dataesr/scientific-paragraphs-categorization huggingface dataset:
Background estimation The expected number of background events   associated to each GRB, at zenith  and azimuth , is evaluated directly from data collected by ANTARES off source and off time (between 27th December 2007 and 30th December 2017) as: 𝜇 𝑏 (𝜃, 𝜙) GRB = 1. 5 T s • 𝑛(𝜃 GRB , 𝜙 GRB ) • C, (5) where T s is the temporal time window around the GRB occurrence, C is the detector efficiency in the specific runs where each GRB occurred and ( GRB ,  GRB ) is the time-averaged rate of events reconstructed in the GRB direction. In the framework of prompt GRB emission, the temporal search window of the neutrino signal was defined in coincidence with the gamma-ray signal, slightly extended to account for uncertainties due to the gamma-ray duration of the event, to the ANTARES data acquisition system and to the propagation time of particles from the satellite to our detector.
Results and discussion ANTARES data from the end of 2007 to 2017 are analysed according to the cuts identified in the optimisation procedure presented above, searching for neutrino events in spatial and temporal coincidence with the prompt phase of GRBs observed by satellite-based gamma-ray instruments. No neutrino events have passed the selection criteria defined through the optimisation procedure and, thus, no neutrino events are found in spatial and temporal coincidence with the GRB sample, for an equivalent livetime of the search of 18. 9 hours. The corresponding 90 % confidence level (CL) upper limit on the computed neutrino signal    is calculated as 𝜙 𝜈 𝜇 90% = 𝜙 𝜈 𝜇 𝜇 90% 𝑠 n s = 𝜙 𝜈 𝜇 2. 3 n s = 𝜙 𝜈 𝜇 • 77 +226 -64 , (6) where the expected number of signal events from the total sample, n s , is estimated to be n s (N GRB = 784) = 0.
(2012) , the expected neutrino flux is observed shifted to lower energies and with a larger normalization, leading to a significantly larger number of expected neutrino events. However, the experimental cuts obtained with an independent optimization procedure are found to remain almost unaltered. As a consequence, the absence of neutrinos associated to GRBs in ANTARES data allows constraints on both models to be derived. The 90 % CL upper limits so obtained lay at a comparable level. For the cumulative fluence of Eq. (  3 ), this limit reads as 1. 3 +4. 1 -0. 8 × 10 -2 < E 2 𝜈 𝜇 F 𝜈 𝜇 < 0. 8 +5. 2 -0. 7 × 10 -1 GeV cm -2 , in the energy range extending from 6. 3 × 10 4 GeV to 1. 3 × 10 7 GeV, which is the region where 90 % of the mean fluence is expected to be detected by ANTARES. The fluence limit translates into 1.
The same spectral trend is reflected in individual upper limits. It is worth keeping in mind that when comparing results from different analyses, one should consider that the spectral and limit shapes depend on the selected sample, the measured parameters of each burst and their uncertainty, namely the set of parameters that are introduced in the chosen model. Here, for the first time, no default value for the model parameters are used and more physical and realistic values are considered (see Sec 4. 2). Finally, the expected quasi-diffuse neutrino flux from the selected 784 GRBs and the corresponding upper limit can be compared with the diffuse astrophysical flux observed by IceCube. To this extent, Fig. 9 , provides the IceCube best fits of the neutrino flux, in both the 10 years   track data sample  (Stettner et al.
Summary and conclusions Using ANTARES data from the end of 2007 to 2017, a search for upward going muon neutrinos and anti-neutrinos in spatial and temporal coincidence with 784 GRBs has been performed. The numerical model NeuCosmA was used to estimate the expected neutrino flux from each burst individually, in the context of one-zone internal shock model. A novel aspect of the search here presented is the inclusion in the data analysis chain of the uncertainty that possible unknown parameters, related to the characteristic activity of the central engine, can introduce in the neutrino flux evaluation. This is crucial in order to correctly interpret the validity of model-dependent results, in terms of upper limits set by non-detections of neutrinos in coincidence with GRBs  (Adrián-Martínez et al.
As a consequence, the expected -fluxes are provided with an uncertainty band of ±2. Analogously to previous ANTARES searches  (Adrián-Martínez et al. , 2013 , 2017b; Celli et al. , 2017) , MC simulations of the signal predicted by NeuCosmA were performed, while the respective background was estimated directly from off-source data collected by ANTARES. Only track-like events reconstructed within 10 • in radius from the expected GRB position were selected, and in temporal correlation with the prompt gamma-ray emission. The analysis was optimised on a burst-by-burst basis so as to maximise the discovery potential of the search, thus allowing the identification of the most promising GRBs for ANTARES. However, because a negligible reduction of the MDP 3 would have been obtained when stacking the entire catalog, the flux from the whole sample of 784 GRBs was investigated.
