nils-herrmann/clean_scirepeval huggingface dataset:
A method of achieving improved radial alignment in an optical package utilizing laser welding techniques

A laser-welding technique for precisely locking the XYZ alignment of the fiber in a laser package is described. Laser welding the fiber with 0.5- mu m precision has been achieved with a high yield. The laser-welded joints' mechanical stability is satisfactory. Piece part geometries, the sequence of tasks needed to secure the fiber within the laser package, and the long-term stability of the welded joints are considered.<<ETX>>
A physically designed 2.2 GHz OOK receiver for minimum power wireless sensor network applications

Low power radio frequency transceiver circuits are critical blocks in wireless sensor network (WSN) applications. The energy consumed by a wireless sensor mote, is largely due to the transceiver. A major design objective for WSN applications is thus to lower the power consumed by the mote transceiver. An appropriate modulation method and a data transmission rate are important factors in reducing the power consumption. Amplitude Shift Keying (ASK) or On-Off Keying (OOK) modulations are less spectrally efficient, but offer better energy efficiency [1][2]. In this paper we present a 1.2V 2mW low power OOK fully integrated receiver implemented using 0.13um CMOS technology. Test shows the chip receives a small radio frequency signal with a sensitivity of -53dBm, and a maximum data rate of 3 Mbps at 2.2 GHz.
Design of grounding systems for Tri-Met Portland Westside light rail traction power substations

The Westside and Hillsboro Light Rail extensions in Portland, Oregon, USA, were open for revenue service in September, 1998. The 18-mile extensions extend the existing Eastside line rail service from downtown Portland to downtown Hillsboro. The alignment includes at-grade street crossings, street-running operation, several fully grade separated sections and a 3-mile tunnel. Primary power is provided by the utilities at 12.5 kV and rectified by 18 mainline traction power substations to deliver 750 DC power to the light rail vehicles. Each substation has three separate grounding systems: (a) an AC ground mat designed per IEEE Std 80-1986 for safe step and touch potentials; (b) a DC ground mat for DC surge arrestor ground; the DC switchgear enclosure is connected to the DC ground mat through a low-resistance grounding scheme; and (c) an isolated ground rod for the utility incoming shields. This paper provides a detailed technical description including grounding calculations for each grounding system. Test and measurement data obtained for the installed grounding systems are presented and discussed.
Ceres flight model 5 on NPP: Pre-launch performance and sensor data record validation

Understanding our climate requires a climate data record CDR of radiation budget spanning many years. The CERES Flight Model 5 instrument on the NPP will continue the CDR begun by the CERES instruments aboard the Terra and Aqua spacecraft, which have provided over a decade of these data. In order to get measurements of sufficient accuracy, the FM-5 has been extensively calibrated in vacuum prior to launch. Once in orbit, each of the three channels will be calibrated by use of the internal calibration module. Special operations will permit comparison of the FM-5 with FM-3 aboard the Aqua spacecraft in order to maintain a seamless CDR during the transition from the present set of instruments to the FM-5.Other validation checks will be performed so as to keep the accuracy level high.
Comparing MFCC and MPEG-7 audio features for feature extraction, maximum likelihood HMM and entropic prior HMM for sports audio classification

We present a comparison of 6 methods for classification of sports audio. For the feature extraction we have two choices: MPEG-7 audio features and Mel-scale frequency cepstrum coefficients (MFCC). For the classification we also have two choices: maximum likelihood hidden Markov models (ML-HMM) and entropic prior HMM (EP-HMM). EP-HMM, in turn, has two variations: with and without trimming of the model parameters. We thus have 6 possible methods, each of which corresponds to a combination. Our results show that all the combinations achieve classification accuracy of around 90% with the best and the second best being MPEG-7 features with EP-HMM and MFCC with ML-HMM.
Conformal Embedding Analysis with Local Graph Modeling on the Unit Hypersphere

We present the Conformal Embedding Analysis (CEA) for feature extraction and dimensionality reduction. Incorporating both conformal mapping and discriminating analysis, CEA projects the high-dimensional data onto the unit hypersphere and preserves intrinsic neighbor relations with local graph modeling. Through the embedding, resulting data pairs from the same class keep the original angle and distance information on the hypersphere, whereas neighboring points of different class are kept apart to boost discriminating power. The subspace learned by CEA is gray-level variation tolerable since the cosine-angle metric and the normalization processing enhance the robustness of the conformal feature extraction. We demonstrate the effectiveness of the proposed method with comprehensive comparisons on visual classification experiments.
Axially Periodic Dielectric-Loaded Circular Waveguide for Microwave/Millimeter-Wave Devices

