dataesr/scientific-paragraphs-categorization huggingface dataset:
For each flight plan, the natural radiation environment can be calculated, which describes the differential energy spectrum of neutron, proton and muon for each point of the flight plan trajectory. Analysis takes place considering different particle types, particle energies and technology cells. The transistors potentially impacted when a particle i of energy E i hits a technology cell and the most relevant induced currents are identified. A current database is created depending on the transistor size and type, the particle and its characteristics. Then, current injection at the circuit level takes place leading to a set databases with fault models characterised with distributions of SET widths in the cell. For instance, Figure  5  summarizes the distribution of the width of the SET pulses created when neutrons hit the 28nm FDSOI cells, normalized to the cell Fig.
the Southern Ocean surrounding antarctica is a region that is key to a range of climatic and oceanographic processes with worldwide effects, and is characterised by high biological productivity and biodiversity. Since 2013, the International Bathymetric Chart of the Southern Ocean (IBCSO) has represented the most comprehensive compilation of bathymetry for the Southern Ocean south of 60°S. Recently, the IBCSO Project has combined its efforts with the Nippon Foundation -GEBCO Seabed 2030 Project supporting the goal of mapping the world's oceans by 2030. New datasets initiated a second version of IBCSO (IBCSO v2). This version extends to 50°S (covering approximately 2. 4 times the area of seafloor of the previous version) including the gateways of the Antarctic Circumpolar Current and the Antarctic circumpolar frontal systems.
Despite its remoteness and hostility, human activities are increasingly extending into this distant part of the world, examples including research, fisheries, and tourism. Precise bathymetric information as e. g. provided by the International Bathymetric Chart of the Southern Ocean (IBCSO) and the Digital Bathymetric Model of the Drake Passage (DBM-BATDRAKE)  9  are paramount to better understand the Southern Ocean and its processes as well as for human activities and conservation and management measures  10. IBCSO aims to provide the most comprehensive compilation of bathymetric data for this region. IBCSO was initiated in 2006 with the first version published by Arndt et al. in 2013  11. It is the southern equivalent of the International Bathymetric Chart of the Arctic Ocean (IBCAO), which was originally produced in 2000 and recently released its fourth version  12, 13.
Following the release of Version 1, the user community expressed the wish for an IBCSO reaching to 50°S to cover the entire ACC and the Antarctic circumpolar frontal systems. This request, the growing demand for bathymetric information of the Southern Ocean, and the availability of numerous new bathymetric datasets collected since the first version of IBCSO were the motivations to produce a new version of IBCSO. Here we present IBCSO Version 2 (IBCSO v2) (Fig. 1 ) covering the area south of 50°S. The resolution is 500 m × 500 m in IBCSO Polar Stereographic projection (EPSG: 9354, see also the usage notes). It covers over 77 million km² of seafloor (approximately 2. 4 times the area of seafloor covered by IBCSO v1). Highlights include improved bathymetries for the important oceanographic gateways of the ACC, the Drake Passage (now entirely included in IBCSO v2), and the Tasmanian Gateway (Fig.
1 ). The IBCSO v2 Digital Bathymetric Model (DBM) is available in two topography versions: one with ice surface elevation on the Antarctic continent and one with bedrock elevation, including sub-ice topography  15. Furthermore, we provide a Type Identifier (TID) grid that indicates the type of data that composes each grid cell. The TID codes adhere to GEBCO standards (Table  1 ). In addition, a unique Regional Identifier (RID) grid links each data cell to the corresponding metadata information and thus the DBM's cell value origin. All grids, a metadata table, and a digital chart of IBCSO v2 are publicly available for download from the PANGAEA data repository  16.
Methods The increase in coverage from IBCSO v1 to IBCSO v2 resulted in a substantial increase in the amount of data processing necessary at all levels from data submission to product generation. To cope with this higher computing workload, we have created a full computational environment surrounding the main processing pipeline (SEAHORSE, Fig. 2 ) of the IBCSO v2 DBM. This environment includes a database management system linked to SEAHORSE. To reduce run times, SEAHORSE is running dedicated code in a high-performance computing environment using parallel computing. On submission, the quality and integrity of datasets is assessed visually and autonomously using designated Python scripts in order to identify major errors (e. g. inverted coordinates, wrong projections, outliers). After these initial checks, weights (Table  2 ) are assigned to datasets for later processing.
