dataesr/scientific-paragraphs-categorization huggingface dataset:
Their studies fo-cused on the evaluation of ocean components from sea surface height, temperature, salinity, mixed layer depth, and kinetic energy to circulation changes. Some aspects of sea ice simulations are assessed in both hemispheres relative to an observational dataset. Tsujino et al. (2020)  provide spatial maps of the 1980-2009 mean ice concentration and time series of ice extent in summer and winter, and Taylor diagrams of the interannual variations of ice extent under CORE-II and JRA55-do forcings. Chassignet et al. (2020)  show spatial maps of the 1980-2018 mean ice concentration and ice thickness in summer and winter, and time series of annual mean ice extent and ice volume under different horizontal resolutions. In this paper, we focus on the sea ice in OMIP simulations available from the Earth System Grid Federation in a more systematic manner, including more sea ice variables (e.
g. , ice-edge location, snow depth, and ice drift). The performance metrics and diagnostics (spatial maps and/or time series diagrams) for each ice variable are provided compared to two sets of observational references when data are available to appreciate the importance of observational uncertainty in the assessment. This paper is organized as follows. SITool (v1. 0) with the details of sea ice metrics and diagnostics is described in Sect. 2. The CMIP6 OMIP models and observational references are introduced in Sect. 3. In Sect. 4, the application of SITool (v1. 0) to CMIP6 OMIP and the results of the model performance are presented and discussed. Finally, conclusions and discussion are provided in Sect. 5. Appendix A presents some additional sea ice diagnostics. The source code of SITool (v1.
0) used to assess the model skills is publicly available in the repository as shown in the "Code and data availability" section. 2 Overview of SITool (v1. 0) A schematic overview of SITool (v1. 0) workflow and its application in evaluating the CMIP6 OMIP model performance is shown in Fig. 1. The input sea ice data from model outputs and observations are detailed in Sect. 3. The methods of the metrics calculation are discussed below in Sect. 2. 1 followed  Massonnet et al. (2011)  with some modifications. Namely, (1) more observational references are used to calculate the observational errors, and the incorporation of observational errors is a prerequisite to do the comparisons here; (2) iceedge location and snow depth metrics are included; (3) the method to calculate the vector correlation coefficient is updated.
SITool (v1. 0) also produces additional sea ice diagnostics (spatial maps and time series diagrams) to help understand why metrics vary from one dataset to the next. Table  1  provides an overview of the diagnostic fields along with input variables, output results and corresponding figures in this paper, Python scripts in the repository, and comments. All the sea ice data from model outputs and observational references are regridded to the polar stereographic 25 km res- olution grid using a kd-tree (k-dimensional,  Bentley, 1975)  nearest-neighbor interpolation method provided by a Python package (a component of the SITool workflow). The kd tree is a binary search tree with a two-dimensional spatial index structure for use in this study. The interpolation yields less than 5 % error for each sea ice variable (not shown), which indicates that the results are not sensitive to the interpolation method used here.
Sea ice concentration, extent, and edge location The methods to calculate the metrics of ice concentration on the mean state, interannual variability, and trend in both hemispheres are introduced here. The consistent equations used to calculate the differences of the mean state (Meandiff), interannual variability (SDdiff), and trend (Trenddiff) between two datasets are shown below: Meandiff = n=12 n=1 i=N i=1 |C n, i 0M -C n, i 1M |×A i i=N i=1 A i × D n n=12 n=1 D n (1) SDdiff = i=N i=1 |SD C i 0 -C i 0M -SD C i 1 -C i 1M | × A i i=N i=1 A i (2) Trenddiff = i=N i=1 |trend C i 0 -C i 0M -trend C i 1 -C i 1M | × A i i=N i=1 A i , (3) where n = 1,. , 12 and i = 1,. , N denotes the 12 months and the grid cells, respectively, C 0M and C 1M are monthly mean ice concentrations from two datasets used to do the comparison, A and D are grid cell area and the days in each month, respectively, C 0 and C 1 are monthly ice concentrations from two datasets, and "SD" is the abbreviation of standard deviation.
