dataesr/scientific-paragraphs-categorization huggingface dataset:
Typically, 50 K•min -1 is applied as the heating and cooling rate. Up to 100 thermal cycles per day are carried out. The thermal cycler allows placing 18 samples at the same time in 0. 5 mL Eppendorf tubes. The Eppendorf tube must not be filled completely, as only the lower part will be subjected to heating and cooling cycles. It is recommended to fill 2/3 of the Eppendorf tube. Depending on the aim of the study, different temperature profiles can be applied. A picture of the stability testing device "GREiA I" is shown in Figure  16. A data acquisition system consisting of temperature, pressure, and flow rate sensors as well as different data loggers and a personal computer were integrated in the facility to measure the HTF flows, HTF pressures, and HTF and PCM temperatures in the boiler and storage tank, respectively.
The stability of the supercooled PCM in contact with the container material is investigated. The setup is shown in Figure  22. A typical temperature profile applied to study the supercooling of SAT consists in the following steps (Figure  23 ): charging from approximately 20 to 90 °C, cooling down to ambient temperature (approximately 20 °C), initialization of crystallization, and discharge to ambient temperature (approximately 20 °C). During the experiments, the following data are measured: temperatures (inlet, outlet) and volume flow rate of the heat transfer fluid, ambient temperature, and heat storage surface temperature.
ACES2030 ( S2018/EM-4319 ) which is supported by  Comunidad de Madrid and European Structural Funds through and SFERA III  ( GA No 823802 ) which is supported by  European Union 's  Horizon H2020 Research and Innovation Programme. The work of  DTU  was supported by  HM Heizkörper GmbH  and the  Danish Energy Agency  through the  EUDP program. The work of  EHU  is part of the project  Sweet-TES  ( RTI2018-099557-B-C22 ), funded by the  Spanish Ministry of Science, Innovation and Universities. The work of GREiA was partially funded by the  Ministerio de Ciencia, Innovación y Universidades de España  ( RTI2018-093849-B-C31-MCIU/AEI/FEDER , UE) and by the  Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación (AEI)  ( RED2018-102431-T ). The authors would like to thank the  Catalan Government  for the quality accreditation given to their research group (GREiA  2017 SGR 1537 ).
GREiA is certified agent TECNIO in the category of technology developers from the  Government of Catalonia. This work is partially supported by  ICREA  under the  ICREA Academia programme. The work of  HSLU  was partially funded by the  Swiss Competence Centre for Energy Research on Heat and Electricity (SCCER HaE ). The authors would like to thank the  SCCER HaE as well as METTLER TOLEDO AG  for the support given. The work of Fraunhofer ISE was part of the projects KOLAN ( FKZ 03ESP357B ) and KOKAP ( FKZ 03ET1463A ) which were administrated by the  Projektträger Jülich (PtJ)  and funded by the  German Federal Ministry of Economic Affairs and Energy. The work of LAMTE was supported by  Intel Corporation , along with the  Natural Sciences and Engineering Research Council of Canada (NSERC)  and the  Canada Foundation for Innovation.
Experimental setup and procedure As depicted in Fig. 2 , the experimental setup was built to examine the thermal performance of PCM with and without PMS in terms of solid-liquid interface evolvement, temperature variation and total melting time. The apparatus mainly consisted of test section, heating system, data measurement and collection system, and snapshot image system. The test section was composed of rectangular enclosure container, test sample and insulation material. The HD camera was employed to capture snapshots of melting interface through transparent plexiglass at a certain time interval. An electric copper heater with size of 40 mm 40 mm 1 mm × × was employed. The heater was mainly made of copper, which is conducive to providing uniform heat flux during the experiment. The constant power was supplied to heater by DC power for the heating system, where the power can be determined by recording the electric voltage and current.
For current experiments, eleven pre-calibrated K-type micro-thermocouples with a wire diameter of 0. 127 mm were utilized along the vertical mid-plane of container. Two typical locations (the center of composite PCM and heater) were selected to trace temperature variation of heated wall and metal structure, in which thermocouples were attached to copper heater and metal skeleton using thermal adhesive. In order to determine heat loss from side wall, eight thermocouples were attached to inner surface of insulation material using thermal tape. The last thermocouple was used to obtain ambient temperature. The temperature data at different locations were traced at every 1 s via the data logger, and then stored in PC for further investigation. PCM was prepared by impregnating porous aluminum structure with paraffin.
