dataesr/scientific-paragraphs-categorization huggingface dataset:
Crit. Rev. Biotechnol. , 37, 163-176. Afgan, E. , Baker, D. , Batut, B. , Van Den Beek, M. , Bouvier, D. , Čech, M. et al. (2018). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. , 46, W537-W544. Akalin, A. , Franke, V. , Vlahoviček, K. ,  Mason, C. E. & Schübeler, D. (2015). Genomation: a toolkit to summarize, annotate and visualize genomic intervals. Bioinformatics, 31, 1127 -1129. Akalin, A. , Kormaksson, M. , Li, S. , Garrett-Bakelman, F. E. , Figueroa, M. E. , Melnick, A. et al. (2012). methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biology, 13, 1-9. A. Mean population growth curve in three different salinities. For each strain (CCAP 19/12 and CCAP 19/15 in red and black, respectively), mean cell density and standard error were calculated for the experiments starting with an initial density of 20000 cells × ml -1 (circle) for both strains (10 distinct populations for each), or 5000 cells × ml -1 (cross) for CCAP 19/15 only (6 populations).
Rug plots illustrate the distribution of the initial (days 0 to 1) and exponential (days 1 to 4) growth rates on their respective axes. Fig. S3. Gene ontology (GO) term enrichment analysis. We adopted GO assignments to classify the functions of D. salina transcripts. Based on sequence homology to known sequence databases (BLAST+/SwissProt), a total of 9,874 D. salina transcripts (30. 93% of 31,926 transcripts) were assigned at least one GO term and categorized into 12,543 GO terms. Enriched GO terms of the DE genes were then identified using Fisher's exact test in the topGO R package  (Alexa and Rahnenfuhrer, 2010). GO categories included molecular function (MF, purple), cellular component (CC, orange) and biological process (BP, green),) and were sorted by decreasing order of evidence within each category, based on the GO enrichment test P-value (for P ≤ 0.
In order to limit GHG emissions related to the usage of EVs, it therefore seems advantageous to provide them with the most environmentally friendly electricity, which leads to turning to renewable energy sources, as photovoltaic (PV)  [2]. EVs charging stations based on PV installations seem able to respond optimally to this challenge. However, while EVs do not directly emit CO 2 when in motion, they do indirectly emit CO 2 during their conception, maintenance, and recycling. Likewise, PV-powered charging stations (PVCS) do not emit CO 2 when they produce electricity, but it is possible to estimate their GHG emissions considering their conception, maintenance, and recycling processes. The GHG emissions of the PVCS require an estimation methodology. There are environmental analysis methodologies such as the single-criteria "Bilan Carbone ® " analysis developed by ADEME and then taken up by the Bilan Carbone association  [3] , the simplified carbon assessment developed by the energy regulatory commission  [4] , Ecoinvent database [5], ISO 14040/44 life cycle assessment (LCA)  [6] , as well as tools for their implementation.
However, the scope of this study is limited to the carbon impact of the EVs' charging strategy. In addition to the study of CO 2 emissions from PVCS, an estimation of the global cost was discussed in the literature over the lifetime of an installation. Optimal technical sizing of the PV system and stationary storage systems for recharging EVs is critical to ensure their economic feasibility, which corresponds to sizing system components with minimum cost. In  [17] , an analysis of the technical and economic performances of autonomous PVCS associated with stationary battery storage is discussed using the HOMER software. In  [18] , an optimal configuration of PVCS is discussed economically and technically with several solar irradiation in Vietnam. The results of the study demonstrate that the irradiation is a crucial factor in choosing to install and invest in a PVCS.
GHG Assessment Methodology In order to assess the carbon impact of the PVCS, a method for calculating the GHG emission has been implemented. Based on the Bilan Carbone Association and a Massive Open Online Course (MOOC) organized by Avenir Climatique  [27] , a calculation method for assessing the carbon impact of PVCS has been established. These databases are used to collect the carbon emission coefficients associated with each emission element of the initially considered PVCS. The first step of this method is to define a study perimeter that sets the carbon emission limits that will be considered. Once this perimeter has been defined, the second step is to fill a data collection matrix, which contains the carbon emission coefficients associated with each emission element. These carbon emission coefficients are based on several references, including the "Ecoinvent" database [5], the study of national renewable energy laboratory in the LCA harmonization project  [28] , and the study presented in  [29] , in order to reduce the total carbon impact of the PVCS.
