Miracle-dz/UniFAR-FTUs huggingface dataset:
Wall shear stress from jetting cavitation bubbles: influence of the stand-off distance and liquid viscosity

Abstract We study systematically the cavitation-induced wall shear stress on rigid boundaries as a function of liquid viscosity $\mu$ and stand-off distance $\gamma$ using axisymmetric volume of fluid (VoF) simulations. Here, $\gamma =d/R_{max}$ is defined with the initial distance of bubble centre from the wall $d$ and the bubble equivalent radius at its maximum expansion $R_{max}$. The simulations predict accurately the overall bubble dynamics and the time-dependent liquid film thickness between the bubble and the wall prior to the collapse. The spatial and temporal wall shear stress is discussed in detail as a function of $\gamma$ and the inverse Reynolds number $1/Re$. The amplitude of the wall shear stress is investigated over a large parameter space of viscosity and stand-off distance. The inward stress is caused by the shrinking bubble and its maximum value $\tau _{mn}$ follows $\tau _{mn} Re^{0.35}=-70\gamma +110$ (kPa) for $0.5<\gamma <1.4$. The expanding bubble and jet spreading on the boundary produce an outward-directed stress. The maximum outward stress is generated shortly after impact of the jet during the early spreading. We find two scaling laws for the maximum outward stress $\tau _{mp}$ with $\tau _{mp} \sim \mu ^{0.2} h_{jet}^{-0.3} U_{jet}^{1.5}$ for $0.5\leq \gamma \leq 1.1$ and $\tau _{mp} \sim \mu ^{-0.25} h_{jet}^{-1.5} U_{jet}^{1.5}$ for $\gamma \geq 1.1$, where $U_{jet}$ is the jet impact velocity and $h_{jet}$ is the distance between lower bubble interface and wall prior to impact.
A Novel Wideband Bandpass Filter by ISGW

A novel ISGW wideband bandpass filter was proposed for 24.7 ~ 36.2 GHz frequency band signal transmission. The filter was implemented by using five coupled stepped-impedance resonators. In this design, the first three resonant frequencies of the SIR are appropriately adjusted and placed in the middle of the structure of the broadband bandpass filter. Then, two SIRs are added to the input and output ports on both sides to generate resonance out of the band, so as to improve the ability of its out-of-band suppression. The five SIRs are coupled together with parallel coupling lines to form a broadband width. By optimizing the filter, good broadband performance is achieved theoretically. The return loss is greater than 12.5 dB and the group delay change is less than 0.45 ns in the whole wide passband.
Reliable constrained control of DC-DC Buck-Boost converter based on signal compensation and inner-outer convex combination optimal control

This paper considers the constrained reliable tracking control problem for a class of Buck-Boost converters. The proposed control strategy is consisted of four parts. First, a signal compensation policy is designed to eliminate the influences from fault/attack and unknown disturbances. Second, an inner robust tracking control policy is designed to basically guarantee the closed-loop stability of tracking error systems when the constraints are not activated. Third, an outer feasible control is constructed based on invariant set theory and it is used to guarantee the feasibility of whole control law. Fourth, the convex combination optimization method is applied to optimize the constrained control performance. It should be noted that this method only involves a linear programming, which has less online computation burden than the conventional predictive control method. Finally, the effectiveness of such reliable control strategy is validated by a simple numerical example.
Research and Design of Greenhouse Environment Monitoring System Based on NB-IoT

The application of Internet of Things technologies in greenhouses is a crucial method to improve agricultural production efficiency and quality. A greenhouse environment monitoring system is designed based on NB-IoT (Narrowband Internet of Things). The system can detect the air temperature, relative humidity, illuminance, and carbon dioxide concentration in greenhouses in real-time and transmit the data to the OneNET cloud platform in a wireless way. Growers can browse the web page through mobile terminals, such as mobile phone, to observe the greenhouse environment information. The experimental results show that the system can detect the greenhouse environment information accurately and run stably and reliably. The automation level of greenhouse environmental monitoring is improved significantly, which lays a good foundation for the automatic control of greenhouse environment.
Research on Collaborative and Confrontation of UAV Swarms Based on SAC-OD Rules