Numerical procedure The CFD software Fluent 18. 0 is used to perform pore-scale numerical calculation. The momentum and energy equations are discretized using second order upwind schemes. PISO algorithm is employed for coupling velocity and pressure field. The PRESTO method is adopted for calculation of pressure term. ICEM CFD 18. 0 is used for dividing computational domain of model. The grids of computational domain are generated using a structured hexahedron element. The structured grids can reduce computation time and improve computation accuracy. Details of the meshes of metal structure and paraffin are presented in Fig. 4 , where the mesh of plexiglass domain is omitted for clarity. The grid independence test is performed for validating accuracy of numerical calculation. Two different numbers of grids (coarse grid with 3156k cells and fine grids with 6735k cells) are tested.
Although the issue of electricity security of supply is extensively discussed in the long-term energy system community, to the best of our knowledge, there is no study using probabilistic measures to tackle power generation adequacy in planning stage. The first objective of this work consists in coupling TIMES with ANTARES as part of a one-directional chain model in The short version of the paper was presented at ICAE2019, August 12-15, Västerås. This paper is a substantial extension of the short conference paper. order to transfer the power generation mix decided by TIMES for a given year of interest to the ANTARES input database. For this target year, the second objective is to focus on the ANTARES dispatch results and adequacy metrics to provide consistent feedback to TIMES to ensure sufficient supply.
Power system model: ANTARES The unit commitment model used in the scope of this work was ANTARES, an open source probabilistic power system model developed by RTE (French power grid operator). ANTARES simulates the balance between supply and demand over an interconnected system. The model is regularly used in European projects and national assessments (e. g. RTE French Generation Adequacy report  [41] [42] [43] [44] , PLEF adequacy study  [45] , TwenTies project  [46]  , e-Highway2050 project  [47] , ENTSOE-MAF TYNDP  [48] ). The paper by  [49]  provides a general description of the architecture of ANTARES. The mathematical problem formulation can be found in  [50]. This power system analysis software determines hydro/thermal schedules and dispatches under the following core specifications: • Network modeling based on node/edge representation.
Then, based on the ANTARES results, the main function of the feedback loop is to ensure an adequate generation mix. However, the feedback with the long-term energy planning model is not straightforward and requires an iterative process. In practice, the stopping criterion of the feedback loop is chosen to be a loss of load expectation limit of 3 hours (see section 2. 1). The short version of the paper was presented at ICAE2019, August 12-15, Västerås. This paper is a substantial extension of the short conference paper. • Global Input database and TIMES initialization: Since TIMES and ANTARES are used to model the reality of the same power system, their input data overlap. Before setting up the feedback loop, a necessary first step of the linking exercise is to identify the differences and similarities between the two modeling approaches.
Typical common inputs in both models are: Electricity demand, PV and wind capacity factors, Technical and economical characteristics of power plants. Since ANTARES is probabilistic, the electricity demand and VRE capacity factors are described by numerous time-series to simulate multiple years. As TIMES-FR is used in its deterministic version, only one year is represented. It was thus decided to use the ANTARES median scenario 5  as the TIMES input. In such a way, the orange block labeled "global input data" contains the key input data required in TIMES and ANTARES, and also provides the starting point of our linking exercise. Once this input consistency is established, the first step is to run the TIMES model for the whole planning timeframe. • Bridge. 1: This step is the core of the linking model.
Based on the TIMES solution, the power generation mix for a specific year of interest is extracted. ANTARES is then run with this targeted mix as input and simulates the supply/demand balance of the power system under several operational and climatic conditions. It is important to recall that within ANTARES, the stochastic nature of VRE, power demand, hydro generation and thermal power plant availability is taken into account by using a large number of annual scenario conditions (Monte Carlo simulations). Each possible future state corresponds to an alternative realization, created using forecasted meteorological data, and is composed by annual time-series, with hourly resolution using RTE data  [54]. Feedback part: This part is designed to assess the power system generation adequacy. • Bridge.
