nils-herrmann/clean_scirepeval huggingface dataset:
Advanced antenna technologies for satellite communication payloads

This paper addresses some of the recent developments at LMCSS in the area of multiple beam and reconfigurable beam antennas for communication satellite payloads. These advancements include (a) multiple aperture dual-band reflector antennas, (b) high efficiency horns covering 50% bandwidth, (c) a novel ?stepped-reflector antenna? (SRA) technology, and (d) reconfigurable beam antennas for HIEO and GEO satellites. It is shown that by combining the reflector improvements through the use of SRA with the feed improvements through the use of dual-band high efficiency horns (DBHEH), significant improvements in EIRP, G/T, and co-polar isolation (C/I) can be achieved. The SRA system produces ?flat-top? radiation patterns for receive beams and highly efficient Gaussian patterns for transmit beams over a geographic coverage as seen by the satellite. The DBHEH designs employ ?slope-discontinuities? to generate desired higher order modes over both transmit and receive bands. By employing a ?frequency-dependant? horn design for the DBHEH, it is shown that the step size can be significantly reduced and can be easily blended into the reflector surface for ease of fabrication. Detailed design of a K/Ka band MBA with about 68 spot beams is presented. The RF performance of this novel MBA has been evaluated and shows significant improvements in EIRP, G/T, and C/I in comparison with a conventional MBA. Comparison results of the advanced MBA with conventional MBA are presented in the paper. Design examples for both FSS and DBS satellites are discussed. A novel reconfigurable antenna suitable for HIEO and GEO satellites has been developed at LMCSS. It employs a non-focused reflector (NFR) antenna using a shaped mesh-reflector, and a small active feed array located in the focal-plane of the NFR in order to continuously reconfigure the beam(s), as the satellite goes through the HIEO orbit. The NFR approach has the advantages of reducing the number of feed array elements significantly while achieving the desired beam flexibility. Feed technology advancement for FSS and BSS satellites that includes high efficiency horns, trifurcated horns, feed networks, and trends in high power TVAC testing are briefly discussed.
Accelerating multi-target tracking by a swarm of mobile robots with network preservation

This paper addresses a decentralized control of robot swarm for multi-target tracking with network preservation - namely swarm dispersion algorithm (SDA). The robot swarm moves towards targets while preserving connectivities for networking communication. The developed controller relied on local information of neighboring robots is synthesized by the rules of behavioral control and connectivity maintenance. The swarm dispersion algorithm accelerates the multi-target tracking by minimizing connectivities but preserving an ad-hoc communication network through all the robots. The decentralized control consists of three major functionalities: (1) maintaining connectivities between the robots for swarm movement towards the targets; (2) breaking down triangle and k-connected topologies to accelerate the target reaching; (3) adjusting the robots' velocity to preserve connectivities for an ad-hoc communication network through all the robots. The developed algorithm is demonstrated and verified in simulation.
CIM compliant multiplatform approach for cyber-physical energy system assessment

With high penetration of distributed renewable energy resources along with sophisticated automation and information technology, cyber-physical energy systems (CPES, i.e. Smart Grids here) requires a holistic approach to evaluate the integration at a system level, addressing all relevant domains. Hybrid cloud SCADA (Supervisory, Control And Data Acquisition), allowing laboratories to be linked in a consistent infrastructure can provide the support for such multi-platform experiments. This paper presents the procedure to implement a CIM (Common Information Model) compliant hybrid cloud SCADA, with database and client adaptive to change in system topology, as well as CIM library update. This innovative way ensures interoperability among the partner platforms and provides support to multi-platform holistic approach for CPES assessment.
CODE: Coherence Based Decision Boundaries for Feature Correspondence

A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model. Once computed, the model can filter the entire set of nearest neighbor matches (which typically contains over 90 percent false matches) for true matches. We integrate our technique into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines where previous techniques provide few or no matches.
A new design for four switch three phase inverter based on FPGA for induction motor control

This paper presents a realization of the space-vector pulse-width modulation (SVPWM) strategy for control system induction motor (IM). In this paper is presented a space vector PWM algorithm for four switch three phase inverters (B4, FSTPI) based on the one for six switch three phase inverters (B6, SSTPI) (principle of similarity) where the αβ plan is divided into 6 sectors and the formation of the required reference voltage space vector is done in the same way as for B6 by using effective (mean) vectors. That has been developed using the ready-to-use field-programmable gate array (FPGA) technology. The programming SVPWM algorithm, deadtime algorithm, and control IM algorithm are described. The proposed SVPWM control scheme can be realized using FPGA (Spartan 3E) from Xilinx, Inc. Experimental results are demonstrated. The interface between PC and FPGA is created by a Visual Basic programming
An improved noise reduction algorithm for speech signals using a microphone array

