dataesr/scientific-paragraphs-categorization huggingface dataset:
Typology retained for the classifications after selection of the training data The typology retained to classify the images is presented in Table  3. It includes all phytosociological units for which training areas could be selected. The other phytosociological units did not cover enough surface area to compose 'pure' pixels; therefore, they could not be integrated into this typology. Ultimately, only 44 of the 74 phytosociological units identified could be included in the final typology. This result corresponds to 39 EUNIS habitats out of the 62 identified and 9 EUR28 habitats out of the identified in the Natura 2000 site. The number of training pixels per typological unit varies from (mud bottoms of waterbodies) to 777 (Alopecurion utriculati Zeidler 1954). Overall, 5,564 training pixels were selected for all the data.
Selected variables to build the multi-date feature dataset Based on the results of the single-image classifications, we selected images of nine dates that had an overall accuracy higher than 80% (Fig. 4 ). For each of these dates, we kept the five variables (spectral bands and vegetation indices) that contribute most to the classification result according to MDG (Table  4 , Appendix B). In total, six different spectral bands and four vegetation indices were included in the multidate feature dataset. The spectral bands B11 and B12 (SWIR) were systematically present for the nine dates, while the bands B5 (red-edge), B3 (green) and B2 (blue) are only present for six, four and two dates respectively and the band B8A (near-infrared narrow) only for one date. Regarding vegetation indices, GNDVI and MTCI are present for seven and four dates respectively, whereas NDWI and EVI both appear for two dates.
In total, 45 variables were retained and stacked to build the multidate feature dataset. Fig. 5  shows the seasonal variations of the main variables (Table  4 ) for habitat discrimination. The GNDVI index and B2 (blue) and B3 (green) spectral bands allow good discrimination of habitats A2. 2 'Littoral sand and muddy sand' and A2. 3 -'Littoral mud' all year long. On the contrary, the phenological profiles of the other habitats cannot be easily distinguished over the period studied. Overall, temporal profiles have more similar values during wintertime. The main reflectance and intensity variations of the indices are observed in spring and summer. More precisely, it is the temporal shifts in spectral responses that allow the distinction of habitats. For example, habitats A2. 523 -'Mediterranean short Juncus, Carex, Hordeum and Trifolium salt meadows' and A2.
Multi-date classification The confusion matrix showing the results of the RF classification of the multi-date feature dataset is presented in Table  5. The predictive habitat map obtained after running the RF model on all pixels of the investigation area is shown in Fig. 6. The results are presented at the EUNIS habitats level after merging the phytosociological units corresponding to the same habitat category, following the crosswalks in Table  3. The overall evaluation of the model shows highly satisfying results with an overall accuracy of 98. 7% and a kappa coefficient of 0. 99. The producer's accuracy is 100% for 19 habitats out of the 39 in the EUNIS habitat typology. The other habitats present moderate errors leading us to obtain a producer's accuracy higher than 90%, except for EUNIS habitats D5.
The importance of field data Using a large amount of field data is paramount to ensure high accuracy in the classification of remotely sensed images  (Calleja et al. , 2019; Yeo et al. , 2020). Several authors insist on using a wide range of vegetation plots to establish a complete typology of habitats  (Chytrý et al. , 2016; Rodwell et al. , 2018). In this study, we relied on a large phytosociological plots dataset which led us to develop a complete and accurate typology of the study site. Beyond confirming the diversity of the habitats present at the Natura 2000 site, we used these field plots to calibrate the RF algorithm for image classification. Indeed, these field data are essential to locate and precisely sample the training areas to optimize image classification  (Millard and Richardson, 2015).
We conducted this step pixel by pixel by selecting only visually homogenous areas. The number of training pixels per habitat varies and depends on the number of field plots and habitat homogeneity. In general, the most widespread habitats are characterized by more training pixels to assess their variability at the scale of the study site  (Colditz, 2015; Raab et al. , 2018). Most of the plots we used are derived from bibliographical data and were collected through an important work of synthesis of the studies previously conducted on site (impact studies, vegetation maps …). Accessing preexisting data is essential to develop a robust typology and to calibrate classification algorithms. Thus, it is essential to centralize and share field plots, and especially to implement common databases  (Gautreau and Noucher, 2013) , such as the European Vegetation Archive which gathers thousands of vegetation plots across Europe  (Chytrý et al.
