nils-herrmann/clean_scirepeval huggingface dataset:
Connecting AnnAGNPS and CE-QUAL-W2 models for reservoir water quality prediction

Jinpen Reservoir is a multi-purpose reservoir with the main objective of supply water for Xi'an city as well as irrigation, electricity generation and flood control . In recent years, nonpoint pollutions have threatened the water quality protection of water source area in the reservoir. To prevent the environmental pollution, protect the water quality of the ground water body, and to exert enough the productive potentialities of resources, it is important to control nonpoint pollutions in the protection zone of headwater area of a reservoir at present. We have connected two powerful watershed and water quality models (AnnAGNPS and CE-QUAL- W2). AnnAGNPS model outputs with the nonpoint source pollution loading as the CE-QUAL-W2 model input. The CE-QUAL-W2 model was applied to simulate water quality in the Jinpen Reservoir, studying of nonpoint source pollution on water quality of reservoir. The results show that when predicting the water quality of reservoir,  source pollutions should be treated differently in the flood and non-flood period.  pollution had great impact for water quality of Jinpen Reservoir in the flood period, but nonpoint source pollution had less impact for reservoir water quality in the non-flood period. The impact made a great difference between longitudinal and vertical water quality. Forest played a significant role for nonpoint source pollution reduction in the watershed.
Numerical Dispersion Analysis and Key Parameter Selection in Laguerre-FDTD Method

In this letter, a theoretical analysis of the numerical dispersion and key parameter selection of the finite-difference time-domain (FDTD) method with weighted Laguerre polynomials is presented. Different from the conventional FDTD method, the numerical dispersion relation is relevant not only to sampling point density in space domain but also to time-scale factor. For a fixed relative error of phase velocity, the suitable sampling point density and time-scale factor can be determined. In addition, the selection of the number of Laguerre functions is derived using the theoretical time-scale factor, and is verified in a numerical example.
A comparison of pixel-based and object-based land cover classification methods in an arid/semi-arid environment of Northwestern China

Land cover classification provides useful information of vegetation for land surface models especially in arid areas. Due to the complex landscapes in the downstream area of the Heihe River basin in northwest China, accurate land cover classification plays a key role in better understanding of the eco-hydrological processes, identification of water consumption by different land cover types and effective use of scarce water resources in this region. The aim of this study is to map land cover classification in the arid environment in the downstream area of the Heihe River basin by employing both pixel-based and object-based image analysis methods using high spatial resolution data. The data used in this study were 2.5 m high spatial resolution imagery (SPOT-5) and field survey. Vegetation indices and land surface texture characteristics were calculated from SPOT-5 imagery and used as input variables for classifications. In this study, overall classification accuracies between pixel-based and object-based classification methods were statistically significant when the same Support Vector Machine algorithm was applied. Object-based classification methods could avoid the salt-and-pepper noise existed in the pixel-oriented results and achieved more accurate depictions of land cover types in this region than the results using pixel-based algorithms. The accuracy assessment on the classification of single tree crown of Populus euphratica revealed more promising results by using object-based image analysis than using pixel-based classification methods. The low accuracy in identifying grassland class was due to the inadequate selection of samples for classifications in the study area. Inappropriate scale value of segmentation resulted in the low producer's accuracy of residential areas when using object-based algorithms, whereas the misclassification led to the low user's accuracy of residential areas when utilizing per-pixel methods.
Multi-objective optimization control of DFIG system under distorted grid voltage conditions

The paper presents the multi-objective optimization control strategy of a doubly fed induction generator (DFIG) under the distorted grid voltage conditions. Based on the mathematical model of DFIG in the positive synchronous reference frame under the distorted grid voltage conditions, the four different control targets are proposed to restrain the harmonic components in stator/rotor current or the ripples in the stator output active/reactive power and electromagnetic torque. Considering that the different control targets are independent and interactional, an enhanced control strategy based on the multi-object particle swarm optimization algorithm is proposed to ensure the compromising optimization of DFIG operation performance under the harmonically distorted grid voltage. The availability of the proposed control strategy is validated by the simulation of MATLAB/Simulink based on the 2MW DFIG_based wind power generation system.
Multi-level diffusion Kalman filter algorithm for adaptive networks

