dataesr/scientific-paragraphs-categorization huggingface dataset:
Extending the same dataset to 2018,  Dodet et al. (2019)  showed that the recovery signature is site specific and multi-annual, with one studied beach fully recovered after 2 years, and the others only partially recovered after 4 years. On open crossshore transport dominated coasts, simple semi-empirical shoreline change models (e. g. ,  Yates et al. , 2009; Splinter et al. , 2014; Lemos et al. , 2018)  accurately reproduce the erosion driven by the 2013/2014 winter and subsequent post-storm recovery, but they largely fail on more complex sites. This further strengthens the recent development of a new generation of reduced-complexity shoreline change models coupling cross-shore, longshore, and other processes such as sea level rise  (Vitousek et al. , 2017; Robinet et al. , 2018) , to be used to investigate, hindcast, and ultimately forecast the erosion and recovery process in more detail on a wide range of coasts.
Short Description of the Observation Network The MOOSE Network was initiated in 2008 and built during the five following years. Since 2015 this organization has developed into a unique multidisciplinary network (Figure  6 ) that pools eff orts and initiatives to converge on best practices, and supports common measurement standards (Figure  7 ). The personnel assigned to these operations represent five FTE posts for researchers and 7. 6 FTE posts for engineers and come from 10 diff erent research institutions. Around 5-10 Ph. D. students have been regularly involved in the processing and analysis of data from the MOOSE network. MOOSE is built as an interactive, distributed, and integrated observatory, based on a multisite network of permanent continental, shelf, shelf-break, and deep-sea stations, and is able to detect and monitor seasonal and inter-annual variability, as well as the impact of extreme events that control fluxes and budgets in the marine environment.
Discussion Employing ILICO ambitions on another scale, the MOOSE network is creating a solid and transparent organization that can provide operational services for the timely, continuous, and sustainable delivery of high-quality environmental data and informational products related to the northwestern Mediterranean environment. Through MOOSE, the French Mediterranean community is now able to have a permanent monitoring system of ocean dynamics covering coastal and open ocean areas. MOOSE also provides a large flux of real-time data to facilitate validation of operational oceanographic models. With many years of research lying ahead for the MOOSE project, it is expected that the system will have an impact on marine science and policy in both the short-and long-term. In terms of marine observation, it is likely that the team's delivery of products, such as measurements and indicators of change and impact, will be widely used for policy decisions.
An example of a collective brainstorming event was the EVOLECO (ECOsystem EVOLution) symposium, whose objective was to draw up an inventory of the long-term evolution of coastal ecosystems (through the study of a 10-year time series from ILICO's elementary observatory) and to identify the associated forcings and processes. Two workshop needs emerged: (i) Comparison of the diff erent statistical tools implemented on the long data series of the diff erent networks, and (ii) the origin of the discrepancies observed between the 1990s and the 2000s on sets of parameters acquired by several networks. In addition to defining scientific hot spots and super sites, the co-localization elementary networks acquisition also allows the sharing of: • Scientific parameters: for example, the SONEL water levels could directly be used for coastline monitoring by DYNALIT.
Members of ILICO are also involved in several pan-European initiatives and numerical infrastructures (SeaDataNet and SeaDataCloud, EMODnet, Copernicus Marine Environment Monitoring Service, etc. ) and other regional data web portals (AODN, IMOS, etc. ). The intersection of all these data sources, combined with the expertise of ILICO scientists, will provide new added-value services and products. Complementary Approaches of Spatial Coverage ILICO elementary observatories were deployed following three major types of spatial strategies: the first type is a regional pattern that aims at revealing the spatial dynamics of the observed processes; the second type involves sampling a range of sites that are representative of diff erent types of dynamics; the third type involves sampling a range of sites with diff erent gradients of drivers and/or functioning.
