Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and Accurate Simulation of Origin-Destination Flow in Telecommunication Systems

Performance evaluation of telecommunication systems is challenging due to the complexity and large-scale of this problem. However, research on obtaining network measures is still scarce and difficult. Majority of previous research mainly applies the traditional simulation method to solve this problem. But in fact, the result of such traditional simulation methods may suffer from many shortcomings. To solve this problem, we adopt the max-plus method to the telecommunication system and establish simulation processes, inspired by the success of using max-plus algebra on the simulation of the production line. In this paper, we focus on the simulation of an origin-destination flow telecommunication system. The efficiency and accuracy of the proposed method are verified by comparative simulation with other simulation methods. On the premise of ensuring the accuracy of simulation, our method performs 20 times faster than traditional event scheduling method. Moreover, we analyze simulation results under different scheduling strategies to illustrate the accuracy of the simulation.
Object-based Loop Closure with Directional Histogram Descriptor

Loop closure can effectively eliminate the accumulated error in Simultaneous Localization and Mapping (SLAM). Appearance-based localization methods tend to fail under large viewpoint changes. In this paper, we propose a monocular SLAM system with object-based loop closure against viewpoint variation to achieve global localization. Objects are represented as cuboids and inferred from 2D object observation. On this basis, we construct a semantic topology graph from the object-oriented map and propose an efficient graph matching method with a directional histogram descriptor to detect the loop. Objects are matched if they satisfy general, graph and geometry verifications. By aligning the matched objects, the accumulated errors can be corrected, and the map can be updated. Experimental results demonstrate that the proposed method shows high accuracy and robustness under large viewpoint differences. Keywords: object mapping, loop closure, SLAM system
Assembler: A Throughput-Efficient Module for Network Driver of Edge Computing Gateways

With the rise of IoT and edge computing, terminal data generated by end devices is also growing explosively. The limited data transmission links between edge data centers and the end devices is becoming a performance bottleneck of IoT edge-based architectures. This paper proposes Assembler, a module integrated into network drivers of the gateway to realize high-throughput forwarding in small-packet-intensive scenarios. Motivated by observations that transmitting large packets obtains far more data throughput than transmitting small ones, Assembler assembles small packets received by the driver into large ones before the driver transmits them. Meanwhile, an adaptive assembling size algorithm is introduced to balance data throughput and packet throughput in scenarios with abrupt changes in data traffic. Our evaluation shows that the network driver incorporated with Assembler can achieve 1.75x the data throughput in small-packet-intensive scenarios compared to the state-of-the-art work. Furthermore, the adaptive assembling size algorithm can help the driver adapt to scenarios with changing data traffic and keep steady throughput.
Data-driven Probabilistic Constraint Elimination for Accelerated Optimal Power Flow

Optimal power flow calculations are run by operators to determine the cheapest operation of electrical grids given a particular system loading. Considering the increasing uncertainty in demand and availability of renewables, there is a need to develop faster solutions which can be run quickly for a variety of loading scenarios. The non-linear constraints in the optimal power flow problem increase computational costs. However, in reality, many of the constraints are non-binding and do not affect the optimum of the problem. In this paper, we propose a data-driven method for determining constraints that may be excluded from the formulation. The framework combines a latin-hypercube sampling method for generating training scenarios, with maximum likelihood estimation of Gamma distributions. We demonstrate that the method can reduce the solution time of a 24k network from 85.4 seconds to 11.1 seconds.
Efficient Clustered Network Telemetry based on Failure Awareness

Nowadays, various network telemetry technologies are proposed to monitor the network and detect failures accurately in real-time, which can be categorized into two types, including the proactive network telemetry (NT) and the passive one. The passive NT can monitor the network with low band-width overhead, yet, cannot guarantee full network coverage. The proactive one can achieve full coverage, yet, lead to high bandwidth cost. To deal with the problem, we propose a failure-aware clustered network telemetry approach, called CNT. CNT leverages the practical objective network operating experience: different network links have various failure probabilities. It is aware of the failure probabilities and assigns the network links into two clusters accordingly. Then, based on the original network topology, CNT designs an active path planning algorithm to connect the two clusters of links into two sub-topologies, respectively. Finally, CNT performs network telemetry with different cycles. We evaluate CNT with various simulation experiments. The results show that compared to existing proactive schemes, CNT can achieve comparable network coverage with less cost.
A Survey on Snake Robot Locomotion

