Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time EMT-Phasor Co-Simulation Modeling for Large-Scale Power Grids: Challenges and Solutions

The use of real-time digital simulators has increased significantly in the last decade due to the rapid technological changes in power systems and the need to assess new solutions through hardware-in-the-loop simulations for various applications and studies. Real-time co-simulation has become a necessity to overcome various challenges such as limited computational power, complicated model conversion process, diverse modeling domains, and rapidly-changing power system models. Co-simulation of large-scale power systems in electromagnetic and phasor domains requires an extensive handling procedure. Defining proper boundaries, avoiding numerical problems, and maintaining system's dynamic characteristics are among the many challenges for setting up such co-simulations. This paper looks into these challenges through creating an TSAT-RTDS co-simulation of the New York State grid model. The results from the co-simulation are compared with those of the full TSAT simulation to assess their accuracy.
Highly-Sensitive Unbalanced MZI Gas Sensor Assisted With a Temperature-Reference Ring Resonator

In this paper we study the gas sensing performance of a compact silicon photonics Mach-Zehnder interferometer (MZI) with a coiled sensing arm. A partially exposed sensor was fabricated using deep UV lithography, with a process resolution of 248 nm. Testing with inert gases, He and N<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>, resulted in a measured sensitivity and limit of detection of <inline-formula><tex-math notation="LaTeX">$\sim\! $</tex-math></inline-formula>1458 nm/RIU and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>8.5×10<sup>-5</sup> RIU, respectively, in a sensing volume of 1.852 picoliters. The temperature sensitivity of the sensor was 166 pm/<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula>C and the inclusion of a cladded ring-resonator, post-MZI, allowed resolving the temperature drift due to gas flow. In order to further enhance the overlap of the optical mode with the measurand and thus the sensitivity, a suspended MZI was designed and simulated with an expected sensitivity of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>5500 nm/RIU, for wavelengths around 1550 nm and a temperature of 300 K.
Solving Unit Commitment Problems with Demand Responsive Loads

This paper focuses on using variations of the Frank-Wolfe algorithm for solving unit commitment problems with high volumes of demand responsive loads on the power grid. We present a formulation of the unit commitment problem with demand responsive loads. We then show through reformulation and relaxations of the problem that variations of the Frank-Wolfe algorithm can be used to determine the time series decisions for the demand responsive loads. We show through computational experiments on the IEEE Reliability Test System that the time series of demand responsive load decisions obtained through our approach are near optimal and describe how large-scale parallel implementations of our approach can be highly computationally efficient.
A QSTS Simulation Tool Using Equivalent Circuit Inverter Models for Permanently Isolated Nanogrids

This paper demonstrates a tool for quasi-static time series (QSTS) simulation for permanently isolated military nanogrids using steady-state equivalent circuit inverter models. Grid-forming and grid-following inverters are connected to PV generation and energy storage systems. The tool facilitates planning and operation of such grids by analyzing steady-state operation, fuel consumption, energy endurance, and faults in grid-forming and grid-following inverter modes. The equivalent inverter models are computationally efficient and well suited for rapid studies requiring QSTS with varying irradiance conditions and loads.
Updating VNF deployment with Scaling Actions using Reinforcement Algorithms

Softwarization of the internet network is promising for network service providers (NSPs) to satisfy various types of user requests while dynamically operating the networking system. However, it is challenging to provide optimal service quality as the complexity of the network increases. In particular, deployment of the VNF instances is a critical issue in providing better QoS while maintaining resources optimally. For the management of the VNF deployment task, dynamic programming algorithms are only feasible in small networks or rely on heuristics. In this paper, we propose a VNF deployment method based on reinforcement learning (RL) that can effectively satisfy the QoS with minimized resource consumption in the network. Our approach makes adjustment decisions, which are scale-in/keep/out for target nodes and target VNF types given ILP-based deployments. We formulate this VNF deployment task as RL by setting the reward as QoS and resources. Moreover, we propose a model architecture for our RL agent's policy, based on Graph Neural Network. In the experiment, our approach optimizes VNF deployment with improved QoS while keeping the similar or slightly less amount of resources, compared to ILP-based deployment.
P2P Energy Trading for Coordinated Home Energy Management and Voltage Regulation

