dataesr/scientific-paragraphs-categorization huggingface dataset:
System simulation is used in many fields to help design, control or troubleshoot various industrial systems. Within the PUMP-HEAT H2020 project, it is applied to a combined cycles power plant, with innovative layouts that include heat pumps and thermal storage to un-tap combined cycle potential flexibility through low-CAPEX balance of plant innovations. Simcenter Amesim software is used to create dynamic models of all subsystems and their interactions and validate them from real life data for various purpose. Simple models of the Gas Turbine (GT), the Steam loop, the Heat Recovery Steam Generator (HRSG), the Heat Pump and the Thermal Energy storage with Phase Change material are created for Pre-Design and concept validation and then scaled to more precise design. Control software and hardware is validated by interfacing them with detailed models of the virtual plant by Model in the Loop (MiL), Software in the Loop (SiL) and Hardware in the Loop (HiL) technologies.
Solutions exist  [5]  such as integrating Heat Pump  [6]  and energy storage  [7]  in CC plants, so that the power plant is able to pick up excess energy during high renewable energy production and use it later when it is required. The presented work has been carried out within the H2020 EU Project Performance Untapped Modulation for Power and Heat via Energy Accumulation Technologies (PUMP-HEAT). In the presented context, the PUMP-HEAT project [8] studies two main layouts of Heat Pump and Energy storage in CC. The first layout considered, a Power Oriented one, conditions the Gas Turbine inlet temperature  [9]  to adapt the electricity production to the market demand. The second layout studies the integration of Heat Pump and Latent Heat storage in Cogenerative CC to extract heat from condensate to provide additional heat to District Network.
Both solutions will be prototyped, the second one will be tested at TRL 6 in the demosite located in Turin (Italy) on the Moncalieri 400 MW CC. The plant is equipped with a Siemens V94. 3A with annular combustion chamber. System dynamic models developed with Simcenter Amesim are involved at different stages in this project. First during the predesign stage the concept has been modelled to confirm its interest and to study its dynamic behaviour. Secondly, as introduced by the authors  [10] , this prototype will include a predictive controller where current scheduling, constraints and plant status will interact to achieve the best thermo-economic efficiency. The controller will be developed in Matlab/Simulink and will integrate Simcenter Amesim linearized model. Finally, model of the plant developed in Simcenter Amesim will be interfaced with the Matlab/Simulink MPC to validate it.
Control Software The developed control software has been already presented in the previous paper  [10]  in its main principles: a framework based on a hierarchic approach was presented. A Model Predictive Controller (MPC) governs the power plant with real time resolution for plant operations. The MPC encloses a linear model of heat pump and storage for the correct management in real time. A virtual heat pump and storage system simulation dynamic model is created using Simcenter Amesim. It is continuously calibrated with real life up-to-date data whose completeness and accuracy are improving during the project at the same time as the MPC:  Predesign phase: thermodynamic cycle of the heat pump, steady state power, pressure and temperature levels are available  Design phase: Subsystems datasheets (compressor, heat exchangers, expansion device) are available  Test phase: steady state and transient test data are available for subsystems (heat exchangers) and complete heat pump system are available  The heat pump model is created with Simcenter Amesim.
This non-linear model is continuously calibrated with real life data starting from the predesign phase, then design phase test phase and finally operational phase.  Using a Jacobian matrix evaluation around a standard operating point, the model is linearized. Thus, we create three matrices A B and C such as: {  ̇=. +. =. Where  is a vector of 32 state variables,  ̇ is its time derivative,  is a vector of one input and  a vector of one output.  The implemented MPC is based on the principle of receding horizon control, where a current information of the plant is required for prediction and control. By doing so, it is implicitly assumed that the input u(k) cannot affect the output y(k) at the same time -thus D=0 in the plant model  [11].  These matrices are injected into the Matlab/Simulink code of the MPC algorithm to be run every second.
 Firstly, MiL simulation. The MPC Matlab/Simulink model, included its linear model of the plant, is exported in C code and imported into the Simcenter Amesim model of the Heat Pump as a control block. In this phase, Simcenter Amesim runs autonomously both the virtual plant and its control systems on any desktop.  Secondly, SiL simulation. The control system and the virtual plant are run together as a cosimulation between Simcenter Amesim and Matlab/Simulink. Given a communication time step at which they exchange the  and  information, each of the two software runs its own system, Simcenter Amesim predicts the behavior of the plant and the MPC gives the command.  Finally, HiL simulation. The same cosimulation is run directly on the industrial control machine the will be implemented on site.
