dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical Modelling Our study focuses on providing an optimised way of embedding FOS within composite plies for best possible coverage of strain signals for wind-turbine structures and/or similar applications. There are some finite element analyses realized for mechanical and thermal stress studies between FOS and host materials  [1, 6, 7]. Our numerical models are developed using the commercial software ABAQUS. Numerical models include various parts of different materials that are assembled together to compose a final specimen model for finite element simulations. The FOS is designed with the significant coating material properties mentioned in Table  1. Two carbon fiber composites are bonded together with an epoxy matrix, which hosts a linear, multi-linear, sinusoidal, and dual-sinusoidal placed optical fiber.
Results of Numerical Simulation of Multi-Linear Alignment of Optical Fibers Embedded in Composite Material Numerical models are simulated using static loading conditions by using standard increments, as proposed by the ABAQUS software. For all models, results were mainly monitored in the epoxy region so as to understand the behaviour of FOSs when embedded in composite. The result analysis was carried for three different finite element models, with different spacings between the FOSs when placed linearly. The following are the results of the experiments performed to see the stress variation in the epoxy part when linear FOSs are placed side by side in the composite material. The stress transfer in the S22 direction (lateral direction) has been measured for the epoxy part when FOSs are present between the composite parts.
Multiresolution analysis: background MRA or multiscale approximation can be interpreted as a decomposition of data at different resolutions, LOD or scales, through a recursive analysis process. It is called exact, reversible or invertible when a synthesis scheme can retrieve the original data. Inter-scale relationships  [9, 10]  often yield sparsification or increased compressibility on sufficiently regular datasets. In discrete domains, each analysis stage transforms a set of values (continuous or categorical, in one or several dimensions), denoted by S 0. The resulting representation consists in one subset S -1 of approximation coefficients at a lower resolution identified by a negative index S -1 that approximates the original signal, plus one subset of details D -1 or a combination thereof.
Finally, detail simplification through multiscale decompositions does not possess well-established quality metrics. All of the above hampers exhaustive objective evaluations such as possible in image processing, where metrics and benchmarks have been evaluated for decades. To evaluate the performance of HexaShrink, we base our analysis on a set of seven geological meshes, with geometries ranging from smooth to fractured, and diverse categorical and continuous properties. Their main characteristics and properties are summarized in Table  1. As will be seen, they appear representative enough to allow one to derive consistent observations and conclusions for different data handling purposes. They are initially stored in the GRDECL ("GRiD ECLipse") file format  [42]. Originally complex geomodels are thus described with details rendering their geometry explicit and structured, an important feature for geomodelers or flow simulation software (Petrel™, SKUA-GOCAD™, Eclipse™.
Compression performance comparison We now provide an objective evaluation of the Hexa-Shrink multiscale representation for compression purposes. Our main objective is to verify that binary mesh formats (beyond mere ontological analyses) are indeed compressible, and whether decomposing them in a progressive manner over different scales remains beneficial in size reduction for needs beyond mere visualization (data storage, transfer). Indeed, multiscale representations are thought to enhance the sparsity of locally regular data, often resulting in better predicted properties that can subsequently be compressed. We first assess lossless (or perfect) compression. All mesh ontological and geological (cf. Table  1 , Section 5. 1) are thus perfectly restored, whatever the number of decomposition levels (Section 5.
Speed performance is highly dependent on mesh size, discontinuity complexity, levels of details. For a baseline evaluation, a Java implementation was run on a laptop with Intel Core i7-6820HQ CPU @ 2. 70 GHz processor and 16 GB RAM. Each mesh (from our dataset in Table  2 ) was compressed to the maximum level, and decompressed, twelve times. As the outcomes were relatively stable, they were averaged. Timings for analysis or synthesis alone, and cumulated with lossless encoding and decoding, are summarized in Table  3. [A]nalysis  [A]   Analysis is slightly slower than synthesis, both taking from a couple of seconds to a couple of minutes. Concerning the coders, gzip is the fastest, adding little overhead to HexaShrink speed. However, bzip2 or LZMA durations can reveal more expensive than analysis alone.
