dataesr/scientific-paragraphs-categorization huggingface dataset:
2020-2050 2050-2080 Ambient temperature [    The installation performance which includes IAM, SMM, soiling, temperature and inverter losses is first studied through P R instant while setting eta ageing (y) = 1 as defined in Section 2. 10. For each projection p, P R p instant (y) is calculated for every year y and is plotted in Figure  3  over the 2020-2050 (red) and 2050-2080 (orange) periods. On another note, the historical P R instant,hist = 88. 1 % (in black) corresponds to the yearly averaged P R instant over the 1981-2019 period obtained from the ERA5 dataset. Due to the natural climate variability, some of the future years are still colder than the historical average and would lead to a higher P R p instant (y) than P R instant,hist. On the other hand, in average, other years have hotter temperature and result in the opposite effect with a lower value than the historical one.
Impact of the temperature characteristics The module losses can change due to different module technology sensitivity and building integration. In this section, two simulations are run to quantify those changes. 1. The first sets back γ, the efficiency loss coefficient to a more conservative -0. 3%/ • C. 2. The second simulation (BIPV) consists of modeling the building integration by changing the coefficients for the temperature model in Sec- tion 2. 6 to U 0 = 20 W m 2 K , U 1 = 3. 2 W m 2 K(m/s) as suggested by PVGIS. [54]  The two simulations are compared in Figure  12  to the base case (orange) with the assumptions applied in the rest of the study with γ = -0. 45 • C and Temperature losses are reduced when using a more conservative temperature coefficient, γ = 0. 30%, and the PR distribution is slightly higher than the base case.
Conclusion In this article, a model chain was established in order to propagate climate projections and quantify the effect of climate change on photovoltaic performance for any installation. To this end, the hourly AC power production taking into account natural aging was obtained through a collection of models that were used to estimate the Performance Ratio, the standard mea- 2019 on the ERA5 dataset of the chosen location. Then, the three-hourly time series are converted into hourly in order to be ready to be injected in the models Section 2.
A. 2. Bias correction In order to correct the bias in climate model outputs because of the location constraints, two bias correction methods of the quantile-quantile correction type are adopted: the Q-MAP and the CDF-t methods. The general principle is to correct the distribution of a variable of the climate model output using the distribution of the same variable from the reference dataset on the reference period 1981-2019. To avoid introducing a bias during the correction, the correction methods are applied over the periods by month and by hour for each variable in order to eliminate the seasonal and diurnal cycles.
A. 2. 1. The Q-MAP method For any selected period, reference or other, the Q-MAP  [23] [24] [25]  method corrects a distribution quantile by quantile so that the quantiles match those of the reference distribution. If it is assumed that the distribution on future periods is the same as the reference, this method is particularly suited. As for the method, let F ref be the cumulative density function (CDF) of the reference dataset ERA5 of a climate variable, such as temperature, over the reference period 1981-2019. More specifically, the function F ref indicates the probability that a value X is less than or equal to a value x, where F ref (x) = P X (X ≤ x). In the same way, F mod is the CDF of one of the climate models on the same variable and period. Then for all model values x mod , it exists a reference value x ref so that F ref (x ref ) = F mod (x mod ) (35) From this relation, the transfer function T can be deducted based on CDFs on the common reference time period 1981-2019 and the T function can then be applied to the same variable on future periods of the climate model.
a b s t r a c t The petrophysical and petrothermal characterization of the sedimentary succession of the Oliana anticline in the Southern Pyrenees has been performed on the basis of mineral density, connected porosity, permeability, P-wave velocity and thermal conductivity measurements of rock samples distributed along this anticline. This dataset was used to explain: (I) the variability of petrophysical rock properties along the Oliana anticline, (II) the distribution of thermal conductivity along the sedimentary units of the anticline, (III) the relationships between the fold and petrology concerning the mineral density, connected porosity, permeability, P-wave velocity and thermal conductivity of rocks and (IV) the tectonic and diagenetic controls underlying the observed relationships, as described in the research article: "Petrological, petrophysical and petrothermal study of a folded sedimentary succession: the Oliana anticline (Southern Pyre-nees), outcrop analogue of a geothermal reservoir -Global and Planetary Change Journal (2023)".
