dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Processing Images were registered in space and time using onboard telemetry data, in this case altitude, heading and the coordinates (of the central point) of each image. In combination with the calculation of camera specific calibration values, using an equation detailed in Appendix A (Equation (A1)), this allowed for the orthorectification of the image calibrating units from pixels to metres. This then permitted calculation of latitude and longitude values for objects of interest and the plotting of physical coverage over ground. Images were examined manually, without the aid of automated techniques, to detect and parameterise kolk-boils within them. This was performed using a graphical user interface (GUI) which was developed within MATLAB (R2019b; MathWorks) to process and collate the multiple images taken within each survey.
Post-Processing This study characterised kolk-boils based on distribution, presence, and area at the surface. Distribution was based on the central point of formed kolk-boils and was examined against tidal phase and current velocity to investigate spatial patterns. Boil presence (using formed and un-formed boils) and area (using formed boils) were then examined in relation to environmental covariates. Boil presence was categorised as an image with a boil detected within it, while absence was classed as an image with no boil detected within. Finally, boil area was calculated from perimeter measurements taken of formed boils. An output file was collated for each survey, with these being merged into a single dataset. This included kolk-boil central point position, kolk-boil area (m 2 ), kolk-boil classification (type and confidence), kolk-boil presence, telemetry data, and relevant explanatory variables taken from the central point of each image (current velocity (m/s), current direction (degrees), tidal phase (assigned based on the average current velocity and direction), depth (m), seabed roughness (m), slope angle (degrees) and aspect (degrees)).
Kolkboils deemed to be visually poorly defined (≤40 confidence score) were excluded due to significant uncertainty around whether they were kolk-boils. Hydrodynamic variables (current velocity, current direction, and tidal phase) were obtained from a depth-averaged TELEMAC-2D model output for the location and timestamp of each image. The hydrodynamic model was constructed using the non-hydrostatic version of the programme which solves the Saint-Venant equations. A 0. 20 m resolution bathymetry raster, from multibeam survey data (MeyGen Ltd. , Edinburgh, UK), was used to calculate depth, seabed slope angle, roughness, and slope aspect values from the central point of each image. This was achieved using the "Point Sampling Tool" and "GDAL Tools" plugins within QGIS (QGIS Development Team, 2020).
Data Analysis A Generalised Additive Model (GAM) was used to analyse the suite of explanatory variables potentially influencing the probability (p) of kolk-boil presence, at the surface, within the Inner Sound (using the mgcv package in R  [43] ). The kolk-boil presence GAM (n = 7236) assumed a Bernoulli distribution with logit link and included smoothing functions for all non-parametric variables. The maximum variation of each smoothing function was specified via the number of knots (k) provided to each spline and tensor product. This was initially set to five (or five by five for tensor products) for each variable, as there was only minor variation (i. e. , some non-linearity which could be suitably estimated using only five knots). It was subsequently checked if the number of knots were sufficient by using the k-index and approximate p-values in 'mgcv::gam.
Kolk-Boil Presence A total of 627 kolk-boil-present images (including formed and un-formed boils) were recorded. When split via tidal phase, the greatest counts were logged in fast-ebb (71) and fast-flood (176) periods (Figure  7A ). While the mean absolute current speed across the data was 1. 44 m/s, 44% of kolk-boils occurred at absolute current velocities exceeding, or equal to, 2. 0 m/s (Figure  7B ). Peaks in boil presence were also observed at current directions going towards approx. 260-310 • (n = 169) and approx. 50-110 • (n = 362) (Figure  7C ). When examining bathymetric covariates, kolk-boil presence predominantly occurred between depths of 32-36 m (n = 533) (Figure  7D ). Seabed slope angle, at boil locations, ranged from 0. 20 to 65. 84 • ; however, 80% fell between 0 and 20 • (Figure  7E ).
While individual current velocity and current direction values were taken directly from the hydrodynamic model for each image, tidal phase categorisation was assigned from the average current velocity and current direction across each survey. This was split into six factors: fast ebb (<-1 m/s), decreasing ebb (-1 to -0. 26 m/s), increasing ebb (-0. 26 to -1 m/s), fast flood (>1 m/s), decreasing flood (1 to 0. 26 m/s) and increasing flood (0. 26 to 1 m/s) as used in Zamon et al. [41] when quantifying tidal phase within a tidal stream environment. A 0. 20 m resolution bathymetry raster, from multibeam survey data (MeyGen Ltd. , Edinburgh, UK), was used to calculate depth, seabed slope angle, roughness, and slope aspect values from the central point of each image. This was achieved using the "Point Sampling Tool" and "GDAL Tools" plugins within QGIS (QGIS Development Team, 2020).
