dataesr/scientific-paragraphs-categorization huggingface dataset:
Upward Looking Sonar Data The Upward looking Sonar (ULS) dataset  [35] , operated by AWI, provides mooring measurements of sea ice draft in the Weddell Sea. The principle is to measure the returning time of sound pulses towards the sea surface reflected by the underside of the sea ice or the water/air interface. Data are collected at 13 fixed positions from November 1990 to March 2008 and from one mooring between March 2008 and January 2011  [66]. They are freely available at https://doi. pangaea. de/10. 1594/PANGAEA. 785565 (accessed in 30 June 2022). Two methods are used to consider the sound velocity variations. The first method uses a correction line interpolated between leads (referring to the sea level) while the second method uses a sound speed model propagation. Only data corrected from the correction line are presented in this study since it is shown to be more accurate  [35].
Operation Ice Bridge Data The data of the Operation Ice Bridge (OIB) missions analysed here are provided by the NSIDC [https://nsidc. org/data/IDCSI4/versions/1] (accessed in 30 June 2022)  [67]. They are also part of the data considered in the framework of the ESA CSAO+ project. It contains total freeboard estimates of the 2009 to 2013 OIB missions derived from elevations of the Airborne Topographic Mapper (ATM) lidar with the approach of Kwok et al. [68]. Unfortunately, most of the OIB campaigns in the Antarctic took place in Summer (March and April). Only 3 missions in 2009 and 3 others in 2010 took place in October, at a period when altimetry observations are available. The freeboards of the other missions have not been yet processed for a public access, and only surface elevations are available Kwok and Kacimi  [53].
The Sea Ice Mass Balance in the Antarctic Campaign The Sea Ice Mass Balance in the Antarctic (SIMBA) expedition was carried out from September to October 2007 in the Bellingshausen Sea to set-up a baseline data of sea ice and snow properties during the winter/spring transition  [31]. The dataset provides in-situ observations of sea ice and snow properties at 3 short-term stations and at a 27 days drifting Ice Station Belgica (ISB) from snow pits, transect lines, ice core ans ice mass-balance buoys. It is available at https://doi. pangaea. de/10. 1594/PANGAEA. 815856 (accessed in 30 June 2022).
Sea Ice Concentration The monthly sea ice extent (SIE) and concentration (SIC) data are provided from the National Snow and Ice Data Center (NSIDC-0051) dataset of the sea ice index archive [https: //nsidc. org/data/g02135] [69] , freely available at ftp://sidads. colorado. edu/DATASETS/ NOAA/G02135 (accessed in 30 June 2022). They are derived from passive microwave brightness temperature signatures from the Special Sensor Microwave Imager (SSM/I, until 2007) and the Special Sensor Microwave Imager Sounder (SSMIS, from 2008 to present)  [70]  of the Defense Meteorological Satellite Program (DMSP) satellites.
Cryosat-2 Radar Freeboard In this section, we present and analyse CryoSat-2 radar freeboards estimated from the methodology presented in Appendix B. Figure  1  presents the climatological mean and variability (standard deviation, Sd) maps of CryoSat-2 radar freeboards of the TFMRA-50 (CS2-TFMRA50) and the SAMOSA+ (CS2-S+) retracker solutions computed using all the monthly mean radar freeboard maps (from May to October) between 2011 and 2020. They are also compared with the SI-CCI dataset (Third). In addition, the maps presenting the evolution of the radar freeboard spatial distributions in winter 2011 are provided in Appendix A, Figure  A1. We found coherent spatial distributions between CS2-TFMRA50 and CS2-S+ with a climatological mean difference by about 1. 5 cm. Note that it is calculated (such as in the rest of the manuscript) from the mean of the radar freeboard differences at each grid point.
A mean difference by about 5-6 cm is found but the spatial distributions are still consistent. One major difference with the CS2-TFMRA50 data is that the SI-CCI radar freeboards are computed using a 40% threshold, while we use 50%. Ricker et al. [71]  showed that it implies a mean positive bias by about 4 cm over First Year Ice (FYI) and 6 cm over Multi-Year Ice (MYI) in the Arctic, which is coherent with the differences found here. The SI-CCI product has also a coarser grid resolution, which could explain the lower values of thick ice in narrow coastal patterns surrounded by strong gradients (such as in the Pacific and the A-B sectors). Note that the limitation to 2016 probably enhances this bias high (cf. Section 3. 4). This could also partly explain the lower variabilities. In the two estimations from TFMRA, it is relevant to observe permanent very thin radar freeboards seawards the Ross continental ice shelf.
