dataesr/scientific-paragraphs-categorization huggingface dataset:
Physicochemical Characterization Thermogravimetric analysis (TGA) was used to confirm the metalloading in the Pd x Rh y /C catalysts and was conducted on a Q600 TA Instruments SDT2960 under synthetic air atmosphere, at a heating rate of 10 °C min À 1 , from 20 to 900 °C. X-ray diffractograms were obtained on an X-ray diffractometer (Bruker -D2 Phaser) with Cu Kα radiation (λ = 0. 15406 nm) generated at 30 kV and 10 mA. Diffraction patterns were collected for 2θ values ranging from 20 to 90°, at 0. 025°s À 1. Phase composition was determined by fitting the experimental angular range of interest to the pseudo-Voigt function per crystalline peak with the Profile Plus Executable refinement program (Siemens AG). To estimate crystallite size, the Debye-Scherrer equation was employed. EDX spectroscopy was performed on a Leica Zeiss LEO 440 apparatus to check the homogeneity of the local elemental bimetallic compositions of the catalyst materials.
XPS was used to probe and to characterize the surface and oxidation states of the prepared Pd x Rh y /C nanocatalysts and was accomplished on a Kratos Axis Ultra DLD spectrometer equipped with a monochromatic Al Kα X-ray source operating at 15 kV and 10 mA. The base pressure of the instrument was 9 × 10 À 8 Pa. The XPS results are demonstrated by using fitting carried out with the CasaXPS software (version 2. 3. 17). The internal reference for C1s spectra is centered at 284. 6 eV. The Shirley background was chosen, and asymmetric Gaussian-Lorentzian profile functions were used to fit the spectra.
Acknowledgements The authors thank the financial support from  CAPES  and  CNPq. This study was partilly funded by  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001. The collaborative program  CAPES/COFECUB  under grant  n°9   14/18  is gratefully acknowledged. The authors also acknowledge the financial support from the  European Union (ERDF)  and " Région Nouvelle-Aquitaine ".
Material and Method In this study, insulation, electricity production, and the consumption data of the energy plant are collected over years. The production data, meteorological conditions, information from the PV generators (PVG), and the diesel engines of the Thermal Power Station (TPS), were all obtained from the plant metering. The models have been carried out from data analysis conducted with the MATLAB Simulink (Version R2015, MathWorks, Netik, MA, USA) software, when suitable. Operating data for the months of February 2019 to November 2020 have been used. For this, we have extracted moments where the TPS operates with an identical number of engines (five, six, seven, eight, and nine) with the solar PV power production. Then, we established equations linking the hourly consumption flow rate (kg/h) and the power of the thermal groups operating.
Categorization of Seasonality of the Solar Irradiance To make this analysis, the satellite data of the daily horizontal global irradiation in kWh/m 2 /day for the mine site have been retrieved from a NASA database (www. power. larc. nasa. gov) accessed on 1 March 2021, for the years 2019 and 2020. Taking these data as a collection of information, the plot of Figure  8  highlights two typical periods in terms of variability of daily irradiation. (Figure  8 ): A period of frequent huge disturbances: this is the period from May to September, which corresponds roughly to the rainy season of the mine site. A period of less disturbance: this is the period from October to April and corresponds to the dry season of the mine site. It is then necessary to understand and characterize the specific behavior of the power plant undergoing the energy impact of these dynamic fluctuations during the two seasons.
This formula is Homer-pro software user-friendly and easily integrates it's the design process. To vary the power during transients, it is necessary to play on the Mean Effective Pressure (MEP), the number of thermodynamic cycles being given. Increasing the MEP is performed by inflating the real cycle, therefore by allowing in the cylinder a mass M (Kg) of fuel mixture as high as possible. Dynamically, due to the need to enrich the mixture to obtain the maximum torque of the engine, the quantity of unburned matter increases, therefore the combustion efficiency is less good. Thus, as will be established, the average power is delivered with a greater efficiency penalty the greater the transient variations involved. This complex internal combustion phenomenon has led to the development of a behavioral model that globalizes the impacts in terms of energy efficiency.
