dataesr/scientific-paragraphs-categorization huggingface dataset:
For running the sequential version of the experiments, your system needs at least 4GB of RAM (running each experiment consumes around 2 GB of RAM). 4) Software requirements: • Git (week requirement): a version control system that is being used for storing all the necessary materials to run and analyze the experiments: the code of the simulations, input files, and scripts for extracting the results; • Nix (weak requirement but strongly recommended): a multi-platform packet manager (run in Linux, Windows with WSL, and MacOS) that allows configuring the experiments in a practical way and allows for reproducibility. If the reader does not want to install Nix, it is recommended to use a Linux distribution (preferably Ubuntu or Debian). Furthermore, it is necessary to install the following programs and packages/libraries: 2).
Finally, if the reader does not want to install Git, the artifact can also be downloaded from Software Heritage, as stated before. 5) Datasets: All the necessary data sets to execute the experiments are available in the input directory. The solar irradiation data was collected from the MERRA-2 service. The main input file is in the. json format, containing all the LP parameters and inputs. Regarding the workload file, it is a CSV file in text format, where for each time slot, there is the total CPU cores demand value. More details about these input files are presented in Section Experiment customization. 6) Installation: Dowload the artifact (either from Software Heritage or by cloning the Git repository). Installing Nix is optional, however, if the reader decides not to use Nix, it is necessary to install Python, R, and all the packages described in the software requirement section.
sh and if you want to run the experiments in a sequential way, execute: bash. /scripts/workflow_sequential. sh. These script files (workflow_parallel and workflow_sequential) automate the execution of the experiments and have 3 main steps. In the first step, the LP will be executed (using the low_carbon_cloud. py program) for each scenario (the years 2018, 2019, 2020, 2021, and the two extra scenarios of only using the grid and only using PVs and batteries). In the second step, after the execution of the LPs, the data for generating the plots and the tables will be extracted (files extract_data_figures. py, extract_table_v_data. py, extract_table_viii_data. py, and extract_table_vi_vii_data. py). Finally, in the last step, the figures will be generated using the R script file plots. r. The total time it takes to run the entire workflow may vary depending on the machine's hardware configuration where the experiments will execute.
For example, on our test machine equipped with an Intel core i9-11950H CPU and 32 GB of RAM, it took about 1 hour for the parallel version, and about 6 hours for the sequential version. This execution time is different from the one reported in the paper because, in this artifact, we are using another solver: the CBC solver (Pulp's default solver). In the paper, we used Gurobi, a commercial solver requiring a license (free license for academics), so we are providing this artifact using CBC to avoid costs for the reader who wants to reproduce the experiments. 8) Evaluation and expected result: Once the experiments are completed, a results directory will be created. In this directory, the reader will find: • For each Table y present in the paper, there is a respective file table_y_data. csv that contains the data used for the table; • Each scenario of the experiment has its own folder.
The reason why some results may be different is that there are multiple alternative solutions for an LP. In other words, the optimal value of the LP regarding its objective function will be the same, but the variable values might differ. In our case, the computed optimal value for the PV surface area and the capacity of batteries is the same. The difference might occur in the variables regarding how much power to charge/discharge from the batteries, use from the grid, or workload to execute at a specific data center at a specific time slot. We provide a script test_output. py that will compare the results of the experiment execution with the expected results to automate the validation process. The validation will consider the sizing and the total carbon emissions. If the validation succeeds, the following messages will be shown: "Sizing results validated!! The value obtained is equal to the expected result.
ACKNOWLEDGEMENT This work has been partially supported by the  LabEx PERSYVAL-Lab  (" ANR-11-LABX-0025-01 ") funded by the  French program Investissement d'avenir , by grant # 2021/06867-2 ,  São Paulo Research Foundation (FAPESP) , by the  EIPHI Graduate school  (contract " ANR-17-EURE-0002 "), by the  EuroHPC EU Regale  project (g. a. 956560 ), and by the  ANR   DATAZERO2  (contract " ANR-19-CE25-0016 ") project.
umanoids fascinate us through their anthro pomorphic appearance, high dexterity, and potential to work in humantailored environ ments and interact with humans in novel applications. In our research, we promote two realworld applications in physiotherapeutic juggling and assisted walking using two compliant humanoids (COMANs), COMAN and COMAN+. We focus on rehabilitation, which, as a result of changing demographics, is becoming an increasingly crucial application field. However, as with most humanoid experiments, the realization of these scenarios is challenging because the hardware is brittle, the software is complex, and control remains highly demanding. In this article, we describe an integrative and transparent control architecture that alleviates this complexity by strictly adhering in design and implementation to a component based approach.
