dataesr/scientific-paragraphs-categorization huggingface dataset:
Improving the representation of microphysical cloud properties and their radiative impact in global climate models may elucidate the role of clouds for Arctic amplification and for future climate change. A1. -3 (99. 4 [35. 7/194. 4   A3. Dupuy et al. , 2022b). Data can be easily reproduced and analyzed by the Python package ac3airborne (https://doi. org/10. 5281/Zenodo. 7305585,  Mech et al. , 2022b)  including a package for flight segmentation (https://doi. org/10. 5281/Zenodo. 7305558,  Risse et al. , 2022) , where each research flight is split up into logical parts like ascends, descends, specific patterns for in situ probing, etc. The data ac3airborne package provides access to sea ice coverage along the flight path as well, extracted from data available at the University of Bremen (https: //data.
Application scenarios The applications cover diferent domains where managing vague and imprecise information is needed, and currently the solutions to represent and operate with this type of information are very limited. (1) In the digital humanities context, we have developed a system using the DICTOMAGRED project  [36]  that studies the location of historical cities and placements in North Africa as they appear in ancient and modern sources, which sufer from vagueness and subjective interpretations from separate experts (historians, geographers, etc. ). (2) In the domain of Software Engineering, we present a scenario where Classifying Terms (CTs)  [23, 25]  have been used to automatically generate test cases. Our approach is used to add beliefs to the generated test cases (i. e. , model instances) and to reason about them before they are accepted and used for testing purposes or discarded.
For instance, he thinks that the birth years of person1 and person2 are not appropriate as they indicate that both persons have a kid when they were 15 years old. Therefore, an opinion of SBoolean(0. 1, 0. 8, 0. 1, 0. 5) is assigned to them. Furthermore, he thinks that not all three people should be named Bob. Therefore, he also assigned the belief b2 to the name of person2. All this information is captured in the object model presented in Fig. 8b. In addition, the designer assigned an opinion to the test case as a whole, i. e. , considering it as a single entity. This was done by assigning a belief to the package that contains the model elements. We can observe how, in Fig. 8b , the package has been stereotyped as an uncertain element and the belief b3 has been assigned to it. In such a case, the belief that an agent holds about a concrete model element is conditional to the belief stated on the test case.
• The list of language translations for in-game information, dialog and/or subtitles. • The list of downloadable content (DLC) that provide extras to the game, such as additional maps, missions, characters, items or skins. • The list of supported platforms as well as the minimum and recommended system requirements (CPU, RAM memory, graphics card, available storage) for each one. Regarding opinions, users can assign tags to games (such as łsingle-playerž o łrole-playing gamež) as well as write textual reviews. Furthermore, users can show their agreement with a particular review by endorsing it (łWas this review helpful?ž). Steam reviews ofer an interesting dataset from the point of view of opinion mining and sentiment analysis, which has been analyzed in the software engineering literature (e.
ACKNOWLEDGMENTS We would like to thank the reviewers of the paper for their insightful comments and very valuable suggestions, which have signiicantly helped us to improve it. This work is partially supported by the  Spanish Government unders  project  LOCOSS  ( PID2020-114615RB-I00 ),  CoSCA  ( PGC2018-094905-B-I00 ) and  MBTI4A  ( P20-00067-FR ); and  TRANSACT , which has received funding from the  ECSEL Joint Undertaking (JU)  under grant agreement No  101007260. The JU receives support from the  European Union 's  Horizon 2020 research and innovation programme and Netherlands ,  Finland, Germany, Poland, Austria, Spain, Belgium. Denmark, Norway.
This smoldering phase can last from several minutes to one or three days or even several weeks in rare cases, making it difficult to accurately identify the lightning that caused the fire. This phenomenon poses a great challenge to the development of predictive methods that can establish a cause-effect relationship, which is currently limited to specific regions  [75]. A robust association between lightning and wildfires can aid in understanding the natural fire regime, such as estimating holdover duration, identifying igniting lightning characteristics, and modelling lightning fire occurrence. The principal possibility of building such a model was demonstrated by  Wotton and Martell [76]  from Ontario, Canada. Unfortunately, there are currently no global datasets that unambiguously link igniting lightning to its corresponding wildfires.
