dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion Once the parameters of both control strategies are obtained, they are evaluated by means of computer simulation of the systems. For this purpose, Matlab ® Simulink and RT-LAB with OP5600 real time simulator from OPAL-RT platforms was chosen as shown in Fig. 6. The first platform is used to describe the control systems, considering their digital realization. The second platform is used for the simulation of  the physical systems to be controlled, that is, the VSI + LCL systems (single-phase and three-phase). The control system is made from basic functional blocks available on the platform. Digital components and the effects of sampling and quantization are considered. Still, the dead time effects were included in the power stage model, considering a value of 65μ. The simulations were carried out considering four scenarios corresponding to the two modes of operation and the two transitions between them.
This objective requires a reconsideration of the management of urban areas and the development of adapted methodology, methods and models. Currently, various studies and projects funded by the EU's Horizon 2020 (H2020) programme are working on a framework to recognize and assess the value of these NBS co-benefits and to guide the design and implementation of urban development projects and cross-sectoral policies. Cooperation between urban scientists (climate change, water and soil management) should therefore be promoted. Consequently, to develop sustainable management of urban areas, it is of utmost importance to use expert models and methods (EMMs) that take into account UCs and the benefits of NBSs. These EMMs inform us about the performance of NBSs using various urban performance indicators (UPIs) (e.
Selected expert models and methods based on the evaluation criteria The comparison between the selected EMMs according to the scoring system and their spatial scale is presented in Table  6  (details of the scoring system are presented in the supplementary material Table  S1 ). In the context of climate change adaptation challenge, SOLWEIG-model has obtained the highest score (84. 0%) in terms of the criteria selected and the neighbourhood scale (Table  2 ). This model works at the object and neighbourhood scales. One important advantage of SOLWEIGmodel among microclimate simulation models is that it is usable at the neighbourhood scale through the GIS representation of buildings and other important elements of modelling. Between 1989 and 2020, almost 219 papers have been identified based on Scopus database; 81.
RayMan can create several year-long output data, while due to the complexity of the model (e. g. spatial representation of wind velocity), ENVI-met requires greater computational capacity and runtime (e. g. , for 2 weeks of neighbourhood data, the computational time is 1 week) (e. g. , Fachinello  Krebs et al. , 2017)  than RayMan (about 3 days on average, depending on resolution). Finally, ENVI-met is a widely used EMM. Tsoka et al. (2018)  reported in a review on ENVI-met use in 2018 that almost 280 respective papers have been identified in the Scopus database; 68% of them concern articles published in scientific journals, while the rest 31% and 1% correspond to conference papers and book chapters respectively. At the neighbourhood scale, ENVI-met got 80%. This model is a three-dimensional, grid-based microclimate model designed to simulate and predict complex surface-vegetation-air interactions in the urban environment.
Consistency between models/methods with parametrization Both RayMan and SOLWEIG models require quite important spatial datasets, as both models need a description of the investigated area, including the geometry of the buildings and vegetation, along with their radiative properties. These data are frequently available in municipalities (e. g. building databases, simple digital elevation models, etc. ), but some of them are not easy to obtain (e. g. tree canopy dimensions and trunk heights) and their integration can be difficult. RayMan model provides good simulation results for radiation flux densities and thermophysiologically significant assessment indices  (Matzarakis et al. , 2010). The model, which takes complex structures into account, is suitable for utilization and planning purposes on local and regional level (Matzarakis and Rutz, 2010).
, 2018). The model is run in order to compute water, energy and momentum surface exchanges over the impervious covers (roofs, roads, walls) at a neighbourhood or city scale, using mean value for the entire area. This approach is less computationally intensive for large scale studies, but the aggregation of parameters datasets must be performed for the results to be relevant. The contributions of gardens take place through the longwave emission that is received by roads and walls  (Lemonsu et al. , 2012). Vegetation is directly included inside the canyon, allowing shadowing of grass or trees by buildings, better representation of urban canopy form and, a priori, a more accurate simulation of canyon air microclimate  (Lemonsu et al. , 2012; Redon et al. , 2017). The radiation and energy budgets and the turbulent exchanges within the canyon of the model are represented in detail in  Lemonsu et al.