Conclusion The paper introduces a model to represent a full scale CHP combined cycle developed within Amesim and the control developed within Matlab/Simulink. The controller is interfaced with the system. The interface is created in Amesim and the system model, which has been linearized and implemented into the Matlab/Simulink controller. The control is based on a MPC linear architecture and the validation process of the control that will be adopted is described. This paper introduces the topic: the MPC controller has been described in  [12] , whilst specification for its real implementation are given in  [13].
Materials 18 healthy subjects were enrolled in this study whose 9 young volunteers having an average age between years 29 ± 7, and 9 middle-old having average age 64 ± 22 years. Blood axial velocity was measured in internal carotid artery at level C2-C3, by using phase contrast magnetic resonance using 1. 5 T and 3 T machines. The velocity was acquired with a 2 D sequence cine phase-contrast and was synchronized with the cardiac cycle with a temporal resolution of the order of 20-31 ms. The length L of the internal carotid is estimated by using Philips DICOM software during the acquisition with a spatial resolution of 0. 5 mm. This estimation is based on the three-dimensional flight time of the cerebral angiogram.
Cepstral analyse of the velocity signal The cepstrum was defined as the inverse Fourier transform (TF) of the logarithm of the Fourier transform  (Peube 2010). The TF V of v(t) is: TF V ðf Þ ¼ TF V i ðf Þð1 þ a 0 e À2jpft 0 Þ (4) Then the logarithm of the square of the modulusallows to separate the contribution of the reflection wave Rðt 0 Þ ¼ ln ð1 þ a 0 2 þ 2a 0 cos ð2pft 0 ÞÞ from the incident wave and we get a periodic function with frequency t 0 : ln jTF V ðf Þj 2 ¼ ln jTF V i ðf Þj 2 þ Rðt 0 Þ (5) Finally, the cepstrum C V of v is calculated by applying the inverse Fourier transform of Equation (  5 ) to return to the temporal space and by analogy by the Fourier transform t 0 is called quefrency: C V ¼ C Vi þ TF À1 ð0:5Rðt 0 ÞÞ (6) where, C Vi is the cepstrum of the incident wave. A script Matlab was developed to perform the calculation of C V.
We then combined OSMOSE with the NEMOMED 12 physical model and the Eco3M-S biogeochemical low trophic level model to build the endto-end model, OSMOSE-MED. We fitted OSMOSE-MED model with observed or estimated biomass and commercial catch data using a likelihood approach and an evolutionary optimization algorithm. The outputs of OSMOSE-MED were then verified against observed biomass and catch data, and compared with independent datasets (MEDITS data, diet composition and trophic levels). The model resultsat different hierarchical levels, from individuals to the scale of the ecosystemwere consistent with current knowledge of the structure, functioning and dynamics of the ecosystems in the Mediterranean Sea. While the model could be further improved in future iterations, all the modelling stepsthe comprehensive representation of key ecological processes and feedback, the selective parameterization of the model, and the comparison with observed data in the validation processstrengthened the predictive performance of OSMOSE-MED and thus its relevance as an impact model to explore the future of marine biodiversity under scenarios of global change.
The selection of the 86 fish species was strongly dependent on data availability both for model parameterization (biological parameters and life history traits, for example) and for comparing the output to observations (species biomass data, for example). Data search and mining for the parameterization of the modelled species' lifecycles represented a significant time investment. Of the 635 fish species included in the FishMed database  (Albouy et al. , 2015) , we were able to find the life history parameters (i. e. growth, reproduction and mortality) required to parameterize the OSMOSE model for only 86 fish species in our search of the scientific literature. Cephalopod and crustacean species were selected for their high commercial value, high contribution to total biomass and data availability.