However, improvements in lightning location systems (LLSs) and fire databases  [77]  have facilitated the identification of possible igniting lightning, and numerous methodologies have been developed to match wildfires and related lightning  [75]. Consequently, identifying the igniting lightning of a wildfire by searching the lightning dataset remains challenging, making predicting single lightning-ignition events difficult. However, utilizing a statistical approach that incorporates lightning data from Numerical Weather Prediction (NWP) models and other environmental factors occurring when a fire is observed may offer a potential solution  [78]. The target variables for prediction could be widely available satellite products, such as Fire Radiative Power (FRP) or Burned Area (BA).
This framework is now widely used to support preparedness, fuels, and response decisions and is embedded in corporate systems like the Interagency Fuels Treatment Decision Support System  [285, 286]. Despite its successes, the framework does have some notable limitations; for instance, a poor ability to account for fire impacts that play out over different time scales and the typical use of expected values masks low-probability high-consequence events  [287]. One notable area of growing emphasis is on wildfire risk to communities and better characterizing risk in the wildland-urban interface. There is a growing availability of national datasets to support community risk assessment (e. g. , https://wildfirerisk. org/, accessed on 30 March 2023), and to prioritize landscape areas for hazardous fuels reduction on the basis of community exposure and fire transmission across landscapes.
Australia Over the last 60 years Australia has been using a two fuel-specific fire danger system, including the Grassland Fire Danger Index (GFDI) and Forest Fire Danger Index (FFDI)  [314]. The McArthur fire danger indexes were operationally applied to all fuel types in Australia and were produced based on the predicted rate of spread and the difficulty of suppression of fires burning in grasslands  [314]  and dry sclerophyll forest fuels  [315]  under different weather conditions using a dataset of over 800 experimental fires, complemented with data from high-intensity wildfires. McArthur's FFDI uses as inputs air temperature, relative humidity, 10 m open wind speed and precipitation. The latest variable is used to determine fuel availability by applying the drought factor (DF), which is determined based on recent rainfall and partly based on the soil moisture deficit, commonly calculated using the ).
A new fuel type-specific Australian Fire Danger Rating System (AFDRS) was implemented in September 2022. This system combines fuel-specific fire behavior models for eight vegetation types with fuel, weather and climate datasets to derive Fire Danger Ratings (FDR) and the Fire Behaviour Index (FBI) across Australia. These vegetation types include grassland, savannah, spinifex, buttongrass, forest, mallee heath, shrubland and pine. The purpose of the FDR is to provide an efficient and easy to understand way to communicate fire danger broadly to the community, with categories including moderate, high, extreme and catastrophic based on the range of possible fire behavior, prescribed burning potential, suppression opportunities, and potential consequences of a fire  [317]. The FBI is a simple numerical scale that can be used consistently across Australia for a decision that needs finer detail than the FDR categories allow.
The final EWRA approach [225] defines risk as the joint effect of wildfire danger (or hazard) and the damage that it may cause (exposure and vulnerability), assessed together by considering the wildfire vulnerability of people, ecosystems and goods exposed to wildfires. An extensive satellite remote sensing data was used to generate the different components and variables of the EWRA. The high-resolution burnable mask was developed using the Global Human Settlement Layer's built-up product [326], the PROBA-V NDVI from the Land Service of Copernicus and the Joint Research Centre (JRC) Global Water Layer  [327]. Historical records (2003-2020) on the location, damage and number of fires (derived from the MODIS thermal anomalies, EFFIS burned area dataset and Corine Land Cover aggregated in two classes (Wildland and Non-Wildland, the latter including urban, industrial and agricultural areas) were used to assess the contribution of fire ignition to wildfire risk.