(1) Studying spatial (as well as temporal) dynamics: This goal requires studying a spatial distribution adapted to spatial dynamics, while the sampling strategy must be homogeneous along the diff erent sites. Within ILICO, the SONEL observatory for sea level, the MOOSE regional observatory of the Mediterranean coastal zone, the REEFTEMPS and CORAIL observation networks in the Pacific, and the PHYTOBS observatory for phytoplankton were conceived in this way. The combination of all observation sites hardly draws a regional picture, and calculating an overall average, for instance, may not make sense. However, data from a sampled site may help understand the processes at work in a diff erent site, not sampled, but with comparable physiographic conditions. (3) Studying sample sites along gradients: Within ILICO, the SOMLIT observatory of water properties, some highfrequency automatic sampling platforms from COAST-HF, and some PHYTOBS sites are designed in such a way.
Ocean observation data life cycles can benefit from many European initiatives supported by the Directorate-General (DG) Mare (EMODnet), the DG Environment (Marine Strategy Framework Directive), the DG Grow (Copernicus   2015 -2019, Farcy et al. , in preparation), Farcy et al. , in preparation). A part of ILICO lies within the thematic scope of JERICO and JERICO-NEXT. The next step is to propose a permanent, sustained, European Observation Infrastructure for the coastal and nearshore marine environment, to be included in the ESFRI Roadmap and with respect to the emerging European Ocean Observing System, EOOS, an inclusive voluntary federation of diverse ocean observation and monitoring communities. Accredited by the French ministry for higher education and research, the ILICO infrastructure would be, entirely or in part, a component of this European infrastructure.
A sober use of resources (including human resources) Constraint being a great source of innovation, one can expect that raising environmental concerns will generate a new generation of sensors and methods more sustainable and creating less damage to nature. Furthermore, as previously described, most of the elementary observatories require high human-resources. Some new techniques will have to be developed in order to optimize staff hours and costs, so that financial support could guarantee all human skills and expertise when necessary. Strong connections (to real-time communications access, to other scientific communities, to end-users) Real-time acquisition could be of great interest in order to limit the step of data recovery. Furthermore, in the perspective of making the observatories more up front and modeled as an 'alert system' rather than the present registering systems, the coastal ocean and nearshore observation system must develop a real-time data connection.
The networks of research infrastructures across Europe strengthen its human capital base by providing world-class training for a new generation of researchers and engineers and promoting interdisciplinary collaboration. As far as marine sciences are concerned, the FP/Infrastructure involves support Research Infrastructures such as research vessels (Euro fleets projects), the Argo floats program (Euro ARGO), the European Marine Seas Observatory (EMSO), and the European Marine Biological Resource Centre (EMBRC). In the field of coastal observation, the European Union has financed two projects: JERICO -Joint European Research Infrastructure for Coastal Observation (FP7 project under grant agreement No. n • 262584 Project coordinator: Ifremer, France -2007-2013) and JERICO-NEXT (H2020 project under grant agreement No.
Building on the theoretical basis, in Section 3, we have conducted a comprehensive review with physical interpretation about the existing P gap and LAI estimation approaches from either point-number-based (PNB) methods or intensity-based (IB) methods. In Section 4, we have demonstrated the utility of the DART model in evaluating these approaches from sensitivity analyses with different footprint sizes, leaf areas, leaf density variations, foliar dimensions, and homogeneous/heterogeneous scene configurations. From the results of the sensitivity studies, we have discussed the optimization of LAI estimation through small-footprint ALS, which could leverage existing and future point datasets to develop more robust LAI map products.
The saturation speeds of LPI weighted and LPI all are slower than those of the other approaches. The total number of returns are illustrated in Figure  4a. The average number of returns per pulse gradually becomes saturated together with LPI weighted and LPI all , as shown in Figure  3. From the sensitivity study above, LPI weighted and LPI all have broader ranges of footprint size and LAI values than the other PNB methods. Regarding the IB methods, the constant value of nearest pure-ground intensity I pure g for LPI nearest estimation is over-idealized for the simulations. Indeed, I pure g is a diverse variable for actual data due to soil moisture, litter, and terrain slope. For LPI fitted , the least-squares approach applied to the dataset of ρ a,g and ρ a,v of every single pulse is used to determine the fitted γ (Figure  5 ).
