dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENT This work was supported by a subcontract from  Northwestern University  from the  United States Department of Energy (DOE ,  DE-SC0000989 ), a grant from the  National Science Foundation (NSF ,  CHE-1506993 ), and a grant from the  National Science Centre Poland  ( DEC-2013/10/E/ST5/00060 ). The  DOE grant  from  Northwestern  supported the salary for C. M. B, M. B, and K. -C. L in carrying out the experimental design, synthesis, and measurements of tunneling current. The  NSF  grant supported the salary for Y. L and M. B. for work characterizing the structure of the SAMs. P. R. acknowledges support from  MRSEC  ( DMR 14-20570 ) in characterizing the EGaIn tips used in the electrical junction. M. G. acknowledges support from  Marie Curie IOF FP7  for project  SAM-TunEGaIn  ( PIOF-GA-2012-328412 ) for DFT analysis.
Acknowledgements We would like to thank  NANOVATION (www. nanovation. com) for providing the -Ga2O3/c-sapphire epiwafers and  G. Bouchez  for help with optical transmission measurements. APT acknowledges  Agencia Estatal de Investigación (AEI)  and  Fondo Europeo de Desarrollo Regional (FEDER)  under contract  ENE2015-74275-JIN  and  ICN2  authors to the  Spanish MINECO  through the  Severo Ochoa Centers of Excellence Program  under Grant  SEV-2013-0295.
Acknowledgments This research was funded by the  Engineering and Physical Sciences Research Council, UK (EPSRC)  under  Critical Mass Grant   EP/J010804/1  ''TOUCAN: Towards an Understanding of Catalysis on Nanoalloys". Calculations were performed on the following HPC facilities: The  University of Birmingham BlueBEAR facility  (see http://www. bear. bham. ac. uk/bluebear for more details) and the  UK's national HPC facility ,  ARCHER , both via membership of the UK's  HPC Materials Chemistry Consortium , which is funded by  EPSRC  ( EP/L000202 ), and via the TOUCAN grant.
Two analytical replicates were also performed during the analyses. TEP values were expressed as xanthan gum (XG) weight equivalent (μg XG l -1 ) calculated by means of a calibration curve using 12 concentrations of xanthan gum (in tri plicate) according to  Claquin et al. (2008). Linear regressions were fitted to the calibration data with a coefficient of determination of R 2 = 0. 98. The carbon content of TEP (TEP-C) was estimated from colorimetric determinations (TEP color ) as: TEP-C = 0. 75 × TEP color (1) where TEP-C is given in μg l -1 and TEP color in μg XG l -1 (Engel & Passow 2001). A control measurement was performed in the culture medium prior to inoculation to confirm the absence of EPS in the medium.
Acknowledgements. This work was supported by the  Energy and Prosperity Chair of the Fondation du Risque, Paris , and by  Dedan Kimathi University of Technology (DeKUT) , whose views it does not claim to represent. It is a pleasure to thank  Hermann Held ,  Axel Kleidon  and two anonymous reviewers for thoughtful and constructive comments that contributed to improve an earlier version of this manuscript. The CoCEB model code is available from the authors upon request. Earth Syst. Dynam. Discuss. , doi:10. 5194/esd-2016-64,2017 Manuscript under review for journal Earth Syst. Dynam. Published: 17 January 2017 c Author(s) 2017. CC-BY 3. 0 License.
Density functional theory calculations To model the catalytic sites and to study the different adsorption modes of CAL on the Ru fullerides, density functional theory (DFT) calculations were carried out using the Vienna ab initio simulation package VASP [28e31], which uses the full-potential projector augmented wave framework  [32, 33]. Spin-polarized calculations were performed with approximating exchange-correlation effects using the Perdew, Burke, and Ernzerhof (PBE) exchangecorrelation functional  [34]. A kinetic-energy cutoff of 400 eV was found to be sufficient to achieve a total-energy convergence within several millielectron volts, considering a k-point sampling in Gamma-point only calculations for isolated molecules and complexes, in conjunction with a Gaussian smearing with a width of 0.