Models and observational references In this study, SITool (v1. 0) is used to evaluate the CMIP6 OMIP model skills in simulating the historical sea ice properties for both hemispheres. The CMIP6 OMIP models and a set of observational references providing ice concentration, thickness, snow depth, and ice drift are introduced in this section. Two sets of observational references for each sea ice variable are used for comparison. The CMIP6 OMIP models used are shown in Table  2  with model details such as atmospheric forcing, ocean models, sea ice models, spatial resolution, and related references. A major improvement in JRA55-do atmospheric forcing relative to the CORE-II forcing is the increased temporal frequency from 6 to 3 h and horizontal resolution from 1. 875 to 0. 5625 •. The surface fields of JRA55-do forcing have been adjusted to match reference datasets based on high-quality 6336 X.
Lin et al. : SITool (v1. 0)  satellite observations and several other atmospheric reanalysis products, as detailed in  Tsujino et al. (2018). Nine models were run with either CORE-II or JRA55-do forcing; five of them were forced by both CORE-II and JRA55-do reanalysis; out of the four remaining models, one of them was forced by JRA55-do reanalysis only, and the other three were forced by CORE-II reanalysis only. The CMCC-CM2-HR4 (∼ 0. 25 • ) and CMCC-CM2-SR5 (∼ 1 • ) models are different in spatial resolution, which provides an opportunity to identify the influence of model resolution on sea ice simulation. The CORE-II forcing dataset has not been updated since 2009 and the two Geophysical Fluid Dynamics Laboratory (GFDL) models only provide the model outputs until 2007. This is why the evaluation period is chosen as 1980-2007 for ice concentration, extent, and edge location (the corresponding observations are available from 1980).
The evaluation period is 2003-2007 for ice thickness, snow depth, and ice drift because some observational references are limited before 2003, and then the corresponding metrics are only on the mean state. The evaluation period can be extended in the future when different model and observational datasets are considered. The observational reference products for sea ice concentration, thickness, snow depth, and ice drift used to compare with model simulations are summarized in Table  3. The first ice concentration product derives from the passive microwave data of the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS), which are processed by using the NASA Team algorithm (NSIDC-0051,  Cavalieri et al.
The Envisat data include ice thickness and thickness uncertainties from November to April for the Arctic with coverage up to 81. 5 • N and May to October for the Antarctic from 2003. The ICESat data used here include 13 measurement campaigns for the Arctic and 11 for the Antarctic during 2003-2007, and these campaign periods are limited to the months of February-March, March-April, May-June, and October-November with each roughly 33 d. The comparisons between individual models and the two observational references are thus restricted to these months when data are available. The months chosen for the comparison are different from two ice thickness observational references, which can contribute to the differences in ice thickness performance metrics. The Envisat thickness data also include snow depth and associated uncertainty.
The other snow depth product derives from a Lagrangian snow-evolution model (SnowModel-LG) forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation (ERA5) atmospheric reanalysis, and NSIDC sea ice concentration and trajectory datasets  (Liston et al. , 2020; Stroeve et al. , 2020). The SnowModel-LG data are only provided for the Arctic Ocean. The SnowModel-LG data used to do the comparison are for the same months as the Envisat data from 2003-2007. The first ice drift product is processed by NSIDC and enhanced by the Integrated Climate Data Center (ICDC-NSIDCv4. 1). This product derives from SMMR, SSM/I, SS-MIS, and the Advanced Very High Resolution Radiometer (AVHRR) for the Antarctic. In addition to the above data, data of the Advanced Multichannel Scanning Radiometer-Earth Observing System (AMSR-E), observations of the International Arctic Buoy Program (IABP), and ice drift derived from NCEP/NCAR surface winds are used for the Arctic Ocean.
The second ice drift dataset is processed by  Kimura et al. (2013)  and derived from the AMSR-E data for both hemispheres from 2003. The ice vectors are reprocessed before calculating the ice drift metrics. The ice vectors from observational references and models are rotated and interpolated to the polar stereographic grid. The monthly mean ice vectors of the observational references are computed when there are more than 10 d with valid daily drift data. The ICDC-NSIDCv4. 1 ice drift data were shown to be biased low (i. e. , too slow) relative to buoy data  (Schwegmann et al. 2011; Barthélemy et al. , 2018)  and is therefore corrected by multiplying the drift components with a correction factor of 1. 357  (Haumann et al. , 2016). The ice vectors from observational references and models are removed when ice concentrations are below 50 %, or the data are closer than 75 km to the coast, or with a spurious value, to reduce the spatial and temporal noise by following  Haumann et al.
