dataesr/scientific-paragraphs-categorization huggingface dataset:
The scaleddown 5 MA VDE retained the time evolution and ψ distribution of the original 15 MA VDE. The time evolution is now more consistently calculated in the 2020 DINA disruption database, giving a much shorter duration for the new 5 MA VDE: ~75 ms. As a consequence, some of the scalings used in  [1]  were clearly overly conservative. The new 5 MA VDE exhibits less total energy deposition to the wall, 29 MJ vs. the scaled MJ. The corresponding P halo values as a function of time are also smaller in magnitude than the scaled predictions, by ~50%. However, over a shorter amount of time, this results in higher heat fluxes to the 3D wall geometries than obtained from the scaled scenario, as seen in Section 3. Radiated power in the core is higher than the scaled value in the new MA VDE case, ~45 MJ vs.
Comparison of DINA model with experiments The question arises: how accurate are these new, broader halo predictions? There have been a number of dedicated halo current measurements presented in the literature across a variety of tokamaks. However, extrapolation of this data to ITER is not straightforward. Compiling and comparing halo current distribution data across multiple devices is extremely challenging for three reasons: 1) Fundamental differences in machine size, wall geometries, and operating capabilities (I p , B t , auxilliary heating, etc. ) 2) Varied definitions of 'halo width' across experiments 3) Lack of adequate diagnostic coverage for measuring the entirety of the halo region Not much can be done to address Issue #3 above, apart from requesting dedicated upgrades to current devices for future experiments.
The area of halo current deposition is then extrapolated along field lines (using the last converged EFIT equilibrium), giving a halo width of ~100 mm at the outer mid-plane. Shortly before the device was shutdown, an array of Langmuir probes dedicated to halo current measurements was installed in the lower divertor tiles (inboard and outboard) of C-Mod. The dedicated experiments in  [15]  report a halo width as a fraction of poloidal flux, similar to the method used in DINA in Eq. (1) for the computational domain. Other notable experiments in the literature include ASDEX-Upgrade  [16] , which uses divertor tile shunts to measure halo current densities, but does not translate those tile geometries to a halo width. An experiment on DIII-D  [17]  utilized tile current arrays in the vessel floor alongside a current reconstruction code JFIT, and reports halo width as a weighted average value across the entire poloidal halo region.
Finally, it is proposed to normalize the values of w h and minor radius to the major radius, to account for variations in machine size. Fig. 4  displays w h as a function of minor radius, both normalized by major radius, for the two DINA models alongside the published experimental data described earlier. Halo current deposition data from COMPASS is extrapolated along field lines via complementary JOREK simulations  [18] , while C-Mod uses a current filament reconstruction code. There is a stark difference between the original DINA model prediction and the newer DINA VDEs, with w h predicted to be almost 5 times wider for the newer model. For comparison, the data in Fig. 3  corresponds to minor radii of ~700 mm for the 2017 VDE and ~750 mm for the 2020 VDE, or a/R = 0. 11 and 0. 12, respectively.
The single halo width measurement from JET  [14]  lies closest to the new DINA predictions for ITER. The COMPASS measurements sit roughly between the two DINA datasets, while the C-Mod data fits closest with the original DINA data, before increasing strongly at lower minor radii. This is where Issue #3 must be taken into account: experimental limitations. Upon further analysis of the COMPASS and C-Mod data, it is seen that Langmuir probe arrays in both devices always measure a halo current from the plasma contact point out to the last probe of the array. In other words, the true halo width always extended past the measurement area of the probe arrays, meaning that calculations of w h from these experiments are always an underestimate. The increase in w h with decreasing minor radius effectively represents the inward motion of the plasma contact point along the array.
Thus, the real halo width would be shifted vertically in Fig. 4 , closer to the 2020 DINA dataset predictions. The upper dump plate measurements from JET appear to capture the entire halo region (see Fig. 6a  and Fig. 7  of  [14] ), although only a single data point is available. Work is ongoing to estimate where the true halo region lies for the COMPASS and C-Mod datasets, based on a rough comparison of the total halo current magnitude (measured from external coils) to the total current current density measured by the probe arrays. Compiling more experimental data to add to that in Fig. 4  is also ongoing. Taking into account the improvements in the 2020 DINA model, comparisons of that model with other disruption simulations, and comparisons with available experimental data, the broad halo width predictions of the 2020 DINA model inspire more confidence than the more conservative, thin halo regions calculated in 2017.