• To guarantee the stability of the hybrid system by means of a spinning reserve and/or the limitation of the active power of the solar PV plant. The spinning reserve is sized to recover the energy surge during hazards like sudden power decrease/increase by cloud shadowing or load and even technical events. The cumulative flowmeter data collection correspondence to the fuel oil consumption meters was verified to prove reliability and accuracy. In the same way, electric energy delivered was computed. The errors (Table  3 ) found are respectively close to 0. 2% and 0. 04%. Data collection for the study took place from February 2019 to November 2020. The data collected were then structured and classified. Multiple levels of data analysis time horizons were created to reflect and focus the main changing weather phenomena (dry and rainy seasons).
• The behavior gives evidence to the instantaneous complementarity of the solar and thermal powering mine activities; • Daily magnitude variation of solar PV production appears. Some days, although few, show superposed disturbances. The behavioral modelling is carried out on the basis of the operating data for the months from September 2019 to February 2020, mixing dry and rainy seasons. For this purpose, we extracted all the instances where the thermal power plant operates with an identical number of groups (five, six, seven, eight, and nine) in the presence of the solar PV production. Then, we established equations linking the hourly consumption rate (kg/h) and the power of the thermal units in operation. The plant operates with a variable number of active thermo electrical generating units (N varying 1 to 11).
The development of the consumption models of the hybrid power plant showed the impact on fuel consumption, according to the regime of interaction of the sources at the point of coupling. The parametric analysis of these consumption model's Equations (  13 ) and  (14)  shows that the slope coefficients are slightly the same but the original stepup coefficient acts as a penalty and performance degradation factor in the presence of disturbances. It is therefore obvious that this phenomenon could be weighted by the intensity, frequency, and duration of disturbances. At this phase of the work, a global consideration by a simple aggregation of the effects of the disturbed periods observed in the multi-year data bank is assumed sufficient. The main qualitative interpretation is that a kilowatt of electricity produced under QSP conditions requires less fuel than the same kilowatt produced under DSP conditions.
Characterization techniques 2. 4. 1. XRD and SEM/EDS A Bruker D8 Advance diffractometer was used to identify and compare the crystallized compounds of the synthesized powders. Each of them was homogenized, using ten 6-mm glass balls for 3 min, before being ground and sieved with an 80-mm mesh for XRD analyses. This homogenization step allowed segregation of species to be avoided, which was confirmed by the low variability in the diffractograms obtained for three powder samples resulting from the same synthesis. Data collection was carried out at room temperature using Cu K-alpha radiation (k = 1. 54 Å). Qualitative identification was conducted with EVA software to collect data necessary for the experimental design method. Anatase and rutile phases exhibited characteristic diffraction peaks at specific 2h values: 25.
Acknowledgements We acknowledge the  National Key R&D Program of China  ( 2021YFA1501503 ), the  National Natural Science Foundation of China  ( 22121004 ,  22108197 ), the  Haihe Laboratory of Sustainable Chemical Transformations  ( CYZC202107 ), the  Natural Science Foundation of Tianjin City  ( 18JCJQJC47500 ), the  Program of Introducing Talents of Discipline to Universities  (No. BP0618007 ) and the  Xplorer Prize  for nancial support.
In all other samples, no radiation damage was observed. Spectra analysis was performed as follows. First, the spectra recorded on each sample were calibrated by setting the absorption edge at 12,658. 0 and 27,940. 0 eV, thanks to the use of spectra recorded simultaneously on reference Se and In foils. Then, these spectra were normalized using the Demeter software package, version 9. 26  [22]. Finally, shell fitting was used to analyze the spectra from pristine QDs and linear combination fitting (LCF) for aged QDs analysis, using the Demeter/ARTEMIS and ATHENA softwares version 9. 26, respectively  [22]. For LCF, we used the following reference spectra: pristine QDs, In acetate and In phosphate (In K-edge), ZnS (bulk and nano), hopeite (Zn 3 (PO 4 ) 2 , 4H 2 O), Zn phosphate dehydrate (Zn 3 (PO 4 ) 2 , 2H 2 O) and Zn-reacted hydroxylapatite at pH 6 as previously described  [15] , Zn-GSH (1:10 mol:mol, pH 4.