SITool application and results SITool (v1. 0) described in Sect. 2 is applied in this section to assess the performance of the sea ice simulations for both hemispheres carried out under the CMIP6 OMIP1 and OMIP2 protocols. Models forced by CORE-II atmospheric reanalysis data (OMIP1) or JRA55-do reanalysis data (OMIP2) are marked as < model name + /C or /J >, respectively. The OMIP1 and OMIP2 model means shown below are from the five models of CMCC-CM2-SR5, EC-Earth3, MIROC6, MRI-ESM2-0, and NorESM2-LM, providing both OMIP1 and OMIP2 model outputs. All the sea ice data from models and observational references are interpolated to the NSIDC-0051 polar stereographic 25 km resolution grid for comparison. The typical errors are the differences between two observational references for the ice concentration, extent, edge location, and ice drift, while typical errors of ice thickness and snow depth are calculated from the thickness and snow depth uncertainties of specific months from Envisat data.
The improvements on the overall ice concentration simulations are not sensitive to the chosen observational reference and then robust. The improved ice concentration simulations are found compared to different observational references except for the interannual variability of the Antarctic ice concentration compared to the OSI-450 data as shown in the fifth column of Fig. 2b. The metrics on the interannual variability of ice concentration (second and fifth columns) are the highest among all metrics, which indicates relatively lower skill on the simulation of ice concentration variability in both hemispheres compared to the mean state and trend. The overall best performance on ice concentration simulations including the mean state, interannual variability, and trend is in NorESM2-LM forced by JRA55-do reanalysis for both hemispheres.
In general, model errors are larger than observational uncertainty, and model performance on the ice concentration, extent, edge location, and ice drift simulations is consistent when comparing to different observational references. For the ice thickness and snow depth evaluation, the rating scores are not consistent compared to different observational references, which is due to the limited observations and to the fact that different months were chosen for comparison during 2003-2007. This finding shows that sea ice thickness and snow depth estimates are still at an earlier stage of maturity compared to datasets of sea ice concentration or drift. The improvements of mean ice concentration simulations in the summer for both hemispheres by changing the atmospheric forcing and increasing the horizontal resolution are also identified in  Tsujino et al.
Acknowledgements. We are grateful to  Julienne Stroeve ,  Noriaki Kimura , and  Sara Fleury  for providing and introducing the snow depth, ice drift, and ice thickness datasets, respectively. We thank the sea ice observational groups and the climate modelling groups for producing and making available their output. We acknowledge three anonymous referees who reviewed the paper. François Massonnet is a F. R. S. -FNRS research fellow. Financial support. This research has been supported by the  Copernicus Marine Environment Monitoring Service (CMEMS)   SI3  project. CMEMS is implemented by Mercator Ocean International in the framework of a delegation agreement with the  European Union. Xia Lin  also received support from the  National Natural Science Foundation of China  (grant nos. 41941007 ,  41906190 , and  41876220 ).
X. Lin et al. : SITool (v1. 0) Code and data availability. The latest release of SITool (v1. 0) is publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 5561722 (Lin et al. , 2021). The source code of SITool (v1. 0) is developed fully based on freely available Python packages and libraries, and is released on a GitHub repository available at https://github. com/XiaLinUCL/Sea-Ice-Evaluation-Tool (last access: 11 October 2021). CMIP6 OMIP data are freely available from the Earth System Grid Federation. Observational references used in this paper are detailed in Sect. 3 and listed in Table  3 , and they are not distributed with SITool (v1. 0) because SITool (v1. 0) is restricted to the code as open-source software.
For example, Sega and Maloya are two popular music styles of Reunion Island, which is a French overseas territory with a multiethnic and multicultural population (28, 29). These styles of music, also known as "Creole music", are based on a traditional ensemble with African and Malagasy rhythmic in uences. Our study seeks to ll this gap in the literature by letting patients create their own, culturally adapted music playlist. Our hypothesis was that music therapy programs, which use speci c rhythm sequences to potentiate the anxiolytic effects of music, could be optimized by adapting them to the preferences and cultural contexts of patients. The aim of our study was to determine whether listening to self-selected music decreases preoperative anxiety in women scheduled to undergo gynaecologic surgery as compared with predetermined music from a music therapy software programme (MUSIC CARE®).
