nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive two-step calibration for high resolution and wide-swath SAR imaging

In this study, the authors focus on the channel calibration for digital beam forming (DBF) synthetic aperture radar (SAR) imagery. Incorporated with DBF processing, multi-channel SAR is promising in high-resolution and wide-swath (HRWS) imaging. It coherently combines recorded multi-channel signals to overcome the spectrum ambiguity. However, the mismatch between channels and baseline errors phase should be compensated before the processing. In this study, the authors propose a novel calibration for DBF SAR imagery by making use of the orthognality between signal subspace and noise subspace. Based on that the imbalance is uncoupled in range and azimuth, the calibration has two steps: range calibration and azimuth calibration. The mismatch in range is estimated by an interpolation in time domain which is applicable in the presence of the Doppler ambiguity. The azimuth calibration is incorporated with the post-Doppler beam forming processing, which estimates the element error caused by baseline errors and channel imbalance. Real data are used to demonstrate the performance of Doppler ambiguity suppression after our calibration. The results confirm the validity of the approach.
A model and dynamics for reticulated supply chain based on multi-agent system

In essence, supply chain is a dynamic complex system that is composed of a multiplicity of entities with independent decision-making ability. Traditional models, such as chain-shaped, radial-shaped and so on, are not fit for this complex setting any more. This paper models a multi-agent based reticulated supply chain, which well describes the relationship among the goods flow, capital flow and information flow exiting in the supply-demand, enterprise groups and virtual factories which are the elements of supply chain. Taking a two-agent system for example, this paper studies the chaos in supply chain through calculation and simulation of Lyapunov exponent, power spectrum and correlation dimension, and also pioneers the study of its application prospect in flexible production, providing a new illuminating insight into supply chain management
An improved DE-KFL for BOC signal tracking assisted by FRFT in a highly dynamic environment

This paper presents a Double Estimator Kalman Filter Loop (DE-KFL), which is assisted by Fractional Fourier Transform (FRFT), for tracking high-dynamic BOC signals. New BOC signals, which can perform better in positioning and spectrum compatibility, are frequently used for the modern Global Navigation Satellite System (GNSS) in modern times. BPSK-LIKE and Double Estimator Technique (DET) algorithms are mostly applied to GNSS receivers to cope with the multi-peaks of the Auto-Correlation Function (ACF) of BOC signals. Since the sub-carrier tracking loop of DET has the performance of a narrow pull-in range, it is more likely to be out of lock under dynamic stress. Partially Matched Filter (PMF) is introduced to implement the acquisition process of BOC signals. Fractional Fourier Transform (FRFT) is also included in the acquisition process to estimate the acceleration of the line-of-sight (LOS) signal, and this technique can enhance the sensitivity of the DE-KFL when tracking BOC signals in a highly dynamic environment. Simulations verify that proposed techniques perform significantly better than the original tracking loop. The proposed DE-KFL assisted by the FRFT (A-DE-KFL) algorithm can obviously improve the sensitivity of the tracking loop for BOC signals in a highly dynamic environment.
Applying evolutionary algorithm in multi-peak decision problems

A typical multiple value decision problem, partnership selection in supply chain management, has been introduced as an example, which is difficult to solve when only using traditional multi-objective decision support methods. To take the self-organization simulation model as the basic complex system theory to analyze the complex interactive mode inside the decision support problems, firstly, the paper briefly introduces a machine learning based decision learning strategy, which could automatically lead to the differentiation of different decision rules. Then, for the only learning strategy that could hardly discover the best solutions in quite a large problem space, an evolutionary algorithm (EA) is imported and designed to help the decision rule discovery procedure. It is difficult to combine EA into a real decision scenario in enterprise life. This paper puts forward an integration simulation model and corresponding algorithm structures which result in satisfying outcomes.
An Asymmetry Bi-directional RSVP for improving the network performance

The classical Resource ReSerVation Protocol (RSVP) is a flow-based signaling protocol used for reserving resources in the network for a given session. The core of Integrated Service (IntServ) Model is RSVP. RSVP is a unidirectional resource reservation protocol; it can only reserve resources for the TCP data flow along the forward direction path, and cannot provide any protection to the ACK packets against congestion in reverse direction. In order to improve TCP performance over RSVP, we proposed a new protocol called ABi-RSVP (Asymmetry Bi-directional RSVP). ABi-RSVP reserves the backward resource by taking account of the ACK packets size, therefore improves the throughput of TCP flows and enhances the efficiency of the network.
A fully integrated CMOS bio-chip aiming at selective assembly of charged nano-particles

