nils-herrmann/clean_scirepeval huggingface dataset:
Defect level calculation: the importance of accurate models for defect distribution and multiple fault coverage in low yield situations

The authors studied multiple-fault coverage versus single-fault coverage and compared these to the traditional models. They found that, for low-to-moderate single-fault coverage, fault exposure and fault masking may be considerable. For single-fault coverages above 90%, however, it was observed that traditional models are surprisingly good. Defect-level calculations reveal that, for the benchmark circuits C432 and C499, satisfactory accuracy is achieved at high single-fault coverages when ignoring fault exposure and fault masking. However, circuit function and circuit topology must be considered in each case.<<ETX>>
A novel fuzzy approach for phishing detection

Phishing is one of the luring techniques used by phishers in the intention of exploiting the personal information. Phishing website is a fake website that looks similar to legitimate site in terms of interface and uniform resource locator (URL) address. Therefore, the numbers of victim have been increasing due to inefficient methods using blacklist to detect phishing. This paper proposed a new technique that apply fuzzy logic based on the features of URL to detect phishing sites. The proposed technique was evaluated with the dataset of 11,660 phishing sites and 5,000 legitimate sites. The results show that the technique can detect over 98% phishing sites.
Adaptive backstepping hierarchical sliding mode control for uncertain 3D overhead crane systems

This paper presents an adaptive backstepping hierarchical sliding mode control algorithm for uncertain 3D overhead crane model. Backstepping sliding mode is constructed based on hierarchical structure to guarantee tracking for trolley and anti-swing for load. Neural network is adopted to approximate the uncertain terms. The disadvantage of sliding mode control is fixed by changing signum function to saturation function. The purposes of this paper are to use RBF neural network to approximate nonlinear function of crane, design hierarchical sliding mode controller based on Lyapunov theory.
Core losses measurement technique for high frequency and flux density of switched reluctance machines

The accurate prediction of core loss play an important role in improving switched reluctance motor (SRM) performance. However, the calculation of iron losses in SRM is especially complex because the flux waveforms are non-sinusoidal. This paper describes a core loss measurement method which comprises core loss model based on finite-element analysis to determine flux waveforms and measurement technique to calculate core loss rates in high-speed switched-reluctance machines. The core loss density of different parts was compared with the loss data supplied by lamination steel manufacturers and evaluated in dynamic experiment. The method was applied to a 30 kW, three-phase 6/4 SRM, and the calculated results were compared with experimentally obtained measurements.
A robust adaptive direct controller for dealing with non-linear first order systems

We present new results on designing a robust adaptive direct controller for a class of nonlinear first order systems, based on the use of a dead zone in the parameter update law. It is shown that the size of the dead zone does not depend on the upper bounds of the disturbances. However, in the ideal case of exogenous signal function and the function represents unmodeled dynamics of the systems equal to zero, the proposed controller does not mean the convergence to zero of the tracking error. Computer simulation results show the effectiveness of the controller in dealing with the stated problems.
Control the photovoltaic grid-connected system using fuzzy logic and backstepping approach

This paper presents a comprehensive method to design a compatible controller system for the photovoltaic (PV) singlephase grid-connected system. The demonstrative PV system in this study consists of four 250W solar panels with a nominal total power of 1 kW, a non-inverting buck-boost DC-DC converter, and a DC-AC inverter, including a LCL output filter. Main objectives of the PV system in use are threefold: tracking and operating at the maximum power point (MPP) of the PV array, regulating the DC link voltage to 200 V, and delivering the power to the 110V/60Hz electric grid with unity power factor (PF). To fulfill three above goals respectively, the designed controller system is composed of three major modules. In which the first is the MPPT Controller module, using an improved incremental conductance (INC) algorithm based on fuzzy logic. Another is the DC Link Voltage Regulator module constituted by a PI-Fuzzy hybrid controller. And the last is the Current Controller module based on the backstepping approach. Simulations show that the proposed controller system completely accomplishes listed aims even when the solar radiation and temperature change suddenly.
Control of propylene - propane distillation process using Unisim® design

The paper addresses the control problem of a binary distillation processes. There are analyzed the quality specifications of separated products and also the distributed and multivariable character of the process. The robustness of the control system lies in its simplicity versus the investment. The paper presents the studies made for development and numerical testing of three types of control structures for propylene-propane separation process: the feedback control structure, the feedforward control structure and the optimal control structure.
A new bitplane coder for scalable transform audio coding