The number and the location of ignitions (based on the proximity between burned areas and thermal anomalies and the percentage of the burnable area in each thermal-anomaly footprint) were used to filter thermal anomalies not corresponding to vegetation fires (i. e. , with footprint outside any potential burnable vegetation fuel) as well as to assess the level of fuzzy-confidence in each thermal anomaly being part of a real wildfire. With this robust fuzzy classification, the possibility for thermal anomalies to be wildfire-related was ranked in a way more tolerant to data uncertainty and misclassification, and an improved version of the wildfire danger by thermal anomalies was estimated (mitigating the otherwise large impact of purely cropland fires). Daily data from the Canadian FWI, for the same period  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020) , were used to assess the weather wildfire danger conditions, since FWI was also proven suitable for European conditions  [33].
As a proxy of high-to-extreme conditions of fire danger by weather  [30] , the number of days where FWI was higher than 30 was computed for each EURO-CORDEX cell. Additionally, the fraction of burnable area for each thermal anomaly footprint was assessed for each land-cover class from the four Corine Land Cover collections (2000 to 2018) and used as a first proxy to assess the vegetation classes most affected by fires. Vulnerability, assessed together with the exposure was designed to encompass people (1), ecosystems (2) and goods (3) prone to fire in vulnerable areas. To estimate the people exposed in those areas, the wildland-urban interface (WUI) model specifically developed by Costa et al. [30]  was used. The ecosystems exposed were estimated by a qualitative approach using the Natura 2000 network, the World Database on Protected Areas and the Ecological irreplaceability score  [328]  for each protected area, which reflects the potential contribution of a protected area to conservation goals.
This index prioritizes the risk for human lives, while also considering ecological and socioeconomic aspects. For this ranking, the mathematical mechanism relied on standard long-established lexicographic sorting algorithms  [224]. High risk may be expected where high wildfire danger affects the most critical areas for people, and secondarily for the other ecological and socioeconomic aspects (Figure  2 ). Next steps of the EWRA development will include testing and validating the current and future versions in close collaboration with the EGFF. At the same time, future research will focus on deriving enhanced datasets and methods for fuel classification, socio-economic components, and live fuel moisture content.
Spatial and Temporal Resolution An integrated risk system should be as scale independent as possible. However, the availability and the quality of input datasets is highly dependent on the target scale. Generally speaking, global or continental scales tend to cover larger territories using coarse resolution data, while regional-local scales cover smaller territories at much higher spatial resolution. For the FirEUrisk project, two target scales have been developed: the European Territory (ET), with a total area of around 5 Mkm 2 of land covering 33 countries at a target resolution of 1 km 2 , and selected pilot sites (PS), covering a few thousand square kilometers and a minimum resolution of 0. 01 km 2 (1 hectare). The ET area covers European Union member states, except for their tropical islands and Cyprus, plus the United Kingdom, Switzerland, Norway and the countries in the Western Balkans.
Since the FirEUrisk project is mostly interested in extreme fires, two reference scenarios leading to very high or high-risk conditions were selected. The first one was defined by selecting the 95th highest percentile of weather parameters at noon (12 h) for high-risk days, while the high-risk conditions scenario was computed from the same set of high-risk days but taking instead the 50th percentile of the weather parameters. Highrisk days were obtained by selecting the ignition dates of any fire that burned more than 2000 hectares in the ET area. The selection of ignition dates was based on the analysis of the 2001 to 2019 time-series of the FireCCI51 burned area product. This dataset was produced within the European Space Agency Climate Change Initiative's program  [331] , based on satellite earth observation data  [4].
