Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Electrical Characteristics in a Ferroelectric L-Patterned Gate Dual Tunnel Diode TFET

This paper presents a ferroelectric L-patter- ned gate TFET with heavily doped (p++ type) dual tunnel diodes (DTDs), which analyzes the concept of negative capacitance as well as vertical tunneling. The tunnel junction and the channel direction are perpendicular that facilitates a broad area of the tunnel junction. Furthermore, ON current is enhanced due to the introduction of <inline-formula> <tex-math notation="LaTeX">$n^{+}$ </tex-math></inline-formula> pocket. To exaggerate the <inline-formula> <tex-math notation="LaTeX">$I_{ \mathrm{\scriptscriptstyle ON}}/I_{ \mathrm{\scriptscriptstyle OFF}}$ </tex-math></inline-formula> ratio, the device architecture is designed systematically by optimizing the ferroelectric and pocket thickness. The cumulated holes are extricated by the tunnel current produced by the DTDs, reducing the kink effect. An enhanced ON current of the order of 10<sup>−5</sup> A/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> with a minimal subthreshold swing (SS) of 29 mV/decade is achieved. The characteristics of the proposed TFET structure is compared with the existing TFET designs, and the proposed design proves to be an appropriate device for high performance and ultra-low-power applications.
Research Status of 6LoWPAN in the Field of Internet of Things

The increasing number of wireless sensor nodes brings more application markets and challenges to the Internet of things (IoT). The traditional IPv4 address resources cannot meet the needs of the development of the IoT, so we need a communication mechanism with better compatibility and scalability. With the proposal of the 6LoWPAN protocol, the application of 6LoWPAN to the communication network of the IoT has become the focus of research. This paper studies the research status of 6LoWPAN in the field of Internet of things in the past four years, and analyzes the system architecture, system composition and monitoring parameters of 6LoWPAN through different application scenarios. Experiments in related literatures show that 6LoWPAN has stable network transmission performance and can be used for precise monitoring and management in combination with web applications. In addition, RPL routing protocol security algorithm can be one of the future research directions.
An Expert Lens on Data Quality in Process Mining

The success of a process mining project is highly dependent on the quality of the event log data, the degree to which quality issues are detected, and the way they are resolved. The detection and resolution of data quality issues requires a systematic approach that is aware of the organisational context in which event log data is created. To this end, the Odigos framework has been developed in prior work. The focus of this paper is the validation of this framework through semistructured interviews with a range of experts in process mining. The experts confirmed the utility of the framework, provided valuable insights into data quality in practical settings, and suggested enhancements to the Odigos framework.
Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication

Along with extensive and successive applications, emotion recognition based on electroencephalogram has attracted more and more researchers. However, how to acquire sufficient and high-quality real emotion electroencephalogram to train emotion recognition model has always been a bottleneck issue in the electroencephalogram-based emotion recognition research field. Because the subject’s emotion is easy to be affected by many factors and subjects do not always evoke emotion well, it’s very hard to determine whether the real emotion electroencephalogram appears or not in the experiments. On the contrary to electroencephalogram, facial expression obtained without any intentional control is easy to be recognized by computers, and is also one of the primary and reliable cues for understanding emotions. Inspired by such a common sense, we proposed an approach to building up ground truth electroencephalogram dataset with visual indication. Firstly, the relationship between facial expression and electroencephalogram is analyzed in details from the viewpoints of biophysics and correlation. Secondly, based on the analysis result that when the subject’s facial expression is evoked, it should be accompanied by the emotional electroencephalogram corresponding to it, the method of building up the ground truth electroencephalogram dataset with visual indication is put forward along with the computer automatic implementation. Thirdly, we have built up a ground truth electroencephalogram dataset which covers 3 kinds of emotions (i.e. joy, sadness, and neutral) of the subjects who are undergraduate and graduate students from Minzu University of China. Lastly, the validation of the established dataset is tested by the comparative experiments between the long short term memory emotion recognition models trained with two different datasets respectively. One dataset contains both truth and false electroencephalograms and another one only contains the truth data.
Clamping Diode-Based Auxiliary Circuit for Suppression of Voltage Ringing in EV Charger

