Exploitation of Electromagnetic Models for Sea Wind Speed Estimation from C-Band Sentinel-1 Images

Among the different available wind sources, i.e. in situ measurements, numeric weather models, the retrieval of wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give high wind resolution cells. For this purpose, one can find two principal approaches: via electromagnetic (EM) models and empirical (EP) models. In both approaches, the Geophysical Model Functions (GMFs) are used to describe the relation of radar scattering, wind speed, and the geometry of observations. By knowing radar scattering and geometric parameters, it is possible to invert the GMFs to retrieve wind speed. It is very interesting to compare wind speed estimated by the EM models, general descriptions of radar scattering from sea surface, to the one estimated by the EP models, specific descriptions for the inverse problem. Based on the comparisons, some ideas are proposed to improve the performance of the EM models for wind speed retrieval.

Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach

Industrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-attacks as their architectures shifted from proprietary hardware, software and protocols to standard and open sources ones. Furthermore, these systems which used to be isolated are now interconnected to corporate networks and to the Internet. Among the countermeasures to mitigate the threats, anomaly detection systems play an important role as they can help detect even unknown attacks. Deep learning which has gained a great attention in the last few years due to excellent results in image, video and natural language processing is being used for anomaly detection in information security, particularly in SCADA networks. The salient features of the data from SCADA networks are learnt as hierarchical representation using deep architectures, and those learnt features are used to classify the data into normal or anomalous ones. This article is a review of various architectures such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Stacked Autoencoder (SAE), Long Short Term Memory (LSTM), or a combination of those architectures, for anomaly detection purpose in SCADA networks.

Shared Resource Quality Monitoring and Dynamic Trust Management in a Community Cloud

The collaboration tools offered by Cloud Computing have increased the need to share data and services within companies or between autonomous organizations. This has led to the deployment of community cloud infrastructures. However, several challenges will arise from this grouping of heterogeneous organizations. One of the main challenges is the management of trust between the actors of the community. Trust issues arise from the uncertainty about the quality of the resources and entities involved. The quality of a resource can be examined from a security or functional perspective. Therefore, ensuring security and monitoring the quality of resources is to ensure a high level of trust. Therefore, we propose in this paper a technique for dynamic trust management and quality monitoring of resources shared between organizations. Our approach consists, on the one hand, in evaluating the quality of resources based on quality of service measurement attributes and, on the other hand, in updating the trust values according to the information deduced from these measurements. The proposed framework is evaluated in terms of resource sharing success rate and execution time. Experimental results and comparison with TNA-SL and InterTrust models show that the framework can identify and track the behavior of malicious organizations with relatively low execution time.

The Gaussian Beam Summation and the Gaussian Launching Methods in Scattering Problem

This paper is mainly devoted to application of the Gaussian beam summation technique in electromagnetic simulations problem. Gaussian beams are asymptotic solutions of the Helmholtz equation within the paraxial approximation. Since they are insensitive to ray transition region, several techniques based on Gaussian beam are used to evaluate high frequency EM wave equation, which overcome partially or fully the difficulties of singular regions (caustics, zero field in shadow zones). This paper concentrates on the explicit formulation of the electromagnetic field scattered from radar target. In this approach, when the incident field illuminates the target, the scattering is accounted in a complex weighing function. The wave field at a receiver is evaluated as superposition of Gaussian beams concentrated close to rays emerging from the target, passing through the neighbor of the receiver.

Trust Assessment Model Based on a Zero Trust Strategy in a Community Cloud Environment

The adoption of Cloud Computing services in everyday business life has grown rapidly in recent years due to the many benefits of this paradigm. The various collaboration tools offered by Cloud Computing have eliminated or reduced the notion of distance between entities of the same company or between different organizations. This has led to an increase in the need to share resources (data and services). Community Cloud environments have thus emerged to facilitate interactions between organizations with identical needs and with specific and high security requirements. However, establishing trust and secure resource sharing relationships is a major challenge in this type of complex and heterogeneous environment. This paper proposes a trust assessment model (SeComTrust) based on the Zero Trust cybersecurity strategy. First, the paper introduces a community cloud architecture subdivided into different security domains. Second, it presents a process for selecting a trusted organization for an exchange based on direct or recommended trust value and reputation. Finally, a system for promoting or relegating organizations in the different security domains is applied. Experimental results show that our model guarantees the scalability of a community cloud with a high success rate of secure and quality resource sharing.

