dataesr/scientific-paragraphs-categorization huggingface dataset:
We considered mediators of two kinds-vector and scalar. For each of the cases, we explored the effect when the mediator is very heavy or when the mediator is very light, using data from XENONnT and Super-K. Boosts due to cosmic ray electrons drastically change the DM flux as seen on Earth. The effect though is quite different for different DM masses, as well as for different mediator masses. For light mediators, the boosted flux is suppressed below the constant cross section flux for relatively heavier DM masses, while it is raised above that level for light DM masses. This is very different for heavy mediators, for which the DM flux is augmented above the constant cross section case for all masses. This behavior is largely independent of the nature of the mediator, though there are some numerical differences in the scalar and vector cases.
This behavior, in turn, leads us to expect that the energy dependence of the cross section can provide stronger bounds for lighter DM in the light mediator case, while providing stronger bounds for a large range of DM masses in the heavy mediator case. Our analysis meets this expectation. The two experiments whose dataset we use differ in two fundamental aspects. While Super-K has a much larger number of target electrons (as can be seen by the different values of ℵ used in our analysis), XENONnT has a much smaller trigger energy. The live time for the dataset from Super-K is also longer than for the dataset from XENONnT. We find that, for the light mediator case, XENONnT gives stronger bounds on both the cross section and the electron-mediator couplings compared to Super-K, while for the heavy mediator case, the reverse is true.
ACKNOWLEDGMENTS D. G. acknowledges support through the  Ramanujan Fellowship and MATRICS Grant  of the  Department of Science and Technology, Government of India. D. B. acknowledges financial support through the  National Postdoctoral Fellowship (NPDF) ,  SERB ,  PDF/2021/002206. Work of A. G. is supported by the  National Research Foundation of Korea  ( NRF-2019R1C1C1005073 ). D. S. has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 101002846 ,  ERC.
To test this hypothesis, we first analysed a dataset from one recent study of our group (Experiment 1) during which human subjects made successive perceptual decisions between reaching movements  (Reynaud et al. , 2020; Thura, 2020). In this experiment, participants were faced with changing sensory evidence favouring one of two possible targets and could invest up to 3 s in the decision process. They then had up to 800 ms to execute the reaching movement expressing a choice. Because our previous results strongly indicate that human subjects coordinate the duration of their decision and movement in order to optimize their rate of reward  (Saleri Lunazzi et al. , 2021) , we also make the more specific hypothesis that the type of error that induces the largest adjustment of behaviour in the subsequent trial is an error of duration.
This hypothesis predicts that posterror adjustments will mainly occur with errors of duration, if participants are 'free' to adjust their decision and/or movement durations (MDs) in the following trial. To test this hypothesis, we analysed a second experimental dataset from our group (Experiment 2) in which the decision component of the task was similar compared to Experiment 1, but in which the allowed duration of reaching movement was strictly constrained, resulting in numerous 'too long' (in duration) and 'too short' (in duration) movements made by the participants.
| Dataset The decision and motor behaviours of these subjects have been described in two recent publications reporting the effects of the decisional context on movement properties  (Thura, 2020)  and the effects of the motor context on decision strategies  (Reynaud et al. , 2020). In these reports, subjects' behavioural adjustments are described either within a given trial (i. e. , the relation between a decision duration [DD] and the duration of the movement produced to express that decision) or between specific conditions designed to set stable decision or motor speed-accuracy contexts in blocks of tens of trials. Here, we aim at describing adjustments of subjects' behaviour from trial to trial, depending on their decision and/or motor performance.
| Data analysis Data were analysed offline using custom-written MATLAB (MathWorks) and R (https://www. r-project. org/) scripts. Reaching horizontal and vertical positions were first filtered using polynomial filters and then differentiated to obtain a velocity profile. Onset and offset of movements were then determined using a 3. 75-cm/s velocity threshold. Reaching MD, peak velocity (PV) and amplitude (Amp) were respectively defined as the duration, the maximum velocity value and the Euclidean distance between movement onset and offset (Figure  1c ). Decision duration (DD) was computed as the duration between the first token jump and the time at which subjects committed to their choice (Figure  1c ). To estimate this commitment time in each trial, we detected the time of movement onset as mentioned above, defining the subject's reaction time in that trial, and subtracted from it her or his mean sensorimotor delays estimated based on her or his reaction times in the DR task performed the same day and in the same condition.