SEAHORSE processing workflow. SEAHORSE consists of four distinct stages (Stage A-D, Fig. 2 ), each containing a number of individual steps. All stages can be run independent from each other. Outputs include extensive reports for quality assurance (QA) and continuous feedback to the IBCSO metadata database (i. e. properties of the data sets derived from processing). SEAHORSE harmonises submitted datasets (harmonisation -Stage A), subdivides them into smaller spatial chunks of data (tiling -Stage B), calculates weighted blockmedians within these chunks (weighted statistics -Stage C) and computes a composite of all data (containing data of all quality). Furthermore, a subset that contains only high-quality data (weights ≥ 15) is computed for subsequent gap-filling to produce the final grid product (product creation -Stage D).
Stage A: harmonisation. The initial Stage A (Fig. 2 ) harmonises incoming datasets line by line and adds the harmonised version of the input data to the IBCSO file database. The harmonisation arranges arbitrarily ordered datasets into standardised XYZ files (consisting of an X, Y, and Z column) with each line representing a single geographic location and depth sounding. A next step identifies and removes potential errors in the data, such as erroneous depths (values exceeding known maximum depths in the study area) and implausible coordinate values (e. g. ship-borne bathymetry with locations on land). Output files from Stage A contain X, Y and Z values rounded to 1-metre accuracy, with duplicates removed, and separated by a standardised column separator. They are stored in a harmonised file database using the dataset identifier and the associated weight as filenames (Table  2 ).
Stage B: tiling. Stage B (Fig. 2 ) subdivides the harmonised file database into smaller spatial regions, pooling data from different sources. For this purpose, we subdivide the area south of 50°S into 100 km × 100 km tiles (in EPSG:9354 projection). Subsequently, a spatial join of all datasets with the defined tiles allows the assignment of each data point to a distinct tile. Points that do not fall into any tile are skipped and reported to QA. The result is a tile database with a single file for each tile. The tiles are further used to identify the origin of outliers and erroneous data visible in the final product. Erroneous data are removed from the harmonised database during iterative cleaning routines using the software suite Qimera ® until all obvious artefacts disappear and a satisfac- tory quality is achieved.
Stage C: weighted statistics. In Stage C (Fig. 2 ), a weighted blockmedian is calculated for each 500 m × 500 m cell using the Generic Mapping Tools 6. 1. 1 (GMT) blockmedian module  17. Five statistic descriptors are calculated: minimum, 25% quartile, 50% quartile (median), 75% quartile, and maximum of the weighted data in each cell. In a subsequent step, the median data points are augmented with additional information from the metadata, i. e. TID and RID. The outputs of this stage are single files per tile containing XYZ values, the summary statistics (min, q25, q75, and max), and categorical values (TID, RID, and the contributing organisation) for each line. Stage D: product creation. In the final Stage D (Fig. 2 ), all files from Stage C are combined and subsets of geographic points are created (XYZ files) depending on the type of data.
Based on the TID, the data are filtered to extract only high-quality data (weights ≥15, Table  2 ) from the database. The complete dataset and the extracted high-quality dataset are gridded using a modified processing sequence that has been initially introduced for the IBCAO Project by Jakobsson et al. 12  and later adapted for IBCSO v1  11  , and the Southwest Indian Ocean Bathymetric Compilation  18. For IBCSO v2, this approach has been further developed. At first, all irregularly spaced geographic points are gridded using a "continuous curvature spline in tension" from the GMT's surface module  17  with a tension factor of 0. 35 (first used in IBCSO v1) to create a 2 km × 2 km background grid. Comparisons of outputs show that this tension factor is appropriate for the SEAHORSE workflow.
