dataesr/scientific-paragraphs-categorization huggingface dataset:
The bias and RMSD per pixel were also computed and are shown in Fig. 10  between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 (top two rows) and between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 (rows 3 and 4). Finally, the bottom row in Fig. 10  shows the annual maximum NDVI for further analysis. 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 present similar spatial features in the IND and LMB. In these areas, a large spread is observed in 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 We computed the spatiotemporal RMSD, bias, and Pearson's correlation over the year in each basin between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 , 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 , and 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 , and they are listed in Table  3. We previously filtered out all the samples having a WF equals to zero in either one of the two datasets used in the comparison. A positive bias is found between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 , especially in the Parana basin (17.
3%). The RMSD ranges from 18. 3% (Mekong) to 26. 2% (Parana), while the correlation ranges from 0. 42 (Parana) to 0. 69 (Mekong). The comparison between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 shows a smaller bias, with a RMSD ranging from 19. 1% (Mekong) to 24. 9% (IND) and a correlation around 0. 60. Finally, the comparison between 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 and 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 shows a strong positive bias up to 23. 3% in the IND. The RMSD ranges from 17. 4% (Mekong) to 31. 8% (IND), and the correlation ranges from 0. 50 (Parana) to 0. 65 (Mekong). Overall, the large deviation between the three datasets is illustrated by a RMSD of ∼20% and correlations of ∼0. 60. Interestingly, MODIS does not have the closest comparison to CYGNSS despite being used in the methodology. Both 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 show a positive bias when compared to 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 , which could be due either to an overestimation of 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 with soil moisture, or to an underestimation of 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 because of the high vegetation cover during the wet season.
It is in both cases much higher than the corresponding seasonal estimations from 𝑆𝑊 𝐸 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑆𝑊 𝐸 𝐺𝐼𝐸𝑀𝑆. It suggests a possible underestimation of flood extent in the Amazon as the GNSS-R coherent signal can be attenuated or even lost with dense vegetation layers  (Loria et al. , 2020). The Lband SAR-based wetland maps over the Amazon and the Congo basins indicate a higher surface water extent at high water stage than both 𝑆𝑊 𝐸 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑆𝑊 𝐸 𝐺𝐼𝐸𝑀𝑆 (see Fig. 9  and Figure  S2 , respectively). However, the SAR data used to create these maps are from  1995-1996 and 2009-2010  and a direct comparison thus neglects the interannual variability as well as possible climate trends. Furthermore, an intercomparison of existing inundation datasets in the Amazon basin was recently performed by  Fleischmann et al.
(2022). It concludes that the SAR-based datasets (and especially the wetland maps from  Hess et al. (2015)  we used in this study) systematically give a higher flood extent than other methods. The long-term minimum and maximum surface water extents given by this inter-comparison are 490,300 ± 204,800 km 2 and 139,300 ± 127,800 km 2 , respectively. Both 𝑆𝑊 𝐸 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑆𝑊 𝐸 𝐺𝐼𝐸𝑀𝑆 at low and high water stages in 2018-2019 are within the range of these results when taking the large uncertainties into account.
Spatiotemporal dynamics of inundations To highlight the potential of this new dataset for hydrological studies, we take here the example of the Llanos de Mojos in the South of the Amazon basin. The inundations in these complex and large wetlands are generated by both an endogenous process fed by local precipitation, and an exogenous process due to a flood wave in the Mamore River generated by intense rainfall over the Andes Range  (Bourrel et al. , 2009). The flooded savannas around the Mamore, Beni, and Itenez rivers correspond well to the spatial extent of inundations derived from both JERS-1 and CYGNSS (Fig. 9 ). If we consider a square box between -68 • and -61 • longitude and between -16. 5 • and -11. 5 • latitude, centered on the Mamoré River, the maximum surface water extent retrieved with CYGNSS and GIEMS are ∼79,300 and ∼80,050 km 3 , respectively.