This is crucial, given the constant evolution of data acquisition density that yields increasingly massive and accurate dataset, associated to more demanding simulations. The Hexa-Shrink decomposition is consistent with respect to mesh rescaling in geosciences, and provides an option for a reversible upscaling;  [37]  recently proposed such a waveletinspired scheme. Finally, it lends itself to an efficient lossless compression, which can be used for storage and transfer. Perspectives can deploy into many directions. Motivated by preliminary progressive lossless compression results, we aim at developing a more versatile multiresolution compression scheme, to manage the potential evolution of mesh geometry or properties over time, with a special care for simulation-related quality metrics. As multiresolution analysis and synthesis were computed independently from compression, we also envision a better matched coding of approximations and details, for additional performance, toward lossy compression  [32, 36].
A common requirement is that the DICOM radiation dose structured report (RDSR) is sent to the RDMS, containing dose indices, acquisition information, and beam and table geometry. There is little data in the literature concerning the validation of PSD and skin dose map distributions for these software applications. The few studies available have some limitations in the comparative assessment as defined by Habib Geryes et al. [18]. In their study, the authors compared the PSDs and the 2D dose distributions calculated by the Radiation Dose Monitor tool (Medsquare, Paris, France) and measured with Gafchromic ® XR-RV3 films. This precise and complete phantom study was performed on two interventional radiology systems for 17 different configurations (simple and complex). The purpose of this study is to compare the PSD and skin dose map distribution as measured with XR-RV3 Gafchromic ® films and estimated with the interactive Skin Dose Map ® tool (SDM Tool ) integrated the RDMS DoseWatch ® (GE Healthcare, Milwaukee, USA).
Skin Dose Map (SDM) calculation formalism The RDMS DoseWatch ® includes the estimation of skin dose as part of its core dose management capabilities. The interactive Skin Dose Map ® tool (SDM Tool ) is integrated in the RDMS DoseWatch ®. At the end of the procedure, each interventional imaging system transfers the RDSR data to DoseWatch ® RDMS. RDSR data include dosimetric (such as kVp, additional filtration, Air Kerma, Kerma Area Product) and geometric (such as primary and secondary angulation, table position) information for each event of fluoroscopy and fluorography. SDM Tool uses RDSR data to calculate the PSD per event following recommendations defined by K. Jones et al. [12] : 𝑃𝑒𝑎𝑘 𝑆𝑘𝑖𝑛 𝐷𝑜𝑠𝑒 = 𝑅𝑃𝐴𝐾 × 𝑆𝑅𝑃𝐷² 𝑑 𝑆𝑘𝑖𝑛 2 × 𝐶𝐹 × 𝐵𝐹 × 𝐴𝐹 𝑆𝑘𝑖𝑛 × 𝐴𝐹 𝑇𝑎𝑏𝑙𝑒 (1) The Reference Point Air Kerma (RPAK) provided by the equipment for each event is the cumulative air kerma at the interventional reference point (IRP) located 15 cm back from isocenter towards the focal spot of an interventional imaging system.
"Height of system" and "Source Object Distance (SOD)" are fixed for each system. 𝑑 𝑇𝑎𝑏𝑙𝑒 = 𝑇𝑎𝑏𝑙𝑒 ℎ𝑒𝑖𝑔ℎ𝑡 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 + 𝑆𝑂𝐷 -𝐻𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑠𝑦𝑠𝑡𝑒𝑚 (2) "Table Height Position", " Second, an average compressed mattress thickness is added to d Table to model the mattress placed between the table and the patient's back. This value was defined especially for this study by measuring the average thickness of the compressed mattress on 20 patients of different morphologies. For each event, SDM Tool defines a Half-Value Layer (HVL) based on kVp and filtrations (inherent and additional) used. The HVL values are calculated beforehand with the Spekcalc software (McGill University, Montreal, Canada) by taking into account the inherent and additional filtrations and kVp available in the system used. Once the beam quality is determined based on the HVLs, the backscatter factor (BF), the skin absorption factor (AF Skin ) and the mattress and table attenuation factor (AF Table ) are determined based on lookup tables in the software.