After unblinding ANTARES data occurred in space and time correlation with GRBs, no event was found to pass the selection criteria, and limits on the contribution of the detected GRB population to the neutrino quasi-diffuse flux were derived. The limits obtained on the cumulative neutrino fluence E 2      , relative to the predictions of NeuCosmA, are 1. 3 +4. 1 -0. 8 × 10 -2 GeV cm -2 and 0. 8 +5. 2 -0. 7 × 10 -1 GeV cm -2 , corresponding to 1. 3 +0. 4 -0. 8 × 10 -9 GeV cm -2 s -1 sr -1 and 1. 0 +0. 9 -0. 5 × 10 -8 GeV cm -2 s -1 sr -1 , respectively, in terms of quasi-diffuse flux E 2      in the energy range from ∼ 60 TeV to ∼ 10 PeV. For the sake of completeness, an upper limit was also calculated relatively to the analysis that assumes the  Ghirlanda et al. (2012)  correlation as a reference model, and it was found to be at a comparable flux level of the one presented here.
With these results, ANTARES data provide a further and independent constrain on the contribution of GRBs to the astrophysical neutrino flux. In particular, within standard assumptions of energy partition among accelerated hadrons, leptons and magnetic fields (baryonic loading equal to 10), GRBs are not the main sources of the astrophysical neutrino flux, possibly contributing for less than 10 % at energies around 100 TeV. (2013). Note that where a measurement of  was missing Γ is obtained by averaging the 1000 values of redshift, possibly extracted for each GRB. On the other hand, for GRB with measured , a single contribution of Γ is present in this plot, as given by Eq. (  2 ). given in Eq. (  2 ). The goal is hence to derive which among these parameters most affects the neutrino flux computation.
Acknowledgements The authors acknowledge the financial support of the funding agencies:  Centre National de la Recherche Scientifique (CNRS) ,  Commissariat  à l'énergie atomique et aux énergies alternatives (CEA),  Commission Européenne (FEDER fund and Marie Curie Program) ,  Institut Universitaire de France (IUF) ,  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ),  Région Île-de-France (DIM-ACAV) ,  Région Alsace  (contrat CPER),  Région Provence-Alpes-Côte d'Azur, Département du Var and Ville de La Seynesur-Mer, France ;  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ;  Istituto Nazionale di Fisica Nucleare (INFN), Italy ;  Nederlandse organisatie voor Wetenschappelĳk Onderzoek (NWO) , the  Netherlands; Council of the President of the Russian Federation  for young scientists and leading scientific schools supporting grants,  Russia ;  Executive Unit for Financing Higher Education, Research, Development and Innovation (UEFISCDI) ,  Romania ;  Ministerio de Ciencia, Innovación, Investigación y Universidades (MCIU) :  Programa Estatal de Generación de Conocimiento  (refs.
At meso-scale, the absolute minimum information is to have at least one class called "urban" within the land cover map, and to assign uniform values to this class for all urban model parameters across the globe. Most databases used in atmospheric models, even recent ones, have only one urban class. Depending on the model requirements and the available data, the urban class can be refined in several ways. For example, it can be separated into different urban densities, which commonly refers to the density of the buildings although population density would be an alternative method. These classes typically use either the land use or land cover (and often both) to describe the city at the neighborhood scale. 2017 ) with an instrumented car, have confirmed that this assumption is correct. For ORMs, however, the above data sets and approaches are not sufficient as the urban canopy must be described explicitly.
, 2018). A land cover classification approach that has been developed specifically for urban climate applications is the World Urban Database and Access Portal Tools (WUDAPT) project. This is an initiative started by the community of urban climatologists  (Bechtel et   One method to extract land use information from vector data sets is to combine cadaster plans of land plots and buildings with the information that is registered about these geographical entities in, e. g. , taxation, planning permits, or ownership records. GIS (or AutoCad) software can be used to develop combined data sets that represent urban land patches and built structures in very high spatial resolution together with detailed information about their uses, which can then be transformed into land use maps. Another method is to use the vector polygons of building footprints and other manmade structures contained in the aforementioned data sets to "tag" those pixels in existing land cover maps as "urban" when these footprints and structures are encountered (see e.
g. Jantz et al. , 2004). This provides the possibility of creating more detailed urban classes since building footprints often contain information on their type and use. Similar to the above, a third method is to access spatial data services, e. g. the OSM query service or national portals to request semantic information about particular coordinate points or polygons. The semantic information can include the type of human activity reported for a specific structure, e. g. , recreation, shopping, dining, etc. , or the general category to which a structure is classified, e. g. , residential, commercial, government, etc. This approach can be used to develop land use maps or enhance existing land cover maps  (Olbricht, 2015; Boeing, 2017)  To achieve this goal, a federated workflow for such content has been designed, and every country must publish data produced by legally mandated organizations through web services according to interoperable specifications at the European level.