Discussion The methodology developed here was shown to be efficient for mapping the spatiotemporal variations of inundation extent over the tropics. A special attention was given to the performances over vegetated regions including the dense tropical forests of the Amazon and Congo basins. While the results presented above prove that both the spatial distribution and the temporal variations of surface water extent are consistent with most of the regional reference datasets, a large dispersion was also highlighted (RMSD ∼20%, R ∼0. 60). We discuss here the choice of the methodology and some sources of error in our retrieval model. We also highlight the large interest of CYGNSS and spaceborne GNSS-R for monitoring the spatiotemporal dynamics of inundations at large scale.
Retrieval model The retrieval model proposed in this study is quite simple as it is based on the assumption of the linearity between 𝛤 𝑚𝑒𝑎𝑛 and the Water Fraction. Previous studies stated, on the contrary, that relationships between CYGNSS reflectivity and the surface water are non-linear  (Chew and Small, 2020b). Here, we use the redundancy of CYGNSS observations within each pixel and compute their distribution statistics. The linearity between 𝛤 𝑚𝑒𝑎𝑛 (in linear units) and the Water Fraction was illustrated in Fig. 3  for different AGB values. These curves were P. Zeiger et al. computed using the average 𝛤 𝑚𝑒𝑎𝑛 per Water Fraction bin (2% bins) instead of all samples in the learning dataset. The linear relationships computed this way are more robust to noise, and they were shown to better represent the spatiotemporal variations of inundations.
The few pixels affected by a large Water Fraction were underestimated when using all the samples of the learning dataset, dominated by low WF. We identified the contribution of the vegetation to the GNSS-R signal and developed a correction for this effect. We used the AGB maps from GlobBiomass  (Santoro, 2018; Santoro et al. , 2021)  to bin the learning dataset. Inside a single AGB interval, 𝛤 𝑚𝑒𝑎𝑛 was expected to vary linearly with the Water Fraction (see Fig. 3 ). On the contrary, the slope and intercept of this linear relationship vary depending on the average AGB in the pixel, as shown in Fig. 4. This was better modeled using a third order polynomial fit, such that the final expression of the relationship between 𝛤 𝑚𝑒𝑎𝑛 , the AGB, and the Water Fraction comprises 8 unknowns (𝑎 𝑖 and 𝑏 𝑖 , 𝑖 ∈ [0, 3] in Eq.
For high-AGB values, GNSS-R signals are strongly attenuated whatever the flooding state. This explains the late and important slope increase in our model, to account for the large decrease of 𝛤 𝑚𝑒𝑎𝑛 over flooded areas. Other geophysical parameters are not taken into account in our retrieval model. The surface roughness and SM are commonly used to describe the coherent reflected power, through the Rayleigh parameter and the Fresnel reflection coefficients  (De Roo and Ulaby, 1994; Voronovich and Zavorotny, 2018). They also have been used to parameterize the response of CYGNSS reflectivity to the presence of surface water  (Chew and Small, 2020b ). In the next section, we investigate the sensitivity of CYGNSS Water Fractions compared to the reference datasets for different geophysical parameters, including SM.
In the IND, a positive bias between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 was also observed for NDVI > 0. 3. It could explain the larger amplitude observed in CYGNSS and GIEMS in the central part of the IND at high water stage (see Figure  S3  and Fig. 10 ). Over the Mekong, a small positive bias is found over the full NDVI range. Finally, the biases computed between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 mostly vary from ±10%, with obvious spatial patterns observed across the three study areas. Due to the lack of a consistent dataset of soil roughness at the scale of the GPS L1 wavelength, this parameter was not studied. According to models, an underestimation of fractional inundations is expected when the surface roughness is high  (Chew and Small, 2020b) , because of the loss of coherency in the GNSS-R signals  (Loria et al.
, 2020). We evaluated the influence of the topography (i. e. large scale roughness) on the statistical comparisons performed between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 , 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 in the Parana and the Mekong basins. For this, we used the SRTM30+ Global 1-km Digital Elevation Model (DEM) Version 11 for land surfaces  (Sandwell et al. , 2014) , distributed by the Pacific Islands Ocean Observing System (PacIOOS). The std height in each 0. 1 • pixel was computed and was used to bin the compared datasets, and the results are presented in Figure  S4. Surprisingly, no significant differences were observed between the samples with high and low large scale roughness. On the same figure, we evaluated the evolution of the statistics depending on the AGB. Logically, consistent results were obtained across the full AGB range in both Parana and Mekong basins, because it was used as input to parameterize our model.
