dataesr/scientific-paragraphs-categorization huggingface dataset:
Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data hosting facility. The results indicate that the integration of process-based features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semi-arid areas, for instance, performance enhancement was observed when the evaporative efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied on observation sites in Tunisia, Italy and South-India that are not part of ISMN.
ISMN soil moisture data The first training and test operations were conducted on eight ISMN networks previously considered in  (Souissi et al. , 2020). Figure  2  shows the distribution of the considered soil moisture networks with different soil textures and climatic parameters. The selected stations exhibit a root zone depth varying between 30 and 60 cm (Table  2 ). In addition to in situ soil moisture, the ISMN optionally includes meteorological and soil variables that are available over specific time periods. Values of the situ surface soil temperature among these variables can be employed as a useful indicator of the soil moisture data quality. The soil temperature was provided in Celsius, and the plausible values range from -60 to 60 °C. Regarding soil moisture data, surface soil temperature data were also provided with quality flags  (Dorigo et al.
Normalized difference vegetation index We considered the remote sensing-based normalized difference vegetation index (NDVI) to quantify vegetation dynamics. We extracted this index from the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices product (MOD13Q1 version 6). MODIS Vegetation Indices (MOD13Q1) version 6 data are generated at 16day intervals and a 250-m spatial resolution as a Level 3 product. This product provides two primary vegetation layers. The first vegetation layer is the NDVI, which is referred to as the continuity index of the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR)-derived NDVI. The algorithm chooses the best available pixel value from all the acquisitions over the 16-day period.
Potential evapotranspiration Similarly, we assessed the impact of considering a remote sensing-based evaporative efficiency on RZSM prediction. The computation details of this variable will be detailed later (cf. Section 3). We employed the remote sensing-based potential evapotranspiration (PET) to compute the evaporative efficiency. We extracted the PET from the MOD16A2 Evapotranspiration/Latent Heat Flux version 6 product, which is an 8-day composite dataset produced at a 500-m pixel resolution. The algorithm used for MOD16 data product collection is based on the logic of the Penman-Monteith equation, which employs inputs of daily meteorological reanalysis data along with MODIS remote sensing data products such as vegetation property dynamics, albedo, and land cover. The MOD16A2 product provides layers for the composite evapotranspiration (ET), latent heat flux (LE), potential ET (PET) and potential LE (PLE).
44% of the stations achieved a correlation value higher than 0. 7 with model ANN_SSM_NDVI, versus 38. 41% of the total stations achieving a similar correlation value with model ANN_SSM. In regard to the ANN_SSM_TEMP model that integrates the soil surface temperature, 54. 35% of the stations (except the stations of the AMMA-CATCH network, as no surface temperature data were available) exhibited higher correlation values than those obtained with the ANN_SSM model. Additionally, 40. 24% of the stations achieved a correlation value higher than 0. 7 with model ANN_SSM_TEMP versus 36. 94% of the stations with model ANN_SSM.
Statistical methods For variables presenting missing data, we conducted multiple imputation analyses with the MICE algorithm  [51] [52] [53]  on 50 datasets and 50 iterations. Imputation details, comparisons between available data, complete cases, and imputed data are presented in the Supplementary Material. First, we described the sociodemographic, lifestyle, and health characteristics, including academic level, psychiatric symptoms, and substance use, in the full sample of participants and according to the history of cannabis use at inclusion. We looked for associations between the cannabis use frequency after 1 year and the other variables using an analysis of variance or the Kruskal-Wallis rank sum test for continuous variables and the Pearson Chisquared test with Monte Carlo simulated p-value based on 10,000 replications for discrete variables.
Strengths and limitations This work has methodological strengths including the population studied, the large sample size, the longitudinal design, and the analysis of the use frequency. Our samples included young adult students with a large proportion of women. Young adults are a population particularly at risk for the short-and long-term consequences of cannabis use, and longitudinal data on this issue are sparse. Most of the longitudinal studies have focused on childhood or adolescence. The use frequency relationship between cannabis use and ADHD is particularly important and has been used to examine the link between cannabis use and psychotic symptoms  [72]. The study also has some limitations. First, since we analyzed only those who completed the follow-up questionnaire, a large number of participants were removed, thus creating a selection bias.