The main action lever in order to reduce the carbon impact is then the PV panels, thereby reducing the impact of the PV system, and therefore of the PVCS. According to the aforementioned analysis, the PV panels and infrastructure will be considered. The carbon impact of new PV panels decreased sharply over the years, due to the use of less carbon emitting processes and materials during manufacture, and their improved efficiency. In this study, the values of emission coefficients used to calculate the carbon impact of the considered PVCS came from the Ecoinvent database and the study carried out by NREL in the LCA harmonization project  [29] , which explains an emission coefficient of 0. 04 kgCO 2,eq /kWh for PV panels. Thus, in order to calculate the reduction of the carbon impact, the emission coefficient of the PV panels will be changed.
In this framework, the reduction of the carbon impact of PVCS is analyzed according to four scenarios. 4. 3. 1. Scenario 1: Reduction of the Emission Coefficient of the PV Panels from 0. 04 kgCO 2,eq /kWh to 0. 025 kgCO 2,eq /kWh By taking PV panels with a carbon emission coefficient of 0. 025 kgCO 2,eq /kWh, the carbon impact of the panels drops from 37,996 kgCO 2,eq to 23,748 kgCO 2,eq. As depicted in Figure  8 , the carbon impact of the PVCS drops from 85,961 kgCO 2,eq approximately to 71,713 kgCO 2,eq , a decrease of 17. 2% compared to the initially considered charging station. of less carbon emitting processes and materials during manufacture, and their improved efficiency. In this study, the values of emission coefficients used to calculate the carbon impact of the considered PVCS came from the Ecoinvent database and the study carried out by NREL in the LCA harmonization project  [29] , which explains an emission coefficient of 0.
In situ XAS data of the 1 and 5 wt% Pd supported on ceria were acquired at the Ce L 3 -edge (5723 eV) and Pd K-edge (24350 eV). XAS spectra were collected in step scan acquisition mode using transmission geometry at the BM26A beamline,  37  ESRF, equipped with a Si(111) double crystal monochromator. Measurements of all the samples were carried out in a transmission mode using ionization chambers. In a typical experiment on the Ce L 3 -edge, pellets were made from 4 mg of the ceria samples mixed with 40 mg of fumed silica. On the Pd Kedge typically 100 mg of the ceria samples were used. The samples were purged under N 2 prior to exposing the catalyst in a flow of 5% H 2 /N 2. Data were obtained at room temperature followed by measurements at various temperatures during the temperature ramp from room temperature to 450 1C at 5 1C min À1.
The samples were then cooled in 5% H 2 /N 2 to room temperature whilst data were collected at various temperatures during this process. Between the first and second cycles of H 2 treatment the samples were exposed to synthetic air at room temperature for 30 minutes. The second reduction cycle was only performed in part, heating to 100 1C in 5% H 2 /N 2 at 5 1C min À1 on the Pd K-edge, as our aim was to monitor the reduction at the initial stages of the reaction. Data processing and analysis were performed using Athena. 43 Linear combination fitting (LCF) analysis of the Ce L 3-edge XANES was performed using a freshly calcined sample consisting of the bare ceria support and cerium nitrate as reference materials representing Ce(IV) and Ce(III), respectively. Similarly, LCF analysis on the Pd K-edge was performed using a Pd metal foil and PdO as reference materials.
MCR combats this by being able to computationally separate the pure spectral components present in a dataset consisting of multiple spectra obtained during an in situ experiment. Using MCR analysis it was possible to extract the significant components which, when compared to standard reference materials, can be readily identified as oxidic Pd(II) and nano-crystalline metallic Pd(0) species, see Fig. S2 (ESI † ). The XANES data of both the 1 wt% Pd/CeO 2 and 5wt% Pd/ CeO 2 samples in their respective initial states clearly resemble that of PdO thus confirming the presence of Pd(II) in the  as-prepared catalysts (black curves in Fig. 2A  and B ). Upon reduction by heating in 5% H 2 /N 2 , the XANES data of both 1 and 5wt% samples are similar to those of the Pd metal, both in the edge position and on the top of the edge and are interpreted as the formation of nano-crystalline Pd particles on the surface of the CeO 2 support (red curve).