Architecture To address the requirements, we have realized the software architecture CoSiMA (Compliant Simulation and Modeling Architecture  [9] ), which is developed through the European joint project CogIMon  [18]. We summarize it only briefly to provide the basis for discussing the specific functional prop erties for humanoid robotic applications. We use Open Robot Control Software (OROCOS)  [11]  as the underlying RT component framework because compo nents intrinsically support reusability in providing selfsus tained and deployable units of computation (as opposed to classes). Specifically, the OROCOS Component Library pro vides the reusable functionality, whereas the deployment is handled by the OROCOS Deployment Component and the Deployer Environment. The RT requirements are handled by the RealTime Tool kit (RTT), which is an integral part of OROCOS.
It utilizes the OpenSoT  [13]  software tool, which simplifies setting up and solving the respective optimization problems while adhering to the kinematic chain abstractions. OpenSoT enables the developer to combine tasks and constraints as atomic entities without explicit definition of Jacobians or interaction with the kinematic or dynamic solvers. For the sake of brevity, we focus only on the SoT, but we can alternatively also employ dynamically consistent task hierarchy projections  [14]  as the motion engine. The latter resorts to computed torque control, thereby switch ing the control paradigm substantially within the same overall architecture. For illustration, let us provide a bird'seye view of the described architecture by considering a single iteration of throwing a ball in the physiotherapy scenario with reference to Figure  3.
Physiotherapeutic-Juggling Scenario The data flow of a single iteration of a throw was described in the "Architecture" section. Here, we provide results of the first empirical evaluations, initially tested in the labora tory with healthy staff (who were familiar with both the robot and the VR) and later with patients. The experimen tal setup validated the RT and NRT integration together with the reliability of the SoTbased motion generation. Subsequently, we exposed patients to the system and evaluated their feedback regarding comfort level, user satisfaction, and practical feasibility. The patients' prelimi nary feedback was highly positive; they were able to catch balls and showed the desired training effects. Figure  6  shows a patient and his view into the VR. We are in the process of obtaining ethical permissions for a systematic evaluation at the time of this writing.
RNA extraction and analyses of the transcriptome RNA extraction and integrity controls were performed as previously described  (Ployet et al. , 2018). Three sequencing libraries per treatment were generated and sequenced (Illumina, 150 bp paired-end reads) by the Genotoul GeT platform (https://get. genotoul. fr/en/; France) using the HiSeq3000 (Illumina). An average of 46 million reads per library was generated, >90% were mapped to E. grandis reference genome (v1. 1, https://phytozome. jgi. doe. gov/) using TopHat2  (Kim et al. , 2013). Sequencing data were registered at NCBI SRA databases (PRJNA514408). For a subset of selected genes, RNAseq expression levels were confirmed by RT-qPCR (Fig. S4 , Table  S1 ) as described in  (Ployet et al. , 2018).
Vector construction and plant transformation The coding region (1050bp) of EgMYB137 (Eucgr. K02806), and a fragment of 2kb upstream of the start codon (promoter) were amplified from E. grandis xylem cDNA and genomic DNA, respectively using specific primers (Table  S1 ). The DNA fragments were then cloned into Gateway-adapted binary vectors pGWAY-0 and pGWAY-1, for overexpression and promoter activity analyses respectively and transformed into Eucalyptus hairy roots as described in  Plasencia et al. (2016). Eucalyptus composite plants harboring transgenic hairy roots were grown in OIL DRI substrate US-Special Substrate (Type III/R; Damolin, Fur, Denmark) in 200-mL pots for a period of 5 months (16 h photoperiod, 25/22°C). Fluorescent roots expressing DsRed were collected on 7 to 9 independent lines for further analyses.
