nils-herrmann/clean_scirepeval huggingface dataset:
A Stream Cipher Engine for Ad-Hoc Security

Overall demands of ever growing ubiquitous environment are security, speed, and power consciousness in processing huge amount of multimedia data. Especially, the critical issue is the security threat due to the spread of mobile platforms. Then, ad-hoc security is very important to keep the temporary security of ubiquitous devices without permanent network infrastructure. A practical solution to meet these demands is a safety aware, high- performed single chip processor. According to this scheme, we have exploited hardware cryptography- embedded processors named RAP (random addressing-accelerated processor) and HCgorilla. We have conceived from RAP and HCgorilla a cryptography called RAC (random addressing cryptography). RAC is a practical cipher promising for multimedia streaming that has ability to keep the temporary security of ubiquitous devices without usual network infrastructure. Abstracting the potential feature of RAC from RAP and HCgorilla, we have developed a multimedia stream cipher engine that has a compact multicore processor structure. In this article, we describe the overall architecture and cryptographic structure of the stream cipher engine, RAC run on the stream cipher engine and its ad-hoc aspects compared with usual cipher techniques.
Frequency Shifts of Spectral Lines Generated by Young's Pinhole Wave Scattering Upon a Quasi-Homogeneous Medium

Frequency shifts of light scattered from either a deterministic or random medium have shown great importance in remote sensing imaging applications, however, such scattering system which combines random scatterer with obstacles has not been specifically discussed so far. To solve this problem, we derive analytical expressions for showing the phenomenon that the Young's pinhole wave scattered from a quasi-homogeneous (QH) medium exhibits the red shift of spectral lines, while the first-order Born approximation is applied to treat the weak scattering between the diffractive wave and the medium. The shifted amount of spectrum is strongly dependent of the scattering angle, correlation length of the medium, and Young's pinhole parameter. Furthermore, we also observe that the red shift of the scattered spectrum converts to the blue shift when the correlation length reaches a certain magnitude. Through numerical simulations, analyses are performed on revealing the effects of Young's pinhole parameter and medium's correlation on the spectral shift and spectral switch of the scattered spectrum.
Low Power Microwave Signal Detection With a Spin-Torque Nano-Oscillator in the Active Self-Oscillating Regime

A spin-torque nano-oscillator (STNO) driven by a ramped bias current can perform spectrum analysis quickly over a wide frequency bandwidth. The STNO spectrum analyzer operates by injection locking to external microwave signals and produces an output dc voltage <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm{ dc}}$ </tex-math></inline-formula> that temporally encodes the input spectrum. We found, via numerical analysis with a macrospin approximation, that an STNO is able to scan a 10 GHz bandwidth in less than 100 ns (scanning rate <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> exceeds 100 MHz/ns). In contrast to conventional quadratic microwave detectors, the output voltage of the STNO analyzer is proportional to the amplitude of the input microwave signal <inline-formula> <tex-math notation="LaTeX">$I_{\mathrm{ rf}}$ </tex-math></inline-formula> with sensitivity <inline-formula> <tex-math notation="LaTeX">$S = dV_{\mathrm{ dc}}/dI_{\mathrm{ rf}} \approx 750~\rm mV/mA$ </tex-math></inline-formula>. The minimum detectable signal of the analyzer depends on the scanning rate <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> and, at low <inline-formula> <tex-math notation="LaTeX">$R \approx 1~\rm MHz/ns$ </tex-math></inline-formula>, is about 1 pW.
A behavior cluster based availability prediction approach for nodes in distribution networks

To predict the availability state of a node in a distribution network, its history trace is usually used. Sometimes, some usage behavior patterns cannot be captured precisely from the insufficient trace, which may lead to unreliable predictors. In this paper, to alleviate the data sparseness problem, the nodes with the similar behaviors are clustered, and all history information in a same cluster is seen as another information source for any node in it. For each node, an N-gram model is used to train the predictor by the combination of the new source and the node's own trace. In addition, because it is hard to capture the trace of all nodes in large scale networks, such as P2P networks, a bagging based prediction algorithm is proposed, which can be applied in the distribution environment and relieve the effect of the noisy data. In our experiments, three datasets are evaluated. Results show that the prediction performance of our cluster based N-gram predictor is better than the results of several other predictors. And the bagging based prediction algorithm presents its validity in the distribution environment.
On capture power-aware test data compression for scan-based testing

Large test data volume and high test power are two of the major concerns for the industry when testing large integrated circuits. With given test cubes in scan-based testing, the ldquodonpsilat-carerdquo bits can be exploited for test data compression and/or test power reduction. Prior work either targets only one of these two issues or considers to reduce test data volume and scan shift power together. In this paper, we propose a novel capture power-aware test compression scheme that is able to keep scan capture power under a safe limit with little loss in test compression ratio. Experimental results on benchmark circuits demonstrate the efficacy of the proposed approach.
Regional landslide susceptibility assessment based on probability index model

