of the MG after the action of secondary controllers. The main challenge in the steady-state modelling of the secondary layer lies in that the secondary controllers consist of integral parts, which can integrate functions with different integral histories, and therefore, under certain circumstances, can imply inaccurate power sharing between the distributed generation units (DGs). This phenomenon is most pronounced under communication failures, as will be shown in the simulations. In this way, this paper proposes a power flow method for calculating, accurately, the steady-state solution of hierarchically controlled islanded AC MGs, including droop-based primary control and secondary control. The paper includes four main features: a) generalized implementation in several communication strategies e.g., centralized, decentralized, consensus, distributed averaging, b) precise simulation of communication links and integral parts of secondary controllers, c) low computation time, and d) accurate 4-wire network representation. Simulations were executed to validate the proposed method against Simulink and to highlight the importance of an accurate modelling of secondary control in the power flow method for islanded MGs

Demand prediction to support appropriate production decisions is being actively studied. Many prediction models are designed to minimize the prediction error, which is measured by determining the difference between the predicted and ground-truth demand. However, these models ignore the effect of the prediction error on downstream production decisions. This prompts our study, which focuses on demand prediction models for two-stage uncapacitated lot-sizing problems. In this paper, we present a linear prediction model that minimizes the decision error, which is measured by the optimization objective of lotsizing problems. Our model mitigates the impact of prediction errors by leveraging the structure of the lot-sizing problems. We subsequently extend the prediction model to a distributionally robust version. A cutting-plane method is proposed to solve the resulting model under the L_{1} and  L_{infinity} norms. The decision errors are used as loss functions to construct decision treeand neural network-based prediction models, respectively. The corresponding training methods are proposed as well. Numerical experiments demonstrate that the proposed prediction models are significantly superior to traditional prediction methods for small samples and high-dimensional data. In addition, the performance of the robust model is highly tolerant to model misspecification and imperfect data.

It is foreseeable that the 100 Gb/s/λ and beyond passive optical network (PON) will be required in future optical access networks to meet the explosive growth of data traffic. The coherent optical systems could be a promising solution for the future beyond 100G PON. Coherent PON using digital subcarrier multiplexing (DSCM) can provide flexible bandwidth allocation to a large number of access subscribers by dividing subcarriers of the DSCM signal into time slots for time-and-frequency division multiple access. When the optical network unit is allocated a new subcarrier, digital signal processing (DSP) should converge fast in the allocated time slot to ensure a low handoff latency for real-time bandwidth allocation. However, the traditional coherent DSP is hard to realize fast convergence due to blind and complex algorithms. In this paper, we design a specific training sequence (TS) structure and propose data-aided DSP to achieve fast convergence for coherent PON. The feasibility of the proposed scheme is experimentally verified in an  8 Gbaud/SC×8 SCs 400 Gb/s-net-rate coherent PON using DSCM with 16 quadrature amplitude modulation. The experimental results show that fast convergence is jointly realized by the proposed TS structure and data-aided DSP using a 416-symbol TS with a 52 ns duration. The receiver sensitivity at the 20% soft-decision forward error correction limit is approximately −27 dBm and an optical power budget of about 35.5 dB is achieved with a booster amplifier.

Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-Temporal Contrastive Learning Enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph (CFG) embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines e.g., with an improvement as much as 27.08% gain on HR@20 and 20.10% gain on MRR@20.

IEEE time-sensitive networks (TSN) offer redundant paths for automation networks that are essential preconditions for network load balancing (NLB) or distribution. They also provide several traffic shapers and schedulers with different impacts on the data flow control. The selection of the right traffic shaper or scheduler for an automation network is challenging. Their influence depends on various network parameters such as network extension, network cycles, application cycles, and the amount of data per traffic class and network cycle. In this study, the data flow control for network load balancing in an automation TSN using different traffic shapers and schedulers was investigated. The effects of the network parameters on the shapers and schedulers were derived and imported into the data flow control model of the automation network. The sample networks were simulated, and performance comparisons were performed. The results show that enhancements for scheduled traffic (EST), strict priority queuing (SPQ), and the combination of SPQ with frame preemption are better scheduler selections in connection with larger networks, fast network cycles, and fast application cycles. The cyclic queuing and forwarding (CQF) shaper and asynchronous traffic shaper (ATS) are only alternatives for data load control in small networks or in conjunction with slow applications.

