dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019a). Vegetation classes were determined based on distinct spectral or structural signatures as well as their importance for ecosystem functioning. Landsat 8 imagery  (Irons et al. , 2012)  during the growing season of WY2017 and WY2018 was used to obtain surface reflectance and calculate the NDVI and GCC across a 200 m side region encompassing the transect. Only the datasets without cloud cover were considered. Due to the Landsat spatial resolution of 30 m, this dataset is only used to evaluate the temporal trends in WY2017 and WY2018 and compare them to the trends observed in the UAVbased imagery.
Point-scale soil moisture monitoring Continuous measurement of soil moisture and temperature were obtained using 5 TE soil moisture sensors (Meter Inc. ) at 0. 1 and 0. 5 m depth at three locations along the transect. The three locations referred further as SM1, SM2 and SM3 are located at 66 m, 103 m and 123 m along the transect, respectively. SM1, SM2 and SM3 are located on the hillslope, at the hillslope toe and on a very small ridge separating the hillslope toe and the floodplain, respectively. SM2 is located in a patch of veratrum, while the two other locations are located in area with predominantly forb and some sparse sagebrush. The time-series of soil moisture and temperature cover most of the 2016-2019 window but data are missing for several time intervals due to logger failure or animal disturbance (<5% data missing).
Soil electrical conductivity time-lapse imaging Electrical resistivity tomography (ERT) data were autonomously acquired over the transect using a 1. 25 m electrode spacing and a dipole-dipole array configuration. The data were collected using a MPT DAS-1 electrical impedance tomography system linked to a field computer, both powered with a solar panel installation. Data were collected daily from October 2016 to November 2017. Power limitation or failure, and animal damage led to several gaps in data with about 20% and 80% data missing in WY2017 and WY2018, respectively. The acquired resistance data were used to reconstruct a 2D model of depth-discrete soil electrical conductivity (or resistivity) along the transect using a smoothness-constraint inversion code named boundless electrical resistivity tomography (BERT)  (Rücker et al.
, 2006; Rücker et al. , 2006). Low-quality measurements were removed prior to the inversion, including signals associated with measured potentials less than 1 mV. Reciprocal measurements acquired a few times during the growing season were used to remove noisy data linked to a few electrodes with high-contact resistance during dry conditions. The inversion of each data set was done with the same mesh and parameterization and independently. An anisotropic ratio of two between horizontal and vertical regularization was used. The obtained tomography shows a smoothed image of the spatial distribution of soil bulk conductivity with a vertical resolution of roughly a third to a half of the electrode spacing near the surface, which corresponds to about 50 cm. The soil electrical conductivity is influenced by subsurface characteristics such as soil moisture, porosity, fluid electrical conductivity, clay content, and soil temperature (e.
Soil moisture mapping The soil moisture spatiotemporal distribution along the hillslope transect is estimated with four approaches that rely on various datasets and thus can provide different spatial coverage. These approaches, which build on the dynamics observed from the soil moisture sensors and the ERT, include 1) a single (i. e. , for space and time) logarithmic linear regression between soil moisture measurements from the point-scale sensor and soil electrical conductivity from the ERT data, 2) the use of soil moisture data under saturated conditions to estimate porosity and soil electrical conductivity to estimate the temporal variability in water saturation, 3) the use of a logarithmic linear regression between soil moisture and soil electrical conductivity during saturated conditions to estimate porosity, coupled with the use of a spatially averaged variation in soil moisture (between minimum and maximum soil moisture observed at each of the three sites), and 4) the use of a simple linear regression between soil moisture under saturated conditions and GCC acquired close to the peak of the growing season-based on the assumption that peak GCC is a direct response to water content during snowmelt-to predict porosity and the use of the spatially averaged variation in soil moisture across the three sites to estimate soil moisture over space and time across the UAV-acquired GCC domain.
In particular, the Pearson correlation coefficients (r) are high for the relations between soil electrical conductivity and GCC (r = 0. 74), plant height (r = 0. 61) and slope (r = 0. 58), and between plant height and slope (r = 0. 63) and GCC (r = 0. 61). Relationship involving NDVI and TWI are weaker (r < 0. 58). These results are consistent with the study of  Falco et al. (2019a) , that evaluated the co-variability of plant types, slope and a single time ERT dataset acquired in September 2016. Locations with similar vegetation types are strongly clustered as a function of surface and soil characteristics that are expected to be adequate in fulfilling vegetation specific physiological demands (Figure  3 ). End members include sagebrush (low GCC) at sites with low electrical conductivity and high slope, and veratrum and riparian shrubs (high GCC) with high electrical conductivity and low slope.