It's the key for regional landslide susceptivity assessment to confirm the regional landslide susceptivity factors. Cameron Highlands of Malaysia was selected as the study area. Bivariate statistical analysis method and ArcGIS were used for analyzing the relationships among landslides and environmental factors. Then, Probability Index Model was developed with Landslide Area Density. Lastly, the assessment of landslide susceptivity of Cameron Highlands was performed. The result shows that the model has higher precision.
Severe Thunderstorm Detection by Visual Learning Using Satellite Images

Computers are widely utilized in today's weather forecasting as a powerful tool to leverage an enormous amount of data. Yet, despite the availability of such data, current techniques often fall short of producing reliable detailed storm forecasts. Each year severe thunderstorms cause significant damage and loss of life, some of which could be avoided if better forecasts were available. We propose a computer algorithm that analyzes satellite images from historical archives to locate visual signatures of severe thunderstorms for short-term predictions. While computers are involved in weather forecasts to solve numerical models based on sensory data, they are less competent in forecasting based on visual patterns from both current and past satellite images. In our system, we extract and summarize important visual storm evidence from satellite image sequences in the way that meteorologists interpret the images. In particular, the algorithm extracts and fits local cloud motion from image sequences to model the storm-related cloud patches. Image data from the year 2008 have been adopted to train the model, and historical severe thunderstorm reports in continental U.S. from 2000 to 2013 have been used as the ground truth and priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing more accurate severe thunderstorm forecasts.
Multi-Task Rank Learning for Image Quality Assessment

In practice, images are distorted by more than one distortion. For image quality assessment (IQA), existing machine learning (ML)-based methods generally establish a unified model for all the distortion types, or each model is trained independently for each distortion type, which is therefore distortion aware. In distortion-aware methods, the common features among different distortions are not exploited. In addition, there are fewer training samples for each model training task, which may result in overfitting. To address these problems, we propose a multi-task learning framework to train multiple IQA models together, where each model is for each distortion type; however, all the training samples are associated with each model training task. Thus, the common features among different distortion types and the said underlying relatedness among all the learning tasks are exploited, which would benefit the generalization ability of trained models and prevent overfitting possibly. In addition, pairwise image quality ranking instead of image quality rating is optimized in our learning task, which is fundamentally departed from traditional ML-based IQA methods toward better performance. The experimental results confirm that the proposed multi-task rank-learning-based IQA metric is prominent against all state-of-the-art nonreference IQA approaches.
A 3-10GHz bandwidth low noise amplifier for ultra-wideband application using SiGe HBT technology

A feedback topology low-noise amplifier (LNA) using advanced SiGe HBT technology for application in ultra-wideband (UWB) systems is presented in this paper. The design consists of single stage topology in two feedback loops to achieve broadband gain together with low noise figure (NF) and good input and output impedance match. Using 0.35 mum SiGe HBT process, the simulated results show 11.9 dB of gain(S21) with 1.7 dB of variation over the 3 GHz to 10 GHz band, and a low NF ranging from 3.6 dB to 3.98 dB while consuming a dc power of only 10 mW. The matched input and output reflectance (S11 and S22) are less than -5 dB and -10 dB respectively. At the same time, LNA is unconditionally stable over the entire band.
Agent Based Intelligent Virtual Environment and Its Entertainment Application

Intelligent Virtual Environment (IVE) is the integration of virtual reality and artificial intelligence (AI), i.e., it incorporates AI technology into virtual environment. Using IVE concept together with intelligent agents and neural networks, we design and implement an entertainment IVE application platform. The humanlike intelligent objects in the virtual environment are implemented with intelligent agents, which are endowed with intelligence for learning by applying artificial neural networks to agent controllers
Arithmetic P Systems Based on Arithmetic Formula Tables

Arithmetic operations are fundamental in computing models. Novel arithmetic P systems are constructed to perform four basic operations: addition, subtraction, multiplication, division. The digits of decimal integers are directly put into hierarchical membranes, one digit one membrane, thus, the number of membranes is reduced and complexity is lowered. Core evolution rules are designed for single digit operations according to the arithmetic formula tables widely used by humans. Some examples are given to illustrate how to compute decimal integers in these P systems and the results indicates that these P systems can efficiently carry out arithmetic computations of integers.
Impaired driving detection based on deep convolutional neural network using multimodal sensor data