SMITER analysis Section 2 has highlighted the inherent differences between the "original 15 MA VDE", the "scaled-down 5 MA VDE", and the "new MA and 5 MA VDEs". Now the effect of those differences on energy deposition and melt damage can be quantified through the rest of the workflow process. First there is the heat flux analysis performed using the SMITER 3D field-line tracing software. SMITER utilizes the time dependent plasma equilibrium information Ψ(r,z), along with the direct profile of q‖(r) calculated in DINA. A scaling factor is applied to q‖(r) in order to take into account the 3D, non-uniform power deposition observed in SMITER, along with the correct pitch angle to convert q‖ within DINA's perpendicular halo area to the q‖ along the magnetic field lines. This ensures that the 3D power balance performed in SMITER matches the 0-D power balance of DINA.
The melt predictions for this case are one of the worst-case scenarios for ITER: a depth of up to ~2 mm of material excavated by melting and subsequent melt motion, with poloidal displacement of melted material up to ~50 cm along the panel surface. This large spatial extent of the melt motion is due to the high toroidal field for the 15 MA case. However, at these heat fluxes and time scales, vapor shielding must be taken into account  [7]. A detailed description of the MEMOS-U results for the new 15 MA VDE (and other cases) is relegated to an upcoming manuscript covering the full study of the 2020 DINA disruption dataset. All of the DINA cases in the disruption database assume a single impurity (Be) ranging from 0 to 3•10 19 m -3 particle density, which is introduced immediately after the thermal quench (TQ).
Tungsten impurity is neglected due to its relatively low cooling power and the low expected core plasma concentrations (prior to the thermal quench) for the scenarios in the DINA disruption database scenarios. For example, for a 5 MA pre-disruptive H-mode scenario with W quantities of ~2•10 16 atoms  [19] , n e = 3•10 19 m -3 , and an upper bound cooling power of ~1•10 -30 W/m 3  [20] , W will radiate less than 1 MW. Beryllium, with an upper bound cooling power of ~1•10 -34 W/m 3  [20] , but at the above range of densities, can produce 100 ′ s of MW. To match the case in  [1] , which was scaled from a 15 MA VDE with the Be density of 3•10 19 m -3 , the "new" 5 MA and 15 MA VDEs assume the same value. Such an impurity density is not unreasonable for a 15 MA case where a VDE or disruption would originate from a steady-state H-mode, full-burn scenario with a starting n e = 3•10 19 m -3.
However, this results in longer CQ times and lower heat flux densities. The effect of impurity density on surface temperature rise is therefore not straightforward. Still, all of these cases remain below the Be melt threshold. The cases of 1•10 19 and 0. 5•10 19 (in blue and cyan on Fig. 8 ) do result in higher surface temperatures, ~1220 K and ~1240 K. From there, the maximum surface temperature decreases with increasing impurity density. The 800 ms case with no impurities only reaches 850 K. Thus, even for large uncertainties in the starting Be impurity density, this analysis indicates that unmitigated upward 5 MA VDE scenarios in the ITER DINA disruption database should not lead to melt damage of the upper Be FWPs.
Acknowledgements ITER is the Nuclear Facility INB No. 174. This publication explores physics processes during the plasma operation of the tokamak when disruptions take place; nevertheless the nuclear operator is not constrained by the results presented here. The views and opinions expressed herein do not necessarily reflect those of the  ITER Organization. The work of EM was supported by  MEYS  project  8D15001  and  LM2018117. SR would like to acknowledge the financial support of the  Swedish Research Council  under Grant No. 2018-05273.