Funding: This work is a contribution to the  Labex Serenade  ( ANR-11-LABX-0064 ) funded by the  French Government 's  "Investissements d'Avenir" ANR program , through the A* MIDEX  project ( ANR-11-IDEX-0001-02 ). It was funded by the  Atomic Energy and Alternative Energies Commission (CEA)  and the  French Environment and Energy Management Agency (ADEME). SyMMES laboratory is part of Labex Arcane  ( ANR-12-LABX-003 ) and  Labex LANEF  ( ANR-10-LABX-51-01 ), both funded by the  French National Research Agency ANR , ISTerre is part of  Labex OSUG  ( ANR10-LABX56 ). P. R. acknowledges financial support from  ANR  (grant  FLUO ,  ANR-18-CE09-0039-01 ).
To record the Raman spectra of the samples, Renishaw inVia Reflex Raman System RL532C, Class 3B (50-3000 cm -1 ), was used. The X-ray photoelectron spectroscopy (XPS) data was collected by employing the Kratos Axis Ultra DLD spectrometer with a monochromated Al Kα X-ray source operating at 168 W (12 mA × 14 kV). The collected XPS data from all of the samples were analyzed using CasaXPS software (v2. 3. 23). XPS depth profiling of NH 2 -MIL-125(Ti/Cu) was carried out to confirm the uniform atomic distribution of each element including loaded Cu. The measurement was performed using a 110 μm-diameter spot after etching over an area of 2 × 2 mm 2 by Ar (1 × 10 -6 Torr) at 4 kV. The UV-vis absorption spectra of the samples were measured using a ThermoScientific Evolution 200 spectrophotometer. The collected XPS spectra were calibrated with respect to adventitious carbon peaks at 284.
The output was a modular prototype named: ThermoGreen®, and a schematic diagram showing the different parts of the assembly is drawn using SolidWorks software as shown in figure Fig. 1. The prototype contains a window and a door that are double glazing. More design details are explained in previous research  [28]. To offer a direct comparison between tested parameters and control, two identical prototypes are implemented following the aforementioned stages (figure Fig. 2-a ), and both are equipped with a hydronic radiant floor heating system (HRFHS) as shown in figure Fig. 2-c. The underfloor heating system comprises a circulating pump, with 100 W rated power, that is utilized to allow circulation of hot water within the spiral PEX tubes installed in the floor. The system is connected into a boiler comprising a tank of 50 L capacity and an electrical resistance heater of 1500 W rated power.
tauri  [37] , suggesting that a single protein with dual functions might be important in dynamic aquatic environments. Interesting novel CPFs have recently been identified by using a new computational method that extracts functional features from large protein sequence datasets  [38]. The actual function of novel CPFs can now be tested experimentally in microalgal model species to confirm whether they are indeed light-responsive proteins with novel features. Diatoms also contain aureochrome photoreceptors, which are unique to stramenopiles  [39]. Containing a light-oxygen-voltage (LOV) domain for blue light perception and a basic leucine zipper (bZIP) domain for DNA binding, these proteins act as light-regulated transcription factors and control several key biological processes such as gene expression, cell cycle and NPQ  [39, 40].
Acknowledgements We would like to thank  Francis-André Wollman  and  Pierre Joliot  for their constant support for our laboratory's research, and for their enthusiasm and critical and constructive insights particularly as we enter this new phase of focusing on major ecological issues. We would like to thank all the non-permanent members of the laboratory, technicians, students and post-doctoral fellows who make the development of our research possible. We thank the  Fondation Bettencourt-Schueller  (Coups d'élan pour la recherche francaise-2018) and the " Initiative d'Excellence" program  (Grant " DYNAMO ,"  ANR-11-LABX-0011-01 ) for supporting our research on the functional biodiversity of microalgae.
Remerciements Nous tenons à remercier Francis-André Wollman et Pierre Joliot pour leur soutien constant aux recherches de notre laboratoire, et pour leur enthousiasme et leur regard critique et constructif notamment au moment où nous entrons dans cette nouvelle phase de focalisation sur des enjeux écologiques majeurs. Nous tenons à remercier tous les membres non-permanents du laboratoire, les techniciens, les étudiants et les post-doctorants qui rendent possible le développement de nos recherches. Nous remercions la  Fondation Bettencourt-Schueller  (Coups d'élan pour la recherche française-2018) et le programme  « Initiative d'Excellence » (Bourse « DY-NAMO » ,  ANR-11-LABX-0011-01 ) pour le soutien de nos recherches sur la biodiversité fonctionnelle des microalgues.