Intelligent vehicle systems, such as advanced driving assistance systems, are relatively popular nowadays, which facilitate the timely prevention of driving-related accidents and human injuries caused by impaired driving. An often ignored problem in existing systems is the heterogeneousness across multimodal sensor data, which is becoming more crucial due to the widespread use of more different sensors. This paper proposes a novel feature fusion based detection approach using deep convolutional neural network to profile driver-related, vehicle-related, and road-related features, and extract collaborative information from them. Experimental results demonstrate that the proposed approach is capable of providing more accurate detection of impaired driving, compared with that achieved by other state-of-the-art classifier.
A Switching-inverter power controller based on fuzzy adaptive PID

Focusing on strong nonlinearity and time variation of switching-inverter power, this paper presents a fuzzy adaptive PID control algorithm. This algorithm not only avoids complex modeling of the controlled object, but also combines advantages of fuzzy control and PID control. In addition, this algorithm realizes real-time and effective online control for switching-inverter power by setting parameters of PID based on fuzzy reasoning. Furthermore, the control algorithm improves steady-state precision and dynamic performance of system, either of which the PID algorithm can be realized simultaneously. A simulation model of system was made aiming at the response of unit step, constant load and cyclical load simulation. The simulation results show that the switching-inverter power based on fuzzy adaptive PID has excellent control performance and self-adjustable ability, and this controller improves the output waveform.
Broadband and High-Isolation Dual-Polarized Microstrip Antenna With Low Radar Cross Section

A Ku-band dual-linear polarized broadband aperture-coupled antenna with high-isolation and low radar cross section (RCS) in special direction is presented, fabricated, and measured based on substrate integrated waveguide technology. The substrate-integrated rows of metallized via holes (SIRMHs) are inserted into the conventional aperture-coupled antenna to enhance the isolation and improve the impedance matching. Monostatic RCS is reduced because the surface current of the metallized ground plane is guided by the SIRMHs. The theoretical and experimental results are in good agreement. Experimental results indicated that the isolation is more than 40 dB, the bandwidth obtains 21% of return loss less than -20 dB, and the RCS is reduced to as much as 23 dB from 12.2 to 16 GHz compared to a traditional antenna when incident angle is 30 °.
A circularly polarized antenna based on EMSIW sub-array with high front-to-back ratio

A high front-to-back ratio circularly polarized planar antenna is presented in this communication based on a quarter-mode substrate integrated waveguide sub-array. It is composed of three parts: an eighth-mode substrate integrated waveguide (EMSIW) sub-array, four coaxial cables which are used to connect the probe-fed sub-array and the corporate feed network, and the feed network of which composed three Wilkinson power dividers is applied to feed the array. The measured front to back radiation ratio and the measured antenna gain for right-hand circular polarization (RHCP) are 28.6 dB and 6.61 dBi at 5.2 GHz, respectively. The measured 10-dB return loss and 3-dB axial ratio bandwidths at the center frequency 5.2 GHz are 21.53% and 10.96%, respectively.
6-DOF Image Localization From Massive Geo-Tagged Reference Images

The 6-degrees of freedom (DOF) image localization, which aims to calculate the spatial position and rotation of a camera, is a challenging problem for most location-based services. In existing approaches, this problem is often tackled by finding the matches between 2D image points and 3D structure points so as to derive the location information via direct linear transformation algorithm. However, as these 2D-to-3D-based approaches need to reconstruct the 3D structure points of the scene, they may not be flexible enough to employ massive and increasing geo-tagged data. To this end, this paper presents a novel approach for 6-DOF image localization by fusing candidate poses relative to reference images. In this approach, we propose to localize an input image according to the position and rotation information of multiple geo-tagged images retrieved from a reference dataset. From the reference images, an efficient relative pose estimation algorithm is proposed to derive a set of candidate poses for the input image. Each candidate pose encodes the relative rotation and direction of the input image with respect to a specific reference image. Finally, these candidate poses can be fused together by minimizing a well-defined geometry error so that the 6-DOF location of the input image is effectively derived. Experimental results show that our method can obtain satisfactory localization accuracy. In addition, the proposed relative pose estimation algorithm is much faster than existing work.
An Adaptive Soft Sensor for Mill Load Measurement Based on PCA and FasArt Neural Fuzzy Networks

Precise load measurement is important for the supervision of the pulverizing process in thermal power plant. This paper presents an adaptive soft sensor based on PCA and FasArt neural networks to achieve this purpose. PCA is firstly used to compress the input secondary variables and the dimension is reduced from 9 to 3 with little loss of information. Then FasArt model derive the knowledge from the training data and construct the relationships between the input secondary variables and target variable automatically. Experimental results show that the proposed model achieve a high accuracy. Moreover, the model has potential advantage of incremental learning capability.
Assessment of Alterations in the Electrical Impedance of Muscle After Experimental Nerve Injury via Finite-Element Analysis