Only research team members were aware of the group to which patients had been allocated. 'Do Not Disturb' signs were placed on patient room doors during the music session. Patients were warned not to mention their group allocation to care team members. Statistical methods Data were expressed as n (percentages) or means, standard deviation (SD). The two groups were compared using the Mann-Whitney U test for quantitative variables and the Chi-square test or Fisher's exact test or the Fisher-Freeman-Halton test for categorical variables, as appropriate. Measures before and after the music session were compared using the Wilcoxon signed-rank tests. The evolution of the STAI state anxiety score was evaluated according to the STAI trait anxiety score level using the Kruskal-Wallis test. Multiple 2 to 2 comparisons of the different components of the STAI trait anxiety score were performed using the Mann-Whitney U test with an alpha level of 0.
83% (Bonferroni correction: 5%/6). Spearman's correlation coe cient between the STAI state anxiety score and the STAI trait anxiety score was calculated. One patient with missing data on the primary outcome was excluded from the analysis. Except for the multiple 2 to 2 comparisons, the signi cance level was set to 5%. Analyses were performed using SAS® version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
Results Recruitment and ow of participants A total of 174 women were included and randomized in the MUANX trial between 1st August 2017 and 20th September 2018. Of these, 89 (51%) were allocated to the control group (predetermined music group) and 85 (49%) to the intervention group (self-selected music group). In the control group, two patients were excluded because they refused to listen to music. One patient had postponed surgery after she received music therapy. For this patient, we analysed the primary outcome evaluation (but data were missing on postoperative measurements). In the self-selected music group, one patient was excluded due to missing data on the primary outcome and one patient included in analysis had an interruption during the music listening. The ow chart describing the process of participant selection is presented in Fig.
5 mm deep for single events on the upper FWPs from upward VDE current quenches (CQ)  [1]. The implication is significant given the ~10 mm Be armor thickness on the majority of the ITER FWPs. Extensive damage to Be surfaces due to such events has already been clearly documented on JET  [2]. Since the first study reported in  [1] , work is actively ongoing to quantify FWP damage during disruptions and VDEs on ITER, analyzing a range of PFPO and FPO scenarios. An extensive disruption database has been completed, with over 80 DINA VDE and disruption scenarios. Variations in the database include plasma current (I p ), disruption direction (up or down), Be impurity density, cross-field thermal diffusivity coefficients, and disruption type (VDE vs. major disruption (MD)). This dataset will allow for a series of broad parametric studies of first wall energy deposition and material surface response using a unique simulation workflow developed at ITER.
The workflow methodology: 2D magnetic flux profiles from the DINA code  [3]  provide input to the SMITER 3D field-line tracing software  [4] , producing 3D maps of perpendicular surface heat flux q ⊥ and magnetic field B → on the FWPs. These maps are then used to compute the time-dependent melt formation and dynamics with the MEMOS-U code  [5, 6] , accounting for heat flux reduction by plasma vapor shielding  [7]. The FWP models are subsequently regenerated in SMITER using the melt deformation data from MEMOS-U, allowing for an assessment of heat loads on the damaged panel from either steady-state scenarios or a subsequent VDE/ disruption. The full results of this analysis effort will be presented in a future paper. Here, the focus will primarily be on a revisit of the analysis presented in  [1]  for the 5 MA VDE, benefitting from the revised DINA simulations and extending the parameter range for this particular scenario.
The shape and time evolution of the parallel heat flux profile q‖(r) was maintained, but the magnitude of q‖(r) was normalized to the reduced P halo. Further characteristics of this VDE scenario are given in Table  1  in  [1]. This first study reported perpendicular heat fluxes q ⊥ up to ~190 MW/m 2 , on the surfaces of upper FWPs #8 and #9, which resulted in surface melt damage up to a depth of 0. 5 mm. Relatively narrow halo regions with exponentially decaying q‖(r) profiles (λ q = 32-82 mm) were assumed. Such drastic melt damage implied that low I p VDEs during ITER early operations, either deliberate or accidental, must be kept to very low numbers. The DINA disruption database now possesses the proper 5 MA/1. 8 T, unmitigated, upward VDE that was unavailable at the time of the previous study  [1].