This contribution presents here the raw and statistical datasets used to discuss the potential of the Oliana anticline as a geothermal reservoir analogue and also includes an extended methodological section that proposes a new procedure to measure the thermal conductivity of highly heterogeneous coarse-grained sedimentary rocks using the Modified Transient Source Plane (MTPS) method on a TCi C-Therm thermal analyzer. These complete datasets can be used to better discuss and understand the principal limitations of outcrop analogue studies applied to unconventional geothermal reservoirs in foreland basins on the basis of the analysis of rock petrophysical and petrothermal properties. Furthermore, the data obtained in the Oliana anticline can be used to understand the structural, diagenetic and petrological factors that can modify the petrophysical and petrothermal properties of rocks and to discuss the potential of foreland basin margins to be used as geothermal reservoirs, comparing the data obtained in Oliana with studies developed in similar geological settings worldwide.
Subject Earth Sciences Specific subject area Petrophysics, petrothermics and petrology Type of data Tables Figures How the data were acquired Mineral and bulk density of sedimentary rocks were measured using the Archimedes method and calculated from mass and volume relations with an analytical accuracy of ±0. 002 g/cm 3. Connected porosity of sedimentary rocks was measured using the Archimedes method by water porosimetry, with an absolute accuracy of ±0. 05%. Permeability of sedimentary rocks was measured using a nitrogen gas permeameter equipped with a Hassler cell, using nitrogen and following a steady-state flow method. Results were corrected from Klinkenberg's effect using the graphical method. Analytical accuracy is ±10% (for K ≈1 D) and ±0. 5% (for K ≈0. 001 mD), detection threshold was 0.
001 mD. P-wave velocity of sedimentary rocks was measured on water-saturated rocks using a portable Pundit 6 system (CNS Electronics LTD) operating at a regular frequency of 1 MHz. Absolute accuracy values range between ±13 -96 ms -1. Thermal conductivity of detrital, carbonate and evaporite rocks was measured with a C-Therm Trident TCi thermal analyzer and the Modified Transient Plane Source (MTPS) method following ASTM D7984-21 measurement protocol. Statistical data has been processed using Excel and SPSS free software on raw measuring data. Please refer to the original research article and Experimental Design, Materials and Methods section for more detailed information on methodological protocols used for collecting data.
Data source location The studied samples were collected from sedimentary formations cropping out at the northern limb, the southern limb, the NE closure and the SW closure of the Oliana anticline (Southern Pyrenees). The coordinates and location of each sample are included in Table  1  and Fig. Value of the Data -Raw and statistical datasets are presented on petrophysical and petrothermal properties measured in carbonate and detrital rocks from the well-exposed Oliana anticline located on the northern margin of the Ebro foreland Basin, at the front of the Montsec and Serres Marginals thrust sheets (Southern Pyrenees). -The article includes an extended and detailed description of the sampling and methodological procedures used to obtain representative values of thermal conductivity in coarsegrained sedimentary rocks of heterogeneous composition, such as conglomerates and coarse-grained sandstones.
Objective The data presented here correspond to mineral density, bulk density, connected porosity, permeability and P-wave acoustic velocity of 40 sedimentary rocks and thermal conductivity data of 35 samples. The main objective of this article is to provide researchers with a more precise and complete scope of the results, analyses and workflow performed during the characterization of the folded alluvial succession of the Oliana anticline as an outcrop analogue of a geothermal reservoir located in the proximal margin of a foreland basin. Thus, all the datasets (raw and statistically processed) and procedures included here were used in the original research article  [2]. The aim of this article is threefold 1) to provide a clearer picture of the analytical and statistical data processing of the results, 2) to extend the petrophysical and petrothermal dataset of the sedimentary succession studied in the Oliana anticline and 3) to present an extended sampling methodology and measurement procedure to acquire thermal conductivity data from petrologically heterogeneous rocks, which due to their grain size and compositional variability are challenging to be represented by a single value.