The last column of Table  4  provides the reference values computed by using P gap as LPI. Additionally, derived results of both f vcc and P gap are shown in Figure  9  for comparison. 9  and Equations (  13 ) and (  12 ). The reference derivations from P gap are in bold. For the derived α of Table  4  from Figure  9a -c, all of the IB methods (LPI nearest , LPI γ=2. 10 , and LPI fitted ) were consistent with P gap with α ≈ 1 for varying LAI and within-crown clumping (bias < 3%). The varying leaf area over the entire scene benefits the building of the slope γ for LPI fitted , as illustrated in Figure  10. The differences between fitted γ were less than 0. 5%, but the introduction of within-crown clumping reduced the fitted γ by 5%, which suggests that within-crown clumping increases the variance of the dataset.
Variables For the derived  of Table  4  from Figure  9a -c, all of the IB methods (LPI , LPI. , and LPI ) were consistent with  with  ≈ 1 for varying LAI and within-crown clumping (bias < 3%). The varying leaf area over the entire scene benefits the building of the slope  for LPI , as illustrated in Figure  10. The differences between fitted  were less than 0. 5%, but the introduction of within-crown clumping reduced the fitted  by 5%, which suggests that within-crown clumping increases the variance of the dataset. The correlation coefficient remained high for all conditions. For the PNB methods, all LPI estimates are strongly biased towards the reference. LPI severely underestimates  because of the small gaps within the crown. LPI tends to give a more   12 )) for a simulated scene with discrete tree crowns (LAI = 1 and 2) and within-crown clumping (LAI = 2).
Our study supports the potential of V-line using online waveforms processed into apparent reflectance ρ a  [38] [39] [40]  with lower cost, smaller divergence, higher pulse repetition frequency, and more efficient data storage without waveforms. For the IB methods, the constant γ assumption in estimating LPI λ heavily relies on the statistically convergent leaf angle projection, clumping index, and natural reflectivity ratio of the leaves and the ground within a region. The understory vegetation and debris can influence the ground reflectivity and the fitting of γ. The full-waveform method used by Armston, et al. [16]  and Chen, et al. [17]  is a practical approach without empirical correlation. However, the method needs a different least-squares approach to consider the inconsistent variances over the dataset.
yOutput exportation to LAS: The DART-simulated waveforms are stored as a binary file. In the next step, this binary file can be converted into standard lidar data formats to adapt to the existing software. Previous work documented the output conversion into sorted pulse data (SPD) format  [58, 83]. Here, we implement an approach that processes the binary file into an LAS 1. 3 point cloud through the LASPY library  [84]. The points are stored in an LAS file, with the possibility to export the waveforms to another WDP file that is linked to each decomposed point [Header description in [85], point type 1 or 4]. The file is supported by existing LAS visualization and processing software (e. g. , CloudCompare  [86] ).
LPI fitted is also influenced by the large divergence of dataset variance. The bias curve gives a parallel displacement. LPI nearest still provides accurate estimations due to the idealized ground reflectance and the adaptation with ρ a filtering. For the PNB methods, the estimated LPIs are slightly lower than the ones from the unfiltered point cloud, because the average number of returns has been reduced (Figure  A3 ). From Figure  A3 , the average number of ground returns decreases with increasing LAI, which means there are more pure-vegetation pulses. For the heterogeneous scene, the results are shown in Figure  A4  and Table  A1. These results correspond to a potential reason for the occlusion effect in ALS applications  [87]. , which may be filtered out. This causes an overestimation by LPI.
, as some energy from vegetation backscattering has been removed. LPI is also influenced by the large divergence of dataset variance. The bias curve gives a parallel displacement. LPI still provides accurate estimations due to the idealized ground reflectance and the adaptation with  filtering. For the PNB methods, the estimated LPIs are slightly lower than the ones from the unfiltered point cloud, because the average number of returns has been reduced (Figure  A3 ). From Figure  A3 , the average number of ground returns decreases with increasing LAI, which means there are more pure-vegetation pulses. For the heterogeneous scene, the results are shown in Figure  A4  and Table  A1. These results correspond to a potential reason for the occlusion effect in ALS applications  [87].