The original product was available at 250 m, organized in continental monthly files that extend from 2001-2019. To estimate ignition points, burned pixels of the FireCCI51 were grouped into burned patches using a contextual algorithm that accounted for the spatial proximity and the date of detection  [332]. Ignition points (and respective dates) were obtained by computing the geographic centroid of the contiguous pixels that have no other neighboring pixels with a previous fire date. A total of 403 high-risk days corresponding to those ignition points was obtained for the 19 years of the FireCCI51 time series. Weather parameters for those 403 days were computed from ERA5-Land global reanalysis data (https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-land? tab=overview accessed on 30 March 2023).
A topography-based transfer function  [333]  was applied to the 9 km-resolution time series of the hourly ERA5-Land reanalysis data (https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview, accessed on 30 March 2023) for the following variables: Temperature, Dew Point Temperature, Relative Humidity, Wind and Daily Precipitation. Particularly, for each climate variable simulated by the reanalysis, the statistical downscaling was based on three steps: (1) For all the variables and for each time step (with 0. 073 • × 0. 073 • resolution), a purely geo-statistical approach was performed to a 1km×1km grid by using AIC-based stepwise regression  [334]  with topological and geographical parameters (altitude above sea level, latitude, longitude and distance to the Atlantic Ocean and Mediterranean Sea).
Human Ignition Modelling of human ignition was approached from empirical models based on historical fire occurrence. Unfortunately, there is not yet a publicly available centralized database in Europe where all fires are recorded, as temporal length and spatial accuracy of available fire reports varies throughout European countries. For this reason, the same method used to define the high-risk days (see Section 4. 1) was selected to define historical fire occurrence for human ignition modelling. However, in this case, ignition points from all sizes of burned patches were considered, not just those from patches above 2000 ha. Since those small patches also included agricultural fires, only those ignition points located in wildland areas were kept. Patches within wetlands or agricultural areas not interfacing with forested covers (>500 m) were removed.
The final ignition point dataset included 55,214 fire observations (Figure  6 ). The Ignition probability model was derived from a Random Forest algorithm [341], using a wide set of explanation variables derived from different statistical and cartographic sources [342]: distance to roads, urban-forest, forest-grassland and forest-cropland interfaces, urbanization degree, livestock density, land cover and land cover change, gross domestic product, population density and senior population proportion (>65 years), and various bioclimatic indices. All were resampled at the target spatial resolution of 1 km 2. The RF model was run using a sample of 70% of ignition points for calibration and the remaining 30% for validation. Non-ignition points were selected randomly in the whole ET area, selecting 1.
Forest fuel types were subdivided as a function of both canopy characteristics: leaf type (broadleaf/needleleaf), phenology (evergreen/deciduous), and fractional cover (open/closed), as well as the understory characteristics, including type (grass/shrub/timber litter) and depth (three height classes). Regarding the other main fuels, subcategories were based on fuelbed depth (grasslands and shrublands) and characteristics (herbaceous or woody, for croplands; tree, shrublands and grasslands for wet/semi peatlands). Urban fuels were split into continuous and discontinuous fabric. This classification system was used to generate all fuel type datasets for the FirEUrisk project, including the whole ET [343] (Figure  7  left) and PS (Figure  7  right). The former were obtained from existing land cover and biophysical models, while the latter were generated from high resolution satellite image classification  [344] , with resolutions ranging from 100 to 20 m.
Both ET and PS fuel type maps were associated to existing fuel models: a preliminary assignment for the ET area was done using the Fire Behavior Fuel Model (FBFM) standard models  [88] , where for the PS the fuel models were estimated from field data or biophysical regional estimations. were based on fuelbed depth (grasslands and shrublands) and characteristics (herbaceous or woody, for croplands; tree, shrublands and grasslands for wet/semi peatlands). Urban fuels were split into continuous and discontinuous fabric. This classification system was used to generate all fuel type datasets for the FirEUrisk project, including the whole ET [343] (Figure  7  left) and PS (Figure  7b  right). The former were obtained from existing land cover and biophysical models, while the latter were generated from high resolution satellite image classification  [344] , with resolutions ranging from 100 to 20 m.