Data Description The analysis of the petrophysical and petrothermal data from the sedimentary succession of the Oliana anticline (Southern Pyrenees) is presented here. Rock samples were collected from all the structural domains of the Oliana anticline covering the northern limb, the southern limb, the NE closure and the SW closure of the fold. The location, structural domain, geological age and lithofacies of each sample are shown in Table  1. Raw petrophysical and petrothermal data of samples are included in the Repository  [1]. The statistical parameters on measured petrophysical properties (i. e. , mineral density, bulk density, connected porosity, permeability and Pwave velocity) are displayed in Table  2  and the statistical descriptors on thermal conductivity are in Table  3. Fig. 1  displays a geological map of the Oliana anticline and the sampling location described in Table  1  ; Fig.
Experimental Design, Materials and Methods The methodology of the petrological description of the samples in Oliana has just been explained in the original research article  [2]. Details on the acquisition of petrophysical data can be found in  [ 8 , 9 ]. This section adds information about the sampling design and treatment of the samples before measuring their petrophysical and petrothermal properties. In addition, an extended methodological description of the petrothermal measurement design for highly heterogeneous rock samples and the subsequent statistical study of the data is also presented. Fieldwork has been carried out during five days in June 2020. Rock samples were oriented in the field with respect to the bedding strike and collected throughout the Oliana anticline to cover the different stratigraphic units and sectors of the structure.
During the measurement time, the temperature of the rock-sensor interphase raises by 1 ºC. The rock sample absorbs the heat generating a simultaneous voltage drop. The petrothermal properties are calculated from the slope resulting from plotting the voltage variation versus measuring time. Samples were dried in an oven by at least 24 h at 70 ºC and were then temperate for other 24 ± 6 h in stable temperature (22 ± 3 ºC) and moisture (52 ± 14%) room conditions. Temperature and moisture were also periodically checked to ensure controlled conditions while measuring (see data in Repository). Before each measurement the sensor was calibrated using reference polymer and pyroceramic test materials. Distilled water was applied as a contact agent between the sample and the sensor. The surface area of the sensor is limited and, as the thermal conductivity is measured by placing the sensor directly over the rock slices, the values acquired are not representative of the whole rock when the sample is heterogeneous.
Instead, they are representative of the thermal conductivity of the measured point. To overcome this, in the case of petrologically heterogeneous samples, the sensor was randomly placed at different points of the surface, usually from 2 to 6 points were measured on each slice (see data in Repository). For each point a cycle of 2 to 3 simultaneous measurements was run to acquire, in the end, a total of 12 values for each slice. After each cycle, the Trident software displays an average thermal conductivity value of the measured point and its Relative Standard Deviation (RSD). RSD must always be less than 1 to comply with the technical considerations of measurement methodology (ASTM D7984-21). Finally, a single, representative value of the thermal conductivity for each sample was calculated by averaging the results of all the measured points in the slice.
The RSD value obtained after averaging all the points must also be less than 1, so the raw data were statistically treated to delete from the cycles the values that place the RSD above the methodological threshold. After this process, a single thermal conductivity value is acquired, which is technically correct and representative of the measured sample because it respects the petrological heterogeneity of the rock. This measuring procedure has been widely implemented in the case of conglomerates and some coarse to medium-grained sandstones of the Oliana anticline. The statistical treatment of the raw petrophysical and petrothermal data was carried out using the free SPSS software and Microsoft Excel to calculate the statistic descriptors presented in Tables 2-4 of this contribution. Simple arithmetic functions were used to calculate the arithmetic mean ( ẋ), arithmetic median (Me), variance (s), coefficient of variation (CV) and difference (d) of each studied property.
Acknowledgments This research was performed within the framework of  DGICYT Spanish  Project  PGC2018-093903-B-C22  and  PID2021-12246NB-C22   Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación/Fondo Europeo de Desarrollo Regional and the Catalan Council  to the  Grup Consolidat de Recerca Reconegut Geologia Sedimentària  (  2021 SGR-Cat 00349  ). DC acknowledges the  Spanish Ministry of Science and Innovation for the "Juan de la Cierva Formación"  fellowship  FJC2020-043488-I AEI/10. 13039/501100 011033. PR acknowledges the  Universitat de Barcelona and Repsol Exploración. S. A. for the " Repsol-UB 2020 " fellowship  D/2260 0 090 0/C2022A/G0 0/ PDZJK0 02  and the  Universitat de Barcelona  for the " PREDOC-UB 2021  " fellowship.