Accès génome (version d'annotation) https://phytozome-next. jgi. doe. gov/info/ CreinhardtiiCC_4532_v6_1 https://genome. jgi. doe. gov/Phatr2/Phatr2. home. html (v2) https://www. chlamycollection. org/chloro/ genome. html http://protists. ensembl. org/Phaeo dactylum_tricornutum/Info/Index (v3) Ressources génomiques et bases de données centralisées https://www. chlamylibrary. org/ https://www. diatomicsbase. bio. ens. psl. eu/ http://genomes. mcdb. ucla. edu/index. html ASAFinder: https://rocaplab. ocean. washington. edu/tools/asafind/ https://www. chlamycollection. org/.
11, 12 The inclusion of nonequilibrium mass transfer is especially critical. 13 The importance of including this in analysis has been shown numerous times in different fields, including in materials engineering,  14  in soil science, e. g. Ref. 12, and for ground heat storage applications. 15, 16 onetheless, the complexity of fully coupling heat and mass transfer including liquid water and vapour flow means that relatively little work has been carried out on the subject within the geotechnical sphere. 8 There are also relatively few recent datasets available for testing theory, with notable recent exceptions including the laboratory scale physical models of Moradi et al. 15  and Smits et al. 12. Experimental data at the micro scale can be particularly challenging to obtain. In recent years, developments in X-ray CT scanning, a nondestructive method that allows imaging of the internal features of objects,  17  have allowed higher resolution images to be achieved in a shorter timeframe.
This means that the experimental technique has expanded its application in geotechnical engineering to include quantification of transient processes in finer grained soils. For example, iterative-algorithm reconstruction approaches allow larger datasets to be reconstructed from fewer frames,  18  and synchrotron X-ray CT enables scans to be completed in seconds. 19 here is also an increasing use of CT scanners in real-time experiments to track movement of soil particles (e. g. Ref. 20), changes in moisture conditions,  [21] [22] [23]  and soil response to mechanical loading. 24, 25 aking advantage of these developments, this paper aims to (i) explore the potential for using X-ray CT techniques to observe and quantify the processes of liquid water movement in soils under thermal gradients; and (ii) make observations about the relative importance of liquid and vapour flows for soils of different grain sizes and degrees of saturation.
Image reconstruction and segmentation Raw 2D projection data from image acquisition were reconstructed into 3D volume data using the cone-beam  33  filtered back-projection algorithm  17  implemented in Nikon's CT Pro v2. 2 software. 34 This was chosen over algebraic reconstruction techniques  35  , following Liu et al. 36  because use of Gaussian decomposition for segmentation of the data (see below), meant there was no need to suppress the noise from the undersampled data. From visual observation of the reconstructed image slices for the two types of soil specimens (see Fig. 2 ), soil solid, liquid water and gas phases could be recognised. Examination of the point spread function  37  for the image data confirmed the image quality, returning values between 5 and 10. Data pre-processing was implemented before quantitative segmentation into soil phases.
This allowed calculation of the µ coefficients for each material (µ g for gas, µ w for liquid water and µ s for solid), which are constant for each specimen. The voxel count of soil solid (a s ) was then determined from the initial scan data in each heating experiment by validating against the weight of the solid particles. a s , µ g , µ w and µ s were then used as fixed inputs to the Gaussian decomposition of the subsequent scans in each experiment. The standard deviation was assumed to be the same for all phases, as all scans were conducted under the same scan settings. The input coefficients and variables are presented in Table  2  for the fine sand and in Table  3  for the silty fine sand. Different mean GVs were calculated for the specimen corresponding to each degree of saturation, according to the calibration process for result optimisation.
c ps is the specific heat capacity of the soil solid and c pw the specific heat capacity of the water. The specific heat capacity of the gas, c pg , is given by Eq. (  11 ): c pg = c pv w v + c pa (1 -w v ) (11) where c pa (1012 J/(kg K)) is the specific heat capacity of dry air and c pv (J/(kg K)) is the specific heat capacity of the vapour (Table  4 ). The governing equations were implemented in COMSOL 5. 4 using the standard modules for Darcy's law (for flow of liquid water and gas), the stabilised convection-diffusion equation (for the movement of water vapour in the gas phase), and the general coefficient based partial differential equation module (for heat flow).