Conclusion In this study we proposed an algorithm for retrieving weekly fractions of water at 0. 1 • spatial resolution based on CYGNSS reflectivity. We assumed that the fraction of water in a pixel increases linearly with 𝛤 𝑚𝑒𝑎𝑛 , within each 5 Mg/ha range of biomass. Third order polynomial fits against the AGB were used to account for the influence of the vegetation on GNSS-R signals. The final model comprises 8 parameters that were fitted using a learning dataset. We used reference surface water maps based on MODIS over the Parana, Niger, and Mekong basins and based on JERS-1 over the Amazon basin, for estimating the model parameters required to further compute CYGNSS fractions of water (𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 ). We also used GIEMS at global scale for an independent comparison with our intertropical estimations of inundation extent.
Our retrieval model demonstrated good performances over most selected river basins. The spatial distribution of inundations at high water stage is in good agreement with the reference maps, except over the Parana basin where MODIS may be affected by the vegetation growth, and over the Cuvette Centrale of Congo where long-term inundation signals are detected but not short-term flood pulses under the densest canopies. 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 matches well the seasonality of 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 datasets and the time series of total inundated areas are consistent with the former, except once again over the Parana basin. Statistically, our estimations were compared at the regional scale with MODIS and GIEMS datasets and we obtained a RMSD of ∼20% and a correlation of ∼0. 60. A positive bias was found between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆.
Acknowledgments P. Zeiger  is funded by a PhD grant from  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI), France. This work was supported by  Centre National de la Recherche Scientifique (CNRS) ,  France  and  Centre National d'Etudes Spatiales (CNES) ,  France  through  INSU   PNTS grant URGENS  and  TOSCA grants  SWHYM and SCOMAG, respectively. We thank all the reviewers for their helpful and detailed suggestions, to help us improve the revised version of this manuscript.
We present here the numerical results for the reinjection process in two different geothermal sites and for two different injection scenarios, focusing on the case of two-phase (liquid-gas) flows. Three commercial software (UniSim®, PIPESIM, and OLGA) and one in-house developed code (GWellFM) were used, and their results were compared. The injection should always take place inside a liquid column in the well to avoid degassing and accumulation of the NCGs. When targeting high pressure reservoirs or when injecting high ratios of water to NCGs flow rates, mixing of the two phases should take place on the surface. Whereas for low pressure reservoirs or for high gas contents mixing must occur deep in the well through one or several mixing points to ensure either complete dissolution of the gas or the downward flow of a two-phase mixture with minimum compression on the surface.
However, with the anticipated imposed regulations to reduce gas emissions, more and more industries are expected to turn their attention to reinjection strategies. Well flow simulations are important for understanding fluid behaviour and modelling crucial parameters in the conception and design phases of a project. Available two-phase flow models have limitations with regards to the types of fluid, the diameter of the pipe and the orientation of the flow used in their development. Since most of them were designed for either the O&G or the nuclear industry, careful consideration should be given when adopting these for geothermal applications. Several commercial and numerical codes exist today for modelling the downward multiphase flow in injection wells. OLGA, PIPESIM, LedaFlow®, and PROSPER are among the software packages that are widely used for steady-state and dynamic calculations.
Injection scenarios Two reinjecting techniques were considered. First, the mixing of the two phases on the surface and the direct injection of the resulting mixture from the wellhead (surface mixing, Inj1). Second, the injection of the two phases separately but simultaneously in the annulus and the central tube of a dual-pipe (coaxial) well and mixing of them at some depth below the surface (deep mixing, Inj2). In the latter case, the gas could be injected either in the annulus part or the central tubing, and water into the other compartment. Simulations were performed under single-phase flow conditions, injecting water-only (Inj0), and results obtained using the commercial software were used to benchmark the inhouse code. For the Kizildere case study, two well configurations were investigated.