The resulting steadystate command law may then be optimized regardless of the OG sensitivity reduction and can easily be tuned so as to compare to the minimum variance control of  Gendron & Léna (1994). Article number, page 1 of 19 arXiv:2103. 09921v1 [astro-ph. IM] 17 Mar 2021 A&A proofs: manuscript no. CLOSE_paper-merged While we started designing CLOSE in the context of the single-conjugate AO (SCAO) module of the MICADO instrument  (Davies et al. 2018; Clénet et al. 2019) , the technique naturally extends to a range of AO systems, namely for non-linear sensors performing well under a locally valid, modal, linearized description, such as the PWFS. This includes other Fourier-like wavefront sensors  (Fauvarque et al. 2019; Chambouleyron et al. 2020) , as well as quad-cell Shack-Hartmanns, or the adaptation of a diffraction-limited, point source AO calibration into an adequate command law for extended natural sources or elongated laser stars, etc.
Offline implementation If the real time computer (RTC) software cannot be altered on an existing system, or if the additional strain is not acceptable within the RTC specifications, CLOSE can be implemented in a block-wise manner. All estimators, gain updates, and command matrix updates are performed in offline time, certainly in another process and preferably on another machine, over batches of contiguously recorded measurements. This buffered strategy enables to deploy CLOSE on nearly any existing AO system that reliably provides its WFS telemetry without excessive delays. A time-continuous buffer of K WFS measurements is forwarded to the CLOSE process, which turns them into modal measurements m i [0],. , m i [K-1] using the modal reconstructor Rec. For each mode, the AC estimators of Eq.
Using the unique setpoint r = 0, we have shown automatic convergence of the modal gains in an wide variety of simulated conditions, using different wind speeds, guide star magnitudes, and seeing parameters. We have also demonstrated the automatic adaptability of the scheme to abrupt changes in observation conditions, hopping between different sets of optimal control gains in a matter of seconds. These simulations use the design parameters of the PWFS of the MI-CADO SCAO system, and, compared to recent design studies for this system  (Vidal et al. 2019) , achieve nominal performance accounting both for optical gain modal compensation and modal transfer function optimization. This performance is achieved while entirely alleviating from extensive situation-dependent optimizations, database look-ups, or manual tune-ups, thus offering CLOSE as an all-in-one baseline strategy for AO modal control.
Virtual Stenting Algorithm Two authors (JD, JFA) developed an algorithm (Mathematica v. 11. 3. 0) called VSA FFR-PB analyser (Copyright 2020 under GNU General Public License; I-depot evidence Benelux Office for Intellectual Property Number 123060 Date 16-04-2020) and able to perform a fully automated analysis of the FFR-PB curve in order to provide: (1) a quantitative analysis of epicardial disease distribution, and (2) a computing of ideal stent positioning in order to remove enough coronary resistance to eliminate the majority of pressure loss across the vessel. At this stage of development, the virtual stenting algorithm (VSA) is currently written in the Mathematica programming language and works offline. The input data is an excel file containing a set of values (x, FFR 0 (x)), where the pullback length x (mm) verifies 0 ≤ x ≤ L with L the total length of the pullback (mm), and FFR 0 (x) is the FFR measured in position x.
Coronary Artery Disease Distribution Analysis The program then computes the mean value -FFR ′ of -FFR ′ (x), which is simply given by -FFR ′ = FFR (0) -FFR (L) L. Then, the program selects the finite set of N pullback lengths (x i ) 1≤i≤N associated with all the peaks (local maxima) in the -FFR ′ function that are larger than -FFR ′ , using a standard Mathematica function (FindPeaks). Thus, we define -FFR ′ , the total difference quotient of FFR along the vessel, as a natural threshold in order to select peaks in the derivative that have a non-negligible impact on the global decrease of FFR. A modified coefficient of variation that we call "lambda" (λ) is then obtained using the following formula: λ = 1 N N i=1 (-FFR ′ (x i ) --FFR ′ ) 2 -FFR ′. The lambda coefficient quantifies the variation magnitude of the non-negligible peaks in -FFR ′.
Sample Size and Power Calculation A sample size estimation was performed on based of the observations made in the second setting using the visual interpretation of angiogram and FFR pullback curves. We used the CI3Cats R-package for kappa size developed by Rotondi et al. (R software, The R Foundation for Statistical Computing)  (13). The sample sizing calculation CI3Cats (kappa0 = 0. 38, kappaL = 0. 23, kappaU = 0. 53, props = c(0. 40, 0. 40, 0. 20), raters = 5, alpha = 0. 05) indicates that a minimum of 41 subjects are required for this study of interobserver agreement. A power calculation was also performed for the analysis of the overall agreement in vessel disease category and PCI eligibility assessment. The observed statistical power to detect significantly different agreement with operator or VSA at the α level of 5% was respectively, 100% for the vessel disease category and 56 % for the PCI eligibility.