Electricity demand and renewables capacity factor datasets Climatic conditions will affect both renewable generation and power demand. Therefore, weather events have a significant impact on the operation and adequacy of the power system. Consequently, it is important to consider the range of climatic conditions that will affect both renewable generation and power demand. Furthermore, wind, solar radiation, temperature and precipitation are mutually correlated. In extremely cold periods, for example, wind energy production often falls while there is a significant increase in consumption due to the thermo-sensitivity of load. In this regard, we employed 200 meteorological time-series databases (wind speed, solar radiation, and temperature) representing different possible states of the linking targeted year (2030).
To compute this, a profile for each end-use consumption is taken assuming reference temperatures (normal conditions). The last phase aimed in applying temperature sensitivity to the end-uses that are sensitive to weather conditions (heating and air conditioning). The 200 temperature time series forecasts from the meteorological database are used in this computation. The demand forecasts for each end-use were then aggregated to produce the total load curve scenario inputs (200 hourly time series) for ANTARES. Figure  5-(a)  shows the shape of the median scenario load curve broken down into sectors of activity, and (b) the temperature-sensitive share of the same load curve. It appears that very intense consumption occurred in winter, mainly due to seasonal electric heating in the residential and tertiary sectors.
By ensuring input-output data consistency, we developed a rigorous methodology for linking a longterm energy planning model (TIMES power system model) with a probabilistic operational power system (ANTARES). For a specific target year, in our model we established an iterative feedback loop with a stopping criterion representing three-hour annual loss-of-load expectation to ensure an adequate power generation mix. Furthermore, most previous studies have highlighted the regrettable lack of consistent long-term energy demand forecasts, as well as capacity factor forecast data sources. As for all models, the quality of our results is conditioned by the quality of the input data. However, our databases benefit from the expertise of the power grid operator (RTE) regarding long-term forecasting studies.
Acknowledgments This research was conducted in joint collaboration between RTE, the French power grid operator, and the  Modeling for Sustainable Development Chair  led by  MINES ParisTech, Ecole des Ponts ParisTech, AgroParisTech. It was supported by  ADEME ,  EDF ,  GRTgaz ,  Total ,  RTE ,  SCHNEIDER ELECTRIC  and the  French Department for Climate and Energy.
limacinum strain SR21 stored at the American Type Culture Collection under the entry ATCC R MYA1381 TM. The taxonomic and systematic environment of thraustochytrids is very complex and requires profound rearrangements  [12]  , but the phylogenetic position of strain CCAP_4062/1 was recently confirmed  [1]. Fig. 1. Schematic representation of the genome assembly pipeline. Raw PacBio reads were corrected using the Illumina data and using three iterations of the program LoRDEC. The corrected PacBio reads were used to create a draft assembly with the tool wtdbg2, thus producing the first raw assembly. The latter was then polished using the Illumina reads and performing five iterations of Pilon corrections and one run of REAPR to remove misassemblies. The polished assembly was used together with the Illumina reads to perform an assembly with Spades.
Genome assembly The starting dataset included PacBio data obtained with the RSII long read technology and Illumina Novaseq 60 0 0 reads ( Table  1  ). The PacBio data included 770,0 0 0 polymerase reads with an N50 of 37,932 bp and a total of 12. 16 Gbp. The Illumina data consisted of 59. 9 million pairedend 2 × 150 bp reads corresponding to 9 Gbp  [13]. Considering that the estimated genome size of A. limacinum is about 60 Mbp, the PacBio and Illumina data corresponded to a 202 × and 150 × coverage, respectively. In order to obtain a highly accurate genome assembly, the Illumina and PacBio datasets were combined at multiple levels following the bioinformatics pipeline showed in Fig. 1. The Illumina dataset was quality-checked and trimmed in order to remove adapters and low-quality sequences thus obtaining 47 million high quality reads.