In particular, most of the 'unreached' trials were trials in which subjects failed to reach the target within 800 ms because of too slow movements. These erroneous movements were corrected with faster and shorter ones in the following trial. But there could also be unreached trials in which subjects failed to hit the target after a fast and short movement landing above or below the circle. In order to more precisely and more specifically assess the impact of MD errors on subjects' subsequent behaviour, we analysed a second dataset from our group (Experiment 2) in which the decision component of the task was similar to the one used in Experiment 1, but in which the allowed duration of reaching movement was strictly constrained, leading to more numerous and more consistent too long (in duration) and too short (in duration) movements.
| Dataset The decision and motor behaviours of these subjects have been described in a recent publication reporting the effects of the motor context on decision strategies  (Saleri Lunazzi et al. , 2021). In this report, subjects' behavioural adjustments are described either within a given trial (i. e. , the relation between a DD and the duration of the movement produced to express that decision) or between specific conditions designed to set stable motor speed-accuracy contexts in blocks of tens of trials. Here, we aim at describing adjustments of subjects' behaviour from trial to trial, depending on their decision and/or motor performance.
ACKNOWLEDGEMENTS This work was supported by a  Centre National de la Recherche Scientifique (CNRS) ,  Institut National de la Santé et de la Recherche Médicale (Inserm) ATIP/Avenir  grant and an  Inserm  young investigator fellowship to DT. We thank  Eric Chabannat  for his help with the statistical procedures,  Sonia Alouche  and  Jean-Louis Borach  for their effective administrative assistance and  Frédéric Volland  for his expertise during the technical preparation preceding the experiments.
In this paper, satellite products developed during the Earth Observation for Science and Innovation in the Black Sea (EO4SIBS) ESA project are presented. Ocean colour, sea level anomaly and sea surface salinity datasets are produced for the last decade and validated with regional in-situ observations. New data processing is tested to appropriately tackle the Black Sea's particular configuration and geophysical characteristics. For altimetry, the full rate (20Hz) altimeter measurements from Cryosat-2 and Sentinel-3A are processed to deliver a 5Hz along-track product. This product is combined with existing 1Hz product to produce gridded datasets for the sea level anomaly, mean dynamic topography, geostrophic currents. This new set of altimetry gridded products offers a better definition of the main Black Sea current, a more accurate reconstruction and characterization of eddies structure, in particular, in coastal areas, and improves the observable wavelength by a factor of 1.
Previous endeavours related to calibration of a regional turbidity algorithm in the Danube Delta coastal area  (Constantin et al. , 2016)  focused on MODIS imagery. As concerns Sea Surface Salinity (SSS), so far, there is no dedicated satellite-sea surface salinity (SSS) product in the Black Sea. In this paper, we present new algorithms and products developed in the frame of the 2-year ESA project EO4SIBS (Earth Observation for Science and Innovation in the Black Sea). During EO4SIBS, Ocean Colour (OC), altimetry and SSS products are produced by improving and developing algorithms and data processing to appropriately tackle the Black Sea's particular configuration and geophysical characteristics. These products are developed for the last decade and are validated with regional datasets. They are developed aiming at better characterizing the Black Sea mesoscale and coastal processes, basin scale productivity and SSS.
Materials and methods EO4SIBS products are summarized in Table  1  and are available from a web interface (http://www. eo4sibs. uliege. be/, Supplementary S1) and at Zenodo  (Grégoire et al. , 2022)  under data doi: 10. 5281/zenodo. 6397223 with a full documentation that includes Products User Manuals (PUM) and Algorithm Theoretical Basis Document (ATBD). All these products are distributed in netCDF files. In this section, we overview the algorithms used to retrieve the satellite products, including the new developments and the calibration/validation procedures.