[2] ez, I. Cantarero, G. Cofrade, D. Muñoz-López, D. Cruset, J. P. Sizun, A. Travé, Petrological, petrophysical and petrothermal study of a folded sedimentary succession: the Oliana anticline (Southern Pyrenees), outcrop analogue of a geothermal reservoir. Global and Planetary Change (2023), 10. 1016/j. gloplacha. 2023. 104057,[2] 1, respectively. Samples are stored at Facultat de Ciències de la Terra, Universitat de Barcelona (UB), Martí i Franqués s/n, Barcelona, 08028, Spain. Data accessibility Data within the article Repository Repository name: Petrophysical and petrothermal raw data of the sedimentary succession in the Oliana anticline (Southern Pyrenees), [1] Data identification number: 10. 17632/23zgg8rtb2. 1 Direct URL to data: https://data. mendeley. com/datasets/23zgg8rtb2 Related research P.
, 2000). Indeed, the HC6 modified membrane exhibits a cross section with more pores, cavities or macrovoids as compared to the reference membrane (Figure  3 ), leading to a more symmetric structure. This observation supports the suggestion that the HC6 particles form aggregates within the polymer matrix, leading to a more hydrated water swelled microstructures this latter being responsible for the permeation properties  (Chuang et al. , 2000). As described previously, from the SEM micrographs of Figure  2 , one can clearly observe the interconnected porous network formed from the crystallization of the polymer globule, coated by the HC6. This hypothesis was confirmed by EDX spectroscopy, providing further evidence of the successful incorporation of the HC6 entities within the PVDF matrix. Indeed, from the data collected in Table  1 , one can determine the following formula C 12 O 3.
The FTIR data confirmed that HC6 compound was successfully incorporated within the matrix of the HC6-modified membrane. TGA and DSC analyses were performed on powders of the prepared membranes samples to evaluate their thermal properties. Two weight losses are found from 400 °C to 500 °C (57. 5%) and up to 1,000 °C (41. 6%) leading to the almost total decomposition of the pellets. The water loss starts at lower temperature of 100 °C while the degradation of the HC6 molecules yield to a weight loss of 91% at 450 °C. At 650 °C, the HC6 is totally decomposed. When looking at the reference M0-a membrane, three weight losses of 4% up to 150 °C, 63% up to 450 °C and a last one of 33% up to 600 °C appeared. They may correspond to water and/or solvent evaporation in a first stage that was not present for the PVDF pellets and to the progressive decomposition of the polymer matrix upon temperature as previously observed.
Tryptic peptides were separated by a twostep linear gradient from 6% to 40% of mobile phase B (0. 1% (v/v) formic acid (FA)/80% (v/v) acetonitrile) in mobile phase A (0. 1% (v/v) FA) for 52 min. For peptide ionization in the nanosource spray, the voltage was set at 1. 9 kV and the capillary temperature at 275 • C. Top 10 Data Dependent workflow was used in a 400-1600 m/z range and a dynamic exclusion of 60 s. The spectra were processed by Proteome Discoverer software (ThermoFisher Scientific, version: 2. 4. 1. 15) using the Sequest HT algorithm with the search following settings: A. aeolicus (taxonomy ID 63363 and subtaxonomies) extracted from Uniprot (3388 sequence entries, version 2020-12-16) plus the sequences of A. aeolicus Fd6 (NCBI protein ID: WP_024015104) and Fd7 (NCBI protein ID: WP_164930743.
1) which were added manually; trypsin enzyme (maximum 2 missed cleavages); fixed modification: carbamidomethyl (Cys); variable modification: oxidation (Met); mass values specific for monoisotopic; precursor mass tolerance: ±10 ppm; fragment mass tolerance: ±0. 02 Da. Peptide validation was based on a score threshold of maximum Delta Cn 0. 05. If at least two unique peptide sequences containing more than six amino acids passed the high confidence filter, proteins were identified. An approximate estimation of the level of proteins in the extracts was based on the number of MS spectral counts (Peptide Spectral Match (PSM) counting)  [27]. Data are available via ProteomeXchange with the identifier PXD040248.