This paper proposes a new bit plane coding method for signed integer sequences. This method consists in mapping successive bit planes onto quinary symbols (+, “, 0, 1, EoP), where the symbol “EoP” stands for “End of Plane”, and applying arithmetic coding. Sign bits are efficiently coded in combination with the corresponding most significant bit of non-zero integers. Moreover, bit planes are scanned and coded in a non-sequential manner to exploit the correlation between successive planes. Results for conversational transform coding of wideband speech and audio signals - sampled at 16 kHz - show that the performance/complexity of the proposed bitplane coder is near equivalent to non-embedded coding (stack-run coding), while offering additional flexibility (bitstream scalability).
A new concept of Bit Geometrical Uniformity and its application to BICM-ID systems

A new concept of bit geometrical uniformity (BGU) is introduced to help the analysis and estimation of performance of systems with iterative detection/decoding (ID). It is also shown that the BGU labelings of the signal constellation allows a fast search for best component binary recursive systematic convolutional (RSC) codes of a BICM-ID system using both of overall and in-line interleaving. Best rate-1/2 RSC codes are reported for 4PSK constellation as an example. The new concept and new search method can be directly extended to MPSK and MQAM constellations.
Capabilities of BIOMASS Tomography for Investigating Tropical Forests

The objective of this paper is to provide a better understanding of the capabilities of the BIOMASS tomography concerning the retrieval of forest biomass and height in tropical areas. The analysis presented in this paper is carried out on airborne data acquired by Office National d'Etudes et de Recherches Aérospatiales (ONERA) over the site of Paracou, French Guiana, during the European Space Agency campaign TropiSAR. This high-resolution data set (125-MHz bandwidth) was reprocessed in order to generate a new data stack consistent with BIOMASS as for the bandwidth (6 MHz) and the azimuth resolution (about 12 m). To do this, two different processing approaches have been considered. One approach consisted of degrading the resolution of the airborne data through the linear filtering of raw data, followed by standard SAR processing. The other approach consisted of recovering the 3-D distribution of the scatterers at a high resolution, which was then reprojected onto the BIOMASS geometry. The latter procedure allows us to obtain a data stack that is the most realistic emulation of BIOMASS imaging capabilities. In both approaches, neither ionospheric disturbances nor temporal decorrelation has been considered. The connection to the forest biomass has been examined in both cases by investigating the correlation between the backscatter at different forest heights and the above-ground biomass (AGB) values from in situ data. As expected, the reduction of the system bandwidth to 6 MHz resulted in significant vertical resolution losses compared with the original airborne data. Nevertheless, it was possible to retrieve the forest height to within an accuracy of better than 4 m, whereas the backscattered power at the volume height (30 m above the ground) exhibited a correlation higher than 0.8 with the in situ data and no bias phenomena over the AGB values ranging from 250 to 450 t/ha.
AHB-master controller formal compliance verification

In this paper, we use the monitor method to develop a verification intellectual property (VIP) core for Advanced Microcontroller Bus Architecture Advance High-performance Bus (AMBA AHB) compliance verification. The VIP is formulated using System Verilog with assertions, so that it can be used in a simulation-based as well as in formal-based verification methodology. The formal interval property intuitive industrial languages such as Interval Property Language (ITL) and System Verilog Assertion are used to formulate the property set. The Advanced Microcontroller Bus Architecture (AMBA) is an on-chip communications standard for designing highperformance embedded microcontrollers. The operations of based AHB checking (IPC) technique is used to formally verify that the operations of two AHB master controllers comply with the standard. We were able to detect some errors in the AHB master controller.
Deep learning for effective detection of excavated soil related to illegal tunnel activities

This paper presents a new deep learning based approach for soil detection using high resolution multispectral satellite images with a resolution of 0.31 m. In particular, a deep convolutional neural network (CNN) is proposed for soil detection to identify potential tunnel digging activities. Spatial and spectral information in the multispectral image cube has been incorporated into the CNN. We also propose a novel method to handle imbalance learning in the context of deep CNN model training. Experimental results on Worldview-2 (WV-2) multispectral satellite images captured at the border between USA and Mexico showed that the proposed CNN model can effectively detect soil in the remote sensed images, and the proposed imbalance learning technique improved the detection performance significantly.
1 MW, 140 GHz, CW gyrotron for Wendelstein 7-X