With the technological advancement in the electric vehicle (EV), EV chargers become more prominent. As the dc–dc power conditioning unit plays an important role in on-board chargers. In this article, an isolated full-bridge dc–dc converters (FBDC) with diode clamping circuit operating with modified pulsewidth modulation (MPWM) is proposed. The proposed configuration eliminates the voltage ringing at the primary and secondary end of the high frequency transformer (HFT), as well as voltage ringing across the rectifier diodes. The proposed configuration saves the overrating of the system rectifier output stage as compared to conventional FBDC. The MPWM provides zero voltage switching during full-load operation. The theoretical methodology first verified on matlab/Simulation, later a 500 W, 80 kHz laboratory prototype is developed for validating the results with 92.10% efficiency.
Selective Protection for Sparse Iterative Solvers to Reduce the Resilience Overhead

The increasing scale and complexity of today's high-performance computing (HPC) systems demand a renewed focus on enhancing the resilience of long-running scientific applications in the presence of faults. Many of these applications are iterative in nature as they operate on sparse matrices that concern the simulation of partial differential equations (PDEs) which numerically capture the physical properties on discretized spatial domains. While these applications currently benefit from many application-agnostic resilience techniques at the system level, such as checkpointing and replication, there is significant overhead in deploying these techniques. In this paper, we seek to develop application-aware resilience techniques that leverage an iterative application's intrinsic resiliency to faults and selectively protect certain elements, thereby reducing the resilience overhead. Specifically, we investigate the impact of soft errors on the widely used Preconditioned Conjugate Gradient (PCG) method, whose reliability depends heavily on the error propagation through the sparse matrix-vector multiplication (SpMV) operation. By characterizing the performance of PCG in correlation with a numerical property of the underlying sparse matrix, we propose a selective protection scheme that protects only certain critical elements of the operation based on an analytical model. An experimental evaluation using 20 sparse matrices from the SuiteSparse Matrix Collection shows that our proposed scheme is able to reduce the resilience overhead by as much as 70.2% and an average of 32.6% compared to the baseline techniques with full-protection or zero-protection.
Localized Modes in Asymmetric Phononic Crystals

This paper reports on a localized mechanical resonance induced by a novel phononic crystal (PnC) design demonstrated in a $1\ \mu \mathrm{m}$ aluminum nitride (AlN) thin film. An asymmetric PnC with diagonal beam bridging discs in a square lattice generates an isolated mode inside the PnC bandgap defined by the flexural mode of the connecting beam. Here we investigate the properties of the PnC dispersion relations as a function of key design parameters, defining resonance cavities to excite the localized mode. PnC resonators with localized mode at 156 MHz are characterized as a function of central bar rotation angle ($\theta$), number of actuation layers ($\mathrm{N}_{act}$) and lateral periods (P). This analysis of electromechanical modes with designed flat dispersion relation is an important first step toward Density-Near-Zero (DNZ) metamaterials and resilient elastic wave manipulation on chip.
Maneuvering Target Detection Based on Three-Dimensional Coherent Integration

Due to the target motion, range cell migration (RCM) and Doppler frequency migration (DFM) always occur. That is harmful to the signal enhancement and detection. In order to solve the problem, a novel three-dimensional (3-D) coherent integration (TDCI) based algorithm is proposed in this article which consists of three stages. Firstly, a 3-D space is generated by the autocorrelation function. After that, TDCI algorithm is realized and TDCI domain is obtained in which the motion parameters can be accurately estimated. Finally, compensating off the RCM and DFM by the estimates, the target signal can be accumulated and detected in range-Doppler frequency domain. Theoretical analyses and simulation experiments are given to demonstrate that the proposed algorithm is able to deal with the problems of velocity ambiguity, shadow effect, and cross-term with superior resolution. Comparisons with several representative algorithms lead us to the conclusion that the proposed algorithm can strike a good balance between computation cost and anti-noise performance. In the end, real measured data processing and result analysis are carried out, which further verify the effectiveness of the proposed algorithm.
An Automatic Detection and Analysis of the Bitcoin Generator Scam

We investigate what we call the "Bitcoin Generator Scam" (BGS), a simple system in which the scammers promise to "generate" new bitcoins using the ones that were sent to them. A typical offer will suggest that, for a small fee, one could receive within minutes twice the amount of bitcoins submitted. BGS is clearly not a very sophisticated attack. The modus operandi is simply to put up some web page on which to find the address to send the money and wait for the payback. The pages are then indexed by search engines, and ready to find for victims looking for free bitcoins.We describe here a generic system to find and analyze scams such as BGS. We have trained a classifier to detect these pages, and we have a crawler searching for instances using a series of search engines. We then monitor the instances that we find to trace payments and bitcoin addresses that are being used over time. Unlike most bitcoin-based scam monitoring systems, we do not rely on analyzing transactions on the blockchain to find scam instances. Instead, we proactively find these instances through the web pages advertising the scam. Thus our system is able to find addresses with very few transactions, or even none at all. Indeed, over half of the addresses that have eventually received funds were detected before receiving any transactions.The data for this paper was collected over four months, from November 2019 to February 2020. We have found more than 1,300 addresses directly associated with the scam, hosted on over 500 domains. Overall, these addresses have received (at least) over 5 million USD to the scam, with an average of 47.3 USD per transaction.
Demonstrating the Architecture for Situation-aware Logistics using Smart Returnable Assets