Acknowledgments. We thank the participants for their commitment to this project. The authors are grateful to the  i-Share  staff for their assistance in data collection and the administration of the project. Author Contributions. C. T. is the scientific coordinator of the iShare project. Data analysis: F. A. M. J. , C. G. , O. C. ; Data collection: C. T. , I. M. , J. A. ; Data interpretation: F. A. M. J. , C. G. The authors F. A. M. J. and C. G. wrote the first draft of the manuscript, and all authors contributed substantially to its revision. All authors have approved and contributed to the final manuscript. Financial Support. The preparation and initiation of the i- Share project was funded by the program  ' Invest for future'  (reference  ANR-10-COHO-05 ). The i-Share Project is currently supported by an unrestricted grant from the  Nouvelle-Aquitaine Regional Council  ( Conseil Régional Nouvelle-Aquitaine ) (grant number:  4370420 ) and by the  Bordeaux 'Initiatives d'excellence' (IdEx) program  of the  University of Bordeaux  ( ANR-10-IDEX-03-02 ).
The  Department of Psychiatry  chaired by him received unrestricted educational and research support from the following companies in the last 5 years:  Eli-Lilly ,  Lundbeck ,  Janssen-Cilag ,  Actelion ,  Shire, Ferrer, Oryzon, Roche, Psious , and Rubió. M. A. reports (past 5 years) outside the submitted work grants from  French Health Ministry Research Grant (PHRC) ,  French Addiction Agency Research  Grant (MILDT/MILDECA),  Aquitaine Regional Council Research Support (CRA) ; grants from  Indivior , personal fees from  Bouchara Recordati, Camurus, Indivior ,  Pfizer.
of the work done within Phase III of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty project (OC6), under International Energy Agency Wind Task 30. This phase focused on validating the aerodynamic loading on a wind turbine rotor undergoing large motion caused by a floating support structure. Numerical models of the Danish Technical University 10-MW reference wind turbine were validated using measurement data from a 1:75 scale test performed during the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project and a follow-on experimental campaign, both performed at the Politecnico di Milano wind tunnel. Validation of the models was performed by comparing the loads for steady (fixed platform) and unsteady wind conditions (harmonic motion of the platform).
Model Definition To validate the accuracy of the rotor loads for a FOWT, measurement data from two wind tunnel experimental campaigns were used. Both campaigns were conducted in the Politecnico di Milano wind tunnel (13. 84 m wide x 3. 84 m high x 35 m long) and used a 1:75 scaled version of the DTU 10-MW RWT. The blades were straight, without cone angle, and rigid. The blade properties can be found in Table  1. The aerodynamic center of the different radial stations is coincident with the blade pitch axis. Two-dimensional sectional-model experiments were conducted in the DTU Red wind tunnel to characterize the airfoil polars in smooth and turbulent flows  (Fontanella et al. , 2021b). The airfoil polars with the lift and drag coefficients for the different angles of attack at the 20 radial stations shown in Table  1  for turbulent flow conditions were provided to the participants.
Each airfoil polar contains seven sets of lift and drag coefficients for Reynolds numbers ranging between 5E4 and 2E5  (Robertson et al. , 2022). The rest of the geometry of the model (e. g. , tower, rotor overhang) was dependent on the testing campaign being studied. The first dataset was developed during the UNAFLOW project  (Fontanella et al. , 2021a). The testing was similar to the one performed during the LIFES50+ project  (Bayati et al. , 2017). However, for the UNAFLOW test the tower was considered rigid and included a negative tilt angle of 5 deg to offset the wind turbine tilt angle; resulting in a rotor perpendicular to the wind tunnel floor. The second dataset is from a follow-on testing campaign performed during 2021 in the same wind tunnel. It used the same rotor but a different nacelle and tower length (also rigid) than the one used in the UNAFLOW project.
