dataesr/scientific-paragraphs-categorization huggingface dataset:
Four temperature sensors were placed inside one serial (no. 2) and one parallel collector (no. 9) to measure temperatures at different levels of the storage tank. Measurements from those sensors allowed calculating the average temperatures T ave,2 and T ave,9. Fig. 3  shows a diagram of the experimental setup and the sensor locations. Data were collected online with a one-minute time step acquisition, then averaged over 5 min. The accuracy of the sensors is given in Table  2. -V-1. 5TC-batt Accuracy ± 5% ± 0. 2 • C ± 5% ± 5% ± 5%.
Conclusion In this study, an in-situ thermal performance investigation was carried out on a solar installation of 24 "Water-in-Glass" evacuated tube collectors intended to provide hot water to a breeding facility. In order to prepare the herd's food, a large amount of water at 80 • C is required. Therefore, the vacuum tube collectors coupled with a storage vessel and their specific arrangement were designed to optimize batch withdrawals twice a day. After having described the solar collector chosen, the actual solar installation located in western France was described. The instrumentation set up in the framework of this study and the method used to acquire the data used to perform energy calculations were presented. Energy calculations were carried out on four experimental sequences representative of the four seasons.
443 GW that same year Enerdata (  2015 ). As we have specified the cost function as C(I) = c 1 I + c 2 2 I 2 , using the 2010 data, this leads to c 1 = 1150 -0. 2215 c 2 expressed in M Є. Second, c 2 is chosen such that our full model reproduces Spanish investment in solar electricity for 2018 (0. 262 GW). This leads to c 1 = 575 M Є/GW, and c 2 = 2 , 596 M Є/GW 2. To calibrate the utility function parameters, we use REE data that provide hourly data during one year (2017) of electricity consumption and compute the average consumption during day (6am-5pm) and night (6pm-5am). The ratio between average consumption at day and total average consumption provides α = 0. 5232. The intertemporal elasticity of substitution is set to σ = 1 (log utility function), absent any relevant empirical information on this parameter.
Data Environmental Debt. We rely on cumulative CO2 emissions to measure environmental debt. To compute cumulative historical CO2 emissions, we use annual data from the Global Carbon Project, and particularly consumption-based emissions that have the advantage of incorporating emissions from international transportation as well as carbon leakages. The data are available from 1990 and are measured in million tons of carbon (MtC); we convert annual carbon emissions in tonnes of CO 2 , using 1 MtC = 3. 664 million tonnes of CO 2 , before dividing them by the population to obtain per capita CO 2 emissions (CO 2 ). Thus, the environmental debt for country  at year  is   = ∑  =1990 (CO 2 ). Public Debt. Following  Mauro et al. (2015) , we draw upon the IMF Historical Public Finance dataset to obtain cross-country-consistent data on gross public debt (measured in % of GDP).
The Vitrobot was set to operate at 22 • C and 100% humidity (blotting time 2 s; blot force 1). Finally, in order to recover the frozen grids, the Dewar was directly removed from the glovebox through the airlock. 2. 4. Cryo-EM Data Acquisition and Image Processing 2. 4. 1. Apoferritin Apoferritin data were collected on a Glacios cryo electron microscope (Thermo Fisher Scientific, Waltham, MA, USA) operating at 200 KV, at a nominal magnification of 45,000× (corresponding to 0. 899 Å/pixel at the specimen level), with a defocus range of -0. 9 to -2. 55 µm. Nanoprobe mode was used and the data collection was performed using parallel beam illumination. Movies were recorded using a K2 summit (Gatan, Pleasanton, CA, USA) direct electron detector operating in counting mode with the SerialEM software  [31].
PFOR PFOR data were collected on a Glacios cryo electron microscope (Thermo Fisher Scientific) operating at 200 KV, at a nominal magnification of 45,000× (corresponding to 0. 899 Å/pixel at the specimen level), with a defocus range of -0. 5 to -3. 5 µm. The Nanoprobe mode was used and the data collection was performed using parallel beam illumination. Movies were recorded using a K2 summit (Gatan) direct electron detector operating in counting mode with the SerialEM software  [31]. SerialEM was also used to perform objective astigmatism and coma-free corrections. A series of 1,304 60-frame movies, were collected with a total dose of 60 electron•Å -2 (Table  1 ). PFOR reconstruction was performed using RELION 3. 1. 1 [32]. Each movie was first drift-corrected using MotionCor2  [33]  (frame 2 to 60) using RELION and CTF was estimated using CTFFIND-4.
