nils-herrmann/clean_scirepeval huggingface dataset:
Research on design process control of ground testing software for the small satellite

Ground testing software for the aerospace, quantify the software development process, to improve the level of control and management of the Testing software development process is to ensure the quality of the software, thereby ensuring the Aerospace small satellite model quality and reliability of an important way to improve the efficiency of software development. In this paper, based on the analysis of small satellite software development process, drawing on mature advanced process control models and thinking, and strive to build models suitable for small satellite testing software process control framework, model software process control method to control the process of the analysis of the development process from the underlying technology, and to establish a set of easy-to-practice testing software process integrated quality management system to improve the ability of small satellite control of the testing software development process.
Monitoring Co-seismic Deformation Fields of Bam Earthquake Using D-InSAR Technique

The Mw=6.5 Bam earthquake, taking place on 26th December 2003 in Iran, caused severe surface deformation. This paper described that it used the differential interferometric SAR (D-InSAR) technique and ENVISAT ASAR data to monitor the Bam co-seismic deformation fields. Firstly, it introduced the principle of D-InSAR and the method to select proper D-InSAR data pairs. Then we used ENVISAT ASAR data to carry out D-InSAR test in the Bam earthquake. We successfully got the Bam co-seismic deformation field. The shape and location of the fault caused by the Bam earthquake was also found in the coherence maps. The test proved that D-InSAR technique is a powerful tool to measure surface deformation and to study earthquake.
Unsupervised snore detection from respiratory sound signals

Acoustic monitoring and diagnosis of Obstructive Sleep Apnea (OSA) has attracted increasing attentions recently due to its advantages over traditional Polysomnography (PSG) based diagnosis. The performances of several existing approaches are often limited by several aspects such as large amount of training data and and high computational cost. In this paper, we proposed a novel snore detection method utilizing the Vertical Box (V-Box) algorithm to detect snore candidate, and then we calculate the spectral feature Mel-Frequency Cepstral Coefficients (MFCCs) of each candidate. Finally, K-Harmonic Means (KHM) clustering algorithm are used to achieve the general classification decision (snore or non-snore). Experimental results demonstrate that the overall accuracy of our proposed method is 95.6% with Positive Predictive Value (PPV) 96.0%, which is superior to the existing approaches.
Recognition of series arc fault based on the Hilbert Huang Transform

Series arc fault is a common fault in a power supply system. It could affect the reliability of power supply seriously and even cause safety accidents. A low-voltage series arc fault experiment platform was developed on the basis of the GB14287.4 standard in this paper. Series arc fault experiments were conducted with different types of load. Arc fault current signals of five cycles before and after arc occurred were analyzed by using Hilbert-Huang Transform. The current was resolved into several Intrinsic Mode Functions (IMF) by using the Empirical Mode Decomposition (EMD). Then the Hilbert spectrum was obtained by calculating Hilbert transform for IMF. The results show that Hilbert spectrum amplitude increased when arc fault occurred. Therefore Hilbert Huang Transform can be used to recognize the series arc fault.
Video Summarization with Global and Local Features

Video summarization has been crucial for effective and efficient access of video content due to the ever increasing amount of video data. Most of the existing key frame based summarization approaches represent individual frames with global features, which neglects the local details of visual content. Considering that a video generally depicts a story with a number of scenes in different temporal order and shooting angles, we formulate scene summarization as identifying a set of frames which best covers the key point pool constructed from the scene. Therefore, our approach is a two-step process, identifying scenes and selecting representative content for each scene. Global features are utilized to identify scenes through clustering due to the visual similarity among video frames of the same scene, and local features to summarize each scene. We develop a key point based key frame selection method to identify representative content of a scene, which allows users to flexibly tune summarization length. Our preliminary results indicate that the proposed approach is very promising and potentially robust to clustering based scene identification.
A Sparse Learning Framework for Joint Effect Analysis of Copy Number Variants

Copy number variants (CNVs), including large deletions and duplications, represent an unbalanced change of DNA segments. Abundant in human genomes, CNVs contribute to a large proportion of human genetic diversity, with impact on many human phenotypes. Although recent advances in genetic studies have shed light on the impact of individual CNVs on different traits, the analysis of joint effect of multiple interactive CNVs lags behind from many perspectives. A primary reason is that the large number of CNV combinations and interactions in the human genome make it computationally challenging to perform such joint analysis. To address this challenge, we developed a novel sparse learning framework that combines sparse learning with biological networks to identify interacting CNVs with joint effect on particular traits. We showed that our approach performs well in identifying CNVs with joint phenotypic effect using simulated data. Applied to a real human genomic dataset from the 1,000 Genomes Project, our approach identified multiple CNVs that collectively contribute to population differentiation. We found a set of multiple CNVs that have joint effect in different populations, and affect gene expression differently in distinct populations. These results provided a collection of CNVs that likely have downstream biomedical implications in individuals from diverse population backgrounds.
Development of fibre bundle probe for coherent anti-stokes Raman scattering microendoscopy

