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Complete set of broadening coefficients and high-order line-shape parameters for N<sub>2</sub>-broadened CO<sub>2</sub> lines, for temperatures ranging from 100 K to 1000 K
No full textBroadening coefficients and high-order line-shape parameters, including the speed dependence of the line width, Dicke narrowing and first-order line-mixing parameters, are predicted for N 2-broadened CO 2 lines over a temperature range from 100 K to 1000 K using requantized molecular dynamics simulations (rCMDS). By employing an intermolecular potential, classical equations of motion, and a requantization procedure, rCMDS enable the calculations of N 2-broadened CO 2 spectra across a wide temperature range without use of any adjustable parameters. For each considered temperature, line-shape parameters were retrieved from fits of the rCMDS spectra using the Voigt, speed-dependent Voigt, and Hartmann-Tran profiles, with first-order line mixing accounted for. The obtained parameters and their temperature dependences were compared with experimental data at various temperatures showing excellent agreements. We demonstrate that parameters and their temperature dependences obtained at Earth's atmospheric temperatures cannot be used to deduce with accuracy those at higher temperatures, relevant for studies of exoplanetary atmospheres and combustion media. Comparisons with databases show that several parameters and their temperature dependences need to be updated by incorporating accurate high-temperature data to correctly simulate the absorption spectra of N 2-broadened CO 2 from low to high temperatures.</p
Tracing online flood conversations across borders:A watershed level analysis of geo-social media topics during the 2021 European flood
No full textIn the face of rapid population growth, urbanisation, and accelerating climate change, rapid and accurate disaster detection has become critical to minimise human and material losses. In this context, geo-social media (referring to georeferenced social media posts) data have proven to be a sensible data source for tracing disaster-related conversations, especially during flood events. However, current research often neglects the relationship between information from social media posts and their corresponding geographical context. In this paper, we examine the emergence of disaster-related social media topics in relation to hydrological and socio-environmental features at the watershed level during the 2021 western European flood, while focusing on transboundary river basins. Building on an advanced machine-learning-based topic modelling approach, we show the emergence of flood-related geo-social media topics in both river-basin-specific and cross-basin contexts. Our analysis reveals distinct spatio-temporal dynamics in the public discourse, showing that timely topics describing heavy rainfall or flood damage were closely tied to immediate environmental conditions in upstream areas, while post-disaster topics about helping victims or volunteering were more prevalent in less affected areas located in both upstream and downstream areas. These findings highlight how social media responses to disasters differ spatially across watersheds and underscore the importance of integrating geo-social media analysis into disaster coordination efforts, opening new opportunities for transboundary collaborations and the coordination of emergency response along border-crossing rivers
FETT: Fault Injection as an Educational and Training Tool in Cybersecurity
Get PDFIn this paper, we present FETT, a fault injection tool for educational and training purposes addressed to educators and students in cybersecurity. Our tool aims to analyze and inject vulnerabilities into existing Django web applications for education purposes. Indeed, security education often relies on either abstract theoretical instruction or overly simplistic examples. This tool bridges the gap between theory and practice by modifying real web applications in a targeted, reproducible way. With its user-friendly interface and modular vulnerability injection, instructors can create challenges tailored to specific learning goals, while students engage directly with code that simulates production-level vulnerabilities. We evaluated \tool based on five publicly available GitHub projects and six student projects from the last three academic years (2022-2024). We successfully managed to efficiently inject vulnerabilities inspired by the OWASP top 10:2021 while keeping the core functionalities of the target application operational.The project is publicly available at \url{https://gitlab.com/fabgilson/django-vulnerability-injector}. A demonstration is available at \url{https://youtu.be/ZYQs2vLzbyE}
