dataesr/scientific-paragraphs-categorization huggingface dataset:
System overview The power management tool proposed in this work is designed in Matlab/Simulink in order to supply electrical energy to a standalone green-energy district. The only source of energy comes from a photovoltaic (PV) unit composed of several PV panels. The rated power of the PV group and the number of panels must be determined. The electrical energy is stored in two distinct ways: • Short-time storage by using a battery unit composed of multiple battery cells. The number of cells and the rated power of the battery group must be determined. • Long-time storage by converting excess electricity into hydrogen. The unit consists of an electrolyzer group, a fuel cell group, and a hydrogen tank. The parameters to be sized include the rated power of the electrolyzer and fuel cell group, as well as the required mass of hydrogen to be stored.
The hydrogen is considered to be stored in gaseous form. To size the studied electrical energy system, the power flows must be known during a complete year of operation depending on the PV production, the battery group State of Charge (SoC), and the stored quantity of hydrogen. Therefore, a power management strategy based on a state machine principle is proposed in this work. Figure  1  provides an overview of the energy system implemented in Matlab/Simulink. The fictive New Era district is considered to be located in the city of Ecully, France. It is composed of several buildings: • A residence including 40 apartments • An office center including 20 offices • Three different stores: a grocery, a bakery, and a pharmacy • An Electric Vehicle (EV) service station comprising four charging points and an office Additionally, two parking areas are available for the residents and some areas are left available and could be used to install some parts of the energy system such as storage units.
Knowing that the reference solar irradiance, ref G , given in datasheet for solar panels is 1,000 W/m², the electrical power generated by the PV group, PV P , is given by equation 1. The block diagram of the PV unit is given in Figure  4b. The presented PV unit model assumes an electrical power production that only depends on solar irradiance data and the number of panels considered. It does not take into account several factors that could impact the produced power such as PV panel orientation, shadow, temperature, or aging effects. Also, the probable use of power electronics systems such as Maximum Power Point Tracking (MPPT) converters and their associated efficiency are not considered here. However, the implementation of this model in the Matlab/Simulink software only requires a few operator blocks and the user can easily modify the PV unit to improve the model accuracy by considering some physical parameters and possibly its own characterization data.
Matlab/Simulink implementation There are many possible ways to implement the previously described PMU in the Matlab/Simulink software. Here, three different possibilities, tested by the authors, will be presented and will give the same results. The first implementation, presented in Figure  9 , is based on the diagram presented in Figure  8. The state machine is carried out using "Switch" blocks that commutate between 0 and 1 states according to defined switch conditions. Then, a "Matlab Function" block which includes Matlab code is performed to specify the actions according to the state value (see Figure  8 ). This solution is robust and is almost instantaneous to simulate. However, this method requires the user to be familiar with the Matlab code. Another drawback is that the modification of the power management strategy will require the user to modify at least the Matlab code and potentially all the block architectures.
A second proposed implementation of the PMU is by using a "Truth Table " block. This can be done by a direct implementation of Table  2  as shown in Figure  10. The "Condition Table " will return actions defined in the "Action Table " according to the state value. In comparison with the previous solution, this method does not require the user to have specific knowledge in Matlab code. As the previously proposed implementation, the simulation time is not increased by this method. Finally, the PMU can be implemented using the "Stateflow" toolbox as shown in Figure  11  using a "Chart" block. This solution gives a graphic view of the different states and transitions as well as an easily adaptive simulation block to the user. Furthermore, when increasing the studied system the state flow methodology will become the most convenient implementation.
System sizing methodology In this subsection, an iterative process to optimize the size of the energy equipment is suggested. In contrast to existing software solutions such as HOMER Pro or H2RES which are mainly driven by economics, the proposed sizing method is only driven by energy requirements. This could be an attractive methodology especially in the case of standalone microgrids. The sizing results obtained with the proposed strategy are presented in the Section 3. It should be noted that the following optimization strategy is not unique and shall be compared in terms of performance to a set of other solutions in future work. The sizing of the studied PV-battery-hydrogen system depends on three parameters that must be optimized: • The number of PV panels, PV n • The number of battery cells, cell n • The maximal quantity of hydrogen to store, H 2 max m   Figure  12  shows a schematic representation of the proposed sizing method.