This grid is subsequently filtered in the spatial domain using GMT grdfilter with an isotropic cosine arch convolution filter (6000 m width). The output is resampled to 500 m × 500 m resolution using a bicubic interpolation (GMT grdsample). The high-quality data are gridded to a separate 500 m × 500 m resolution grid using GMT nearneighbor to preserve the high-quality direct measurements in the final product. Background and high-quality grids are combined using the bending algorithm from Arndt et al. 11  that follows the remove-restore concept described in Hell and Jakobsson  19  and Jakobsson et al. 12. The algorithm is implemented using the programming language Python and its scientific ecosystem, e. g. SciPy  20  , NumPy PyGMT  22  , and Dask (https://dask. org/) as an interface for GMT  17.
Pre-bending, the predicted bathymetry grid is adjusted to the IBCSO database to minimise artefacts caused by varying depths by calculating an offset factor between both grids on a cell-by-cell basis. A 1000 m × 1000 m blockmedian is computed with the GMT blockmedian module to suppress small-scale artefacts in the grid. The factor values are re-gridded and filtered using GMT surface and grdfilter with a cosine arch filter (2000 m × 2000 m) via PyGMT before the resulting grid is resampled to 500 m × 500 m using GMT grdsample. Then, this factor grid is used to adjust the predicted bathymetry grid by multiplying both grids. Areas are masked out, if the adjusted predicted bathymetry differs significantly from the surface grid (e. g. continental shelf areas and around islands). There, the background surface spline grid is used instead.
This approach successfully prevents artefacts caused by differences in data resolution and accuracy. In the final step, ice-surface and ocean mask grids are dynamically generated from the datasets created in previous processing steps (Fig. 2 ). The ice-surface mask is derived from the BedMachine 15 surface elevation grid. The ocean mask is calculated from the gap-filled composite grid considering the ice-surface mask and RID grid (excluding all values above 0 m). It is used to assure that all ocean cells are modelled below sea level and all topographic cells are modelled above sea level. Grid cells that failed this logical test are set to the value -1 for ocean cells and to the value 1 for topographic cells. The ice-surface mask is used to create IBCSO v2 with ice surface elevation from BedMachine  15.
Data Records IBCSO v2 is available for download from the PANGAEA data repository  16. It comprises a variety of datasets (Table  1 ) ranging from digitised contours and lead line soundings to high-resolution multibeam data. If possible, the use of gridded compilations was avoided and source datasets were used instead to achieve the most consistent interpolation and prevent an overestimation of the covered area (Fig. 3 ). Therefore, each dataset mostly refers to a single expedition with its unique RID value  16. Bathymetry. High-resolution multibeam datasets make up the basis of the compilation with a total of 464 datasets. In addition, 766 singlebeam datasets provide measured bathymetric information (Table  1 , Fig. 3 ). The datasets were received in various formats and were standardised as ASCII XYZ data with associated metadata information when available (e.
g. data contributor, source survey, year of survey). However, many datasets lack detailed information regarding their origins making it difficult to assess their quality. Furthermore, the spatial distribution of data shows a high degree of heterogeneity. For example, Drake Passage and the Ross Sea areas display high multibeam data coverage while along East Antarctica mostly singlebeam data exist (Fig. 3 ). IBCSO v2 uses SRTM15+ v2. 2  23  as the predicted bathymetry. It, however, contains numerous artefacts especially in areas of sea-ice cover and on the continental shelves. To avoid the incorporation of those artefacts, after interrogation of the available high-resolution multibeam data, critical areas are masked out for the infill with predicted bathymetry. Sub-ice shelf bathymetry. Sub-ice shelf bathymetry in IBCSO v2 is constrained by direct measurements (e.
g. from seismic campaigns), and in the absence of direct measurements by bathymetry estimations from gravity inversions, interpolation, and artificial steering lines. Seismic measurements from 21 datasets conducted since the 1950s are included (Supplementary Table  1 ). We only include bathymetry inferred from gravity inversion that rely on airborne gravity measurements and only in areas that are further away than 5 km from direct measurements in the IBCSO v2 database. In addition, we do not use bathymetry inferred from gravity inversions in areas where the models produce unrealistically shallow topography. Such areas have been identified either by a large discrepancy between the depths modelled by the gravity inversion and depths determined by seismic measurement, or by very small water column thicknesses (less than 100 m) in the sub-ice shelf continuation of narrow, deeply incised subglacial troughs beyond the grounding-line.
