nils-herrmann/clean_scirepeval huggingface dataset:
Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three earlyto mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients R of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04 m·px−1). Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction. Remote Sens. 2014, 11 10336
Impedance-based fault location in transmission networks: theory and application

A number of impedance-based fault location algorithms have been developed for estimating the distance to faults in a transmission network. Each algorithm has specific input data requirements and makes certain assumptions that may or may not hold true in a particular fault location scenario. Without a detailed understanding of the principle of each fault-locating method, choosing the most suitable fault location algorithm can be a challenging task. This paper, therefore, presents the theory of one-ended (simple reactance, Takagi, modified Takagi, Eriksson, and Novosel et al.) and two-ended (synchronized, unsynchronized, and current-only) impedance-based fault location algorithms and demonstrates their application in locating real-world faults. The theory details the formulation and input data requirement of each fault-locating algorithm and evaluates the sensitivity of each to the following error sources: 1) load; 2) remote infeed; 3) fault resistance; 4) mutual coupling; 5) inaccurate line impedances; 6) DC offset and CT saturation; 7) three-terminal lines; and 8) tapped radial lines. From the theoretical analysis and field data testing, the following criteria are recommended for choosing the most suitable fault-locating algorithm: 1) data availability and 2) fault location application scenario. Another objective of this paper is to assess what additional information can be gleaned from waveforms recorded by intelligent electronic devices (IEDs) during a fault. Actual fault event data captured in utility networks is exploited to gain valuable feedback about the transmission network upstream from the IED device, and estimate the value of fault resistance.
Tracking-based deer vehicle collision detection using thermal imaging

Deer vehicle collision (DVC) is constantly a major safety issue for the driving on rural road. It is estimated that there are over 35,000 DVCs yearly in the US resulting in about 200 deaths and close to 4,000 reported property damages of one thousand dollars or more. This justifies many attempts trying to detect deer on road. However, very little success has been achieved. In order to reduce the number of DVCs, this work focused on the study of using an infrared thermal camera with tracking system to detect the presence of deer to avoid DVCs. The prototype consists of an infrared thermal temperature image grabbing and processing system, which includes an infrared thermal camera, a frame grabber, an image processing system and a motion tracking system, which includes two motors with their motion control system. By analyzing the infrared thermal images which are independent of visible light, the presence of an animal can be determined in either night or day time through pattern recognition and matching.
Body Composition Changes After Very-Low-Calorie Ketogenic Diet in Obesity Evaluated by 3 Standardized Methods.

Common concerns when using low-calorie diets as a treatment for obesity are the reduction in fat-free mass, mostly muscular mass, that occurs together with the fat mass (FM) loss, and determining the best methodologies to evaluate body composition changes.



This study aimed to evaluate the very-low-calorie ketogenic (VLCK) diet-induced changes in body composition of obese patients and to compare 3 different methodologies used to evaluate those changes.



Twenty obese patients followed a VLCK diet for 4 months. Body composition assessment was performed by dual-energy X-ray absorptiometry (DXA), multifrequency bioelectrical impedance (MF-BIA), and air displacement plethysmography (ADP) techniques. Muscular strength was also assessed. Measurements were performed at 4 points matched with the ketotic phases (basal, maximum ketosis, ketosis declining, and out of ketosis).



After 4 months the VLCK diet induced a -20.2 ± 4.5 kg weight loss, at expenses of reductions in fat mass (FM) of -16.5 ± 5.1 kg (DXA), -18.2 ± 5.8 kg (MF-BIA), and -17.7 ± 9.9 kg (ADP). A substantial decrease was also observed in the visceral FM. The mild but marked reduction in fat-free mass occurred at maximum ketosis, primarily as a result of changes in total body water, and was recovered thereafter. No changes in muscle strength were observed. A strong correlation was evidenced between the 3 methods of assessing body composition.



The VLCK diet-induced weight loss was mainly at the expense of FM and visceral mass; muscle mass and strength were preserved. Of the 3 body composition techniques used, the MF-BIA method seems more convenient in the clinical setting.
A Machine Learning Based Approach for Hand Gesture Recognition using Distinctive Feature Extraction

