dataesr/scientific-paragraphs-categorization huggingface dataset:
These differences are further exacerbated when evaluating the fine meshes data-set where the variance of PBD-lin is many times higher compared to the variance in PBD-opt. For the outlier analysis we must make a distinction between two types of outliers. The first type are slow convergence (SC) outliers, which surpasses the upper limit of the box plot but did not reach the stopping time. The second type are very slow convergence (VSC) outliers, which reach the stopping time. For PBD-opt, at most 2% of the runs were SC outliers and 0% were VSC outliers. However for PBD-lin, in the coarse meshes dataset, 10% of the runs were SC outliers. In the fine meshes dataset, around 34% of the runs were VSC outliers. This means that PBD-lin has a higher risk of getting stuck in iterations whose convergence time would be way higher compared to their median convergence speed.
The Nyqvist frequency f N is defined by the fact that at least two sampling points are needed for each frequency to reconstruct the signal. Therefor f N is given by Equation (6. 8): f N = 1 2Dt (6. 8) where Δt is the sampling time interval. Adding frequencies above this limit results in 'aliasing'. In most software development environments, e. g. Matlab ® , pre-programmed functions that take this limit into account automatically are available. Parts of the signal having frequencies larger than the Nyqvist frequency cannot be identified from the measurements. The underlying principle of a Fourier transform is that a signal f(x) can be written as a summation of periodic functions (Equation (6. 9)): F(k) = j=N e -1 j=1 f ( j)e -i2pkj N e (6. 9) where i is the imaginary unit. The real part of F(k) is the amplitude belonging to a frequency of 2πk/N e , with N e the number of elements in the time series.
The inverse transformation (from frequency domain to time domain) reads as Equation (6. 10): f (n) = 1 N e k=N e -1 k=0 F(k)e i2pk N e (6. 10) The 'power' of a certain frequency in the signal is defined as Equation (6. 11): P(k) = F(k)F(k) (6. 11) The overlined F(k) indicates the complex conjugate. For practical applications, built-in functions in packages like Matlab ® can be used (for Matlab ® that is the Fast Fourier Transform, F(t)=fft(f) and its inverse f=ifft(F)). For detailed information on Fourier transforms and analysis, the reader is referred to the literature on the subject, e. g. in Grafakos (2014).
Networks of rain gauges In virtually every monitoring project related to urban drainage and stormwater management, data on precipitation are indispensable. As discussed in Chapter 2, radar based or microwave attenuation measurements (e. g. Fencl et al. , 2015)  are employed successfully for covering relatively large areas, also in the urban environment, with up to 5 minutes time resolution and, depending on the technology applied, a spatial resolution of 1 km 2 or less. There is however still a need for validation with ground-based point measurements. The data for the latter is usually obtained from a network of rain gauges. Installing 'the optimal rain gauge network' in an urban environment is challenging for a number of reasons: • Demands set on the environmental conditions for a rain gauge set-up as formulated in standards (e.
To avoid systematic deviations in the result this should be compensated for. Heikkila & Silvén (1997)  describe a method to do so in some detail, which is implemented in e. g. a Matlab ® function. • There must be a quantified link between pixel positions in the camera's sensor and the real world in 3D. This implies that the exact locations of a number (at least four, but preferably more) of reference points in the field of view (FOV) of the camera have to be known. These reference points should preferably be in, or in the direct vicinity of, the plane on which the camera focusses. • Make sure the field of interest is covered by the camera's FOV after mounting it. • In underground spaces, an external light source is needed. Safety issues, especially ATEX requirements (see Chapter 7) are a matter of concern.
Meta-modeling platforms serve as means to implement DSML. Those platforms usually provide many features that can be used to edit the abstract syntax of a language in the form of a meta-model, to specify the graphic representations of the meta-model's elements, and to implement mechanisms & algorithms that process the knowledge expressed in the models  (Karagiannis et al. , 2016). Hence, to prepare the ground for the implementation of our BCM dedicated modeling tool, we investigated and analyzed several meta-modeling platforms such as MetaEdit+ 1 ,Eclipse Modeling Framework (EMF 2 ), Graphical Editing Framework (GEF 3 ), Sirius 4 and ADOxx 5 An extensive comparative study of the most relevant platforms is provided in  (Lamine et al. , 2020). Accordingly, the ADOxx meta-modeling platform was deemed the most appropriate for our needs.