Modelling high trophic level species distribution We used a niche modelling approach based on environmental data to generate species distribution maps in the Mediterranean Sea; these maps were then used as input in OSMOSE. Species occurrence was compiled and merged from multiple sources: the Ocean Biogeographic (https://www. nodc. noaa. gov/OC5/woa13/woa13data. html). To take into account the vertical distribution of species in the water column, six environmental metrics were derived from monthly temperature and salinity climatologies: mean sea surface temperature and salinity (0-50 m depth), mean vertical temperature and salinity (0-200m depth) and mean sea bottom temperature and salinity (50 m -maximum bathymetry depth). These metrics were used to model bioclimatic envelopes for each species.
The use of environmental variables assumed that current species ranges are mainly driven by the abiotic environment, which is a reasonable hypothesis for marine species as water temperature is commonly considered as the main driver of fish geographic ranges  (Ben Rais Lasram et al. , 2010; Ben Rais Lasram and Mouillot, 2009; Cheung et al. , 2009; Sabatés et al. , 2006). Current distribution was modelled using eight climate suitability models (generalized linear models, generalized additive models, classification tree analysis, boosted regression trees, random forests, multivariate adaptive regression splines, artificial neural networks and flexible discriminant analysis) embedded in the BIOMOD2 R package  (Thuiller et al. , 2009). As OBIS and GBIF databases provide occurrence data only at world scale  (Hattab et al.
Evaluation of OSMOSE-MED outputs with independent data In order to evaluate the ability of OSMOSE-MED to predict the spatial distribution of the entire biomass in a realistic way, we compared the model output to observed data that was not used either for the calibration of OSMOSE-MED, or its parameterization, or for the climate niche modelling used to generate the species distribution maps. The ranking of geographical sub-areas (GSAs), based on cumulated biomass estimates by species (in kg. km -2 ) from the MEDITS survey (International bottom trawl survey in the Mediterranean,  Bertrand et al. , 2002)  in 2006-2013 was compared to the ranking predicted in OSMOSE-MED (see Appendix F for the correspondence between GSA numbers, names and sizes). To evaluate the consistency of the OSMOSE-MED model at the community level, the mean trophic level (mTL) of each species was calculated and compared with three different sources: the FishMed database, which contains ecological and biological traits for 635 Mediterranean fish species  (Albouy et al.
71 between MEDITS and OSMOSE-MED biomass ranking. Differences that exist between the rankings of certain GSAs can be explained in two ways. For instance, around the island of Corsica, OSMOSE-MED predicted less relative biomass (ranked 15 out of a total of 16 GSAs) than was estimated by MEDITS (ranked 8). This is partly due to the very narrow continental shelf around Corsica and to the resolution of our model (20×20 km²), which may be too coarse to represent the dynamics in this area, with the result that the climate niche models and distribution maps input in OSMOSE-MED did not resolve precisely enough the spatial distribution of species closely associated to the Corsican continental shelf. Developing OSMOSE-MED at a finer resolution was attempted in the early stages of model configuration, but the computing cost for the calibration process was judged too high (at least two to three times the computing time required for a 10×10 km² resolution).
In contrast, for GSAs that ranked higher in OSMOSE-MED than in MEDITS (i. e. below the 1:1 line in Figure  3 ), these differences could be explained by the fact that MEDITS is a demersal trawl survey with low catchability of small pelagic fish. While data from the trawl survey is useful in assessing the spatial and temporal trends of pelagic species in the Mediterranean (Brind'Amour et al. , 2016), some biases may exist, such as the survey's potential significant underestimation of the biomass of some small pelagic fish.
Catch outputs could be improved with spatialization of the fishing effort -this is being implemented in the latest version in development. However, data on fishing effort and distribution is either unavailable or difficult to access in some Mediterranean regions  (Katsanevakis et al. , 2015). One solution might be to use data from the new Global Fishing Watch database, which collects data from an automatic identification system (AIS) of fishing fleets around the world  (Kroodsma et al. , 2018). An index of the fishing effort in the Mediterranean Sea could then be calculated by evaluating the fishing time by vessel characteristics  (Kroodsma et al. , 2018). However, as most of the fishing boats in the Mediterranean fleet are less than 10 m and AIS is only compulsory for large European vessels, effort would remain underestimated  (Ferrà et al.