With the introduction of the new generation of artificial intelligence technology into the military field, unmanned aerial vehicle (UAV) swarm operation, as an important form of intelligent operation, has attracted the attention of various countries. In this paper, we consider the problem of coordinated confrontation between UAV swarms in the plane area. An improved “SAC-OD” rules and combat strategy of UAV swarm are employed to establish the decision-making model for UAV swarm conflict where each UAV in the swarm is regarded as an independent individual. With the introduction of SAC-OD, each UAV keeps on interacting with its neighboring environment and the UAV swarm conflict is dynamic. Simulation experiments are conducted using MATLAB and the results demonstrate the effectiveness of the built decision-making model for UAV swarm conflict.
Text Visualization and Close Reading for Journalism with Storifier

Journalistic inquiry often requires analysis and close study of large text collections around a particular topic. We argue that this practice could benefit from a more text- and reading-centered approach to journalistic text analysis, one that allows for a fluid transition between overview of entities of interest, the context of these entities in the text, down to the detailed documents they are extracted from. In this context, we present the design and development of Storifier, a text visualization tool created in close collaboration with a large francophone news office. We also discuss a case study on how our tool was used to analyze a text collection and helped publish a story.
Tracing Relevant Twitter Accounts Active in Cyber Threat Intelligence Domain by Exploiting Content and Structure of Twitter Network

Due to the enormous volume of data and rate of data generation on Twitter, a challenging task is to trace user accounts to monitor these as instances of Cyber Threat Intelligence (CTI). In this paper, we propose a novel approach for cyber threat-associated user accounts tracing in the Twitter data stream based on the ranking of users according to their contextual relevance and topological information extracted from finding user communities in the Twitter network. In our approach, we use both structural information of the graph network and user accounts’ tweet contents to find relevant user accounts concerning previously identified seed user accounts. Our proposed method outperforms over two relevant user recommendation methods on an annotated data of CTI related Twitter user accounts in tracing relevant user accounts as instances of cyber-threat intelligence.
Fault Localization-Guided Test Data Generation Approach for Novice Programs

Online judgment (OJ) systems can automatically evaluate program results by executing corresponding test cases. Among them, automatic fault localization techniques can assist students in localizing the root cause of program faults. However, artificially constructing many valid test cases is a very time-consuming and challenging task. Therefore, we propose an approach for automatically generating test data to solve this problem. Specifically, we utilize the real faulty novice programs from the OJ system as training data, and then we leverage the search algorithm to evolve the test data according to the fitness function. To evaluate the effectiveness of our approach, we conduct experiments on 1,136 faulty novice programs collected from the OJ system. The experimental results show that the test data generated by our approach can achieve the same fault detection ability as the artificially designed test data while has better fault localization accuracy for most fault localization techniques.
Clinical Investigation as an Iteration Tool for the Design of Low-Risk Medical Devices: A Case Study of an IV Drip Monitoring System

The design of a medical device comprises multiple stages, beginning with task clarification, and followed by conceptual design, embodiment design, detailed design, testing, and a clinical study. Involvement of stakeholders at every stage is essential for translating an idea into a usable product. In this work, we adopt a ‘user-in-the-loop’ approach to Engineering Design (ED), to explore the use of clinical investigation as an iteration tool. An IV drip monitoring system designed for resource-constrained settings, proposed in our earlier work, is used as a case study to illustrate this. A basic prototype was investigated at multiple clinical settings with requisite approvals. Device operation by clinicians was observed, which elicited embodiment, ergonomic and human-machine interface related issues. While establishing device efficacy, the investigation also gathered significant inputs for subsequent design stages. The adopted approach ensures that only validated designs proceed to the next stage. However, this can only be undertaken for low-risk medical devices with appropriate precautions.
Optimal Task Allocation Policy for Energy Efficient D2D-enabled Mobile Edge Computing

