dataesr/scientific-paragraphs-categorization huggingface dataset:
A 0. 5 mL aliquot was collected from the reactor at different time points. In case of P25, after degradation reaction, the solution was centrifuged to separate the catalyst and obtain a transparent solution. High-performance liquid chromatography (HPLC) was used to determine the concentration of phenol and to study its degradation. A Varian Prostar 230 ternary gradient pump was combined with a Prostar 330 photodiode array detector (D2 lamp). For elution, an isocratic mobile phase consisting of 75% H 2 O and 25% acetonitrile (ACN), at a 1 mL min -1 flow rate, was used, with detection at 270 nm. The column was an Adsorbosphere C18 reverse phase (5 μ m, l: 150 mm, ID: 4. 6 mm, Alltech) combined with an All-Guard cartridge system TM (7. 5× 4. 6 mm, Alltech). For data acquisition, Star software was used.
154056 nm) and an image plate detector. The X-ray beam passes through Kirkpatrick-Baez mirrors, ensuring low divergence and spot-like shaping of the X-ray beam. Beam-size is adjusted using three consecutive cross-slits systems. The sample to detector distance was calibrated using a silver behenate reference sample. The collected intensity was corrected from capillary contribution and radial averaging was performed using the Fit2D software  [19]. Wide angle X-ray scattering data were recorded in Debye-Scherrer geometry using a sealed source at Mo Ka wavelength (k = 0. 071069 nm) and a solid-state detector. This combination makes instrument background and absorption corrections close to negligible in our case. Data were thus only corrected for polarisation and capillary contribution.
Acknowledgments L. T. was supported by a PhD grant from  Delegation Generale de l'Armement (DGA). P. S. would like to thank the  European Research Council  for supporting the research (Advanced Grant No. ERC-2011AdG , Project No. 291543-IONACES ). Pr Y. Gogotsi  is thanked for the supervision of the preparation of selected microporous carbon. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
5, corresponding to an activation energy below 10 kJ mol À1 and 20 kJ mol À1 , respectively. These low activation energy values show that the current is likely limited by the gas transport processes in the bulk of the electrode materials. We then performed X-ray absorption spectroscopy (XAS) measurements to identify the active species at work in the device. X-ray absorption spectra were measured directly on the MEA, before and aer 1 hour of continuous operation. X-ray absorption near edge structure (XANES) data recorded at the Co K edge on Co-N-C cathodes before and aer O 2reduction catalysis are presented in Fig. 3. The spectra exhibit two distinct contributions from metallic Co and a non-metallic ionic cobalt compound. Metallic cobalt is the major species in the as-prepared material but this contribution is lowered to 28 AE 15% of the total Co concentration aer fuel cell operation.
This is followed by an B2 ps electron localization, resulting in the formation of the [ 2 Ru III ¼ 4 Co II (HS)] chargeseparated species. Since these timescales are typical of intramolecular vibration, energy redistribution and heat dissipation from vibrationally hot states to the environment, the complete characterization of the charge localization requires obtaining further information about the global structural changes and the interaction of the complex with its surroundings. Whereas this usually challenges optical spectroscopies, both aspects are readily amenable to investigation by the analysis of the XDS difference signal DS XDS (Q,t) acquired simultaneously with DS XES (t) in the present experiments. Figure  5a  shows the measured DS XDS (Q,t) after data reduction and background subtraction  51  (Supplementary Notes 5 and 6; Supplementary Fig.
For the energy scans of the XES intensity at constant t, the signal was integrated for 4 s (corresponding to 40 X-ray pulses) at each energy point. XES data analysis. The fine structure of the K a spectra originates from multiplet and spin orbit interactions. In transition metal systems, they are highly sensitive to the oxidation state and to the number of unpaired electrons  24, 26, 66. The twodimensional images containing the XES signal were recorded by the MPCCD detector at the focus of the analyser crystal. The images were corrected, and the photons of each exposure were explicitly counted for every X-ray pulse as described in Supplementary Note 2. The Ka 1 spectra were constructed from the energy scans, resulting in the transient XES signal DS XES (t) curves shown in Fig. 2a  (main text).