We study the problem of distributed state estimation over adaptive networks, where agents collaborate to estimate a common state parameter vector. If the sensing target area is too large or we want to improve the convergence speed of a large adaptive network, single-level diffusion algorithms do not have better performance, so we study the multi-level diffusion Kalman filter algorithm where a network running a diffusion strategy is enhanced by defining some special nodes called supernodes. In order to improve the estimation accuracy, we also study the weight-normalized diffusion Kalman filter algorithm. Simulation results show that multi-level diffusion Kalman filter algorithm has better accuracy and convergence performance than single-level diffusion Kalman filter algorithm. Furthermore, in order to further improve the algorithm's performance, we studied weight-normalized methods which are better than average weight methods.
Protective Exploitational Model of Marine Cultural Landscapes

Marine cultural landscape is one of the important types of the new world heritage-cultural landscapes. It was being destroyed by progress of urbanized tourism development. Correctly disposing the relation of protection and development of marine cultural landscapes reasonably become a hot topic studied by people. Base on discussing the significance of protective exploitation of marine cultural landscape, the authors set out some basic principles of utilization of the landscape. At last, the authors put forward that legislation, project, succession, festival and base are main protective exploitative model of marine cultural landscape.
Bidirectional CLLC resonant DC-DC converter with integrated magnetic for OBCM application

It has been understood the large drawback of a CLLC bidirectional serious resonant converter is using three magnetic components, i.e., the transformer and two resonant inductors. In this paper, an integrated magnetic transformer for simplifying and boosting the electrical vehicle (EV) and plug-in electrical vehicle (PHEV) on board charger module (OBCM) implementation and performance of a bidirectional CLLC resonant converter is proposed. The new integrated magnetic transformer including two primary windings, one secondary winding, bobbin, and a magnetic core, are designed and implemented. With the magnetic integration, the converter with the high power density is achieved while the number of its components and cost are reduced. The effectiveness of the proposed integrated magnetic transformer is evaluated with a 3.3kw bidirectional DC/DC converter. The experiential results show that our transformer can operate on the full voltage range of the converter with a high efficiency.
Sensor fault detection and isolation in small-scale autonomous helicopters

This paper deals with the problem of Fault detection and isolation for a small-scale autonomous helicopter. By using the unobservable and unobservability subspace concept, the smallest unobservability subspace is constructed to describe the fault characteristics. The residual designed by geometric approach is only sensitive to single sensor. Our proposed method is then applied to an autonomous helicopter. The component of the gyroscope angular velocity sensor has been used as representative case of sensor fault detection and isolation for three fault types (stuck with constant bias, additive type and multiplicative type). Numerical examples for sensor faults are given to show the validness of our proposed fault detection and isolation algorithm.
A New Reliable Routing Method Based on Probabilistic Forwarding in Wireless Sensor Network

Probabilistic forwarding is one of the routing methods that neighbors are selected as the next hop according to probability. The probability calculation method is different for different forwarding methods. Our work is based on selective forwarding and the selective forwarding probability based on the node degree and link loss is presented to increase the reliability of selective forwarding. The effect of parameters on performance is reflected in the simulation results and the qualitative analysis is done based on regular square network. Through the qualitative analysis, the calculation of forwarding probability is improved further
Multi-polarity text segmentation using graph theory

Text segmentation, or named text binarization, is usually an essential step for text information extraction from images and videos. However, most existing text segmentation methods have difficulties in extracting multi-polarity texts, where multi-polarity texts mean those texts with multiple colors or intensities in the same line. In this paper, we propose a novel algorithm for multi- polarity text segmentation based on graph theory. By representing a text image with an undirected weighted graph and partitioning it iteratively, multi-polarity text image can be effectively split into several single-polarity text images. As a result, these text images are then segmented by single-polarity text segmentation algorithms. Experiments on thousands of multi-polarity text images show that our algorithm can effectively segment multi-polarity texts.
Formal Analysis of Crosstalk Noise in Mesh-Based Optical Networks-on-Chip with WDM

Optical networks-on-chip (ONoCs) using wavelength division multiplexing (WDM) technology is one of the most promising candidates in tacking multichannel communication on the single waveguide in multicore systems-on-chip. However, compared to the traditional ONoCs with single wavelength transmitting, ONoCs employing WDM can be more susceptible to the influence of the crosstalk noise, especially the nonlinear noise generated by the four-wave mixing (FWM) effect. Consequently, the crosstalk noise can result in the signal distortion and performance degradation, even constrain the scalability of the WDM-based ONoCs. In this paper, a formal systematical analysis model including the crosstalk noise and optical signal-to-noise ratio (OSNR) analysis in mesh-based ONoCs using WDM is presented at device, router, and network levels. To demonstrate the feasibility of the proposed method, we exploit a numerical simulation example of mesh-based ONoCs using the optimized crossbar and Crux optical routers with eight optical wavelengths. The simulation results show that the crosstalk noise is the critical element for the OSNR diminution and the scalability constraint of the mesh-based ONoCs using WDM. For example, the input power is 0 dBm in the worst case, when the mesh-based WDM-based ONoCs size using optimized crossbar is 4 × 4, the average FWM noise power, linear noise power, and OSNR are -37.61 dBm, -38.86 dBm, and 4.26 dB, respectively, and these values are -37.78 dBm, -38.95 dBm, and -13.65 dB when the scale of network is 6 × 6.
A Cascaded Tunable DFB Semiconductor Laser With Compact Structure