A fibre bundle has been developed for coherent anti-Stokes Raman scattering (CARS) microendoscopy. The polarisation scheme was employed to suppress the four-wave mixing (FWM) effect in the fibre. High collection efficiency of epi-CARS signals and isolation of excitation lasers are achieved. Epi-CARS and epi-FWM images were compared in different polarisation schemes. The use of a broadband dual wavelength waveplate offers spectroscopic imaging capability to the system, which was shown by using ex vivo imaging mouse tissues.
Analysing on characteristics of lightning shielding failure of UHVAC double circuit transmission line based on leader progression method

Shielding failure of lightning is the key problem for safe operation of UHV transmission lines. Taking the feature of 1000KV AC double circuit transmission lines into account, the leader progression method with proper criterion was employed. The lightning shielding failure rate of top phase, middle phase and bottom phase of two different transmission line tower types were compute, in three different geography conditions. The research work is helpful for improving the lightning performance of UHV transmission lines.
Characterization of Thin Metal Films Using Terahertz Spectroscopy

A theoretical model is presented to simulate the transmission of thin metal films. Using terahertz spectroscopy, the two key characteristic parameters, conductivity and thickness, are determined simultaneously by fitting the experimental data to the theoretical model. Three different metal films, aluminum, copper, and silver, are investigated. The measured conductivities are significantly less than the bulk values. The sample thicknesses obtained are consistent with those estimated by the quartz thickness monitor.
Optical intensity-based long-period fiber grating biosensors and biomedical applications [Life Sciences]

In this article, we present a novel long-period fiber grating (LPFG) biosensor that can employ the simple and cost-effective optical intensity-based signal process method instead of the complex and expensive optical spectrum-based method. The ionic self-assembled multilayers (ISAM) technique is utilized to immobilize a thin layer of biorecognition film on the surface of a specially designed turnaround-point (TAP) LPFG. The new sensor maintains the optical resonant feature to achieve the desired high sensitivity but can be interrogated using optical intensity-based signal-processing. The new TAP-LPFG biosensor may be used for real-time DNA forensic analysis.
A PDE-Based Noise Suppression Method of Contaminated Remote Sensing Images Generated by Increasing CCD Integration Time

After operating on orbit for certain periods, natural degradation of CCD will result in the issue that the image SNR begins to apparently decreasing. To increase image SNR, one of the feasible ways is to increase CCD integration time. However, more Gaussian-like thermal noise, which could blur the image and even decrease the compression ratio of onboard lossless compression, will be added into the image afterwards. Therefore, it is critical to adequately consider a way to mitigate this problem. PDE-based anisotropic diffusion has been used for a range of fields in image processing such as feature extraction and edge enhancement. Nevertheless, determination of gradient threshold K value in conventional methods still remains to be uncertain. Particularly, for noise suppression of the image contaminated by exposure-induced Gaussian noise, yet, there is no method proposed to estimate more accurate K value to make the restored image have similar quality as the one acquired before increasing integration time. For this reason, UK-DMC multispectral images are selected as the test data and a new thought is proposed in this paper to estimate K value based on estimation of local noise variance and root-finding algorithm. It is finally indicated that proposed estimation method can provide a more adaptive way to determine K value for anisotropic diffusion. ; Gaussian; integration time; anisotropic diffusion; gradient threshold
Monitoring the coseismic deformation of L'Aquila earthquake using D-InSAR technique

The Mw=6.3 L'Aquila earthquake, taking place on 6th April 2009 in Italy, caused severe surface deformation. This paper described that the differential interferometric SAR (D-InSAR) technique and ENVISAT ASAR data were used to detect the L'Aquila co-seismic deformation fields. Firstly it introduced the basic principle and data processing flowchart of D-InSAR. Then it introduced the ENVISAT ASAR data and SRTM DEM. And it selected 3 interferometric pairs. It illustrated the procedure of two-pass InSAR data processing. Then it got the earthquake coseismic deformation field. Through interpreting and analyzing the coseismic deformation fields in detailed, it got the maximum deformation about 23cm in LOS direction. At last, it got some valuable conclusions in monitoring the earthquake deformation using two-pass D-InSAR technique. The test proves that SRTM DEM can be used in two-pass D-InSAR for monitoring the earthquake deformation.
Area-detection fibre-optic system for spatially offset Raman spectroscopy and Raman tomography in reflection mode