Indeed, compared to the others, ADOxx does not need a high skill programming level like GEF and EMF. It has provided tool support for more than 60 domain-specific modeling languages. Besides, it is a multi-user platform that provides a repository based on a relational database for models and supports multiview modeling. In addition to all of these arguments, the local expertise about ADOxx in our research Laboratory helped us in choosing it and proceeding with it as our modeling framework to create our proposed tool for Business continuity management.
Secondly, water-reuse options are specified arbitrarily. It begins first with the estimate of the mass flowrate of freshwater (Equation (  9 )) using the maximum concentration at the outlet (C max ki,out ). Finally, the last step consists of a debugging procedure by detecting errors in the material balances (Equations (  1 )-(  3 )), unexpected mass transfer of pollutants (recalculation by Equation (  4 )), or even exceeding maximum concentrations (Equations (  6 ) and (  7 )). At the end of the third step, complementary routines (developed in Python software) provide a simple interface to display a detailed water-network drawing. Thanks to the debugging information, the user will be able to refine the waterreuse matrix. For each tuning in the water-reuse matrix, the calculation resumes from calculating the mass flow rate of freshwater (Equation (9), step 2), and the result is updated.
6 m 3 /h) of reuse from process 1 and mix it with the freshwater. By considering all mentioned tradeoffs, the final table of reuse opportunities and their total amounts (m 3 /h) are presented in Matrix 13: R = ⎡ ⎢ ⎢ ⎢ ⎢ ⎣ -24. 3 2. 6 0 8 0 -0 0 0 0 0 -0 0 0 8 0 -0 0 0 0 0- ⎤ ⎥ ⎥ ⎥ ⎥ ⎦ (13) Table  2  and Fig. 2  present the final results of the simulation. The total freshwater consummation is 111. 93 m 3 /h. Also, around 43 m 3 /h of required water for all the systems supplied by reuse water. For the same dataset and by using The MINLP procedure  (Boix et al. , 2011) , results reported within the set of 30 answers combined fresh and regenerated water ranged between; [30 m 3 /h freshwater + 223. 4 m 3 /h regenerated water], and [1627 m 3 /h freshwater + 30 m 3 /h regenerated water]. The different approaches for mathematical modeling and also solving procedures can explain these differences.
Discussion In the work of  (De-León Almaraz et al. , 2016)   (Boix et al. , 2011) , MINLP procedure is applied in a lexicographic strategy, and the resolution is performed in GAMS software. For the ranking of the answers, TOPSIS and GEC methods are used. Furthermore, these methods are usually very time-consuming also require the right expertise in mathematical optimization tools. In the present study, the selection of solutions is performed manually while considering the user targets (i. e. , limitations about flowrates, concentration, connections, etc. ). Being user-friendly is the main benefit of using the actual method, besides the accuracy and less time-consuming. The method also provides graphical insights with less mathematical calculation. The user can modify the network and flows based on different limits and considerations and obtain new results.
Acknowledgements The authors wish to thank the  French National Research Agency  ( ANR-17-CE10-0015 ) for their funding, and the partners of the project  MINIMEAU  (https://minimeau. fr/)led by  AgroParisTech  (French higher education and public research institute), in collaboration with ProSim (Expert in process simulation) ACTALIA, CRITT, CTCPA, IFV, ITERG (Centers of expertise for the food industry), and  INRAE ELSA  ( French institute  for agriculture, food and environment).
Remotely Sensed Data In September 2016, an airborne campaign funded by the Centre National d'Etudes Spatiales (CNES) and the Center for the study of biodiversity in Amazonia (labex CEBA) led to the acquisition of imaging spectroscopy, LiDAR data, and very high spatial resolution red, green and blue (RGB) imagery. The acquisitions were performed in clear sky conditions at an average flying altitude of 890 m.
Imaging Spectroscopy Imaging spectroscopy data were acquired with a Hyspex VNIR-1600 sensor (Hyspex NEO, Skedsmokorset, Norway) over 160 spectral bands in the visible and near infrared (VNIR) regions, ranging from 415 nm to 993 nm. Spectral information corresponding to spectral bands > 902 nm were discarded because of the low signal-to-noise ratio. Images were orthorectified and geo-referenced using the PARGE software (http://www. rese. ch/ products/parge/ accessed on 4 March 2021) with the canopy DSM (Digital Surface Model) produced from the LiDAR point cloud. Atmospheric corrections were performed using ATCOR-4 software  [58]  to produce top of canopy (TOC) reflectance with 1-m spatial resolution. Airborne acquisition over the full site and its surroundings resulted in 23 flight lines covering 14 Km 2.