An Hybrid Model for Rectal Tumour Response Prediction during Radiotherapy

A hybrid model is proposed in this study to predict rectal tumour response during radiotherapy treatment. As the oxygen partial pressure distribution (pO2) is a data which is naturally represented at the microscopic scale, we firstly estimate the optimal pO2 distribution using both a diffusion equation and a discrete multi-scale model (that we proposed in a previous study). The aim is to use the effectiveness in algorithmic complexity of the discrete model and its multi-scale aspect in this work to estimate biological information at cellular scale and then construct them at macroscopic scale. Secondly, the obtained pO2 distribution results are used as an input of a biomechanical model in order to simulate tumour volume evolution during radiotherapy. FDG PET images of 21 rectal cancer patients undergoing radiotherapy are used to simulate the tumour evolution during the treatment. The simulated results using the proposed hybride model, allow the interpretation of tumour aggressiveness.

A Decision Support Framework for the Choice of Languages and Methods for the Design of Real Time Embedded Systems

The choice of methods or design languages is a crucial phase in the development of systems and software, also for real time and embedded systems. An open question that remains in the design of these types of systems is to build a method, or to choose one among those existing, capable to cover the life cycle of a project, and particularly the development phases. This article contributes to answer the question, by proposing an approach based on a multi-criteria comparative study, of few languages and methods dedicated to the design of real time and embedded systems. The underlying objective of this work is to present to designers a wide range of approaches, and elements that can guide their choices. In order to reach this goal, we propose different comparison criteria. Each criterion is divided into sub-criteria, so that the designers can refine their choices according to the qualities they prefer and wish to have in the method or language. We also define a rating scale which is used to assess the retained languages and methods. The scores obtained from this assessment are presented in tables, one table per criterion, followed by a summary table giving the overall scores. Graphics built from these tables are provided and intend to facilitate the judgement and thus the choice of the designers.

Habits on Social Networks at Workplace: A Survey of Motivations and Behaviour

This article explores the use of social networks by workers in Abidjan, Côte d’Ivoire, with particular emphasis on a descriptive or quantitative analysis aimed at understanding motivations and methods of use. More than five hundred and fifty questionnaires were distributed, highlighting workers’ preferred digital channels and platforms. The results indicate that the majority use social media through their mobile phones, with WhatsApp being the most popular app, followed by Facebook and LinkedIn. The study reveals that workers use social media for entertainment purposes and to develop professional and social relationships, with 55% unable to live without social media at work for recreational activities. In addition, 35% spend on average 1 to 2 hours on social networks, mainly between 12 p.m. and 2 p.m. It also appears that 46% believe that social networks moderately improve their productivity. These findings can guide marketing strategies, training, technology development and government policies related to the use of social media in the workplace.

Performance analysis of space-shift keying over negative-exponential and log-normal FSO channels

The average bit-error rate （ABER） performance of free-space optical （FSO） communication links is investigated for space-shift keying （SSK） over log-normal and negative-exponential atmospheric turbulence channels. SSK is compared with repetition codes and a single-input single-output system using multiple pulse amplitude mod- ulations. Simulation results show that the signal-to-noise ratio gain of SSK largely increases with greater spectral efficiencies and/or higher turbulence effects. A tight bound for ABER is derived based on an exact moment generation function （MGF） for negative-exponential channel and an approximate MGF for log-normal channel. Finally, extensive Monte Carlo simulations are run to validate the analytical analysis.

Genetic algorithm-based multiple moving target reaching using a fleet of sailboats

This study addresses the problem of Dynamic Travelling Salesman Problem for a multi-agent system using a fleet of sailboats.A genetic algorithm(GA)is proposed,which attributes to each agent a varying number of targets to be collected.GA allows obtaining a suboptimal solution in the shortest time possible.Moreover,this study adapts it to the specific problem involving a fleet of sailboats,which is a challenging task with comparison to autonomous underwater vehicles or motorised vehicles in terms of the propulsion.Therein motors can be flexibly controlled while sailboat movements are constrained by available wind direction and speed.Thus the method takes into account wind conditions at various locations of the sailboat.Simulation results demonstrate the effectiveness of the proposed approach.