Snake robots have been a topic of discussion among researchers for decades. They are potentially strong enough to bring substantial contributions to the fields which are unsafe/ narrow/ dirty/ hard reachable to human operators, such as inspections, rescue missions, firefighting, etc. Though the inventions of the wheel and legged mechanisms are amazing, they often fail when coming to these scenarios. Terrain adaptability is the vital essence of locomotion over constrained surfaces in biological snakes. But how this natural adaptability is accomplished in snake-like robots? Therefore, this paper focuses a study on factors behind the recreation of a physical snake, like the kinematics and dynamics modelling, mechanical design, and locomotion control approaches from existing literature. With their feature comparison, the simulators available for verifying the mathematical model and the feasibility of the mechanical design are also made for researchers new to the field.
Automatic Term Extraction with Joint Multilingual Learning

Automatic term extraction using deep learning achieves promising results if sufficient training data exists. Unfortunately, some languages may lack these resources in some domains causing poor performance due to under-fitting. In this study, we propose a joint multilingual deep learning model with sequence labeling to extract terms, trained on multilingual data and aligned word embeddings to tackle this problem. Our evaluation results demonstrate that the multilingual model provides an improvement for automatic term extraction task when it is compared with a monolingual model trained with limited training data. Although the improvement rate varies according to domain and the size of the data, our evaluation shows that the highest improvement in F1-score is 10.1 % in the domain of Computer Science, the least improvement is 7.6% in the domain of Electronic Engineering. Our multilingual model also achieves competitive results when it is compared with a monolingual model trained with sufficient training data.
Lightweight Shufflenet Based CNN for Arrhythmia Classification

Recent advances in artificial intelligence (AI) and continuous monitoring of patients using wearable devices have enhanced the accuracy of diagnosing various arrhythmias, from the captured Electrocardiogram (ECG) signals. Achieving high accuracy when using Deep Neural Network (DNN) for ECG classification is accomplished at the cost of compute and memory intensive operations, thus limiting its deployment to devices with high computing capabilities, and makes it unsuitable for wearable edge devices. To facilitate the deployment of deep neural networks on wearable mobile edge devices with limited resources, a lightweight Convolution Neural Network (CNN) model based on the ShuffleNet architecture is proposed and implemented as a solution in this paper. A sliding window of variable stride is used to increase the number of under-represented classes in the database. Moreover, a novel encoding scheme is employed for labelling and training test set samples, allowing the model to detect multiple classes in one ECG segment. A loss function (Focal loss) that proved to be effective when applied for DNN training on an imbalanced dataset was also explored in this work. The proposed model outperformed traditional CNN with 9x less trainable parameters and improved the F1-score by 2%.
Electroweak bubble wall speed limit

In extensions of the Standard Model with extra scalars, the electroweak phase transition can be very strong, and the bubble walls can be highly relativistic. We revisit our previous argument that electroweak bubble walls can “run away,” that is, achieve extreme ultrarelativistic velocities γ ∼ 1014. We show that, when particles cross the bubble wall, they can emit transition radiation. Wall-frame soft processes, though suppressed by a power of the coupling α, have a significance enhanced by the γ-factor of the wall, limiting wall velocities to γ ∼ 1/α. Though the bubble walls can move at almost the speed of light, they carry an infinitesimal share of the plasma's energy.
Spin transport parameters of NbN thin films characterized by spin pumping experiments

We present measurements of ferromagnetic-resonance - driven spin pumping and inverse spin-Hall effect in NbN/Y3Fe5O12 (YIG) bilayers. A clear enhancement of the (effective) Gilbert damping constant of the thin-film YIG was observed due to the presence of the NbN spin sink. By varying the NbN thickness and employing spin-diffusion theory, we have estimated the room temperature values of the spin diffusion length and the spin Hall angle in NbN to be 14 nm and -1.1 10-2, respectively. Furthermore, we have determined the spin-mixing conductance of the NbN/YIG interface to be 10 nm-2. The experimental quantification of these spin transport parameters is an important step towards the development of superconducting spintronic devices involving NbN thin films.
Sufficient Conditions for Robust Frequency Stability of AC Power Systems

This paper analyses the frequency stability of ac grids in the presence of non-dispatchable generation and stochastic loads. Its main goal is to evaluate conditions in which the system is robust to large, persistent active power disturbances without recurring to time-domain simulations. Considering the ongoing energy transition to more renewable sources, defining robustness boundaries is a key topic for power system planning and operation. However, much of the research on long-term studies has not dealt with robust dynamic constraints, while short-term analyses usually depend on time-consuming simulations to evaluate nonlinearities. To bridge this gap, the authors derive an algebraic equation that provides sufficient conditions for robust frequency stability in ac power systems and a relationship among four key quantities: the maximum active power perturbation, the minimum system damping, the steady-state and the transient frequency limits. To achieve this goal, it uses a nonlinear average model of the ac grid and Lyapunov's direct method extended by perturbation analysis requiring only limited knowledge of the system parameters. The algebraic calculations are validated using time-domain simulations of the IEEE 39-bus test system and results are compared to the traditional Swing Equation model.
2050 Power Grids: Control Through Demand