This paper proposes an improved multichannel Wiener filter (IMWF) algorithm for noise reduction of the speech signals by introducing a new factor when deriving the filter coefficients. This factor, which depends on the multichannel speech presence probability (MC-SPP), helps significantly reduce background noise while suffers only a little speech distortion. In addition, we also propose to modify the minima controlled recursive averaging (MCRA) algorithm, by exploiting shorter time frames and slower statistic change in speech signals, which helps increase the computing speed with almost no degradation in the estimation quality of noise statistics. The performance of our algorithms are simulated and verified for various noise scenarios.
Balanced QPSK space-time trellis codes

All the best known space-time trellis codes (STTCs) belong to the balanced STTC class. The first method to design balanced STTCs is proposed. The exhaustive code search of optimal codes can then be reduced to this class. Finally, the proposed method is used to construct new QPSK codes for three and four transmit antennas, which outperform the best previously published codes of the same complexity.
A message-passing approach to min-cost distributed clustering in wireless sensor networks

Clustering is an effective approach to hierarchically organizing network topology for efficient data aggregation in wireless sensor networks (WSNs). In this paper, we present a new approach to energy-efficient, distributed clustering in WSNs using the recent modeling and computational methodology of factor graphs and message-passings. We first formulate the sensor clustering as an optimization problem that minimizes the total data transmission cost weighted by node residual energy. We then derive simplified, localized, min-sum recursive message-passing rules which can elect a near-optimal set of cluster heads. We show through simulations that the proposed algorithm quickly achieves a good approximation of the minimum cost found by a centralized algorithm, and effectively prolongs the network lifetime compared to a popular sensor clustering algorithm.
A Universal Dynamic and Kinematic Model of Vehicles

Motion control of a vehicle requires a model that can present the relationship between forces applied to the vehicle, acceleration and velocity in given direction, slip ratio of individual wheels, road condition and many more. By using the dynamics of rigid body and the Magic Formula in Pacejka's tire model, we propose in this paper a vehicle representation that include both dynamic and kinematic models of vehicle. The validity of the model is demonstrated by simulation in Matlab/Simulink. This model can be served as powerful tool for motion control of vehicles and of electric vehicles in particular.
A company frequency reconfigurable MIMO antenna with low mutual coupling for UMTS and LTE applications

In this paper, we propose a compact frequency reconfigurable (FR) multiple-inputmultiple-output (MIMO) antenna in a overall size of 51 × 53 × 0.8 mms. The MIMO antenna consists of two symmetrical FR ones using two PIN diodes on ground plane and has a simple bias circuit without capacitor. By switching the PIN diodes, the FR MIMO antenna can operates in the three states covering bands of tte universal mobile telecommunication system (UMTS), of the long-term-evolution (LTE) at 2.3 GHz, and 2.6 GHz with peak gains of 1.55, 1.81, and 0.87 dBi, respectively. A slot is added on the ground plane to achieve a low mutual coupling between antenna elements of less than −20 dB in all operating bands. The process of antenna design as well as calculating desired frequency bands are presented in detail. The antenna is fabricated on FR4 substrate and the simulated S-parameter results of the antenna are in good agreement with measurement results.
Constructivist learning: operational criteria for cognitive flexibility

Constructivism is a learning theory which states that people learn best when they actively construct their own knowledge. Many different perspectives exist on constructivist pedagogical principles and on how to apply them to instructional design. It is thus not only difficult to evaluate the conformity of existing learning systems with constructivist principles, it is also quite hard to ensure that a new learning system being designed ultimately facilitates and stimulates constructivist learning. Our research aims to help designing truly constructivist learning systems. For that purpose, we must clarify what constructivism entails in an operational manner: we need criteria enabling one to determine which aspects of a learning system actually embody constructivist principles. Cognitive flexibility is a pedagogical principle that is often mentioned among the basic characteristics of constructivism. This paper proposes operational criteria for cognitive flexibility and presents both justifications and examples of their use. These criteria should be applicable to many instructional situations.
Acquisition of Causal Models for Local Distributions in Bayesian Networks

To specify a Bayesian network, a local distribution in the form of a conditional probability table, often of an effect conditioned on its n causes, needs to be acquired, one for each non-root node. Since the number of parameters to be assessed is generally exponential in n, improving the efficiency is an important concern in knowledge engineering. Non-impeding noisy-AND (NIN-AND) tree causal models reduce the number of parameters to being linear in n, while explicitly expressing both reinforcing and undermining interactions among causes. The key challenge in NIN-AND tree modeling is the acquisition of the NIN-AND tree structure. In this paper, we formulate a concise structure representation and an expressive causal interaction function of NIN-AND trees. Building on these representations, we propose two structural acquisition methods, which are applicable to both elicitation-based and machine learning-based acquisitions. Their accuracy is demonstrated through experimental evaluations.
A Model for Predicting Transition in Railgun Fiber Brush Armatures

