nils-herrmann/clean_scirepeval huggingface dataset:
Temperature characteristics of sliding friction pair under high-speed and strong-current conditions

To reduce the influence of temperature distribution of pantograph-catenary system on the electrical contact characteristics, a temperature field simulation model was established with COMSOL Multiphysics software. The validity of the model was verified with temperature experiments. Both thermal time constant and steady temperature rise of contact wire were simulated under flexible contact pressure conditions. The thermal time constant decreases gradually and the steady temperature rise firstly decreases then increases with the increase of sliding speed. The thermal time constant are kept constant and steady temperature rise changes slightly with the increase of contact current. When sliding speed and contact current are kept constant, both the thermal time constant and steady temperature rise under flexible contact pressure conditions are smaller than that of under rigid contact pressure conditions. The transient temperature field was also analyzed and the high temperature trailing phenomenon was found and discussed. The conclusions can be used to further research the temperature characteristics of pantograph-catenary system.
Interaction between microspheres of collagen/biphasic calcium phosphate and mesenchymal stem cells

Collagen has been widely used as biomaterials for its excellent biocompatibility and the potential to guide tissue regeneration. Based on the inducing regeneration concept, collagen/biphasic calcium phosphate microspheres (BCP) were prepared by utilising emulsion polymerisation and the intrinsic self-assembly of collagen. The bioactivity of the micro collagen/BCP composites was preliminarily evaluated by in vitro co-culture with mesenchymal stem cells (MSCs) and the results indicated that the composites can provide an appropriate environment for the proliferation and osteogenic differentiation of MSCs and have a potential clinical application in the bone tissue engineering field.
Exploring Kernel Based Spatial Context for CNN Based Hyperspectral Image Classification

Convolutional Neural Network (CNN) has received remarkable achievements in hyperspectral image (HSI) classification. However, how to effectively implement spatial context that has been demonstrated to be crucial for classification of HSI is still an open issue. Current CNNs for hyperspectral classification are restricted into a small scale due to small-scale input and limited training samples. Therefore, in this paper, two different ways are proposed to implement both spatial context and spectral signature into CNN based classification of HSI: 1). fixed kernels in which weights are determined by prior information, i.e., mean, mean and standard deviation of pixels in a spatial neighborhood, and Gaussian kernel; 2). learnable kernels in which weights are learned from training samples, i.e., 2D learnable kernel, 3D convolutional kernel, and 2-Layer kernel. In the successive CNN for classification of HSI, dropout and batch normalization are also used to improve the classification performance of hyperspectral images under small sample conditions. Experiments on two- known HSIs demonstrating that, in the considered small-scale CNN, fixed kernels are more effective than learnable kernels to explore spatial information for classification of HSIs, especially for the case with small number of training samples.
Detecting and assessing the land subsidence in coal mining area using PALSAR data based on D-InSAR technique

Land subsidence induced by mining coal is one major issue in terms of damage to infrastructures. In this paper, the potential of L-band repeat-pass differential SAR interferometry for land subsidence in coal mining area is evaluated using ALOS PALSAR data. The focus is to detect and assess the land subsidence using PALSAR data based on D-InSAR technique. Firstly it introduced the basic principle and some applications of D-InSAR. Then it introduced the information of PALSAR and test data of coal mining area. It described the procedure of data processing of two-pass D-InSAR. And it got the deformation of coal mining area. It carried out interpretation and analysis on the D-InSAR results in details. The maximum settlement of land subsidence in this coal mining area was up to 42.4cm in vertical direction in 46 days. At last, it got some valuable conclusions in monitoring the land subsidence in coal mining area using two-pass D-InSAR technique. The test proves that PALSAR data is suitable for monitoring the land subsidence in areas with vegetated land cover, such as coal mining area.
Video summarization via temporal collaborative representation of adjacent frames

The ever increasing volume of video content demands to develop efficient and effective video summarization (VS) techniques to manage the video data. Recent developments on sparse representation have demonstrated prospective results for VS. In this paper, in consideration of visual similarity of adjacent frames, we formulate the video summarization problem with a temporal collaborative representation (TCR) model, in which the adjacent frames instead of an individual frame are taken into consideration to avoid selecting transitional frames. In addition, a greedy iterative algorithm is designed for model optimization. Experimental results on a benchmark dataset with various types of videos demonstrate that the proposed algorithms can not only outperform the state of the art, but also reduce the probability of selecting transitional frames.
Improving News Video Annotation with Semantic Context