Many Internet-of-Things (IoT)-over-satellite applications rely on affordable location-aware but energy-constrained IoT sensors. In this paper, we propose a novel method to estimate the satellite visibility window in IoT devices based on simple Doppler measurements. We present two scenarios where the orbital information of the serving satellite’s is initially unknown to the IoT device: (i) we assume that the geographic coordinates are known to the IoT device, and (ii) we assume that the coordinates are completely unknown. Accordingly, we  derive the Doppler measurement likelihood function, and simplify it to a root mean square error (RMSE) minimization problem. From a sequence of Doppler measurements, we estimate the orbital parameters of the serving satellite using a stochastic optimizer to minimize the RMSE. From the orbital estimation, we then predict the satellite visibility window (satellite pass). To gauge the accuracy of the window estimation, we apply the intersection-over-union metric to compute the overlapping visibility window between the ground truth and the estimation, and consequently present the results based on extensive Monte Carlo simulations.

This paper investigates a cooperative non-orthogonal multiple access network where a near user (NU) harvests energy using time-switching (TS) or power-splitting (PS) protocol to assist information transmission to the far user (FU). Considering nonlinear energy harvesting and opportunistic cooperative mode (CM) to non-cooperative mode (NCM) switching, expressions are derived for FU and NU outage probabilities and throughput. We demonstrate that CM/NCM switching can guarantee NU’s performance similar to OMA without any degradation in FU throughput. Also, the choice of optimum PS/TS parameter is essential to maximize FU throughput while ensuring desired NU throughput. CM/NCM switching ensures higher energy efficiency.

This manuscript addresses the problem of detecting, classifying, and localizing sound sources in an acoustic scene of spatial audio. We propose using bio-inspired Gammatone auditory filters for the acoustic feature extraction stage and a novel deep learning architecture encompassing convolutional, recurrent, and temporal convolutional blocks. Our system exceeded the state-of-the-art metrics on four spatial audio datasets with different levels of acoustical complexity and up to three sound sources overlapping in time. Furthermore, we also performed a comparative analysis of the gap between machine and human hearing, evidencing that our results have already exceeded the human performance in non-reverberant scenarios.

This paper provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel. Our main goal is to minimize the average query age of information (QAoI) subject to average sampling, average transmission, and per-slot transmission constraints. To this end, an optimization problem is formulated and solved by casting it into a linear program. We also provide a low-complexity near-optimal policy using the notion of weakly coupled constrained Markov decision processes.

Road cracks are an important concern of administrators. Visual inspection is labor-intensive and subjective, while previous algorithms detecting cracks from optical camera images were not accurate. Furthermore, the actual length and thicknesses of a crack cannot be estimated only from images. Light Detection and Ranging (Lidar) is a standard feature introduced on the latest smartphones. In this research, for completely automatic, accurate and quantitative road crack evaluation using smartphones, an up-to-date segmentation technique, U-Net with morphology transform adopting data augmentation was proposed. Lidar 3D point cloud data of smartphones is linked to color data obtained from cameras. By registering images to Lidar data, geometrical relationships were estimated to calculate the length and thicknesses. The proposed algorithm was validated by a standard database of road cracks and dataset constructed by the authors, showing 95% length accuracy and 0.98 coefficient of determination for thickness estimation irrespective of various crack shapes and asphalt pavement color patterns.

Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers. Nonetheless, GNSS signals are susceptible to intentional or unintentional RF interferences (RFIs), which may alter the calculation of the scintillation indices, thus compromising the quality of the scientific data and the reliability of the derived space weather monitoring products. Upon the observation of anomalous scintillation indices computed by a GISTM receiver in the Mediterranean area, the study presents the results of the analysis and characterization of a deliberate, unclassified interferer acting on the L1/E1 GNSS signal bands, observed and captured through an experimental, software defined radio setup. The paper also highlights the adverse impacts of the interferer on the amplitude scintillation indices employed in scientific investigations, and presents a methodology to discriminate among regular and corrupted scintillation data. To support further investigations, a dataset of baseband signals samples affected by the RFI is available at IEEE DataPort.

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles (CAVs) at unsignalized intersections. The constraint-coupled bi-level optimization is operated with a limited view of incoming traffic using the rolling horizon procedure to reduce computational complexity. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established while demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios with high penetration market rate of CAVs based on the assumption of tracking errors of Human Driven Vehicles (HDVs) trajectories. Numerical experiments are conducted on extensive scenarios with different traffic densities, where the Constraint Programming (CP) technique is used to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

This letter reports the design techniques for a class of inline waveguide bandpass filters with sharp-rejection capabilities at the lower stopband based on a novel nonlinear-frequency-variant-coupling (NFVC) structure. The proposed NFVC consists of a modified-T-shaped metallic post (MTP) that is placed at the center of the waveguide broad wall with its open arms lying along the waveguide width. The engineered NFVC structure produces a first-order bandpass filtering transfer function with a pair of transmission zeros (TZs) located below the passband range. To demonstrate the usefulness of the proposed NFVC inverter, a 9.9-GHz third-order inline waveguide bandpass filter prototype with two TZs is developed and tested. It consists of two half-wavelength cavity resonators coupled together via the conceived MTP coupling structure. The measured results are in close agreement with the EM simulated ones, thus validating the devised waveguide filter design principle.