A circular waveguide loaded with periodic dielectrics is considered. Two theoretical methodologies have been developed to investigate the wave dispersion characteristics for such structure. The feasibility and accuracy of the theoretical approaches have been verified by high frequency structural simulator (HFSS) simulations. Using the theoretical approaches, effects of the material properties and structure dimensions on the dispersion characteristics, power distribution, coupling impedance, and wave attenuation have been analyzed, and results obtained for different structures have been compared. Examples of W-band structures with periodic dielectric loading that can be used in various microwave devices have been given. The presented methodologies and results can be used for design optimization of microwave devices with dielectric loading.
A Comparative study on calibration technique for SFCW ground penetrating radar

An important factor for the precise interpretation of ground penetrating radar (GPR) records is to know as much as possible about the transmission features of our antennas. The accuracy of the retrieved parameters in the GPR data inversion depends on the accuracy of the GPR external calibration. In this paper we studied the calibration technique for step frequency continuous wave (SFCW) GPR by two different methods namely, calibration by averaging and calibration by using linear transfer function (LTF) model. The SFCW GPR is implemented with the help of a vector network analyzer (VNA: E50715 of Agilent) and 0.8–4.5 GHz single TEM horn antenna. GPR system using their antenna is calibrated several times at different heights over a metal plate. The error in the calibration based on inaccurate antenna height over metal plate.
Accelerated simulation of complex waveforms in nonlinear amplifiers with memory

We present a framework for efficient, physics-based computer simulation of complex time-dependent waveforms (i.e. wide-band, with large number of frequency components) in nonlinear amplifiers with memory. It is built upon a well established pseudo-spectral, multi-frequency, large-signal code and relies on an adaptive algorithm for signal splitting and splicing in the time domain. Included in the model, and calculated from first-principles, are memory effects, such as dispersion and wave reflections. We validate our approach on such complex waveforms by operating the simulation kernel in the large-signal, nonlinear regime and comparing against the output of the benchmarked serial version.
A Derivation of a General Order, Multichannel Fast Transversal, Recursive Least Squares Filter Algorithm

This paper examines the derivation of a general order, multiple input version of the fast transversal recursive least squares filter algorithm. The new algorithm, in which the order (or number of taps) associated with each input channel is independent, is distinguished from the previously published multichannel form of the fast transversal RLS algorithm, wherein each input channel was con strained to have the same order. This additional flexibility allows assignment of filter resources to particular channels, or independent assignment to the pole/zero estimators in an ARMA system identification application. A summary of the new algorithm is given, in proper order of execution. An operations count is also provided for each equation
Design and financial analysis of a micro grid-tie solar PV system for a small scale industry in Kerala

The difficulty in identifying project sites for installing new hydel power plants or thermal power plants has given a big push for micro power plants particularly near the loads (DIstributed Generation). The paper proposes the selection a suitable small scale industry, base on a few criteria, which can harness solar energy and convert it to electrical energy. The design which is in-line with the load of the industry is explained and a financial analysis of the proposed system is also shown.
Efficiently Managing Context Information for Large-Scale Scenarios

In this paper, we address the data management aspect of large-scale pervasive computing systems. We aim at building an infrastructure that simultaneously supports many kinds of context-aware applications, ranging from room level up to nation level. This all-embracing approach gives rise to synergetic benefits like data reuse and sensor sharing. We identify major classes of context data and detail on their characteristics relevant for efficiently managing large amounts of it. Based on that, we argue that for large-scale systems it is beneficial to have special-purpose servers that are optimized for managing a certain class of context data. In the Nexus project we have implemented five servers for different classes of context data and a very flexible federation middleware integrating all these servers. For each of them, we highlight in which way the requirements of the targeted class of data are tackled and discuss our experiences
Analysis of stray current, track-to-earth potentials and substation negative grounding in DC traction electrification system

In the first part of the paper, we look at a stray current model under the ideal conditions with uniformly distributed track-to-earth resistances and develop the track-to-earth potentials and stray current relationships within a segment of track. We then expand the model to include buried metallic structures in the vicinity of the track using field theory to calculate the potential gradients generated due to flow of rail current and show that a potential difference experienced by a crossing or paralleling underground structure that traverses these gradients could cause stray current corrosion. We discuss the limitations of these models with the actual conditions such as nonuniform back-to-earth resistances, nonuniform soil resistivities. In the second part, we look at the stray current model when there are inadvertent breakdowns in the track insulation. The points of rail insulation breakdown may be approximated using the spherical ground electrode model and equipotential lines to illustrate the flow of stray current. Common grounding of the substation negative bus, substation AC ground mat and electric utility neutral are examined and discussed.
Analysis of the Local Quasi-Stationarity of Measured Dual-Polarized MIMO Channels