, 2008). URBS-MO is based on urban databanks to print the morphology of the urban surface: the parcels and the street network  (Rodriguez et al. , 2007). Thus, urban catchments are represented as a set of elementary surfaces connected to a hydrographic network, based on the city's main structural components. URBS-MO requires both morphological features and physical parameters  (Rodriguez et al. , 2007). This detailed representation is well adapted for the introduction of plot-scale greening strategies  (Rodriguez et al. , 2007). Most entries should have been deduced from physical considerations, either literature reviews, field measurements or parameters adjustment  (Rodriguez et al. , 2008). The method for water quality evaluation is divided into two steps: 1) evaluation of ecological status (physicochemical); 2) evaluation of chemical status.
In the first step, basic physicochemical data are required to obtain a global evaluation of the quality. If relevant, some complementary chemical analysis on metallic and organic contaminants may be performed, after characterization of pollutant sources in the vicinity of the NBS or due to maintenance. To parameterize the Ecotox EM, doses of chemical stressors that affect soil organisms (regarding EC50, LD50, etc. ) can be acquired from freely available databases (see below). The question arises about the bioindicators to be included in the method. So far, there is no consensus. In France, 18 bio-indicators were approved by ADEME and some combinations were suggested for different purposes  (Pérès et al. , 2011). EC50, LD50 and TD50 and several other parameters regarding physicochemical characteristics of soils to be taken into account (texture, Henry constant, solubility, etc.
) are quite easily available in database, e. g. EPA 1  (USA); INERIS 2 and INRS 3 (France). Concerning Fertility EM, soil moisture status is largely determined by porosity, which is a key attribute of soil structure. The size or diameter of pores regulates the energy state at which moisture is held in soil and its availability to plants  (Jim and Peng, 2012). Fertility EM requires input data obtained from direct measurement (e. g. SWI, SCF), derived from models or equations (e. g. SMP, SAW, SOM)  (Cambou et al. , 2018; Yilmaz et al. , 2018). Concerning Textural function method, statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions  (Saxton and Rawls, 2006). The texture function-based method reported by  Saxton et al.
Status of documentation on models/methods Sufficient amount of information about RayMan has been available since its creation in 2007  (Matzarakis et al. , 2010 (Matzarakis et al. , , 2007;; Matzarakis and Rutz, 2017, 2010). Concerning SOLWEIG, an online manual is available  (Lindberg and Grimmond, 2011). There is a wiki and a forum group to help, but SOLENE-Microclimat is dedicated to research so that it is needed to use Python and to be able to change parameters in scripts. ENVI-Met is well documented  (Bruse, 2004)  concerning its use, but not with regards to the physical modelling. FLUENT-ANSYS is a commercially available software that is largely used in the industry. Publications using the software are numerous (e. g. Chatzidimitriou and Yannas, 2016; Hanna et al. , 2006). Nevertheless, applications of this EMM including vegetation are not so common and require specific developments through user defined function (implemented in routines)  (Ansys, 2017).
It should be noted that both ENVI-met and FLUENT-ANSYS benefit from the support provided by the commercial entities responsible for their developments, for a cost. TEB-Hydro is a part of SURFEX. The structure and the workflow of the calculations of the modelling platform SURFEX, in which the model TEB-Hydro is available, are well documented. The equations and the structure of the TEB-Hydro module are well explained in  Chancibault et al. , (2014)  and  Stavropulos-Laffaille et al. , (2018). It will be soon available, in the version 8 of SURFEX. The structure of the URBS-MO and the workflow of calculations is well developed in  (Rodriguez et al. , 2008). The basic documentation for the water quality evaluation is those of the European WFD, which was transcribed in each European country. The documentation of the national institutions in charge of water management will be the basic sources of information.