Introduction The scientific progress observed in the last few decades had a remarkable impact on modern human lifestyle. The development of countless chemical substances part of our daily routine activities is now severely affecting the quality of water resources. The widespread presence of contaminants of emerging concern (CECs) is a major threat to aquatic ecosystems worldwide (UNESCO World Water Assessment Programme, 2019). According to NORMAN network ("NORMAN Databases"), a CEC is defined as "a substance currently not included in routine environmental monitoring programs that may be a candidate for future legislation due to its adverse effects and/or persistency". These substances are not necessarily new, most of them are "emerging" as result of the improvement in analytical techniques that recently allowed to detect and track trace concentrations in aquatic media  (Hollender et al.
Each sample was prepared before analysis by adding 10% volume of a mixture acetonitrile/formic acid (0. 1%). Electrospray ionization was used as the ion source in positive mode with a sample flow of 0. 2 mL/min. The capillary voltage was 4000 V and the spray shield was set at -500 V. Nitrogen was used as nebulizer gas (1 bar) and drying gas (8 L/min, 250 °C). The detection range was 57. 7-1000 m/z, in broadband mode, with a data acquisition size of 4 Mpts and a data reduction of noise of 97%. For MS 2 experiments, isolation was carried out with a 1 m/z window and CID experiments were performed with collision energies of 5, 10, 15 and 20 V. Preliminary in-cell experiments were performed to assess the optimal parameters (isolation window, quadrupole voltage, and collision energy) for the MS 3 experiments.
The Bruker Data Analysis software was used for data processing. Total organic carbon (TOC) was measured using a Shimadzu TOC-5000 analyzer (catalytic oxidation on Pt at 680 °C). The calibration was performed using standards of potassium phthalate. Inorganic ions (NO 3 -, NO 2 -and NH 4 + ) generated during maprotiline degradation were identified by ion chromatography analysis using a Dionex chromatograph equipped with a Dionex 40 ED pump and a Dionex 40 ED conductimetric detector. For nitrate anions, a Dionex Ion Pac AS9-HC 4 × 250 mm column, and Ion Pac ASRS-ULTRA 4 mm conductivity suppressor were used while ammonium cations were analyzed using a Dionex Ion Pac CS12A 4 × 250 mm column, and an Ion Pac CSRS-ULTRA 4 mm conductivity suppressor, using 10 mM NaHCO 3 and 4 mM K 2 CO 3 as eluent at 1 mL/min.
g. persistence, bioaccumulation, mobility, endocrine disruption potential) and risk indicators. This workflow, originally designed to work with target monitoring data, is currently exploring the automatic query of non-target screening (NTS) mass spectral information archived in the Digital Sample Freezing Platform (DSFP). As part of the NORMAN Database System (NDS) ("NORMAN Database System") and its interconnected databases (SusDat database ("NORMAN Substance Database") ECOTOX database ("NORMAN Ecotoxicology Database"), the NORMAN Digital Sample Freezing Platform (DSFP)  (Alygizakis et al. , 2020 ) is a virtual platform able to store an extensive number of raw HRMS data converted into a common (open) format, designed for retrospective screening. Thanks to a set of fully integrated tools NORMAN DSFP can provide reliable qualitative and semi-quantitative data on the occurrence of CECs, can represent the spatial distribution of contaminants and the degree of exceedance of threshold values and in this way support identification and prioritization of CECs frequently detected in environmental samples.
Maprotiline was identified as a compound of potential interest further to a suspect screening test carried out for a list of more than 40,000 substances (present at the time of the study in the SusDat database, 5th September 2019) on 46 composite effluent wastewater samples collected from Danube river basin (August 2017)  (Alygizakis et al. , 2019a)  and from a national wastewater effluent campaign in Germany (May 2018)  (Freeling et al. , 2019)  archived in DSFP. Considering mass accuracy, plausibility of the chromatographic retention time (RT), isotopic pattern fit, number of experimental and predicted fragments, and % similarity of experimental and library spectra as criteria supporting the tentative identification of the suspects, maprotiline was elucidated at level 2A (probable structure by library spectrum match) as compared to other substances for which actions to improve the elucidation of the structure are needed before implementation of further monitoring effort.
