Resultados de investigación

[EN]This dataset was developed to support the development, training and evaluation of artificial intelligence and machine learning models for wildfire risk prediction in the Iberian Peninsula. The dataset integrates satellite observations from Sentinel-2 with meteorological information from ERA5 and fire-weather indicators derived from the Fire Weather Index (FWI) system. Data were collected for the period from 16 July 2024 to 31 August 2024, with observations obtained approximately at midday following the temporal criteria established by the Spanish State Meteorological Agency (AEMET). Sentinel-2 data include ten spectral bands (B2, B3, B4, B5, B6, B7, B8, B8A, B11 and B12). ERA5 meteorological information includes 2-metre temperature, 2-metre dewpoint temperature, the U and V components of 10-metre wind, and total precipitation. These variables were used to calculate the components of the Fire Weather Index using the FWIfunctions library (version 0.1.10), including Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Buildup Index (BUI), and the resulting Fire Weather Index (FWI). The continuous FWI values were subsequently classified into five wildfire-risk categories: low, medium, high, very high and extreme. The class thresholds were established using the 40th, 65th, 85th and 95th percentiles of the FWI distribution, respectively. A common spatial grid with a resolution of 0.1° × 0.1° was used to harmonize Sentinel-2 and ERA5 information. The study area was defined using the bounding box [44, -10, 36, 4]. Each record represents a spatiotemporal observation linking satellite-derived information with meteorological and FWI-derived wildfire-risk information for a given location and date., This work was supported by the International Chair Project on Trustworthy Artificial Intelligence and the Demographic Challenge within the framework of the National Artificial Intelligence Strategy (ENIA). Reference: TSI-100933-2023-0001. Funded by the Secretary of State for Digitalization and Artificial Intelligence and by the European Union (NextGenerationEU).

[EN]Quantum dynamics of collisional cooling of the BN− anion in a cold trap is carried out by using the ortho- and para-H2 molecules as a buffer gas. The calculations are performed using the full four-dimensional interaction potential energy surface in which both BN− and H2 are treated as vibrationless rigid rotors, computed from ab initio methods and further accurately fitted using an artificial neural network approach. Relevant state-to-state rotationally inelastic cross sections are computed from quantum methods and the ensuing inelastic rate coefficients for the temperatures of interest are obtained. The latter are employed within a modeling of the cooling process at a variety of trap conditions. The results are discussed in the context of earlier data obtained using the rare gases He and Ar as buffer gases in the same trap. The effects of having either para- or ortho-H2 as collision partner are analysed., The Material includes: (1) all the original cross section data obtained for rotationally inelastic collisions of BN− with ortho- and para-H2 as projectile, (2) the corresponding state-to-state rate coefficients obtained from the cross sections, (3) the relative rotational state populations from the time evolution analysis obtained using the rate coefficients, and (4) the original routine that produces the NN fit of the initial raw data describing the 4D interaction.