Peer-to-peer (P2P) energy trading allows direct interactions among participants without centralized resource integration. Therefore, P2P energy trading is suitable for local energy transactions among small scale consumers to promote the consumption of renewable energy localized. Whereas, for electricity commodity, system operation reliable and security is a critical and inevitable problem when designing a P2P energy transaction market. For this consideration, a novel P2P transaction model coordinating consumers' home energy management with system voltage regulation problem is proposed to consist the profits seeking purpose of consumers with the system operation requirements. The proposed P2P transaction model solves consumer-utility maximized energy trading problem with network security operation constraints. An alternating direction method of multiplier (ADMM) based distributed algorithm is proposed to protect the privacy of consumers. The effectiveness and efficiency of the proposed model is verified through a simulation on a modified 9-bus low voltage network.
Centralized versus Distributed Nonlinear Model Predictive Control for Online Robot Fleet Trajectory Planning

In this paper, we formulate and evaluate a centralized vs. a distributed approach for online trajectory generation for a fleet of mobile robots in the presence of static and dynamic obstacles. Due to dynamic obstacles, the trajectories need to be updated online and this is formulated as a nonlinear model predictive control problem. We show that both centralized and distributed MPC solvers manage to generate smooth collision-free trajectories. The distributed approach is shown to scale to many robots very well. In contrast, the computational cost of the centralized approach increases with the number of robots. However, the trajectories generated by the distributed control approach have larger deviations than those generated by the centralized approach. The experiments suggest that the centralized method should be chosen with sufficient computation resource while the distributed approach is a viable alternative when the number of robots is considerable.
Melanoma recognition using deep learning and ensemble of classifiers

The paper presents the special arrangement of an ensemble of classifiers for melanoma recognition. It is based on the application of deep learning. The study applies the structure of the convolutional neural network to create the numerical descriptors of dermoscopic images of melanoma. The flattened vector of image descriptors is subjected to diagnostic feature selection by applying different selection methods. As a result, different sets of a limited number of diagnostic features are generated. In the next stage, these sets of features represent the input attributes for two types of classical classifiers: support vector machine and random forest of decision trees. An additional set of softmax classifiers supplied by a set of features randomly selected from the available descriptors is also applied. By combining different selection methods with these classifiers an ensemble classification system is created and integrated by majority voting. Thanks to the fusion of results of many classifiers forming an ensemble the accuracy and all other quality measures have been significantly increased in the recognition of melanoma from non-melanoma images.
Metamaterial-Based, Vertically Polarized, Miniaturized Beam-Steering Antenna for Reconfigurable Sub-6 GHz Applications

A miniaturized, vertically polarized (VP), pattern switchable/beam-steering antenna based on a metamaterial radiator is presented for sub-6 GHz applications. Different from the traditional split-ring resonator (SRR), the proposed T-shaped SRR can work as a miniaturized VP omnidirectional radiator. To steer the radiation beam, a novel switchable mushroom reflector with a low profile and low loss is explored and utilized. Multiple switchable mushroom reflectors are loaded around the T-shaped SRR radiator to obtain various steerable beams. To verify the proposed design principle, a VP beam-steering SRR-based antenna with a compact size of 0.36 λ0 × 0.36 λ0 × 0.07 λ0 is designed by loading four mushrooms. By controlling the off/on states of four switches, the proposed reconfigurable antenna can realize beam-steering in the horizontal plane (eight states with 45°-step). The measured overlapped −10 dB impedance bandwidth is wider than 3.40–3.80 GHz (11.1%), and the realized peak gain is larger than 5.3 dBi. Therefore, the proposed antenna is a good solution for the widely deployed fifth generation (5G)-NR and smart antenna applications due to its compact size, flexible beam-steering capacity, broad bandwidth, and good scanning coverage.
Comparison of Parallel and Sequential Algorithms for Compressed Sensing

Iterative signal recovery algorithms for compressed sensing (CS) usually employ a parallel scheduling, where all elements of the signal vector are estimated simultaneously under one of the two constraints imposed by the CS observation model (channel), and signal structure, respectively. Most often, they work with average variances to characterize the reliability of the estimated signal in a single parameter. In contrast, sequential algorithms allow a variation in the scheduling of the processing of the signal components, to that end employing individual variances per component. In this paper, we consider several parallel and sequential algorithms for signal recovery in CS and compare them in different sensing scenarios via numerical simulations. Among these algorithms, we introduce the sequential version of an existing algorithm, which bridges the gap between other sequential algorithms.
Directed Data Association for Single Object Tracking in Point Clouds*