A new design of a fibre-optic probe system is presented which can be used to simultaneously detect scattering signals from multiple channels for spatially offset Raman spectroscopy and Raman tomography in reflection mode. The fibre setup contained 32 source fibres with an auto-controlled optical switch and 40 detector fibres, which were deployed in a large-area planar interface. Phantom experiments were conducted using a prototype of the Raman instrument, and have demonstrated the practical applicability of the system design. The presented area-detection instrument provides an efficient platform for various reflectance applications of Raman modalities over a large area. It could also be potentially adopted for other diffuse optical-based spectroscopic and tomographic applications.
Performance of Monobit Digital Receivers with Inter-Symbol Interference

Implementation complexity and power consumption are the two most crucial problems in impulse radio (IR) communications, which is seen as a promising contender for broadband wireless systems. To address such issues, monobit sampling with Nyquist rate was introduced, and the corresponding receiver without inter-symbol interference (ISI) was obtained. In this paper, we consider the case with ISI, especially when the data rate is high, e.g. 100 mega-bits per second (Mbps). For implementation simplicity, the interference from other symbols is approximated as Gaussian noise, and thus the monobit receiver for the ISI case is obtained. To boost receiving performance, high autocorrelation training sequence is employed. The performance loss of the proposed receiver is analyzed, based on the typical Saleh-Valenzuela (S-V) model of indoor channels. The simulation and analysis results show that the proposed monobit receiver has only about 1dB signal-to-noise ratio (SNR) loss compared with the one without ISI, when the data rate is up to 250Mbps.
Social-aware data forwarding in smartphone-based Delay-Tolerant Networks

With the growing number of smartphone users, peer-to-peer ad hoc data forwarding is expected to occur more often. The forwarding performance improves when knowledge regarding the expected topology and the social context information of the networks. In this paper, we introduce a new metric for data forwarding based on social context information, in which node's social context information is used to calculate the social similarity utility between a node and destination, and the social connection of networks is used to calculate the betweenness centrality utility of a node. We combine two utility functions to derive the social strength among users and their importance. We also present social context-based data forwarding algorithm for routing decision. Extensive simulations on real traces show that the introduced algorithm is more efficient than other state-of-art algorithms.
Two-level indexing for high-dimensional range queries in peer-to-peer networks

Supporting complex and efficient lookup queries in peer-to-peer networks is challenging, though simple keyword based lookup queries are well supported by most deployed systems. This paper presents a two-level indexing structure built on Distributed Hash Table (DHT) aiming to support range queries on high-dimensional feature space in peer-to-peer network. Unlike most existing systems, where every node is responsible for a data partition, our design only utilizes a small part of the nodes to manage partitions. These partition nodes form the first level index. The second level index consists of one or more server nodes, which maintains links to each partition node. Additionally, a merge and split mechanism is designed to dynamically adjust the workload among nodes. Experimental results indicate that our system offers promising performance in terms of workload balance in churn networks. The flexibility to work with any DHT and the capability to support multiple feature spaces further make our proposed approach a feasible extension for file sharing networks.
Low-loss, all-fibre higher-order-mode dispersion compensators for lumped or multi-span compensation

A low-loss higher-order-mode dispersion compensator that enables practical implementation of lumped or multi-span dispersion compensation schemes is demonstrated. The connector-to-connector module loss is 3.16 dB, and yields -886 ps/nm of dispersion, corresponding to compensation of 200 km of NZDSF.
Research on Current-Carrying Wear Characteristics of Friction Pair in Pantograph Catenary System

To solve the serious wear problem of pantograph slide in electrified railway pantograph catenary system, lots of current-carrying wear experiments under different conditions were carried out with a self-developed sliding electrical contact testing machine. The experimental materials are immersed copper carbon slide and copper contact wire respectively. Relationships between wear rate of slide and contact pressure, contact current and sliding speed were analyzed. The F-test method was used to compare the influence degree of three factors on wear rate based on variance analysis table. The optimal nonlinear functions between wear rate and single influencing factor were obtained by curve fitting method. A mathematical model of the wear rate was established by the multi-variable nonlinear regression method. The conclusions have certain reference value to research of the current-carrying wear characteristics of friction pairs in pantograph catenary systems.
Partial diffusion Kalman filter for communication bandwidth constraints

In this paper, we propose a novel distributed Kalman filter (KF) over sensor networks using partial diffusion strategy, where each sensor only communicates with its neighbours and individual estimation vectors are partially transmitted to obtain local ones through convex combinations under communication bandwidth constraints. Proposed solution can obtain a trade-off between communications burden and estimation accuracy. Simulation results show the effectiveness of our algorithm, and it can obtain similar estimation accuracy with less information exchange when compared with the weighted KF.
A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network