The tool has been applied to a case study, consisting of a PV-battery-hydrogen power system, to demonstrate its effectiveness. It provides a deep view in the power exchange in the system hour by hour on a yearly basis, which could be of great help for energy designers. Moreover, the obtained results conform to the expectations for a standalone microgrid supplied by solar panels. Furthermore, the proposed simulation tool presents various advantages: • Comfortable implementation: the tool is based on mathematical models. In this work, it has been implemented in the Matlab/Simulink software but other simulation environments can be used. • Easy model improvement: the energy unit models presented in this study can be straightforwardly improved to better fit the user needs. For example, the temperature and pressure conditions or aging could be taken into account.
As an approximation to the full-order solutions, the reduced-order solutions are recovered as a linear combination of the RB functions, where only the combination coefficients, also named the reduced-order or projection coefficients will be calculated during the online stage. In the NIMOR method, the reduced-order coefficients are approximated over the parameter domain by only using a database of reduced-order information  [10]. More recently, NIMOR methods (whose mainly advantage is that they are 'equation-free' and do not require access to the governing equations of original FOM) have been developed for solving large-scale complex physical problems. For instance, Hesthaven and Ubbiali  [5]  proposed a non-intrusive RB method combining the POD method with feed forward neural networks (FNNs) for parameterized steady-state PDEs, which is extended to time-dependent nonlinear problems  [6] , in particular to unsteady flows.
Nested proper orthogonal decomposition method Based on the above data preparation, we perform a low-rank approximation by constructing a low-dimension reduced basis (RB) subspace V rb,u with rank L u min{N h , N tp } (u ∈ {E, H}), which has the lowest dimension possible, while still maintaining a good approximation of the original dataset. The proper orthogonal decomposition (POD) method with snapshots is a popular approach to form the RB subspace. However, for the parameterized electromagnetic scattering problems, the snapshot matrix S u (u ∈ {E, H}) has a considerable size because time is added as an input parameter, so a direct approach of a singular value decomposition (SVD) is very time consuming. Therefore, a nested POD method  [9]  is employed to extract the time-and parameter-independent RB functions, where the POD method is implemented separately for time and parameter spaces.
The detailed pseudo-code implementation of the nested POD method is shown in Algorithm 1. Fig. 1  Algorithm 1 Nested POD method. Input: Time trajectory matrices S i [U, , V ] = svd(S) u (i = 1, 2, • • • , N p , u ∈ 8: k = argmin{E(k) : E(k) ≥ 1 -ρ} with E(k) = k i=1 σ 2 i / r i=1 σ 2 i being the relative information content, where r is the rank of S and σ i is the singular value of S shows the framework of the classic POD and nested POD. For a study of the corresponding projection errors we refer to  [9]. Let u = [ψ u,1 , ψ u,2 , • • • , ψ u,L u ] ∈ R N h ×L u (u ∈ u h (t, θ) ≈ u r h (t, θ) = u α u (t, θ) + u h = L u i=1 α u,i (t, θ)φ u,i + u h , u ∈ {E, H}, (4) where α u (t, θ) = [α u,1 (t, θ), α u,2 (t, θ), • • • , α u,L u (t, θ)] T ∈ R L u denotes the reduced-order or projection coefficient vector.
Dynamic mode decomposition method The dynamic mode decomposition (DMD) method with data-driven framework is proposed for model reduction of timedependent problems. Based on the database of the reduced-order coefficient vectors over a time domain for each parameter, the DMD is defined as an approximate eigendecomposition of the operator that relates one time step to the next. For each training parameter θ ( j) ∈ P tr h , we seek an operator (or a Koopman matrix) K ( j) u ∈ R L u ×L u such that α u (t (n i+1 ) , θ ( j) ) = K ( j) u α u (t (n i ) , θ ( j) ), i = 1, 2, • • • , N t -1, u ∈ {E, H}, (6) where the operator K ( j) u can be considered as a finite approximation of the Koopman operator  [34] , which is infinitedimensional. In order to establish the operator K ( j) u satisfying (  6 ) to an acceptable approximation, we consider the time sampling T tr h with equal interval t.
