nils-herrmann/clean_scirepeval huggingface dataset:
A novel autofocusing algorithm for ISAR imaging based on sparsity-driven optimization

A novel algorithm of phase adjustment joint with ISAR imaging based on sparsity-driven is proposed in this paper. In the scheme, both phase adjustment and imaging are established in an optimization problem to seek for phase errors and reconstruct ISAR image by exploiting the prior sparsity of target scattering field. The novelty of this method lies in its capability of de-noising and overcoming phase errors, resulting in well-focused image. Besides, the optimization problem is solved by using fast Fourier transform and matrix Hardmard multiplication, promoting the efficiently. The experimental results using both simulated data and measured data confirm the validation of the proposal.
Discrimination of winter wheat and seed rape using multi-date Landsat TM/ETM images

In this study, three Landsat ETM images obtained at different period of the growth of seed rape and winter wheat are used in a study of analyzing the potential of multi-date ETM in crop discrimination for an area near Kaifeng city in Henan province, China. We compared the spectrum development between winter wheat and seed rape before, during and after the flowering stage of seed rape. Then, we provide two different classification schemas to discriminate the two crops depend on different image combination. The first one using images between the jointing stage and flowering stage of seed rape, the other one using images between the jointing stage and leguminous stage. At last, we selected two ETM images, on March 8 2001 and April 1 in 2001, to build a decision tree to discriminate seed rape and winter wheat. And the result was compared with that from IKONOS image classification and proved that the discrimination accuracy was 87% high. And the results also told us that discrimination errors were happened in two kinds of regions. One is the regions that crop growing status were very poor, another is that the planting patches were very thin or small
Application and performance analysis of various AEAD techniques for space telecommand authentication

Secure communications in the context of civil space missions gained a major attention in the last few years, mainly thanks to the activities promoted in this field by the Consultative Committee for Space Data Systems. Risk analyses performed by several space agencies have provided indications of the impact of different security threats on several categories of space missions. As a result, to ensure a minimum level of security, at least Telecommand authentication should be applied to all missions. Besides standard and well known algorithms, alternative authentication solutions are to be considered, and tested for possible adoption in the space context, in order to provide a scalable and flexible authentication framework. To this aim, this paper focuses on some Authenticated Encryption with Associated Data techniques, and on their thorough evaluation by a detailed model of the space Telecommand channel and protocol stack, in order to achieve an optimal selection for application in the real space communication environment.
An intelligent multi-modal affect recognition system for persistent and non-invasive personal health monitoring

This paper presents a multi-modal affect recognition system that is capable of effectively estimating human affective states through analyzing and fusing a number of non-invasive external cues. The proposed system consists of a probabilistic information fusion model based on the influence diagram and a set of data analysis, feature extraction and affect recognition modules for processing heterogeneous sensory data (e.g., visual appearance and speech) to accurately estimate human affective states. Experimental results demonstrate that the proposed affect recognition system can automatically and robustly recognize human affective states. Our results also prove that by fusing complementary information such as facial expression with voice intonation and/or body posture, the affect recognition performance can be boosted and outperform the performance based on each individual information. The proposed multi-modal affect recognition system can be integrated into existing remote health monitoring and rehabilitation systems to provide objective, non-intrusive, and persistent affect recognition, thus assisting patients' health monitoring and diagnosis in a natural living environment.
Design and Implementation of Double NAT-Traversal Data Transmission System

Transfer data in only one network segment is not a sticky work. However, communication between different network segments is worth studying. Especially when one or both sides network segment behind NAT devices. This text has a brief description about the classification of NAT, and explains the problems that need to be solved about NAT traversal. According to the features of NAT and the needs of our system, this article comes up with a solution which is based on SIP. This article illustrates how to overcome the problems of NAT by SIP and the design and implementation of our system.
Design and implementation of high reliable spaceborne reconfiguration system

As the improvement of the SRAM-based FPGA's production technology, the number of gates has reached 10 million-scale, it becomes more powerful, increasingly used in the space field. Due to the impact of space radiation, SRAM FPGA couldn't always be stable for space applications, it needs to strengthen reliability. High reliable spaceborne reconfiguration system is designed to ensure correct operation of the SRAM FPGA onboard. The system can achieve three functions: first, reconfiguring SRAM FPGA using external PROM or E2PROM memory; second, accepting the ground configuration file and coding it in Hamming method, then storing it in E2PROM; last, operating EDAC during idle time. The system can reduce the impact of SEU on SRAM FPGA, store different programs in E2PROM, realizing multiple functions in a single FPGA in different time. The cyclic EDAC function, not only guarantees configuration data correct in the E2PROM, but also achieves statistics of single event effect occurrence probability in external memory device, such as E2PROM.
A Reliable Distributed Process Management System