The quality of these algorithms is assessed by comparing the spectral reflectance (R rs ) retrieved from S3 OLCI using different atmospheric corrections with that measured in the field by the two Black Sea Aeronet-OC stations i. e. , Gloria (25/01/2011-07/03/2018) and Galata (12/04/2014-31/10/2018). All available matchups from 2017 are taken into consideration. These two stations are located in the western part of the basin relatively close to the coastline (app. 22-23 km) and are representative of different optical conditions (e. g. , Gloria is sometimes exposed to the Danube River plume). Another important aspect concerning data processing refers to the flagging scheme used. As suggested by the latest EUMETSAT   Kubryakov et al. , 2019; Kubryakova et al. , 2021) , it was decided not to remove these areas from subsequent analysis.
Inversion algorithm Each OC product is estimated using an approach that merges two distinct algorithms adapted to low and high values of the retrieved variable. The final product is then obtained by combining these two products according to specific thresholds and using an interpolation scheme over an overlapping range. This combination is expected to offer a more accurate retrieval over broader ranges of concentrations. For the three biogeochemical variables of interest (i. e. , Chl-a, SPM and TURB), the algorithms are calibrated regionally using in-situ data when available. For Chl-a, in-situ measurements are available only for the low-end part of the concentration spectrum. Thus, for the low Chl-a regions, a blue-to-green ratio approach is adapted regionally (n=31, R=0. 91, Root Mean Square Difference (RMSD)=0.
17 and median absolute percentage difference (MAPD)=20. 33%). A fourthorder polynomial relationship (OC4-type) between Chl-a and the maximum band ratios of reflectance at 490 nm and 510 nm with that at 560 nm is developed. For the high Chl-a regions (i. e. Chl-a > 2 mg m -3 ), the development of a regional algorithm is not possible due to the limited number of available in-situ datasets for its calibration and validation during the period of Sentinel data acquisition. We then use the neural network algorithm (C2RCC) including during coccolithophores blooms. C2RCC computes the absorption by phytoplankton pigments (a pig ) at 442. 5 nm, based on the waterleaving reflectance and then links a pig to Chl-a using predefined scaling factors  (EUMETSAT, 2021). A merging scheme combines then results from OC4-type and C2RCC algorithms.
Filling the clouds Level-4 (gap-free) Chl-a products are obtained using DINEOF (Data Interpolating Empirical Orthogonal Functions,  Beckers and Rixen, 2003; Alvera-Azcaŕate et al. , 2005). DINEOF infers the missing information (e. g. , due to the presence of clouds, dust or smoke in the atmosphere) thanks to a spatio-temporal analysis of the available data using an Empirical Orthogonal Functions (EOFs) basis extracted from the data themselves. Given the large size of the datasets, a separate analysis for 2016 and 2017 is realized. A first DINEOF run is performed on each of these datasets and the EOFs retained are used to detect and remove outliers, following Alvera-Azcaŕate et al. (2012). The final DINEOF reconstruction is done using 4 and 5 EOFs for respectively 2016 and 2017 respectively.
Salinity Level-2, Level-3 and Level-4 SMOS SSS datasets are generated starting from the brightness temperature derived from the SMOS Level-0 data. The complete description of the algorithms as well as the quality assessment of the resulting Level 3 SMOS SSS products are provided in  Olmedo et al. (2022). Here, we briefly summarize the main improvements introduced from the data processing point of view for taking into account the Black Sea's particular geographic and geophysical conditions and we refer to  Olmedo et al. (2022)  for further details.
Quality estimation of the SSS products The quality of the SMOS SSS products is estimated by using the correlated triple collocation method developed by  Gonzaĺez-Gambau et al. (2020). The method compares three datasets that resolve similar spatial scales, with two of them affected by correlated errors. Here, the two error correlated data sets are the different Level SSS products while the uncorrelated one is the Black Sea physical reanalysis system (version E3R1) freely distributed by the Copernicus Marine Service with the identifier BLKSEA_MULTIYEAR_PHY_007_004 and doi https://doi. org/10. 25423/CMCC/BLKSEA_MULTIYEAR_PHY_ 007_004. A flagging is defined for the SSS products (Level-2, -3 and -4) based on the range and variability of the products as described in  Olmedo et al. (2022).