The HF solutions to the scattering problem of a dielectric disk and a multi-layer disk are calculated by using a discontinuous Galerkin time-domain (DGTD) solver formulated on an unstructured simplicial mesh  [38] , which combines a centered flux scheme with a second order leap-frog (LF2) time scheme. The HF solutions to the scattering problem of a 3-D dielectric sphere are obtained by using the Mie series method  [39] , and the corresponding computer implementation has been developed by Zhu  [40]. The DGTD, Mie series and NIMOR methods are implemented in MATLAB and all simulations are run on a workstation equipped with an Intel Core i7-10700F CPU running at 2. 90 GHz, and with 16 GB of RAM memory. All SVDs are implemented via the MATLAB function svd.
Conclusion In this paper, we proposed and studied an 'equation-free' NIMOR method for time-domain electromagnetic wave propagation. From the HF database on a given training parameter set with uniform or uneven sampling, a set of RB functions were extracted by using the nested POD method, which performs a prior dimensionality reduction. The DMD method, which is defined by finding the best-fit linear model of the Koopman operator, was then used to predict the low-dimensional reduced-order coefficient vectors in future time instants on this training parameter set, where the dimension of NIMOR method is further reduced. The RBF interpolation was finally used to estimate the reduced-order coefficient vectors at a new specified parameter value from the DMD calculations. The main contribution of this work is that we exploited the POD, DMD and RBF techniques to obtain a quick approximation for new specified parameters in future time instants.
2004; Donovan et al. 2011). Consequently, functional traits are key to determining the interactions between plant species and their environment  (Satdichanh et al. 2015). Large datasets of functional diversity measures have been developed in the literature  (Walker et al. 1999; Petchey & Gaston 2002 , 2006; Dı ´az et al. 2007; Villéger et al. 2008; Poos et al. 2009; Schmera et al. 2009; Laliberté & Legendre 2010; Mouchet et al. 2010; Chiu & Chao 2014). Functional diversity and phylogenetic diversity are expected to be related, as functional traits are often characterized by polygenic inheritance  (Kelly et al. 2014) , which generally relies on the hypothesis that closely related species are more functionally similar than distantly related species, and therefore a more phylogenetically diverse community will have greater functional complementarity  (Srivastava et al.
This region stands out due to its high topographic, edaphic and climatic heterogeneity that has allowed the evolution and long-term persistence of a diversity of conifer and angiosperm tree taxa, both sclerophyllous and deciduous  (Quézel & Médail 2003; Aurelle et al. 2022). Their current distribution reflects the complex palaeogeography of the region  (Thompson 2005). Surprisingly, comprehensive phylogenetic and functional trait datasets are scarce for Mediterranean tree species. The only available large-scale, dated phylogeny was produced very recently and was done at the genus level  (Cheikh Albassatneh et al. 2021). And the first species dataset of traits and occurrences has only recently been assembled  (Monnet et al. 2021). Both concern the tree species of the European part of the Mediterranean Basin.
Acquisition of DNA sequence data We used data from 12 DNA regions commonly used for phylogenetic and taxonomic barcoding purposes: (i) plastid genes  (matK, rbcL, ndhF, atpB, psbB, rpl16, trnQ-rps16) , (ii) plastid intergenic spacers (trnL-trnF, psbA-trnH), (iii) mitochondrial genes (atp1, matR), and (iv) ribosomal DNA (Internal transcribed spacer -ITS). In a first step, we gathered all available sequence data for our target taxa from NCBI (https://www. ncbi. nlm. nih. gov/nucleotide/; accessed July 2020) and from the datasets of  Monnet et al. (2021)  using matrixmaker  (Freyman & Thornhill 2016 ) (Appendix S2). The only species that did not have any sequence available was Salix appendiculata Vill. and was thus absent in our DNA sampling. According to data availability, we finally obtain sequences for 277 species.
Supermatrix data assembly We used a supermatrix approach  (McMahon & Sanderson 2006; Sanderson et al. 2007 ) to combine our large datasets. We concatenated the DNA region clusters into a combined matrix, making sure there was species overlap between each cluster and at least one other cluster. In total, the supermatrix has 277 species and 11,722 sites (Appendix S3). This step was performed using the Biopython package  (Cock et al. 2009)  implemented in python  (Van Rossum & Drake 2009).