Building on earlier work, this paper aims to demonstrate and discuss an instantiated architecture for situation-aware logistics in an operational environment using smart returnable assets. The demonstration is based on a motivation scenario focusing on exception management. The system outline and its components, interfaces, and enabling technologies are described and linked to the different layers of the architecture. This paper documents and illustrates the use of the system with detailed models and screenshots. Earlier work is extended using business rules to identify and quantify exceptions and potential disruptions. Specifically, it is shown how shipment data are enhanced with data from IoT sensors of smart returnable assets to provide situation-aware decision support based on data analytical methods. This demonstration provides scholars and practitioners, active in the fields of enterprise computing, insights into the concepts, models and engineering technologies used to implement an architecture for situation-aware logistics. The instantiated architecture provides a rich testbed for experiments, measurements, and incorporate the ideas promoted by the international data space initiative. An online recording is available to support the demonstration and ignite discussion about the potential of IoT technologies and future research directions in pursuit of the smart logistics vision.
Network Fingerprinting: Routers under Attack

Nowadays, simple tools such as traceroute can be used by attackers to acquire topology knowledge remotely. Worse still, attackers can use a lightweight fingerprinting technique, based on traceroute and ping, to retrieve the routers brand, and use that knowledge to launch targeted attacks.In this paper, we show that the hardware ecosystem of network operators can greatly vary from one to another, with all potential security implications it brings. Indeed, depending on the autonomous system (AS), not all brands play the same role in terms of network connectivity. An attacker could find an interest in targeting a specific hardware vendor in a particular AS, if known defects are present in this hardware, and if the AS relies heavily on it for forwarding its traffic.
Adaptive Integral Backstepping Control for a Quadrotor with Suspended Flight

Aiming at the problem that the stability of a quadrotor with a suspension system is affected by the swing of the slung-load due to the wind and so on, a nonlinear control method is proposed to reduce the influence of the slung-load on the flight of the quadrotor. Firstly, the mathematical model of the suspension system of the quadrotor is established by using Hamilton principle and Lagrange formula; secondly, the adaptive backstepping controller is designed to control the position and attitude of the quadrotor, and eliminate the steady-state error of the system and enhance the robustness of the system; thirdly, the integral backstepping controller is designed to reduce the swing of the slung-load; finally, the simulation experiment system of the quadrotor with a slung-load suspension is built. The simulation experiment results verify the validity and accuracy of mathematical model and designed controller.
Replicated Testbed Experiments for the Evaluation of a Wide-range of DDoS Defenses

Cyber security attacks and defenses are constantly evolving, requiring us to re-tool our networks at a regular pace. New systems are usually evaluated within restricted corporate or enterprise networks, with limited or no access for researchers and cyber security students. In this paper we present a longitudinal study of the design and evaluation of six novel defense technologies to thwart next generation DDoS attacks. While the technologies were developed independently as part of the DARPA XD3 program, the evaluations were conducted over the last year on the DETER cyber security testbed and on Amazon Web Services. We present the evaluation framework, the results, and the inherent challenges in evaluating a wide range of defense technologies on the testbed. We believe the technologies presented in this paper will aid in the development of relevant and plausible living in the future operational scenarios for cyber ranges.
Research on Neutral Network Based Impact Angle Control Considering the Field-of-view Limitation

The impact angle control guidance problem considering the strapdown seeker’s field-of-view has become an interested topic and it has been solved by various techniques. However, most of the existing solutions suffer from undesirable fluctuation in their guidance commands due to the disturbance of the nonlinear system. In this paper, we design a field-of-view constrained impact angle control guidance law by using an adaptive RBF neural network based sliding mode controller. In the design of the controller, a logarithmic barrier Lyapunov function and a quadratic Lyapunov function are used for forcing the system to reach the sliding mode in limited time and a hyperbolic tangent function is introduced to solve the field-of-view limitation. An adaptive RBF neural network is constructed to approximate the system’s uncertain disturbance and the approximation serves as compensation in the guidance command to mitigate the adverse fluctuation. Finally, the performance of the proposed solution is verified by numerical simulations through two engagement scenarios.
Spherical Harmonic Energy Over Gaussian Sphere for Incomplete 3D Shape Retrieval