3D building data and CityGML for microscale modelling The CityGML standard 2  , developed by the Open Geospatial Consortium (OGC), defines threedimensional geometry, topology, appearance and semantics of all relevant topographic urban objects at multiple well-defined level of details (LOD), ranging from simplified bounding boxes (LOD1: constant building height, LOD2: with roof shape) to explicit details like doors (LOD3) or indoor design (LOD4)  (Gröger and Plümer, 2012). Because they represent the explicit form of buildings, they benefit ORMs more than UCMs in being able to use this finer scale data (e. g. , LOD2, cf Figure  4 ). CityGML is very generic and does not only include all 3D objects of interest to ORMs, like buildings, trees, bridges, tunnels or even park benches, but it contains also basic classes such as relief, water bodies, roads or land use.
Most available CityGML models have been automatically generated from a fusion of LiDAR and catastral data. Important landmarks are often manually added at a higher level of detail. Creation and improvement of CityGML data sets is an ongoing project for many municipalities. Germany, for example, plans to offer a comprehensive LOD2 building model by 2019 and to amend it with bridges and tunnels by 2020. The detailed CityGML data can then be rasterized and aggregated to the resolution and requirements of the ORM model. For ORMs at the meter resolution, information about textures can also be mined using machine learning approaches, e. g. , to extract information like building materials and window or vegetation fractions for single facades. As an example of the processing of CityGML data to represent buildings in a microscale simulation of Berlin at a grid spacing of 1 m, Figure  4  shows the original LOD2 CityGML while Figure  5  is the voxeled building configuration of the Reichstag building in Berlin, Germany.
Further classification steps may be necessary to obtain the required input data structures for the respective ORM. The design of 3D data products at the national level has been considered for several years now  (Stoter et al. , 2015). Optical imagery with different angles is used to generate elevation models with photogrammetric technologies. This technique can be applied to airborne imagery using several images from the same flight but it can also be improved with UAVs or street imagery. LiDAR offers a higher geometric accuracy but a smaller coverage. In some countries, nationwide LiDAR products are available (as over the USA,  Ching et al. , 2009). For some areas, simplified 3D databases can be built automatically out of existing topographic databases based on height attributes. For example, Brasebin et al.
Crowdsourcing or deep learning methods Data on building heights can also be crowdsourced directly (Over et al. , 2010; Fan and Zipf, 2016) through OSM. OSM has an agreed set of tags for 3D building models including 'height' for height of the building in meters and 'levels' for the number of floors. Although this information is not often added to buildings footprints in OSM, there are some demo cities with this information that can be visualized via the OSM Wiki 3  , but building height is often missing  (Lao et al. , 2018). They report that less than 3% of the buildings globally have a height value and less than 4% have a level value. For the city of Paris, the values are 0. 1% and 51. 2%, respectively. It would be possible to use crowdsourcing to manually extract building height or the number of floors from photographs in Google Street View, Mapillary or from other geo-tagged photo repositories such as Flickr.
Developing comprehensive architectural databases There exists for no large city an exhaustive database on building architectural practices or building material characteristics at the scale of the building. Even with the increasing use of Building Information Modelling (BIM), such information is, at best, limited to a very small part of buildings in a city, which are mostly recently constructed large buildings. Due to the specific lack of spatial data about architectural structures at the city scale, how can such parameters be defined? The main problem is that all of these parameters depend on the materials used to construct the buildings, and this varies considerably across time and space. Almost no generic information exists about these architectural features and certainly not globally. Even at the local scale, the only comprehensive way to gather data on building materials is limited to roof material identification using hyper-spectral remote-sensing  (Heiden et al.
, 2007). Architectural knowledge is mostly available at the building scale, but not extensively. The issue arises of how to upscale such sparse information. Some attempts have been made to derive surface parameters like wall albedo and emissivity as well as window fractions by manual observations within case studies  (Resler et al. , 2017). As databases of the architectural parameters needed by UCMs and ORMs are not available, an approximate approach is possible, i. e. , the development of comprehensive architectural databases. The objective is to describe, for several archetypes of buildings, their typical architectural characteristics. For example, the material of the main structural wall is documented. To link this information to the models, the thermal characteristics of these materials are recorded.