1  [34]. We initially manually selected 1178 particles from 6 randomly selected micrographs to perform a preliminary 2D classification. The most promising 2D classes were used as templates to perform an autopicking on 50 micrographs. Next, 13,938 new particles were extracted and 2D sorted. The best classes were selected and used to calculate an initial 3D model. This model was subsequently used as template to perform an autopicking on the full dataset (913,217 particles). After a step of 3D classification, 180,868 particles corresponding to the best 3D classes were used to calculate a first 3D reconstruction. Post-processing of the corresponding map, using a B-factor value of -178. 9 Å 2 , gave a resolution of 3. 72 Å. Subsequently, refinement of both the asymmetrical and symmetrical aberrations and the per-particle defocus values was performed.
Apoferritin A cryo-EM structure of horse spleen apoferritin (PDB code: 4V1W  [38] ; resolution: 4. 70 Å) was used as a starting model. After initial manual building in COOT  [39] , the model was iteratively improved using PHENIX-real space refinement  [40]  and further manual building. Statistics validation was performed using both PHENIX validation tool and MolProbity until the statistics ceased to improve (Table  2 ). The map-to-model resolution was finally estimated at the 0. 5 Fourier shell correlation (FSC) cutoff in PHENIX. The apoferritin cryo-EM map has been deposited to the EMDB (code: EMD-13487).
PFOR The crystal structure of the PFOR monomer (PDB code: 1B0P-chain A  [26] ; resolution: 2. 31 Å) was initially fitted into the cryo-EM density as a rigid body. The dimer was then generated using the non-crystallographic C2 symmetry. Several iterative cycles of manual modification using COOT  [39]  and refinement with PHENIX  [41]  were necessary to build the complete model into the reconstructed cryo-EM density map (Table  2 ). Residues that did not match this map were removed from the model. Finally, those water molecules that were visible in the cryo-EM map and also present in the reference crystal structure (PDB code: 1B0P) were added to the model. The PFOR atomic coordinates and the corresponding cryo-EM map have been deposited to the PDB and EMDB, respectively (codes: PDB-7PLM and EMD-13493).
Several 2D classes display a clear C2 symmetry axis, as expected from protein dimers. As many as 180,868 particles were used to calculate the final 3D reconstruction at an estimated resolution of 2. 90 Å by applying the gold standard FSC criterion (Figure  4c ). Calculation of the local resolution indicates that the protein core, which represents about 83% of the volume of the reconstruction, is at a resolution between 2. 75 Å and 3. 05 Å, while the most exposed loops at the surface are at lower resolution ranging from 3. 05 to 3. 55 Å (Figure  4d ). An atomic model of the PFOR was subsequently built into the reconstructed 3D density map using the available crystal structure (PDB code: 1B0P  [26] ; 2. 31 Å resolution) as a starting model. All of the PFOR structures published to date are very similar with a rootmean-square deviation (RMSD) of about 0.
21 ± 0. 05 Å. Similarly, the cryo-EM structure monomers display RMSDs of 0. 53 and 0. 54 Å, when compared to the crystal structure (PDB code: 1B0P). The most significant difference corresponds to a rotation of 1. 6 • between monomers when compared to the D. africanus PFOR crystal structures and up to 2. 6 • when compared to the enzyme from Moorella thermoacetica (Figure  S6 )  [46]. The reconstructed cryo-EM density map was of good-enough quality to fit almost all of the protein residues (Figure  5a ). As expected, less well-defined regions are found at protein surface loops. The largest disordered region corresponds to the first 14 residues of domain VII (residues 1170-1183), which consists of a domain-swapping extension (Figure  S7 ). In addition, the Cys1195-Cys1212 disulfide bridge  [47] , the Ca 2+ and Mg 2+ ions and the thiamine pyrophosphate (TPP), essential for the protein activity, are clearly visible in the cryo-EM reconstruction (Figure  S8 ).
Acknowledgments: We thank  C. E. Hatchikian  for the quality of the proteins sample he and his team prepared and we used for all our PFOR studies and  C. Cavazza  for making the protein sample available to us. We also thank  L. F. Estrozi  and  G Effantin  for help with the calculations and  M Soler Lopez ,  E Kandiah ,  M Saïdi  for providing us with the apoferritin sample.
Funding: This work was supported by the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Univ. Grenoble-Alpes  to Y. N and M. V. C. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UMS 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-05-02 ), and GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). The IBS electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fonds FEDER , the  Fondation Recherche Médicale (FRM)  and the  GIS-Infrastructures en Biologie Santé et Agronomie (IBiSA).