Single object tracking in point clouds is a fundamental component in enabling autonomous vehicles to understand dynamic traffic environments. Earlier tracking confidence only relies on IoU between two static boxes, ignoring the motion properties of objects, which may weaken the association abilities of trackers. To comprehensively associate an object with the estimated motion state, we introduce a directed representation. This representation factorizes the box of an object into its central position and orientation. To handle under-detection and over-detection problems, we also present an undirected range suppression mechanism that automatically enlarges and stabilizes the view field at the current time step. As a result, we build a single object tracking system that achieves high accuracy and real-time performance. On both KITTI and nuScenes tracking datasets, we demonstrate that our system outperforms other recent single object trackers in both accuracy and speed. Besides, we also validate the superiority of our approach compared to multiple object tracking methods.
Simultaneous Update Model of Multiple Service Function Chains Guaranteeing State Consistency

Service chaining concatenates virtual network functions (VNFs) in the network to automatically process packets in an appropriate order. In a situation where network traffic fluctuates, routes of service function chains (SFCs) need to be updated to achieve users' requirements. States of VNF instances that make up the SFC need to be kept consistent when updating the SFC routes. Existing models update SFCs one by one. If these models are adopted to the update of multiple SFCs, the total time required to update all SFCs can be long; problems such as leaving VNFs out-of-date can occur. This paper proposes a model that determines the update schedule of multiple SFCs guaranteeing state consistency. The objective function is minimizing the total time required to update all SFCs. Numerical results show that the total time required to update all SFCs can be shortened by updating multiple SFCs at the same time. The numerical results also show that the total time can be shortened by dividing a large buffer into an adequate number of small buffers.
An Enhanced EWMA for Alert Reduction and Situation Awareness in Industrial Control Networks*

Intrusion detection systems (IDSs) are widely deployed in the industrial control systems to protect network security. IDSs typically generate a huge number of alerts, which are time-consuming for system operators to process. Most of the alerts are individually insignificant false alarms. However, it is not the best solution to discard these alerts, as they can still provide useful information about network situation. Based on the study of characteristics of alerts in the industrial control systems, we adopt an enhanced method of exponentially weighted moving average (EWMA) control charts to help operators in processing alerts. We classify all detection signatures as regular and irregular according to their frequencies, set multiple control limits to detect anomalies, and monitor regular signatures for network security situational awareness. Extensive experiments have been performed using real-world alert data. Simulation results demonstrate that the proposed enhanced EWMA method can greatly reduce the volume of alerts to be processed while reserving significant abnormal information.
The Small Magellanic Cloud Investigation of Dust and Gas Evolution (SMIDGE): The Dust Extinction Curve from Red Clump Stars

We use Hubble Space Telescope (HST) observations of red clump stars taken as part of the Small Magellanic Cloud Investigation of Dust and Gas Evolution (SMIDGE) program to measure the average dust extinction curve in a ∼200 pc × 100 pc region in the southwest bar of the Small Magellanic Cloud (SMC). The rich information provided by our eight-band ultraviolet through near-infrared photometry allows us to model the color–magnitude diagram of the red clump accounting for the extinction curve shape, a log-normal distribution of AV, and the depth of the stellar distribution along the line of sight. We measure an extinction curve with . This measurement is significantly larger than the equivalent values of published Milky Way (MW) RV = 3.1 ( ) and SMC Bar RV = 2.74 ( ) extinction curves. Similar extinction curve offsets in the Large Magellanic Cloud (LMC) have been interpreted as the effect of large dust grains. We demonstrate that the line-of-sight depth of the SMC (and LMC) introduces an apparent “gray” contribution to the extinction curve inferred from the morphology of the red clump. We show that no gray dust component is needed to explain extinction curve measurements when FWHM depth of 10 ± 2 kpc in the stellar distribution of the SMC (5 ± 1 kpc for the LMC) is considered, which agrees with recent studies of Magellanic Cloud stellar structure. The results of our work demonstrate the power of broadband HST imaging for simultaneously constraining dust and galactic structure outside the MW.
Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions

Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors’ knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks.
Comparative Analysis of ML-CSC Based CS-MRI Framework With State of the Art