Three additional load cases (2. 12, 2. 16, and 2. 17) were included to examine conditions that might create more impactful unsteady aerodynamic responses due to changes in the flow conditions. There is no experimental data available for these conditions and they are thus used as verification cases only. Load case 2. 12 includes a platform surge oscillation at the same frequency as load case 2. 7, but with an amplitude that is one order of magnitude higher. Finally, load cases 2. 16 and 2. 17 are based on load case 2. 12 but include some rotor speed and blade pitch variations. Some numbers are skipped in the load case numbering sequence because there were more load cases that did not provide additional insight and are left out of the results of Section 5. The presence of the scaled wind turbine in the test section reduces the flow area compared to an unrestricted freestream.
For example, the band around the median contains 50 % of the participants results for a given modeling approach. The upper and lower ranges contain the remaining 25 % of the participant results, respectively. This statistical information can be considered equivalent to the one obtained from a box plot. Using the median instead of the mean avoids the potential impact of outliers in the data. The subindex next to each modeling approach indicates the number of results available from the participants. For the GDW approach there is only data from two participants. In this case, the median is 275 equivalent to the mean and the range is determined by the maximum and minimum value. For the experiment 1, hot-wire measurements were taken during the steady wind condition. A hot-wire anemometer probe traversed the along-wind direction (x-direction in Figure  2 ) and the cross-wind direction (y-direction in Figure  2 ).
Acknowledgements The authors would like to thank  Politecnico di Milano  for providing the data to characterize the numerical models, the data recorded during the UNAFLOW and the follow-on campaigns, as well as for their ongoing support. This work was authored in part by the  National Renewable Energy Laboratory , operated by  Alliance for Sustainable Energy, LLC , for the  U. S. Department of Energy (DOE)  under Contract No. DE-AC36-08GO28308. Funding was provided by the  U. S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the  DOE  or the  U. S. Government. The  U. S. Government  retains and the publisher, by accepting the article for publication, acknowledges that the  U.
METHODS Data were derived from a phase 3, randomized, double-blind, placebo-and tramadol-controlled trial investigating the efficacy and safety of SC tanezumab (ClinicalTrials. gov, NCT02528253)  [16]. The study protocol was approved by an institutional review board or independent ethics committee for each participating investigational center and all patients provided written informed consent. This study was conducted in compliance with ethical principles of the Declaration of Helsinki and Good Clinical Practice Guidelines.
Assessments Patient's overall assessment of disease status was assessed using the self-administered patient global assessment of low back pain questionnaire, with scores ranging from 1 (''very good'') to 5 (''very poor''). The PGA-LBP was administered at baseline and at weeks  2, 4, 8, 16, 24, 32, 40, 48, 56, and 64. This manuscript reports mean change in PGA-LBP from baseline at week 16 (end of placebo-controlled treatment) and week 56 (end of overall treatment). Pain severity and impact on daily functions over the past 24 h were assessed using the selfadministered Brief Pain Inventory-short form (BPI-sf) questionnaire. Data on worst pain, average pain, and pain interference were assessed in this study. Scores for individual questions ranged from 0 to 10, with higher scores indicating greater pain severity or pain interference.
Treatment preference and willingness to use study treatment were assessed at weeks 16 and 56 using the self-administered 4-item modified Patient Reported Treatment Impact (mPRTI) questionnaire. Data on three of the four items were collected in this study including types of treatments used prior to study enrollment, patient preference of study treatment relative to previous treatments, and patient willingness to use study treatment in the future. Preference and willingness questions were scored using a 5-point Likert scale, with lower scores indicating greater preference or willingness for study medication. Rescue medication use was recorded, using an electronic diary, daily up to week 16 and then weekly from weeks 16 to 80. The incidence and number of days of rescue use were assessed at weeks  2, 4, 8, 12, 16, 24, 32, 40, 48, 56 , and 64, while the amount of rescue medication taken was assessed at weeks 2, 4, 8, 12, and 16.