Automatic video annotation has been proposed to bridge the semantic gap introduced by content based retrieval so as to facilitate concept based video retrieval. Recently, utilizing context information has emerged as an important direction in automatic visual information annotation. %Most of existing approaches aim to assign linguistic terms to visual information without considering its given context. In this paper, we present a novel video annotation approach by utilizing the semantic context extracted from video subtitles. The semantic context of a video shot, which is formed by a set of key terms identified from subtitles of the video, is utilized to refine the initial annotation results by exploiting the semantic similarity between those key terms and the candidate annotation concepts. Similarity measurements including Google distance and WordNet distance have been investigated for such a refinement purpose. In addition, visualness is utilized to further discriminate individual terms for better refinement granularity. Extensive experiments on TRECVID 2005 dataset have been conducted to demonstrate significant improvement of the proposed annotation approach and to investigate the impact of various factors.
Monobit Digital Receivers for QPSK Modulation Using Impulse Radio

Future communication system requires large bandwidths to achieve high data rates, rendering high-resolution analog-to-digital converter (ADC) a key bottleneck due to its high complexity and large power consumption. In this paper, we consider monobit digital receivers for QPSK modulation. First, the optimal monobit receiver under Nyquist sampling is derived. Its performance is calculated in the form of deflection ratio. Then a suboptimal but low-complexity monobit receiver is obtained. The impact of the phase offset is investigated, and the interface with error-control decoder is given. Numerical simulations show that the low-complexity suboptimal receiver suffers 3dB signal to noise ratio (SNR) loss in AWGN channels and only 1dB SNR loss in multipath channels compared with the matched-filter based monobit receiver with full channel state information (CSI).
Green Design and Humanized Design in High Speed Train's Industrial Design Management

High speed train, been as a new transportation, revealed its advantage in speed and clean energy of power when compared to the other modern transportation means. Meanwhile, green design and humanized design have drawn a great concern within contemporary design trends, such trends originated from the rising environmental consciousness and continuously focusing on the needs of human-being in modern age. In the process of high speed train's industrial design development, the introducing of green design and humanized design could benefit this new transportation to adapt itself better to meet the contemporary social requirements. But it can only be achieved in maximum under the instruction of effective design management, and in this way, the expected macro-economical and social profit will be produced successfully.
Harvesting Web Images for Realistic Facial Expression Recognition

Large amount of labeled training data is required to develop robust and effective facial expression analysis methods. However, obtaining such data is typically a tedious and time-consuming task that is proportional to the size of the database. Due to the rapid advance of Internet and Web technologies, it is now feasible to collect a tremendous number of images with potential label information at a low cost of human effort. Therefore, this paper proposes a framework to collect realistic facial expression images from the web so as to promote further research on robust facial expression recognition. Due to the limitation of current commercial web search engines, a large fraction of returned images is not related to the query keyword. We present a SVM based active learning approach to selecting relevant images from noisy image search results. The resulting database is more diverse with more sample images, compared with other well established facial expression databases CK and JAFFE. Experimental results demonstrate that the generalization of our web based database outperforms those two existing databases. It is anticipated that further research on facial expression recognition or even affective computing will not be restricted to traditional 7 categories only.
Efficient high-dimensional retrieval in structured P2P networks

Known by its exceptional scalability and flexibility, Peer-to-peer (P2P) technique is arguably one of the most important mechanisms for sharing massive data (e.g. media data). It has been challenging to support similarity search in structured P2P networks, though it provides efficient indexing for exact search. In this paper, we present an efficient indexing technique to support complex similarity retrieval on high-dimensional data by improving existing approach Multi-dimensional Rectangulation with Kd-trees (MURK). In order to make search more user-centric, relevance feedback techniques are also investigated. To the best of our knowledge, it is the first attempt of utilizing relevance feedback in structured P2P networks. Simulations for content based music retrieval with multiple acoustic features have been conducted to investigate the properties and efficiency of the proposed approach.
Adaptive reference frame selection for near-duplicate video shot detection

Near-duplicate video shots provide critical visual link between videos and detecting such video shots efficiently and effectively is of paramount importance in many applications such as detecting copyright infringement. In this paper, we propose an improved near-duplicate video shot detection approach by adaptively selecting reference frames for more effective shot representation. The correlation between adjacent frames is measured with Pearson's Correlation Coefficient (PCC) so that a set of compact yet representative reference frames can be selected adaptively in terms of content variation within video shots. Interest points are further extracted from the selected frames to effectively represent shot contents for similarity matching. Comprehensive experimental results on TRECVID-2008 corpus demonstrate that our proposed approach outperforms the state-of-the-art method effectively.
Atmospheric thermal lens induced by high-power CO/sub 2/ laser beams in laser materials processing