A continuous wave, 1 MW, 140 GHz gyrotron oscillator with a CVD diamond window has been designed and constructed as a joint collaboration between FZK Karlsruhe, CRPP-Lausanne, IPF Stuttgart, CEA Cadarache and TTE Velizy for the 10 MW ECRH system of the new stellarator plasma experiment Wendelstein 7-X at IPP Greifswald, Germany. The gyrotron will operate in the T/sub 28,8/ cavity mode and provides a linearly polarized, fundamental Gaussian output beam. It is composed of a diode type electron gun, an improved beam tunnel, a high mode purity low-Ohmic-loss cavity with rounded transitions. The design parameter for the cavity were chosen to minimize power reflection and mode purity. The electron gun and the magnetic field are designed for a velocity ratio of 1.3. The RF field is guided by an optimized non linear uptaper and converted to a Gaussian beam by a highly efficient quasi-optical mode converter consisting of a rippled wall waveguide launcher followed by a mirror of quasi-elliptical shape and two toroidally focussing mirrors. A large single stage depressed collector on ground potential is capable to withstand the electron beam power in continuous operation assuming a total efficiency of 45% which is achieved by a voltage of 30 kY to the cavity and -50 kV to the cathode. The RF beam is extracted from the gyrotron horizontally through an edge-cooled single-disk CVD-diamond window with an aperture of 88 mm and a disk thickness of 1.8 mm. With a loss tangent of 4.10/sup -5/ and a dielectric permittivity of 5.67, the absorbed power is only 700 W and thermal finite element computations a temperature rise of only 61/spl deg/C at the center of the window.
Contourlets: a directional multiresolution image representation

We propose a new scheme, named contourlet, that provides a flexible multiresolution, local and directional image expansion. The contourlet transform is realized efficiently via a double iterated filter bank structure. Furthermore, it can be designed to satisfy the anisotropy scaling relation for curves, and thus offers a fast and structured curvelet-like decomposition. As a result, the contourlet transform provides a sparse representation for two-dimensional piecewise smooth signals resembling images. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing tasks.
An Online Unsupervised Feature Selection and its Application for Background Suppression

Background suppression in video sequences has recently received great attention. While there exist many algorithms for background suppression, an important and challenging issue arising from these studies concerns that for which attributes of the data should be used for background modelling. It is interesting and difficult because there is no knowledge about the data to guide the search. Also, in real application for background suppression, the video length is unknown and the video frames are generated dynamically in a streaming fashion and arrive one at a time. Thus, it is impractical to wait until all data have been generated before feature learning begins. In this paper, we present an online unsupervised feature selection for background suppression. The advantage of our method is that it avoids any combinatorial search, is intuitively appealing, and allows us to prune the feature set. Moreover, our method, based on the self-adaptive model, has an ability to adapt and change through complex scenes. Experiments on real-world datasets are conducted. The performance of the proposed model is compared to that of other background modelling techniques, demonstrating the robustness and accuracy of our method.
Analysis of UN Voting Patterns via Diffusion Geometry and Thematic Clustering

We apply a range of data mining techniques to analyze voting patterns in the United Nations. We begin with non-linear dimensionality reduction, showing that diffusion geometry reveals an historically relevant organization of countries based on their UN voting patterns. Key historical events can be ``read out'' from these embeddings, such as de Gaulle's influence on France and the breakup of the Soviet Union. These events are not apparent in other (e.g., PCA) embeddings. We then switch to an organization of resolutions, revealing dominant themes during different political epochs. Formally themes are introduced as summaries (eigenfunctions) within a modified hierarchical clustering algorithm.
An interdigital bandpass filter embedded in LTCC for 5-GHz wireless LAN applications

This letter presents a compact interdigital stripline bandpass filter embedded in low temperature cofired ceramic for 5-GHz wireless LAN applications, including design, simulation, fabrication, and measurements. The filter measures 8 mm/spl times/7 mm/spl times/1.1 mm and exhibits an insertion loss of 3.6 dB, a return loss of 20 dB, and a 212-MHz passband with the midband frequency at 5.28 GHz. The filter is highly reproducible with good tolerance. A low noise amplifier (LNA) built on the top of the LTCC substrate with an embedded filter has the same bandwidth and midband frequency as those of the filter. Using this filter and an integrated chip, a small RF front-end receiver has been achieved.
BER performance analysis of 100 and 200 Gbit/s all-optical OTDM node using symmetric Mach-Zehnder switches

In future high-speed self-routing photonic networks based on all-optical time division multiplexing (OTDM) it is highly desirable to carry out packet switching, clock recovery and demultiplexing in the optical domain in order to avoid the bottleneck owing to the optoelectronics conversion. We propose a self-routing OTDM node structure composed of an all-optical router and a demultiplexer based on the symmetric Mach-Zehnder (SMZ) for high bit rate OTDM packets. Here, we investigate both numerically and by means of simulation the noise and crosstalk characteristics of the SMZ and the bit error rate (BER) performance of the proposed OTDM node. For BER of 10 -9 and the total bit rates of 100 and 200 Gbit/s the power penalty incurred is about 2 and 2.5 dB, respectively compared with a 2.5 Gbit/s back-to-back (B-B) system.
Commonsense knowledge extraction for Tidy-up robotic service in domestic environments