The magnetic field surface integral equation for perfect electrically conducting scatterers suffers from accuracy problems when discretized with lowest-order Rao-Wilton-Glisson (RWG) functions. For high-frequency scattering scenarios, one of the various reported countermeasures are hierarchical higher-order (HO) functions. We demonstrate that the accuracy of these HO methods of up to 1.5th order may be further improved by employing a weak-form discretization scheme for the identity operator inside the magnetic field integral equation (MFIE), in particular for scatterers with sharp edges. As expected, the presented numerical results indicate that this approach becomes less effective for increasing order. Moreover, since the weak-form discretization overcomes only the anisotropy problems of the standard discretizations, parts of the accuracy problems of the MFIE persist for HO discretizations if the testing is performed with non dual-space conforming functions.

Lifetime of motes in wireless sensor networks can be enlarged by decreasing the energy spent for communication. Approaches like time slotted channel hopping pursue this goal by performing frame exchanges according to a predefined schedule, which helps reducing the duty cycle. Unfortunately, whenever the receiving radio interface is active but nobody in the network is transmitting, idle listening occurs. If the traffic pattern is known in advance, as in the relevant case of periodic sensing, proactive reduction of idle listening (PRIL) noticeably lowers energy waste by disabling receivers when no frames are expected for them. Optimal PRIL operation demands that, at any time, the transmitter and receiver sides of a link have a coherent view of its state (either enabled or disabled). However, this is not ensured in the presence of acknowledgment frame losses. This paper presents and analyzes some strategies to cope with such events. An extensive experimental campaign has been carried out through discrete event simulation to determine what consequences above errors may have from both a functional and performance viewpoint. Results show that, although no strategy is optimal in all circumstances, different solutions can be profitably adopted depending on the specific operating conditions.

*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. This article is intended to provide practical information about development of a cost-effective test model to achieve low-cost radar cross-section (RCS) measurement. To reduce the manufacturing cost of a model, two major possible approaches are: replacing the molding material with inexpensive one, and measuring a scaled-down model with a higher operating frequency. In this study, the former was achieved by using a resin-made model coated with conductive paste to imitate a complete metallic model. According to the scaling laws, the entire geometry, including the surface roughness of the model owing to the conductive paint, must be properly scaled down. As this roughness scaling is practically infeasible, we experimentally prove that the shape-only scaling is valid without the roughness scaling, provided that appropriate molding material is selected. As a preliminary study, two types of polyurethane resin with different density were tested for their suitability of the RCS measurement, and the resin with higher density was shown to be appropriate for the RCS test model. Based on this preliminary study, 1/20- and 1/40-scale aircraft models made with aluminum or polyurethane resin were manufactured, and their RCS were measured to demonstrate that the almost equivalent RCS patterns can be obtained; therefore, low-cost RCS testing was achieved by the resin-made scaled model.

Under the implementation of the ‘Two Rules’, large-scale electric vehicles (EVs) with their V2G capability have become important incremental resources in the frequency regulation (FR) scenario. Based on the information interaction framework between the power grid and EVs in the form of the aggregator, the optimal decomposition of real-time automatic generation control (AGC) command from the power grid to EVs are discussed in this paper by proposing a bi-level decomposition algorithm considering the network model in combination with real-time dispatchable information of EVs. In the bi-level decomposition algorithm, on the one hand, the upper level decomposes the total AGC command between the aggregator and the AGC unit with economy as the goal and safety as the constraint. On the other hand, the AGC command is decomposed between each EV in the aggregator based on the real-time dispatchable power and the battery life loss under the condition of meeting the demand for EVs. The study case of the real-time AGC command decomposition shows that the proposed bi-level algorithm can realize the real-time command decomposition in the information interaction framework from the power gird to EVs in the perspective of economy and safety.

Study estimated the number of real-world, unsupervised steps that need to be recorded before various gait-related outcomes show consistency (i.e., adding additional data would not change the outcome distribution). The data were collected over 1-week of walking by healthy adults and adults with symptomatic knee osteoarthritis using a shoe-embedded inertial sensor.