On the other hand, producing electricity from a 1 kWp solar panel avoids on average 266 kgCO 2 2 eq per year in Germany and 69 kgCO 2 2 eq per year in France (elec-panels and storage systems is highly dependent on the load profiles that shall be met by both local assets and imports from the main grid. Indeed, some load profiles are naturally more inclined to absorb -i. e. , self-consume -solar production when associated together. For instance, an office building with greater consumption during daytime is naturally better equipped to consume solar generation, than a residential building with an 8 pm peak demand. Unfortunately, real-world consumption profiles are scarce resources, especially when looking for a variety of consumer types. The existence of open-source databases is one solution to studying the effect of various load profiles on the optimal sizing of solar panels and storage capacities  (Quoilin et al.
| Data description and preliminary perusal The data employed in our study covers different streams of crude oil including WTI, Brent, OPEC, Tapis, Dubai, and Heating oil, as well as natural gas. The stock market indices and exchange rates for the E7 + 1 emerging countries were also used, as well as geopolitical risks. The data on crude oil prices were obtained from (www. eia. gov), the data on the stock market indices were downloaded from (www. investing. com), and the data on the exchange rates were sourced from (www. imf. org). The global geopolitical risk index was introduced by Caldara and Iacoviello (2018) in order to measure the geopolitical risk. This index was constructed via the text-search results of 11 major international newspapers covering words related to geopolitical tensions, terrorist attacks, and other forms of geopolitical events.
These included the Financial Times, The Times, The New York Times, Los Angeles Times, The Guardian, The Boston Globe, Chicago Tribune, The Daily Telegraph, The Washington Post, The Wall Street Journal, and The Globe and Mail. The data was collected from the website (https://www. matteoiacoviello. com//gpr. htm). To distinguish between the effect of geopolitical tensions and the effect of pure geopolitical risks,  Caldara and Iacoviello (2018)  also developed two determinants of political risk: The geopolitical threat (GPRD_Threat) index and the geopolitical acts (GPRD_Act) index. The geopolitical threat index includes words related to military force threats and nuclear menaces, while the geopolitical acts index includes words related to terrorism and war, such as the 1990-1991 Gulf war, the November 2015 Paris terror attack, the March 2004 Madrid bombing, the 2003 Iraq invasion, the 2014 Russia-Ukraine tension, the 9/11 terrorist attack, and so on.
, 2004). The deflection became known as the 'Great Acceleration'  (Steffen et al. , 2007)  echoing Karl  Polanyi's 1944  holistic societal analysis The Great Transformation. The Great Acceleration expressed a similarly holistic, comprehensive and interlinked depiction of post-1950 changes covering socio-economic factors and biophysical processes as well as environmental and climatic changes  (Steffen et al. , 2015). It represents both a range of overwhelming human impacts from the mid-20 th century onwards and the Earth System responses to them. The Great Acceleration dataset and its analysis, having been published by  Steffen et al. (2004 Steffen et al. ( , 2007)) , was then extended by  Steffen et al. (2015;  see also  Broadgate et al. , 2014  for the online dataset). Human global energy consumption, economic productivity, and population growth have recently been analysed for the past 12,000 years  (Syvitski et al.
Against this significant body of analysis, Nielsen (2018a, b, and 2021a) mathematically analysed the Great Acceleration dataset of  Steffen et al. (2015; also Broadgate et al. , 2014)  and combined these data with an earlier analysis  (Nielsen, 2017)  of human population and economic growth. Nielsen asserted that the growth rates of most of the datasets fit hyperbolic trajectories up to around 1960 or later, at which point they depart from that historical trend into periods of decreasing growth rates, leading to his introduction of the term 'Great Deceleration'. He concluded that the Great Acceleration data cannot be used to support a beginning for the Anthropocene, and indeed with no sudden intensification of growth there is no evidence to justify a new geological epoch  (Nielsen, 2021a).
Acknowledgements We thank the  Max Planck Institute for Chemistry, Mainz , where the late  Paul Crutzen  worked for many years, for elucidating Nielsen's (2021a) comment that Crutzen had endorsed the results of his work. Prof. Crutzen  clarified that he did notice and acknowledge but did not review or endorse the paper 'The Great Deceleration and proposed alternative interpretation'. MJH acknowledges support from a  Natural Sciences and Engineering Research Council of Canada   Discovery Grant. Simon Turner  kindly supplied Figure 11. This article has benefitted from stimulating ongoing discussions with colleagues in the Anthropocene Working Group. We are most grateful to two anonymous reviewers for their insightful comments and to  Brian Marker  as Associate Editor for helpful guidance.
