Sentinel-1-based flood mapping: a fully automated processing chain

This article presents an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-real-time (NRT). Since no user intervention is required at any stage of the flood mapping procedure, the processing chain allows deriving time-critical disaster information in less than 45 min after a new data set is available on the Sentinel Data Hub of the European Space Agency (ESA). Due to the systematic acquisition strategy and high repetition rate of Sentinel-1, the processing chain can be set up as a web-based service that regularly informs users about the current flood conditions in a given area of interest. The thematic accuracy of the thematic processor has been assessed for two test sites of a flood situation at the border between Greece and Turkey with encouraging overall accuracies between 94.0% and 96.1% and Cohen’s kappa coefficients (κ) ranging from 0.879 to 0.910. The accuracy assessment, which was performed separately for the standard polarizations (VV/VH) of the interferometric wide swath (IW) mode of Sentinel-1, further indicates that under calm wind conditions, slightly higher thematic accuracies can be achieved by using VV instead of VH polarization data.     

Dynamic behaviour of a magnetically actuated floating liquid marble

This paper reports the dynamic behaviour of a magnetically actuated floating liquid marble by analysing the oscillation of the marble. A liquid marble is a liquid droplet coated with hydrophobic powder. Magnetite particles inside the marble make it magnetic. The marble floats on a carrier liquid that contains aqueous glycerol of various concentrations. A permanent magnet located under the carrier liquid drives the floating marble with the initial velocity. Stopping the magnet abruptly causes the marble to oscillate around its final position for a few seconds. The oscillation was recorded and analysed using customised image processing and evaluation software. The damped harmonic motion model was then applied to the data and tested. Subsequently, critical parameters of the system such as the initial displacement, friction correction factor, the apparent frequency and the spring constant were determined and discussed. The simple experimental set-up and convenient theoretical approach allow us to characterise the marble motion under the influence of a magnet with good accuracy.     

Local closed world reasoning with description logics under the well-founded semantics

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases (Motik and Rosati, 2010 [52]), and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.     

Stability radius of implicit dynamic equations with constant coefficients on time scales

This paper deals with the stability radii of implicit dynamic equations on time scales when the structured perturbations act on both the coefficient of derivative and the right-hand side. Formulas of the stability radii are derived as a unification and generalization of some previous results. A special case where the real stability radius and the complex stability radius are equal is studied. Examples are derived to illustrate results.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.     

Ab initio determination of heavy oxide perovskite related structures from precession electron diffraction data

Two complex perovskite-related structures were solved by ab initio from precession electron diffraction intensities. Structure models were firstly derived from HREM images and than have been confirmed independently using two and three-dimensional sets of precession intensities. Patterson techniques prove to be effective for ab initio structure resolution, specially in case of projections with no overlapping atoms. Quality of precession intensity data may be suitable enough to resolve unknown heavy oxide structures.     

Effects of starter cultures on the formation of flavour compounds in dry sausage

The purpose of this work was to study the impact of starter cultures on the production of flavour compounds in dry sausages. The effect of six starter cultures corresponding to different combinations of lactic acid bacteria (Lactobacillus sake L110, Pediococcus acidilactici 725, P. pentosaceus 716) and different Staphylococcus species (S. carnosus 833, S. warneri 863, S. saprophyticus M31) strains were tested in a total of 30 dry sausages without spices. The analysis of flavour compounds using a dynamic headspace apparatus coupled to a gas chromatograph and a mass spectrometer enabled us to identify about 80 volatile compounds. They were of various origins—lipids oxidation, fermentations, amino acid catabolism and animal feedstuffs. The influence of the starters and especially the flavouring strains proved to have a major effect on the level of volatile compounds in dry sausages. The flavour tests led to more accurate determination of the sensory characteristics of important molecules in the flavour of dry sausages. The sensory analyses showed that the butter odour of dry sausages largely depends on the catabolism of carbohydrates and that curing and rancid odours were correlated with some typical compounds of lipid oxidation.     

Lagegeregelte servoantriebe ohne mechanische sensoren

Mechanische Sensoren zur Drehzahl- und Lageerfassung bei elektrischen Antrieben verursachen Kosten, vergrößern das Bauvolumen des Antriebs, bedingen die Datenübertragung zwischen Regler und Sensor und schränken das Einsatzgebiet des Antriebs ein. Daher wurden verschiedene Konzepte zum Ersatz dieser mechanischen Sensoren entwickelt. Sie unterscheiden sich durch den Aufwand, die benötigte Genauigkeit (Pumpenantrieb Servoantrieb) und dem gewünschten Betriebsbereich.In diesem Beitrag wird die Realisierung des sensorlosen Betriebs einer permanenterregten Synchronmaschine durch ein Kalman-Filter beschrieben. Durch Simulation und durch praktische Untersuchungen konnte gezeigt werden, daß die Schätzung von Lage und Drehzahl die Genauigkeitsanforderungen für eine indirekte Lageregelung erfüllt. Eine solche Lageregelung besitzt keinen mechanischen Sensor mehr.     

Male response to natural sex pheromone ofMigdolus fryanus westwood (Coleoptera: Cerambycidae) females as affected by daily climatic factors

Males of the sugarcane borer,Migdolus fryanus (Coleoptera: Cerambycidae), are attracted to females by means of a sex pheromone. Mating usually occurs during a few days from October to March under field conditions in São Paulo State, Brazil. This work reports on mating of this species as affected by daily climatic factors, during a single nuptial flight. Maximum male capture by the natural sex pheromone occurred from 1000 to 1100 AM at air and soil temperatures of 30.0°C and relative humidity of 57.0%. As these temperatures increased, females burrowed into the soil, as they are more sensitive to heat than males. Thus, it was concluded that sex pheromone-mediated mating in this cerambycid is directly affected by temperatures of air and soil.