To extract information on the lattice parameter of the metallic Pd from the (111) reflection a custom peak fitting tool was written in python. At the start of the experiment the Pd was noted as being oxidic Pd(II) from XAS measurements and therefore no metallic Pd is expected to be present allowing for the baseline correction to be achieved through subtraction of the first dataset corresponding to the initial starting material. Fig. S8 (ESI † ) gives the surface colour map of the baseline corrected data which were used for further analysis. Examples of the quality of the peak fitting are given in Fig. S9 (ESI † ). Attempts to also perform the same analysis on 1 wt% Pd/CeO 2 were made; however, no peak related to the Pd(111) reflection could be clearly detected as a consequence of the lower Pd content and expected lower crystallinity.
Acknowledgements This research used resources of the  Advanced Photon Source , a  U. S. Department of Energy (DOE) Office of Science User Facility , operated for the  DOE Office of Science by Argonne National Laboratory  under Contract No. DE-AC02-06CH11357. We thank  EPSRC  and  Johnson Matthey Plc for Case Awards (HRM and AHC). We acknowledge the  European Synchrotron Radiation Facility  for provision of synchrotron radiation facilities.
Without the ability to be certified, energy auditors -especially those not affiliated with a professional entity -face difficulty in convincing enterprises to take certain energy-saving measures as their creditability is often questioned. (p. 356) According to  Moya et al. (2016) , energy auditors require intensive training to provide support in the decisionmaking process about economic investment on energy savings opportunities. Kubule et al. (2016)  have collected the data from the mandatory energy audit program initiated in Latvia in 2016. Figure  6  from this paper shows that: In only 18 audits, or 16% of the analysed companies, energy auditors have suggested energy efficiency measures that exceed 10% of the company's total energy consumption. The logical analysis after manual reviewing of the audits indicates that the actual energy efficiency potential in Latvian companies could be much higher.
Tools According to  Kluczek & Plazswkoi (2017) : Audits vary widely in their level of detail. Depending on comprehensive audits and the plant's energy profile, various energy audit tools are used. By using a checklist that includes the major areas for energy consumption, an individual can take a baseline measurement, take notes and identify the areas where obvious improvements can be made. Table  4  categorizes auditing tools. Basic energy audits do not require advanced measurements. In most cases information provided on nameplates supplemented with basic measurements are sufficient to perform analysis. Abdelaziz et al. (2011)  indicate some tools to use to get relevant data for motor (as motor is one of the most consuming elements in industry): 1. Fuel efficiency monitor: This measures oxygen and temperature of the flue gas.
Acknowledgements This work has been supported by  ECOS Sud-Chile CONICYT program  (Project  C17E10/170037 ),  FONDECYT  Project  1161117 ,  FONDECYT  Project  11221073 ,  FONDECYT   Postdoctoral  Project  3170330 , and  ACT192175. S. Juillard  thanks  Sorbonne Université  for the PhD contract. L. Zhang thanks the PhD contract obtained in the frame of the  joint program  ( Sorbonne Université -China Scholarship Council ). The authors acknowledge the support of the  Centre National de la Recherche Scientifique (CNRS). Karina Muñoz-Becerra : Conceptualization, Methodology, Formal Analysis, Writing Original Draft, Writing Review and  Editing. Sophie Juillard  and Lin Zhang: Formal Analysis. Rubén Oñate: Validation, Formal Analysis. Ingrid Ponce: Supervision, Project administration, Funding acquisition.
Based on the close agreement between the EMFP from our present ab initio ADCS and the EMFP from our previous tting of experimental PAD and EAL (diamonds and stars in Fig. 3a , respectively), we use the analytical extrapolation shown as a thick blue line for the EMFP in this work. This EMFP also captures the general trend of the reported datasets. Inelastic scattering. Inelastic scattering denotes collisions in which the free electron loses energy by exciting or ionizing a water molecule. Following customary notations in the dielectric modelling of electron scattering in water, the energy transferred in such a collision will be denoted by u. These events can be divided in two main categories. The rst contains electronically inelastic events, in which energy is transferred to a bound electron that either gets promoted to a bound excited state (excitation) or gets emitted (ionization), becoming a secondary electron.
The origin of this discrepancy remains unclear, but it should be noted that energy losses above z1 eV were not directly detected in the ice measurements  20  and hence cumulatively described by a single cross section. The ice dataset features electronic channels even below the excitation threshold: they are negligible with respect to vibrational ones, and likely correspond to dissociative electron attachment (DEA) based on the comparison to gas-phase data. In our simulations, this channel is not included, as we derived differential cross sections from a dielectric model based on optical data: this channel is not optically observable, as it inherently requires an electron impact to occur, and therefore our cross sections do not include it. However, given the low amplitude of the associated cross section, it can be neglected in good approximation.