A fully integrated bio-chip targeting at electrical selective assembly of charged nano-particles is proposed and designed in SMIC 0.18 ¿m CMOS mixed signal process. The proposed circuit integrates the electrode array, potentiostat circuit, and logics on a single chip, and provides a rail-to-rail range of assembly voltage, a potential resolution of 9 bit, and a maximal assembly current up to 0.46 mA, biased at a current of 1 ¿A. Meanwhile, a novel electrode-reuse scheme is also proposed to further simplify the architecture and save chip area as well, without degrading the functionalities. Experimental results from on-chip selective assembly of 50 nm poly-styrene nano-particles (PS) are included and discussed to verify the feasibility of the proposed circuits.
A novel fiber with ultra-low-loss and large-effective-area for the next generation communication

Fiber loss and nonlinear effect are the main important factors for high-speed coherent communication. A novel communication fiber with ultra-low loss (0.165dB/km) & large effective area (about 110μm2) at 1550nm wavelength is proposed. Compared to the conventional single-mode fiber, it can contribute more benefits for high speed large capacity communication system. Its optical characteristics are discussed here, including splicing, macrobending and cabling performance.
A novel paralleling session setup mechanims in IMS

To reduce the session setup delay there have been many methods used widely in traditional 2G networks, as well as the IP Multimedia Subsystem (IMS). However, the existing approaches to solve the problem depend on optimization of Session Initiation Protocol (SIP) message transmitting and signaling flows. The article proposes a novel paralleling session setup mechanism (PSSM) based on the exploration of the performance of IMS session establishment. The scheme involves local routing policy and paralleling algorithm, which can decrease call setup delay. The analytical model is deduced, and then the delay and cost functions are formulated. By the mathematical analysis and performance simulation, the result shows that PSSM can reduce the call setup delay effectively comparing to the current session setup mechanism.
A Novel Virtual Network Fault Diagnosis Method Based on Long Short-Term Memory Neural Networks

Network virtualization has emerged as a significant trend to solve the issues caused by ossification of traditional network. Under the circumstance of network virtualization, substrate network and virtual network are inextricably interdepending each other. The substrate network serves many virtual networks. Substrate network faults may lead to different virtual network faults. A service''s failure may introduce additional influence on other services. Therefore, it has become a big challenge to predict when and where a fault happens in the network. In this paper, we propose a fault diagnosis method by deep learning to predict the failure of virtual network. Our deep learning model enables the earlier failure prediction by the Long Short-Term Memory (LSTM) network, which discovers the long-term features of network history data. Simulation results show that the proposed method performs well on faults prediction.
A virtual touch event method using scene recognition for digital television

Because of rich applications, smart operating systems on cell phones are now being migrated to home appliances like televisions. However, applications that are originally designed to be operated by touch screen are not suitable for televisions with these systems. This paper presents a method to manipulate applications with infrared remote control instead of touch screen on televisions without rewriting the code of these applications or adding extra expense on hardware. The principle of the method is to map keystroke events on the remote control to virtual touch-based events according to specific mapping relationship corresponding to each application. Since the mapping relationship is various in each scene within one application, scenes should be recognized with feature information before the mapping process. The feature information and the mapping relationship in each scene have been set up prior to running of the application. When one application is running, the current scene of the application could be identified by scene recognition algorithm, the mapping relationship related to the current scene is able to be acquired, and then keystrokes on the remote control would be mapped to touch-based events. The proposed method is tested on a smart television platform, and the result indicates the method can operate most applications by remote control, while the input response delay brought by the event mapping is negligibly less than one millisecond.
Common-mode current inductively coupled emission of AC PWM drives

The most common solution for modern adjustable speed drives is the use of induction motors fed by voltage-source inverters. The inverters using fast switching insulated gate bipolar transistors (IGBTs) generate pulse-width modulated (PWM) voltage pulses. The high carrier frequency along with fast rise and fall time of the PWM switching results in nontrivial common-mode or ground currents noise. The current component can cause electromagnetic interference (EMI) noise to neighbour equipments and installations by either radiation from the motor cable or by cross-talk (inductive and capacitive coupling) with other conductors. This paper discusses the inductively coupled EMI noise from an ac drive system where there susceptible devices and a common ground plant are presented. The model describing the sophisticated EMI phenomenon is proposed and measurement results are presented.
An Energy Balanced Clustering Algorithm for Mobile Ad Hoc Networks