Statistical analyses and data integration For transcriptomic analyses, we combined CuffDiff v2. 2. 1  (Trapnell et al. , 2012) and DEseq2 R package v1. 10 (Love et al. , 2014)  to detect, a total of 5,573 differentially expressed genes (fold change ≥ 2, adjusted P-value (FDR) < 0. 01, Table  S1 ). For metabolomic analyses, 2,250 mass signatures were obtained by LC-MS (850) and GC-MS (1400). Mass signatures detected in less than 2 technical repetitions and 2 conditions were discarded. We identified 516 differentially accumulated mass signatures (DAMs), 231 and 285 for LC-MS and GC-MS respectively (fold change ≥ 2, FDR < 0. 01, Table  S2 ). Missing values were substituted by random forest imputation  (Gromski et al. , 2014) , using missForest R package  (Stekhoven & Buhlmann, 2012). In total 6,089 DEGs and DAMs were used to perform datamining.
Discrimination of the different treatments and biological replicates was done using Partial Least Square -Discriminant Analysis (PLS-DA), from R package mixOmics  (Lê Cao et al. , 2016). We used BiNGO package (Cytoscape© software,  Maere et al. , 2005)  and g:Profiler  (Reimand et al. , 2016)  to perform gene ontology enrichment based on Arabidopsis thaliana GO classification on the first blast hit of Eucalyptus DEGs in Arabidopsis genome (Phytozome V12). Two networks of GO enrichment were obtained for genes regulated by either water availability (+W treatments versus -W treatments) or K supply (+K treatments versus -K treatments). They were merged and manually curated to remove redundant GO terms (Fig. 2 ). Network analyses based on Pearson pairwise correlations, calculated from three values per condition, were performed as described previously  (Ployet et al.
, 2018). We used WGCNA method  (Langfelder & Horvath, 2008)  to detect modules (softPower=10, deepSplit=3 and minModuleSize=100) and compute correlations between average module profiles and phenotypic variables. Correlations between standardized profiles of DAMs and DEGs of modules M1 to 6, were further investigated using mixDIABLO (Data Integration Analysis for Biomarker discovery using a Latent component method for Omics studies,  Singh et al. , 2016). In the sparse Partial Least Square Discriminant Analysis (sPLS-DA), a full design was chosen in which all datasets are connected, all variables were kept and number of components set to 3. Correlation values (Table  S3 ) were used to graphically represent networks using Cytoscape© software. For WGCNA, significant Pearson correlations were used (FDR < 5.
Sixty percent of the 155 DAMs were spread across the network and correlated to DEGs but the remaining 40% were clustered in a specific subnetwork poorly connected with DEGs (i. e. only 6 DAMs were connected to DEGs). Using the WGCNA method, we detected 12 modules containing highly correlated variables and therefore potentially involved in the same biological process (Fig. 3 ). For each module, we performed a GO enrichment analysis (Table  S1 ) and represented by heatmaps DEGs' profiles in the four treatments (Fig. 3 ). The expression profiles of the genes in all modules, except module 12, were more dependent on K-fertilization than on water status (Fig. S6 ). The modules M1 to M5 located in the core of the network, were enriched in cell wallrelated genes, especially in genes involved in the synthesis and in the regulation of the three main SCW polymers (cellulose, xylan, lignin) (Fig.
Correlations between identified DAMs and network modules In an attempt to optimize the integration of the metabolomic and transcriptomic datasets, we decided to use the mixDIABLO method of MixOmics R package, a method designed for testing correlations between heterogeneous "Omics" datasets. As shown in Fig. 4a , where the correlations between DAMs and modules are represented, the number of highly connected DAMs (282) was substantially increased as compared to the first method where only 93 were correlated with DEGs (Fig. 3 ). None of the DAMs was connected to the M7 and M12 modules in agreement with the first correlation analysis. The vast majority of the highly connected DAMs (213, 76% of the DAMs) were associated with the modules M1 to M6 related to cell wall formation and cytoskeleton. Most of these DAMs accumulated in [+K] conditions and were negatively correlated to expression profiles of SCW-related genes.