05 eV. During geometry optimization runs, all the atoms were fully relaxed until forces on individual atoms were smaller than 0. 01 eV/Å. Calculation cells for isolated molecules and complexes were (25 Â 26 Â 27) Å 3 to avoid spurious interactions between periodic images. Figures of the different geometries were produced with the three-dimensional visualization program VESTA  [35]. The calculations including MeOH solvent effects were performed with the implicit solvent model described in Mathew et al. [36]  and compared with an explicit treatment of the interaction between the adsorbed CAL and three MeOH molecules. As in a previous study  [37] , we have used a C 60 eRu 13 eC 60 complex to investigate the most stable adsorption modes of CAL over the Ru NPs. In addition, the hydrogenation of the Ru NP surface was also considered with a coverage value of 1H per surface Ru atom.
Acknowledgments This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , which we gratefully acknowledge. The authors acknowledge financial support from the  program of China Scholarships Council (CSC)  to F. L. I. C. G. acknowledges the  Calcul en Midi-Pyr en ees initiative-CALMIP  (Project p0812) and  GENCI-CINES  and  GENCI-IDRIS (x2016096649 ) for allocations of computer time.
The plane-view and cross-section SEM images of the undoped and Fe-doped ZnO NWs (Figure  2 ), with the same growth time of 4 h and the same growth temperature of 95 • C, showed that the high aspect ratio ZnO NWs were obtained and the morphology of the nanowires was not modified by the iron doping. From the SEM plane-view images, by using ImageJ software (Version 1. 6. 0, 2014) and by processing on ten different regions for each sample, we can estimate the nanowire density which was not significantly changed: 63 ± 2 nanowires/µm 2 for ZnO NWs vs. 61 ± 2 nanowires/µm 2 for ZnO:Fe NWs. Furthermore, the X-ray diffraction (XRD) measurements showed the typical ZnO Würtzite structure with preferred growth direction towards the c-axis both for undoped and Fe-doped ZnO nanowires (Figure  3 ). ZnO:Fe NWs.
Methods By applying the barriers theories framework to the Loviisa 3 NDH megaproject, we conducted a disciplined interpretative case study, according to  Odell (2001). Such research is  (Odell, 2001) ; it allows us to sharpen and refine existing theories while working with them. Easterby-Smith et al. (  2015 ) helped design the case study, which aims at answering the research questions mentioned in the introduction. The sampling gathers views from a medium sample of people likely to have different perspectives and experiences (see Appendix A for the details of in-depth interviews). Individuals were selected with regards to their knowledge of Finnish energy systems and of the Loviisa 3 NDH megaproject in particular. The data for the case study has been collected through semi-structured interviews as well as by examining the relevant documents.
To counterbalance this trend, academicians should provide reliable, impartial and qualitative studies on NDH experiences, bringing the discussions a step back from purely technical aspects and inviting the civil society into the debate. Transparent and systematic data sharing of NDH experiences should be encouraged through international cooperation programs. Russia, which has the largest experience with NDH  (IAEA, 2003) , should actively take part in these programs. Local authorities experiencing NDH should be interviewed and the opinion of citizens heated by nuclear heat should be collected through large-sample questionnaires. This would highlight whether or not NDH is supported by the communities that use it. Eventually, it would also provide material for NDH promotion and dissemination programs, with the aim of increasing recognition of this alternative.
Acknowledgements The authors are indebted to the staff at the  VTT Energy Systems department  for their assistance and for giving the opportunity to collect many data. Special thanks also to the interviewees for their time and patience. Discussions were very collaborative, trusting and friendly. Within a broader approach, this research is part of a PhD thesis co-directed by the  Laboratoire de Génie Industriel (LGI) of CentraleSupelec  and the  Institute for Techno-Economics of Energy Systems (I-Tésé ) at the Commissariat aux énergies atomique et aux énergies alternatives (CEA).