The time evolution of the XES signals was then modelled by convoluting the transient concentrations introduced above with a Gaussian IRF. Taking model A as an example, the resulting kinetics were given by: I Dt ð Þ ¼ I t À t 0 ð Þ ¼ g 0 Z 1 À 1 1 s ffiffiffiffiffi 2p p e À y 2 = 2s 2 H t À t 0 À y ð Þ 1 À e À t À t0 À y ð Þ =t0 dy ð4Þ where s is the width of the Gaussian broadening, t 0 is time 0, g 0 is the amplitude of the difference signal at t 0 and H is the Heaviside step function. The corrected Akaike information criterion (AICc) is then used to compare the relative goodness of the fit weighted by the model complexity  70. Briefly, the AIC is introduced as: AIC ¼ 2k À 2 lnðLÞ; ð5Þ where k is the number of fit parameters and L is the maximized likelihood value of the fit defined as: lnðLÞ ¼ c À 1 2 X N i¼1 y i À f ðx i Þ ð Þ 2 s 2 i ¼ c À w 2 2 ; ð6Þ where N is the number of data points, y i is the measured value, f(x i ) is the fit value, s i is the standard deviation of the measured values and c is a constant.
5  shows the fits resulting from models A (cyan) and B (blue) against the XES data. Model A systematically underestimates the data in both the region around t 0 , as well as the region between 2 and 6 ps. XDS data analysis. Two-dimensional X-ray scattering images were recorded by the MPCCD detector behind the sample. These images were corrected (for sample absorption, solid angle coverage and detector efficiency as a function of angle and polarization), azimuthally integrated and individually scaled to yield onedimensional radial curves S(Q,t), which were used to construct the difference X-ray scattering signals DS XDS (Q,t) ¼ [S ON (Q,t)-S OFF (Q)] for each time delay t, as described in detail in Supplementary Note 5. The set of DS XDS (Q,t) was subsequently analysed in a framework developed for treating such data containing a significant contribution from a fluctuating background.
Acknowledgements This project was supported by the  X-ray Free-Electron Laser Priority Strategy Program of MEXT, Japan  (to SA), the  Swedish Research Council , the  Knut and Alice Wallenberg Foundation , the  Crafoord Foundation , the  Swedish Energy Administration , the  Science Faculty at Lund University  (MAXIV and  ESS  initiative grant), the  Danish National Research Foundation Center for Molecular Movies, DANSCATT ,  The Carlsberg Foundation , the  Lundbeck Foundation , the  European Research Council  ( ERC-AdvG-VISCHEM-226136  to V. S. and  ERC-StG -259709  to G. V. ), by the European XFEL (CB, WG, AG), by the  German Research Foundation  (DFG,  SFB925 ,  TP A4 ), by the  Centre of Ultrafast Imaging (CUI) , the  Hungarian Academy of Sciences  via the Lendu ¨let ( Momentum) Program  (G.
Regarding the specific case of Egypt for monthly means of daily solar irradiation, it was demonstrated that performances of the Meteosat-derived HelioClim-3 (HC3) database are currently similar to or better than those obtained by empirical models  [11]. Another approach is the use of meteorological reanalyses such as the MERRA (Modern-Era Retrospective Analysis for Research and Applications) reanalysis proposed by the NASA Global Modeling and Assimilation Office or the ERA-Interim reanalysis of the ECMWF (European Center for Medium-range Weather Forecasts) which offer estimates of the SSI. The overestimation of the SSI by reanalyses has already been documented and several publications have reported on a tendency of a majority of reanalyses to overestimate the SSI and noted that deficiencies in clear-sky radiative transfer calculations were major contributors to these excessive SSI.
In a very detailed study using SSI measured at Egyptian stations among many others, it was found that the MERRA and ERA-Interim reanalyses often predict clear-sky conditions while actual conditions are cloudy and that the opposite is also true though less pronounced: actual clear-sky conditions are predicted as cloudy  [12]. This overestimation of occurrence of clear-sky conditions leads to an overestimation of the SSI by the reanalyses. It was also found that the uncertainty in SSI by reanalyses varies strongly within the same area exhibiting fairly homogeneous SSI, making accuracy of a mapping of the SSI difficult to predict. Furthermore, it was demonstrated that the HelioClim-1 database should be preferred to these reanalyses as it offers better performances. HelioClim-1 is a database derived from images of the first generation series of Meteosat satellites  [13].