We propose an energy balanced clustering algorithm, which considers several important clustering parameters integrally, including node's relative velocity, distance, remaining energy and identification. The algorithm uses a filter mechanism to elect the most appropriate nodes to be the cluster heads, which breaks through the limitation of setting the weight parameters manually according to the network state in the weight based clustering algorithm; it also introduces a cluster head rotation mechanism to balance the energy consumption among all nodes and prevents some nodes' energy to be exhausted too early. Simulation results show that the algorithm not only improves the clusters stability but also prolongs the node's lifetime significantly.
Adsorption isotherm and thermodynamic of triclosan on activated sludge

Batch experiments were conducted to investigate the adsorption behavior of triclosan (TCS) at μg/L level in activated sludge. Effects of time, temperature, and MLSS on adsorption behavior were studied, and adsorption isotherm and thermodynamic parameters were studied as well. Sorption of TCS on the sludge was found to be physics-sorption, and partitioning played a significant role. TCS sorption process on sludge is basically exothermic and spontaneous. The study was meaningful for understanding the removal behavior of TCS in biological sewage treatment system.
Coupled torsional and axial nonlinear vibration model of the crankshaft with a propeller

The nonlinear model of the crankshaft system with a propeller is established, in which the variable moment of inertia of the linkage and the piston, coupling effect between torsional and axial vibration, the actuating force applied on the piston, the actuating torque and force applied on the propeller is included. 6 linkages and pistons are involved in the model. The governing equations of the model denote a strong nonlinear and non autonomous system. By numeric calculation, the time history and frequency spectra of the system response at working speed are obtained. It is found that two times frequency component is significant in the relative torsional vibration spectrum between the propeller and the first crank, between the fifth and the sixth crank. In the relative axial vibration response, fundamental frequency, six times and twenty times frequency components are discovered. Four times frequency components are not discovered, with which, the actuating force is applied on the propeller. Therefore, more errors may be caused, if the propulsion system of the marine diesel is designed according to the linear vibration model.
Dam safety informaiton system survivability impact factors importance assessment based on rough sets

As it's quite difficult to assess the importance of survivability impact factors of dam safety information system with traditional methods, a new method is developed based on rough sets theory. Considering the superiority of rough sets theory on knowledge mining, this method takes useful information from the results of survivability assessment and makes an assessment on importance of survivability impact factors of dam safety information system. At last, an example was given out for application of this method. It shows that this method is quite helpful in reducing the influence of subjective factors. It proves a new approach for dam safety information system survivability impact factors assessment.
3D visual phrases for landmark recognition

In this paper, we study the problem of landmark recognition and propose to leverage 3D visual phrases to improve the performance. A 3D visual phrase is a triangular facet on the surface of a reconstructed 3D landmark model. In contrast to existing 2D visual phrases which are mainly based on co-occurrence statistics in 2D image planes, such 3D visual phrases explicitly characterize the spatial structure of a 3D object (landmark), and are highly robust to projective transformations due to viewpoint changes. We present an effective solution to discover, describe, and detect 3D visual phrases. The experiments on 10 landmarks have achieved promising results, which demonstrate that our approach provides a good balance between precision and recall of landmark recognition while reducing the dependence on post-verification to reject false positives.
A new surface inspection method of TWBS based on active laser-triangulation

An vision inspection sensor system for the automation of laser welding processes in heavy industries is presented. The system consists of PC based vision camera and stripe type laser diode. For image de-noising, a new surface inspection method of Tailor-welded blanks including in: median filtering, bi-level thresholding, opening and closing, mid-line of the laser stripe extraction, has been developed. After extracting the middle line of the laser stripe, the algorithms of calculating bead width, mismatch, weld slope, convexity, concavity, overthickness and undercut are proposed. Finally, A series of experiments show that the proposed method in this paper is effective.
Denoising by spatial correlation thresholding

This paper presents a spatial-correlation thresholding scheme for noise reduction by wavelet transform. Observing that edge structures are of high magnitude across wavelet scales but noise decays rapidly, we multiply two adjacent wavelet scales to form a spatial-correlation function to enhance significant structures and dilute noise. Dissimilar to the traditional thresholding schemes that apply threshold to the wavelet coefficients, the proposed scheme applies threshold directly to the scale correlation. A robust threshold is presented and experiments show that the proposed scheme outperforms the traditional thresholding methods.
A novel design of GMI magnetic sensor and its hybrid analysis

