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Scenario 2: intentional damage of the selected infrastructure.
In this paper, we evaluated the following anomaly/attack scenarios: Scenario 1: electromagnetic distortion Scenario 2: intentional damage of the selected infrastructure Scenario 3: attacks performed by means of the WSN IP gateway.
Government ultimately decides which infrastructure assets to develop, guided by these priorities, and uses rigorous Value-for-Money analyses to select the delivery approach used to finance and develop the selected infrastructure assets.
In SensApp, with the exception of the Notifier (N), all other services depend on the database, and this remains regardless of the selected infrastructure, software stack and allocation scheme.
The following scenarios of anomaly attacks are analyzed and calculated for the sake of efficient protection of railroad crossings: (i) electromagnetic distortion; (ii) intentional damage of selected infrastructure; and (iii) attacks performed by means of the important WSN component, i.e., the WSN IP gateway.
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For the definition of the interface (buffer) distance between WUI infrastructural and wildland components, we computed the empirical cumulative distribution functions (ECDF) of the percentage of ignition points (observed and simulated) arising at increasing distances from the selected infrastructures.
The hybrid method hires scatter search to derive the optimal routing plan of mixed fleet and variable neighborhood descent to select infrastructure locations.
In such a model, the government selects infrastructure projects, guided by a non-partisan, expert infrastructure prioritization panel, and contracts for the delivery of these prioritized infrastructure services with a private concessionaire financed by long-term institutional investment capital.
Based on the results of those interviews, we develop a governance model for infrastructure service delivery: the government selects infrastructure projects, guided by a non-partisan, expert infrastructure prioritization panel, and contracts for the delivery of these prioritized infrastructure services with a private concessionaire financed by long-term institutional investment capital.
In the upper-level problem, the government optimizes his location strategy, i.e., selects infrastructures from candidate locations, to minimize the construction budget and meet desired EV adoption rate.
In this paper, we demonstrated the use of fuzzy inference system for aggregating selected railway infrastructure performance indicators to relate maintenance function to capacity situation.
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CEO of Professional Science Editing for Scientists @ prosciediting.com