Short term load forecasting (STLF) has an essential role in the operation of electric power systems. In recent years, artificial neural networks (ANN) are more commonly used for load forecasting. However, there still exist some difficulties in choosing the input variables and selecting an appropriate architecture of the networks. This paper presents a novel fuzzy-rough sets based ANN for STLF. The fuzzy-rough sets theory is first employed to perform input selection and determine the initial weights of ANN. In the sequel, an improved k-nearest neighbor (K-NN) method is used for the selection of similar days in history as the training set of ANN. Then ANN module is trained using historical daily load and weather data selected to perform the final forecast. To demonstrate the effectiveness of the approach, short-term load forecasting was performed on the Hang Zhou Electric Power Company in China, and the testing results show that the proposed model is feasible and promising for load forecasting
Developed case-based reasoning system for short-term load forecasting

Short term load forecasting (STLF) has an essential role in the operation of electric power systems. A developed case-based reasoning (CBR) system is presented to solve STLF problem with the aid of self-organizing maps (SOM) and fuzzy-rough sets method. CBR is composed of the steps of case representation, indexing, retrieval, and adaptation, and the key idea in CBR involves the use of already existing knowledge about objects or situations to predict aspects of similar objects. SOM are trained as a cluster tool in order to organize the old cases with the purpose of speeding up the CBR process. Fuzzy-rough sets method further extends the rough set concept through the use of fuzzy equivalence classes and is presented as a tool to extract principal case attributes. This method uses not only case-specific knowledge of past problems, but also uses additional knowledge derived from the clusters of cases and it provides a new way for selecting proper feature subset and feature weights. To demonstrate the effectiveness of the approach, short-term load forecasting was performed on the Hang Zhou Electric Power Company (HZEPC) in China, and the testing results show that the proposed model is feasible and promising for load forecasting
Experimental research on radiated electroma gnetic noise of pantograph arc

In recent years, with the continuous increasing of electric locomotive running speed, electromagnetic noise of pantograph arc gets more and more serious and has become a kind of security risk of electric railway's operation. To study the electromagnetic noise and suppression methods of pantograph arc, an electromagnetic noise experimental system was developed. The experimental system is composed of an echoic chamber, pantograph arc generator and pantograph arc detection system. With the experimental system, 64 groups of radiation electromagnetic noise experiments were conducted under different contact force, contact current and sliding speed conditions. The experimental materials are metal impregnation carbon slide and Cu contact wire respectively. The radiated electromagnetic noise of pantograph arc is mainly electric field noise. Most of the electric field noise is distributed in the frequency ranges from 30MHz to 500MHz. And the maximum interference is between 30MHz and 80 MHz. With the increase of contact current, the distribution of the electric field noise becomes denser and the disturbance level increases. With the increase of contact pressure or sliding speed, the distribution of the electric field noise firstly becomes thinner and then denser.
Hierarchical Gaussian Mixture Model for Image Annotation via PLSA

In order to mimic the representation of textual documents, some approaches have recently been proposed to represent visual contents in terms of visual words in many applications such as object recognition and image annotation. In this paper, we propose to build an effective visual vocabulary by using Hierarchical Gaussian Mixture model instead of traditional clustering methods. In addition, Probabilistic Latent Semantic Analysis is employed to explore semantic aspects of visual concepts and to discover topic clusters among documents and visual words so that every image is projected on to a lower dimensional topic space for more efficient and effective annotation. Experimental results obtained on TRECVID 2005 dataset demonstrate that the Hierarchical Gaussian Mixture model can achieve better annotation performance than hierarchical k-means clustering even by using simple k-NN annotation scheme.
Research on Model Criterion and Mapping of Simulation Computation

Methods of natural computation development (such as simulation computation), have not been completely standard. These concrete problems are as follows: nonstandard state of models, and irregularity of the procedure of model developing. Because of the deficiency of common standard and criterion, the models of simulation systems under "chimney pattern " are repeatedly developed, hard to be interconnected , interacted, and efficient, leaving total waste of money and efforts. By making the criterions of concept model, component model and federate model (adopting HLA/RTI standard, IEEE 1516), this paper focuses on the standardization of the procedure of simulation system development. Different layer's model criterions are the mapping base between different layers that makes the realization of conversion more efficient and convenient. At the same time, these conversion patterns also provide an important function for automatic generation tools: supporting the automatic mapping of different models, which makes development work more efficient and valid.