Acknowledgments: The research presented in this study was supported by  NASA's Earth Science Division  and  NASA 's  Terrestrial Ecology Program. Shanshan Wei  was supported by the project " Computerized Management of Urban Trees: Tree Inspection Using Satellite Data " funded by the  Ministry of National Development Research Fund  awarded to the  National Parks Board and Singapore-MIT Alliance for Research and Technology, Singapore. The authors are also thankful to the  French National Space Center (CNES)  for supporting DART development in the frame of the  TOSCA  project " Fluo3D " and the computer scientists in DART group ( Nicolas Lauret ,  Jordan Guilleux.
We describe SuperIso Relic, a public program for the calculation of dark matter relic density and direct and indirect detection rates, which includes in addition the SuperIso routines for the calculation of flavour physics observables. SuperIso Relic v4 incorporates many new features, namely the possibility of multiprocessor calculation of the relic density, new cosmological models, and the implementations of the calculation of the observables related to direct and indirect detection experiments. Furthermore, the new version includes an implementation of the nuclear and astrophysical uncertainties, from namely nuclear form factors, dark matter density and velocity, as well as cosmic ray propagation through the galactic medium.
Introduction SuperIso Relic is a mixed C / Fortran program devoted to the calculation of dark matter and flavour observables in supersymmetry (SUSY). It is an extension of SuperIso  [1] [2] [3]  which contains the calculation of the flavour observables, and includes additional routines from AlterBBN  [4, 5]. The previous versions of SuperIso Relic were devoted to the calculation of the relic density in standard and modified cosmological scenarios  [6, 7]. Many new features have been added in the current version of SuperIso Relic, which justifies a major version number change. The main features are the possibility of multiprocessor calculation of the relic density using the OpenMP library, new cosmological models, and the implementations of the calculation of observables related to direct and indirect detection experiments, with a focus on the possibility to alter the astrophysical description of dark matter and propagation of dark matter annihilation products.
These features distinguish SuperIso Relic from the other existing public codes such as DarkSusy  [8] , micrOMEGAs  [9] , MadDM  [10]  and DarkBit  [11, 12]. SuperIso Relic is public and open-source, and is released with the GPL version 3 licence. A detailed description of the code is provided in the following; the content of the SuperIso Relic package is described in section 2, the compilation and installation instructions are provided in section 3, the input and output descriptions are given in section 4, and some illustrative results are presented in section 5. The appendices contain the description of the cosmological scenarios as well as the calculation of indirect and direct detection rates. 2 Content of the SuperIso Relic package.
General description The main directory of SuperIso Relic contains 23 main programs in C language. sm. c, sm chi2. c, flha. c, , slha chi2. c and thdm. c are only relevant for SuperIso and do not involve dark matter observables. The other programs relies on the SUSY Les Houches Accord (SLHA) format  [13, 14]  as input format for supersymmetric models. 10 programs are common to both SuperIso and SuperIso Relic: • amsb. c: calculation of observables for an AMSB model point. • cmssm. c: calculation of the observables for a CMSSM model point. • cnmssm. c: calculation of the observables for a semi constrained NMSSM model point. • gmsb. c: calculation of the observables for a GMSB model point. • hcamsb. c: calculation of the observables for a hypercharged AMSB model point. • mmamsb. c: calculation of the observables for a mixed moduli AMSB model point.
Compilation and installation instructions The SuperIso Relic package and the latest version of the manual can be downloaded from: http://superiso. in2p3. fr/relic The package can be uncompressed with tar xjvf superiso relic vX. X. tar. bz2 Before configuration, the user can choose the compilers with BASH commands such as export CC=gcc export FC=gfortran export CXX=g++ The automatic configuration can be run with for example. /configure --with-mp "--with-mp" is an option to activate the OpenMP multiprocessor compilation and calculation. The configure options can be accessed with configure --help. At this step, it is possible to modify several configuration options in Makefile: • RELIC can be set to 0 to deactivate the dark matter routines (this would correspond to the usage of SuperIso only for the calculation of the flavour observables), 1 to activate them only for the MSSM, and 2 for the MSSM and NMSSM.