The specific urban pollutants were selected from French review studies. The structure of Ecotox EM is documented in  Huber and Koella, (1993). This method is based on an interpolation log method using the following formula to calculate EC50 value. Direct Interpolation method is similar to interpolation log method but without logarithmic transformation of concentrations. The formula for EC50 calculation is set according to  Alexander et al. , (1999). Fertility EM is well documented in  Vidal-Beaudet et al. , (2017). Furthermore, some studies described certain UPIs: SMP (e. g. Bouzouidja et al. , 2018b; Jim and Peng, 2012) , SCR (e. g. Szymański et al. , 2015). A total of 20 institutions from 12 European countries collaborated in establishing the database of HYdraulic PRoperties of European Soils (HYPRES) using textural function method.
, 2014; Lindberg and Grimmond, 2011). This can be explained by the fact that the main calculated parameter (mean radiant temperature) can be measured with different methods. Concerning SOLENE-Microclimat, different modules have been validated. In particular, attention has been paid to validate the radiative part with comparison with measurements (e. g. Hénon et al. , 2012a Hénon et al. , , 2012b;; Idczak et al. , 2010). Some modules have also been validated as soil, green wall, building (e. g. Azam et al. , 2018; Musy et al. , 2015). However, the overall model has not been validated considering the difficulties to measure a wide range of parameters covering different physical problems, on a large number of locations. Several articles were published on the validation of ENVI-met during the last two decades.
g. stygofauna)  (Reboleira et al. , 2013). In most publications, where Fertility EM was used, field validation exists and is presented (among others,  Damas and Rossignol, 2009; Gosling et al. , 2013; Vidal-Beaudet et al. , 2017). Due to the heterogeneity of texture classification systems around the world, textural classes should be harmonized to the international system. For instance,  Minasny and McBratney (2001)  observed that the silt texture was not included in the Australian texture classification. They found that the USDA/FAO occupied 60% of the Australian soil texture triangle. The soil texture representation with the standard textural fraction triplet "sand-silt-clay" is commonly used to estimate soil properties. The differences between the texture triangles, in terms of textural classes, come mainly from the soil databases used to build them.
A timeincreasing process compared to some other models: the wind speed must be reduced manually before the simulation  ( Égerházi et al. , 2014). Because RayMan can model only the surrounding radiation (e. g. MRT) and bioclimatic indices (e. g. PET), the simulation CPU time is less compared to ENVI-Met. Depending on the amount of data the running time at a given grid point ranges from several seconds to 5-10 min (even in case of several-year long databases). The time for SOLWEIG build up can be higher if the input data needs much preparation time by the user (e. g. tree cadastre data for vegetation DSM). In addition, at the micro scale, the running time does not limit the use of it in expert modelling. In terms of build-up, preparation and running time compared to ENVI-met, SOLENE-Microclimat was about 10 times quicker.
For the simulation with TEB-Hydro, of a domain with an area of 46 km 2 , at a 200 m × 200 m spatial resolution, a 1 h resolution forcing time step and a 5 min numerical time step during 884 days takes a few hours. The water quality evaluation requires water samplings or on-site measurements. Before this step, the study of the urban context is mandatory to determine the target pollutants. Moreover, the hydraulic functioning should be taken into account to optimize the water sampling. This preparation of the sampling/measurement campaigns may be then more time-consuming than the data collection itself. To build up and run Ecotox EM, data has to be acquired first (some of them from databases and others from experiments that may sometimes take time (a few days to a month). The Fertility EM build up and run require relatively little time.
User friendliness Due to its clear structure, RayMan model can be applied not only by experts in human-biometeorology, but also by people with less experience in this field of science  (Matzarakis and Rutz, 2010). The presented model provides: (i) diverse opportunities in applied climatology for research and education  (Matzarakis and Rutz, 2010) , (ii) user friendly windows-based interface  (Charalampopoulos et al. , 2013)  and (iii) the estimation is very flexible and practical  (Mahmoud, 2011). RayMan model is a free software and available for general use  (Zaki et al. , 2020). SOLWEIG-model is now integrated in QGIS (as a part of the UMEP module), which is one of the leading GIS software, used by urban microclimatologists or green infrastructure experts as well. This makes the usage easy for these experts.