Airborne Laser Scanning (ALS) ALS data were acquired with a RIEGL LMS Q780 simultaneously with imaging spectroscopy data. The sensor operated with a frequency of 400 kHz and a scanning angle of ±30 • which ensured an average pulse density of 33 pulses/m 2. A point cloud filtering was applied to remove points classified as Low point (noise) according to the ASPRS Standard LIDAR Point classification  [59].
gitlab. io/pytools4dart/ accessed on 12 March 2021) a python API (Application Programming Interface) aiming at preparing, running and analyzing DART simulations, specially dedicated to the design of large and complex simulations. DART was set in flux tracking mode to simulate TOC reflectance, with the sun and the atmosphere as the only radiation sources. Topography was defined with a digital surface model produced from ALS point clouds. A generic litter spectrum was used for the optical properties of the soil/background. Simulations were performed for 135 bands ranging from 415 nm to 902 nm, following instrumental characteristics of the airborne acquisition for sites A and B. Sensor height, solar zenith and azimuth angles as well as spatial resolution were set according to the geometry of acquisition during the airborne campaign.
Definition of 3D Structure of the Canopy The 3D forest structure was derived from ALS acquisition: the ALS point cloud and associated trajectory file were voxelized using the AMAPvox v. 1. 7. 3-BETA software (http://amap-dev. cirad. fr/projects/amapvox accessed on 3 April 2021). AMAPvox tracks every laser pulse through a 3D grid (voxelized space) from the top of the canopy to the last recorded hit. The effective sampling area of each laser pulse (or fraction of pulse in case of multiple hits) is computed from the theoretical beam section (a function of distance from laser and divergence of laser beam) and the remaining beam fraction entering a voxel. In case more than one hit is recorded for a given pulse then it is assumed that the surface intercepted by each hit (the "hit area") is equal to the inverse of the total number of hits times the beam section.
This information is combined with the optical path length of each pulse entering a voxel to compute the local transmittance per voxel. Different estimation procedures are provided in the AMAPVox software  [67]. In the present study we used a numerical resolution to compute for each voxel the transmittance, or gap probability Pgap, from the equation below: ∑ n i BFent i × S i × Pgap l i = ∑ n i BFout i × S i (1) with l i the optical potential path length of pulse i (from entry point to the expected exit point). S i the cross section of pulse i, computed at voxel center. BFent and BFout being the beam surface fraction entering and outgoing the voxel. The equation above expresses that the estimated Pgap per unit length of a voxel is the value that yields the light transmission rate (given the actual path length and effective surface of all contributing pulses) equal to the observed transmission rate.
Simulation of Leaf Optical Properties through the Estimation of Leaf Pigment Content Leaf pigment content controls LOP and canopy reflectance in the VIS region of the electromagnetic spectrum as well as in the red-edge domain and first part of the NIR domain  [36]. Here, our first goal was to define a strategy to assign pigment content to each pixel, crown or species in our simulations, based on canopy reflectance. A variety of methods can be used to assess foliar pigment content from remotely sensed canopy reflectance acquired with imaging spectroscopy. Verrelst et al. [82]  categorized these methods into four categories: (i) parametric regression, including vegetation indices, shape indices and spectral transformations, and assuming a simple relationship between spectral data and vegetation biophysical variable, such as a linear monovariate relationship; (ii) nonparametric regression, based on data-driven techniques that directly link spectral data and biophysical variables (e.
g. , linear and non-linear machine learning regression algorithms); (iii) inversion of RTM using numerical optimization and look-up table approaches; and (iv) hybrid methods, which combine RTM for the simulation of a training dataset with parametric or non-parametric regression methods. Parametric regressions based on vegetation indices and linear or polynomial models are the most straightforward means of estimating pigment content  [83] [84] [85]. However, the adjustment of such empirical models requires a large amount of field data. Hybrid approaches provide a means to overcome this dependency on field data: they are based on the simulation of a training dataset (corresponding to LOP or canopy reflectance) using appropriate physical models and sampling strategy, in order to adjust a regression model applicable to experimental data.