The vertical grey lines indicate dates when UAV data were acquired in WY2017, and approximately in WY2018. It can be noted that the high and relatively constant soil moisture values observed during the spring of WY2017 correspond to soil water saturated conditions and a groundwater table close to the ground surface. WY2018 occurred only 1 day after the first bare-ground date, while this event occurred between 14 and 30 days after the first bareground date in WY2017. It clearly shows the combined effect of WY2018's thinner snowpack, frozen ground and earlier first bareground date on the reduced amount of water infiltration and time the soil was water saturated. Also, the short period of time involving soil water saturated conditions in WY2018 (~3 days) and its end right after the first bare-ground date indicates that the high moisture content was primarily driven by the snowmelt induced vertical percolation at these locations.
Soil moisture dynamics along the geophysical transect estimated from soil moisture sensors and electrical conductivity data To evaluate the soil moisture variability along the transect from a few soil moisture sensors and the ERT dataset, we tested three different approaches (described in Section 2. 2. 5) The first approach, which involves a single logarithmic relationship between both variables provides a Root Means Square Error (RMSE) of 0. 0495 m 3 /m 3 and a Mean absolute Error (ME) of 0. 0435 m 3 /m 3 (Figure  5 ). The second approach, which makes use of locationspecific relationship using Eq. 2, with soil porosity obtained from the soil moisture data under saturated conditions, enables more reliable estimation of soil moisture with a RMSE of 0. 037, 0. 039 and 0. 039 m 3 /m 3 , and a ME of 0.
An approach to reduce error in soil moisture estimation is to involve models or data that account for the direct effect of water infiltration events on soil moisture, which is obviously easier to achieve in rain-than snow-impacted environment (e. g. ,  Acharya et al. , 2022). Our results indicate that the identified link between vegetation indices and soil moisture in the studied snow-dominated environment is promising to constrain the reconstruction of the spatiotemporal distribution of soil moisture, although soil moisture and/or snowmelt rate datasets are essential. Currently, extending the approach used in this study beyond a single hillslope would require a strategic placement of sensors to capture the range of soil moisture dynamics. Finally, while the UAV provided unparallel spatial resolution in plant greenness in this study, the increasing emergence of satellite products having meter scale resolution is quickly becoming a preferable alternative to reduce the cost of acquisition and processing time.
The data coverage can be spaced out in sparsely populated areas, which is a limitation for global-or large-scale studies  [12]. Weather radar provides useful information about the rain rate at a high spatial and temporal resolution. Nevertheless, as for the rain gauges, they cannot be installed over marine surfaces. Projects based on radar networks allow for the building of rain-products over larger areas, such as over France (PANTHERE project;  [13] ) or Europe (OPERA project;  [14] ). Note that all the acronyms used in this paper are defined in the Abbreviations. Largely developed in recent years, remote sensing observations from satellites have enabled improvements in precipitation estimates on a large spatial scale. Different sensors are used to estimate precipitation amounts: infrared (IR) or visible (VIS), and passive or active microwave (PMW or AMW) sensors.
Dataset Type of Data by Météo-France. This network covers mainland France and contains nearly 600 operational stations providing data in real time  [25]. It constitutes tipping-bucket rain gauges with a precision of 0. 1, 0. 2, or 0. 5 mm from one rain gauge to another, corresponding to the bucket capacity. The mean distance between two stations of the RADOME network is approximately 30 km. For our study, we use the RADOME dataset aggregated over 1 h provided by the company Weather-Measures.
• The rain-gauge adjustment. In our case, we used the aggregated 15 min PANTHERE dataset provided by the company Weather-Measures. The "European Operational Program for Exchange of weather RAdar information" (OPERA) was established in 1999 and constituted of 30 member states operating over 200 radars in the C-, X-and S-band  [28]. The goal of this program is to organise a European cooperation of radar-products exchange with the help of the European national weather services and to build radar composites for the European Meteorological Network EUMET-NET  [14]. Hence, it is worth noting that the French ARAMIS radar network serves as a foundation of the radar coverage for OPERA over France, with the notable exceptions of the X-band radars that are not included; the locations are given in Figure  1.