It was created within the HelioClim project of the MINES ParisTech  [14]  after preliminary works in 1980's  [15]  and is widely used in many domains of application  [16]. In particular, the SSI in the northern African desert climate and its long-term variations were studied by the means of HelioClim-1  [17]. The HC3 database has been created from images of the second generation series of Meteosat satellites and offers better performances than HelioClim-1  [14]. The present article deals with HC3. HC3 is widely used by professionals in solar energy and is regularly improved  [14]. Versions HC3v4 and HC3v5 are currently available. The objective of this article is to present and discuss the results of a validation of these two versions with high quality measurements performed at seven sites located in Egypt.
The results will support the selection of the satellite-derived database for mapping the solar resource over Egypt. The validation is performed for all-sky conditions as well as in cloud-free conditions. In the latter case, another comparison is made with the estimates of the McClear database, based on the McClear model which predicts the SSI that should be observed if the sky were cloud-free  [18]. Such a model predicting cloud-free SSI is called clear-sky model in the following. The article is organized as follows: the study area and available data sets (Section 2), the presentation of the HC3 database and its versions HC3v4 and HC3v5, and the McClear model (Section 3), the results (Section 4), the discussion on the results (Section 5) and finally the conclusions (Section 6).
At each station, the data loggers receive the mean, standard deviation, maximum and minimum values of the measurements every minute. However, in order to increase the storage capacity of the data loggers, only hourly averages were stored. The measurements are actually hourly irradiation and were converted in this study into hourly mean of irradiance, and expressed in W• m -2. The hourly mean of global irradiance received on a horizontal plane, abbreviated in GHI, and noted G hereafter, was measured at all stations by the Eppley Laboratory Precision Spectral Pyranometer (PSP), in Cairo the measurements were sometimes collected by the Kipp and Zonen CM-11 pyranometer (CM11). The hourly mean of direct irradiance received on a plane normal to the sun rays, abbreviated in DNI, and noted Bn hereafter, was measured by the Eppley Laboratory Normal Incidence Pyrheliometer (NIP) at only Aswan and Cairo, and some DNI measurements in Cairo were collected by the Kipp and Zonen CH1.
For all stations, the hourly mean of diffuse irradiance received on a horizontal plane, abbreviated in DHI, and noted D hereafter, was measured by the Eppley Laboratory Model 8-48 pyranometers (8/48) with a shading ball. Table  1  lists the station names, their coordinates, their altitude above mean sea level (a. m. s. l), the number of hours with available data from HC3v4, HC3v5, McClear, and the ground measurements for solar zenith angles less than 75°, the available hours with all the data which have passed the quality check procedures, and the number of hours which are classified as cloud-free and have passed the quality checks. The pyrheliometer and pyranometer measurements of the EMA network are traceable to the World Radiometric Reference standards for solar data, where spare instruments are calibrated every five years in Davos, Switzerland.
The HelioClim-3 Database and the McClear Model At MINES ParisTech, Meteosat satellite images are routinely processed by means of the Heliosat-2 method  [28]  and the estimated irradiation values are stored in the HelioClim databases covering Europe, Africa, the Atlantic Ocean and the Middle East  [14]. The Heliosat-2 method is based on the general fact that the appearance of a cloud over a pixel results in an increase of reflectance in visible imagery, and further on the principle that the attenuation of the downwelling shortwave irradiance by the atmosphere over a pixel is related to the magnitude of change between the reflectance that should be observed under a cloud-free sky and that currently observed  [6, 7]. This magnitude of change is quantified by a cloud index. The Heliosat-2 method and its application to the Meteosat images are well presented in  [13, 28]  and the method is not detailed any further.
Implementations of the Heliosat-2 method have been used in several places with adaptations  [29] [30] [31]. The influence of uncertainties in input variables on the outcomes of the Heliosat-2 method was discussed by  [13, 32]. The HC3 database has been created and is being updated from images of the second generation series of Meteosat satellites  [14]. HC3 covers the period from February 2004 to the day before the current day. It is available for any location within the field of view of the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) instrument on board the Meteosat Second Generation satellite. The field of view and spatial resolution of the SEVIRI instrument are shown in Figure  2. It is evident from Figure  2  that the spatial resolution of SEVIRI over Egypt is between 3. 5 km and 4 km.