It is common practice in wireless communications to assume strict or wide-sense stationarity of the wireless channel in time and frequency. While this approximation has some physical justification, it is only valid inside certain time–frequency regions. This paper presents an elaborate characterization of the non-stationarity of wireless dual-polarized (DP) channels in time. The evaluation is based on urban macrocell measurements performed at 2.53 GHz. To define local quasi-stationarity (LQS) regions, i.e., regions in which the change of certain channel statistics is deemed insignificant, we resort to the performance degradation of selected algorithms specific to channel estimation and beamforming. Additionally, we compare our results to commonly used measures in the literature. We find that the polarization, the antenna spacing, and the opening angle of the antennas into the propagation channel can strongly influence the non-stationarity of the observed channel. The obtained LQS regions can be of significant size, i.e., several meters; thus, the reuse of channel statistics over large distances is meaningful (in an average sense) for the considered correlation-based algorithms. Furthermore, we conclude that, from a system perspective, a proper non-stationarity analysis should be based on the considered algorithm.
A parallel Monte Carlo search algorithm for the conformational analysis of proteins

The EDMC (electrostatically driven Monte Carlo) method has proven to be an effective computational tool for searching the potential energy hypersurface of polypeptide molecules consisting of up to 20 amino acid residues. Such a Monte Carlo search combined with gradient-based energy minimization of molecular conformations results in the need for 100 gigaflop or higher performance levels. The parallel EDMC algorithm has been designed to exploit currently available supercomputing technology. The implementation on the iPSC/2 described appears to represent an improvement over the original version for the IBM 3090. A performance analysis indicates that the attainable parallelism is limited by the underlying acceptance rate of search. It is demonstrated that a coarse-grained approach is suitable for architectures such as the CRAY-XMP, particularly if vectorization techniques can be exploited. Tests on the Intel iPSC/2-VX computer have shown, however, that even the easily vectorized parts of the computation may not overcome a large vector pipeline latency.<<ETX>>
Deep Interactive Object Selection

Interactive object selection is a very important research problem and has many applications. Previous algorithms require substantial user interactions to estimate the foreground and background distributions. In this paper, we present a novel deep-learning-based algorithm which has much better understanding of objectness and can reduce user interactions to just a few clicks. Our algorithm transforms user-provided positive and negative clicks into two Euclidean distance maps which are then concatenated with the RGB channels of images to compose (image, user interactions) pairs. We generate many of such pairs by combining several random sampling strategies to model users' click patterns and use them to finetune deep Fully Convolutional Networks (FCNs). Finally the output probability maps of our FCN-8s model is integrated with graph cut optimization to refine the boundary segments. Our model is trained on the PASCAL segmentation dataset and evaluated on other datasets with different object classes. Experimental results on both seen and unseen objects demonstrate that our algorithm has a good generalization ability and is superior to all existing interactive object selection approaches.
Application of reciprocity to the design of electron guns

We develop a sensitivity function for the design of electron guns, based on a form of reciprocity implicit in Hamilton's equations of motion. The sensitivity function allows for the determination of the effect of small, arbitrary changes in electrode potentials, electrode positions, and magnet locations on specific beam quality figures of merit with a single, time-reversed run of a beam optics code. The sensitivity function can thus be used to predict the sensitivity of a design to manufacturing errors, or it can be used in an optimization cycle of a gun's design.
A survey on extracting frequent subgraphs

Mining on graphs has become quiet popular because of the increasing use of graphs in real world applications. Considering the importance of graph applications, the problem of finding frequent itemsets on transactional databases can be transformed to the mining of frequent subgraphs present in a single or set of graphs. The objective of frequent subgraph mining is to extract interesting and meaningful subgraphs which have occurred frequently. The research goals in the discovery of frequent subgraphs are (i) mechanisms that can effectively generate candidate subgraphs excluding duplicates and (ii) mechanisms that find best processing techniques that generate only necessary candidate subgraphs in order to discover the useful and desired frequent subgraphs. In this paper, our prime focus is to give an overview about the state of the art methods in the area of frequent subgraph mining.
Detecting small, moving objects in image sequences using sequential hypothesis testing