The PEGASUS railgun at the French-German Research Institute of Saint-Louis ISL is capable of launching 0.3-kg projectiles with muzzle velocities greater than 2500 m/s without mechanical failure of the sabot which is made of fiber-reinforced plastics. Unfortunately for these high velocities the metallic fiber brush armature shows contact transition. One of the main long term objectives is to improve the railgun performance by delaying this armature transition, leading to an increase of the overall railgun efficiency and a reduction of the rail erosion. In order to obtain a better understanding of the transition mechanisms, several series of PEGASUS shots were examined with projectile masses ranging from 250 grams up to 2 kilograms. As a result, a model was developed in order to predict the transition. It determines the mass loss of the brush armature due to electrical heating, mechanical shearing and viscous heating. It can be shown for the low-weight high-velocity projectiles that friction heating becomes a predominant transition mechanism. For the 13-stage PEGASUS railgun the transition model was implemented in a lumped parameter simulation code which takes into account the time dependent armature voltage drop and the armature mass loss. The comparison of the computed transition time with measured values shows a very good agreement.
Choosing SNPs using feature selection

A major challenge for genomewide disease association studies is the high cost of genotyping large number of single nucleotide polymorphisms (SNP). The correlations between SNPs, however, make it possible to select a parsimonious set of informative SNPst known as "tagging" SNPs, able to capture most variation in a population. Considerable research interest has recently focused on the development of methods for finding such SNPs. In this paper, we present an efficient method for finding tagging SNPs. The method does not involve computation-intensive search for SNP subsets but discards redundant SNPs using a feature selection algorithm. In contrast to most existing methods, the method presented here does not limit itself to using only correlations between SNPs in local groups. By using correlations that occur across different chromosomal regions, the method can reduce the number of globally redundant SNPs. Experimental results show that the number of tagging SNPs selected by our method is smaller than by using block-based methods.
A simplified and automated fault current estimation approach for grid-connected low-voltage AC microgrids

For a grid-connected AC microgrid (MG), when a fault occurs at the microgrid, adaptive overcurrent (OC) or directional overcurrent (DOC) relays must be activated as fast as possible to protect all distributed generation (DG) units and loads. Fault currents inside the grid-connected AC microgrid can be significantly varied because fault current contributions from the main grid and DG units are different depending on various fault locations, fault types, and high penetration of inverter-based distributed generators (IBDGs) and rotating-based distributed generators (RBDGs) in the microgrid. A traditional fault analysis method cannot be applicable for AC microgrids with the presence of both rotating-based distributed generators and inverter-based distributed generators. Therefore, this paper proposes a simplified and automated fault current estimation approach for grid-connected AC microgrids, which is effective to quickly and accurately calculate fault current contributions from IBDGs and RBDGs and the grid fault current contribution to any faulted sections in the microgrid. This simplified and automated fault current calculation approach is mainly focused on grid-connected and small-sized low-voltage (LV) AC microgrids with the support of communication system. Under the grid-connected operation mode of the MG, fault tripping current thresholds of adaptive OC/DOC relays can be properly adjusted by the proposed fault analysis method. In particular, relying on fault current distribution coefficients of IBDGs, RBDGs, and the utility grid, pick-up currents of the adaptive OC/DOC relays in the LV AC microgrid are instantly and accurately self-adjusted according to different MG configurations as well as the operation status of DG units during the grid-connected mode.
A component architecture for an extensible, highly integrated context-aware computing infrastructure

Ubiquitous context-aware computing systems present several challenges in their construction. Principal among them is the tradeoff between easily providing new context-aware services to users and the tight integration of those services, as demanded by the small form factor of the devices typically found in ubiquitous computing environments. Performance issues further complicate the management of this tradeoff. Mechanisms have been proposed and toolkits developed for aiding the construction of context-aware systems, but there has been little consideration of how to specialize, organize, and compose these mechanisms to meet the above requirements. We motivate and describe a software architecture that provides the desired integration and extensibility of services in a context-aware application infrastructure. A key result is the fissioning of intuitive class organizations, both across layers and within layers, to achieve the required integration of services and separation of concerns.
Accurate positioning for real-time control purpose integration of GPS, NAV200 and encoder data