Additionally, the DINA model from 2017 that produced the original 15 MA VDE scenario has been updated and improved in the process of generating the 2020 disruption database (see Section 2). For the analysis workflow, the DINA simulations supply the parallel heat flux profile q‖(r) and current densities J → p (r) in the halo region from a refined power balance. They include radial transport, ohmic heating, and radiation losses, all of which affect the time evolution of the VDE. Any differences in the calculated q‖ profile within the halo, and the time evolution of that profile, will have direct consequences on the q ⊥ magnitudes, surface temperature rise, and resulting melt damage. The purpose of this paper is to introduce updates made to the DINA disruption model, and assess the impact of those updates on the 5 MA/1.
In the process of creating the 2020 DINA disruption dataset for ITER, several important improvements were made to the 2017 DINA version (the version featured in  [9;10] ). Those updates include: improved energy balance in the halo region, limitations on the safety factor q = aBt RBp , and a constant plasma + halo poloidal crosssection for the halo computational domain. When q drops below 1 inside the LCFS, the value is manually set to q = 1, with the current and plasma temperature profiles within the core modified accordingly. Maintaining a safety factor of at least 1 within the core region allows for a more consistent evolution of the VDE/MD dynamics, conservative from the standpoint of prolonging the duration of the event. Later in the analysis workflow, it is seen that longer-lasting VDEs and MDs produce more severe melt damage for high I p scenarios, and are therefore of greater concern.
In reality, a VDE where q becomes <1 within the core would result in 3D instabilities (which DINA cannot model), a flattening of the current profile, extremely high magnitudes of the total plasma current density (J p ) and q‖, and most importantly a rapid plasma termination. Setting q = 1 in the core maintains the minimum Kruskal-Shafranov criterion for plasma instability  [12]  and provides conservative scenarios that the 2D, axisymmetric DINA code can model. Importantly, the halo current profiles in the halo region (outside the LCFS) remain unchanged and allow for q values <1. This maintains a smooth distribution of plasma current density in the halo region, J h. It should be noted that manually adjusting the safety factor in DINA does slightly violate energy conservation; this is currently unavoidable and will be addressed in a future DINA model with 2D transport.
For the 2020 DINA disruption dataset, the parameter C is effectively set to infinity, such that S 0 = S(t, w domain ). Since w domain is unbounded, it will increase as the core flux Ψ m -Ψ b decreases in time over the course of the VDE  [9]. This assumption is consistent with experimental observations (described in Section 2. 3) as well as with code comparisons with the TSC code in  [9] , which features a more robust calculation of current flows by solving electric circuit equations alongside 2D axisymmetric Maxwellfluid equations. Eq. (  1 ) represents the domain in which a halo current is calculated. How the halo current distribution evolves within that region is determined separately based on ohmic heating, perpendicular diffusion, and power balance  [10]. Multiple cases were tested to explore the VDE/MD dynamics for ITER, combining the options for constant vs.
5 MA VDE model comparison The original DINA VDE scenario used in  [1, 8]  was a 15 MA, 5. 3 T, unmitigated, upward VDE with an assumed Be impurity density n Be of 3•10 19 m -3 and perpendicular heat transport coefficient χ = 1m 2 /s. This will be hereafter referred to as the "original 15 MA VDE". The scaleddown version used in  [1] , with scalings described in Section 1, will be the "scaled-down 5 MA VDE". The 2020 DINA database now includes the equivalent cases for both the 15 MA/5. 3 T and 5 MA/1. 8 T VDE scenarios (upwards, n Be = 3•10 19 m -3 , χ = 1m 2 /s), hereafter referred to as the "new 15 MA VDE" and the "new 5 MA VDE". This section will compare the intrinsic properties of these 4 VDE scenarios as calculated by their respective DINA versions. Section 3 will compare the corresponding differences in heat flux and melt damage found in the SMITER and MEMOS-U portions of the workflow.