Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarimetric Signatures of Moving Automotive Vehicles Based on H/A/α-decomposition: Preliminary Results with PARSAX Radar Data

Polarimetric radar responses from moving automotive targets are studied aiming at target classification using the polarimetric H/A/α-decomposition technique. A signal- and data processing chain has been proposed for the detection and tracking of targets in a multi-target environment in the range-Doppler domain. Polarimetric information of the vehicles is collected during tracking and is analyzed by the H/A/α-decomposition technique. Employing both time averaging and spatial averaging of the statistical coherency matrix, the polarimetric signatures of both vehicles and static clutter have been presented in the two-dimensional H/α-plane. It has been found that the spatial averaging approach results in a polarimetric signature that can be very helpful to distinguish automotive vehicles from static clutter.
SLAM Loop Detection Algorithm Based on Improved Bag-of-Words Model

Aiming at the problem that the vocabulary tree in the traditional bag-of-words model algorithm is not strong enough to represent the scene, which will affect the performance of the loop detection module in the visual SLAM system and produce accumulated errors, this paper proposes a slam loop detection algorithm based on the optimized bag-of-words model, improves the clustering algorithm for generating offline vocabulary tree, and uses the improved K-Means algorithm to build the vocabulary tree; At the same time, the image feature data of the experimental scene is used to update the vocabulary tree to improve the representation ability of the vocabulary tree. In this paper, the data sets of CityCenter, NewCollege, KITTI00 and KITTI06 are compared and analyzed experimentally. Compared with the experiments before and after updating the vocabulary tree, when the accuracy is 100%, the maximum recall rate of the algorithm on the four data sets is improved by 11.28%, 24.73%, 16.58% and 17.73% respectively; Then, compared with the five classical algorithms, the maximum recall rates of the four data sets are 0.39%, 11.27%, 2.39% and 22.67% higher than the best results of the five algorithms, respectively. The improved algorithm reduces the processing time, and the experimental results verify the correctness and effectiveness of the improved algorithm.
Reliability Analysis and Optimization for Sensing Data Collection and Processing in New Energy Internet Systems

In this paper, we consider a sensing network in the future energy internet systems. The network has a processing server collecting sensing data from multiple sensors and processing them together. The whole framework of data collection and processing is required to satisfy a low overall latency and high- reliability. Therefore, the data collection phase is short and the transmission in such phase is carried by finite blocklength codes, while the data processing should be finished within the remaining time duration (taking out the spent time for data collection from the overall latency constraint/budget). We characterize the overall reliability of data collecting and processing. Following the characterization, we provide an optimal framework design of time-allocation aiming to minimize the instantaneous overall error probability. In particular, we prove that the proposed framework is jointly convex in the lengths of data collection and data processing phases. Via simulations, we validate the convexity proven in our analytical and show the performance advantage of proposed analytical solution.
A study on how organizations perceive synergy between Lean and Industry 4.0

Over the last few decades, the advent of lean spread over most manufacturing industries as a best practice for minimizing waste, increasing efficiency, and maximizing resource usage. However, in the recent past, Industry 4.0 is under debate to be taking over the existing best practices of the manufacturing industry as digitalized production systems. This has sparked concern as to how these changes would affect the well-established lean systems. Hence, through this research, we explore how lean would transition into the digital era in the apparel industry. The information and insights generated were used to map relationships between Lean and Industry 4.0 as well as to understand the strength of the given relationships using non-parametric statistical methods. The paper discusses key elements of compatibility as well as incompatibilities under different segments of both Industry 4.0 and Lean in the form of a comprehensive matrix. It also reflects the nature of synergy relationships compared against different levels of Industry 4.0 maturity.
Detecting Web Application Injection Attacks Using One-Class SVM