■ ACKNOWLEDGMENTS This work was supported by the  JST PRESTO program  and the  European Research Council (ERC)  grant (PLASMHACAT # 280064 ). Support from the  Research Foundation, Flanders (FWO)  ( G0B5514N ,  G081916N ,  G056314N , and  G025912N ) and  KU Leuven Research Fund  ( GOA 2011/03 ,  OT/12/059 ,  IDO/12/008 , and  C14/15/053 ) and funding from the  Belgian Federal Science Policy Office  ( IAP-VI/27 ) and  JSPS KAKENHI  ( JP17H03003 ,  JP17H05244 , and  JP17H05458 ) are gratefully acknowledged. G. L. acknowledges  FWO  for postdoctoral fellowships.
Results As outlined above, our analysis focused on two key thresholds, 350 and 550 ppm by 2100. Here we present the results of the analysis in terms of key attributes of the structure of the control variable and the most important state variables to track in order to remain viable. We will first address the 550 ppm goal followed by the more restrictive 350 ppm target. Carbon neutrality and staying below 550 ppm until 2100. We analyzed all combinations of the world states from 2010 and 2100 in order to characterize the adaptive policies, for each world state, that enable the planetary system to stay below 550 ppm until 2100. Our computations generated all viable states as a function of time. In order to represent this very large data set for 8 variables, we focus the analysis on the most influential variables for the viability of the system using Sobol indices (see Figs  2a-c  and SI ).
Statistics Data are reported as the mean ± standard deviation of three or more biological replicates. Statistical significance of differences among the means was determined by an unpaired two-tailed t test when only two groups were compared, or by one-way analysis of variance (ANOVA) and Tukey's post hoc test when more than two groups were compared. Graph-Pad Prism 4. 03 software (GraphPad Software, San Diego, CA, USA) was used, with the level of significance set at 95%. P values were classified as follows: <0. 001 extremely significant***, 0. 001-0. 01 very significant**, and 0. 01-0. 05 significant*.
Comparision between VSG Control and Classical Decoupling Control To validate the proposed VSG control scheme, some numerical simulations are carried out by using MATLAB/Simulink. The simulation results of two control strategies, the VSG control and classical decoupling control, to an inverter are compared. The simulation structure is illustrated in Figure  18  and its VSG control scheme of an inverter is shown in Figure  1. The values of important simulation parameters are summarized in Table  1. The difference between the VSR PUs and NPUs is the reference value of the reactive power: a PU is given a reference value which is the sum of the scheduled reactive power and VSR adjustment; while the reference of a NPU is only the scheduling reactive power. When the methods of setting these two references are swapped, the roles of PUs and NPUs can be easily changed.
Comparision between VSG Control and Classical Decoupling Control To validate the proposed VSG control scheme, some numerical simulations are carried out by using MATLAB/Simulink. The simulation results of two control strategies, the VSG control and classical decoupling control, to an inverter are compared. The simulation structure is illustrated in Figure  18  and its VSG control scheme of an inverter is shown in Figure  1. The values of important simulation parameters are summarized in Table  1. During the simulation process, before 0. 8 s, the reference values of active and reactive power are 6000 KW and 500 Kvar respectively. At the instance of 0. 8 s, the load active power is increased to 8000 KW, and the reactive power is increased to 800 Kvar. At the moment of 2 s, the power demands of load come back to their initial values.
Finally, simulations are carried out with MATLAB/Simulink to verify the effectiveness of the proposed control scheme. The effect of the VSG-based inverter control method is shown by comparing the control results with the decoupling control strategy. The theoretical analysis results of controller parameters are verified by the simulations. Further, three layers' hierarchical control simulations for both frequency and voltage secondary regulations have been done. The simulation results show the VSG-based hierarchical control of parallel DGs can realize error free control for frequency and voltage, and can control the DGs to share the load proportionally with the capacities of DGs to meet the demand of loads. From the design and analysis procedure of the VSG-based hierarchical control scheme, and also the simulation results, it can be seen that the core idea of VSG control is to embed the synchronous generator equations including the virtual inertia J and the virtual damping coefficient D into the control loop of the inverters to emulate the external dynamic characteristic of a traditional synchronous generator set.