Deep neural networks have been used extensively for inverse problems in image processing research. Subsequently the interest has been shown to theoretically model deep nets with celebrated sparse coding theory resulting in emergence of convolutional sparse coding (CSC) theory. The CSC theory which is a peculiar case of sparse coding works on the premise of representing underlying data (natural or biomedical images) with learned filters/dictionaries and their corresponding sparse feature maps. The dictionaries have special structure unlike their counter part of sparse coding and pursuit algorithms works on global scale instead of patches. This global pursuit results in mitigating the effects of patch aggregation process during traditional regularization-based techniques. Further extending the CSC model, the learning features are again processed through the CSC model representing them with another layer of filters/dictionaries and their corresponding sparse maps. This process is continued until the last layers of model making the multi-layer convolutional sparse coding model. The pursuit algorithms can be employed layer wise of a global pursuit settings utilizing iterative thresholding algorithms. In this work we have implemented a ML-CSC model for the reconstruction of Knee MR images on different CS ratios. The results have been compared with state of the art ISTA-Net+ model in terms of PSNR/SSIM and restoration times for different CS ratios. It is shown that ML-CSC has better quality results than the state of the art ISTA-Net+model.
Exploring the Quark Flavour Puzzle within the 3 Higgs Model

We extend the standard model with two extra Higgs doublets. Making use of a symmetry principle, we present flavour symmetries based on cycle groups $Z_N$ that oblige each Higgs doublet to contribute to the mass of only one generation, thus accommodating the quark mass hierarchy. We systematically search for all charge configurations that naturally lead to the alignment in flavour space of the quark sectors, resulting in a CKM matrix near to the identity, determined by the quark mass hierarchy, and with the correct overall phenomenological features. The minimal realisation is by the group $Z_7$. We show that only a limited number of solutions exist, and that any accidental global symmetry that may occur together with the discrete symmetry is necessarily anomalous. A phenomenological study of each class of solutions concerning predictions to the flavour changing neutral current (FCNC) phenomena is also performed: for some solutions, it is possible to obtain realistic quark masses and mixing, while the flavour violating neutral Higgs are sufficient lighter to be accessible at LHC.
Multi-Class Traffic Density Forecasting in IoV using Spatio-Temporal Graph Neural Networks

Internet of Vehicles (IoV) is an emerging archetype that is a distributed network of various vehicles armed with sensors, actuators, technologies, and applications to connect and exchange data with each other over the Internet. The primary goal of IoV is to provide a vehicular platform to enable better communication and Quality of Service (QoS) for vehicles, pedestrians, and roadside infrastructure in real-time, through the use of Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N), and Vehicle-to-Cloud (V2C) channels. However, the increasing number of vehicular services poses serious concerns and challenges to the researchers: real-time traffic forecasting, service placement, security, reliability, and routing. The primary focus of this work is concerned with the challenge of multi-regional forecasting of multi-class traffic. These traffic forecasting models enable pro-activeness in systems by providing real-time and accurate predictions. Also, they can explore traffic densities over spatial and temporal domains for various vehicle types. However, current literature cannot provide forecasts for multi-region and multi-class vehicles at the same time. This study aims at enabling a proactive platform which could make decisions based on the integrated Graph Neural Network (GNN) and Gated Recurrent Unit (GRU) based traffic forecasting model, i.e., Spatio-Temporal GNN (STGNN) based traffic forecasting model. The STGNN-based traffic forecasting model uses the GNN and GRU models to explore spatial and temporal features of varying vehicular multi-class traffic densities. In GNN, spatial data consisting of multi-class traffic densities are utilized for the feature extraction that results in graph embeddings. In the GRU, these graph embeddings are utilized for temporal feature extraction. This approach enables the forecasting of multi-class vehicular traffic densities and the pro-activeness of an IoV platform. In addition, the performance results show that an intelligent platform can be built upon the proposed traffic forecasting model that is capable of inspecting complex and nonlinear traffic accurately.
Catalog of two-dimensional vortex patterns

The two-dimensional patterns that correspond to minima in the free energy of identical parallel vortices inside a rotating cylinder of liquid such as superfluid /sup 4/He are listed. All known stable patterns are listed for N = 1, 2,. , 30, 37, 50, where N is the number of vortices. The two lowest energy patterns are shown for all ''triangular'' numbers, N = 1+6(1+2+3+.), through N = 217. For each value of N the different patterns are ordered according to their relative free energy. 37 figures, 3 tables.