1 × 10 16 cm -3 and T = 8800 ± 300 K for the late delay. Depth-resolved measurements were performed by recording spectra for consecutive laser pulses applied to single ablation sites. Each recording was obtained by accumulating the signal over 100 ablation sites to reach a sufficiently large signal-to-noise ratio. The depth of the laser-produced craters was measured via optical microscopy (Nikon, model Eclipse LV100ND). The spectral lines used for the quantification of major, minor, and trace elements are listed in Table  1  with the data used for the calculations. Most Stark broadening parameters have been measured in a separate experiment as illustrated in Refs. [38, 39]. Although the applied calibration-free approach accounts for self-absorption, all measurements have been performed with lines of optical thickness < 1.
Impact of surface contamination on optical properties As shown in the previous section, the near-surface glass layer is contaminated by trace elements originating from operating materials used in optics manufacturing. This leads to an alteration of the chemical composition of this layer up to a depth of several microns. Since the optical properties of any glass strongly depend on the glass stoichiometry and admixtures, the observed manufacturing-induced contamination results in a modification of optical parameters such as the index of refraction. This was verified by ellipsometric measurements of the polished heavy flint glass surfaces. As shown in Fig. 6 , the actually measured index of refraction differs from the theoretical or nominal one as specified by the manufacturer's data sheet. The measured index of refraction of 1.
Acknowledgements The authors thank  Lutz Müller  and  Daniel Tasche  from the  University of Applied Sciences and Arts  for the preparation of the investigated samples and the help during experimentation. Special thank is also dedicated to  Y. Ralchenko ,  A. Kramida ,  J. Reader , and the  ASD Team  at the  National Institute of Standards and Technology, Gaithersburg, MD , for the free access to the  NIST Atomic Spectra Database.
Due to the high switching speed of Gallium Nitride (GaN) transistors, parasitic inductances have significant impacts on power losses and electromagnetic interferences (EMI) in GaN-based power converters. Thus, the proper design of high-frequency converters in a simulation tool requires accurate electromagnetic (EM) modeling of the commutation loops. This work proposes an EM modeling of the parasitic inductance of a GaN-based commutation cell on a printed circuit board (PCB) using Advanced Design System (ADS ® ) software. Two different PCB designs of the commutation loop, lateral (single-sided) and vertical (double-sided) are characterized in terms of parasitic inductance contribution. An experimental approach based on S-parameters, the Cold FET technique and a specific calibration procedure is developed to obtain reference values for comparison with the proposed models.
The obtained experimental results are then used to validate the developed models but also to adjust the GaN transistor intrinsic inductance. Two different PCB designs of the commutation loop are studied: a lateral structure (singlesided) and a vertical structure (double-sided) involving high inductive coupling between PCB tracks  [24]. Both designs are characterized by S-parameters and modeled using Advanced Design System (ADS ® ) software. Experimental switching waveforms in a 1 MHz Energies 2021, 14, 1495 3 of 17 DC/DC converter are given for both PCB layouts of the commutation loop. The advantages of the proposed commutation loop modeling for the development of high-frequency power converter design tools are finally discussed.
2 is the relative permittivity of FR4, h is the thickness of the dielectric, w is the width of the line and t is the conductor thickness. A ground plane is located on the PCB bottom side. the developed models but also to adjust the GaN transistor intrinsic inductance. Two different PCB designs of the commutation loop are studied: a lateral structure (single-sided) and a vertical structure (double-sided) involving high inductive coupling between PCB tracks  [24]. Both designs are characterized by S-parameters and modeled using Advanced Design System (ADS ® ) software. Experimental switching waveforms in a 1 MHz DC/DC converter are given for both PCB layouts of the commutation loop. The advantages of the proposed commutation loop modeling for the development of high-frequency power converter design tools are finally discussed.
B. Applied research methods The research presented in this paper follows a systematic approach method, aiming to identify papers that presented a classification or an organization of DTs. The bibliographic database used for sourcing the articles was Web of Science. To keep the number of articles reasonable and to ensure the quality of the sources, the search was restricted to peer-reviewed journal articles. Web of Science was queried in February 2021 using the terms : ((Digital Twin OR Digital Shadow OR Digital Model) AND (catalog* OR categor* OR classif* OR assignment OR denominat* OR organiz* OR allot* OR systemiz*). The search for papers was restricted to the keywords, abstract and title of the articles after 2012, as it was the first apparition of the term "Digital Twin" in this context of research.
This paper focuses on robust wind disturbance rejection for nonlinear quadrotor models. By leveraging on nonlinear unknown observer theory, it proposes a nonlinear dynamic filter that, using sensors already on-board the aircraft, can estimate in real-time wind gust signals in the three dimensions. The wind disturbance is then treated as input to the PD controller for a quick and robust flight pathway in presence of disturbances. With this scheme, the wind disturbance can be precisely estimated online and compensated in real-time. Hence, the quadrotor can successfully reach its desired attitude and position. To show the effective and desired performance of the method, simulation results are presented in Matlab/Simulink and ROS-enabled Gazebo platform.