Both ET and PS fuel type maps were associated to existing fuel models: a preliminary assignment for the ET area was done using the Fire Behavior Fuel Model (FBFM) standard models  [88] , where for the PS the fuel models were estimated from field data or biophysical regional estimations. Fuel moisture content was computed for fire propagation potential. For dead fuels, the FMC was computed from weather variables using an adaptation of the  Nelson model [345]  to estimate the 10h Dead FMC for high-risk and extreme-high conditions (see section 4. 1). The weather inputs were obtained and rescaled from ERA-5 (https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era5, accessed on 30 March 2023) and Worldclim (https://www. worldclim. org/data/worldclim21. htmland, accessed on 30 March 2023), and downscaled to 1 km 2.
DFMC values were divided into four classes, according to the 10-h DFMC code: D1L1: Very low dead FMC, fully cured herb, Fuel moisture content was computed for fire propagation potential. For dead fuels, the FMC was computed from weather variables using an adaptation of the  Nelson model [345]  to estimate the 10h Dead FMC for high-risk and extreme-high conditions (see Section 4. 1). The weather inputs were obtained and rescaled from ERA-5 (https://www. ecmwf. int/en/ forecasts/datasets/reanalysis-datasets/era5, accessed on 30 March 2023) and Worldclim (https://www. worldclim. org/data/worldclim21. htmland, accessed on 30 March 2023), and downscaled to 1 km 2. DFMC values were divided into four classes, according to the 10-h DFMC code: D1L1: Very low dead FMC, fully cured herb, D2L2: Low dead FMC, 2/3 cured herb, D3L3: Moderate dead FMC, 1/3 cured herb, D4L4: High dead FMC, fully green herb.
These variables were computed for the ET area using the two reference weather scenarios, with the DFMC modelled for those conditions and the fuel type dataset described in Section 4. 3. 3, using the adaptation to the Scott and Burgan fuel models  [88] , done by  Aragoneses et al. [343]. Elevation data was downloaded and rescaled from NASA Shuttle Radar Topography Mission data (https://lpdaac. usgs. gov/products/srtmgl1v003/, accessed on 30 March 2023). Both aspect and slope density data were extracted from this dataset. The method used to estimate propagation for the different PS of the project was based on a different approach to obtain a more detailed analysis of spatial variations within those sites, based on higher resolution spatial datasets. Propagation modelling was based on the Minimum Travel Time algorithm provided by FlamMap  [49] , which estimates burn probability for every pixel based on certain weather conditions, fuel parameters and topography, using a stochastic method.
ctfc. cat/en/index. html, accessed on 25 March 2023). to define extreme conditions, while 50th percentile values were used to define high-average ones. Outputs of Rothermel equations were: Rate of Spread (m/s), Reaction intensity (kW/m 2 ), Fireline Intensity (kW/m) (see Figure  9 ) and Flame Length (m). These variables were computed for the ET area using the two reference weather scenarios, with the DFMC modelled for those conditions and the fuel type dataset described in Section 4. 3. 3, using the adaptation to the Scott and Burgan fuel models  [88] , done by  Aragoneses et al. [343]. Elevation data was downloaded and rescaled from NASA Shuttle Radar Topography Mission data (https://lpdaac. usgs. gov/products/srtmgl1v003/, accessed on 30 March 2023). Both aspect and slope density data were extracted from this dataset.
The method used to estimate propagation for the different PS of the project was based on a different approach to obtain a more detailed analysis of spatial variations within those sites, based on higher resolution spatial datasets. Propagation modelling was based on the Minimum Travel Time algorithm provided by FlamMap  [49] , which estimates burn probability for every pixel based on certain weather conditions, fuel parameters and topography, using a stochastic method. To obtain the reference weather conditions, historical fire ignitions were obtained from national statistics, taking at least the last 20 years. Only weather scenarios responsible for more than 10% of overall detected burned area were taken for further examination. Fuel types were obtained from high-resolution products (as indicated in  Section 4.