Consequently, the VSG control-based inverter can realize power exchanges between the power source and the grid in patterns similar to the external behavior of the synchronous generator set. By doing in this way, it helps to stabilize the output parameters. And the introduction of secondary voltage and frequency regulations, which are very important concepts in the conventional power grid, can help removing the static errors existing in the VSG control. Although in this paper, the effeteness of the proposed VSG-based hierarchical control scheme of distributed generation systems is verified only by MATLAB simulations, the feasibility of this method in practical systems still need to be further confirmed. Accordingly, realize this scheme in an experimental platform for verifications is the next research mission of our research team.
Rigorous Optical Simulation of Nanojets Created by Microsphere Arrays In order to predict the distribution of the electric field from the array of microspheres, the geometry of the elementary cell of a hexagonal grating of silica microspheres was defined using a MATLAB routine for later use in an rigorous coupled-wave analysis (RCWA)  [13]  based optical propagation code ("MCGratings"  [14] ), which allows the calculation of the electromagnetic field distribution behind the microspheres. Each microsphere of 1 µm diameter was longitudinally (i. e. , in the direction of light propagation) discretized in 35 layers, which is a good compromise for determining a sufficiently exact representation of the electromagnetic field in an acceptable calculation time. The number of considered Fourier-orders was determined by repeatedly calculating several representative structures with an increasing number of Fourier-orders and verifying the convergence of the results towards a solution that is sufficiently stable.
Hybrid Renewable Energy Systems (HRES) use multiple renewable resources such as hydro, solar and wind collaboratively to produce energy that can meet a defined load demand continuously. Their combination can lead to the improvement in the systems efficiency and overall reliability. However, the level of penetration of HRES in the Caribbean region is less than its expected potential. The constraints generated by their complexity and the costly access to useful energy planning tools is a limitation to their implementation. Therefore, in collaboration with the Alternative Energy Research Group, UWI Mona, we develop a free Linear Optimization software, Photurgen, for the design and analysis of hybrid solarwind systems within the Caribbean region. Solar-wind hybrid systems are simulated based on historic climatological resources and instantaneous load consumption data, providing the user with graphics and advice for their optimal configuration.
Objectives of study Consider the application of an open source software of computations, Photurgen 2 that can determine the most suitable capacity of solar-wind system to be installed at locations within the Caribbean region (i. e. ; tropical insular areas of developing countries with high solar radiation rate and a continuous wind regime). This numerical tool is both quantitative and qualitative in its output, focusing on system design and optimization. This is an initial step in providing platforms by which human capacity within the region can increase in alternative energy. The development of Photurgen, seeks to address the lack of regionally intrinsic tools which are free for use, open for collaborations amongst regional experts and effective for energy planning involving solar-wind systems.
Development policy The Python 2. 7 programming language is used for the development of Photurgen due to its wide accessibility and high level understanding when compared to other languages. The Linux OS is also the current platform for the authors. Python built-in libraries such as numpy, matplotlib and scipy are supplemented with the authors' own libraries to form computational scripts for each of the functionality (see Sections 2. 1-2. 3) of the tool. Photurgen is a data-driven, open source, numerical tool used for the optimization of hybrid solar-wind systems. According to  Watson (2016) , HRES requires at least one year of climatological and load consumption data from the proposed installation site. Using a combination of modules and sub-modules (computational code), the end user is able to empirically determine the optimal mix of solar panels and wind turbines applicable to the specific locations.
Currently, only locations on the island of Jamaica (see Fig. 3 ) are accessible. This limitation only exists due to the authors' use of Jamaica as the experimental location for the tool. Additional Caribbean territories will be added in keeping with the regional expansion of the software. The tool is accessible in two forms; Command Line Interface (CLI) and Graphical User Interface (GUI) (see Fig. 3 ). Both versions are only compatible with the Linux Operating System. The former version seeks to attract contributors for its continued development, while the latter is for regular application by the public. The developers are also working on a Windows compatible version for release in 2017. Notwithstanding, both versions are freely accessible and can be obtained if requested from the software's website.