Moreover, the putative 5-FormylTHF cyclo-ligase was not detected at all, and the glycine cleavage system H proteins were hardly detected (Tables  1  and S1 ). One possibility is that this route may not operate in A. aeolicus, at least under the growth conditions used in the present study, and that the corresponding enzymes that are identified by mass spectrometry may potentially be involved in other metabolic pathways. Glycine and serine could be synthesized as described in H. thermophilus, via a novel phosphoserine phosphatase, which is essential for serine anabolism in this bacterium  [17]. The corresponding enzyme in A. aeolicus (accession number O67797; Locus tag Aq_1990; annotated as Phosphoglycerate mutase; 65% sequence identity with the H. thermophilus enzyme) was identified in the soluble content of the bacterium with 10 unique peptides and a sequence coverage of 56% (Tables  1  and S1  and Figure  1 ).
Enzymes are indicated in blue (except PFOR and OGOR, highlighted in red), and intermediates in green (except glycine, serine, and phospho-serine, highlighted in red). THF: tetrahydrofolate; SAM: S-Adenosyl Methionine; Fd and Fdred: ferredoxin and reduced ferredoxin. In addition to enzymes related to carbon assimilation, the proteins Aq_866 (misannotated as NuoL2, accession number O67027) and Aq_863 (O67026) were also detected in the membrane fraction. These two proteins are probably part of the recently discovered membrane-bound heterodimeric complex DabAB that functions as an energy-coupled inorganic carbon pump in many prokaryotes  [31]. This complex seems to be highly abundant in the membrane of A. aeolicus, as the PSM number of DabA is one of the highest (PSM number of 190, not shown).
Results reported in this table were obtained after protein preparation with the "stacking method", as described in the Material and Methods section, and correspond to one of two independent experiments. Results for all replicates and those obtained with the "S-Trap method" are reported in Table  S1. The protein GcvH5 (renamed LbpA2 [32]), accession number O66720; locus tag aq_402), was identified by mass spectrometry in the soluble extract but not included in the table because it was proposed to be involved in the sulfur oxidation pathway and not in the glycine cleavage system. 1 Accession: accession number in the UniProt database. 2 Mw: theoretical molecular weight of the identified protein in kDa. 3 PSM: peptide spectrum match number (given by the algorithm and corresponding to the total number of identified peptide sequences for the protein, including those redundantly identified).
However, this enzyme looks to be incomplete in A. aeolicus since no ACS genes have been found in the genome. Consequently, its function and, especially, why there are two copies located within the OGOR and PFOR gene clusters is intriguing. The A. aeolicus PFOR gene cluster also harbors one additional open reading frame named aq_1194  [5] , which codes for an uncharacterized protein (accession number O67253) that has homology with Uma2 family endonucleases (Figure  S5 ). This protein, which was hardly or not detected in the total extract or soluble fraction of the bacterium in our proteomic analyses (data not shown), was not identified by mass spectrometry in the PFOR activity gel band (Table  2 ), suggesting that it is not part of the enzyme complex. ble for the efficient electron transfer to/from an external electron acceptor/donor consequently annotated as Fd2 (FORε) and Fd3 (PORε)  [42].
The β subunits c conserved GDGx4ID domain, which is responsible for binding the TPP cofactor additional conserved cysteines, including an atypical CX2C motif, which are involved in the coordination of a third [4Fe-4S] cluster as suggested for D. africa  [43]. PORδ and FORδ are found exclusively in five-subunit type 2-oxoacid:Fd ox tases from Aquificaceae  [5]  and conserve high sequence similarity with the β carbon monoxide dehydrogenase/acetyl-CoA synthase ɛ subunit (CODH/A methanogenic archaea and acetogenic bacteria performing carbon fixation via t Ljungdahl pathway  [44]. However, this enzyme looks to be incomplete in A. aeo no ACS genes have been found in the genome. Consequently, its function and, why there are two copies located within the OGOR and PFOR gene clusters is i The A. aeolicus PFOR gene cluster also harbors one additional open reading fram aq_1194  [5] , which codes for an uncharacterized protein (accession number O6 has homology with Uma2 family endonucleases (Figure  S5 ).