In this work the classification of Sentinel-3 Sea and Land Surface Temperature (S3-SLSTR) images with a focus on volcanic cloud detection through a Neural Networks (NNs) approach is presented. Since the hazardous nature of eruptions, a fast and reliable method to monitor the evolution of volcanic clouds in real time is of primary interest. NNs represent a suitable tool for this purpose given their short processing time once trained, and their ability to solve complex problems as those related to natural events. The present research starts from the generation of the training patterns by means of MODerate resolution Imaging Spectroradiometer (MODIS) data collected during the 2010 Eyjafjallajokull (Iceland) eruption, it goes through the training of the NN, and ends with the application of the NN-based model to SLSTR data collected during the 2019 Raikoke (Kuril Island, Russia) eruption.

In this study we present a novel approach dedicated to the retrieval of volcanic ash parameters by means of data acquired from the Sea and Land Surface Temperature Radiometer on board of the Copernicus Sentinel-3. In this framework, we developed a procedure combining Radiative Transfer Model simulations and Neural Network for estimating three volcanic ash parameters such as aerosol optical depth, effective radius and ash mass. The Radiative Transfer Model simulations have been considered for producing synthetic training sets, which have been used in the training phase of the Neural Networks development. In particular, nine latitude's belts have been identified for training several Neural Networks ensuring the global coverage of the method. The approach has been tested by comparing the results of the trained NN with the ones obtained by applied the state-of-art Look Up Table and the Volcanic Plume Retrieval procedures. The results of the methodologies applied on Raikoke, 2019 eruption demonstrated the feasibility of the proposed approach by registering values of the correlation coefficient between all the three methods ranging between the 65% and the 94%.

The rapidly evolving field of open science has made scientists agree on the idea that data, workflows and services, should be findable, accessible, interoperable, and thus optimally reusable. These principles apply to the geohazard community also, dealing with natural phenomena such as volcanic and seismic activity. However, there is still a weakness regarding research sharing and re-use through the scientific community, due to lack of technological solutions and their long-term implementation. We present a lifecycle management service ecosystem based on Research Objects to manage scientific works, in terms of methods, workflows and intermediate or final results, according to open science principles. We show a data cube service for scalable structured data discovery, access and interoperation. These services foster the adoption of remote sensing (e.g., Copernicus) data in the geohazard scientific community enabling research lifecycle documentation. We present test cases of ground deformation analysis and modelling, and volcanic cloud mapping.

In recent years many studies concerning the monitoring of volcanic activity have been carried out to develop ever more accurate and refine methods which allow to face the emergencies related to an eruption event. In our work we present different approaches for the volcanic ash cloud detection and retrieval using Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) data. As test case the SLSTR image collected on Raikoke volcano the 22 June 2019 at 00:07 UTC has been considered. A neural network based algorithm able to detect and distinguish volcanic and meteorological clouds, and the underlying surfaces, has been implemented and compared with two consolidated approaches: the RGB (Red-Green-Blue) and the Brightness Temperature Difference procedures. For the ash retrieval parameters (aerosol optical depth, effective radius and ash mass), three different methods have been compared: the reliable and consolidated LUTp(Look Up Table) procedure, the very fast VPR (Volcanic Plume Retrieval) algorithm and a neural network based model.

During a volcanic eruption, large quantities of Sulphur dioxide (SO2) are sometimes emitted into the atmosphere. Rapid detection and tracking ofvolcanic SO2 clouds might be beneficial to air traffic security and to predict any correlated impact on the environment; for example, the possibility of acid rain events. Within the presented work, we exploited Sentinel-5p radiance data (Level 1 b) to detect and retrieve SO2 volcanic emissions through a neural network based algorithmthat produces rapid SO2 vertical column estimates. The dataset used for training the net was composed of 13 TROPOMI Level 2 “Offline” SO2 data collected during the Etna Volcano eruption that occurred in 2018 from 22 December to 1 January. Experimental results are very encouraging and open to the perspective ofmake available a new and stable product for monitoring atmospheric SO2 clouds on a global scale based on Sentinel-5p acquisitions.

In this work data observed by the geostationary MSG-SEVIRI and the polar NASA-Terra/Aqua-MODIS orbiting satellite instruments, have been used for the proximal and distal monitoring of the 24-30 December 2018 Etna eruption. The combined use of the SEVIRI high repetition time and the MODIS high spatial resolution allows a reliable near real time volcanic characterization from the source to the atmosphere. For the proximal monitoring the parameters estimated are the eruption starts and duration and the volcanic plume top height, while the distal monitoring was inverted relying on the determination of the volcanic cloud altitude and the ash/SO2retrievals. Achieved products were validated by comparing these results with those observed remotely by ground based networks.Results obtained in this study show the ability of satellite-based systems to entirely follow eruptive events in near real time, offering a powerful tool to mitigate volcanic risk on both local population and airspace.

This paper considers the Softcast joint source-channel video coding scheme for data transmission over parallel channels with different power constraints and noise characteristics, typical in DSL or PLT channels. To minimize the mean square error at receiver, an optimal precoding matrix design problem has to be solved, which requires the solution of an inverse eigenvalue problem. Such solution is taken from the MIMO channel precoder design literature. Alternative suboptimal precoding matrices are also proposed and analyzed, showing the efficiency of the optimal precoding matrix within Softcast, which provides gains increasing with the encoded video quality.

In this paper, an in-vehicle power line channel mathematical multipath representation is proposed. The selected approach aims at describing the transmission of a signal on a possibly complex power network by means of a finite number of delayed echoes. Model parameters are computed via a well-defined step-by-step procedure from frequency-domain channel characteristics. The feasibility and strength of the method are demonstrated by means of a measurement campaign. Two-port scattering measurements have been carried out on a commercial automobile and the effect of the measurement setup has been considered in the analysis.

This paper focuses on the design of stopping rule criteria for duo-binary turbo codes. In particular, single-binary stopping rules are generalized and iteration/semi-iteration based criteria are developed. The proposed solutions are tested using the framework of HomePlug AV, which is the last generation technology for the in-home distribution of audio and video data over existing power-line wiring.

In this paper a new bit-loading algorithm for the OFDM-based turbo coded HomePlug AV system is proposed. To better exploit the error correction capabilities of the turbo code, this algorithm tries to maximize the throughput while guaranteeing a target BER over a set of sub-carriers using a new metric based on the LLRs. The performance of the considered system, in terms of throughput and BER, is evaluated, using a realistic power-line channel model. Furthermore, data- aided channel and SNR estimates are investigated and their performance compared with the ideal case.