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  • Egu-Copernicus  (1)
  • Wiley-Blackwell  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Chichester [u.a.] : Wiley-Blackwell
    International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 1 (1988), S. 45-59 
    ISSN: 0894-3370
    Keywords: Engineering ; Electrical and Electronics Engineering
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Electrical Engineering, Measurement and Control Technology
    Notes: The conventional transverse resonance method, which has been widely used for the approximate characterization of a number of guiding structures and leaky wave antennas, is formulated in a generalized form. The transverse resonance viewpoint combined with the generalized matrix representation of discontinuities yields a rigorous transverse equivalent circuit of the guiding structure.This technique is used to compute the characteristics of both symmetrical and asymmetrical double slot unilateral finlines in terms of phase constant and characteristic impedance of the dominant as well as higher-order modes. Numerical aspects of this method are discussed.In a double-slot finline structure, a coupled-mode regime is established with the onset of the first higher-order mode in addition to the dominant quasi-TEM mode. Symmetrical structures are simply modelled in terms of even and odd mode characteristics, but a more general coupled-line model, in terms of the C-mode and the II-mode must be applied to asymmetrical coupled finlines.
    Additional Material: 10 Ill.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2022-12-28
    Description: Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this context, machine learning techniques applied to satellite data acquired from recent spaceborne sensors have shown promising results in the last few years. This work focuses on the application of a neural-network-based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. A classification of meteorological clouds and of other surfaces comprising the scene is also carried out. The neural network has been trained with MODIS (Moderate Resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the comparable latitudes of the eruptions permit an extension of the approach to SLSTR, thereby overcoming the lack in Sentinel-3 products collected in previous mid- to high-latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared to RGB visual inspection and BTD (brightness temperature difference) procedures. Moreover, the comparison between the ash cloud obtained by the neural network (NN) and a plume mask manually generated for the specific SLSTR images considered shows significant agreement, with an F-measure of around 0.7. Thus, the proposed approach allows for an automatic image classification during eruption events, and it is also considerably faster than time-consuming manual algorithms. Furthermore, the whole image classification indicates the overall reliability of the algorithm, particularly for recognition and discrimination between volcanic clouds and other objects.
    Description: Published
    Description: 7195–7210
    Description: 5V. Processi eruttivi e post-eruttivi
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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