Distributed systems have been used to solve some complicated problems. In order to make better use of resources in distributed systems, people have designed many approaches and developed many systems to manage processes in distributed systems, but most of the management systems are paid little attention on reliability of them. There may be some attacks in complex network environment. Therefore the reliability of the management system is very important. In this paper, we propose a reliable distributed process management system (RDPMS), which provides a reliable and smart way to manage a lot of computers running numerous processes, not only do they improve the reliability but also put forward an innovative method for load sharing.
A Novel Multi-Cell OFDMA System Structure using Fractional Frequency Reuse

In this paper, we propose a novel frequency reuse scheme for multi-cell orthogonal frequency division multiplexing access (OFDMA) systems for co-channel interference reduction. Each cell is partitioned into three sectors. All the available subcarriers are divided into two groups. One is reused in the central region of the sectors, and another is divided into three parts used orthogonally for the edge of the three sectors (no overlap). Which subcarrier group a user can use is dependent on the location or the received signal to interference plus noise ratio (SINR) of the user. Thus, intra-cell orthogonality is guaranteed, and inter-cell interference can be greatly reduced. All the subcarriers may use the same transmit power in this scheme. Simulation results show that this scheme can bring higher system throughput and lower CCI, and it also can increase the data rate at the cell edge.
Distributed composition services for self-adaptation wireless sensor networks

The heterogeneity and mobility inherent in a large-scale wireless sensor network makes it difficult to develop different applications. The difficulty primarily arises from weak and intermittent connections, dynamic reconfiguration, and limited power availability. The challenge in building a distributed service adaptive to environmental changes lies in the degree of data transmission and the adaptation to the dynamics can be automated or transparent to applications. Most applications in wireless sensor networks involve a group of nodes coordinating to perform one task. Distributed composition service enables data centric nodes to form a dynamic task group and maintain the group. In this paper, we demonstrate the composition service and its implementation and show that the re-organization process can be automated in collaboration with the composition service by an adaptation server. Our testing validates the framework of the composition service and the performance improvement.
A Factor-Based Model for Context-Sensitive Skill Rating Systems

Estimating agent’s skill ratings from team competition results has many applications in the real world. Existing models assume skills are the same for all contexts. However, skills are context-sensitive in a variety of cases. In this paper, we present a Factor-Based Context-Sensitive Skill Rating System(FBCS- SRS). Instead of estimating agent skills under every context, which is hard due to data sparisity, we propose a factor model where individual skills are modelled by the inner product of an agent factor vector and a context factor vector. Collapsed Gibbs sampling is used for approximate inference. We formulate the problem of sampling linear constraint factors as a variant of MAX-SAT, and solve it by linear programming algorithms . We validate our model on two real-world datasets. Experiments show that FBCS-SRS achieves significantly higher prediction accuracy than other skill rating systems. The improvement is even more obvious when there are a lot of contexts.
A parent search method in peer-to-peer media streaming networks

Providing media streaming services over a peer-to-peer (P2P) network has attracted much attention in recent few years. In this paper, we focus on a tree structured P2P network for live media streaming. In our proposed P2P framework, the Prim-Dijkstra algorithm is used to construct the network spanning tree. A new fast parent searching method based on network triangle inequality is then developed for the newly arrived peers to join the P2P media streaming network. Simulation results show that the complexity of the parent searching procedure is greatly reduced using the proposed method.
Component Refinement and Its One Type in the Model of EOOPN

This paper studies the issue of component refinement on the bases of component evolution processes that has been proposed in authors previous research work focused on Extended Object-Oriented Petri Net. Components are refined through basic elements of software system's architectural components. The detailed idea of component refinement is discussed and a set of methods and rules relating to component refinement are provided under the framework of EOOPN model.
Dedicated Reference Signal Based Channel Estimation Using Weighted Averaging Scheme in OFDM Systems

The paper studies channel estimation methods with DRS in an OFDM system. As the DRS may only locate on part of the bandwidth, conventional methods, such as 2D Wiener filter and DFT based time domain estimation, cannot function well. A weighted averaging channel estimation method is proposed by which the channel responses estimated by LS estimation is averaged using Lagrange method. To verify the efficiency of the proposed method, both the MSE and block error rate BLER performances are evaluated. Simulation results show that the proposed method always outperforms LS estimation, and has similar performance as 2D Wiener filter when the influence of noise is more significant than channel variation in both the time and frequency domains.
Dispersion stability of indium tin oxide nanoparticles in butyl acetate