Hence, the N parameter was set to 1. 8, assuming that the vegetation scattering induced by the detailed canopy structure would provide sufficient variability. The influence of EWT and LMA on canopy reflectance is limited in the VNIR spectral domain, thus they were set to 12. 9 mg•cm -2 and 12 mg•cm -2 respectively, which correspond to the average values reported by Féret et al. [88]  for a leaf dataset collected at the Paracou research station. The linear regression models between leaf pigment content and the spectral indices (computed from simulated canopy reflectances) were trained using sunlit pixels only, i. e. , pixels with more than 20% reflectance in the NIR spectral band centered on 800 nm, in order to reduce confusions induced by low illumination in further comparisons  [23].
In our study, we performed classification on ITCs corresponding to the nine most abundant species detailed in Table  1. For each species the ITCs were randomly distributed between two folds of equal size: one for training and the other for testing. The split is made at the crown level instead of the pixel level in order to avoid using pixels from the same crown in both training and test datasets. Sampling pixels from the same crown in both training and test dataset may lead to overestimated scores due to spatial correlation among neighboring pixels and possibly lower variability within crown than among crowns. This resulted in 4 to 23 ITCs/species used to train the LDA model, and 4 to 22 ITCs/species used to test the model. The procedure was applied on imaging spectroscopy data, with training and test datasets built from the same pixel selection for experimental and simulated data, and repeated 100 times in order to account for the influence of the random selection.
The complementary fraction of PAD corresponding to LAD was characterized by LOP with pigment content defined as described in Section 4. 3. ing and test datasets. Sampling pixels from the same crown in both training and test dataset may lead to overestimated scores due to spatial correlation among neighboring pixels and possibly lower variability within crown than among crowns. This resulted in 4 to 23 ITCs/species used to train the LDA model, and 4 to 22 ITCs/species used to test the model. The procedure was applied on imaging spectroscopy data, with training and test datasets built from the same pixel selection for experimental and simulated data, and repeated 100 times in order to account for the influence of the random selection. The Pearson's correlation coefficient was used to compare the average confusion matrices of tests on observations against simulations.
Analysis of Spectral Dissimilarity The spectral dissimilarity was computed within crowns, among crowns corresponding to the same species and among crowns corresponding to different species using the spectral angle. DART simulations were compared to imaging spectroscopy to test if the spectral dissimilarity observed in imaging spectroscopy data was correctly simulated for the different LOP scenarios (Figure  11  and Table  2 ). When defining LOP per species in DART (LOP species ), low agreement was obtained between spectral dissimilarity computed from imaging spectroscopy and from simulations for all levels (within crowns, within species and among species), with systematic underestimation. Intermediate performances were obtained when defining LOP per ITC in DART simulations. LOP ITC scenario showed a systematic underestimation of spectral dissimilarity within crowns and a good agreement between spectral dissimilarities within species and among species.
Analysis of Spectral Dissimilarity The spectral dissimilarity was computed within crowns, among crowns corresponding to the same species and among crowns corresponding to different species using the spectral angle. DART simulations were compared to imaging spectroscopy to test if the spectral dissimilarity observed in imaging spectroscopy data was correctly simulated for the different LOP scenarios (Figure  11  and Table  2 ). When defining LOP per species in DART (LOPspecies), low agreement was obtained between spectral dissimilarity computed from imaging spectroscopy and from simulations for all levels (within crowns, within species and among species), with systematic underestimation. Intermediate performances were obtained when defining LOP per ITC in DART simulations. LOPITC scenario showed a systematic underestimation of spectral dissimilarity within crowns and a good agreement between spectral dissimilarities within species and among species.
Three-Dimensional (3D) Forest Reconstruction The 3D forest structure was reconstructed by discretizing ALS data in 3D space using AMAPVox software. By applying Beer-Lambert's turbid medium approximation, local transmittance enables estimation of PAD and PAI. Infravoxel clumping may have caused an underestimation of LAI and PAI  [77, 97]. Qu et al. [98]  estimated the PAI of a moist tropical forest at Fazenda Cauaxi in the Paragominas Municipality of Pará State, Brazil. They reported a mean PAI of 6. 94 m 2 /m 2. Another study was conducted by  Pimmasarn et al. [69]  in Khao Yai National Park in central Thailand. They obtained a mean PAI of 8. 02 m 2 /m 2. These results are slightly lower than the PAI reported here (mean PAI of 9. 4 m 2 /m 2 ). By contrast, Vincent et al. [72]  mapped PAI at the same site (Paracou experimental station) by ALS (using a different laser system, i.