Such areas with steep topography and abrupt elevation changes are usually poorly resolved by gravity inversions due to the long wavelength and are therefore typically inadequately modelled  24, 25. This mainly occurs at the western Ross Ice Shelf close to the Transantarctic Mountains. Supplementary Table  2  summarises the gravity inversions that are incorporated directly or as part of the BedMachine Antarctica dataset  15. For the Amery Ice Shelf cavity, we use the bathymetric model created by Galton-Fenzi et al. 26. This model also uses seismic point data and an interpolation guided by tidal modelling for the deepest, most inland section of the ice shelf which is difficult to survey due to crevasses. For the remaining ice shelf areas, i. e. where neither direct measurements nor good quality gravity inversions exist, we have investigated the adjacent bathymetry and subglacial bedrock measurements for glacially incised troughs.
Where such troughs are located, we introduce artificial steering lines to guide our interpolation to model a continuation of these troughs. For the remaining areas, we use the seafloor depths as provided in the bed layer of BedMachine  15. Sub-ice sheet topography, ice surface topography, and island topography. Sub-ice sheet topography is entirely derived from the bed layer of BedMachine  15. BedMachine in these areas builds on ice-thickness measurements from airborne radio-echo sounding and a mass-conservation approach that uses ice sheet dynamics to interpolate between measurements  15. The topography is derived from various datasets (Fig. 3 ). Their selection depends on the geographical region and the quality of the different datasets in these regions. For the Antarctic mainland, we use the surface layer of the BedMachine dataset  15  derived from the "Reference Elevation Model of Antarctica" (REMA) that has a spatial resolution of 8 m 27.
Despite this higher resolution, we use the BedMachine topography information in continental ice-covered areas of Antarctica to ensure consistency with the ice thicknesses reported within BedMachine. For some coastal, ice-free areas of East Antarctica and for some small islands that are not resolved in BedMachine, we have directly added elevation information from the REMA dataset  27. For many Antarctic islands, REMA, and thus also BedMachine, yields no (e. g. South Orkney Islands, Balleny Islands) or incomplete (e. g. King George Island) topographic information. For these islands, most sub-Antarctic islands, and for South America we use elevation data from the ALOS Global Digital Surface Model Version 3. 2 of the Japan Aerospace Exploration Agency  28. For a few islands, including some smaller reefs, and for a few parts of larger islands, the ALOS model does not provide elevation information.
While this is not a perfect way to measure the uncertainty in a given grid cell, we regard this as the most practical way to get an estimate of the expected range of depth values for every given grid cell (Fig. 4 ). Overall variability increases with the number of datasets in a grid cell. The interquartile range of depth values at any given cell falls mostly between 0 m and 100 m. High values occur along the regular supply routes for Antarctic stations and within areas of high scientific interest where many datasets overlap. These areas have been visited across multiple generations of technical proficiency. On the other hand, areas with low variability indicate areas with little survey effort or areas that have produced similar data across multiple data sources. This can be expected for measurements e.
g. in shallower waters. However, variability does not immediately quantify the reliability of the reported depth value. We can conclude that our blockmedian approach is robust against outliers in the 25 th and 75 th quartiles. Only areas where both low coverage and high variation in measured data coincide have a detrimental effect on the final depth value in a grid cell. The RID grid (Fig. 1b ) gives a first impression, where the IBCSO v2 grid is constrained by actual data. Data coverage per tile (Fig. 5 ) provides an additional indication of how many grid cell values per tile originated from   1  and 2 ) with tiles featuring only low-quality data (weights <15) masked out in grey. (b) Data coverage based on all datasets. measured data. This coverage map highlights distinct distribution patterns.
