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.properties is a for mainly used in Java-related technologies to store the configurable parameters of an application. They can also be used for storing strings for Internationalization and localization; these are known as Property Resource Bundles. Each parameter is stored as a pair of strings, one storing the name of the parameter (called the key), and the other storing the value. Unlike many popular file formats, there is no RFC for .properties files and specification documents are not always clear, most likely due to the simplicity of the format. Format Each line in a .properties file normally stores a single property. Several formats are possible for each line, including key=value, key = value, key:value, and key value. Single-quotes or double-quotes are considered part of the string. Trailing space is significant and presumed to be trimmed as required by the consumer. Comment lines in .properties files are denoted by the number sign (#) or the exclamation mar

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Characterization of disperses dyes and application properties in inks for ink-jet printing

Marion Debellemanière

2013

Lightweight HI source finding for next generation radio surveys

Frédéric Courbin, Aymeric Alexandre Galan, Jean-Paul Richard Kneib, Austin Chandler Peel, Mark Thomas Sargent, Emma Elizabeth Tolley

Future deep HI surveys will be essential for understanding the nature of galaxies and the content of the Universe. However, the large volume of these data will require distributed and automated processing techniques. We introduce LiSA, a set of python modules for the denoising, detection and characterization of HI sources in 3D spectral data. LiSA was developed and tested on the Square Kilometer Array Science Data Challenge 2 dataset, and contains modules and pipelines for easy domain decomposition and parallel execution. LiSA contains algorithms for 2D-1D wavelet denoising using the starlet transform and flexible source finding using null-hypothesis testing. These algorithms are lightweight and portable, needing only a few user-defined parameters reflecting the resolution of the data. LiSA also includes two convolutional neural networks developed to analyze data cubes which separate HI sources from artifacts and predict the HI source properties. All of these components are designed to be as modular as possible, allowing users to mix and match different components to create their ideal pipeline. We demonstrate the performance of the different components of LiSA on the SDC2 dataset, which is able to find 95% of HI sources with SNR > 3 and accurately predict their properties. (C) 2022 The Author(s). Published by Elsevier B.V.

ELSEVIER2022

Applying model checking to industrial-sized PLC programs

Simon Bliudze, Borja Fernandez Adiego, Jean-Charles Tournier

Programmable Logic Controllers (PLCs) are embedded computers widely used in industrial control systems. Ensuring that a PLC software complies with its specification is a challenging task. Formal verification has become a recommended practice to ensure the correctness of safety-critical software but is still underused in industry due to the complexity of building and managing formal models of real applications. In this paper, we propose a general methodology to perform automated model checking of complex properties expressed in temporal logics (e.g., CTL, LTL) on PLC programs. This methodology is based on an Intermediate Model (IM), meant to transform PLC programs written in various standard languages (ST, SFC, etc.) to different modeling languages of verification tools. We present the syntax and semantics of the IM and the transformation rules of the ST and SFC languages to the nuXmv model checker passing through the intermediate model. Finally, two real cases studies of CERN PLC programs, written mainly in the ST language, are presented to illustrate and validate the proposed approach.

Institute of Electrical and Electronics Engineers2015