The shortest temporal step is that of SEVIRI, i. e. , 15 min. The HC3 estimates are also provided at integration periods of 1 h, 1 day and 1 month. The HC3 database can be accessed by the means of a Web service, i. e. an application that can be invoked via the Web  [33, 34]. The Heliosat-2 method provides estimates of the GHI only. As a consequence, a post-processing step is applied to estimate the other components: DHI and DNI. The DHI is computed using the decomposition model of Ruiz-Arias  [35]. Knowing the solar zenith angle, the DNI is computed from the GHI and DHI. The post-processing also includes improvements and corrections to the original HC3 database. The strategy to account for improvements in HC3 was to leave the original database unchanged because it would have required several re-processing of the whole set of images dating back to 2004 and to include changes in the post-processing.
Results The validation of the estimates of GHI and DNI made by respectively HC3v4, HC3v5 and McClear was made by comparing them to the aforementioned 1 h measurements. Following the ISO (International Organization for Standardization) standard  [42] , the deviations were computed by subtracting measurements for each instant from the estimates for each database and they were summarized by the bias, the root mean square error (RMSE), and the squared correlation coefficient, also known as the coefficient of determination (R 2 ). Relative values are expressed with respect to the mean observed value. The validations of KT and KTB n are also included, as they are stricter measures of the performance of a model with respect to the optical state of the atmosphere. The 2-D histograms between measured and estimated values, also called scatter density plots  [43] , are presented for selected cases.
Cloud-Free Conditions The results of the comparisons for cloud-free cases are presented in The bias differs from one database to the other. It is most often negative (underestimation) for HC3v4 and ranges between -59. 1 W• m -2 (-9%) and +19. 4 W• m -2 (+2%). It is always positive for McClear that overestimates the irradiance: it ranges from +33. 0 W• m -2 (+4%) to + 91. 2 W• m -2 (+12%). The case of HC3v5 is in-between, with a bias ranging from -21. 8 W• m -2 (-3%) to +34. 4 W• m -2 (+4%). The RMSE depends on the bias as it is the square root of the sum of the bias and the standard deviation. It exhibits fairly constant relative values, ranging between 6% and 15%. HC3v5 exhibits the smallest range, from 59. 3 W• m -2 (7%) to 79. 6 W• m -2 (12%). The standard deviation, not reported, is always the lowest for McClear.
2 W• m -2 (-1%) to +29. 4 W• m -2 (+5%). The RMSE is very similar for both versions and all stations. Since the biases are different, it means that there are compensations by the standard deviation which is low when the bias is large. Figure  7  exhibits a time series of the ground measured, HC3v4 and HC3v5 GHI for the month of June 2004 for the Arish station. This specific month and station were selected based on the temporally continuous ground data which have passed the quality control procedures. It is evident from Figure  7  that both databases capture the temporal variability and the absolute values of the GHI very well. The results of the comparison for the all-sky KT are presented in Table  7. Like for GHI, it is observed that as a whole, both versions exhibit satisfactory results, except for Barrani.
This shows that as for the DNI, McClear is better able than HC3v4 and HC3v5 to predict the temporal variability of the GHI. However, the bias of the McClear GHI estimates is always worse than those of HC3v4 or HC3v5. This bias is also positive, which indicates that in spite of the correct estimation of the DNI, the GHI is overestimated by McClear. In turn, this means that the diffuse component is overestimated. This could be explained by errors made when estimating the types of aerosols in McClear  [18]. For instance an overestimation of the fine, strongly diffusing pollution particles associated with an underestimation of the coarse, not so diffusing, mineral dust particles would not affect the DNI but certainly the DHI and consequently the GHI. It should be added that the MACC data on aerosols have a temporal resolution of 3 h and a spatial resolution of 1.
Interestingly, the bias of +31 W• m -2 (+4%) in the DNI in Cairo is similar to the one of the GHI: +37 W• m -2 (+4%), which suggests that the bias in the GHI is induced by that in DNI and this may be explained by an underestimation of the AOD in the MACC database. On the contrary, the bias of -21 W• m -2 (-2%) in the DNI in Aswan is opposed to that in the GHI: +33 W• m -2 (+4%), which, as discussed earlier, is due to an overestimation in the DHI. One conclusion to be drawn for this variety of figures and situations is that errors in the DNI and GHI estimates by McClear depend on the location of the station and may vary with the season of the year. Another observation is that the bias in the McClear GHI is largest for the Arish (+12%), Matruh (+9%) and Barrani (+13%) stations, which are all coastal stations.