For the Philips system used during this work, the information is provided through the position of the shutters DICOM Tag "99PHI-IXR-XPER/005". The beam position is determined using various data fields in the RDSR (left/right and cranial/caudal angulations, C-arm position). The phantom is positioned on top of the table. The software rotates the phantom, if needed, to consider the patient position provided in the RDSR. The patient position relative to the table top has to be manually adjusted as this information is not present in the RDSR; the default setting is 10 cm between the table top edge and the patient's head. The dose information is cumulated on all the irradiation events to provide a comprehensive skin dose map distribution for the whole exam. DoseWatch ® presents the skin dose map distribution as a 3D interactive view that the user can rotate and zoom in or out to focus on relevant body regions (Figure  1 ).
Films were scanned, orange-side down in the scanner, at 1 week ±1 h post exposure, with an Epson 10,000 Expression XL as per manufacturer recommendations. Each exposed film (7 cm x 8 cm) is scanned individually in the center of the scanner to overcome the non-uniformity of the scanner  [27; 32]. The software Film Qa-XR (Ashland, Advanced Materials, USA) was used to analyze film images. A square region of interest (ROI), approximately 2 cm x 2 cm, was placed in the center of each film image. According to the manufacturer recommendations, the red color channel was used  [27; 28]. The mean measured reflective density was associated to the measured AK and a calibration curve was obtained. An uncertainty assessment was performed for the skin dose measurements with XR-RV3 Gafchromic films (Table  2 ).
Two configurations were performed to compare the combined impact of previous parameters on skin dose map calculation. The description of the different configurations was presented in Table  3. The 13 exposed films were read 1 week ±1 h after the exposure, respecting the same process described previously for the film calibration. Based on the beam quality selected by the X-ray system when exposing the PMMA phantom, the 70 kVp calibration curve most closely matched the beam quality for all film exposures. A square ROI of 1 cm² was manually positioned by the medical physicist in the maximum dose region with the software Film Qa-XR ®. The average value of this ROI represented the PSD Film. SDM Tool calculates the skin dose for each event from the previously defined calculation formalism. At the end of the calculation, SDM Tool provides the PSD SDM and dose distribution obtained on the flat phantom and the ICRP phantom.
Comparison of dose maps The dose maps obtained with SDM Tool are similar to those recorded with the software Film Qa-XR after film assessment. Sizes, shapes and positions of the different exposure fields on the dose maps with SDM Tool are relatively the same to those present on the Gafchromic ® films, especially for simple configurations. Figure  5  shows five dose maps obtained with the two softwares for simple configurations (C4, C5, C7, C8, C9) and Figure  6  shows the dose maps obtained for the two combined configurations (C12 and C13). Variations in dose map representation are observed for two configurations: C10 and C11. Figure  7. a shows that software does not model one-sided and two-sided wedge filters. The PSD being located in the center of the field, this omission does not involve a significant variation of the PSD obtained with the film and the software (10% for both phantoms).
[18]  found an average difference of 10%±7% for a Siemens Artis Zee and 9%±7% for GE Innova IGS interventional systems. This robust and comprehensive study carried out on phantoms for 17 configurations is the most appropriate for comparison. Indeed, the methodology used in our study was modeled on that described by Habib Geryes et al. [18]. However, our study is carried out with 13 configurations on the same system. It should also be noted that in the present study, the PSD is calculated on both 2D flat phantom and 3D ICRP phantom whereas in the Habib Geryes study  [18]  it is only calculated on a flat 2D phantom with the Radiation Dose Monitor software. The results of this study also show that the obtained dose maps with SDM Tool are relatively similar to those obtained from the Gafchromic ® films.