Steady-state flow simulations with PIPESIM PIPESIM is a steady-state flow simulator that can solve multiphase flow problems using physics-based governing equations (see  Appendix 1). Two injection scenarios were investigated for a well injecting CO 2 or NCG and water in the Kizildere site (Table  1 ). Gas and water injection rates as well as reservoir injectivity sensitivity simulations were performed. For each scenario completion configurations were set up, one for injecting through the casing (Fig. 1a, K-Inj1 ) and one with a central tubing for mixing the two phases in a certain depth in the well (Fig. 1b,  K-Inj2 ). Although PIPESIM software does not have a direct interface and the tools to model separate flows in the annulus and the tubing (K-Inj2), the available features are enough to mimic such conditions.
Transient flow simulations with UniSim® UniSim® is a design suite software that allows the construction of dynamic and steady-state simulation models under variable environmental conditions. The dynamic simulations were conducted for the Castelnuovo case study, and the outline is shown in Fig. 2. The model consisted of four blocks: the well, the drain valve, the compressor, and the reinjection valve or valves (multi-point injections) located along the well. The well-block was the main part; the whole well depth of 2500 m was divided into 25 vertical segments, with 100 elements each, in order to calculate with a sufficient accuracy the dynamic momentum, the pressure gradient, the heat transfer, the solubility, and the void fraction. Refining further the mesh improved slightly the calculation accuracy, especially for the two-phase flows, but significantly increased the computational time.
Steady-state flow simulations with GWellFM GWellFM (Geothermal Well Flow Model) is a steady-state 1D nonisothermal multicomponent and two-phase flow simulator. The model considers the single-phase flows of liquids and gases, the hydrodynamics of the two-phase downward and upward flows, constitutive laws for mixtures, the flow of fluids through orifices/valves, and finally the heat exchange between the well completion and the surrounding formation. The code was developed in Python and discretises and solves iteratively along a mesh the mass, the momentum, and the heat balances (see  Appendix 1). Hydrodynamic modelling consists of calculating the velocities of fluids and pressure drop along with the flow. For single-phase flows, the calculations are quite simple and well established. However, for twophase flows the calculations are more complicated, and additional flow parameters, such as the void fraction (or liquid hold-up), the frictional pressure drop, and the phase distribution (flow regime), must be defined.
The commercial Multiflash (MF) software, which has a comprehensive PVT (pressure, volume, temperature) and physical properties package that quickly and reliably allows the complete modelling of the phase behaviour of complex mixtures and pure substances through equations of state; and the Carnot library, which is an in-house thermodynamic library, performing flash calculations using robust isothermal-isobaric algorithms coupled to various real-fluid EoS to compute the vapour-liquid equilibrium as well as the transport properties. Due to the presence of water and aqueous mixtures, the Cubic-Plus-Association (CPA) model was chosen to be used, in both models, since it explicitly considered the association of water molecules (see Appendix 3). This EoS can reproduce the properties of the vapour and water phases with the same model  [41].
Comparison of modelling tools In addition to the comparisons between the results of the commercial tools and the in-house code for the water-only injection, simulations were performed for the mixing of water and NCGs. OLGA simulator with the black-oil option was also used to model one case from Fig. 9  (m L = 200 t/h, m G = 5 t/h) for the injection of a mixture of water and NCG from the surface (K-Inj1) and compared with PIPESIM and GWellFM (Fig. 12 ). Again, the behaviour of the mixture is different between the compositional and black-oil flow models. The models with the compositional option predict that at one point in the well the gas is fully dissolved in the liquid, whereas the black-oil models estimate that the mixture is always under two-phase conditions. The complete dissolution of the gas can be also seen in the temperature profiles, where an increase in the local temperature is observed.