The integration of mobile edge computing (MEC) and device to device communication (D2D) is considered one of most promising techniques to realize reliable communication and computation performance. In this paper, we present a dynamic task allocation policy for saving energy in D2D-MEC system, in which two users have their dynamically arrived tasks. Specifically, the transmission energy consumption and computing energy consumption is minimized, which is constrained by task causality and task completion within a limited time-interval composed of multiple slots. The task allocation of local computing and task offloading is optimized jointly. By using convex optimization methods, the optimal solution to the sum-energy minimization problem is obtained. Simulation results are provided to confirm that the proposed dynamically task allocation can significantly save energy, when comparing with other schemes.
Optimal Transmission Strategy for Solar Harvesting Communication

We consider a solar harvesting point-to-point communication system, where the transmitter powered by solar energy and equipped with a battery of finite capacity communicates over an AWGN channel. The solar harvesting rate is described by the real data-based quadratic solar power model, which characterizes the solar power levels by a quadratic equation with the time of the day. By analyzing the effect of battery capacity, we consider two cases and propose an optimal transmission strategy for each case to maximize the channel capacity. Furthermore, we prove the optimality of the proposed strategy through the proof by contradiction. We find that for a certain amount of energy, longer transmission time and more uniform energy allocation for transmission lead to greater capacity.
Leader-following consensus of linear multi-agent systems with strict negative imaginary uncertainties

This paper considers the leader-following consensus problem of linear multi-agent systems with strict negative imaginary (SNI) uncertainties. It is assumed that the followers have homogeneous nominal dynamics while subject to different model uncertainties with SNI property, leading to weakly heterogeneous multi-agent systems. In the case of undirected topology, the control protocol is designed for multi-agent systems based on the relative states of neighbouring followers and the leader. It is shown that the control protocol can achieve leader-following state consensus for networked systems that followers have SNI uncertainties. A necessary and sufficient condition for the nominal networked system to be negative imaginary (NI) and satisfying gain condition is presented by decoupling the feedback controller from communication topology. Furthermore, a sufficient condition in terms of linear matrix inequalities (LMI) is derived for the leader-following consensus controller. Simulation example is provided to validate the effectiveness of the results.
Computer Vision Based Conveyor Belt Congestion Recognition in Logistics Industrial Parks

Various automatic and intelligent technologies have been employed to facilitate the logistics operations in recent years promoted by the trend of industry 4.0. As the most frequently used automatic equipment in the logistics industrial parks, conveyor belt plays a critical role on the efficient sorting of packages. Due to the reasons like non-ideality of scheduling and inappropriate operations, conveyor belts can potentially be impacted by congestion, thereby inducing a series of consequences such as delay, lose and damage of packages. In order to tackle these issues, a computer vision-based method is proposed to recognize the congestion on conveyor belts. Other than the popular deep learning-based techniques, the proposed method comprehensively analyzes the characteristics of conveyor belt congestion using statistical approaches and extract informative features for decision making. Finally, the proposed method is evaluated on the data collected from real package sorting scenarios, where it outperforms the deep learning and conventional pattern recognition-based methods on both detection accuracy and capability of generalization.
A 40-nm 202.3nJ/Classification Neuromorphic Architecture Employing In-SRAM Charge-Domain Compute

In recent years, temporal neural network (TNN) emerges as a promising direction of development of spiking neural network (SNN) for low power applications. This paper proposes a compact digital-analog hybrid SRAM in-memory computing TNN architecture and circuits, using bionic low-power spike communication and the synaptic array operation based on charge domain computing. Simulation shows the proposed circuits achieve 94.73% accuracy on MNIST hand-written character recognition, consume only 202.3nJ per inference, and occupy a small area of 0.6mm2.
A Double-Layer, High-Gain and Dual-Polarized Huygens’ Metalens with Complete Phase Coverage