5 ). The lowest carbon allocation for trunk growth (GPPstem) was found in [-W-K] treatment whereas unexpectedly the highest value was found in the [-W+K] treatment. We then used the WGCNA method to compute the correlations between the module profiles (mean profiles of all the variables contained in a module) of M1 to M12 and the phenotypic traits. In the network represented Fig. 5 , the phenotypic traits were correlated with the 12 modules profiles as well as with 2,184 variables contained in these modules (2,148 DEGs and 36 DAMs). The core-network modules M1 to M6 were positively correlated with vessel density and saccharification yield (Fig. 5 ). In contrast, these modules, except M2, were negatively correlated to vessel diameter (Fig. 5 ). The correlations obtained between traits and module profiles reflected well the correlations between traits and variables (Fig.
We represented in green dots the GOs    S3 ). Two small isolated subnetworks (<10 variables) were not considered in the analyses. Twelve modules, detected by WGCNA analyses  (Langfelder & Horvath, 2008) , were represented by different node colors. For each module, gene expression profiles in the four treatments (Table  S1 ) were represented by heatmaps. [+K], [-K], with and without potassium; [-W], [+W], with and without water exclusion respectively. We performed a Gene Ontology (GO) enrichment approach using functional annotation of the first BLAST hit of Eucalyptus grandis genes in A. thaliana genome (Table  S4 ) to detect biological processes associated to DEGs enclosed in each module.
This work was supported by the Centre National pour la Recherche Scientifique (CNRS), the French Laboratory of Excellence project 'TULIP' (ANR-10-LABX-41; ANR-11-IDEX-0002-02), Agropolis Fondation (project ID 1203-003, Investissements d'avenir programme, Labex Agro/ANR-10-LABX-372 0001-01) associated with CAPES (project: 017/13), CNPq (projects: 444793/2014-3 and 014/2012) and FAPESP (project 2013/25642-5). The authors acknowledge the University of Toulouse, the Genotoul GeT Platform, the TRI-Genotoul platform for microscopic analyses, M. Bermudez and A. C. Siqueira for their contribution in field trial. The authors acknowledge S. Déjean and K-A Lê Cao for their nice advices for the use of MixOmics. R. P. was supported by a PhD grant from the Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche.
The validation of our SA retrievals is very difficult because of the lack of other SA products from satellite or other adapted ground-based or in-situ datasets. In addition, for this event, CALIOP satellite observations are not available during this day and even this region. Nevertheless, more indirect evaluations of the quality of these retrievals can be performed. In order to further investigate the spatial consistency of our SA retrievals, they are compared with SO 2 observations using the IASI-SO 2 Oxford algorithm (Fig. 1b, e ) and CHIMERE simulations (Fig. 1c, f ). The SO 2 and SA plumes, for the same eruptive event and after such a short time from the beginning of the event, are expected to be reasonably co-located in space and time. From Fig. 1 , it can be seen that both observations and simulations of the SO 2 plume are geographically consistent with AEROIASI-Sulphates retrievals and the two plume sub-sections are generally co-located in space and time, including vertical distributions, even if IASI-SO 2 Oxford high plume seems higher than AEROIASI-Sulphates and CHIMERE.
Acknowledgements. The authors are grateful for the financial support of:  EC 7th Framework Program  under grant No. 603557  (StratoClim),  CNES  under grant  TOSCA/IASI ,  PNTS  under grant  MIA-SO2 ,  ANR  under grant TTL-Xing and  AID (Agence de L'Innovation de Défense)  under grant TROMPET. We acknowledge the AERIS database and support for providing IASI Level 1 and AVHRR-CLARA2 data, and  ECMWF  for providing meteorological analyses. CHIMERE simulations were performed using HPC resources from  GENCI TGCC  under.
While the second process is useful to document existing web services, it cannot leverage the benefits of building web service using a top-down approach: from a high-level (model) to a low-level (implementation). Yet  [6]  shows the advantages of using different languages for programming-in-the-large and for programming-in-the-small. The first process makes possible to partially leverage these advantages, but the issue is that it lacks the ability to keep the OpenAPI model and the implementation aligned throughout the life of the project. Indeed, implementation is often obtained by a projection of the model that is then manually modified to implement business logic. Any following model evolution needs to be projected again which would require manual modifications to be re-applied from the beginning.