In order to take into account multiple parameters (not only the PP), we use a neuronal approach developed by  [76]  in the context of the FDR4ALT project. The correction model is a multi-layer perceptron neural network that has the specification to handle well with non-linearities. By considering the data of the common winter 2011 (the training period), it computes a calibration function from the TFMRA-50 radar freeboards of CS2 and various Envisat parameters. The calibration function characterizes, at each grid point, the state of the sea ice from the Envisat inputs: LRM radar freeboard, date (month), sea ice concentration (SIC), leading edge slope, pulse peakiness and sea ice type. The correction model is then able to retrieve the radar freeboards of any month of the Envisat flight period.
Sea Ice Mass Balance in the Antarctic (SIMBA) Transects We analyse the sea ice freeboard and snow depth along transect lines conducted during the SIMBA campaign at 3 sites (Fabra, Brussels and Patria) of the Ice Station Belgica (ISB) drifting station, and at 3 short term stations (Stat1-3) in late September and October 2007. In-situ sea ice freeboards are calculated from the difference between the surface elevation, obtained by drilling auger holes, and the snow thickness, measured using a meter-long ruler. The snow and sea ice properties relative to the datasets are described in Lewis et al. [31]. For each station, Table  2  compares the mean in-situ transect measurements with the calibrated monthly mean Envisat sea ice freeboard data (ENV/AMSR) and the AMSR snow depth data. It also indicates the « extended SIT » which is computed from the sea ice freeboard and the snow depth mean data.
In the case of negative ice freeboards, it is assumed that the « underwater » layer between the ice interface and the sea level is constituted of slush that has the property of the ice. Then, the total freeboard is the sum of the snow depth and a (negative) layer of slush under the seawater level (total freeboard = snow depthice freeboard). In addition, Figure  A3 , in Appendix A, specifies the comparison by indicating the results of each transect of the different sites. Table  2. Mean sea ice freeboard and snow depth along each transect line of the SIMBA campaign. The ENV/AMSR column presents the Envisat sea ice freeboard that considers AMSR data to account for the radar speed decreasing into the snow. The SIMBA "Mean extended SIT" values are SIT estimations assuming that the layer between the ice interface and the sea elevation is only constituted of snow.
Mean Ice The Patria and Brussels sites are both considered as First Year Ice. The Brussels site has thin snow layers with mainly positive freeboards (no flooding), while Patria has much thicker snow which induces flooding events. At these 2 stations, the altimetry ice freeboards are comparable (with a mean of 0. 12 m) and generally higher than the in-situ freeboards. At Brussels, where the snow is very thin, the Envisat mean freeboard is equal to the addition of the in-situ mean snow depth and freeboard (respectively equal to 0. 07 and 0. 05 m). This result is consistent with the study of Nandan et al. [80] , that shows that Ku-band radar echoes can not penetrate saline thin snow layers over FYI. At Patria, the altimetry data always overestimate the in-situ ice freeboard, indicating that the Ku-band radar should not completely penetrate the snow either.
Mean sea ice freeboard is again overestimated from about 50% of the mean in situ snow depth. Overall, we found that AMSR-E underestimates snow depth over thick and mediumthick snow layers, and strongly overestimates over thin layers. Very similar results are obtained when using the AMSR-clim dataset. The altimetry mean sea ice freeboards are always overestimated. It likely reflects both a radar echo mean scattering horizon within the snow layer, and the difficulty to retrieve freeboards in the presence of slush layer and flooding. By consequences, mean SIT altimetry estimations tend to be overestimated. However, it is important to recall that the SIMBA in-situ data account for a very local area, and at a particular time, with associated large standard deviations and uncertainties. In fact, the A-B sector is particularly affected by strong variabilities and small scale effects that can not be observed by the spatial and temporal resolution of altimetry measurements.