In line with the purposes of the prioritization methodology (worstcase scenario approach), the lowest Provisional No Effect Concentration (PNEC) values were usedrepresenting the most conservative ecotoxicological threshold values for the investigated suspect compounds. All PNEC values used in this study were extracted from the NORMAN Ecotoxicology database. In cases where no experimental data on the toxicity of detected substances were available, predicted PNECs (P-PNECs) were derived by QSTR models  (Aalizadeh et al. , 2017). The substances were ranked based on three indicators: (i) Frequency of Appearance (FoA), calculated as percentage of sites where the substance was detected above the limit of detection (LOD); (ii) Frequency of PNEC exceedance (FoE), calculated as percentage of sites where the substance was detected above the PNEC, and (iii) extent of PNEC exceedance (EoE), calculated as the 95th percentile (MEC95) of the substance's maximum observed concentration at each site divided by the PNEC.
2017 ) question the origin of HIP 65426 b and makes the system an interesting testbed for planet formation theories. Marleau et al. (2019a)  explored formation scenarios for HIP 65426 b, including the evolution of the protoplanetary disc and the gravitational interaction with possible further companions in the system. In this paper, we analyse medium-resolution (R λ ∼5500) Kband data of HIP 65426 b collected with the VLT/SINFONI IFS to characterize the exoplanet and evaluate the potential of the molecular mapping technique. We describe in Section 1 the observations and data reduction based on the molecular mapping and classical ADI extraction techniques. We characterize the planet in Section 3 using the new spectroscopic data, and discuss our results in the Section 4. 1 ). SINFONI is made of a custom adaptive optics module (MACAO) and of an IFS (SPIFFI).
in  prep for another application of this tool). The toolkit first corrected the raw SIN-FONI data from various noises occurring on the detector described in  Bonnefoy et al. (2014). TExTRIS also mitigated two different effects affecting the cubes: we corrected an incorrect estimate of the slitlet positions on the raw science frame and masked the first two and last two columns of spaxels in the cubes which are affected by crosstalk at the slitlet edges. This step is critical for removing important stellar flux residual at the edges of the field of view and getting an accurate knowledge of the star position outside the field of view. we re-examined the wavelength calibration performed by the ESO pipeline by comparing the many telluric absorption lines contained in each spaxel to a model generated for the observing conditions with the python module skycalc_ipy 2  and based on the ESO Skycalc tool  (Noll, S.
These smaller shifts are visible at the same position for all cubes along the sequence and point toward differential errors on the wavelength solutions between the slitlets. We validated this recalibration on the final products in the Appendix A using an independent method relying on OH sky emission lines. We used TExTRIS to re-compute the parallactic angles and rotator angular offsets following the method we described in  Meshkat et al. (2015)  to allow for realigning the cubes to the North. To conclude, the software retrieved the position of the star outside the field of view. SINFONI lacks an atmospheric dispersion corrector, which makes the registration more complex. We followed a three-step strategy: the position of the star was first evaluated at each wavelength in the last cube corresponding to the 10 s exposure using a Moffat function.
The drift was fitted with a low-order polynomial; we accounted for the field selector offsets, the field rotation, and the change in atmospheric refraction to measure the star position outside the field of view in the first 100 s exposure. The theoretical change of the star position due to atmospheric refraction was computed using empirical formulas reported into the source code of the SINFONI data handling pipeline recipes and adapted to the case of the pupil-tracking mode (see the pipeline user manual 3  ); we iterated over the next 100 s exposures using our computed change of theoretical atmospheric refraction and the evolution of the parallactic angle. We assumed for that purpose a pivot point located at the center of the field of view (X=32, Y=32; see the ESO manual 4  ). The accuracy on the position of that pivot point is however tied to the proper correction of the slitlet edges which we evaluate to be accurate to ∼1 pixel.