The peak detection method is the conventional way to design a giant magneto-impedance (GMI) magnetic sensor. However, it is difficult to extract the actual signal from the raw data outputted from the GMI probe when the signal-to-noise ratio (SNR) of the raw data is less than 0dB. Therefore, a novel design of GMI magnetic sensor and a hybrid analysis method of wavelet transform in combination with correlation analysis are proposed in this paper which are proved effective and able to process the raw data whose SNR is even -15dB.
Blue single photon emission from a single InGaN/GaN quantum dot in nanowire up to 200K

Short wavelength single photon sources are useful for quantum optical devices, in particular for applications such as free space quantum cryptography. Semiconductor quantum dots (QDs) have proven to be ideal for the generation of single photons and single photon sources have been demonstrated with InAs, InP, ZnSe and GaN quantum dots at temperatures ranging from 4K to 300K.1-4 GaN nanowires with InGaN disks inserted in them have been successfully grown on (001) and (111) Si by us and other groups.5-7 By shrinking the diameter of these nanowires to 15-20nm, the disks electronically behave like quantum dots. The optical emission from the InGaN dots can be varied over a wide range of the visible spectrum by varying the In composition. We report here, single photon emission at λ=403nm (blue) from a single InGaN dot in a GaN nanowire at 200K.
A SVM-based compound-word recognition method in information security

With the emergence of mobile Internet, Internet of things and cloud computing, the domain of information security is in a rapid development. As a result, a constant stream of compound-words describing new concepts and new technologies has arisen. However, the existing dictionary does not collect those new compound-words in time, so it cannot identify them correctly. In order to solve this problem, this paper presents a SVM-based compound-word recognition method in information security. The method is based on the outputs of the existing word segmentation system. It constructs adjacent atom-word digraph according to the statistical co-occurrence features and lexical rules. Next, it produces compound-word candidate set through deep traverse the digraph by the longest match principle. It further filters the candidate set by using a SVM classifier with the help of domain contrast corpus and computer dictionary. We use this method to identify new compound-words from 2200 vulnerability description texts. It achieves a precision of 82.25% and recall of 77.44%. The results show that our method is able to effectively identify new compound-words in information security from large scale of corpus.
An Implementation of Electronic Commerce Recommender System Based on Improved K-means Clustering Algorithm

Recommender system plays an important role in modern Electronic Commerce. An excellent recommender system is the key to make electronic commerce network run well. But now because of many reasons most recommend effects are not good enough. So a recommender system based on Improved K-means Clustering Algorithm (IKCA) is designed and implemented in this paper. The whole system includes user clustering module, prediction recommending module and evaluating module. This paper also studies and analyzes the influence factors of recommend effect and improves recommending accuracy. Traditional K-means Clustering Algorithm (TKCA) often falls into local optimal solution. IKCA uses the moving operator to adjust distance from user to cluster centre so it can more easily escape from local optimal solution and approach the global optimal. The experimental result shows that IKCA is better than TKCA. This system can be generally applied in the other fields.
Compensation Management based on mental accounting theory

Mental Accounting is the psychological process of individual, family and company making decision of economic result. People always divide wealth into different mental account unconsciously, while different mental accounting has different booking ways and psychological running rules. Compensation management based on mental accounting theory provides a more real and feasible theory framework for human resource management. This paper analyses the appliance criteria of mental accounting on compensation management, and proposes the key points of compensation management design based on mental accounting.
A New Approach to Automated Retinal Vessel Segmentation Using Multiscale Analysis

Computer based analysis for automated segmentation of blood vessels in retinal images help eye care specialists screen larger populations for vessel abnormalities. However, automated retinal segmentation is complicated by the fact that the width of retinal vessels can vary from very large to very small, and that the local contrast of vessels is unstable, especially in unhealthy ocular fundus. We propose a novel method that takes these facts into account. Our method includes a multiscale analytical scheme using Gabor filters and scale production, and a threshold probing technique utilizing the features of retinal vessel network. Our method is good for detecting large and small vessels concurrently. It also offers an efficient way to denoise and enhance the responses of line filters, allowing the detection of vessels with low local contrast
Cloud computing and exploration of its application to test field and PHM

Cloud computing is a syncretic development result of traditional computer technology and network technology. The core idea of cloud computing is to manage and schedule huge computing resources linked by network and then provide services for users from computing resources pool. The birth and speedy development of cloud computing bring new opportunities to test field, which is the foundation of PHM-enabling technology for engineering system. Firstly, this paper discusses the requirements of applying cloud computing to test field from two aspects (hardware testing, software testing), and then presents a cloud-based architecture model of the automatic test system. In turn, the key technologies of cloud-based autotest are proposed. At last, the expectation, advantages and obstacles of applying cloud computing to test and PHM are presented.