Phylogenetic analysis Phylogenetic analyses using maximum likelihood  (Felsenstein 1973)  were conducted using the Randomized Accelerated Maximum Likelihood program (RAxML version 8; Stamatakis 2014). We conducted analyses on all individual genes using the supermatrix dataset. An appropriate substitution model of sequence evolution was selected for each of the 12 DNA regions using the Akaike Information Criterion (AIC) as implemented in the JModeltest 2 program  (Darriba et al. 2012). The optimal substitution model identified was for all 12 DNA regions sequences: GTR + G. The GTRGAMMA nucleotide substitution model was used for all partitions, with the default settings for the optimization of individual per-site substitution rates. We applied the constraint tree option with a backbone tree using edge null tree of 277 species from V.
phyloMaker, scenario 3 (Jin & Qian 2019) (Appendix S4 and S5). We added the missing species, Salix appendiculata, to the tree of 277 species by applying the Simulation with Uncertainty for Phylogenetic Investigating (SUNPLIN) implemented in R, using the branch-based method as random insertion  (Martins et al. 2013)   (Appendix S6 and S7). We estimated a chronogram of 278 species using the software treePL (Smith & O'Meara 2012) with 42 calibration points from the literature  (Magallón et al. 2013; Rothfels et al. 2015)  (Appendix S8 and S9). Finally, we dropped the supplementary species from the extended tree of 278 species using the package ape  (Paradis et al. 2004 ) implemented in R (R Core Team 2020) (Appendix S10) to limit the phylogenetic tree to the 203 European Mediterranean tree species.
Functional traits selection We assembled a dataset of functional traits for the 203 tree species of the European Mediterranean region  (Monnet et al. 2021)  and calculated the mean value for each species. Only continuous traits were used that have demonstrated links to forest productivity. These were: (i) stem specific density  (Swenson & Enquist 2007; Chave et al. 2009 ); (ii) seed dry mass (Ben-Hur et al. 2012)small-seeded species produce more seeds and the probability that one seed might reach a suitable new site is higher, consequently increasing naturalization success  (Hamilton et al. 2005) , while large seeds might also help in establishment because they have more storage tissue, which allows the seedling to be independent from external resources for a period of time  (Jensen & Gutekunst 2003) ; (iii) specific leaf area (SLA) species with higher SLA take up nitrogen easily and have a high relative growth rate  (Hamilton et al.
Topological difference metrics A functional tree was then inferred using hierarchical clustering calculated from Euclidean distance and UPGMA on the functional traits  (Legendre & Legendre 2012 ) using the R vegan package  (Oksanen et al. 2022). We performed a Mantel test to compare distance matrices  (Mantel 1967) , with pairwise Abouheif's distance for the phylogenetic tree and Euclidean distances for the traits  (Abouheif 1999; Pavoine et al. 2008; Hardy & Pavoine 2012 ) using the R adephylo package  (Jombart et al. 2010 ) and R vegan package  (Oksanen et al. 2022). After which the phylogenetic and functional trees were compared using the topological difference metric from  Penny & Hendy (1985). The relative topological difference (RTD) was calculated as the proportion of the topological difference between the two trees, as: RTD ¼ Topological difference 2nÀ6 : Ranging from 0 (no difference) to 1 (completely different), where n is the number of species and 2n-6 is the maximum number of topological differences.
In addition, λ can be >1, which would suggest that the rate of evolution of a trait is higher at the root than it is at the tip  (Pagel 1999; Freckleton et al. 2002). The P-value likelihood ratio was estimated using the likelihood ratio test (LRT). This method compares the likelihood of an evolutionary model with the observed λ against that of a model where λ is fixed to zero. These analyses were performed in R using the Geiger package  (Harmon et al. 2008) , function fitContinuous. After calculating λ, the nature of the autocorrelation of traits at different phylogenetic scales (distances) was investigated using I, Moran's autocorrelation coefficient  (Moran 1950; Gittleman & Kot 1990 ). Moran's I is usually known as a measure of spatial autocorrelation in landscape ecology used to describe the spatial structure of a trait.