Species trophic levels In general, the trophic levels predicted by OSMOSE-MED were consistent with the results obtained by other studies in the Mediterranean (Figure  6 ). A significant majority (69%) of the OSMOSE-MED mTLs were close to previously estimated mTLs (by less than 0. 3). Of the 81 species that had several mTL data sources, OSMOSE-MED mTLs stood within the range of previously estimated mTLs for 58 species (72% of the species). Trophic levels from OSMOSE-MED were generally higher than those of the Ecopath model and generally lower than those of FishMed, which were mainly from the Fishbase database  (Albouy et al. , 2015). The significant Spearman's correlation coefficients between the OSMOSE-MED trophic levels and the FishMed, Ecopath and  Karachle and Stergiou (2017)  trophic levels were 0.
, 2010; Coll et al. , 2007; Corrales et al. , 2015; Halouani et al. , 2016; Hattab et al. , 2013a). Figure  6  Mean species trophic levels predicted by OSMOSE-MED (in orange), the FishMed database  (Albouy et al. , 2015)  (in blue), the Mediterranean Ecopath model  (Piroddi et al. ,2017 (Piroddi et al. , , 2015a) )  (in green) and  Karachle and Stergiou (2017)  (in purple).
g. fishing). By modelling the spatial dynamics of the community across the whole Mediterranean as well as in geographical sub-areas, we can predict the potential cascading effects of these changes on food webs and ecosystem services. To our knowledge, OSMOSE-MED is the most complete model built at the scale of the entire Mediterranean in terms of species and process representativeness. We used existing biological and ecological data from various databases and also took advantage of the high-performance computing (HPC) platform DATARMOR, which allowed the calibration of such a complex model. As for most end-to-end models, one challenge lies in searching through and integrating a large amount of data from various sources -databases, scientific and 'grey' literature, output from other models  (de Mora et al.
, 2014). We exploited the capacities of the evolutionary optimization algorithm in order to find a set of estimated parameters within a 195-dimensional search space that reproduced state variables and indicators close to observations  (Oliveros-Ramos et al. , 2017; Oliveros-Ramos and Shin, 2016). This is the first time the 'calibrar' R package has been used to configure such a complex model (a large number of parameters in a stochastic model with many nonlinearities), and it proved its ability to solve complicated minimization problems (Oliveros-Ramos and  Shin, 2016). Due to computing time and the need for continuous iterative trials and feedback between model parameterization and observations, the calibration of OSMOSE-MED took more than a year and required highperformance computing facilities.
Given its importance in the Mediterranean, this trophic compartment would merit improved representation: for example, by considering multiple functional groups with common biological and ecological characteristics (e. g. meiofauna, bivalves, echinoderms)  (Grüss et al. , 2016). As the data to do this is lacking for both the parameterization and calibration of the model, an intermediate complexity approach could be adopted to model these more refined benthic compartments as 'background taxa' for which only predation, mean growth rate and spatial distribution are modelled. This new category of 'intermediate complexity' species would deal with limited datasets and allow more species of interest to be included while keeping the model reasonably complex: this has recently been coded in OSMOSE  (Fu et al.
(Albouy et al. , 2015)BIS: www. iobis. org), the Global Biodiversity Information Facility (GBIF:. fao. org/geonetwork) and the atlas of Fishes of the Northern Atlantic and Mediterranean in the FishMed database(Albouy et al. , 2015)(Appendix D). Values of environmental predictor variables for climate data were extracted from the World Ocean Atlas 2013 version 2.
Acknowledgements The authors would like to thank the  Pôle de Calcul et de Données Marines (PCDM)  for providing access to their DATARMOR computing resources (http://www. ifremer. fr/pcdm). We are also grateful to the participants of the MEDITS survey, conducted within the Data Collection Framework (DCF) since 1994. Sabrine Drira 's help was very valuable in developing the species distribution models. Fabien Moullec  was funded by a PhD grant from the  French Ministry of Higher Education, Research and Innovation. This work was partially funded by the  Mediterranean Centre for Environment and Biodiversity Laboratory of Excellence (LabEx CeMEB)   USBIO  project ( Uncertainty in Scenarios of Biodiversity ), an  Agence Nationale de Recherche (ANR)  ' Investissements d'avenir' program  ( ANR-10-LABX-04-01 ), and the  SOMBEE project of the joint BiodivERsA and Belmont Forum "BiodivScen 2018" call  ( ANR-18-EBI4-0003-01 ).