10 ). Each data point corresponding to a certain height at each isochrone (under each scan) was determined from the Gaussian decomposition analysis on the corresponding subsection image data (comprising 300 image slices per subsection), and the middle position of the subsection was taken as its vertical location within the sample height. Consistent with the specimen overall saturation data (Fig. 9 ), the coarser specimens and those with a lower initial saturation dry more rapidly and to a greater degree. In all cases, drying is greatest at the base of the specimen closest to the heat source. Here progressive drying is seen with each time step. However, in the upper part of some specimens, particularly the fine sand at lower degrees of saturation, the degree of saturation may increase again later in the test.
Characterization of the Metal Content. Scanning electron microscopy (SEM) experiments were performed using an ESEM instrument Quanta 250 FEG (FEI, Hillsboro, OR) equipped with an energy-dispersive spectrometer for Xray microanalysis (Bruker Nano GmbH, Berlin, Germany). The energy-dispersive X-ray spectrometer is equipped with a QUANTAX XFlash 6 | 30 detector with energy resolution ≤126 eV full width at half maximum (FWHM) at Mnkα. The spectra were collected and analyzed using ESPRIT 1. 9 software (Bruker Nano GmbH). ICP-AES analyses were performed with an ULTIMA 2 instrument JOBIN YVON in the radial configuration, with a JY 2501 monochromator calibrated against carbon lines. The optical path was continuously purged with nitrogen (2 L/min). The samples of the functionalized frameworks were dissolved in 2 mL of a mixture of HNO 3 65% and H 2 O 2 30% and then heated by microwave irradiation (Milestone, MLS-1200 MEGA, equipped with TFM inner vessels).
Calibration was performed with standard solutions, 10% of HNO 3 on six different metal concentration levels, ranging from 0. 5 to 100 mg/L. No significant spectral interferences were detected. Data were acquired by considering the following emission lines: Fe 238. 204 nm, Co 228. 616 nm, and Mo 202. 030 nm. Data acquisition and processing were performed using the ICP JY v 5. 4. 2 software (Jobin Yvon). Electrochemical Measurements. Electrochemical measurements were performed using an electrochemical workstation (Metrohm-Autolab potentiostat/galvanostat, PGSTAT100N) with a standard three-electrode setup, with Ag/AgCl (in 3. 5 M KCl solution) as the reference electrode, a platinum plate as the counter electrode, and a glassy carbon electrode (GCE, 3 mm in diameter) coated with as-prepared catalysts as the working electrode.
* sı Supporting Information The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acsomega. 1c06522. Synthesis of polymers, solid-state NMR, FT-IR spectra, TGA profiles, EDS spectra, ICP analysis of the loaded polymers, and controlled potential electrolysis (PDF) Accession Codes CCDC 2100398 contain the supplementary crystallographic data for this study. These data can be obtained free of charge via www. ccdc. ca-m. ac. uk/data_request/cif by emailing data_request@ccdc. cam. ac. uk or by contacting The Cambridge Crystallographic Data Centre, 12, Union Road, Cambridge CB2 1EZ, UK; fax: +441223 336033.
Thanks to a "classification" supervised machine learning from Matlab, in which an algorithm learns to classify new observations, and thanks to a simple principle of the superposition approximation model developed for flexible GaAs solar cells, our harvestable energy estimation error is less than 5% after more than 2 weeks of observation. To measure this error, the data collected leading to an estimate of the harvestable energy is compared to what has been experimentally harvested in a real IoT system Li-ion battery and compared to what has been estimated using an expensive spectrometer during the same period. Our prototype should allow the development and the massive deployment of a new generation of low cost 'indoor light energy harvesting' sensors for future reliable indoor energy harvesters.
Introduction The development of the Internet of Things (IoT) or Wireless Sensor Networks (WSNs) applications is growing significantly. Reaching the energetic autonomy of the associated sensors remains a challenge. On one hand, a lot is done to reduce their power consumption, and on the other hand, a growing community of researchers works on improving the technologies able to harvest enough power from the nearby environment to supply electrical energy to such devices  [1, 2]. One of the most common means to achieve such energy recovery is photo-electrical conversion, based on photovoltaic technologies. For decades, databases and software have been used to accurately estimate the energy that can be harvested outdoors from square meters of sun light, wherever on earth  [3]. On the contrary, under indoor light conditions, no standards have been created yet.