Missing data were imputed using a last-observation-carriedforward approach. For all comparisons, significance was declared if the 2-tailed test for the difference between treatment groups was significant at the 0. 05 level. All comparisons were unadjusted for multiplicity. Patient disposition values and all efficacy analyses were based on the efficacy population, which included all patients who received at least one dose of study treatment grouped according to assigned treatment (i. e. , the intent-to-treat population). See primary publication for more detail  [16].
Editorial Compliance with Ethics Guidelines. The study protocol was approved by an institutional review board or independent ethics committee for each participating investigational center (Supplementary Table  2 ) and all patients provided written informed consent. This study was conducted in compliance with ethical principles of the Declaration of Helsinki and Good Clinical Practice Guidelines. Data Availability. Upon request, and subject to review, Pfizer will provide the data that support the findings of this study. Subject to certain criteria, conditions and exceptions, Pfizer may also provide access to the related individual de-identified participant data. See https://www. pfizer. com/science/clinicaltrials/trial-data-and-results for more information. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4.
The m-THPC derivatives were characterized using 1D NMR spectroscopy, MALDI-TOF mass spectrometry (MS), UV/Vis and fluorescence spectroscopy. The NMR spectra recorded in deuterated chloroform (CDCl 3 ) for PS 1, 5 and deuterated dimethyl sulfoxide (DMSO-d 6 ) for PS 3, respectively and MS data are included in the Supporting Information (SI) (Figures S1-S21). The 1 H NMR spectra of compounds 1, 3 and 5 are consistent with the structure of m-THPC which was originally evaluated in detail by Bonnett et al. [35]  Hydrogen atoms on the reduced pyrrole ring, characteristic for chlorins, appear as a singlet at 4. 15-4. 25 ppm, and the NH protons of the macrocycle with a singlet at À 1. 5 to 1. 6 ppm. Moreover, the NMR spectra of the derivatives contain characteristic signals for the functional groups -aldehyde for 1 and 5 at 10.
9 %). [38]  To our knowledge there is no FLQY data of m-THPC reported in DMF. Although the difference between fluorescence intensity of m-THPC and derivatives 1 and 3 is not significant, a decrease of over 20 % of the emission intensity was observed for PS 5. This decreased fluorescence intensity may be due to the reduced rigidity of the corresponding molecular structure. Moreover, this is reflected in the increased value of the non-radiative constant (k nr ) (0. 089 ns À 1 ) for PS 5 in comparison with other derivatives (0. 059-0. 067 ns À 1 ). [12]  The fluorescence decays of the m-THPC derivatives were recorded at 650 nm using time-correlated singlephoton counting technique (TCSPC). The obtained decay curves were fitted using mono (m-THPC, PS 1, PS 3) or bi-exponential (PS 5) functions (Figure  2 ).
Singlet Oxygen Quantum Yield The results indicate that there are no significant differences in 1 O 2 generation efficiencies between the derivatives 1, 3, 5 and m-THPC, being in the range from 25 % to 31 % when quantified in DMSO (Table  3 ). Slightly larger differences could be noted for the values determined in DMF, varying in the range from 64 %, for the hydrophilic PS 3, to 76 % for hydrophobic derivative 1. The difference between 1 O 2 quantum yields calculated in both solvents is attributed to quenching processes occurring in different solvents with 1 O 2. This phenomenon has been extensively elaborated by Ogilby et al. [39]  Nevertheless, it can be concluded that the expansion of the m-THPC skeleton does not negatively impact its photophysical properties. Obtained experimental values of the 1 O 2 quantum yield of m-THPC in both solvents corresponds with the literature data (68 % in DMF and 40 % in DMSO)  [40, 41]  whereas small discrepancies are within the experimental error.