Positioning of a mobile robot is critical to its movement in an outdoor environment to accomplish tasks with success and accuracy. For outdoor automation with a fleet of mobile robots in a local area, a common positioning system for all vehicles is essential for maneuvering and coordination. The positioning system should be referenced to a global coordinate frame and requires few artificial landmarks only for the areas that are out of the GPS positioning system. In addition, positioning system is desirable to have a high update rate for real-time control purposes. In this paper, we propose an integrated positioning system (IPS), which is able to provide an accurate and high update rate position estimate. The integrated system utilizes GPS as the global coordinate positioning system, Nav200 as the local positioning system for areas that are not covered by GPS signals and encoders/compass as the supplementary relative positioning system. The structure of the IPS and techniques are described. Experimental results are presented to show the working and output of the IPS.
An arc-length warping algorithm for gesture recognition using quaternion representation

This paper presents a new algorithm, called Dynamic Arc-Length Warping algorithm (DALW) for hand gesture recognition based on the orientation data. In this algorithm, after calculating the quaternion for each orientation measurement, we use DALW algorithm to obtain a similarity measure between different trajectories. We present the benefits of using quaternion alongside the implementation of Dynamic Arc Length Warping to present an optimized tool for gesture recognition.We show the advantages of this approach compared with other techniques. This tool can be used to distinguish similar and different gestures. An experimental validation is carried out to classify a series of simple human gestures.
Decentralised feedback controllers for uncertain interconnected dynamic systems

This paper presents a method for the design of decentralised controllers for interconnected dynamic systems with structured uncertainties. The design problem is formulated as a constrained linear quadratic optimal regulator problem, where the cost function is designed to incorporate the system uncertainties. Conditions are then derived for choosing the state and control weighting matrices that guarantee closed-loop stability in the presence of the uncertainties. Once these matrices are appropriately chosen, a generalised Riccati equation is solved and a robust decentralised controller is obtained. Two numerical examples are given to illustrate the controller design method.
Design of a CPG-based close-loop direction control system for lateral undulation gait of snake-like robots

This paper aims to propose a paradigm of constructing the system which is capable of controlling the locomotion direction of Lateral Undulation gait of snake-like robots. Firstly, we explain the function of each essential component of such system as well as their means of implementation. Secondly, we deliver a control scheme which unifies all these components to make a single complete system that helps reduce many degrees of freedom of snake-like robots to just one in the context of direction control. Finally, some simulations are conducted to verify the ability of such system. A part of which is also tested with a real robot.
An Early Warning System Based on Fuzzy CMAC

Many statistical models such as the Cox's model have been applied to the study of bank failure. However, these classical models have not attempted to identify the possible traits of financial distress that eventually leads to bank failure. It is difficult to explicitly specify what constitutes a financial distress and the intrinsic relationship between financial distress and a failed bank. This paper attempts to apply a fuzzy system named FCMAC-TVR to bank failure analysis. The FCMAC-TVR network is a generic network, in which the numerical operation is carried out by neural network, but the readable rules are generated by TVR inference scheme. The trained FCMAC-TVR operates as a bank failure classification and prediction system and the formulated fuzzy rule base shed lights on the inherent contributions of the selected financial covariates to bank failure. Experiments have demonstrated that the FCMAC-TVR network consistently outperforms the Cox's model in classifying failed and survived banks using a set of US banking data.
An underwater acoustic OFDM communication system with shrimp (impulsive) noise cancelling

OFDM based acoustic communication system for underwater application is proposed. In order to mitigate a shrimp (impulsive) noise created by ocean living creature, time and frequency domain noise canceller is introduced. 2 dimensional DFT based channel estimation is also adopted to enhance the mobility of the communication system. Ocean measurement was performed in the experiment, and 53.0 m distance communication with Bit Error Rate of 0.01 was successfully confirmed. BER reduction by the proposed time and frequency domain shrimp noise canceller is also realized with 0.3 to 0.6 m/s mobility.
Consensus-based distributed estimation of Laplacian eigenvalues of undirected graphs

In this paper, we present a novel algorithm for estimating eigenvalues of the Laplacian matrix associated with the graph describing the network topology of a multi-agent system or a wireless sensor network. As recently shown, the average consensus matrix can be written as a product of Laplacian based consensus matrices whose stepsizes are given by the inverse of the nonzero Laplacian eigenvalues. Therefore, by solving the factorization of the average consensus matrix, we can infer the Laplacian eigenvalues. We show how solving such a matrix factorization problem in a distributed way. In particular, we formulate the problem as a constrained consensus problem. The proposed algorithm does not require great resources in both computation and storage. This algorithm can also be viewed as a way for decentralizing the design of finite-time average consensus protocol recently proposed in the literature. Eventually, the performance of the proposed algorithm is evaluated by means of simulation results.