As with any software, SDM Tool has differentiators and limitations. First, the results of this study show that this tool can accurately calculate the PSD for simple and combined configurations. PSD can be calculated for all patients tracked in the system. PSD values are computed automatically without additional manual action, are easily accessible from the RDMS, but are not provided with calculation uncertainties. Limitations of the SDM Tool are not due to algorithm gaps but from data gaps in the RDSR. Upon connection of a new system to the RDMS, the reliability of RDSR data generated by the system needs to be assessed. As with Radiation Dose Monitor, the PSD is obtained retrospectively but much faster than with Gafchromic ® films or software like em. dose. In addition, the PSD is not obtained in real time as with Dose Tracking System ® or Dose Map ® during the procedure.
A similar study should be performed to assess the reproducibility of the results on the other systems and with rotational events. Third, this experimental study is performed on a PMMA phantom with uniform density, which is relatively far from the patient density. A study on patients should be performed to assess the reproducibility of the results for interventional cardiology and other fluoroscopically guided procedures. Finally, as defined by Habib Geryes et al. [18] , a gamma index analysis between the obtained dose maps with the films and with SDM Tool could not be performed because of the lack of suitable software.
A R Y Earth observation satellites yield a wealth of data for scientific, operational and commercial exploitation. However, the redistribution of mass in the system Earth is not yet part of the standard inventory of Earth Observation (EO) data products to date. It is derived from the Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On mission (GRACE-FO). Among many other applications, mass redistribution provides fundamental insights into the global water cycle. Changes in continental water storage impact the regional water budget and can, in extreme cases, result in floods and droughts that often claim a high toll on infrastructure, economy and human lives. The initiative for a European Gravity Service for Improved Emergency Management (EGSIEM) established three different prototype services to promote the unique value of mass redistribution products for Earth Observation in general and for early-warning systems in particular.
2012; Flechtner et al. 2016). More than 17 yr after the launch of the GRACE satellites, the exploitation of the GRACE data is still ongoing. A growing number of Analysis Centres (ACs) inside and outside the GRACE Science Data System (SDS) is still challenged with the Level-1B data processing. To date each new release of monthly gravity fields, provided to the scientific user community as Level-2 products in a (usually) spherical harmonic (SH) representation, still represents a substantial and significant improvement with respect to previous releases. Official SDS solutions are provided by the Center for Space Research (CSR; Bettadpur 2012) and the German Research Center for Geosciences (GFZ;  Dahle et al. 2012). Additional solutions are computed by the Jet Propulsion Laboratory (JPL) for validation  (Watkins & Yuan 2012).
2015). As a lesson learned one may conclude that a certain agreement on common processing standards will help to ensure the compatibility of the different solutions in view of a proper geophysical interpretation. On the other hand, the example of the GFZ RL05 solution also nicely shows that the availability of several competing solutions is very beneficial to detect outliers. The current situation is, however, sub-optimal from the user perspective to fully exploit the information content offered by the data of the past GRACE and the recently launched GRACE-FO mission. Today the end user is essentially left with the difficult choice of deciding (1) which solutions from which AC to use and (  2 ) what actions to perform for converting SH coefficients (Level-2 products) into gridded mass values (Level-3 products) appropriate for their study when not selecting one of the three official SDS solutions.
1 n contrast to other Earth Observation (EO) data, satellite-based measurements of gravity represent total water storage variations, that is, variations of all surface and subsurface water storage compartments. As such the past GRACE and the recently launched GRACE-FO mission provide unique information on the wetness state of a river basin with regard to its actual flood generation potential or its susceptibility to a drought. Reager & Famiglietti (2009)  estimated flood potential at the regional scale by means of determining repeated maxima in water storage anomalies, which suggest an effective storage capacity in a region, beyond which additional precipitation must be met by increases in runoff or evaporation. Thomas et al. (2014)  presented a quantitative approach for measuring hydrological drought occurrence and severity based on GRACE data by calculating the magnitude of the deviation of regional, monthly total water storage anomalies from the time-series' monthly climatology.