The atmospheric thermal lens induced by high-power CO/sub 2/ laser beams in laser materials processing and its influence on beam propagation and focus are studied in this paper. It is found in experiments that the atmospheric thermal lens is generated when the air in the beam propagation path is stationary. But it can be eliminated by blowing air with a certain pressure into the beam's guidance system. Also, the behavior of the beam's propagation under the case of the thermal lens deviates from Gaussian theory. This deviation is calculated by the linear and nonlinear models of the atmospheric thermal lens under the conditions of a large beam diameter and high power intensity. In addition, the influence of the thermal lens on the beam's focus size and position is also discussed in this paper.
Computer aided system of screening and sorting in data processing for non-patent literature

A computer aided system is proposed to screen and sort the non-patent literatures in this paper. It is introduced that the present situation and strategy of screening in data processing. The system here is a man-machine interactive system. First, knowledge and experience which stand for the know-how of the experts were inserted into the knowledge base, including the screening rules and principle in corresponding fields. Then, reasoning was carried out, including the rule matching, fuzzy matching and further data fusion. International Patent Classification-IPC is acquired, this non-patent literature was matched to the right type of indexing table. According to the self-learning function of expert system, the outcome was inserted to knowledge base as the new rule for next matching. It is known that each one had submitted the IPC fields which he was skillful in. The literature then was selected into the optimum index person. The result shows that it is satisfied that the error could be avoided. The work efficiency could be increased. The screening and sorting result could be normalized and standardize.
Joint Viterbi decoding and decision feedback equalization for monobit digital receivers

In ultra-wideband (UWB) communication systems with impulse radio (IR) modulation, the bandwidth is usually 1GHz or more. To process the received signal digitally, high sampling rate analog-digital-converters (ADC) are required. Due to the high complexity and large power consumption, employing multibit high-rate ADC is impractical. However, monobit ADC is appropriate. The optimal monobit digital receiver has already been proposed. This kind of receiver has been derived under the assumption that the intersymbol interference (ISI) either does not exist or can be regarded as random noises. When encountered with heavy ISI, these receivers are not excellent as we expected. There are many approaches to solve the ISI problem in regular communication systems. When applied to monobit systems, unfortunately, most of them turned out to be unavailable due to the great loss of quantification. Decision feedback equalization (DFE) is an effect way to deal with ISI in monobit digital systems. In this paper, we propose an algorithm that combines Viterbi decoding and DFE together for monobit receivers. In this way, we suppress the impact of ISI effectively, thus improving the bit error rate (BER) performance. In addition, we introduce a method called state expansion by which better BER performance can be achieved. Under the condition of perfect channel state information(PCSI), the simulation results show that the algorithm has about 1dB SNR gain compared to separate monobit BPSK demodulation with convolutional decoding and 1dB SNR loss compared to the BER performance of optimal monobit receiver in the channel without ISI. Compared to the full resolution detection in fading channel without ISI, it has 3dB SNR loss after state expansion. Under the CSI that achieved from iterative evaluation, both the performance of optimal monobit receiver and joint receiver have 1dB loss compared to that of PCSI.
Monitoring Land Subsidence in Suzhou City Using D-InSAR Technique

This paper mainly discusses how to monitor the land subsidence in Suzhou city using differential Interferometric Synthetic Aperture SAR (D-InSAR) technique. It firstly introduces the principle and data processing flowchart of D- InSAR. Then it carries out test and gets the land subsidence in Suzhou city using two pass D-InSAR technique. At last, it analyzes some problems which influence the application of D- InSAR in monitoring the urban land subsidence in detail. D-InSAR;land subsidenc;2-pass;ERS
Monitoring the Coal Mining Subsidence in Jibei Mine Area Using D-InSAR Technique

This paper mainly discusses the application of D- InSAR technique for monitoring the coal mining subsidence in Jibei mine area. Firstly, it introduces the principle and data processing of D-InSAR technique. Then it uses ENVISAT ASAR data and SRTM DEM to carry out 2-pass D-InSAR test. It gets the deformation map and interprets the result. At last, it analyzes some problems to study further in monitoring the mining subsidence using D-InSAR technique. The test proves that D- InSAR technique is a powerful and effective tool to measure the mining land subsidence.
Leaf Image Classification with Shape Context and SIFT Descriptors

Nowadays leaf image classification is very useful for both botanists and ordinary users since advanced imaging devices such as smart phones make it ever easier to capture leaf images for various tasks such as retrieval and classification. Most of existing approaches mainly utilize global shape features. In this paper, we propose to improve leaf image classification by taking both global features and local features into account. As one of the most effective shape features, shape context is utilized as global feature. And SIFT (Scale Invariant Feature Transform) descriptors that have been successfully utilized for object recognition and image classification are selected as local features. Finally, weighted K-NN algorithm is utilized for classification. Experimental results on the large ICL dataset demonstrate that the proposed method outperforms the state-of-the-art.
Application of TOFSIMS for Contamination Issues in the Assembly World