PacBio reads were corrected using the high-quality Illumina reads and used to perform a draft assembly with the software wtdbg2. The obtained genome sequence was polished using the Illumina reads and then used as a guide for a new assembly with Spades. The newly obtained genome was subjected to error correction, scaffolding and gap-closing. The procedure led to the final assembly. The obtained assembly was aligned against the A. limacinum ATCC R MYA1381 TM reference genome  [13]  assembly as showed in Fig. 2. The analysis highlighted a high degree of collinearity between the two genomes with more than 93% of the sequences having an identity higher than 75%. Indeed, the Average Nucleotide Identity (ANI) between the two genomes was 98. 89%. Given the high collinearity and similarity of the two genomes, a scaffolding step was performed with the software DGenies using the A.
RNA-seq guided annotation of the genome Once the final genome assembly was produced, genome annotation was performed. For this purpose, RNA-seq dataset  [8]  was used to assist in the gene prediction. The bioinformatics pipeline consisted in mapping the RNA-seq reads on the genome assembly followed by a com- bination of GeneMark and Augustus in order to predict the gene models. The Braker2 pipeline was used for this purpose. As a first step, the aligned RNA-seq reads were used to detect introns and transcribed loci. More than 95% of the reads could be correctly mapped on the obtained assembly. Then, the information used in the previous step was used to feed GeneMark to create an HMM model of splicing sites which, in turn, was used to train Augustus. In addition, the proteins from the A. limacinum ATCC R MYA1381 TM genome were used as input to identify coding genes by sequence similarity.
The strain By means of pollen grain bait method  [14]  carried out on samples from coastal seawater gathered in Mayotte (Indian Ocean, 12 °48 51. 8 'S, 45 °14 21. 7 'E), a cell was isolated and an axenic culture established. Once the culture was proved to be monospecific and axenic, it was deposited at the Culture Collection of Algae and Protozoa (CCAP) under the accession number CCAP_4062/1.
Bioinformatics methods Raw Illumina reads were analysed with FASTQC [15] to obtain quality statistics, then BB-Duk v38. 75  [16]  was used to perform trimming and clipping (minimum base quality 35 and minimum read length 35 bp). PacBio reads were corrected using three iterations of the software LoRDEC v0. 3  [17]  together with the trimmed Illumina reads, the three iterations were performed with three different K-mer lengths: 19, 31 and 41 bp. The software wtdbg2 v2. 5  [18]  was used to obtain the first draft assembly with the PacBio corrected reads, an estimated genome size of 60 Mbp was indicated. The Illumina reads were then mapped against the obtained assembly with minimap2 version 2. 17-r954-dirty  [19]  and the results treated with Pilon v1. 23  [20]  and REAPR v1. 0. 18  [21]  in order to fix mismatches and assembly rearrangements.
Genome assembly statistics were produced with the software QUAST  [25]  whereas BUSCO analyses were performed with the software version 3  [26]  and the eukaryote_odb9 dataset. Average Nucleotide Identity between A. limacinum ATCC R MYA1381 TM and A. limacinum strain CCAP_4062/1 genomes was calculated with FastANI  [27]. For genome structural annotations, RNA-seq reads  [8]  were trimmed and clipped using BB-Duk using the same parameters mentioned above. Reads were mapped against the genome assembly using STAR v2. 7. 3a  [28]  in double pass mode. The obtained mapping was used as input for Braker2 v2. 1. 0 [29] also providing a FASTA file containing the protein sequences of A. limacinum ATCC R MYA1381 TM. The genome assembly was repeat masked with RepeatMasker version open-4. 0. 9 [30] before performing the annotation selecting the option -species stramenopiles.
Gene expression levels were obtained using Kallisto v0. 46. 0  [31]  against the predicted transcript sequences. Finally, gene functional annotations were obtained using the software PANNZER2  [32]  with the following options: Minimum query coverage 0. 4 or minimum sbjct coverage 0. 4, and Minimum alignment length 50. The ARGOT  [33]  scoring function implemented in PANNZER2 as default advanced option for Gene Ontology Annotation that proved to be the best  [32]  , was chosen. The functional annotation table (available at doi: http://dx. doi. org/10. 17632/v3w485jnz5. 2 ) contains the description, Gene Ontology and KEGG Enzymes identified through PANNZER2. The four columns contain 1. Locus : The transcript ID created during the gene prediction step; 2. Annotation Type , which can include: DE (general description), MF_ARGOT (Gene Ontology Molecular Function), BP_ARGOT (Gene Ontology Biological Process), CC_ARGOT (Gene Ontology Cellular Component), EC_ARGOT (EC number); 3.