Humphrey et al. (2016)  surveyed key features of temporal variability in the GRACE record by decomposing gridded times-series of monthly equivalent water height (EWH) into linear trends, interannual, seasonal and intra-annual components, with an additional focus on extreme dry anomalies and their relation to documented drought events. Today it takes approximately two months from the time that the Level-1B data is collected onboard the GRACE satellites to the time when scientists can access and examine the Level-2 or Level-3 products provided to the scientific community. The temporal sampling of the gravity field solutions is at best 10 d but most reliably restricted to one month when not using regularization techniques  (Bruinsma et al. 2010). Both of these time constraints currently limit the potential of using the results from satellite gravimetry in time-critical monitoring applications  (Pail et al.
2015). This applies in particular for early-warning and forecasting systems of extreme hydrological events. Flood forecast models need, for example, near real-time (NRT) information to estimate the probable development of the event in terms of flood stage or river discharge with typical lead times of a few days for larger river basins (see overview by  Emerton et al. 2016). Also the usefulness of high-resolution follow-up observations such as optical and radar EO data for emergency management is strongly influenced by the time-span from alert reception, satellite programming, satellite acquisition and data reception  (Voigt et al. 2016). Given these limitations, the GRACE ACs in Europe have joined forces and established the European Gravity Service for Improved Emergency Management (EGSIEM) project, an initiative supported by the Horizon 2020 Framework Program for Research and Innovation of the European Commission and the Swiss State Secretariat for Education, Research and Innovation (SERI).
In this article the EGSIEM concept is presented, the current implementation status is reported and future perspectives are outlined. The article is structured as follows: Section 2 introduces the general concept of the EGSIEM initiative. Section 3 presents the complementary data prepared within EGSIEM, whereas Section 3. 2 is dedicated to the GRACE Level-1B data processing. Sections 4-6 introduce the EGSIEM prototype services and present the current achievements. Section 7 closes the article with a summary and outlook.
C O N C E P T O F E G S I E M A N D I T S P RO T O T Y P E S E RV I C E S The main driver of the EGSIEM initiative was that gravity-based observations of the redistribution of water and ice masses provide valuable and complementary information with respect to more traditional EO data as provided by, for example, Copernicus, the European Programme for the establishment of a European capacity for Earth Observation. 2  We, therefore, defined three key objectives: (1) demonstrating that superior gravity products can be derived by combining solutions from different ACs, (2) providing NRT gravity products and (3) assessing the potential of these NRT products for flood and drought monitoring and forecasting. These objectives are achieved by setting up three dedicated prototype services, namely a scientific combination service, a NRT service and a hydrological service.
Scientific combination service The main input data to the first two EGSIEM prototype services are GRACE Level-1B data. For combination purposes, Level-1B data needs to be consistently processed by several ACs in the postprocessing mode to derive monthly gravity field solutions. These are then combined by the Scientific Combination Service to derive high quality scientific products with a time resolution of one month and latencies of up to 60 d. Since all EO systems must refer to one and the same reference frame, the consistent use of the reference frame is an essential step in the EGSIEM concept. To the extent possible a generic formulation of processing standards has been developed and consequently been applied for the GRACE processing at all EGSIEM ACs (EGSIEM 2015). It is expected that the measures pave the way for a long awaited standardization in the gravity field community and to significantly increase the quality, robustness and reliability of products derived from satellite gravimetry.
A dedicated GPS reprocessing campaign was conducted for a consistent integration of data from the global tracking network of the International GNSS Service (IGS; cf. Dow et al. 2009 ) as described in Section 3. 1 and to provide one single set of up-to-date GPS products to all EGSIEM ACs (see Section 3. 1). Subsequently, ACs may then apply their preferred, individual approach to process the GRACE input data. The approaches differ by the used observables, the parametrizations, the stochastic noise models applied and the relative weighting of the different observables. All ACs are required to provide free solutions, that is, solutions that do not depend on an a priori gravity model. This is of crucial importance to avoid biases in the combined solutions. If all solutions are unbiased, the strengths and weaknesses as well as the different background models of the different solutions are expected to average out in the combination and the combined solution will be statistically better and more robust.