Within the assembly world, problems related to package contamination, which stem from material sourcing and assembly process excursions, can impact product reliability. From the reliability perspective, it is essential to achieve integrity at all internal interfaces in the flip-chip, wire-bond, and novel mixed technology packages. In many instances, both organic and low-level ionic contaminants have resulted in interface delamination, metal migration, microcracking, etc., which leads to a premature failure of products either in reliability tests or in the field. Several examples of failure analysis relating to package contamination in the assembly world and the effectiveness of time-of-flight secondary ion mass spectroscopy in isolating and understanding failures are highlighted in this paper.
Modeling closed kinematic chains via singular perturbations

Closed kinematic chains (CKC) are constrained multibody systems the dynamics of which are characterized by differential-algebraic equations. In this paper we present a novel approach to modeling CKC that supports model-based control. The approach is based on a singular perturbation formulation with attractive properties including (i) the number of slow second-order differential equations is the same as the number of degrees of freedom of the system, (ii) the domain of definition of the singularly perturbed system is the entire singularity-free workspace of the CKC. We illustrate the effectiveness of our approach by simulating the dynamics of the parallel Rice Planar Delta Robot.
High throughput analysis of drug effects on single breast cancer cells using droplet-microfluidic devices

This paper reports the analysis of drug effects on single breast cancer cells using the droplet-microfluidic devices for the first time. Hundreds of droplets can be generated in less than 5 minutes. Each droplet serves as a nanoliter-volume titer plate. About 50% of the droplets contain a human breast cancer cell MDA-MB-231 and Cytochalasin D drug solution. The drug effects on each cell are monitored by analyzing the fluorescence image of each cell. Clear change of the cell shape and size has been observed after incubation, suggesting this approach is a potential viable technical platform for drug effect analysis and for high throughput drug screen and discovery.
Spectral-spatial Endmember Extraction by Singular Value Decomposition for AVIRIS data

Spectral Mixture Analysis (SMA) has been widely utilized for hyperspectral remote sensing image analysis and quantification to address the mixed pixel problem, in which Endmember Extraction (EE) plays an extremely important role. Distinct from the traditional EE algorithms which are only based on spectral information, a novel EE algorithm integrating spectral characteristics and spatial distribution is proposed in this paper. Purity of pixels presenting in a spatial neighborhood (SN) is examined by the Singular Value Decomposition (SVD) based on not only spectral characteristic but also spatial distribution, which effectively addresses the spectral deviation problem. Spectral deviation inside an SN is eliminated by selecting the average of the pixels in pure SNs as endmember candidates, while spectral deviation among different areas in an image is eliminated by clustering these endmember candidates. In addition, a graph theory based spatial refinement algorithm is proposed to reduce the number of endmember candidates, which can save a lot computation in the subsequent clustering step. Experimental results on AVIRIS hyperspectral data demonstrate that the proposed Spectral-spatial EE algorithm outperforms the other three popular EE algorithms, N-finder algorithm (N-FINDR), unsupervised fully constrained least squares (UFCLS) algorithm, and the automated morphological endmember extraction (AMEE) algorithm.
Band gap engineering of graphene: From the point view of polymerization of porphyrins

The electronic properties of one-dimensional graphene nanoribbons, which could be regarded as the polymerized porphyrins, have been investigated using the first-principle methods. The types of the porphyrins have a great influence on the band gaps (Eg) of the graphene nanoribbons. The Eg as large as 1.48eV can be reached, which is large enough for the design of the logic nanoelectronic devices. The results suggest that the design and polymerization of porphyrins could be a promising band gap engineering method for graphene.
Differential diagnosis of human lung cancer — A label-free and chemistry-sensitive approach

Lung carcinoma is the most prevalent type of cancer, and it is responsible for more deaths than other types of cancer . Unlike other imaging modalities used for lung cancer diagnosis, such as magnetic resonance imaging (MRI) and computed tomography (CT), coherent anti-Stokes Raman scattering (CARS) offers submicron spatial resolutions and video-rate temporal resolution along with chemical specificity. This method potentially allows label-free differential diagnosis of lung cancer at high resolution. This study aims at demonstrating the feasibility of using CARS to differentiate normal, benign and different subtypes of lung cancers. Human lung lesions, including small cell carcinoma, adenocarcinoma, squamous cell carcinoma and intestinal fibrosis, were imaged ex vivo using a CARS microscope and compared to their corresponding hematoxylin and eosin (H&E) staining results. Our data shows that CARS is capable of identifying cellular features in a similar way as H&E stain, and these features could be used to characterize different types of lung lesions from each other. Therefore, the CARS technique offers a new strategy for differential diagnosis of lung lesions using pathologically prevalent criteria, and thus could be further developed for in vivo and real time diagnosis of early stage lung cancer.