The tools set included UniSim®, OLGA, and PIPESIM, as well as an in-house code (GWellFM), which were all used to simulate different injection scenarios for two geothermal fields (see Table  1 ). The main findings of the present study are: a) Deep mixing helps fully entrain the gas mixture in the liquid with minimum work on the surface, driving the two-phase mixture down into the reservoir. b) Although, for high-pressure reservoirs, deep mixing (K-Inj2) needs more NCG compression power on the surface than surface mixing (K-Inj1), it requires less pumping demand. c) For specific geothermal conditions (as in the 2 nd case study) with low reservoir pressure, limited water injection rate, or high NCG injection rate, the deep mixing is the only feasible solution. d) Deep mixing through valves at different depths (C-Inj2b) is more technically promising compared to the single mixing point deep in the well (C-Inj2a).
Particularly, the black-oil approach by PIPESIM and OLGA was found to not be flexible enough to consider both single-phase and two-phase properties in a single simulation. It was shown that a negligible amount of CO 2 (0. 1 %wt) in water gave different results from UniSim® and GWellFM. g) Existing pipe flow models are highly dependent on the phase fraction; therefore, the uncertainty of the phase fraction and physical properties will be projected on the flow model for downward flow on the reinjection well. h) The effect of the thermodynamic and hydrodynamic models becomes more evident when the in-house code is compared to the commercial tools. A good agreement was found between UniSim® and the developed in-house code for property profiles, making them reliable to simulate the behaviour of the downward two-phase flows of water and NCG mixtures.
Acknowledgments The present research was funded by the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement No  818169  ( GECO  Project). The authors would like to acknowledge  Zorlu Enerji  and  Graziella Green Power  S. p. A, industrial partners of the  GECO  project and operators of the two geothermal sites, for providing the permission to use the field data for the simulations.
Moreover, the fast rate of stimulation ensured that only one gaze fixation was performed on each stimulus, eliminating the contribution of potential eye gaze exploration differences between species. From an analysis in the Fourier domain, we identified face-selective clusters with a high signal-to-noise ratio (SNR) in both monkeys, at typical locations, and we demonstrated that our results are robust and reproducible. To promote this new approach as a standard localizer of face-selective regions in NHP, the experimental paradigm, stimuli, and analysis scripts will be provided online (Open Science Framework) upon acceptance of the paper.
Visual stimulation The stimuli used in the present study consisted of color images of human faces, monkey faces, and non-face objects from a wide variety of categories (e. g. , houses, cars, animals, etc. ; Fig. 1  ). The set included natural images (i. e. , unsegmented) of 100 different and non-famous human faces and 195 non-face objects images from the dataset created by Rossion and colleagues at the Face Categorization Lab of the University of Louvain, Belgium (from  Gao et al. , 2018 ). The monkey face images were 89 natural photographs of rhesus macaques collected on the internet, thus unfamiliar to the macaque subjects. Each face image contained only a single human or monkey face. Face and object images significantly varied in size, lighting conditions, color, 3D orientation, and background ( Fig.
1  ). Unlike when using highly homogenous sets of stimuli, this variability minimizes the contribution of low-level cues to face-selective neural responses and ensures that these responses reflect generalization across variable exemplars (i. e. , a category-selective response;  Rossion et al. , 2018 ). Our dataset contained natural photographs of human (upper rows) and monkey (middle rows) faces and of non-face objects (lower rows). These stimuli differed in size, lighting condition, color, 3D orientation, and background. Phase-scrambled versions of the images in the Fourier domain (below each face and object image) were also included as control conditions. To further control for the potential contribution of low-level visual cues to category-selective responses, the stimuli were resized to 256 × 256 pixels and equalized in terms of intensity and contrast (averages values of 115 ± 0.
Anatomical and functional templates Data collected during the anesthetized sessions were used to create individual functional and anatomical templates. The anatomical template was obtained by realigning and averaging the four T1-weighted (MPRAGE) volumes. It was then aligned to the MNI space of the 112RM-SL  ( McLaren et al. , 2009 ( McLaren et al. , , 2010 ) ). Cortical surface reconstructions were performed using the CARET software  ( Van Essen et al. , 2001 ). The functional template was obtained by realigning and averaging the 300 functional (GE-EPI) volumes. It was then aligned with the anatomical template. Spatial normalization parameters (affine and non-rigid) between the functional and anatomical templates were determined based on the gray matter probability maps of both templates.