Acknowledgments This work took place within the frameworks of the  SUSIE (SUrface Solar Irradiance in Egypt for energy production)  project (# 5404 ) co-funded by the  Science and Technology Development Fund (STDF) of Egypt  and the  French AIRD , and of the  MISRA (MIsr Solar Radiation Atlas ) project funded by the  French Ministry of Foreign Affairs  and the  UAE government. The research leading to these results has partly received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 218793  ( MACC project ) and No. 283576  ( MACC-II  project). The authors thank the  Egyptian Meteorological Authority  for generously providing the solar radiation measurements, and all personnel who installed and maintained the stations in Egypt. The authors thank the company Transvalor which is taking care of the  SoDa Service  for the common good, therefore permitting an efficient access to HelioClim databases.
Database # of Samples Mean W• m -2 Bias RMSE R 2 W• m -2 % W• m -2 % Arish HC3v4 HC3v5 12,892 12,892 504. 6 504. 6 +2. 8 +20. 9 +1 +4 87. 9 90. 1 17 0. 878 18 0. 883 Aswan HC3v4 HC3v5 4550 4550 692. 4 692. 4 -23. 3 -9. 2 -3 -1 66. 4 68. 4 10 0. 923 10 0. 909 Asyut HC3v4 HC3v5 19,778 19,778 572. 3 572. 3 +24. 0 +29. 4 +4 +5 72. 6 76. 6 13 0. 925 13 0. 922 Barrani HC3v4 HC3v5 8207 8207 512. 3 512. 3 -16. 4 +1. 3 -3 0 153. 6 151. 7 30 0. 673 30 0. 680 Cairo HC3v4 HC3v5 3529 3529 590. 4 590. 4 -2. 1 +27. 9 0 +5 77. 7 89. 8 13 0. 913 15 0. 906 Kharga HC3v4 HC3v5 16,732 16,732 586. 0 586. 0 -15. 9 +23. 6 -3 +4 100. 9 78. 0 17 0. 827 13 0. 914 Matruh HC3v4 HC3v5 14,859 14,859 521. 9 521. 9 -10. 3 +16. 6 -2 +3 117. 0 116. 9 22 0. 798 22 0. 809.
The physical models describing the functioning of PV modules, such as the single diode model need parameters that are not given in the manufacturer's data sheets. Therefore their determination is required. Some authors have presented an implementation of the single diode model in LabVIEW  [2] ,  [3]  for simulation needs. These techniques require the introduction of model parameter values. These values vary from PV module to another and are also affected by atmospheric conditions such as temperature. Several techniques for calculating the single diode model parameters from experimental measurements of the IV characteristic are found in the literature  [4]. LabVIEW software is a powerful tool for data acquisition using specially designed cards or by controlling measuring instruments  [5] ,  [6].
It is widely used in photovoltaic for the monitoring of installations for example  [7]. It is ideally more convenient to have a single LabVIEW Graphical User Interface (GUI) that controls data acquisition and also performs all other operations such as parameter extraction and simulation. Taking into account all previous considerations, we present and validate in this contribution an original and complete outdoor and computer controlled test bench via a LabVIEW GUI for researchers and professionals working in the application and development fields related to renewable energies, mainly photovoltaic. This tool allows the measurement of the I-V characteristics of PV modules and the extraction of all the working parameters and all the parameters of the physical model and the simulation of the I-V and P-V characteristics under measurement conditions and under STC.
Presentation The principle of the test bench developed in the context of this contribution is based on the block diagram of Figure  3  and the experimental realization is shown in the photo of Figure  4. It consists of a structure on which are installed the PV modules to be tested, an electronic load built with MOSFETs, a Data Acquisition and Switch Unit (Agilent 34972A). We have developed under LabVIEW environment an interface to drive and control this test bench. The solar irradiance is measured with a pyranometer fixed on the same plane as the PV module to be tested, the temperature of the PV module is measured with a PT100 sensor bonded to its back and the current is measured with a Hall Effect sensor.