This paper contrasts today's electricity markets, where fossil units supply most of the energy and flexibility services (FS), with a future year where renewables provide almost all of the energy and the load provides most of the FS. A co-optimized expansion planning application is used to illustrate. Results show a diverse portfolio of demand response types are needed to achieve this.
Self-Recalibrating Micromanipulator System for Resilient Robotic Vision-Based Control

A self-recalibrating micromanipulator system is developed in this work to perform vision-based control under unpredictable scenes and poor imaging conditions. To ensure resilient vision-based control in such challenging conditions, we proposed a self-recalibrating mechanism that combines the estimates from both the robot manipulator and microscope vision. Our method is demonstrated for micropipette cell aspiration. In the initialization stage, the system automatically locates the tip and selects the templates for visual tracking. At the self-calibration phase, accurate tracking of the tip is achieved by the background subtraction template matching (BSTM) method in order to estimate the calibration matrix in complex environments. During the operation, the control error is detected according to the visual information and the self-recalibration is executed when the error exceeds the threshold value. Experimental analysis shows that the BSTM method for self-calibration achieves accurate tracking with an average error of less than 1 pixel (0.18 μm). And the system has an average control error of 4.18 pixels (0.75 μm) under complex calibration backgrounds, satisfying the accuracy requirements for cell aspiration. Moreover, the system achieved stable tracking in complex operating environments, including cell interaction, occlusion, tip leaving the focal plane or beyond the field-of-view. By designing a self-recalibrating micromanipulator equipped with resilient vision-based control system, we hope to robotize the procedures for the study of cell mechanics during micropipette aspiration and pave the path for the advancement of robotic micromanipulation in biomechanical research.
EAFT: Evolutionary Algorithms for GCC Flag Tuning

Due to limited resources, some methods come to the fore in finding and applying the factors that affect the working time of the code. The most common one is choosing the correct GCC flags using heuristic algorithms. For the codes compiled with GCC, the selection of optimization flags directly affects the speed of the processing, however, choosing the right one among hundreds of markers during this process is a resource consuming problem. This article explains how to solve the GCC flag optimization problem with EAFT. Rather than other autotuner tools such as Opentuner, EAFT is an optimized tool for GCC marker selection. Search infrastructure has been developed with particle swarm optimization and genetic algorithm with diffent submodels rather than using only Genetic Algorithm like FOGA.
A Novel Distributed Data-Driven Strategy for Fault Detection of Multi-Source Dynamic Systems

In this brief, a distributed data-driven fault detection method based on sparse integrated dynamic principal component analysis is designed to enhance the fault detection performance of multi-source dynamic systems. First, by dividing the complex system into multiple sub-sources according to the structural principle, the fusion and complementation of process mechanism knowledge and data-driven are realized. Second, by introducing the Kronecker product covariance structure, the individual modes within each sub-source as well as joint modes shared between sub-sources are captured. Notably, by developing a truncated contribution power method, key variables are selected and the interpretability of principal components is improved. Finally, experimental results demonstrate that the proposed strategy can significantly improve the detection performance compared with the existing PCA-based methods.
Energy Storage Integration into Fast Charging Stations Installed on e-Highways

With the development of electric mobility, today's population is preparing to face numerous changes in the way they move around, use vehicles and live in cities. The need to electrify transport stems from an ever-increasing need for energy efficiency and, simultaneously with the development of Renewable Energy Sources (RESs), smart distribution networks and a sustainable development model. The purpose of the work is to evaluate different energy storage alternatives for integration into Fast Charging Stations (FCS) installed on highways aiming to exploit renewable overgeneration.
Detecting Crossfire-Attack Hosts in Search Phase

A new type of Distributed denial of service (DDoS) attack, called a crossfire attack (CFA), has appeared, whereby attackers prevent packets from arriving at servers in the target area by sending many packets on some links connecting the target area and other areas and overloading those links. In the CFA, bot hosts repeatedly send traceroute packets to multiple servers in the target area within a short time frame to select the target links prior to the attack. Therefore, to prevent the CFA, it might be effective to filter all the traceroute packets sent within a time interval less than a threshold. However, legitimate hosts which send traceroute packets within a short time interval will be also detected as bot hosts. In this paper, we propose a method using two thresholds to detect the bot hosts of a CFA: one threshold is used for detecting the target servers of traceroutes; the other threshold is used for detecting the bot hosts. By using two thresholds, we can expect to detect bot hosts while avoiding false identification of legitimate hosts. We also propose a method for optimally designing detection thresholds that maximize the detection accuracy against bot hosts from the given upper limit of false identification probability of legitimate hosts.
Ranking Actionable Mental Health Advice through a Personalized Video Recommendation System