A novel dual-polarized Huygens’ metasurface is proposed. The meta-atom is composed of two symmetric metallic patterns printed on both sides of a dielectric substrate. Based on induced magnetism, the magnetic dipoles is formed in the meta-atom and satisfies a balance with the electric dipoles, which achieve resonant transmission effect. Hence near-unity transmission magnitude and a 360° transmission phase shift are achieved. An ultrathin dual-polarized metalens comprised of 27×27 meta-atoms with size 124.2×124.2 mm2 is designed. The simulated results show that the proposed metalens exhibits excellent antenna performances. The simulated maximum gain is 29dbi at 27.6GHz, resulting in an aperture efficiency of 48.1%. In addition, the simulated 3-dB gain bandwidth is 26–29.8 GHz or 13.8%.
Design of Planar Inverted-F Antenna (PIFA) for 5G Communication Indoor Micro-base station

In this paper, a planar inverted F antenna is designed for indoor distributed micro-base station of 5G communication. The overall size of the antenna is 16 mm × 15.3 mm × 10 mm, and the antenna works in the 3.5 - 3.6 GHz, which band number named n78. The antenna has the advantages of miniaturization, easy machining and convenient installation. The simulation results of ANSYS HFSS 2020 shows that in the working bandwidth range, when the central frequency of the designed antenna is 3.55 GHz, the S11 is -23.6 dB, and the VSWR is 1.1, which meets the requirements of impedance matching. In terms of directivity, the antenna coverage also meets the requirements of 5G micro-base station.
A Miniaturized Signal Processing Circuit with High Near-distance Suppression Abilities for 60 GHz FMCW Radar Applications

In this paper, a miniaturized signal processing circuit with high near-distance suppression abilities is designed. The circuit consists of an amplifier, a three-stage active bandpass filter, a detector diode circuit and a two-order decision circuit. Experimental results show that this circuit can fully satisfy the requirement of 60 GHz radar, enabling its system to detect (suppress) the target within 9-11 m (0.1-9 m).
An Adaptive Dynamic Programming Algorithm Based on ITF-OELM for Discrete-Time Systems

Adaptive dynamic programming (ADP) is a kind of intelligent control method, and it is a non-model-based method that can directly approximate the optimal control policy via online learning. The gradient algorithm is usually used to update weights of action networks and critic networks, however it is clear that gradient descent-based learning methods are generally very slow due to improper learning steps or may easily converge to local minimum. In this paper, in order to overcome those disadvantages of gradient descent-based learning methods, a novel ADP algorithm based on initial-training-free online extreme learning machine (ITF-OELM), in which the critic network link weights of hidden nodes to output nodes can be obtained by least squares instead of gradient algorithm, is introduced. Finally, the ADP algorithm based on ITF-OELM is tested on a discrete time torsional pendulum system, and simulation results indicate that this algorithm makes the system converge in a shorter time compared with the ADP based on gradient algorithm.
Improved Multi-Motor Synchronization Control of Underwater Robot Based on Virtual Shaft

Underwater robot is the main equipment used to develop underwater resources. This paper solves the problem of speed and load imbalance of multi-motor when the underwater robot works in the random interference environment. A multi-motor synchronization control strategy based on the virtual shaft is proposed. A normalized proportional coefficient distribution method is constructed to achieve all the different speed motor cooperation. Furthermore, it solves the problems of low control accuracy, and slow dynamic response caused by multi-motor cooperation. A backstepping sliding mode controller with dynamic surface is constructed. Combining backstepping control and sliding mode control to eliminate the underwater random nonlinear disturbance. The simulation results demonstrate the multi-motor propulsion under the control of this scheme has good rapidity and robustness when dealing with random disturbance and large instantaneous load disturbance.
Exploring the use of Deep Reinforcement Learning to allocate tasks in Critical Adaptive Distributed Embedded Systems