For both TEB-Hydro and URBS-MO models, once the EMM built up is realized on a specific site or catchment, their use is rather easy thanks to relevant publications or user's guide. TEB-Hydro is accessible on open source: the codes of the surface schemes TEB and ISBA can be downloaded 7. The hydrological module TEB-Hydro will be soon available at the same address. URBS-MO is also accessible as an open source. The adapted method for water quality evaluation requires some statistical analysis in optimal conditions of monitoring and a flowchart. It is under development but the code for the WFD calculations is freely available in France. Ecotox EM cannot be used easily because data has to be acquired first (most of them from databases and the others from experiments) that may sometimes take time.
Acknowledgements This work was supported by the  European Union  program SCC-03-2016: New governance, business, financing models and economic impact assessment tools for sustainable cities with nature-based solutions (urban re-naturing) [grant number  730468 ]. This work was carried out under the research project  Nature4Cities (N4C) -"Nature Based Solutions for re-naturing cities: knowledge diffusion and decision support platform through new collaborative models ". The research was also supported by grant  NKFIH-1279-2/2020  of the  Ministry for Innovation and Technology, Hungary.
Live-cell studies of circadian oscillators in individual cells have thus far remained limited to one gene product at a time, and hence the properties of circadian oscillators in single cells across multiple genes remain largely uncharacterised  (Nagoshi et al, 2004; Welsh et al, 2004). We thus aimed at characterising circadian oscillators at the transcript level by using multichannel mRNA smFISH, which is a sensitive measure of single-cell transcript counts for multiple genes simultaneously  (Raj et al, 2008; Itzkovitz & van Oudenaarden, 2011). While such smFISH measurements provide transcript counts at a single snapshot, the resulting mRNA distributions contain rich information about the underlying dynamical processes. Specifically, the mRNA distributions can be analysed using the telegraph model of transcription  (Peccoud & Ycart, 1995; Munsky et al, 2012)  to estimate the transcriptional bursting kinetics of individual genes using either smFISH  (Raj et al, 2006; G omez-Schiavon et al, 2017; Nicolas et al, 2018; Zoller et al, 2018; Mermet et al, 2018)  or even scRNAseq count data  (Kim & Marioni, 2013; Bahar Halpern et al, 2015; Larsson et al, 2019) , though the latter is less sensitive.
The extent to which each of these sources of noise contributes to cell-tocell heterogeneity in mRNA expression remains an open question for circadian clock genes. Here, we aimed to quantitatively study how the mRNA distributions of core circadian genes evolve over the circadian cycle while considering multiple sources of intrinsic and extrinsic variability. We used smFISH to simultaneously target pairs of core clock transcripts (Cry1 and Nr1d1, or Cry1 and Bmal1) in confluent NIH3T3 mouse fibroblasts every 4 h over the circadian clock. We mathematically modelled the mRNA counts using mixtures of distributions to account for intrinsic transcriptional fluctuations (bursting), cell-tocell variability and time-varying (periodic) parameters. We investigated several models of increasing complexity and, using a Bayesian model selection approach, we found that the preferred model favours inclusion of measured cellular area as an explanatory variable, with gene-specific scaling of mRNA counts with cell size.
Time-resolved mRNA count distributions of core clock genes in single cells To characterise the circadian oscillator at the single-transcript level, we performed smFISH in synchronised, confluent (non-dividing) NIH3T3 mouse fibroblasts and measured transcript numbers every 4 h for 24 h (7 time points) (Fig  1A ). The cells were synchronised using dexamethasone (Dex), and sampling started 17 h after the treatment to avoid the initial transient response. We performed multichannel imaging with fluorescent probes targeting exons of Bmal1 and Cry1 or Nr1d1 and Cry1 in the same cells. We measured the number of transcripts per cell for each gene in approximately 450 single cells per time point from three replicates (Materials and Methods). To estimate the population phase and amplitude of each gene, we fitted a two-harmonic cosinor model (Materials and Methods).