A colloidal dispersion of indium tin oxide (ITO) nanoparticles in butyl acetate solvent was achieved using a milling process in which PVP or PEG were used as a dispersant. The influences of Polyvinyl pyrrolidone (PVP) and Polyethylene glycol (PEG) dispersants on the stability of ITO suspension were studied by Zeta potential value, Nano-particle size measurement and sedimentation experiment. The results indicate that PEG dispersant dosage 5.5%, PVP 3.5% of the amount of slurry when the effect of dispersion can play, but in the storage time of 30 days, the dispersing agent PVP on the stability of slurry particles better than the PEG.
Area spectral efficiency of infrastructure-relay enhanced single-cell wireless systems

This paper investigates the area spectral efficiency (ASE) of infrastructure-relay enhanced single-cell wireless system. ASE serves as an attractive alternative performance metric by taking into account of the relatively smaller spatial footprint of relay transmission steps. We extend the notion of ASE into relay transmission scenario and derive its accurate mathematical expressions as a function of the coverage distances and transmitting power of both the BS and RSs with the consideration of the random user locations and the impact of path loss and fading effect. We show through selected numerical examples that the relay enhanced systems have the potential to achieve much better ASE performance over the conventional systems.
A 24GHz low power and low phase noise PLL frequency synthesizer with constant KVCO for 60GHz wireless applications

In this paper, a fully integrated 24GHz integer-N PLL for a 60GHz wireless transceiver is presented. A VCO with tail-feedback technique is used to improve the phase noise by modulating the tail current, and a switched varactor array is also adopted together with a switched capacitor array to compensate the variation of KVCO over different bands. Moreover, benefiting from the careful optimization of CML pre-scaling divider, the PLL operates correctly when VCO buffer is turned off, coming up with an extremely low power. The whole PLL is fabricated in standard 90-nm process. Measurement shows that the VCO achieves a phase noise of -102dBc/Hz@1MHz offset with a tuning range of over 16%. The KVCO variation over all bands is less than 16%. In close-loop operation mode the PLL shows an integrated phase error of 3.3°rms (from 100kHz to 100MHz) over prescribed frequency bands, and a total power dissipation of only 26mW.
A clonal selection algorithm based optimal iterative learning control algorithm

Improved clonal selection algorithms were proposed as a method to implement optimal iterative learning control algorithms. The strength of the method is that it not only can cope with non-minimum phase plants and nonlinear plants even there are uncertainties in their models, but also can deal with constraints on input signals conveniently by a specially designed mutation operator. Simulations show that the convergence speed is satisfactory regardless of the nature of the plants and whether or not the models of the plants are precise.
An integrated control strategy of battery energy storage system in microgrid

As an important smart-grid technology, microgrid is being developed rapidly for its advantages of high reliability, less pollution and improved system flexibility. This paper proposes an integrated control strategy of battery energy storage system (BESS) in a microgrid. It includes three control strategies, namely, PQ control, Droop control and V/f control. The integrated control strategy can switch among these three control strategies as required. In this paper, the simulation is performed and the whole model of the microgrid is built. The BESS uses the integrated control strategy, while the rest microsources use PQ control. Through the comparison and analysis, the applicability and effectiveness of the integrated control strategy has been verified in simulation.
An Adaptive Longitudinal Driving Assistance System Based on Driver Characteristics

A prototype of a longitudinal driving-assistance system, which is adaptive to driver behavior, is developed. Its functions include adaptive cruise control and forward collision warning/avoidance. The research data came from driver car-following tests in real traffic environments. Based on the data analysis, a driver model imitating the driver's operation is established to generate the desired throttle depression and braking pressure. Algorithms for collision warning and automatic braking activation are designed based on the driver's pedal deflection timing during approach (gap closing). A self-learning algorithm for driver characteristics is proposed based on the recursive least-square method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data in the manual operation phase, and the identification result is applied during the automatic control phase in real time. A test bed with an electronic throttle and an electrohydraulic brake actuator is developed for system validation. The experimental results show that the self-learning algorithm is effective and that the system can, to some extent, adapt to individual characteristics.
Dictionary Learning for Promoting Structured Sparsity in Hyperspectral Compressive Sensing