(Albouy et al. , 2015)BIS: www. iobis. org), the Global Biodiversity Information Facility (GBIF:. fao. org/geonetwork) and the atlas of Fishes of the Northern Atlantic and Mediterranean in the FishMed database(Albouy et al. , 2015)(Appendix D). Values of environmental predictor variables for climate data were extracted from the World Ocean Atlas 2013 version 2 www. gbif. org), the Food and Agriculture Organization's Geonetwork portal (www.
catch data was extracted from the International Commission of the Conservation of Atlantic Tuna (ICCAT) statistics database. For all other exploited species, reported fishery landings were provided by the UN Food and Agriculture Organization General Fisheries Commission for the Mediterranean (FAO-GFCM) database (http://www. fao. org/gfcm/data/capture-production-statistics) and reconstructed catches were obtained from the Sea Around Us (SAU) project (Zeller and Pauly, 2015). The latter were used in order to reduce data gaps and take into account discarded bycatch and illegal, unreported and unregulated fishing in the Mediterranean Sea, where actual catches are often underestimated (European Commission, 2003; Moutopoulos and Koutsikopoulos, 2014). Cumulated biomass from stock assessments in different geographical sub-areas (GSA) of the and reconstructed catches averaged over the period 2006-2013 (called hereafter the 'reference state period') as target data.
| Bioinformatics Barcodes were removed, and reads were trimmed to 80 pb using cutadapt  (Martin, 2011)  allowing for an error rate of 0. 2 and demultiplexed using the "process_radtags" module of Stacks v1. 44  (Catchen, Hohenlohe, Bassham, Amores, & Cresko, 2013). They were aligned to the Northern Pike (Esox lucius) reference genome Eluc_V3  (Rondeau et al. , 2014)  using bwa-mem  (Li, 2013)  with default parameters. Here, 19 randomly distributed individuals with <2. 5 million reads were removed, and 643 of 662 Muskellunge were retained for subsequent analysis. Then, aligned reads were processed with Stacks v. 1. 44 for SNPs calling and genotyping. The "pstacks" module was used with a minimum depth of 3, and up to three mismatches were allowed in catalog assembly. We then ran the "populations" module to produce a vcf file that was filtered with a custom python script.
To control for paralogs and HWE disequilibrium, SNPs were retained with a read depth higher than 5, and presence in 70% of each individual at each sampling location and had heterozygosity <0. 60. We removed any SNPs that lacked presence in at least 70% of  A haplotype file was exported from the "populations" module in Stacks providing information on each RAD locus instead of single SNPs. These loci were then used to estimate a range of statistics described below and to perform FineRADstructure analysis.
| Genetic diversity analysis Hierfstat  (Goudet, 2005)  was used to compute patterns of observed heterozygosity and gene diversity after excluding monomorphic markers from each sampling location. We then computed the proportion of polymorphic loci in each sampling location using custom R scripts. Contemporary effective population size (N e ) was estimated using the bias-corrected version based on linkage disequilibrium and assuming random mating  (Waples, 2006; Waples & Do, 2008)  and implemented in NeEstimator  (Do et al. , 2014). We kept one SNP per locus and filtered the dataset for Notes. Code = acronyms for each sampling site, n = number of individuals genotyped, H o = observed heterozygosity, H s = gene diversity, P = proportion of polymorphic loci. Pi = nucleotide diversity  (Tajima, 1989) , N e = effective population size with 95% CI obtained from jackknifing over individuals.
Other DAPC model-based clustering methods  (Jombart, Devillard, & Balloux, 2010)  produced highly congruent results to those obtained using Admixture or LEA. All admixture analyses were performed by maintaining a single SNP per locus. Keeping either a random SNP or the SNP with the highest minor allele frequency produced similar results (not shown), and we report only the results of analyses performed with SNPs showing the highest minor allelic frequency (MAF) at each locus. We then correlated the individuals "stocking source" ancestry coefficient to individual levels of observed heterozygosity (Spearman's rho) and tested it for significance with a t test. Finally, we used a modification of the fineSTRUCTURE package  (Lawson, Hellenthal, Myers, & Falush, 2012)  implemented in FineRADstructure  (Malinsky, Trucchi, Lawson, & Falush, 2018)  to infer levels of population genetic structure and ancestry from haplotype data derived from RAD loci.