As the important component of the Internet, the Web makes it easy for us to access information anytime, anywhere. However, the widespread adoption of web applications has introduced new security risks and expanded existing attack surfaces that many organizations are not effectively protecting. Among the various threats facing the web applications, injection attacks are one of the most dangerous. In this work, we propose to use one-class Support Vector Machine (SVM) for detecting web application injection attacks. We treat the detection of injection attacks as an anomaly detection problem. In the training stage, a number of legitimate HTTP requests are used to train a one-class SVM model. In the testing stage, the trained one-class SVM is used to detect whether an HTTP request is legitimate or malicious. We adopt 2v-gram algorithm (a variant of n-gram) to extract features from HTTP requests. The experimental results show that one-class SVM achieves good performance in detecting web application injection attacks by achieving a detection rate of 94.04% and a false positive rate of 1.62%.
Temperature field measurement based on the Chirped Fiber Bragg Grating

The measurements of the temperature field are very important and difficult for generally single-point measuring instrument. Here we presented a simple temperature field sensor by using a chirped fiber Bragg grating (CFBG) as the sensing element. As the temperature varies around the CFBG along the optical fiber Z-axis, because of the photo-thermal effect, the temperature change in the gradient temperature field will lead to the shifting of the CFBG's spectrum. We believe that this simple configuration will open the way to research in CFBG-derived theories and configurations for the detection of temperature fields.
Linear Vlasov theory of a magnetised, thermally stratified atmosphere

The stability of a collisionless, magnetised plasma to local convective disturbances is examined, with a focus on kinetic and finite-Larmor-radius effects. Specific application is made to the outskirts of galaxy clusters, which contain hot and tenuous plasma whose temperature increases in the direction of gravity. At long wavelengths (the ‘drift-kinetic’ limit), we obtain the kinetic version of the magnetothermal instability (MTI) and its Alfvénic counterpart (Alfvénic MTI), which were previously discovered and analysed using a magnetofluid (i.e. Braginskii) description. At sub-ion-Larmor scales, we discover an overstability driven by the electron-temperature gradient of kinetic-Alfvén drift waves – the electron MTI (eMTI) – whose growth rate is even larger than the standard MTI. At intermediate scales, we find that ion finite-Larmor-radius effects tend to stabilise the plasma. We discuss the physical interpretation of these instabilities in detail, and compare them both with previous work on magnetised convection in a collisional plasma and with temperature-gradient-driven drift-wave instabilities well known to the magnetic-confinement-fusion community. The implications of having both fluid and kinetic scales simultaneously driven unstable by the same temperature gradient are briefly discussed.
PointOT: Interpretable Geometry-Inspired Point Cloud Generative Model via Optimal Transport

Point cloud generative models have aroused increasing concern for their realistic generation potentialities. However, most existing methods adopt deep-neural-network (DNN) models for continuous mapping. DNN usually induces mode collapse and mixture problems without clear interpretation. Consequently, in this paper, we design a geometry-inspired point cloud generative framework called PointOT. PointOT decouples the generative model into two separate sub-tasks: manifold learning of the point cloud and distribution transformation. Then, we propose corresponding instances according to the requirements in each sub-task, where they can be established by the point cloud auto-encoder (AE) and the semi-continuous optimal transportation (SCOT) mapping, respectively. In particular, the transportation map between the source and the target distributions is discrete rather than continuous in geometric view. The learned continuous shape model of the DNN point cloud does not conform with the discrete distribution transformation. Therefore, the proposed SCOT efficiently relieves these problems by connecting the continuous-to-discrete domain. Besides, we provide theoretical explanations from a geometric view and analyze the fundamental reason for mode collapse and mixture in point cloud generative models. The proposed SCOT algorithm without the DNN model is computationally efficient and makes the original black box semi-transparent. Final experiments validate the virtue of the proposed approach, including the designed decomposition framework and the rigorous theory.
Spatial Point Pattern Analysis of Inbound Logistics Activities Based on GPS Trajectory Data