From 1. 2 Â 10 À7 eV to 7 eV, the OELF was built from complex refractive index n(u) + ik(u) measurements,  46  using the relations  15  3 1 (u,k ¼ 0) ¼ n 2 (u) À k 2 (u) (15) 3 2 (u,k ¼ 0) ¼ 2n(u)k(u) (16) J À1 3ðu; kÞ ¼ 3 2 ðu; kÞ 3 1 2 ðu; kÞ þ 3 2 2 ðu; kÞ : From 7 eV to 100 eV, we used recent inelastic X-ray scattering measurements of the OELF. 35 From 100 eV to 405 keV, we used the mass photoabsorption coefficient m(u) from the FFAST database,  47  and the formula J À1 3ðu; k ¼ 0Þ ¼ ħcrmðuÞ u ( 18 ) where c is the speed of light and r ¼ 998 kg m À3 is the mass density of liquid water. 13 The full OELF is shown in the top panel of Fig. 11 , with each source shown in a different color. The green line marks the 7 eV gap in the excitation spectrum, above which the OELF is attributed to electronic excitations and ionizations.
Acknowledgements The  École Normale Supérieure , Paris (http://www. chimie. ens. fr/?q¼en) is gratefully acknowledged for T. Gadeyne's fellowship. We thank  ETH Zürich  for nancial support. AS also thanks the  Swiss National Science Foundation  for supporting this research with an  Ambizione grant  (Grant No. 174 212 ). The calculations were run on the Euler cluster of  ETH Zürich.
X-ray diffraction data collection and analysis Both powder samples and single polycrystalline grain samples from the pisolitic cortex and core were analyzed for their mineralogical composition using XRD. Samples were loaded in borosilicate capillaries either as powder fillings or as stuckup grains. Laboratory X-ray experiments were performed at the X-ray diffraction platform of the Institut de Minéralogie, de Physique des Matériaux et Cosmochimie (IMPMC), Sorbonne Université (Paris, France). The single polycrystalline grain samples from the pisolitic core were first analyzed using a Panalytical XpertPro MPD 2-circle diffractometer (Co sealed tube in Debye-Scherrer transmission geometry -λKα1= 1,789010 Å, λKα2= 1,792900 Å -at 45 KeV and 40 mA, with a range scan of 3-90 º2 and a step size of 0.
03 º2 s -1 ). The signal collected from the grain was too low, even after several hours of exposure (Fig. S2 ). Therefore, more intense and collimated sources were required to analyze small grains in transmission geometry. A Rigaku MM007HF diffractometer equipped with Varimax focusing optics, a RAXIS4++ image plate detector placed at a distance of 200 mm from the sample and a Mo rotating anode (λKα1= 0. 709319 Å and λKα2= 0. 713609 Å) at 50 KeV and 24 mA was used, with a range scan of 3 to 45 º2 and the acquisition time of 60 minutes. The Fit2D program  (Hammersley, 2016)  was used for the integration of 2D images into 1D patterns after a calibration with LaB6 standard. Rotating anode generators are one of the most powerful X-ray sources available in the laboratory. In these experimental setting operating under a high vacuum, a cooled rotating surface is continuously irradiated by an intense electron beam (Fig.
1 ), resulting in an improved signal-to-noise ratio and in the possibility to analyze small samples. Higher X-ray fluxes require the use of a synchrotron facility. Therefore, XRD_synch patterns were collected on the same grains at the CRISTAL beamline of the SOLEIL Synchrotron facility (Saint Aubin, France). We used the 2-circle diffractometer of the CRISTAL beamline equipped with a MYTHEN2 X 9K detector (DECTRIS). The measurements were performed in continuous mode at 17 keV (0. 72896 or 0. 727913 Å) using 5 min exposure time, with a scan range of 3-65 º2 and a scan speed of 0. 04 º2 s -1. Rietveld refinements of the multiphase XRD patterns were performed with the FullProf software  (Rodriguez-Carvajal, 1993)  and included hematite, goethite, kaolinite and anatase as mineral phases. Starting crystal structure data were taken from respectively  Finger and Hazen (1980) ,  Gualtieri and Venturelli (1999) ,  Bish and Von Dreele (1989)  and  Howard et al.