The comparison with in-situ data of the S3 OLCI retrieved SPM and TURB shows that the use of a regional algorithm improves the quality of the products with respect to a generic one (see Figure  4  for SPM). The use of two spectral bands (i. e. , l = 665 nm and l = 865 nm) and the calibration of the algorithm's parameters with regional data improve the quality of the product compared to an approach that uses a single spectral band (l = 665 nm). The RMSD is reduced from 7. 26 to 1. 93 g m -3 while the MAPD is reduced from 35. 15 to 15. 89% when using a regionally fitted algorithm. The improvement is particularly visible for high SPM values. This is due to the better performance of the NIR band for these high SPM concentrations, for which the red band tends to saturate and gives false estimations.
e. <= 10 m), decouples the skin surface from waters below the pycnocline and prevents a straightforward comparison between the satellite and in-situ SSS. In addition, sampling error (or representation error) can also be significant especially in coastal areas where this error may amount 0. 4-0. 7 psu (e. g. ,  Vinogradova et al. , 2019). Nevertheless, the SMOS retrieved SSS is compared with that provided in-situ Then, the estimated error statistics include the error associated to the satellite product and that associated to different sampling time and depth. Besides the comparison with insitu, the uncertainty of SMOS SSS is also estimated by using correlated triple collocation with models and other satellite products (SMAP). The quality of Level-2 dataset corresponding to descending satellite overpass (i.
Mean sea level trend The monitoring of the long-term evolution of the Mean Sea Level (MSL) for climate applications and the analysis of Ocean/Climate indicators (e. g. , MSL trends) require a homogeneous and stable sea level record. Such a dataset is produced within the Copernicus Climate Service (C3S,  Legeais and Meyssignac, 2021) , which is considered as the reference product for MSL change detection. Trend in the MSL estimated from C3S is compared with that estimated with products from the Copernicus Marine Service and EO4SIBS (Figure  12 ). These last two products are more designed for mesoscale activities. The MSL trend is analyzed over the 10-year period common to the 3 products considered. Although a 10-year period is too short to give an accurate estimation of the MSL climate trend over the Black Sea, it allows to qualify the consistency of the different gridded products.
4-0. 7 psu  (Olmedo et al. , 2022). This is a success taking into account that since the beginning of the SMOS mission it is evident that the environmental conditions of the Black Sea basin hamper the SSS retrieval. In comparison, the seasonal variability of the Black Sea SSS in the open sea in response to dilution, evaporation, mixing and river water intrusion amounts to a few tenths of psu (~0. 1-0. 5 psu). For OC, Chl-a, SPM and TURB datasets are produced using regional calibrated algorithms. The application of a water classification algorithm highlights the heterogeneity of the Black Sea from optical point of view (e. g. mostly case-1 in open sea areas, with periods of class-2 and very localized case 3 waters). These regional products need to be further validated using datasets independent from the calibration ones, representative of the entire Black Sea and of all possible environmental conditions, including during coccolithophores blooms.
Coastal applications We deliver a 5Hz altimetry along-track product obtained by processing the full rate (20Hz) altimeter measurements from C2 and S3A. This 5Hz product is combined with existing 1Hz product and interpolated to produce gridded datasets for the SLA, mean dynamic topography, geostrophic currents. We show that these EO4SIBS altimetry products offer, compared to a 1Hz one, a better definition of the main Black Sea current, a more accurate reconstructions of eddies structure in coastal areas and improve the observable wavelength by a factor 1. 6. It also allows a better characterization of the mesoscale circulation with, in particular, an improved identification and characterization of eddies. For SSS, we show that the SMOS product tracks the basic characteristics of the Danube's plume dynamics with an increased extension at the plume at the end of spring when the discharge is maximum.
However, SMOS SSS underestimates the variability of the plume dynamics. An increased resolution of Level-2 products for coastal applications can be achieved by combining the SMOS retrieved brightness temperature with SST from S2 and S3, using non-linear fusion schemes. A consideration of the day-night temperature cycle is also expected to improve the resolution of the Level-2 product. For OC, the combination of different datasets from S2 and S3 using appropriate fusions techniques is promising for the delivery of very high-resolution products for coastal applications. For instance, fusion between S2-MSI and S3-OLCI data can bring benefits in areas where a detailed inspection of the spatial patterns combined with improved spectral information is needed  (Alvera-Azcárate et al. , 2021). The comparison of cloud-free S2-MSI and S3-OLCI satellite images obtained over three coastal optically complex areas (i.