Acknowledgements. The authors are grateful to  Dr Emmanuel Flahaut  of the  Centre Inter universitaire de Recherche et d'Ingénierie des Matériaux (CIRIMAT) Toulouse, France  for providing technical assistance. The authors are also thankful to the  Higher Education Commission (HEC) of Pakistan  and the  Laboratoire Matériaux et Durabilité des Constructions (LMDC) Toulouse  for finanical support of the thesis that is the source of this work.
X-ray Diffraction Analyses The XRD diffraction profiles are shown in Figure  1. By analyzing the spectra obtained using WinPLOTR software, we can see the presence of several well-defined peaks, which proves that the samples are relatively well-crystallized, corresponding to the orthorhombic BWO phase referenced in the JCPDS files (JCPDS card no. 73-1126). Starting at 600 • C, we managed to synthesize a well-crystallized Bi 2 WO 6. The variation in the half-value width β of the diffraction lines between the three samples can be seen. This variation is generally explained by the effect of crystallite size. The half width β decreases with increasing calcination temperature, and since the half width is inversely proportional to the crystallite size, the latter increases with calcination temperature.
The density of the BWO calculated from Rietveld analyses is 9. 2 g cm -3 , which corresponds to 96. 8 % of the theoretical value. The calculated surface areas of the prepared electrocatalysts BWO-600, BWO-700, BWO-800, and BWO-900 are 38. 13, 16. 63, 13. 44, and 7. 79 m 2 g -1 , respectively. The Rietveld refinement (Figure  2 ) of the well crystallized sample at 900 °C for 8 h was performed by the FullProf PCR software, which allows the refinement of several parameters until the optimal factors are obtained. Table The theoretical surface area S was calculated using the formula as follows: S = 6/(ρD) ( 3 ) where D is crystallite size using Scherrer method determined from XRD data, ρ is the density of the Bi 2 WO 6. The density of the BWO calculated from Rietveld analyses is 9. 2 g cm -3 , which corresponds to 96.
8 % of the theoretical value. The calculated surface areas of the prepared electrocatalysts BWO-600, BWO-700, BWO-800, and BWO-900 are 38. 13, 16. 63, 13. 44, and 7. 79 m 2 g -1 , respectively. The Rietveld refinement (Figure  2 ) of the well crystallized sample at 900 • C for 8 h was performed by the FullProf PCR software, which allows the refinement of several parameters until the optimal factors are obtained. Table  1  summarizes the atomic positions, the obtained cell parameters, and the reliability factors. During refinement, we refined the positions of the heavy atoms Bi and W by fixing the positions of the oxygen atoms. These light atom positions cannot be refined because of the small contributions of oxygen to the structure factors. This is the reason why these coordinates were fixed during the refinements.
Characterization Methods For X-ray diffraction analysis, the apparatus used is a PANALYTICAL EMPYREAN, equipped with a copper anticathode bombarded by accelerated electrons at a voltage of 45 kV, the current being 35 mA. Scanning electron microscopy (SEM) analyses were performed using an Hitachi S-4700 SEM. Energy dispersive spectroscopy and elemental mapping were carried out to compare the experimental composition to nominal synthesis concentrations. Thermogravimetry analysis (TGA) coupled with differential thermal analysis (DTA) was performed on the decomposition of the precursor, which was followed under air between 25 and 1150 • C with a rate of 10 • C/min (using Shimadzu Instruments DTG-60 equipment). Electrochemical analysis was performed using a VoltaLab PGZ 301 equipped with Volta Master 4 software.
B. Characterization The crystal structure was examined by employing Xray powder diffraction (XRD) using an Empyrean PANalytical diffractometer equipped with Cu X-ray source (K α1 of 1. 54056 A ˚) with the Bragg-Brentano geometry θ to 2θ and PIXcel-3D detector. Infrared measurements were performed using an Agilent Cary 640 FTIR spectrometer equipped with a standard source and a DTGS Peltier-cooled detector. The spectra were collected using the GladiATR accessory with Ge plate (Pike Technologies) in the 4000-750 cm -1 range with a spectral resolution of 4 cm -1 and recorded by accumulating 16 scans. The water vapor and carbon dioxide were subtracted from the spectrum, while the baseline correction and peak fitting analysis were performed using the GRAMS 9. 2 software package. The transmission electron microscopy (TEM) studies were performed using Jeol JEM 3010.