7b ) or slopes around islands (Fig. 7d ) display more pronounced discrepancies. The change in data coverage and quality is obvious when  looking at the area seaward of Totten Glacier (Fig. 7c ) where a multibeam dataset acquired in 2017 by the Australian research vessel RV Investigator improves the morphology of the shelf break and resolves a network of submarine channels at the slope. Similar improvement is visible at the South Scotia Ridge where incised slopes facing towards the Powell Basin have been mapped in high-resolution by the RV Polarstern 32 in 2019 resulting in a larger discrepancy. The benefit of incorporating actual source data rather than gridded compilations is seen in Fig. 7d. In IBCSO v1, the slopes around the Balleny Islands are based on a data compilation with a resolution of 1000 m.
However, for IBCSO v2 we were able to receive the source data in full multibeam resolution resulting in a much more detailed DBM for this area compared to IBCSO v1. Improvements can also be observed when comparing IBCSO v2 with the predicted bathymetry grid (SRTM15 + v2. 2, Fig. 7 ). The Williams Ridge and the adjacent Labuan Basin at the Kerguelen Plateau were mapped by the RV Investigator and the RV Sonne in 2020. These additional data substantially improved the bathymetry for this region (Fig. 7e ). Distinct improvements are also visible when examining the region around the South Sandwich Islands and Trench (Fig. 7f ). Although not covered by IBCSO v1, the comparison with the SRTM15 + grid highlights an increased grid quality caused by large seafloor areas now constrained by multibeam measurements.
Usage Notes The IBCSO DBM is provided in GeoTIFF and netCDF-4 file formats with coordinates and depth stored as 16-bit integers and a pixel node registration. These formats can be imported into all major GIS packages (e. g. QGIS, ArcGIS). All grids are available in geographic coordinates (WGS84, EPSG:4326) and in projected Cartesian coordinates defined in the IBCSO Polar Stereographic projection registered with the EPSG Geodetic Parameter Dataset using the code EPSG:9354 (https://epsg. org/crs_9354/WGS-84-IBCSO-Polar-Stereographic. html). The projection's true scale is set at 65°S and coordinates in X and Y directions are given in meters. The horizontal datum is WGS84 whereas the vertical datum is approximately Mean Sea Level. Due to limited acquisition parameter information, there are uncertainties associated with the vertical datum information, especially for older data.
The grid cell value of the DBM is given in meters with negative values representing depths below sea level and positive values corresponding to topographic elevation. For the RID and TID grids, the cell values represent a unique dataset and type identifier value, respectively. An overview of the TID codes is given in Table  1 , whereas a list of all incorporated datasets is provided at the PANGAEA data repository  16. When using the native IBCSO projection, it is important to consider the following: The EPSG code was registered in March 2020 and first included in EPSG v9. 8. 11 database published on the 30 th April 2020 that was again included in the PROJ 7. 1. 0 database (from 1 st July 2020). However, QGIS versions prior to release 3. 20. 0 (from 19th June 2021) are using older PROJ database versions that do not include the IBCSO projection (EPSG:9354).
Code availability The GMT and GDAL routines used in the SEAHORSE workflow are Open Source and can be accessed on their respective webpages (https://www. generic-mapping-tools. org/ and https://gdal. org/). All relevant code related to the main SEAHORSE workflow are available at https://github. com/SeaBed2030/IBCSO_v2_Dorschel_ et_al_2022. Data for the technical validation are hosted on figshare  33. Since the SEAHORSE workflow was customised to fit the existing architecture of AWI's high performance cluster, most of the code is specific and requires severe adjustments when moved to a different environment.
g. Generic no weight [1] 42 Digital bathymetric contours from charts Depth value taken from a bathymetric contour dataset 5 1 45 Predicted bathymetry based on flight-derived gravity data Depth value is an interpolated value guided by helicopter/flight-derived gravity data 10, 5 3 46 Draft of a grounded iceberg Depth estimated by calculating the draft of a grounded iceberg using satellite-derived freebord measurement 10 1 70 Pre-generated grid Depth value is taken from a pre-generated grid that is based on mixed source data types, e. g. single beam, multibeam, interpolation etc. 5 [2] 71 Unknown source Depth value from an unknown source 5 1 72 Steering points Steering points -depth value used to constrain the grid in areas of poor data coverage no weight [2].