Annotation ID , which contains the accession number in the case of MF_ARGOT, CC_ARGOT, BP_ARGOT and EC_ARGOT and the score of the prediction in the case of DE; 4. Description of the annotation ID, which includes the gene description in the case of DE, the Gene Ontology description for MF_ARGOT, CC_ARGOT, BP_ARGOT and the associated GO ID for the EC_ARGOT. The sequences of CCAP_4062/1 predicted proteins were aligned with the A. limacinum ATCC R MYA1381 TM proteins using the BLASTp algorithm setting a minimum evalue of 0. 001.
Acknowledgments CM, EM, FR, and AA were supported by the  French National Research Agency  ( ANR-10-LABEX-04 ,  GRAL Labex  ;  ANR-11-BTBR-0 0 08 ,  Océanomics ;  ANR-17-EURE-0 0 03 , EUR CBS) and by the  Trans'Alg Bpifrance   PSPC partnership.
Acknowledgments The research reported in this work was supported by the  National Natural Science Foundation of China  (No. 51508317 ), the  Fundamental Research Funds for the Central Universities  ( GK201802108 ),  China Postdoctoral Science Foundation  (No. 2016M602762 ), and the  Special Financial Grant  from the  Shaanxi Postdoctoral Science Foundation  (No. 2017BSHTDZZ09 ).
FUNDING This work was partly supported by the  CEA-DRF PTNTE Greenfuels project , the  ANR   Harley  project ( ANR-19-CE44-0017-03 ), and the platform of Biophysics of I2BC supported by  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-05-05. ED-S received a post-doctoral fellowship from the  CEA  ( PTNTE  project  Greenfuels ). CC and SG received a Ph. D. fellowship from the  CEA  ( SL-DRF-17-0106 ) and the  MENESR , respectively.
A bibliographic database of scientific papers published by authors affiliated worldwide, especially focused in Europe and in the European Atlantic Area, and containing the keywords "microalga(e)" or "phytoplankton" was built. A corpus of 79,020 publications was obtained and analyzed using the Orbit Intellixir software to highlight the evolution of the research domain. Publication rates from 1960 to 2019, organization of the research, collaboration networks between countries and organizations, emerging and fading research concepts, major studied species, and associated concepts, as well as journals publishing microalgae research were considered. As a result, of the 79,020 papers published worldwide, 26,137 included authors from Europe (33% of world production) and 6989 from the European Atlantic Area (AA) (27% of European production, 9% of world production).
96, suggesting that the scientific production does not follow the European and world publication production trends. This observation suggests that European countries and regions located outside the European AA contributed more to the European publication rate increase (Figure  1 ). Table  1  details the number of authors, affiliations, and concepts listed in the three databases. Europe occupies an important place in the world scientific production, as 33% of total publications were written by authors containing at least one European co-author. A total of 42% of the total world authors or co-authors were European, and these authors represented 54% of the world affiliations. In the same way, by comparing the scientific output of the AA with Europe, a large part of AA institutes/affiliations contributed to the European production of publications: 27% of the total European scientific publications were written by a list of authors containing at least one AA author or co-author, 37% of the total European authors or co-authors came from the AA, and these authors represented 69% of the European affiliations.
concepts listed in the three databases. Europe occupies an important place in the world scientific production, as 33% of total publications were written by authors containing at least one European coauthor. A total of 42% of the total world authors or co-authors were European, and these authors represented 54% of the world affiliations. In the same way, by comparing the scientific output of the AA with Europe, a large part of AA institutes/affiliations contributed to the European production of publications: 27% of the total European scientific publications were written by a list of authors containing at least one AA author or co-author, 37% of the total European authors or co-authors came from the AA, and these authors represented 69% of the European affiliations. Concerning European countries, the United Kingdom, France, Germany, Spain, and Italy were the main European countries producing scientific publications in the field of microalgae worldwide, with 5818, 5448, 5248, 4106, and 2925 publications, respectively.