The agglomeration and dissipation behaviors of large-scale logistics activities lead to significant traffic congestion and delays. In order to reveal the operation and movement rules of logistics vehicles in the urban road network, this paper studies the spatial distribution characteristics of inbound logistics clusters based on the GPS trajectory data. Firstly, the kernel density estimation method with road network constraints is employed to extract the spatial characteristics of inbound logistics enterprises. Secondly, the weighted kernel density estimation method is adopted to analyze the point aggregation characteristics of inbound logistics transportation demand. Besides, the Moran index is applied to analyze the spatial autocorrelation characteristics of inbound logistics activities in the urban road network.
Imaging the dust sublimation front of a circumbinary disk

We present the first near-IR milli-arcsecond-scale image of a post-AGB binary that is surrounded by hot circumbinary dust. A very rich interferometric data set in six spectral channels was acquired of IRAS08544-4431 with the new RAPID camera on the PIONIER beam combiner at the Very Large Telescope Interferometer (VLTI). A broadband image in the \textit{H} band was reconstructed by combining the data of all spectral channels using the SPARCO method. We spatially separate all the building blocks of the IRAS08544-4431 system in our milliarcsecond-resolution image. Our dissection reveals a dust sublimation front that is strikingly similar to that expected in early-stage protoplanetary disks, as well as an unexpected flux signal of $\sim$4\% from the secondary star. The energy output from this companion indicates the presence of a compact circum-companion accretion disk, which is likely the origin of the fast outflow detected in H$\alpha$. Our image provides the most detailed view into the heart of a dusty circumstellar disk to date. Our results demonstrate that binary evolution processes and circumstellar disk evolution can be studied in detail in space and over time.
Infrared and Visible Image Fusion Based on Gradientlet Filter and Low-Rank Decomposition

Infrared and visible image fusion is an important research topic in the field of image fusion. In this paper, we propose a novel infrared and visible image fusion algorithm based on gradientlet filter and latent low-rank decomposition, in which source images are firstly decomposed into local texture information, global texture information, and basis information. Among them, the gradientlet filter is used to extract the local texture information and the basis information of the image, and the low-rank transform is used to extract the global texture information and the basis information of the image. Then, according to the different characteristics of the information, different methods are used to fuse them, respectively. For both local texture information and basis information, we use the weighted average method. For the global texture information, we use weighted least square (WLS) optimization scheme. Finally, the content we want to highlight is enhanced by determining the weight, and the final fused image is obtained by adding the three parts of the information. The experimental results show that our proposed method outperforms the other five traditional methods in terms of subjective visual effects and objective indexes.
Configuration of Inverted-V Dipole NVIS HF Antenna for Offshore Fast Patrol Boat

This paper describes the configuration of Inverted-V half-wave dipole antenna which was designed, simulated and deployed for offshore ship-patrol of a 28 Meter class Fast Patrol Boat (FPB) for its High Frequency (HF) communication system. The antenna is used for medium range of HF radio coverage by Near Vertical Incidence Skywave (NVIS) mode for sea to port and sea to sea communication scheme for radius around 300 kilometers. The effect of sea surface and ship's hull material on the radiation pattern was studied. Furthermore, the antenna height and the V-shape angle configuration were determined to compromise between the limited space, maximum antenna gain as well as its Standing Wave Ratio (SWR) value. The Numerical Electromagnetics Code (NEC) computer simulation describes not only the flat-bottomed radiation pattern due to high conductivity of the sea surface but also the 3-dB gain of the vertical main lobe as the typical characteristic of V-inverted dipole antenna. Designed antenna has the SWR value as low as 1.16 at 42-meter band with 40-degree angle of inverted V-shaped mounted on boat with 10 meters tall of mast above sea level coupled with a 100-Watt ICOM HF radio.
Trust Management Model of VANETs Based on Machine Learning and Active Detection Technology