Three-Dimensional (3D) Forest Reconstruction The 3D forest structure was reconstructed by discretizing ALS data in 3D space using AMAPVox software. By applying Beer-Lambert's turbid medium approximation, local transmittance enables estimation of PAD and PAI. Infravoxel clumping may have caused an underestimation of LAI and PAI [77,97]. Qu et al. [98] estimated the PAI of a moist.
Data Availability Statement: Not applicable. Acknowledgments: D. Ebengo, F. de Boissieu and J. -B. Féret acknowledged financial support from Agence Nationale de la Recherche (BioCop project-ANR-17-CE32-0001-01) and from TOSCA program grant of the French Space Agency (CNES) (HyperTropik/HyperBIO projects). We thank the Region Occitanie for funding of D. Ebengo doctoral researches. Vincent has benefited from Investissement d'Avenir grants of the ANR (CEBA: ANR-10-LABX-0025). We also acknowledge Jean-Philippe Gastellu-Etchegorry, Nicolas Lauret and the DART Team for their advice and support in using the DART model. Thanks to A. Laybros, M. Aubry-Kientz, R. Dutrieux and C. Baltzer for field support and data cleaning and preparation Hyperspectral imagery acquisition was funded by the CNES (Centre National des Etudes Spatiales) and acquired by Hi-Tech Imaging.
5. Discussion 5. 1. 551 Three-Dimensional (3D) Forest Reconstruction The 3D forest structure was reconstructed by discretizing ALS data in 3D space using AMAPVox software. By applying Beer-Lambert's turbid medium approximation, local transmittance enables estimation of PAD and PAI. Infravoxel clumping may have caused an underestimation of LAI and PAI [77,97]. Qu et al. [98] estimated the PAI of a moist.
The aim of a forward approach is simple: for a given lens model, utilize the constraints from the extended surface-brightness profile of the lensed source to reconstruct its intrinsic profile. Various methods have been developed for the inversion of extended lensed images; more so for galaxy-scale lenses  (Warren & Dye 2003; Claeskens et al. 2006; Brewer & Lewis 2006; Tagore & Keeton 2014; Nightingale, Dye & Massey 2018; Galan et al. 2020 ) compared with the more complex mass distributions of group scale/cluster lenses  (Yang, Birrer & Treu 2020; Suyu et al. 2006). In this letter, we combine a forward approach with a fully automated image inversion technique newly implemented in the publicly available software LENSTOOL 1  (Kneib 1993; Jullo et al. 2007)  for the source plane reconstruction of strongly lensed galaxies.
With the pixelated approach, we obtain a discretized SBD on a uniformly pixelized source plane grid using a fixed mass profile of the lens. We use a Bayesian Monte Carlo Markov Chain (MCMC) algorithm called Massive Inference (MassInf; part of the BAYESYS package in LENSTOOL  Skilling 1998)  to optimize the brightness of every pixel in the source-plane grid. The constraints come from the extended image plane distribution of the target galaxy. A weighting map is also supplied to LENSTOOL that accounts for the uncertainty in the observed data. We ensure regularization or smoothness in the source profile by allowing every grid-based pixel to be defined by a Gaussian radial basis function (RBF) of a known size. The Bayesian MCMC algorithm samples the posterior distribution and outputs a number of reconstructed source frames and their corresponding image reconstructions.
APPLICATION TO CSWA128 We extract 14 wavelength channels in the vicinity of the Hα emission line from the IFU data corresponding to a1 and a2 images in the lensed system. Then we reconstruct these channels in individual LENSTOOL runs using two different methods: direct reconstruction (or traditional raytracing) and pixelated grid algorithm. In terms of code, the source reconstruction is performed by the cleanlens task in LENSTOOL. The direct method of reconstruction converts the image plane fluxes to the source plane with ray-tracing provided by the lens model that transfers the PSF to the source plane. We introduce the pixelated approach as a new feature of the cleanlens task in the LENSTOOL software. The source plane resolution enabled by the lensing amplification is obtained after a sub-sampling of 5 on the source grid, resulting in a pixel scale of 0.