(ii) Soil variables from SoilGrids 250 m, resolution 250 m, from  (Hengl et al. 2017; Shangguan et al. 2017 ) (http:// soilgrids. org): "Texture class" (USDA system) at a depth 0. 30 m, "Soil organic carbon stock" in tons per ha for depth interval 0. 00-0. 30 m. Soil variables from https://zenodo. org/record/ 4139912#. YOHzeej7S00  (Hájek et al. 2021 ) at a resolution 250 m: pH in water, Ca + in water. We extracted the data for each cell using the R package raster  (Hijmans et al. 2016) , adjusted it to resolution 10 × 10 km, then calculated the mean value of all raster pixels contained in the each cell. All description and acronyms of variables are provided in Appendix S11. All variables were standardized (mean = 0 and SD = 1) to make the regression coefficients comparable. Relationships between diversity indices and associated variables were then estimated using ordinary least squares (OLS) models.
To model spatial effects as well as incorporate predictors into the analysis, we used the spatial autoregressive error model (SARerr)  (Kissling & Carl 2008). We constructed the SARerr model using the package spdep  (Bivand et al. 2006 ) implemented (R Core Team 2020) R version 4. 0. 3 for all analysis. We specified the neighbourhood distance based on the coordinates (longitude and latitude) of 10042 centroid grids and specified a maximum distance of 50 km, as outlined in Kissling & Carl (2008) using the function dnearneigh. Then, using the function nb2listw, we calculated the spatial weight matrix by weighting the neighbours with coding of the variance-stabilizing coding scheme (S) (Tiefelsdorf et al. 1999), and used maximum neighbour distance 50 km  (Kissling & Carl 2008). The neighbourhood weighted was then defined as 1/x where x equals 50 km.
Finally, the SARerr model was specified by the function errorsarlm. As recommended in  Kissling & Carl (2008) , to minimize the Akaike information criterion (AIC), the environmental predictors were then added to this spatial model using a forward selection strategy. All descriptor variables were examined for multicollinearity using the function vif in the package car  (Fox & Weisberg 2019). To illustrate the relationship between all SARerr coefficients of alpha diversity indices, we prepared a table with diversity indices as rows and environmental variables as columns. We then standardized SARerr coefficients using Z-scores across the rows (indices) and computed a Principal Components Analysis (PCA) using the package FactoMineR  (Lê et al. 2008 ) implemented in R. All R functions were run using R version (R Core Team 2020).
Trait values tended to be similar for closely related species, demonstrating phylogenetic conservatism (see  Kelly et al. 2014). In another example,  Swenson & Enquist (2007)  examined the evolutionary ecology of wood density using a large database for seed plants in the American Neotropics. In particular, they focused on the geographic and phylogenetic variation in wood density for both gymnosperms and angiosperms. They demonstrated that wood density was generally highly conserved across the entire seed plant phylogeny, yet large divergences were found within the Rosid clade. In this study, we found that a pure Brownian motion model was not sufficient to explain the variation of each trait along the evolutionary distance gradient (time divergence) when the whole phylogeny was considered (Table  1 ).
ACKNOWLEDGEMENTS This study was funded by the  Programme national d'aide à l'Accueil en Urgence des Scientifiques en Exil (PAUSE) of Collège de France , through the  National Museum of Natural History in Paris. The study complies to the  Nagoya Protocol on Access to Genetic Resources  and the  Fair and Equitable Sharing of Benefits Arising from Their Utilization of the Convention on Biological Diversity.
In the human brain, learning is continuous, while currently in AI, learning algorithms are pre-trained, making the model non-evolutive and predetermined. However, even in AI models, environment and input data change over time. Thus, there is a need to study continual learning algorithms. In particular, there is a need to investigate how to implement such continual learning algorithms on-chip. In this work, we focus on Oscillatory Neural Networks (ONNs), a neuromorphic computing paradigm performing auto-associative memory tasks, like Hopfield Neural Networks (HNNs). We study the adaptability of the HNN unsupervised learning rules to on-chip learning with ONN. In addition, we propose a first solution to implement unsupervised on-chip learning using a digital ONN design. We show that the architecture enables e cient ONN on-chip learning with Hebbian and Storkey learning rules in hundreds of microseconds for networks with up to fully-connected digital oscillators.