As they compensate each others, the annual mean deviation is only of a few centimeters. These results suggest that early spring modifications of snow and ice properties impact the satellite draft retrieval. This is coherent with the SIT underestimations detected at the Fabra site (which has comparable sea ice characteristics) during the in-situ September/October SIMBA campaign (cf. previous section). When using AMSR-E snow depths, we found a mean underestimation by ≈13 cm (≈9%) from May to September and by ≈55 cm (≈37%) in October. The annual mean deviation is of 20 cm (≈13%). This result tends to show that AMSR-E data underestimate snow depth over thick ice. Table  3  also underlines large RMSD's, on average by about 1 m. In spite of coherent mean estimations, such deviations are the consequence of strong variabilities that can not be detected from monthly mean smoothed estimations.
Compared to SIMBA, the greater consistency with ULS drafts is therefore most likely due to a larger and more representative statistical sample, that better fit with the space and time resolution of the satellite observations. Therefore, analyses presented in Section 3. 4 rather consider large scale informations. Note also that ULS data are known to potentially provide higher drafts because of the geometry of the underwater sea ice bottom. 5  compare the mean total freeboards of Envisat, computed with AMSR-E (ENV-AMSRE) and AMSR-clim (ENV-AMSRclim), with the ATM airborne data for 6 OIB missions during the 2009 and 2010 campaigns (cf. Section 2. 3). The 21 October 2009 and 30 October 2010 missions took place in the coastal Amundsen-Bellingshausen sector (CoA-B) and the 4 others in the Weddell sea.
To analyse along-track variabilities, the satellite datasets are projected onto the OIB tracks, using a simple linear interpolation. Figure  6  presents the results for the 21 October 2009 and the 26 October 2010 missions. The comparison with the 4 other missions are provided in supplementary (Figure  A4 ). The Envisat radar freeboards and the AMSR-E and AMSR-clim snow depths are also indicated in these figures. Note that a 12. 5 km radius rolling mean smoothing is applied to OIB data in order to make comparisons at corresponding spatial scales  [60]. Considering the 6 missions, these results indicate a mean freeboard underestimation by 10 cm for ENV-AMSRE and 5 cm for ENV-AMSRclim. In the CoA-B zone, OIB data show a strong inter-annual variability between the 2 years, with values in 2010 higher by about a factor 2.
Standard deviations of the datasets are comparable, which mostly reflects equivalent spatial scale resolutions (due to the rolling mean smoothing of OIB data). Still, the strong RMSDs logically illustrate small scale variabilities in OIB that are not captured by monthly mean satellite observations. The analysis of along-track variability (cf. Figures  6  and A4  in Appendix A) shows relatively good agreement (although Envisat freeboards can be biased, such as for the 30 October 2010 mission). As a result, considering the 6 OIB missions together, a correlation by about 0. 5 is found (cf. scatterplots in Figure  5 ). It is coherent with validations in the Arctic. At the beginning of the 21 October 2009 mission, it is relevant to observe AMSR-clim snow depths comparable with ATM total freeboards.
ICESat-2 We compare total freeboard estimations of ICESat-2 with CryoSat-2 computed from CS2-TFMRA50 and ASD or AMSR-2 snow depth data. Figure  7  presents the annual mean maps of these two datasets for winter 2019. Overall, the CS2 total freeboard estimations are in good agreement with ICESat-2 in terms of magnitude and spatial distributions. The use of AMSR-2 tends to overestimate the spatial mean by 4. 5 cm, while the use of ASD leads to a mean underestimation by about 3. 5 cm. In both cases, the correlation, computed from all the monthly data (instead from annual means), is about 0. 55. Using AMSR-2, the mean overestimation is mainly the result of thicker freeboards in the Weddell sea and the A-B (and CoA-B) sectors. In particular, the pattern of thick freeboards drifting eastwards in Weddell is far too expanded compared to ICESat-2.
Deviations with SAMOSA+ probably provide first guess estimations of the impact of the roughness. Overall, the data presented in this study generally overestimate the ice freeboard, and confirms the results suggested by Price et al. [45]  and Kwok and Kacimi  [53]. Kwok and Maksym  [43]  still argue that a zero ice regime may not be dominant and that this assumption, analysed in e. g. , Kurtz and Markus  [51] , leads to important SIT underestimations. Although altimetry radar freeboards are likely biased, its knowledge remains essential for sea ice thickness. Additional studies, as for instance on the impact of snow temperature, density and/or surface roughness is yet necessary to characterize Ku bandradar penetration. Our results have also highlighted that snow depth underestimations would counterbalance freeboard overestimations, leading to consistent total freeboards.
