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This model considers hazard location and hazard covered area, as well as the spatially spreading impact of hazards, and proposes two spatial vulnerability indices the ASVI and the RSVI to quantitatively assess the vulnerability of network systems in the face of spatially localized hazards.
It uses two spatial parameters in particular that characterize propagation environments in which there are spatially spreading signals due to local scattering: the angle of arrival and the degree of scattering related to the angular spread of the received signals.
According to the definition of spatial vulnerability in this article, two basic factors are important to calculate the spatial vulnerability of a network system: area covered by hazards (multiple hazards may occur simultaneously in different locations in a network system), and spatially spreading impact of hazards (including direct impact and indirect impact of hazards).
This model considers hazard location and area covered by a hazard, and spatially spreading impact of the hazard (including direct impact and indirect impact through network topology) and proposes an absolute spatial vulnerability index and a relative spatial vulnerability index to reflect the vulnerability of a network system to local hazards.
Therefore, when an animal uses different habitats at different stages, ontogenetic niche shifts have spatially spreading demographic impacts (e.g., [3]-[6]).
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This paper talks about the DOA estimation of spatially spread wideband sources i.e. sources having both spatial and temporal bandwidth.
Although the offices are spatially spread across the map, they are hierarchically grouped by regions.
The six antennas composed of the electromagnetic vector-sensor are spatially spread in the space with some constrains.
In this paper, the geometry of a general spatially spread electromagnetic vector sensor (SSEMVS) array is firstly presented.
Such decomposition facilitates the extraction of sources with a fixed spectral structure which spatially spread over time or sources which evolve in frequency but retain a fixed localisation.
Sensor nodes in a group were adjacent to each other, and hence, representative nodes were spatially spread out (and hence not concentrated around the location of the event).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com