Commonsense is one of the keys to enable human-robot communication in daily life scenarios. It is very difficult for a robot to do tasks ordered by a human without having some basic knowledge to understand the human's commands. This paper proposes a method to automatically build commonsense knowledge for the “Tidy-up” service, in which a robot is asked to take objects such as books, cups, dishes on a table to appropriate places automatically. We defined three object classes that are necessary for the service, namely “Washable”-objects that need to be washed, “Reusable”-objects that need to be stored for reuse, and “Trashable”-objects that need to be disposed of. For each object, multiple attributes were extracted from both the ConceptNet knowledge base and the Google search engine, and fed to classifiers to classify the object into the appropriate class. To evaluate the proposed method, output from classifiers were compared with the result from actual human. The result showed that the proposed approach is efficient in classifying objects and in providing object type as commonsense knowledge, hence, helping the robots to understand human intention and to provide intuitive service.
Design dual-band microstrip monopole antenna for 3G mobile handsets

This paper concentrates on studying, designing and manufacturing a dual-band and high-bandwidth antenna applied for 3G mobile handsets. This antenna can work in GSM/UMTS bands by generating two resonant modes of 900 MHz and 1900 MHz frequencies. Antenna is printed on FR4 substrate with size of 58 mm × 80 mm, thickness of 1.6 mm and relative permittivity of 4.4. The proposed antenna is experimentally studied.
A Target Discrimination Methodology Utilizing Wavelet-Based and Morphological Feature Extraction With Metal Detector Array Data

In this paper, a methodology for target discrimination utilizing wavelet-based and morphological feature extraction is proposed. The proposed methodology is implemented into a landmine classification decision system utilizing metal detector array data as input. The classification performances of a number of feature vectors composed of different combinations of feature elements are assessed. This is conducted using a Fuzzy ARTMAP neural network classifier and majority voting decision fusion. The classification classes trialled during processing are target type and burial depth, both combined and individually. The majority of the results achieve correct classification percentages of above 80% both prior to and after decision fusion, with generally higher accuracies and lower misclassification percentages achieved after decision fusion.
Asymmetric mixture model with variational Bayesian learning

Bayesian detection for the symmetric Gaussian mixture model has recently received great attention for pattern recognition problems. However, in many applications, the distribution of the data has a non-Gaussian and non-symmetric form. This study presents a new asymmetric mixture model for model detection. In this paper, the proposed asymmetric distribution is modeled with multiple Student's-t distributions, which are heavily tailed and more robust than Gaussian distributions. Our method has the flexibility to fit different shapes of observed data such as non-Gaussian and non-symmetric. Another advantage is that the proposed algorithm, which is based on the variational Bayesian learning, can simultaneously optimize over the number of the Student's-t distribution that is used to model each asymmetric distribution, and the number of components. The performance of the proposed model is compared to other mixture models, demonstrating the robustness, accuracy, and effectiveness of our method.
Decompose Document Image Using Integer Linear Programming

Document decomposition is a basic but crucial step for many document related applications. This paper proposes a novel approach to decompose document images into zones. It first generates overlapping zone hypotheses based on generic visual features. Then, each candidate zone is evaluated quantitatively by a learned generative zone model. We formulate the zone inference problem into a constrained optimization problem, so as to select an optimal set of non-overlapping zones that cover a given document image. The experimental results demonstrate that the proposed method is very robust to document structure variation and noise.
A Resource-Constrained Asymmetric Redundancy Elimination Algorithm

We focus on the problem of efficient communications over access networks with asymmetric bandwidth and capability. We propose a resource-constrained asymmetric redundancy elimination algorithm (RCARE) to leverage downlink bandwidth and receiver capability to accelerate the uplink data transfer. RCARE can be deployed on a client or a proxy. Different from existing asymmetric algorithms, RCARE uses a flexible matching mechanism to identify redundant data and allocates a small sender cache to absorb the high downlink traffic overhead. Compared to existing redundancy elimination algorithms, RCARE provides a scalable sender cache that is adaptive based on resource and performance. We evaluate RCARE with real traffic traces collected from multiple servers and a campus gateway. The trace-driven simulation results indicate that RCARE achieves higher goodput gains and reduces downlink traffic compared to existing asymmetric communication algorithms. We design an adaptation algorithm for resource-constrained senders sending multiple data streams. Our algorithm takes samples from data streams and predicts how to invest cache size on individual data streams to achieve maximal uplink goodput gain. The adaptation algorithm improves the goodput gain by up to 87% compared to the baseline. In first 10% of data streams (sorted by the optimal goodput gains), RCARE achieves up to 42% goodput gain on average.
A 1-157 GHz InP HEMT traveling-wave amplifier

We report traveling wave amplifiers with 1-157 GHz 3-dB bandwidth, 5 dB gain. These amplifiers were fabricated in a 0.1 /spl mu/m gate length InGaAs/InAlAs HEMT MMIC technology. The use of gate-line capacitive-division and low-loss elevated coplanar waveguide lines have yielded record bandwidth broadband amplifiers.