Unfortunately, the recent decrease of snow depth strongly limits the representativeness of such climatology in the past. This argues instead for the use of the AMSR-clim snow depth to construct time series including Envisat data. Deviations with in-situ data (cf. Section 3) also showed that the complexity and high variability of the Antarctic sea ice occurs at space and time scales that can not be well captured by the current resolution of satellite altimeters. For this reason, the comparisons presented in this work rather consider large scale statistics (global to regional mean analysis). The absence of ASD data prior to 2013 is an important obstacle to the analysis of long term sea ice volume trends and variabilities. The combination of AMSR-E/AMSR-2 data is not recommanded as there is a bias between the 2 datasets.
Conclusions In this paper, we presented the first altimetry-based sea ice freeboard, thickness and volume series over 2003-2020, at global and regional scales in the Antarctic. Such dataset is an important step towards an « Arctic-like » knowledge of the Antarctic sea ice, which is becoming an urgent need in the context of the recent warming events. The sea ice minimum extent record of the last summer 2021/2022 confirms this. In the first part of this study, we presented sea ice radar freeboard estimates computed from CryoSat-2 heights measurements using a 50% threshold TFMRA retracker and the SAMOSA+ physical retracker. These 2 solutions have been compared with the SI-CCI dataset. Overall, we found very consistent spatial distributions between the different datasets, with a 5-6 cm bias low compared to the SI-CCI product.
Envisat LRM measurements can not directly provide consistent freeboards from heuristic retracking, and physical retracker techniques are still under development. In order to produce consistent time series, the LRM Envisat radar freeboards are calibrated onto CS2 using a neural network approach trained over the common flight period. This methodology results in Envisat radar freeboards with a mean deviation from CS2 of the order of a centimeter. Nevertheless, this dataset has lower thick and higher thin freeboards, which is relevant with the estimation from SAMOSA+. Our analyses showed that such calibration is only efficient when using identical retracking techniques for CS2 and Envisat. Then, in this study, we have mainly focused on the TFMRA50 solution. Results from physical retracking are still very promising and should be considered in the future.
Freeboard and thickness estimates were compared with ULS draft data, some in-situ measurement of the SIMBA campaign and 6 missions of the 2009 and 2010 OIB campaigns. Overall, these comparisons mainly highlight mean radar freeboard overestimations and snow depth underestimations. These deviations are very likely related to the particular, and rapidly changing, sea ice conditions in the Antarctic (e. g. , flooding or snow-ice formations). In such cases, the ku-band penetration is strongly reduced, leading to freeboard overestimations. However, such conditions are not always dominant. On the scale of the month, our comparisons show that regional means are generally quite consistent. At the SIMBA transects, the predominance of flooding event lead to higher freeboards (by about 15 cm). The AMSR-E data underestimate the snow depth by about 10 cm.
The good statistical representativity of the ULS draft data sample lead to good global mean consistencies. Using AMSR-clim we found an identical mean draft and an underestimation by about 20 cm using AMSR-E. Comparisons with OIB also lead to consistent mean total freeboards. It also highlights that along-track variabilities of OIB can be quite well observed by Envisat. Still, specific events related to local temporary conditions can hardly be observed in monthly gridded estimations. It could be improved by using optimal interpolation techniques, that enables to consider time in the gridding process. Finally, the comparisons with ICESat-2 demonstrate very good consistency in terms of spatial distributions and magnitudes. Our results indicate that the abrupt decline of sea ice extent in 2016 was associated with an important sea ice thickness and volume reduction.