In the first, the as-prepared material was studied directly after its storage (1 step). Afterwards, the same specimen was sputtered by Ar + ion beam for 30 min (2 step), and then the sample was measured. All the XPS spectra obtained were calibrated using C1s peak (BE = 284. 8 eV) as carbon peak, which usually originates from the carbon adsorbed at the surface of the sample and is used as a reference for charge correction. All spectra measured were processed with the use of MultiPak 9. 7 software. The magnetic properties of as-received samples were determined based on wide-range Superconducting Quantum Interference Device (SQUID) magnetometer MPMS XL7 Quantum Design, while a commercial Physical Property Measurement System (PPMS, Quantum Design) with VSM and VSM Oven options was used in magnetic behavior studies of annealed specimens.
-The chemical speciation of the nitrogen functionalities introduced by nitrogen plasma treatment was determined by X-ray photoelectron spectroscopy (XPS) using an ESCALAB 250 spectroscope from Thermo Electron with an Al K α ray (1486. 6 eV) as monochromatic excitation source. The analysed surface area was 400 μm 2. The emitted photoelectrons were detected perpendicularly to the sample surface with a constant analyser energy mode (20 eV pass energy). Data analysis was carried out using the Avantage software. Binding energies (BEs) of all core levels are referenced to the C-C bond of C 1 s component at 284. 4 eV. X-ray fluorescence (XRF). -The platinum loading on the Pt/VC samples was determined by XRF. Samples were prepared by grinding 30 mg of Pt/VC with 160 mg of H 3 BO 3. The powder was placed in a cavity and subsequently pressed to obtain a pellet of 13 mm diameter with scanned surface of ca.
Acknowledgement This research was supported by the  European Commission  under the  Horizon 2020 framework  (grant number  665085 , DIACAT). AK thanks the  BMBF  for support under project  CarbonCat  (grant number  033RC009B ). We thank  Mario Borgwardt ,  Iain Wilkinson  and  Igor Kiyan  for access and technical assistance with the ULLAS femtosecond laser facility at  Helmholtz-Zentrum Berlin. The authors also acknowledge the kind support from  Jian Ren  and the staff of the  BESSY II synchrotron facility , especially  Ronny Golnak  and  Jie Xiao. The authors thank the  Helmholtz-Zentrum Berlin  for the allocation of beamtime at the U49/2 PGM-1 synchrotron beamline at BESSY II. AK and AHD thank  Martin Kamp  for support with TEM.
Photocatalytic Hydrogen Production The catalytic activity of Rh-tpy for the light-driven production of H 2 was evaluated in the presence of the Ru photosensitizer and ascorbate as a sacrificial electron donor in deaerated aqueous solutions (5 mL) at 298 K under visible light irradiation (400-700 nm). The light-driven evolution of H 2 was determined by gas chromatography in the course of the photocatalysis, and these data were used to calculate the TON (turnover number) and initial TOF values (turnover frequency) per molecule of catalyst (respectively denoted as TON Cat and TOF Cat ), which characterize the performance of the photocatalytic system (Table  S7 ). The catalytic activity of Rh-tpy was first investigated in a HA -/H 2 A buffer (total concentration 1. 1 M, see Table  S6 ) at pH 4.
Acknowledgments: We are grateful to the  CNRS , the  Université Grenoble Alpes  and the  French National Research Agency  through the project  TATADyes  ( ANR-20-CE05-0041 ),  Labex ARCANE  and  CBH-EUR-GS  ( ANR-17-EURE-0003 ) for their financial supports. This work was also supported by  ICMG Chemistry Nanobio Platform  ( FR2067 ). F. C. and B. D. thank the " Université Grenoble Alpes"  for their PhD grant, and T. G. thanks the  French National Research Agency  ( TATADyes ;  ANR-20-CE05-0041 ) for his Master1 grant.
There is therefore a need for data linking the instant microalgae productivity with its physiological status. This study therefore aimed at studying and quantifying the relationship between lipid accumulation and growth rate for microalgae. A high-lipid-accumulating microalgae Chlorella vulgaris NIES 227 was cultivated at pilot scale in order to characterize the joint evolution of its physiological status and photosynthetic productivity. Aiming at giving an accurate snapshot of the microalgae photosynthetic productivity, these measurements were performed by short assays of oxygen productivity under light-limited conditions.