17 of HabDir  (European Commission, 1992; DG Environment, 2017). These data are essential for managers to define conservation measures to maintain the HICs. Thus, the habitat map produced during this study meets the needs of the manager of the Natura 2000 site 'Estuaire de la Loire' and will be essential to preserve this natural site. Overall, our results show that it is possible to obtain habitat maps quickly and at a low cost compared with conventional fieldwork  (Ichter et al. , 2014). This method can be transposed to other natural sites, in or outside the Natura 2000 network, which has the advantage of promoting a common and standardized methodological approach to habitat mapping. In addition, the approach we offer completely meets the European requirements as part of the reporting required by Europe for each member state.
Acknowledgments The authors would like to thank  Kristell Le Bot  ( GIP Loire-Estuaire ),  Bérengère Autret  ( Grand Port Maritime Nantes Saint-Nazaire ) and  Aline Corbeaux  ( Nantes Métropole ) for providing vegetation-plot data for this study. We would like to thank all the people involved in collecting field data. The authors would particularly like to thank  Hermann Guitton  of the  National Botanical Conservatory of Brest  for his assistance in field investigations, bibliographic data collection and useful discussions. Stéphanie Trécant and  Frédéric Moré  are gratefully acknowledged for their assistance in financially setting up this project. We also wish to thank  Cassandra Carnet  for her contribution to translating the work into English. We are grateful to the two anonymous reviewers for providing constructive comments on the manuscript, improving the overall quality of the paper.
The program (controlled by Gen5 3. 04) consisted in 4 shaking phases, 60 seconds each: linear shaking 567 cpm (3 mm), orbital shaking 282 cpm (3 mm), linear shaking 731 cpm (2 mm), orbital shaking 365 cpm (2 mm). After 3 shaking cycles absorbance OD600 was measured. Raw data were calibrated to 1 cm-wide cuvette measured OD600 values according to ODcuvette = ODplate / 0. 23. Matlab was used to calculate growth parameters, repeatedly based on at least three technical replicates. Average values were used to generate the growth curves. Variability between triplicate measurements was less than 5% in all cases displayed. Reverse engineering. The pORTMAGE system which allows an efficient directed genome editing in E. coli  [25]  was used to introduce into the naïve ancestor strains the mutations fixed in the genes maeA and lpd during the evolution experiments.
AHM proposes also a process, allowing for both the creation of the global model, and its consistency control. Partial automation of this process is done through a refining mechanism supported by a semantics expression described in a Domain Specific Language (DSL). The application of AHM is illustrated by the example of a conference management system. A prototype of a tool called Heterogeneous Matching and Consistency management Suite (HMCS) has been developed to support this approach.
2009 ) -is widely used in software engineering and allows designers to focus on different parts of the system in isolation. Among problems that typically arise in this multi-modeling situation, we can mention the fact that different terminologies and terms are used to represent the same concept or that the same term can be used to express different concepts. This issue, typically known as heterogeneity problem is a common problem widely shared by the community of complex software systems  (Yousafzai et al. 2016) ,  (Mosterman & Zander 2016). GEMOC provides a classification of the different levels of heterogeneity in software engineering in  (Baudry et al. 2012 ). Authors propose a three-level classification: heterogeneity of systems (such as subsystems orchestration), heterogeneity of execution platforms (this is the case for example with simulation engines), and finally heterogeneity in modeling which deals with coordination of domain specific models.
Persistence viewpoint model The meta-model of Figure  4  represents the core elements of a relational database and the relationships among them. A schema includes tables and views of the system. Both of them Figure  2  CMS Software Design meta-model contain columns that can be specified as primary or foreign keys. Figure  5  shows the persistence model of the CMS. The Table  AuthorTable  stores information about every author such as its identifier, its affiliation, etc. ArticleReviewsTable contains data concerning the evaluation of an article. For example, the columns review and decision are used respectively for remarks and acceptance/rejection notifications. In the same table, a primary key column type is used to uniquely enumerate the various evaluations. Foreign keys columns type, reviewerId and articleId, are used to identify respectively the reviewer and the article.