Initially, the PERSIANN algorithm  [18]  was based on the artificial neural network technique to estimate rainfall accumulation. Then, the PERSIANN-Cloud Classification System (PCCS) resumed the same PERSIANN features; however, the PCCS classifies cloud-patch features based on the texture, geometry, dynamic evolution, and cloud-top height of clouds from IR sensor-based satellite estimates  [40]  and does not use PMW data unlike PER-SIANN  [33]. This cloud classification is performed by using a variable threshold cloud segmentation algorithm and varies the T b -R relationships. The PDIR algorithm is a nearreal-time (NRT) precipitation product like the PCCS, with several modifications developed by Nguyen et al. [33] :.
• The integration of climatology data from WorldClim version 2  [41]  over land and PERSIANN Climate Data Record (PCDR;  [42] ) over the ocean to create a dynamical T b -R curve model. In this study, PDIR is an NRT precipitation product and provides 4 × 4 km 2 gridded rain-rate estimations with an hourly temporal resolution. Microwave Sensor-Based and Blended Products Two types of microwave-based sensing techniques were used in this study:.
The space mission Global Precipitation Measurement (GPM) was launched in 2014 as part of a joint project between NASA and JAXA. In that framework, level 3 satellite precipitation products Integrated Multi-satellitE Retrievals for GPM (IMERG) were developed  [21]. IMERG provides precipitation fields every 30 min, with a horizontal resolution of 0. 1 • × 0. 1 • in latitude and longitude. These products emerge from a combination of data collected by IR and PMW sensors from the GPM constellation satellites. In this study, we used two different products from IMERG: IMERG-Early and IMERG-Late, which were both considered as NRT versions but with various post-processing procedures  [52]   1. Carrier-to-Noise-Based Precipitation Product DATABOURG is a commercial product provided by the Databourg Systems Company based in Luxembourg.
• Deterministic methods (e. g. , the inverse distance weighting or the Thiessen polygons;  [56, 57] ); • Geostatistical methods (e. g. , the different Kriging methods;  [58] ). The choice of the selected interpolation mainly depends on factors such as the complexity of the topography or the density of the rain-gauge network  [59]. In this study, we consider the original spatial resolution of the RADOME network as a reference. Hence, we compare the observed punctual rain-gauge values to their respective nearest estimated pixel by using the widely used point-to-pixel approach  [8, [59] [60] [61] [62]. Moreover, the comparisons between the rain-gauge data and the diverse rain products were performed on the accumulated rain values obtained at the end of both case studies presented in Section 2.
One hypothesis that partially explains the north shift of both case studies' estimations is the parallax effect. This effect is caused by satellites with a high distance from the nadir, thus leading them to affect the clouds located in higher latitudes from the nadir more, such as the case in observed over Poland by using the Meteosat dataset  [68]. The Meteosat dataset is included in PDIR and GHE algorithms as one of the GEO satellites inputs. For GHE, the algorithm might have observed the cold clouds present over France during the April event, which could explain the overestimation over the region.
Thus, the mean absolute biases (Table  3 ) obtained for EARLY and LATE products were quite small for this specific area: -4. 55 mm and -0. 58 mm, respectively. The two IMERG products provide similar precipitation estimations for both case studies. However, we can notice that LATE showed greater values of precipitation accumulations than EARLY for both cases (see Table  3 ). Initially, in the study of O et al. [52]  over southeastern part of Austria, using the WegenerNet rain-gauge gridded dataset as a reference, the authors found that the backward morphing scheme of IMERG-Late (V03) had only little effects compared to the simple forward scheme of IMERG-Early (V03). Furthermore, in their study they marked that EARLY and LATE tended to overestimate low rain rates, such as is observed with the case study over the JUR area and, inversely, that high-intensity precipitation amounts were underestimated by the IMERG family, as is observed over the NMC area during the June case.