Since 2013, we found a diminution by about -40% per decade for thickness and by about -60% per decade for the volume. Unlike the sea ice extent, post-2016 thickness estimations remain low. Over the entire 2003-2020 period, the absence of non-climatological snow depth dataset does not allow for consistent trends. Note that they are still indicated in Table  A1. The regional analysis confirms a sea ice thickness and volume decreases everywhere except in the CoA-B sector, where the SIT has increased by about 65% (considering SIC > 50%). In all region except the CoA-B sector, 2016 is a thickness and volume maximum high. Except for 2016, we have not identified clear first order correlations between sea ice thickness and sea ice extent. To go further, an analysis of correlations between sea ice summer conditions and the winter sea ice thickness would be of great interest.
One other important point is to investigate the seasonal variability of sea ice. It should provide insights into whether 2016 impacted the timing of the melt and freeze. An update of the freshwater budget could also be made by using these estimations of sea ice volume variations. Finally, the datasets presented and validated in this study provide a very important amount of information, aiming to impulse other studies in the Antarctic. It is crucial to better understand the climate alarm currently rising from the Antarctic. The radar freeboards trends are calculated from the CS2-TFMRA50 dataset and the snow depth from the ASD product (only for 2013-2020). The SIT and SIV trends are computed from CS2-TFMRA50+AMSR-clim over 2003-2013 and from CS2-TFMRA50+ASD over 2013-2020. The NSIDC Sea ice extent and concentration trends are also indicated.
Prior to this calculation, H lead are linearly interpolated under the floes within a 25 km window. In the absence of leads in this interval, observations are discarded. Based on the National Snow and Ice Data Center (NSIDC) sea ice concentration product (cf. Section 2), freeboards in areas of less than 75% of sea ice are removed. The radar freeboard uncertainties δ f br are estimated from Equation (A1) assuming that the errors are unbiased, uncorrelated and follow the Gaussian propagation law. Uncertainties on the leads δ H leads are calculated along-track from the statistical dispersion (standard deviation) of heights within a 12. 5 km radius. Since sea ice topography can significantly vary, we assume that the statistical dispersion of floes is equivalent to that of the leads, as expressed in Equation (A2).
Acknowledgments: This research has received support by the  CryoSat+ Antarctic Ocean projects (CSAO+) , the  CNES  TOSCA  CASSIS  project and the  ESA  living planet fellowship. It has also benefited from the outcomes of the  ESA POLAR+ Snow on Sea Ice  and the Fundamental Data Record for Altimetry (FDR4Alt) projects. We also would like to thanks  Stephan Kern , from  AWI , for providing the AMSR climatology. We also thank the  LEGOS laboratory  and the reviewers for their relevant and constructive comments.
Our data treatment was in accordance with the Methodology of reference Methodology Reference-004 specification for data policy. The study findings will be disseminated via peer-reviewed publications and conference presentations, especially to the Neurosphynx's rare disease healthcare network. Trial registration number NCT04679792; Pre-results. ⇒ This is the first trial which evaluates postural control strategies in a paediatric population affected by Chiari I malformation using computerised dynamic posturography. ⇒ Quantitative and qualitative data inherent to postural ability will be observed and collected, in order to ensure reproducibility of the research. ⇒ This protocol could represent the starting point for a wider, multicentre study. ⇒ This trial is a multidisciplinary approach to Chiari I malformation, combining neurosurgical, ears, nose and throat and biomechanical expertise.
► Dynamic posturography recordings in paediatric population. Systematic review of the literature at the time of study design was performed using an Index Medicus and PubMed electronic database, looking for typical symptoms of CM1 reported in neurosurgical articles. 0] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  Seven studies were selected to express the normal development of postural control in children,  [35] [36] [37] [38] [39] [40] [41]  two of which underline the feasibility and effectiveness of Equitest. 39 42 osturographic evaluation in CM1 patients has been used in a study published in 2019 by Palamar et al. 43  Static posturography (Tetrax Interactive Postural Balance System (Sunlight Medical, IT) was used in 36 adult subjects, trying to find a correlation between the risk of fall (Fall Index) and degree of tonsillar ectopia, as well as presence of syringomyelia.
Data collection method The data are collected by the principal investigator in an electronic and anonymous database which is password protected and stored on the principal investigator's professional computer. Data collection will be carried out in accordance with the National Commission on Informatics and Liberties (Commission Nationale de l'Informatique et des Libertés, CNIL) policy. During the study, the collection of data will be interrupted and the data previously collected will not be used if the person participating in the research objects to the use of their data.