Effective energy and entropy densities The program test standmod. x calculates the relic density using the parameters contained in a given SLHA file, in a cosmological scenario where the energy and entropy content is modified. In addition to the name of an SLHA file, this program needs four additional parameters and five optional ones, and is run with:. /test standmod. x filename κ ρ n ρ κ s n s (T ρ T s N n N T N ) where • κ ρ is the ratio of dark energy density to photon energy density at T BBN = 1 MeV, • n ρ is the temperature exponent of the dark energy density, • κ s is the ratio of dark entropy density to photon entropy density at T BBN = 1 MeV, • n s is the temperature exponent of the dark entropy density, and the optional parameters (which, if not specified, are set to 0) are: • T ρ is a temperature cut in GeV below which the dark energy density is set to 0, • T s is a temperature cut in GeV below which the dark entropy density is set to 0, • N is the non-thermal production rate of supersymmetric particles at T BBN = 1 MeV, • n N is the temperature exponent of the non-thermal production rate of supersymmetric particles, • T N is a temperature cut in GeV below which the non-thermal production is set to 0, which are further described in Appendix A.
Decaying scalar field The program test phi. x calculates the relic density using the parameters contained in a given SLHA file, in a cosmological scenario with a decaying scalar field. In addition to the name of an SLHA file, this program needs two additional parameters and two optional ones, and is run with:. /test phi. x filename ρφ T RH (T init η φ ) where • ρφ is the ratio of the scalar field density to photon energy density at T = T init. • T RH is the reheating temperature in GeV, and two optional ones: • T init is the initial temperature in GeV at which the calculation starts (100 GeV by default), • η φ is the non-thermal supersymmetric particle production parameter, which are further described in Appendix A. Note that for this scenario full comput is set to 1 in order to compute the evolution of the scalar field before freeze-out.
B. 3 Constraints from AMS-02 antiprotons Contrary to gamma rays, antiprotons are diffused by turbulent magnetic fields in the galaxy. Therefore, it is necessary to describe the propagation of cosmic rays in the galactic medium in order to deduce the antiproton flux reaching Earth. To this end, we integrated in SuperIso Relic a code developed by Pierre Salati and Mathieu Boudaud, which is detailed in  [29]. The propagation model used in this code is briefly described below.
D. 4 Experimental limits We implemented the constraints from the latest results of XENON1T  [17] , PANDAX-2  [16]  and PICO60  [18]. No events were observed during the time of exposure of these experiments, except for PANDAX-2, which observed one event during RUN 9. For each constraint, we use a Poisson likelihood defined as: L (µ|N o ) = (b + µ) No e -(b+µ) N o ! , (D. 24) where µ is the total expected number of events from WIMP-nucleus scattering, N o is the number of observed events and b is the expected background. We marginalise over b by choosing its value according to: We use the efficiencies provided in the GAMBIT package DDCalc  [11]. They were calculated using the TPCMC Monte Carlo code  [76]  to model the detector response, which relies on NEST  [77] [78] [79]  for modelling the microphysics of a recoiling xenon atom.
Introduction Three-dimensional geoinformation practitioners dealing with cities and infrastructure often struggle while shuttling 3D models back and forth between BIM (Building Information Modelling) and GIS (Geographical Information Systems) software, which often only have support for a few proprietary native formats. This forces users to attempt to convert between formats, often losing information, and sometimes even having to manually recreate whole datasets  [2, 43, 74]. The integration of BIM and GIS, often dubbed as GeoBIM, is thus widely acknowledged as a crucial step for 3D city modelling and its applications  [2]. BIM models are much more detailed and semantically rich than GIS models  [2]. BIM is more than a data model, it is an approach to deliver a reliable digital representation of a building during its development.