C/N-Based Product Figure  19a ,b presents the precipitation amounts estimated by DATABOURG during both the April and June case studies. The DATABOURG product uses an innovative method to estimate and process precipitation at high spatial and temporal resolutions as described in Section 2. 1. 3. No similar observations-estimations comparison at this spatial scale has been conducted by using DATABOURG estimations but it has been previously found that the dataset is closer to rain-gauge observations than the estimations of the German DWD radars of Neuheilenbach  [22]. Figure  19a ,b presents the precipitation amounts estimated by DATABOURG during both the April and June case studies. The DATABOURG product uses an innovative method to estimate and process precipitation at high spatial and temporal resolutions as described in Section 2.
1. 3. No similar observations-estimations comparison at this spatial scale has been conducted by using DATABOURG estimations but it has been previously found that the dataset is closer to rain-gauge observations than the estimations of the German DWD radars of Neuheilenbach  [22]. For the April case (Figure  19a, c ), DATABOURG estimated moderate-to high-intensity precipitation amounts over the south of France, most notably over the WMC, SAL, and CEV areas. The locations of the precipitation patterns agree well with the observations made by RADOME (Figure  2a ). Moreover, the distance between the maximum estimated by DATABOURG and the maximum observed by RADOME was less than 20 km for the WMC, DUR, and CEV areas (Table  2 ). However, the DATABOURG maximums were lower than the RADOME maximums by more than 20 mm (Table  2 ).
• The reprocessing applied to IMERG-Late does not improve the estimations compared to IMERG-Early; • CMORPH-RT showed traces of raw LEO satellite observation swaths over parts of the country. This was traduced by strong differences at the very-local scale, over the JUR area for example. CMORPH-RT showed low bias medians but CMORPH was better at the spatial representation of both events; • Despite slightly overestimating during the June event, DATABOURG showed promising results with low biases for both cases with the highest spatiotemporal resolution (1 km 2 and 5 min). PANTHERE gives closer results to rain gauges than OPERA for radar precipitation product, DATABOURG shows the most promising results with low biases over parts of France, and this dataset can be used for nowcasting applications.
This study can also be performed by using other methods of comparison such as the pixel-to-pixel method by interpolating the observed rain rates to a gridded dataset or by using Kriging methods. But this work must be conducted after a careful reflection on the remapping or interpolation method used to obtain the identical grid for all products. Further studies are also encouraged to conduct over a longer study period, over more case studies, or even over other study areas in order to test the robustness of the conclusion's made on the performance of satellite precipitation products.
Data Availability Statement: Publicly available datasets are analysed in this study and available via the links below: GHE data: https://www. noaa. gov/nodd/datasets, accessed on 19 November 2023; PDIR data: ftp://persiann. eng. uci. edu/CHRSdata/PDIRNow, accessed on 19 November 2023; IMERG data: https://doi. org/10. 5067/GPM/IMERG/3B-HH-E/06, accessed on 19 November 2023 for IMERG-Early and https://doi. org/10. 5067/GPM/IMERG/3B-HH-L/06, accessed on 19 November 2023) for IMERG-Late; CMORPH data: http://ftp. cpc. ncep. noaa. gov/precip/CMORPH_RT/, accessed on 19 November 2023 for CMORPH-RT; https://ftp. cpc. ncep. noaa. gov/precip/CMORPH_ V0. x/, accessed on 19 November 2023 for CMORPH; and https://sharaku. eorc. jaxa. jp/GSMaP/, accessed on 19 November 2023 for GSMaP data. The ERA5 reanalysis used in this study was provided by the ECMWF (https://doi.
8 kHz for 327 lines (Table  2 ). The internal rotation constant F 0 is correlated to V 3 and was fixed to the calculated value. The reduced constant F = h2 /2rI α and the rotation-torsion coupling constant ρ were derived parameters. e From geometry optimization. f Derived parameter. g Fixed to the value of the XIAM fit. h Calculated at the B3PW91/6-311+G(d,p)//MP2/6-311++G(d,p) levels of theory, see text. i Derived from χ bbχ cc = -0. 9554(97) MHz. j Fixed due to symmetry. k Root-mean-square deviation of the fit. l Number of A and E species transitions as well as number of the nuclear quadrupole hyperfine components. For the BELGI-C s -hyperfine fit, presented in Table  3  in the RAM system, the same dataset of 327 hyperfine components were fitted with an rms of 3. 9 kHz, floating A, B, and C, ∆ K , ∆ JK , the 14 N NQCCs χ aa and χ bb (χ cc = -χ aa -χ bb , following Laplace condition), V 3 , and ρ.