With the continuous development of vehicular ad hoc networks (VANETs), it brings great traffic convenience. How-ever, it is still a difficult problem for malicious vehicles to spread false news. In order to ensure the reliability of the message, an effective trust management model must be established, so that malicious vehicles can be detected and false information can be identified in the vehicle ad hoc network in time. This paper presents a trust management model based on machine learning and active detection technology, which evaluates the trust of vehicles and events to ensure the credibility of communication. Through the active detection mechanism, vehicles can detect the indirect trust of their neighbors, which improves the filtering speed of malicious nodes. Bayesian classifier can judge whether a vehicle is a malicious node by the state information of the vehicle, and can limit the behavior of the malicious vehicle at the first time. The simulation results show that our scheme can obviously restrict malicious vehicles.
Research on the novel hybrid amplifier based on Raman and ZBLAN-TDFA

For S-band wavelength division multiplexing (WDM) systems, the $\text{ZrF}_{4}-\text{BaF}_{2}-\text{LaF}_{3}-\text{AlF}_{3}-\text{NaF}$ based thulium doped optical amplifier (ZBLAN-TDFA) is a main amplifying media. However, the main limitation of TDFA is the low gain and lack of gain flatness, which requires Raman amplifier (RA) to compensate for its shortcomings. In this paper, the gain is achieved by using a hybrid optical amplifier (HOA) composed of a RA and ZBLAN-TDFA. The effects of variations in amplifier fiber length and RA pumping power on HOA gain were investigated.
Energy Management Applications in Battery And Supercapacitor Hybrid Electric Vehicles Using Fuzzy Logic

The battery is primarily used in electric vehicles as an energy storage unit because it has a large energy storage capacity. The disadvantages of the typical battery are that it has a small power density, a limited-service life, and a relatively high price. Due to the driving cycle characteristics of electric vehicles, which are often not constant, because an excessive charge-discharge process causes them, it decreases battery life. Additional energy storage systems such as the supercapacitor are recently applied to overcome energy fulfilment. Since the battery and the supercapacitor have different characteristics in charging and discharging operation, a proper energy management system should be applied. An intelligent method to share the energy consumption of both battery and supercapacitor is proposed in this research. This paper proposed a fuzzy logic controller (FLC) be applied to the power distribution control for the hybrid energy storage system. The proposed power distribution is carried out by attaching a buck converter to the battery. The duty cycle value of the buck converter is controlled using FLC according to vehicle starting, normal, and regenerative braking operation. The simulation uses MATLAB Simulink software. The simulation result shows that the FLC can successfully manage the energy sharing between the battery and supercapacitor according to vehicle operation when in the starting condition, the supercapacitor fully supplies the power demand. Under normal conditions, most of the power demand is supplied by the battery. Under the regenerative braking condition, the supercapacitor fully receives the current and power.
A rate-compatible punctured Polar code decoding scheme based on deep learning

In order to improve transmission reliability and flexible cooperation in optical communication, rate-compatible punctured Polar codes have become a research hotspot. Aiming at the problem that the traditional decoding performance and transmission efficiency is limited, based on deep learning, a rate-compatible punctured Polar code decoding scheme is studied. We use convolutional neural network model as the basic structure of rate-compatible Polar code decoder. The log likelihood ratio values of the received sequence are input into the decoder for training. Simulation results show that the proposed decoder outperforms the traditional punctured Polar code decoder under high signal-to-noise ratio.
Savitzky-Golay Parameter Optimization by using Linear Discriminant Analysis for FTIR Spectra