The surface measurement of electrical impedance of muscle, incorporated as the technique of electrical impedance myography (EIM), provides a noninvasive approach for evaluating neuromuscular diseases, including amyotrophic lateral sclerosis. However, the relationship between alterations in surface impedance and the electrical properties of muscle remains uncertain. In order to investigate this further, a group of healthy adult rats, a group of rats two weeks postsciatic crush, and a group of animals six months postcrush underwent EIM of the gastrocnemius-soleus complex. The animals were then killed and the conductivity and permittivity of the extracted muscle measured. Finite-element models based on MRI data were then constructed for each group. The characteristic EIM parameter, 50 kHz phase (±standard error), obtained with surface impedance measurements was 17.3° ± 0.3° for normal animals, 13.8° ± 0.7° for acutely injured animals, and 16.1° ± 0.5° for chronically injured animals. The models predicted parallel changes with phase values of 24.3°, 18.8°, and 21.2° for the normal, acute, and chronic groups, respectively. Other multifrequency impedance parameters showed similar alterations. These results confirm that surface impedance measurements taken in conjunction with anatomical data and finite-element models may offer a noninvasive approach for assessing biophysical alterations in muscle in neuromuscular disease states.
Numerical Simulation of Roughness Effect on Gaseous Flow and Heat Transfer in Microchannels

A three-dimensional compressible model of microchannel was established. Flow and heat transfer characteristics of nitrogen flows in microchannels (hydraulic diameter is 100 mum) with designed roughness in form of obstructions placed along channels walls have been investigated numerically through a finite element CFD code consideration of the effect of compressibility and viscosity heating. The model was verified through comparing with experiment data. Then the effects of the Reynolds number, obstruction height, obstruction pitch, obstruction geometry and width to height Ratio of obstruction on the flow and heat transfer characteristics were investigated.
A New Kind of PSO--Convergent Fuzzy Particle Swarm Optimization and Performance Analysis

In this paper, a new kind of FPSO is proposed, called convergent fuzzy particle swarm optimization (CFPSO) to enhance the convergent performance. A convergent gene is introduced in the velocity equation of the FPSO, thus it turns to CFPSO. And it differs from convergent particle swarm optimization (CPSO) in that it employs the fuzzy membership function in the velocity equation. The CFPSO performance is evaluated with four popular benchmark functions, compared with the performance of FPSO, CPSO and standard PSO. The experimental results show that better performance can be gained with the new optimization algorithm, both in convergent ability and high precision.
Discussion on evaluation for emergent resistance of urban raw-water system on pollution accident

“Emergency resistance” of urban raw-water system (URWS) is used to character the ability of a URWS is possessing with to safeguard itself against sudden strike from outer environment. This paper describes and analyzes URWS by conducting the theory of periphery (boundary and shell, or JIEKE), and presents a comprehensive similarity evaluation model for measuring the resistance of URWS based on theory and methodology of vague sets. Taking the URWS of Chengdu city as an application, this paper builds up an index system for valuing the emergence resistance of URWS; analyzes pollutant spreading and diffusing in different condition of designed scene through numerical simulation; structures relation functions and calculates vague values for each index; calculates and analyzes with similarity model for evaluation the emergent resistance of URWS. The result shows: the evaluation model is available for describing and measuring the ability of emergent resistance for an URWS on unexpected pollution accident reasonably.
Analysis of Landscape Pattern Changes in Miyun County under Urbanization

Based on RS and GIS technology, use the landscape pattern analysis software FRAGSTATS3.3, analyzed landscape changes in Miyun county in 1990, 2000, 2009 nearly 20 years. From landscape structure, spatial distribution and relationship aspects discussed about the landscape changes. Using patch area index, path density index, edge density index, shape index, connection Index and cohesion index describe the landscape heterogeneity. Forest is the main landscape elements in Miyun County, the area keep about 1150 km2, accounting for 50% of Miyun County area. The area of water gradually decreased, mean patch area reduced from 11.79 km2 to 5.36 km2. Grassland area was decrease and urban land was increase. Grassland and urban land trend to fragmentize, forest to formula. Urban land mixed with other land use is growing, while others stay stable. The results indicated that urban land was gradually expanding outward; the urbanization process is in developing period. Urbanization accelerated the landscape changes
Real-time obstacle avoidance for fixed-wing vehicles in complex environment

In this paper, we present a real-time obstacle avoidance method for fixed-wing unmanned aerial vehicles (UAVs) in complex environment. The Rapidly-exploring Random Tree(RRT) algorithm is used in this method. In order to adapt the RRT algorithm on fixed-wing platforms, we make extensions on it. The algorithm demonstrates very good performance in trajectory planning for fixed-wing vehicles in three-dimensional environment and dynamic environment. We analyze the algorithm theoretically and provide simulation results. We also demonstrate our method on a fixed-wing vehicle flying over a runway with multiple obstacles. The application of this method will greatly improve the survival ability of fixed-wing vehicles in complex environment. It is significant in elevation of autonomous level of UAV.