Acknowledgements This work was supported by the  Technical University of Munich -Institute for Advanced Study  through a  Hans Fischer Senior Fellowship  (M. O. S. ). This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No. 764837 , the  NCN  under Opus  UMO-2019/35/B/ST4/03280  Grant Agreement, from  Science Foundation Ireland  ( IvP 13/IA/1894 ) and was supported by the  Higher Education Authority  and the  Department of Further and Higher Education, Research, Innovation and Science (Ireland). We thank  Dr. Fábio A. Schaberle  ( University of Coimbra ) and  Krzysztof Nadolski  ( Wroclaw University of Science and Technology ) for their guidance and explanation of measurements regarding 1 O 2 generation efficiency.
A "natural reference" simulation In this study, we use the LSM Organizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) from the Institut Pierre Simon Laplace (IPSL). It includes biophysical and biogeochemical processes to simulate the global carbon cycle and quantify terrestrial water and energy balance. It can be run coupled to an atmospheric model or in off-line mode. In that latter case, the atmospheric conditions are forced by an independent dataset. In the following, we will use the model in stand-alone conditions, with the forcing dataset GSWP3 which covers 1901 to 2013  (Hyungjun, 2017)  with a resolution of 0. 5°for all climate variables. The hydrological network of the ORCHIDEE LSM is constructed from the hydrological elevation model HydroSHEDS  (Lehner et al. , 2008)  which covers the area studied with the resolution of 30 arc seconds (approximately 1 kilometer at the equator).
The hydrological information is then upscaled to the resolution of the atmospheric grid, the hydrological coherence being preserved by the construction of Hydrological Transfer Units (HTU) at the sub-grid level  (Polcher et al. , 2022). From a database of gauging stations, upstream basins are reconstituted on the hydrological elevation model grid and then projected on the atmospheric grid during the process. We have access to 2134 stations over the area studied for which the LSM calculates a discharge and for which we have the reconstituted upstream basin. We consider that the final product correctly reproduces the hydrological network and the water partitioning over a watershed due to climatic phenomena. The modeled watersheds have fixed constant land surface characteristics which will react to the climate data input at each time step (30 min time step).
Therefore the LSM output depend on both the evolving annual average and the evolving distribution over the year of the climate variables. The watershed parameter of the Budyko curve is calculated over each catchment with a fit of the equation curve E/P = f (P ET /P ) (equation 2), using the minimum root mean square error (RMSE) for a given set of annual averages of evapotranspiration E, precipitation P and potential evapotranspiration P ET data  (Jiang et al. , 2015; Yang et al. , 2007). The climatic variables defining P and P ET are from a forcing dataset, GSWP3 here. P is the sum of rainfall and snowfall. P ET is calculated through Penman-Monteith a For forcings 2 to 4, P has been modified compared to the reference: the average value of P over the year and/or the distribution of precipitations over the year have been fixed for each year to the value of the year 2000.
equation applied at a 30 min time step  (Barella-Ortiz et al. , 2013). E is the output of the LSM forced with the same forcing. The gridded outputs (P ET , E) are at the resolution of the forcing dataset (0. 5°). Then we consider the annual mean P , P ET and E over hydrological years, integrated over each catchment. The catchments' shape has been reproduced at finer resolution and then projected on the 0. 5°grid. We fit the parameter once with all points over the entire time period covered by the climate dataset to get ν representing the average behavior for each catchment (fig. 2, A).
Synthetic forcings to analyze the effect of variation of seasonality In order to better understand the effect of inter and intra-annual climate variations on the Budyko framework and on discharge Q, we constructed synthetic climate forcings. To separate the effect of changes in annual averages of climate variables from the effect of changes in the intra-annual covariance of P and P ET , we constructed synthetic forcings fixing one or the other. The reference forcing is the GSWP3 dataset from September 1901 to September 2012 (3h time step). Then we constructed three forcings which were modified over hydrological years (table 1, fig. 3a ): • f2000 : A forcing where all 3h values of P are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore all component of P (average and intra-annual variations) are set constant.