The Savitzky-Golay (SG) smoothing technique has been successfully applied to filter the noise in spectra or chromatograms. In this research, SG smoothing was applied as a pre-processing method to analyze Fourier Transform Infrared (FTIR) spectra of chilli plants infected by Pepper Yellow Leaf Curl Virus (PYLCV) and PYLCV-undetected plants. SG smoothing has two parameters that can be optimized to achieve the best result, namely the polynomial order and the window length. For the former, orders of zero, two, four, six and eight whilst the latter used lengths from the polynomial order + 1 to 59. Linear Discriminant Analysis (LDA) was used to optimize the parameters. The results showed that the best LDA classification result was achieved using the zeroth and second order polynomials. For the zeroth order, a 100% classification result was achieved by window lengths in the range nine to twenty-five, while the second order polynomial window lengths to achieve the same results were from twenty-nine to forty-one. From the two polynomial orders, the mean squared error (MSE) of the SG smoothed, and the original signal was calculated. From that process, the zeroth order SG smoothing curve with a window length of nine produced the best parameter combination to classify the samples.
On the Impact of Spread Spectrum EMI on Communication Performance

Spread spectrum is a technique that is commonly used today with the intent to reduce electromagnetic interference (EMI) from clocks and devices, such as e.g. switching DC/DC converters. The principle idea behind the technique is to spread the power at one frequency to a larger bandwidth and thereby decrease the interference amplitude and make it more probable to pass EMC emission requirements. This work analyzes the impact on radio communication receivers from this interference reducing technique. The relation between the communication receiver bandwidth and the spreading bandwidth of the spread spectrum operation is clarified. Furthermore, the effect of the aggregated EMI from multiple is investigated. Spread spectrum techniques may have a reducing effect on the communication degradation, as long as the spreading bandwidth is large in relation to the communication bandwidth and the number of EMI sources is limited to just a few. In all other cases, spectrum spreading does not reduce the interference impact.
A Nine-Level T-Type Converter for Grid-Connected Distributed Generation

This article presents a new high-efficiency nine-level T-type converter (9L-T2C) for grid-connected applications based on the three-level T-type converter (3L-T2C). The proposed 9L-T2C outperforms other common dc-link nine-level converters in terms of the required number of active switches and capacitors, flying capacitors (FCs) voltage ratings, and efficiency. Only ten power switches, eight gate drivers, and two FCs are required for each phase. Exploiting the available pole-redundant states, an FCs balancing algorithm is developed to stabilize the two FCs with one voltage sensor in steady-state and dynamic operation. Moreover, an effective balancing method is proposed for dc-link capacitors without the need for further redundant states and integrated into FCs balancing. The FCs and dc-link balance are integrated into the phase-disposition pulsewidth modulation (PD-PWM) method, eliminating the need for an additional controller. Considering the designed PD-PWM method, a mathematical analysis is performed to establish the relationship between the FCs size and the desired ripple. A comprehensive comparison with other converters is provided to demonstrate the merits and application areas of 9L-T2C. The operation of the proposed 9L-T2C with the capacitors’ balancing scheme is validated for stand-alone and grid-connected operation via simulation investigations and experimental setup.
ON THE EXTREME POSITIVE STAR FORMATION FEEDBACK CONDITION IN SCUBA SOURCES

We present a detailed study of the hydrodynamics of the matter reinserted by massive stars via stellar winds and supernovae explosions in young assembling galaxies. We show that the interplay between the thermalization of the kinetic energy provided by massive stars, radiative cooling of the thermalized plasma, and the gravitational pull of the host galaxy lead to three different hydrodynamic regimes. These are: (1) the quasi-adiabatic supergalactic winds; (2) the bimodal flows, with mass accumulation in the central zones and gas expulsion from the outer zones of the assembling galaxy; and (3) the gravitationally bound regime, for which all of the gas returned by massive stars remains bound to the host galaxy and is likely to be reprocessed into further generations of stars. Which of the three possible solutions takes place depends on the mass of the star-forming region, its mechanical luminosity (or star formation rate), and its size. The model predicts that massive assembling galaxies with large star formation rates similar to those detected in Submillimeter Common-User Bolometric Array sources (∼1000 M☉ yr−1) are likely to evolve in a positive star formation feedback condition, either in the bimodal or in the gravitationally bound regime. This implies that star formation in these sources may have little impact on the intergalactic medium and result instead into a fast interstellar matter enrichment, as observed in high redshift quasars.