Presentation To assist the expert and put in practice our approach, we have developed HMCS (Heterogeneous Matching and Consistency management Suite). It is a suite of plugins that gives stakeholders two core features. The first one concerns the matching of heterogeneous models. The second one addresses the management of inconsistencies when models evolve. This part is not addressed in this paper. For more information about it, please refer to  (El Hamlaoui, Ebersold, Coulette, et al. 2014). In this section we illustrate the semi-automatic creation of M1C that contains the correspondences between the 3 models of CMS. The architecture of HMCS (Figure  19 ) is based on a set of modules (AMT, RT, etc. ) developed using the following Frameworks: EMF, KOMMA, GMF, Xtext, JET, EMFCollab, TwoUse and CDO.
The Matching Tool (MT) is provided as an Eclipse plugin. As shown in Figure  20 , it includes four modules represented by gears, namely: AMT (Assisted Matching Tool), RT (Refining Tool), M2T (Model To Text) and T2M (Text To Model). AMT takes as input the meta-models of the application domain as well as MMC. To be suitable for different uses, AMT module produces as output a correspondence model in textual (sample. m2cT) or graphical (sample. m2cG) form depending on the expert's needs. It must be noted that the two representations are synchronized, meaning that the expert may start by exploiting the graphical representation and continue on the same model of correspondences with the textual representation and vice versa. This is achieved through the M2T and T2M modules. The first module serializes sample.
Produced correspondences can be characterized according to (1) their arity (binary vs n-airy with n>=3) and (  2 ) the possibility to keep them permanently. In AMW  ((Del Fabro et al. 2005 ), (Del Fabro & Valduriez 2007)), the authors describe a language that allows using M2M transformations for model comparison. These transformations produce a model of correspondences in the form of a weaving model. However, AMW can be used only when source and target model are very similar and requires that developers add extensions to the meta-model to define relationships, even for the obvious ones (e. g. similarity). Moreover according to  (Kolovos 2009 ) used transformations are generally verbose since M2M languages do not provide customized constructs for model comparison. To optimize the representation of a composed model, the authors of the same team propose a model virtualization technique (VirtualEMF)  (Clasen et al.
2011). Such technique may be useful for implementing models tracing and impacts calculation in case of source models evolution. As en extension to AMW, AtlanMod Matching Language  (Garcés et al. 2009)  proposes a DSL to express matching heuristics defined as transformation rules. It allows combining these heuristics as chains of model transformations in order to produce a weaving model. However, heuristics concern only similarity relationships. Always based on AMW,  (Jouault et al. 2010)  proposes a framework for model matching. It establishes both links among models and their respective elements by combining the components AMW and AM3 of the AMMA toolkit (AtlanMod Model Management Architecture). This approach considers only two input models simultaneously and produced correspondences do not have precise semantics so it is not easy neither to detect them nor to verify their correctness by the end of the matching.
The model of correspondences can be used for different purposes. Firstly, it allows for having a global view on the system (Figure  1 ). Each stakeholder has not only a view on his own model but also a light view on the other models through the model of correspondences. This model uses the virtualization mechanism for accessing the elements used in the various correspondences. Secondly, it can be used for interoperability allowing different stakeholders to exchange information and exploit them especially since it reinforces the semantics associated to the system via the dedicated DSL. Thirdly, through the model of correspondences, that we can consider as a PIM (Platform Independent Model) and by using the M2M and M2T Frameworks, it is possible to generate code (depending on the associated PDM (Platform Dependent Model)), for instance, database schema of the system, whole domain application code in Java, etc.
This research direction has been considered in  [58] , where kriging and Artificial Neural Network (ANN) models have been used to solve explicit Model Predictive Control (MPC) problems. Specifically, ML techniques are employed to build control laws that approximate the optimal control variables that must be applied to the process at future time step as a function of the current values of the state variables. The training data used to develop these ML-based control laws are generated by solving the MPC problem several times, considering different values of the initial state variables. Katz et al. [27]  and Katz et al. [28]  used multilayer ANNs with ReLU activation functions as surrogate models that approximate highly nonlinear objective and/or constraints, and, then, the MPP problem is traditionally solved on the basis of the surrogate models.