An algorithm is proposed for the solution of the class of multidimensional detection problems concerning the detection of small, barely discernible, moving objects of unknown position and velocity in a sequence of digital images. A large number of candidate trajectories, organized into a tree structure, are hypothesized at each pixel in the sequence and tested sequentially for a shift in mean intensity. The practicality of the algorithm is facilitated by the use of multistage hypothesis testing (MHT) for simultaneous inference, as well as the existence of exact, closed-form expressions for MHT test performance in Gaussian white noise (GWN). These expressions predict the algorithm's computation and memory requirements, where it is shown theoretically that several orders of magnitude of processing are saved over a brute-force approach based on fixed sample-size tests. The algorithm is applied to real data by using a robust preprocessing procedure to eliminate background structure and transform the image sequence into a residual representation, modeled as GWN. Results are verified experimentally on a variety of video image sequences.
A planar dual-band antenna design for RF energy harvesting applications

Past work on radio frequency (RF) energy harvester design mostly focused on harvesting RF energy in a single RF band [1, 2,]. While a significant portion of RF energy is concentrated in the communication band around 900 MHz, there is also abundant RF energy present in the band covering 1800 MHz to 2100 MHz. For example. Li [2011] demonstrated the design and implementation of a dual band RF energy harvester chip that has two cascade voltage multipliers in parallel and can operate at 900 and 1900 MHz bands. The chip can generate 1.2 V output DC voltage at 900 MHz for −19 dBm RF input power and 1.05 V output voltage at 2000 MHz for −18 dBm RF input power. The RF energy conversion efficiency is ∼12% at 900MHz and ∼8% at 2000MHz which is comparable with its single band counterparts at both frequencies in the literature. The design greatly increases the energy harvesting efficiency over two bands. It is found that the input impedance of the chip is ∼ 50 ohm at 900 MHz, 26 ohm at 1.9 GHz, and 70+j10 ohm at 2 GHz. To operate the harvester chip in two bands, the antenna is required to be matched to the chip impedance at 900 MHz for one band and at either 1.9 GHz or 2GHz for the other band.
5.5: A new complex envelope ADI-FDTD algorithm for 3D simulation of slow wave structures

We present a new time-domain algorithm designed for self-consistent 3D simulation of the nonlinear interaction between an electron beam and RF fields in planar slow wave structures, based on a modified alternating direction implicit (ADI) finite difference time domain (FDTD) time advance scheme. The algorithm forms the basis of a new simulation code, NEPTUNE.
Album-based object-centric event recognition

We explore event recognition in the personal photo album setting where the task is not to identify event in individual photo but in the whole album. This setting arises from the way people organize their own collections and the fact that individual photo in these albums often fails to convey meaningful event semantic behind the the album. We work on this problem in a object-centric manner, i.e. we train detectors for objects relevant to the events in the holiday dataset we built, and then detect these holidays based on object detector outputs. The prior knowledge, i.e. what objects are relevant to the event, is obtained statistically from mass image collection web site and thus tends to be more accurate and less biased.
An audio-visual fusion framework with joint dimensionality reducton

By combining audio and visual modalities, the speech recognition systems achieve higher performance and robustness. The fusion strategies to this point are mainly three types: feature level fusion, model level fusion, and decision level fusion. In this paper, we present a novel audio-visual fusion framework, in which a joint dimensionality reduction approach is used to project the audio and visual features into more compact subspaces. With correlation preserving criteria, the representations of projected audio and visual features will be able to preserve the correlation conveyed in the original audio and visual feature space. At the same time, the better model efficiency is achieved in the more compact feature spaces. The experiments on audio-visual person verification demonstrate the efficiency and effectiveness of the proposed fusion framework.
Bio-inspired robot platform powered by a commercial PEM fuel cell

Robots have come up as an emerging technology to assist humans in performing those repetitive and dangerous tasks which humans prefer not to do, or are unable to do due to size limitations and extreme environmental conditions. So the design of robots is required to be stringent to be able to operate at any environmental conditions. A wheeled robot is the one mostly associated in mobile robot operations. But a wheeled robot can travel only on smooth surface and not at irregular surface conditions. To overcome this, the robot's mobile platform equipped with an efficient structure and function is required. Another issue with mobile robot is the power source which is generally battery. The disadvantage of battery is its low efficiency and low energy density. To override the above two issues, A competent robot platform suitable for irregular areas is designed and the conventional power source is replaced with a fuel cell to develop an efficient and environmental independent robot.
Block-based, memory-efficient JPEG2000 images indexing in compressed-domain

The contribution of this paper is the development of a fast, block-based JPEG2000 image indexing system in the compressed domain which achieves high memory efficiency. By high memory efficiency we mean the entire wavelet-decomposed coefficient matrix never needs to be buffered. The feature extracted is the variance of each rectangular block in the compressed domain. The extraction of this feature is of very low complexity. Retrieval performance on the VisTex image database has shown its effectiveness.