A compact cascaded tunable distributed feedback semiconductor laser is proposed and theoretically analyzed. Each laser section (LS) is formed by two adjacent grating sections (GSs) with slightly different Bragg wavelengths and a <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> phase shift in the joint between them. A step-wise grating period profile is designed to realize multi-channel lasing. Since two LSs share a common GS, the total cavity length of the tunable laser is significantly reduced. As an example, a tunable laser with six GSs and five <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> phase shifts was designed and analyzed, resulting in a continuous tuning range of 12 nm. The total length of the laser is reduced by 40% compared with the conventional structure. Furthermore, an improved structure with apodized grating in each GS is also proposed. The single mode stability and fabrication tolerance are significantly improved. Due to the compact structure, the cost of the proposed laser can be highly reduced. Therefore, we think the proposed structure would benefit the practical applications to the low cost tunable lasers in wavelength-division multiplexing systems.
Context-based multiscale classification of document images using wavelet coefficient distributions

In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, and graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature-it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as well as fast classification overall-and its use of accumulated context information for improving classification accuracy.
Design of emergency map symbol

According to the three phrases of outburst accidents, this paper classes the emergency map as follows. They are forecast map, instant emergency map and recovery map. In broad sense, emergency map includes all above. But in narrow sense, it is referred to instant emergency map. This paper mainly discusses this kind of map. Then, the problems of emergency map are analyzed and concluded. There are three main problems: ignoring the characters of emergency environment, appearances of symbols are single and the deficiency of innovation on symbol design. What is more, the characters of emergency are analyzed. Based on this, principles of emergency map symbols are talked. At last, design methods of emergency mobile map and emergency great screen map are studied.
Design the overall framework of external information support system for GSS

External information plays an important role in group decision making process, yet research about the acquisition and use of external information in group decision making process has been lacking. Inherent weaknesses in individuals and groups to acquire external information have made it necessary to build an external information support system. This paper proposes an overall framework of external information support system for GSS and introduces its system architecture, major components and related technical issues in detail. Conclusions and future work are also discussed.
Cooperative spectrum sensing with sensing task allocation in cognitive radio networks

In the cooperative spectrum sensing with multi-primary-user, one secondary user may have different detection performance for different primary user, due to the different distance to the primary users. A novel idea for the cooperative sensing is to allocate the secondary users with excellent detection performance to sense the corresponding primary user. The sensing task allocation is based on this idea in this paper. We formulate the problem to an optimization problem, but its a NP-hard problem. Then we propose a greedy search algorithm with which have comparable performance with that of brute-force search and have much less computational complexity. At last, we analysis the impacts of the user number and the distance to the systemic cooperative sensing time, validating the advantage of sensing task allocation.
An improved bidirectional reflectance distribution function (BRDF) over rugged terrain based on moderate spatial resolution remote sensing data

Land surface albedo is derived from a bidirectional reflectance distribution function (BRDF) model with angular integration of BRDF. The kernel-driven model is based on the Algorithm for MODIS Bidirectional Reflectance Anisotropic of Land Surface (AMBRALS) to retrieve land surface albedo. However, at moderate/low spatial resolution scale, topography results in mutual shading of sub-pixels, the kernel-driven model does not deal explicitly with such topographic effects. This paper proposes an improved kernel-driven model by adding a topographic factor to the kernel-driven model. This model was applied to MODIS land surface reflectance and the results were compared with the ones obtained with AMBRALS. The results were evaluated against ground measurements.
Micro-localized cell lysis by low power Focused Acoustic Transducer