Optimization for training data generation Once a good sampling plan, [θ] n×k , is obtained, the optimization problem must be solved ntimes, each time considering one of the generated UPs combinations, so as to obtain the matrix of the optimal values of the objective and decision variables [Z * , x * ] n×K+1. State-of-art optimization algorithms can be used to solve the optimization problem depending on its characteristics  [2, 3]. Particularly, this work addresses different types of continuous optimization problems that include linear, quadratic and nonlinear ones, and employs the algorithms included in the Matlab optimization toolbox for their solutions. Specifically, for linear problems, the solver "linprog", based on a dual simplex algorithm, is used; for quadratic problems, the solver "quadprog", based on an interior-point-convex algorithm, is selected; and for nonlinear problems, the optimizer "fmincon", based on a sequential quadratic programming algorithm, is adopted.
x* (θ new ) = μ ok + r T R -1 (x * -1μ ok ) (5) ŝ2 (θ new ) = σ 2 ok ( 1 + λ -r T R -1 r + ( 1 -1 T R -1 r ) -1 / ( 1 T R -1 1 ) ) (6) In practice, the maximization of the concentrated log-likelihood function is computationally challenging because of the high effort associated to the repetitive calculation of the correlation matrix inverse [R] -1 n×n during the optimization iterations, which quickly grows with the size of the training data set and/or the model input dimensionality. Besides, the nature of the concentrated log-likelihood function itself is quite complicated because it is flat near the optimum. More details about these computational challenges, and the numerical methods and optimization techniques to overcome or reduce these obstacles can be found in  [13, 16]. After the training of the MPMs, they are validated using a new and different dataset, [θ v ] n v ×k [Z *,v , x *,v ] n v ×K , )), for each of the K +1 MPMs.
[Z * , x * 1 , x * 2 ] 75×3. The optimization problem is solved using the "linprog" optimizer of the Matlab optimization toolbox, based on a dual simplex algorithm. Finally, using each of the five input-output training datasets (e. g. , [θ 1 , θ 2 ] 15×2 - [Z * , x * 1 , x * 2 ] 15×3 ), three metamodels are fitted to approximate the optimal objective and decision variables, Ẑ* = f 0 (θ 1 , θ 2 ), x* 1 = f 1 (θ 1 , θ 2 ), x* 2 = f 2 (θ 1 , θ 2 ).
One validation dataset [ θ v 1 , θ v 2 ] 400×2 -[Z *,v , x *,v 1 , x *,v 2 ] 400×3 is generated and used to assess the performances of the five sets of MPMs, f 0 , f 1 , f 2 , trained with the five different datasets. It is worth highlighting that the validation set is in the form of a uniform grid of 20 × 20 over the UPs space, so as to achieve a credible assessment of the MPMs predictions in all the local regions of the UPs. The NRMSE of the MPMs prediction is calculated by comparing their estimated outputs [ Ẑ*,v , x*,v 1 , x*,v 2 ] 400×3 with the exact ones [Z *,v , x *,v 1 , x *,v 2 ] 400×3 provided by the rigorous optimization itself. Fig. 3  shows that the NRMSE of the MPMs decreases when the training dataset size increases, while the required computational efforts for the MPMs training and for prediction (of the validation set) increase.
The Figure also indicates that even with few (i. e. , 15) training patterns, the MPMs are able to achieve a satisfactory accuracy (less than 3 % of NRMSE). Table  1  illustrates the NRMSE and computational efforts required for the MPMs training and validation when the largest training dataset (75 patterns) is considered. Fig. 3  and Table  1  shows i) the high accuracy of the MPMs trained by the dataset containing 75 instances (NRMSE less than 0. 5 % of all the MPMs), ii) affordable "offline" computational effort required for training the MPMs (2. 79 s = 0. 86 + 0. 80 + 1. 13) and iii) very low computational effort required to estimate the optimal solutions of the validation dataset (0. 00127 s = 0. 51/400), saving 75. 7 % ((2. 1-0. 51)/ 2. 1) of the computational effort required for the "wait and see" optimization procedure.
It is also worth to emphasize that only one multiparametric relation (i. e. , a MPM) is able to describe the optimal solution behavior over the entire space of the UPs, which makes its online usage to react to the UPs variation easier and more flexible than using several multiparametric relations. In the literature  [50] , the conventional MPP approach provides a solution for the same problem that divides the UPs space into two critical regions and, consequently, two sets of mathematical parametric functions are obtained each of them is valid only for one of the two partitions of the UPs space. As an additional assessment of the performance of the developed MPMs, the deterministic multiparametric solution provided in  [50]  is used to calculate the optimal objective and decision variables values for the validation dataset and compared with the one obtained by the MPMs in Fig.