In order to improve the quality of life in today’s digital culture, one of the most important areas of health to consider is mental health. Over the past several years, mental health has become increasingly important as the digital landscape has gotten more complicated, and as more and more people seek ways to relax, connect with others, and find mental balance in their life. While there have been advances in technology, particularly in information retrieval and recommendation systems, very few of these advances have been applied to the mental health industry. In this paper, we bridge this gap by introducing a ranking system that suggests personalized videos for users with the intention of helping them improve their mental health. By updating the relative ranking of videos by marginal improvement for different users, then aligning the distribution of videos depending on the areas that the user needs the most help with, the website helps provide personalized and actionable mental health advice.
Analysis on Induced Voltages in Wind Farms Close to Distribution Lines on Frequency-Dependent Soil

This paper investigates the impact of the frequency-dependent soil electrical parameters on the transient voltages generated along the wind tower (WT) and on the induced voltages on an overhead distribution line (DL) close to the WT for a lightning striking at the WT. For this objective, a full-wave electromagnetic commercial software XGSLab® based on the Partial Element Equivalent Circuit (PEEC) is used. The frequency-dependent soil characteristic is incorporated into the software using the CIGRE recommended expressions for the resistivity $\rho(f)$ and relative permittivity $\varepsilon_{r}(f)$. The analysis is carried out for three different soil resistivities $\rho_{0}$ of 1000, 2500, and $5000\ \Omega \mathrm{m}$. All the transient responses are compared with those computed assuming the ground modeled by its frequency-constant parameters ($\rho,\ \varepsilon_{r}$). Results demonstrated that the transient voltages at the WT base are significantly reduced when frequency-dependent soil is assumed, however, the voltage peaks at the injection point have presented no expressive impact for both soils. The induced voltages have a pronounced variation as the ground becomes more conductive with the increasing frequency for the frequency-dependent soil.
A Robust Controller Design for Mitigating Control Loop Interactions in Multi-VSC Systems Built by Multiple Vendors

Control loop interactions among voltage-sourced converters (VSCs) sharing the same alternating current (AC) system and the disruptive impacts of these interactions on the stability of multi-VSC systems have been widely studied in the literature. This paper proposes a controller design method for mitigating the negative impact of these interactions on the stability of multi-VSC systems. The proposed controller design approach incorporates the external interactions as uncertainties into the system model and obtains the outer controllers of individual converters using $H_{\infty}$ controller synthesis. The controllers are designed based on the model of individual VSCs rather than the exact model of the interconnected multi-VSC system, and thus they require only the exact model of individual VSCs and a reduced-order model of coupling dynamics. This modeling requirement ensures design confidentiality in multi-VSC systems built with multiple manufacturers due to the individual design of converter controllers, which does not require the exact model of adjacent converters. The performance of the proposed robust controller is evaluated using time-domain simulations and eigenvalue analysis in MATLAB/SIMULINK.
Skill Transfer for Surface Finishing Tasks Based on Estimation of Key Parameters

This paper presents an approach for transferring surface finishing behaviors to new surfaces while preserving the quality of the process. The idea is to let a human demonstrate the desired grinding behavior on a planar surface and subsequently generate an equivalent grinding behavior on new surface geometry. The transfer of the process quality is accomplished by imitating the material removal rate of a human. This is achieved with an adaptive control that relies on the online estimation of the material removal rate, which depends on the contact area, normal force, tool speed, and tool wear. The proposed approach is verified in simulation and experimentally validated on the grinding of planar and curved surfaces.
A Broadband Radial Power Combiner Using a Dipole Antenna-Based TM Mode Transducer

A TM01 mode transducer based on dipole antenna is proposed to design a broadband and low-loss radial power combiner at Ka-band (26.5–40 GHz). It is an essential component in the radial power combiners to convert the electromagnetic modes between rectangular and circular waveguides. The latter feeds the cylindrical cavity in TM01 mode of which the fields exhibit a circular symmetry. The proposed TM01 mode transducer consists of the dipole antenna, microstrip, and probe on a dielectric substrate. It is an in-line mode transducer. In other words, the rectangular and circular waveguides are aligned along the same axis of wave propagation. Therefore, it does not require the waveguide components such as tee junctions, steps, and bends which are normally used in the traditional mode transducers and increase the complexity, size, and loss of entire power combiner. The eight-way radial junction is also designed using a cylindrical cavity, where the rectangular waveguides are placed along the circumference of the cylindrical cavity. The dimensions of each component are determined based on the full-wave electromagnetic simulations. The designed eight-way radial power combiner consisting of the TM01 mode transducer and radial junction is fabricated in four-split blocks by mechanical machining. In the measurements, the proposed power combiner exhibits a broadband and low-loss performance under a compact size while maintaining the magnitude and phase balances between the eight ports.