In this study, we designed and fabricated Self Focused Acoustic Transducer (SFAT) for micron-sized localized cytolysis. Monolayer 22RV1 prostate cancer cells were cultured in the cell culture chamber and locally lysed by the SFAT. Various electric powers and operating frequencies of actuating pulsed signal were applied to characterize the localized cell lysis effects. The cell lysis area was around 2.28×10-9 m2 and 1.64×10-9 m2, when the acoustic waves produced by the transducer were 17.3 and 52 MHz, respectively. The minimum electric power required for the cell lysis of 22RV1 is as low as 9 mW, which produces an acoustic intensity 0.15 W/cm2 at the focal spot. The amount of mRNA released in the culture media was increased more than 10 times after the cytolysis. According to experiment results, the size of lysed cells area is determined by the acoustic-wave frequency, and very little by the electric power applied to the device above a threshold. Signs of inertia cavitation phenomena such as bubble generation or temperature raise were not observed. Therefore, low-power micron-sized cell lysis without cavitation may have practical applications relating to cancer diagnosis and therapeutics.
A Data-Driven Metric for Comprehensive Evaluation of Saliency Models

In the past decades, hundreds of saliency models have been proposed for fixation prediction, along with dozens of evaluation metrics. However, existing metrics, which are often heuristically designed, may draw conflict conclusions in comparing saliency models. As a consequence, it becomes somehow confusing on the selection of metrics in comparing new models with state-of-the-arts. To address this problem, we propose a data-driven metric for comprehensive evaluation of saliency models. Instead of heuristically designing such a metric, we first conduct extensive subjective tests to find how saliency maps are assessed by the human-being. Based on the user data collected in the tests, nine representative evaluation metrics are directly compared by quantizing their performances in assessing saliency maps. Moreover, we propose to learn a data-driven metric by using Convolutional Neural Network. Compared with existing metrics, experimental results show that the data-driven metric performs the most consistently with the human-being in evaluating saliency maps as well as saliency models.
Neural network for prediction of composite mechanical properties based on niche genetic algorithm

This paper proposes to apply the artificial neural network theory and the genetic algorithm to solve the difficulties of predicting composite mechanical properties. Niche technique based on crowding mechanism is used in genetic algorithm, and punishing function is adopted to adjust individual fitness, so as to promote global search capability. Taking the wheat straw-reinforced composite for instance, four influence factors (mold temperature, mold pressure, fibre content and time) are selected to test its tensile strength and toughness. The simulation results show the founded network model has preferable learning and generalization capabilities, which performs effectively in predicting mechanical properties. Besides, the model is used to optimize process parameters of injection molding and find the range of best parameters.
A Compact Frequency Reconfigurable Rectenna for 5.2- and 5.8-GHz Wireless Power Transmission

A compact reconfigurable rectifying antenna (rectenna) has been proposed for 5.2- and 5.8-GHz microwave power transmission. The proposed rectenna consists of a frequency reconfigurable microstrip antenna and a frequency reconfigurable rectifying circuit. Here, the use of the odd-symmetry mode has significantly cut down the antenna size by half. By controlling the switches installed in the antenna and the rectifying circuit, the rectenna is able to switch operation between 5.2 and 5.8 GHz. Simulated conversion efficiencies of 70.5% and 69.4% are achievable at the operating frequencies of 5.2 and 5.8 GHz, respectively, when the rectenna is given with an input power of 16.5 dBm. Experiment has been conducted to verify the design idea. Due to fabrication tolerances and parametric deviation of the actual diode, the resonant frequencies of the rectenna are measured to be 4.9 and 5.9 GHz. When supplied with input powers of 16 and 15 dBm, the measured maximum conversion efficiencies of the proposed rectenna are found to be 65.2% and 64.8% at 4.9 and 5.9 GHz, respectively, which are higher than its contemporary counterparts.
Chinese prosodic phrasing with the source-channel model

The prosodic phrasing is a classic problem in nature language process, which is not only useful for text-to-speech(TTS), but for speech recognition, statistic machine learning etc.. This paper introduces and discusses the source-channel model for Chinese prosodic phrasing. Based on the basic idea, the Hidden Markov Model (HMM) and the improved source-channel model are both used to describe the phrasing problem. In the improved source-channel model, maximum entropy model is used, and the discriminative training is introduced. And the rhythm model is proposed to describe the property of the utterance. The phrase-length model and the foot-pattern model both are used to describe the rhythm model, respectively. The experiments show that this approach achieved a good performance for prosodic phrasing. The improved source-channel model achieve a better performance than the Hidden Markov Model. And the foot-pattern model is the better one as a rhythm model.
Classification of textured and non-textured images using region segmentation

The classification of general-purpose photographs into textured and non-textured images is critical for developing accurate content-based image retrieval systems for large-scale image databases. With the accurate detection of textured images, we may retrieve images based on features tailored for the corresponding image type. In this paper, we present an algorithm to classify a photographic image as textured or non-textured using region segmentation and statistical testing. The application of the system to a database of about 60,000 general-purpose images shows much improved accuracy in retrieval.