The ability to accurately represent a hyperspectral image (HSI) as a combination of a small number of elements from an appropriate dictionary underpins much of the recent progress in hyperspectral compressive sensing (HCS). Preserving structure in the sparse representation is critical to achieving an accurate reconstruction but has thus far only been partially exploited because existing methods assume a predefined dictionary. To address this problem, a structured sparsity-based hyperspectral blind compressive sensing method is presented in this study. For the reconstructed HSI, a data-adaptive dictionary is learned directly from its noisy measurements, which promotes the underlying structured sparsity and obviously improves reconstruction accuracy. Specifically, a fully structured dictionary prior is first proposed to jointly depict the structure in each dictionary atom as well as the correlation between atoms, where the magnitude of each atom is also regularized. Then, a reweighted Laplace prior is employed to model the structured sparsity in the representation of the HSI. Based on these two priors, a unified optimization framework is proposed to learn both the dictionary and sparse representation from the measurements by alternatively optimizing two separate latent variable Bayes models. With the learned dictionary, the structured sparsity of HSIs can be well described by the reweighted Laplace prior. In addition, both the learned dictionary and sparse representation are robust to noise corruption in the measurements. Extensive experiments on three hyperspectral data sets demonstrate that the proposed method outperforms several state-of-the-art HCS methods in terms of the reconstruction accuracy achieved.
Analysis of unrestrained curve of rectangular piston ring based on energy principle

A rotary engine with rectangle pistons and cylinders is claimed to have higher mechanism efficiency for eliminating the lateral force between piston skirt and liner. Constrained by the present machining and assembly accuracy, surfaces of piston could not fit the internal surface of cylinder well which might lead to gas leakage from cylinder. A novel ring used to seal rectangular cylinder was invented, and the working theory of the piston ring was present. Then based on the assumption of uniform pressure distribution on the contact surface of the ring the mathematical model of unrestrained curve of the ring was established. The modeling process was present in detail. Furthermore a finite element analysis was carried out as a way to validate the mathematical model of the piston ring. Results show that deformation of piston ring achieved from calculation and simulation meets well at initial and ending segments. The maximum error appears in the middle segment, which is only 11.52%. The accuracy of mathematical model is acceptable.
All-Fiber Passively Q-Switched Laser Based on Tm3+-Doped Tellurite Fiber

We report all-fiber passively Q-switched Tm3±-doped tellurite fiber lasers. The composite tellurite fiber is specially designed to improve the mechanical strength. Both carbon nanotubes (CNTs) and semiconductor saturable absorber mirror (SESAM) are inserted separately into the laser cavities as SAs to demonstrate a fiber-integrated setup. In a short, 9-cm tellurite fiber, 1.86-μm-pulsed lasers without self-modelocking effect are demonstrated by in-band pumping at 1.59 μm. An average power of 84 mW is obtained in CNT-pulsed laser with 860-ns duration, whereas in SESAM-pulsed laser, the average power reaches 21 mW with 516-ns pulsewidth.
Attitude control for multi-rotor aircraft with output constraints

In this study, an attitude control method for multi-rotor aircraft with output constraints and various disturbances is presented. To prevent output constraints violation, a Barrier Lyapunov Function (BLF) is introduced and the controller is designed based on backstepping algorithm. To enhance the robustness of the system, a linear extended state observer (LESO) from linear active disturbance rejection control (LADRC) is employed to estimate the disturbances and compensate the impact. It is proved that the proposed control algorithm guarantees the tracking error converging to zero asymptotically. Finally, simulation experiments validate the effectiveness and superiority of the presented control method.
Different mode conducted EMI of three-phase SCR rectifier: prediction and reduction

This paper analyzes the EMI that is generated from a three-phase SCR rectifier and the effect of some common reduction measures. According to the switching function, the noise equivalent circuit is obtained. Based on these equivalent circuits, the noise spectrum calculation can be performed in the frequency-domain. The experiment results show the validity of the calculation. Since the calculation is in the frequency-domain, it does not need many parasitic parameters and the calculation time can be greatly decreased compared with time-domain simulation.
Design and Manufacturing of Clutch in Tunnel Boring Machine

A non-asbestos semi-metallic stainless steel/carbon fiber reinforced PEEK-based friction material was developed and applied in Tunnel Boring Machine(TBM) driven clutch friction plate. The uniform design method of 5 factors and 27 levels was used to design and optimize the match composition. The friction plates were manufactured by dry-processing procedure, comprising mixing, pre-heating, cold pressing, adhesive coating, hot pressing and heat treatment. Friction and wear tests were carried out using a pin-on-disc type high temperature tribo-tester. The developed stainless/carbon fibers reinforced PEEK friction plates were used in Tunnel Boring Machine. The friction and wear mechanism was analyzed through observing the worn surface morphology by scanning electron microscope (SEM). The test results showed that the developed compound has better heat stability and wear resistance with slightly lower hardness and higher compress strength and impact toughness than asbestos composite, and these can decrease the wear of counter discs. The wear mechanism is mainly adhesive wear with lighter abrasive wear. The worn traces of developed friction plate were indistinct and damage degree of the counter plates was little, showing that the developed friction plate is suitable for TBM clutch Disc in hard working conditions of tunnel boring.