Contribution. This work is an attempt to solve the problem of building web services using safe abstractions on top of an existing programming language in order to ease development and reuse of show-stopper features, while keeping the web services in sync with their documentation (i. e. an OpenAPI model). We introduce a meta-model to express web services and a corresponding semantics for verification. This leverages existing theory in Model Driven Engineering (MDE)  [3, 16, 17]  and a component-based approach in order to provide an expressive and language-agnostic solution to build web services. This metamodel does not allow to completely specify web services but is rather limited to a high-level representation in order to provide support while keeping models simple. In  [19]  we have proposed a tool (i) to check models consistency and (ii) to generate working web services from a given valid model.
The popularity of OpenAPI is also highlighted by its use in other domains. For example  [30]  describes a case where OpenAPI 2. 0 is used in combination of other tools from the life sciences community and points out that the specification extension mechanism of OpenAPI 3. 0 (that we use in this article) might be an interesting opportunity of improvement. Another example in the telecommunication domain is presented in  [14]  which provides a section to emphasizes the trade-offs of Model-Driven Engineering and argues that they can be overcomed "with increased investment in the tools that support the development process"; we share the same vision in the present proposal. One of the tools featured in the OpenAPI ecosystem is Swagger Code Generator  [22]. It aims at generating client librairies, server stubs or documentations from an OpenAPI model.
The server stubs generation supports many languages and frameworks, but, as its name states, only generates stubs. This helps developers to write new services by generated boilerplate code, which is a tedious task, but they still need to add a lot of code on top of it. Moreover, when services evolve, developer need to manually propagate evolutions into the codebase because Swagger Code Generator isn't able to merge them automatically. Our approach solves this issue because it gives flexibility to developers before the code generation step, making useless editing generated code. It is worth mentioning that the code generators in themselves are based on similar model-to-text mechanisms: Swagger Code Generator uses the mustache 5  or the Handlebars 6  (depending on the version) template format whereas SWSG uses Twirl 7.
A Meta-Model of Web Services A meta-model of web services is defined by the BNF grammar given in Figure  1. Figure  2  shows a slightly simplified version of this grammar in the form of a UML class diagram, to provide a quick glance. This meta-model does not aim to replace existing standard meta-models like for instance the OpenAPI Specification  [11]  or RAML  [15] , but rather to be compatible and complementary with them (see Section 5). These meta-models allow to define programming language-agnostic interface descriptions for HTTP APIs (i. e. informal contracts) in order to be able for both humans and computers to discover and understand their capabilities, while our meta-model allows to express actual implementations of such HTTP APIs. Precisely, our meta-model is designed in order to match needs and requirements about verification (see Section 4), generation and ease of writing (see  Section 6).
A Common Usage of OpenAPI The OpenAPI Specification  [11]  defines a standard to express interfaces to HTTP APIs in a language-agnostic way. It aims at allowing "both humans and computers to discover and understand the capabilities of the service without access to source code, documentation, or through network traffic inspection"  [11] : that is a meta-model. As in MDE, the point of having meta-models is to have tools that can rely on them in order to safely manipulate models and offer support to developers. Indeed, an ecosystem of tools was developed around OpenAPI by various actors. Such tools have several purposes, including but not limited to: providing an interactive graphical user interface from a model  [23, 24] , generating functional tests from a model  [1]  or generating a model from an annotated implementation  [28].
The Petstore example. To ease the development of tools, some official examples of OpenAPI models are shipped with the specification. In this article, we focus on the Petstore example  [12]. It describes a simple application that exposes four services to list, show, add and remove data records representing animals. We assume that we are in the context of a company such as Startup Palace; that means the goal is to develop these web services in order for them to be consumed by user interface applications; for example, desktop and mobile applications for the owner of the store. Development process. The common top-down development process follows. First, developers make several iterations on writing an OpenAPI model. This model must match the functional specifications and describe web services that are fully exploitable by consumer applications.