3 The tool has three (3) main modules as shown in Fig. 2 ; Photurgen Analysis (PTGA) for data analysis; Photurgen Optimization (PTGO) for system design; and Photurgen Geographic Information System (PTGGIS) for map viewing. 2 Pronounced as ''fo-ter-gen''. Pho-photovoltaics, tur-turbines, gengenerators. 3 Visit www. photurgen. com for more information.
Autocorrelation Functions (ACF) and Partial Autocorrelation Functions (PACF) are obtained from the datasets which then provides the insight for the selection of the most accurate order of the ARIMA(p, d, q) model for forecasting  (Watson, 2016). An exponential decrease of the ACF coupled with a spike of the PACF in the initial lags would suggest an autoregressive process being most ideal. On the other hand, an exponential decrease of the PACF coupled with a spike of the ACF in the initial lags would suggest a moving average process. However, if both the ACF and PACF decline slowly, then a mixed process is suggested. This mode of determining p and q nonetheless is more intuitive (i. e. graphical) than empirical and is mostly used in the initial phase of order determination.
Photurgen geographical information system (PTGGIS) Geographic Information Systems (GIS) are important when assessing climatological conditions and topographical features of PTGGIS is designed to read and modify different GIS file formats for site specific assessment. It also presents the findings of these processes graphically in the form of maps as depicted in Figs. 6  and 7. In Figs. 6  and 7 , the experimental location is highlighted and isolated into a sub-plot; showing the referencing capability of the tool. This feature is most convenient when working with resource databases such as wind and solar maps. It should be noted that PTGGIS will not have the full capabilities of tools such as ArcGIS. The authors' main intention is to provide energy planners with the ability to view resource maps conveniently, streamlining all the processes required for assessment into one simple tool.
Concluding remarks This paper details the objectives achieved in the development of the Photurgen energy planning tool, its modular structure and functionalities. At the risk of stating the obvious, each module has different capabilities according to their namesake i. e. PTGA deals with data analysis and statistics, PTGO focuses on system economics and optimization while PTGGIS involves geo-referencing. This is one of the possible solutions for increasing human capacity in the energy sector within the Caribbean. Proprietary tools such as Homer Energy have often been applied for the optimization of renewable energy systems on numerous occasions. Nonetheless, there exist limitations such as inaccessibility, financial investments (licensing) among others that have little positive result on the lack of penetration in this region.
Acknowledgements The authors would like to acknowledge the  Department of Physics at The University of the West Indies  for its continued support. We thank our funding partners,  Dr. Keith Duncan  at the  Southeastern University  and the Office of the Deputy Principal at the  University of the West Indies  for the  New Initiative Grant  ( Feasibility Study of Renewable Energy Production in Jamaican Communities 5  ). Also, we wish to thank the reviewers for their valuable comments and criticisms.
Machine learning methods Machine learning is a subfield of computer science and it is classified as an artificial intelligence method. It can be used in several domains and the advantage of this method is that a model can solve problems which are impossible to be represented by explicit algorithms. In  [23]  the reader can find a detailed review of some machine learning and deterministic methods for solar forecasting. The machine learning models find relations between inputs and outputs even if the representation is impossible; this characteristic allow the use of machine learning models in many cases, for example in pattern recognition, classification problems, spam filtering, and also in data mining and forecasting problems. The classification and the data mining are particularly interesting in this domain because one has to work with big datasets and the task of preprocessing and data preparation can be undertaken by the machine learning models.
High values of bias can cause an algorithm to lose its ability to establish relations between actual and target outputs (under-fitting). -The variance is the error created from actually caprturing small fluctuations in the training set. It should be noted that high variance can cause overfitting which results in modeling the random noise in the training dataset, rather than the intended output. The breakdown of bias-variance relationship is a way of investigating the expected generalization error of a learning algorithm for a particular problem that is the sum of three terms, the bias, variance, and irreducible error, which result from noise in the problem itself. In this part we present the different machine learning models used in forecasting, initially the models for classification and data preparation, secondly the supervised learning models, thirdly the unsupervised learning models and finally the ensemble learning models.