(2022)  evidence that the EO4SIBS Level-4 altimetry dataset better characterize subsurface anomalies of oxygen and salinity within cyclonic and anticyclonic eddies. The imprint of mesoscale structure on the oxygen distribution inside eddies is not limited to a vertical displacement. Other changes potentially induced by within-eddy biogeochemical processes are suggested. This finding is in agreement with that of  Karstensen et al. (2015)  who show that eddies are local sites of enhanced production and degradation of organic matter. The combination of SST, SSS and altimetry satellite products with increased resolution and quality in connection with models and insitu platforms offer information to progress in the understanding of the role of mesoscale and SSS in the formation of the CIL and evolution of Black Sea's thermohaline state.
Towards a multiplatform system to monitor the Black Sea state and its variability The development of OC, altimetry and SSS products for the period 2010-2020 has suffered from the lack of fit for purpose in-situ data for the calibration and validation of the algorithms. Table  2  summarizes the requirements for in-situ observations and gaps that are identified for developing the products listed in Table  1. In general, except for Argo, most in-situ data available in public databases (e. g. , EMODnet, CMEMS, WOD) are collected in the frame of local campaigns, are very coastal and mostly cover the western basin (e. g. ,  Palazov et al. , 2019). The quality is sometimes not optimal and would benefit from best practices documentation and intercalibration exercises across regions and basins. For OC, the available in-situ data over the S3 and S2 periods are very limited and do not allow a validation of the products over the full spectrum of contrasted optical conditions present in the Black Sea.
For Chl-a, high quality data, determined ideally by High Performance Liquid Chromatography (HPLC), is usually recommended. Yet, such data are very rarely available. For turbidity, SPM and CDOM, very few (no) data are available in the open sea. For SSS, most of the in-situ data used are collected from 1-10m (the shallowest). In the past, the Black Sea MSL has been quite well monitored by TG with data series longer than 100 years along the north-eastern coast (Figure  13 , Table  S2. 2 and Supplementary S2). However, presently, the number of active TG significantly decreases and is mainly limited to the southern coast where two radars have been recently installed. In addition, the disparity in temporal series (e. g. gaps in time series), sampling and anomalies detected on the data series (e.
g. , no clear quality flagging) challenge to obtain significant results. Then, our recommendations for the evolution of the in-situ Black Sea observing system to support the development of high-quality satellite products on OC, SSS and altimetry are: 1. To reinforce and sustain the existing AERONET-OC network, which could inform and assess the quality of atmospheric correction algorithms. So far, two stations are present, and both are located along the western coast. 2. To deploy and permanently maintain 3 BGC-Argo floats, equipped with CDOM, Chl-a, backscatter and irradiance sensors in the eastern, central and western parts of the open sea (Figure  13 ). 3. To deploy at least 1 BGC-Argo float specifically equipped with hyperspectral radiance and irradiance instruments to provide high quality spectrally resolved radiometric data (e.
Funding This work has been carried out as part of the  European Space Agency  contract  Earth Observation data For Science and Innovations in the Black Sea (EO4SIBS ,  ESA  contract  n°4000127237/19/I-EF ). MG received fundings from the  Copernicus Marine Service (CMEMS) , the  European Union 's  Horizon 2020 BRIDGE-BS project  under grant agreement No. 101000240  and by the Project  CE2COAST  funded by  ANR (FR),  BELSPO (BE) ,  FCT (PT) ,  IZM  (LV),  MI (IE) ,  MIUR (IT) ,  Rannis (IS) , and  RCN (NO)  through the 2019 " Joint Transnational Call on Next Generation Climate Science in Europe for Oceans" initiated  by  JPI Climate and JPI Oceans. The research on  SMOS SSS  has been also supported in part by the  Spanish R&D  project  INTERACT  ( PID2020-114623RB-C31 ), which is funded by  MCIN/AEI/10.