Two-gene mRNA count distributions reveal gene-pair specific correlations We next exploited the ability of smFISH to measure multiple transcripts simultaneously to explore the joint relationship between transcript numbers of different clock genes. Our dual-channel imaging allows either Bmal1/Cry1 or Nr1d1/Cry1 to be measured in the same cells  (Fig 2B). The bivariate relationships between the gene pairs show that Bmal1/Cry1 are positively correlated at each time point (R from 0. 12 to 0. 53), whereas Nr1d1/Cry1 show negative correlations (R from 0. 0 to -0. 19)  (Fig 2C). To also estimate the correlation between genes while accounting for cell area, we regressed out the area for each gene and recalculated the correlation coefficients  (Padovan-Merhar et al, 2015; Hansen et al, 2018). Since all genes are positively correlated with area  (Fig 2A) , this processing shifted the correlations for both pairs of genes.
Since we measured cellular area instead of volume, we set the dependence between burst size and cell area using an additional, gene-specific exponent β, which is also supported by the linear relationship observed between log area and the log mean mRNA  (Fig 2A). For both models M1 and M2, the likelihood of observing the data given the parameters of the model is evaluated using the modelspecific NB distribution and the mRNA counts for both genes in each cell. This is performed for both Bmal1/Cry1 and Nr1d1/Cry1 pairs across all time points, and this likelihood is combined with model priors to define the posterior parameter distribution for each model (Materials and Methods). We applied Hamiltonian Monte Carlo sampling within the STAN probabilistic programming language to sample the posterior distribution and infer model parameters (Carpenter et al, 2017) (parameter estimates for each model shown in Appendix Tables  S2-S5 ).
F3: the burst size in each cell scales with measured cell area using a gene-specific exponent β g. F4: to model incomplete synchronisation, each cell has a cell-specific phase offset compared to the average phase. F5: the burst size in each cell is proportional to the cell area multiplied by a cell-specific burst parameter ϵ i,g. These cell-specific ϵ i,g are further modelled using a bivariate log-normal distribution. B PSIS-LOO calculated for each of the four models using the whole dataset of both gene pairs across all time points. C Posterior parameter estimates. Burst frequency is plotted as a function of time, where the solid line represents the posterior mean and the shaded area represents the 90% confidence interval. Posterior probability densities are shown for the average burst size, β (which controls the dependence between burst size and cell area) and ρ (representing the correlation in burst size between genes (in log space)), where β is inferred from model M2 and the average burst size and ρ are calculated from model M4.
D Comparison of the probability density of the data (kernel density estimates) with the model (using model M4). To estimate the probability from the model, the dataset was simulated 15 times using the posterior mean parameter values combined with the measured cell areas. lowest average burst size  (Fig 3C). The burst frequencies (normalised by mRNA half-life) were in the upper range when compared to the transcriptome-wide estimates using single-cell RNA-seq, while the burst sizes spanned the observed range  (Larsson et al, 2019). The correlation parameter ρ of the random variable ϵ i,g was positive for Cry1/Bmal1 and negative for Cry1/ Nr1d1 (Fig 3C  preferred model (M4) that incorporated transcriptional bursting, cell size and circadian time variation and which was able to capture the measured bivariate smFISH densities.
Discussion Single-cell measurement of gene expression with smFISH goes beyond bulk mRNA measurement as it allows for the full characterisation of transcript distributions in populations of cells. Until now, the temporal evolution of circadian mRNA distributions through the clock remained uncharacterised. Here we performed a bivariate measurement of mRNA number across two pairs of genes every 4 h over the circadian clock. There were several notable features of our data including: an average mRNA expression level that oscillated over the circadian clock, a coefficient of variation for each gene that largely exceeded that from a Poisson process, a transcript number that correlated with cell area and a correlation between genes that depended on the pair considered. To capture these phenomena in a unified quantitative framework, we proposed several probabilistic, generative models of our data and used model selection to balance goodness of fit and model complexity to identify the optimal model.