Critical Adaptive Distributed Embedded Systems (CADES) must carry out a set of funcionalities while fulfilling their associated real-time and dependability requirements. Moreover, they must be able to reconfigure themselves in a bounded time as the operational context changes. Finding a proper configuration can be non-trivial and time-consuming. Several studies have proposed Deep Reinforcement Learning (DRL) approaches to solve combinatorial optimization problems. In this paper, we explore the application of such approaches to CADES by solving a simple tasks allocation problem using DRL and comparing the results with three popular heuristics. The results show that DRL beats two of them and gets very close to the third, while requiring significantly less time to generate a solution.
Determining Confused Brain Activity from EEG Sensor Signals

Human brain, being the most remarkable logic processing unit, sometimes fails to act properly. Often the task at hand becomes too cumbersome for the brain to perceive, which is known as confusion in simple terms. In some certain critical moments, it is expected for the brain to perform optimally. Hence, being able to determine the confused state of brain and providing arrangements to re-focus the brain is of utmost significance. Electroencephalography (EEG) sensors captures the electrical activity of the brain, and therefore can be used to infer the state of the brain. In this work a novel algorithm has been presented that extracts the spectral features from multi-band EEG signals using an Convolutional Neural Network (CNN) and reconciles the temporal signatures, leading to the classification of confused brain state. We find our proposed framework yields an accuracy of 82.92% which outperforms some popular methods. Our claim is supported by a benchmark dataset using cross validation.
UAV-aided Secure NOMA Transmission via Trajectory and Resource Optimization

The application of NOMA in UAV networks is an effective solution for communications. However, the security risk becomes more serious with the LoS channels and higher transmit power for weaker users in NOMA-UAV networks. In this paper, a UAV-assisted NOMA transmission scheme is proposed to achieve secure downlink transmission via artificial jamming, where a UAV flies straightly to serve multiple ground users in the presence of a passive eavesdropper. Only the closest users to the UAV can be connected in each time slot to achieve high LoS probability. To balance the security and transmission performance, the tradeoff between the jamming power and the sum rate is investigated by jointly optimizing the power allocation, the user scheduling and the UAV trajectory. To address the problem, we first decompose the problem into two subproblems of power allocation and trajectory optimization. Then, they are transformed into convex ones for an iterative algorithm via the first-order Taylor expansion. Finally, simulation results are presented to show the effectiveness of the proposed scheme.
Improved Adaptive Feedback Scheduling Algorithm based on LATE in Hadoop Platform

Hadoop is the mainstream cloud platform for data analysis and processing. Job scheduling algorithm directly affects job response time and system resource utilization. The research and improvement of scheduling algorithm has always been an important topic. Based on the original LATE scheduling algorithm, this paper proposes an adaptive feedback LATE (AF-LATE) algorithm to improve the autonomous selection and feedback of execution nodes and backup tasks. In the process of scheduling, according to the load type of the task, the idle node with highest ratio of task success rate to node load is selected to back up the backward task. At the same time, the feedback of the task and node working data is obtained to dynamically adjust the fast and slow node set. The algorithm improves the resource utilization and load balance, and improves the reliability of task execution and reduces the running time of scheduling algorithm. In this paper the experimental environment is built to verify the algorithm. The results show that the scheduling algorithm is more reasonable in judging backward tasks and selecting execution nodes in heterogeneous environment, which can shorten the response time of jobs, improve the utilization and efficiency of the cluster, and can adaptively adjust the performance of execution nodes to improve the cluster reliability.
A Generalized Broadband Sip Package for Microwave Transceiver Module

Broadband microwave transmission and receiving modules are the core components of radar and electronic Countermeasure (ECM) systems. In order to meet requirement of small size and light weight, a generalized broadband Sip package for microwave transceiver module of radar and ECM system has been developed. It has the advantages of miniaturization, lightweight and high reliability. Performance of the generalized broadband Sip package is tested. Results show that it has a broadband operation frequency covering S, C, X and Ku band. Transmission loss of the generalized Sip package is less than 3dB at 18 GHz and reflection standing wave ratio (SWR) between 2 GHz and 18 GHz is less than 2.