Bilinear optimization In this mathematical example (Eq. (  9 ))  [23] , a bilinear objective function is to be minimized, subjected to two linear constraints which are affected by one uncertain parameter: Min x Z = x 1 x 2 S. T. : 2x 1 + x 2 ≥ θ x 1 + 3x 2 ≥ 0. 5θ -1 ≤ x 1 , x 2 ≤ 1, 0 ≤ θ ≤ 1 ⎫ ⎪ ⎪ ⎬ ⎪ ⎪ ⎭ (9) The proposed method is applied starting by designing a sampling plan [θ] 60×1 over the space [0: 1] of the UP (i. e. , the MPMs input). The optimization problem is solved 60 times considering the values of the UP in the sampling plan, to obtain the corresponding optimal decision variables and objective values (i. e. , the MPMs outputs) [Z * , x * 1 , x * 2 ] 60×3. The "fmincon" optimizer of the Matlab optimzation toolbox, based on a sequential quadratic programming algorithm, is used.
This input-output training dataset is used to build three MPMs, one for each of the optimal objective and decision variables ( Ẑ* = f 0 (θ), x* 1 = f 1 (θ), x* 2 = f 2 (θ)). A different validation dataset, including a uniform grid of 150 samples, is generated ([θ v ] 150×1 -[Z *,v , x *,v 1 , x *,v 2 ] 150×3 ) and the trained MPMs are used to estimate the optimal solutions [x *,v 1 , x*,v 2 , Ẑ*,v ] 150*3. Table  1  reports the results obtained by the proposed methodology, which indicate the very high accuracy of the three MPMs (NRMSE of 0. 01 %, 0. 8 % and 0. 4 %) and, also, the significant reduction (98. 9%) in the required computational effort with respect to the use of standard optimization tools. Also, the deterministic MPP solution provided in  [23]  is used to calculate the optimal objective and variables of the validation dataset, and the extend of matching between both solutions is   *Intel core i5-6200U CPU@2.
Quadratic optimization The second application  [10]  involves the minimization of a quadratic objective function subjected to a set of six constraints, which are affected by two UPs in their right-hand side (Eq. (  10 )). Min x Z = c T [x 1 , x 2 ] T + 0. 5[x 1 , x 2 ]Q[x 1 , x 2 ] T S. T. : A[x 1 , x 2 ] T ≤ b + F[θ 1 , θ 2 ] T -1 ≤ x 1 , x 2 ≤ 1, 0 ≤ θ 1 , θ 2 ≤ 1 ⎫ ⎪ ⎬ ⎪ ⎭ ( ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ , A = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ 1 0 -1 0 -0. 0609 0 -0. 0064 0 0 1 0 -1 ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ , F = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ 3 ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ The same procedure is considered. A sampling plan [θ 1 , θ 2 ] 80×2 is designed over the UPs space. The problem is solved 80 times to obtain the matrix [Z * , x * 1 , x * 2 ] 80×3 , using the "quadprog" optimizer of the Matlab optimization toolbox based on an interior-point-convex algorithm.
Using this input-output dataset, three MPMs are fitted: Ẑ* = f 0 (θ 1 , θ 2 ), x* 1 = f 1 (θ 1 , θ 2 ), x* 2 = f 2 (θ 1 , θ 2 ). The validation is accomplished using a different dataset including 400 samples ( [ θ v 1 , θ v 2 ] 400×2 - [Z *,v , x *,v 1 , x *,v 2 ] 400×3 ) , where the MPMs are used to estimate the outputs [x *,v 1 , x*,v 2 , Ẑ*,v ] 400*3 and the NRMSE of the MPMs predictions is calculated (Table  1 ). In this case, the accuracy of the MPMs (NRMSE of 0. 0001 %, 0. 028 % and 0. 018 %) is significantly higher than that of the MPMs in the previous two examples, which can be explained by the smooth and continuous multiparametric behavior of the optimal solutions over the entire UPs space (Fig. 6 ), which is relatively easy to capture by the datadriven models. On the contrary, the multiparametric behavior of the optimal solution in the previous two cases shows discrete features, which represent a challenge for the data-driven models.