Spherical harmonic (SH) has shown excellent advantages in terms of accuracy and efficiency in the retrieval of complete three-dimensional shapes. However, since the spherical function directly takes the shape centroid as the global reference point, the SH features depend heavily on the central position. In this context, the features become no longer reliable in the query of incomplete shapes that may cause erratic centroid. In this work, we propose a novel shape descriptor, namely spherical harmonic energy over the Gaussian sphere (SHE-GS), especially for the incomplete shape retrieval. Firstly, all unit normal vectors on the shape surface are mapped to points on a Gaussian sphere, which has the constant center. Secondly, kernel density estimation is used to establish a Gaussian Sphere Model (GSM) to describe the density change of these mapping points. Finally, the shape descriptor is generated by applying an SH transformation on the model. According to the way of GSM being regarded as a surface model or a volume model, we have separately designed two specific algorithm implementations. Experimental results, on two engineering shape sets containing artificially defective shapes, indicate that the proposed method outperforms other traditional methods also defined in the sphere space for incomplete shape retrieval. The superiority is verified for both similar objects in the same category or the single specific object of query.
Bidirectional Volatile Signatures of Metal–Oxide Memristors—Part I: Characterization

The multistate capabilities as well as the intrinsic integrating properties of memristors deem them suitable candidates for the realization of novel neuromorphic applications. To date, much of their prestige arises mostly from the versatility that is promised by the nonvolatile device families. However, memristors also exhibit volatile characteristics, which for as long as they remain unknown, will hinder their integration to large-scale applications. In this article, we present a comprehensive study for characterizing the relaxation dynamics of TiOx resistive RAM (RRAM) devices within a predefined volatility framework. These dynamics are tightly linked to the total energy of stimulation, and device relaxation can be accurately described using simple mathematical models. Moreover, we show that RRAM volatility is bidirectional and that relaxation time constants heavily depend on the level of invasiveness caused by programming stimulation. Our work further includes a demonstration of how volatility can be characterized within a specific time window. Moreover, our protocol can be altered to fit the specific needs of potential applications. We anticipate that the universality of our method can act as a stepping stone toward the understanding and modeling of volatile memristors across different technologies and materials, enabling the realization of a new family of time-related applications.
Drimia trichophylla (Hyacinthaceae, Urgineoideae), a New Species from the Eastern Cape Province, South Africa

Abstract Within the framework of a taxonomic revision of Drimia sensu lato we here describe a new species from the Eastern Cape Province in South Africa. Drimia trichophylla sp. nov. is at first sight related to D. vermiformis, but it can be clearly differentiated by the flower and leaf morphology and distribution. A complete description is presented for this species, and data on morphology, ecology, and distribution are reported. Affinities and divergences with other close allies are also discussed.
A Distributed Reward Algorithm for Inverse Kinematics of Arm Robot

Traditional methods of inverse kinematics of robots always adopt analytical approach and numerical approach to solve the continuous state and action problems with experience and experiment mostly, which require much time and work in reality work scene, especially for robots with complex structure. This paper proposes a method based on reinforcement learning TD3 network, which is constructed by PyTorch to find the inverse solution from another point of view. A set of improved distributed multiple rewards which choose the position difference between adjacent joints as the reward standard are designed to optimize the solution, avoid solving unreachable points and prevent the mechanical structure from being damaged also in the environment of five-degree-of-freedom arm robot. The validity of above method is verified by simulation experiment results.
Testability modeling method based on MSFG-BN composite model

The multi-signal flow graph model is the most widely used Testability model, but its ability to handle uncertain information is weak, and the failure information utilization rate is low, which cannot provide researchers with more data support. Moreover, the model cannot update the structure and parameters through learning. The model established may not be suitable for all stages of the entire life cycle of the equipment, and there is a risk of requiring multiple modeling. In order to solve the above problems, a Bayesian network was introduced and a MSFG-BN combined model was proposed. The multi-signal flow graph was used to reduce the difficulty of modeling the Bayesian network. The Bayesian network was used to increase the processing of uncertain information and improve the information utilization. At the same time, with the help of Bayesian network learning capabilities, the model can update its own structure and parameters, improving the applicability of the model at all stages. It has been verified that the model can play its role effectively.