In a world of almost 7 billion people more than 500 million suffer from some physical, sensory or mental disability. Their lives are often impeded by such deformities which bars them from full participation in society and the enjoyment of equal rights and opportunities. Sign language is common for the deaf and the dumb. Sign language is an efficient alternative to talking, where the former is replaced by hand gestures. Hand gestures are combination of hand shapes, orientations and movement of the hands, alignments of the fingers and positioning of the palm which are used to express fluidly a conveyer's thoughts. Signs are used to communicate words and sentences to audience. The objective of this paper is to optimize an algorithm for recognition of hand gestures with reasonable accuracy, where the input to the pattern recognition system will be given from the hand. Possible reference models are already available such as ASL or American Sign Language. Image is collected from a webcam followed by preprocessing. Further segmentation of the figure is done through polygon approximation and approximate convex decomposition. Feature extraction is done by recording the unique feature among the various convex segments of the hand. The resultant singularities are then used as extracted feature vectors. This involves training with the obtained features which are approximately unique for different hand gestures. Thus, we will be able to identify sign languages and successively make disabled individuals socially acceptable.
2-D Beam-Steerable Integrated Lens Antenna System for 5G  $E$ -Band Access and Backhaul

The new services available through smart devices require very high cellular network capacity. The capacity requirement is expected to increase exponentially with the forthcoming 5G networks. The only available spectrum for truly wideband communication (>1 GHz) is at millimeter wavelengths. The high free space loss can be overcome by using the directive and beam-steerable antennas. This paper describes a design and the measurement results for a lens antenna system for E-band having 2-D beam-steering capability. Continuous beam-switching range of about ±4° × ±17° is demonstrated with the lens having the maximum measured directivity of 36.7 dB. Link budget calculation for backhaul application using the presented lens antenna system is presented and compared with the measurement results of the implemented demo system.
Meandered Variable Pitch angle Printed Quadrifilar Helix Antenna

A new method of Meandered Variable Pitch angle Printed Quadrifilar Helix Antenna (MVPQHA) is described. This type of antenna helps in reducing the size of the Printed Quadrifilar Helix Antenna (PQHA) and makes the bandwidth of the antenna broader. The new type of antenna is compared with other existing types of Printed Quadrifilar helix antenna in terms of size, return loss and bandwidth.
Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of these submanifolds for a particular data set is an open research problem for which hierarchical manifold structures appear to have merit. To date, most work in manifold learning has focused on feature extraction from single images, assuming stationarity across the scene. Research is also needed in joint exploitation of global and local embedding methods in dynamic, multitemporal environments and integration with semisupervised and active learning.
A Strategy for Identifying Locomotion Modes Using Surface Electromyography

This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% plusmn 5.0%, 6.0% plusmn 4.7%, 7.5% plusmn 5.1%, and 5.2% plusmn 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.
Multitechnique paraphrase alignment: A contribution to pinpointing sub-sentential paraphrases

This work uses parallel monolingual corpora for a detailed study of the task of sub-sentential paraphrase acquisition. We argue that the scarcity of this type of resource is compensated by the fact that it is the most suited type for studies on paraphrasing. We propose a large exploration of this task with experiments on two languages with five different acquisition techniques, selected for their complementarity, their combinations, as well as four monolingual corpus types of varying comparability. We report, under all conditions, a significant improvement over all techniques by validating candidate paraphrases using a maximum entropy classifier. An important result of our study is the identification of difficult-to-acquire paraphrase pairs, which are classified and quantified in a bilingual typology.
Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments

Researches on Location-Based Service (LBS) have been emerging in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered. In this paper, we propose a novel algorithm, namely, Cluster-based Temporal Mobile Sequential Pattern Mine (CTMSP-Mine), to discover the Cluster-based Temporal Mobile Sequential Patterns (CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In CTMSP-Mine, user clusters are constructed by a novel algorithm named Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by the proposed measure, Location-Based Service Alignment (LBS-Alignment). Meanwhile, a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. To our best knowledge, this is the first work on mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through experimental evaluation under various simulated conditions, the proposed methods are shown to deliver excellent performance.
Stereo Vision in Structured Environments by Consistent Semi-Global Matching

This paper considers the use of stereo vision in structured environments. Sharp discontinuities and large untextured areas must be anticipated, but complex or natural shapes of objects and fine structures should be handled as well. Additionally, radiometric differences of input images often occur in practice. Finally, computation time is an issue for handling large or many images in acceptable time. The Semi-Global Matching method is chosen as it fulfills already many of the requirements. Remaining problems in structured environments are carefully analyzed and two novel extensions suggested. Firstly, intensity consistent disparity selection is proposed for handling untextured areas. Secondly, discontinuity preserving interpolation is suggested for filling holes in the disparity images that are caused by some filters. It is shown that the performance of the new method on test images with ground truth is comparable to the currently best stereo methods, but the complexity and runtime is much lower.
Correlation Hashing Network for Efficient Cross-Modal Retrieval

Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which improves the quality of hash coding by exploiting the semantic correlation across different modalities, has received increasing attention recently. For most existing cross-modal hashing methods, an object is first represented as of vector of hand-crafted or machine-learned features, followed by another separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, because the quantization error is not statistically minimized and the feature representation is not optimally compatible with the binary coding. In this paper, we propose a novel Correlation Hashing Network (CHN) architecture for cross-modal hashing, in which we jointly learn good data representation tailored to hash coding and formally control the quantization error. The CHN model is a hybrid deep architecture constituting four key components: (1) an image network with multiple convolution-pooling layers to extract good image representations, and a text network with several fully-connected layers to extract good text representations; (2) a fully-connected hashing layer to generate modality-specific compact hash codes; (3) a squared cosine loss layer for capturing both cross-modal correlation and within-modal correlation; and (4) a new cosine quantization loss for controlling the quality of the binarized hash codes. Extensive experiments on standard cross-modal retrieval datasets show the proposed CHN model yields substantial boosts over latest state-of-the-art hashing methods.
Enhancing Windows Firewall Security Using Fuzzy Reasoning

Firewall is a standard security utility within the Microsoft Windows operating system. Most Windows users adopt it as the default security option due to its free availability. Moreover, Windows Firewall is a widely used security tool because of the large market share of the Microsoft Windows operating system. It can be customised for filtering of network traffic based on user-defined inbound and outbound rules. It is supplied with only basic functionality. As a result it cannot be considered as an effective tool for monitoring and analysing of inbound and outbound traffic. Nonetheless, as a freely available and conventional end user security tool, with some enhancement it could perform as a useful security tool for millions of Windows users. Therefore, this paper presents an enhanced Windows Firewall for a more effective monitoring and analysis of network traffic, based upon an intuitive fuzzy reasoning approach. Consequently, it can be used to prevent a greater range of attacks beyond the simple filtering of inbound and outbound network traffic. In this paper, a simulation of ICMP flooding is demonstrated, where the created firewall inbound and outbound rules are insufficient to monitor ICMP flooding. However, the addition of fuzzy reasoning system monitored it successfully and enhanced the standard Windows Firewall functionality to prevent ICMP flooding. The use of this Windows Fuzzy-Firewall can also be extended to prevent TCP flooding, UDP flooding and some other types of denial of service attacks.
The status of HTS ship propulsion motor developments

The development of ship propulsion synchronous motors with high temperature superconductor (HTS) field windings for Naval electric ship applications has progressed to the point where a full scale motor is now under construction. A 5 MW, 230-rpm prototype ship propulsion motor was built and tested by the Center for Advanced Power Systems (CAPS) on behalf of U.S. Office of Naval Research (ONR). It met or exceeded all its design goals. Currently, a 36.5 MW, 120-rpm ship propulsion motor is being built for delivery to ONR at the end of 2006. This paper presents test results of the 5 MW motor and the status of the 36.5 MW motor
Design of One Stage Operational Transconductance Amplifiers at 65 nm and 90 nm for Low Power Applications

A novel design technique for one stage CMOS Operational Transconductance Amplifier (OTA) for nanometer application. The idea is to achieve improved gain-bandwidth product (GBW), DC gain, settling time, and slew rate. This is well known that, these parameters are important for high frequency, fast settling applications, such as switched capacitor filters, analog to digital converters, oscillator. They have vast application areas through analog design field, implemented in 32nm process parameters. We have designed OTAs for 65nm and 90 nm technologies. DC gain, Average Power, Bandwidth, Output resistance, Phase Margins and Unity Gain Frequency have been simulated for both 65nm and 90 nm OTAs. At 65nm technology node DC gain (24.6 dB), Average Power(24.17uW), Bandwidth(1.16 MHz), Output resistance(7.7K-Ohms), Phase Margin(86.760) and Unity Gain Frequency(18.6MHz). At 90nm technology node, DC gain (29.3 dB), Average Power(14.54uW), Bandwidth(0.43 MHz), Output resistance(12 K-Ohms), Phase Margin(88.200) and Unity Gain Frequency(11.32MHz) are simulated using Hspice.
ATRIUM: A Radar Target Simulator for Complex Traffic Scenarios

Future autonomous automobiles will require extremely reliable radar sensors to guarantee the safety of road users. This requires test environments to verify that automotive radars work properly even in complex traffic situations. Radar target simulators can generate virtual scattering centers in front of the radar sensor and are therefore a promising verification technique. However, currently available systems only offer a very basic approach to manipulating scattering centers. With the workflow presented in this publication it becomes possible to decompose high-level descriptions of traffic scenarios into parameters required for radar target simulation. The workflow can therefore be used to generate realistic traffic scenarios and thus increases the reliability of automotive radar sensor tests.