Funding: This research was funded by the  Université de Paris-King's College London Joint Research  Award and was co-funded by the  Agence Nationale de la Recherche  ( ANR , grant number  ANR-18-CE29-0011 ), by the  European Union  ( ERC grant   101040480-LACRIDO , and  PCIG12-GA-2012-334525 ), and  Ministerio de Ciencia e Innovación  (grant  PID2019-107307RB-I00 ). S. B. thanks  King's College London  for a PGR International Scholarship. We acknowledge use of the research computing facilities at King's  College London, Rosalind  (https://rosalind. kcl. ac. uk) and  CREATE (King's Computational Research, Engineering and Technology Environment. https://doi. org/10. 18742/rnvf-m076,accessed on  31 January 2023 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
5 • C every 2 days for 2 weeks until reaching 24 • C. The acidification treatment (A) represented the second condition. Water acidification was performed in the water reservoir with the addition of CO 2 (IKS aquastar, iks ComputerSysteme GmbH, Germany), allowing a drop of initial water pH of 0. 1 unit every 4 days for 2 weeks until reaching a decrease of the initial pH of the water reservoir equal to 0. 4 unit, i. e. , 7. 6. The water pH in the water reservoir was precisely monitored with a pH probe (826 pH mobile, Metrohm, Swiss) to adjust the pH on IKS device by taking into account the alkalinity using the seacarb package on RStudio software (version 4. 2. 1© RStudio, R Core Team, 2020). This probe was calibrated in standard solutions (pH 4, 7, and 10, HANNA (instruments, France). The third treatment combined warming and acidification treatments (WA).
Statistical analyses The bioinformatic analyses on the DNA raw data obtained after sequencing were performed using the SAMBA (Standardized and Automated MetaBarcoding Analyses workflow) (v3. 0. 0) workflow written by SeBiMER, the IFREMER's Bioinformatics Core Facility  (Noël et al. , 2021). Two replicates of the control condition at t44 and t58 were removed because they didn't pass the quality control test during the data integrity step of the workflow. The taxonomic affiliation was performed against the Silva database v138  (Quast et al. , 2012; Yilmaz et al. , 2013)  with 99% of similarities. All calculations and statistical analyses were performed on RStudio software (version 4. 2. 1 (RStudio, R Core Team, 2020). The statistical analyses used to interpret the physical-chemical data, the pigmentary composition, and the extracellular polymeric substances (EPS) concentration were described in  Mazière et al.
A monochromatized beam (λ = 0. 41125 Å) was used, focused down to a 10 × 10 µm 2 area. Data were registered with an angular step of 0. 5 • using an MAR555 flat-panel detector with a 430 × 350 mm 2 (555 mm diagonal) active area. The wavelength and the sample-to-detector distance (279. 88 mm) were calibrated using Si standard and Fit2D software  [26]. The investigated pressure range varied from 1. 26 to 16. 6 GPa. Measurements were made in 7 steps in increasing pressure. Pressure was measured before and after each data collection, and the gasket relaxation was ensured by waiting ~15 min after changing the load. CrysAlisPro software (Agilent Technologies U. K. Ltd. , Yarnton, UK) was used for data extraction, reflection intensity correction, reflection merging and refinement of the lattice parameters.
Refinement Protocol and Elasticity Analysis The structural refinements were performed using ShelXle software  [27]. The crystal structure of jamesonite was refined up to 6. 76 GPa, starting from the crystal structure published in Léone et al. [15] , in the space group P2 1 /c. The unit cell and crystal structure refinements of data collected from 9. 70 to 16. 60 GPa showed the beta angle of jamesonite approaching 90 • and the x atomic positions of all the atoms in the unit cell approaching special positions. Thus, the occurrence of a phase transition from monoclinic to orthorhombic symmetry was checked, and SHELXT  [28]  software was used to solve the crystal structure of jamesonite after phase transition. Satisfactory structure refinements were performed in the Pmcb space group for the last four data collections.