Acknowledgments This research was funded by the  São Paulo Research Foundation -FAPESP  (grants No. 2017/20788-2; 2017/22292-4 ;  2019/10708-7 ). This study was also financed in part by the  Coordination for the Improvement of Higher Education Personnel (CAPES) -Finance Code 001. Thanks to the  National Council for Scientific and Technological Development (CNPq)  for the research grant to the last author ( 301818/2017-7 ). We thank  Jessica Brest  for help with sample preparation and  Thierry Roisnel  for fruitful discussions about Rietveld refinements. In the framework of the  CNRS ЯECIPROCS network , this work has been accepted for synchrotron beamtime by the SOLEIL scientific proposal committee (BAG proposal 20191509). The authors would like to thank  Erik Elkaïm  for his help on CRISTAL beamline.
We also thank to  Imène Esteve ,  Stéphanie Delbrel  and  Béatrice Doisneau  who is working at the  Scanning Electron Microscope (SEM)  facility of the  Institut de Minéralogie, de Physique des Matériaux et Cosmochimie (IMPMC -Sorbonne Université). It is supported by  Région Ile de France  grant  SESAME 2006 N°I-07-593/R ,  INSU-CNRS, INP-CNRS, University Pierre et Marie Curie -Paris 6 , and by the  French National Research Agency (ANR)  grant no. ANR-07-BLAN-0124-01. ( U-Th )/He analysis were funding by the  RECA   ANR-17-CE01-0012-01  project. https://doi. org/10. 5194/gchron-2022-9Preprint. Discussion started: 5 April 2022 c Author(s)  2022. CC BY 4. 0 License.
While such issues are generally mitigated with low radiation dose methods and comparative control experiments, one cannot keep reducing the dose without running into signal-to-noise ratio issues or sacrificing temporal resolution with longer acquisitions. As operando methods become more common and the instrumentation improves, data management becomes a looming problem. In this aspect, there are already emerging challenges in both the processing and interpretation of large operando datasets, and in the compilation of data streams from different instruments. In this respect, machine learning approaches would become increasingly important in the analysis and interpretation of operando microscopy and spectroscopy data  [78].
Acknowledgments Twelve would like to thank the  United States Small Business and Innovation Research Program  within the  National Science Foundation ,  NASA , the  US Air Force , and the  Department of Energy  for their financial support of our work. We also acknowledge support from the  following Department of Energy  programs and offices: the  Advanced Manufacturing Office, Chemical Catalysis for Bioenergy , the  Office of Fossil Energy, Small Business Technology Transfer, High Performance Computing for Energy Innovation, SCALEUP , the  Bioenergy Technologies Office , and the  Technology Commercialization Fund. We further acknowledge the  California Energy Commission's RAMP and CalSeed funding  and private financial support through the  Roddenberry ,  Keeling Curve ,  Ocean Exchange ,  TomKat Center , and  Echoing Green  awards.
Acknowledgments This work was performed under the auspices of the  US Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344  and IM release number LLNL-JRNL-825464 and was supported by  Laboratory Directed Research and Development  funding under Project  19-SI-005. Acknowledgments A B and J R acknowledge the  Danish National Research Foundation Centers of Excellence, The Center for High Entropy Alloys Catalysis  (Project  DNRF149 ).
Acknowledgment B X and X C acknowledge the support of  Beijing National Laboratory for Molecular Sciences. Acknowledgments The authors acknowledge the support by the  Institute for Basic Science  ( IBS-R006-D1 ) and the  National Research Foundation of Korea (NRF)  grant funded by the  Ministry of Science and ICT  (No. 2020M3H4A1A02084590 ). This work was supported by  Creative-Pioneering Researchers Program  and the  New Faculty Startup Fund from Seoul National University. Acknowledgments The research leading to the present results has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant No. 101006701  (ECOFUEL) and Grant No. 851441  ( SELECTCO2 ). Acknowledgments M R acknowledges the  French National Agency for Research  ( ANR-16-CE05-0010-01 ) and the  Institut Universitaire de France (IUF)  for partial financial support.
Acknowledgments We gratefully acknowledge the  Villum Foundation  for financial support through a  Villum Young Investigator Grant  (Project Number:  19142 ), and the  Danish foundation  through the  DFF-Research Project 1 (Thematic Research , green transition) Grant with Number:  0217-00213A. We also acknowledge support from the  Danish National Research Foundation Center of Excellence 'Center for High Entropy Alloy Catalysis'  ( DNRF-149 ).