While these parameters still only represent an educated guess, they allow the variability in building architectural characteristics to be taken into account. Jackson et al. (  2010 ) have described the thermal and radiative properties of buildings for 4 building types in 33 regions in the world (defined by socio-economics, architectural practices and climate). Tornay et al. (2017) , using an architect's expertise approach, have defined building archetypes in each of the 95 administrative regions of France. This has allowed the spatial variability between the different cities in France to be described, but not within each city. Such building architectural databases can be completed based on expertise or crowdsourcing. One persistent issue is the fact that there is generally no cadaster for monitoring changes in the state of buildings (e.
g. , due to improved insulation during reconstruction works). The actual building properties are thus likely to be very different from those at construction. However, the architectural database would describe elements that are at the building scale (but without spatial information, since they are only referencing archetypes). Therefore, in a second step, once such a comprehensive architectural database is completed, a link must be made between the building archetypes documented within the database and the buildings in the city. This can be accomplished by observing the characteristics of the buildings in the field. Four building parameters that are relevant for how the building has been built are: • the period of construction of the building; • the use of the building (i. e. , residential, commercial, offices); • the building type (i.
e. , house, mid-rise building, high-rise building, industrial building); and • the geographic area (this can be at the scale of neighborhoods, the city, the region, or the country), but can also be related to the climate type. Mapping this information is possible. For example, the building type can be linked directly to the LCZ (see section 2). The building use and period of construction can come from socio-economic databases and a census (see section 5). We now concentrate on how to gather the architectural information itself.
As a consequence, the French database on building archetypes provides three options for the construction of the wall, roof and floor. These expert approaches require an exhaustive collection of information (Figure  6 ), knowledge to generate reference building databases (step 1) and characterize their materials (step 2). To extract results (step 3) that are exploitable and realistic, they can be cross checked with an extended literature review and statistical data in order to be generalized. At present, existing databases are still heterogeneous, and the main factor to be further developed is the different uses of buildings (not just residential buildings). Figure  6. Overview of the expert methodology to define the architectural parameters Known for its compact high-rise urban environment, which is distinctly different from typical European or North American settings, Hong Kong provides an exemplary testing ground for the approach described here.
Although databases detailing Hong Kong's heterogeneous urban morphology are available, they are not sufficient for characterizing building architectural parameters. Several unique features in building characteristics have been identified through reviewing the urban development history and building ordinances in Hong Kong (Shelton et al. , 2011; Wong, 2014), from interviews with practitioners, and from field surveys. First, there is a small variation in construction materials across buildings of different type, use and age. Reinforced concrete, often in the form of prefabricated component blocks, is the predominant structural material for all building types. A notable exception is tall commercial skyscrapers with curtain walls. Wall insulation is not of major concern for the subtropical climate in Hong Kong.
e. , it requires some expert knowledge or training of the crowd to identify features associated with different age categories, where these can also vary by country. As with height, building age can be collected using a crowdsourcing application like that described previously. An alternative approach is to build a model that predicts building age if a sample of building age data exists. For example, Rosser et al. (  2019 ) built a predictive model of building age using parameters such as area of the building footprint, the perimeter, the number of buildings in the block, etc. By aggregating the results from different methods and using some simple rules for filtering the data, the overall accuracy achieved was 77%. A similar approach was undertaken by  Biljecki and Sindram (2017) , who fitted a random forest regression model to predict building age in Rotterdam.
From inventories The anthropogenic heat flux is mainly due to traffic, industrial activities, the building sector, and human and animal metabolism. It can be quantified using three strategies  (Sailor, 2011) : observing the urban surface energy balance; inventories (top-down approach); and building energy consumption modelling (bottom-up approach). The observation-based method can only be employed for locations with available urban surface energy balance observations (e. g. Pigeon et al. , 2007  for Toulouse). This method is also subject to uncertainty due to observation errors, especially since the anthropogenic heat flux is determined as the residual of all other fluxes. The top-down inventory method consists of disaggregating large-scale energy consumption data in space and time. It is the state-of-the-art for the construction of global anthropogenic heat flux databases.
Population is disaggregated in space using the total residential floor area as a spatial weight. The residential floor area is determined using the building's geometric properties (i. e. , footprint, height) and use, which is available from administrative data and was processed following  Bocher et al. (2018). The GPWv4 captures the population density pattern well in the Paris Metropolitan Region. However, it misses sharp heterogeneities at the kilometric scale compared to the MAPUCE data set, which uses the fine building database. We, therefore, conclude that the effective resolution of the GPWv4 population density, which has the advantage of global availability, is in reality coarser than 1 km² for the Paris Metropolitan Region. The bottom-up method consists of simulating building energy consumption as a function of the building type, use and the prevailing meteorological conditions.
Crowdsourcing It is also possible to obtain building use from crowdsourcing, e. g. OSM has specific tags for users to indicate the building function. In a study by  Fonte et al. (2018) , OSM data were extracted for a section of the city of Milan. By analyzing the tags associated with buildings as well as the points of interest layer, more than 80% of the buildings in Milan could be assigned a building function. Moreover, the analysis could also help to identify mixed function building types, e. g. buildings that might be commercial on the ground floor but contain residences above. A similar study was undertaken by  Kunze and Hecht (2015) , who specifically focused on using OSM to determine the amount of nonresidential use in residential buildings in order to calculate the non-residential floor area  (Kunze and Hecht, 2015).