Then, it holds U = -D(CD) † =   0 3×3 0 3×3 0 3×3 0 3×3 0 3×3 -I 3×3 0 3×3 0 3×3 0 6×3 0 6×3 0 6×3 0 6×3   , V = I p -(CD)(CD) † =   I 3×3 0 3×6 0 3×3 0 6×3 0 6×6 0 3×3 0 3×3 0 3×6 I 3×3  . Moreover, considering the Lyapunov candidate V (k) = e T (k) P e(k), the following forward difference is to be considered:  The above equation can be solved using the Linear Matrix Inequality (LMI) technique in Matlab. For the error to be convergent, it is required for V < 0. To begin with, by using the above expression V = e T (k)[X T P -1 X -P]e(k), where X = PA(α)+P U C A(α)+P s V C A(α)-K C, P s = P S and P k = 0. This further implies X T P -1 X -P < 0. Then, by using LMI with the condition P > 0, the solutions for P, P s P k can be found. Moreover, the matrix inequalities P A(α) + P U C A(α) + P s V C A(α)-K C and P < 0 are verified for all α ij , with i, j = 1, • • • , n.
A. SIMULINK VALIDATION The first step of validation is to use the proposed scheme in the Matlab/Simulink environment. The nonlinear mathematical model of the quadrotor together with the wind estimation scheme of the NUIO have been implemented. The system parameters are the same as discussed in Table  1. The control system has been tested under two different scenarios: 1) military gusts winds affecting the aircraft when it is required to tracking a circular trajectory, and 2) time-varying wind spiral trajectory of the quadrotor.
B. ROS/GAZEBO VALIDATION The proposed design is then tested within the ROS/Gazebo framework using Ardupilot 3. 5 controller. The Ardupilot has its own nonlinear controller, which proves that the NUIO proposed works for any type of controller, provided the rotor speeds and partial outputs are available to the NUIO for estimation of wind gusts. The ROS nodes communicate to the Simulink and Gazebo software. Fig. 8  shows the communication links between Gazebo, ROS, and Ardupilot. Gazebo is designed to accurately reproduce the dynamic environment of a quadrotor  [39]. The simulated prototype has mass, inertia, wind, friction, and numerous other attributes that allow it to behave realistically when testing. These actions are also integral parts of an experiment. The aircraft prototype has been developed by Erle Robotics for Gazebo, and it has a dynamic structure composed of a rigid body with joints, forces, and torques to generate propulsion and interaction with the environment.
More precisely, the architecture implemented is as follows: 1) ROS provides a middleware layer for the SITL (software in the loop) emulation, 2) the Gazebo software provides a reliable simulation platform for the aircraft physics, including wind gust forces, and 3) Matlab/Simulink scheme implements the proposed NUIO filter. Gazebo receives rotor speeds from subscribed ROS topics and publishes the aircraft pose (position and attitude) to ROS, while the Matlab/Simulink node subscribes and receives such pose topics.
VI. CONCLUSION To conclude, the paper presents an innovative approach to accurately estimate and compensate the external disturbances in real-time such as wind gusts acting on the nonlinear model of the quadrotor aircraft. The quadrotor has robust performance as it moves accurately along the desired path when exposed to different types of wind gusts which is validated by Matlab/Simulink and Gazebo environment. This shows that the proposed method requires no extra sensors, is simple low computation and has the ability to obtain a fast respond to different types of disturbances during the flight. Furthermore, the nonlinear unknown input observer works with any type of controller, provided the controller can compensate for the estimated wind gusts by changing the aircraft rotor speed. This has been proved as for simulation by using PD controller and in Gazebo environment using Ardupilot controller.
Note that several recent works focused on ignition of RDX mixed with nanoparticles of gold  [7]  or aluminum  [8]. We recently published  [9 , 10]  separately studies on ignition and combustion of these two propellants. The aim of the present paper is to compare their behavior, and therefore to increase literature database on RDX and nitrocellulose combustion. Detailed studies on insensitive propellants combustion is of interest for the field of internal ballistics, for system performances but also for security of property and people. Experimental setup and energetic materials used are presented in the next section. Section three is devoted to the presentation of results and discussions.
Acknowledgments Authors want to thank the " Conseil Régional du Centre-Val de Loire", in France , which has financially supported this work ( LILOVEM  project). Authors are also thankful to ArianeGroup for the supply of  P1  and  P2  propellants. Authors also thank the  French Direction Générale de l'Armement (DGA)  for the supply of  P3  and  P4  propellants.
Therefore, enzymatic production routes using chitin deacetylases (CDAs) have been proposed, that may yield polymers with defined non-random PA and thus, may open the way to third generation chitosans  12, 17. According to the carbohydrate-active enzymes (CAZy) database, CDAs (EC 3. 5. 1. 41) are classified in the carbohydrate esterase 4 (CE4) family, together with four other deacetylase and esterase activities  18. In vivo, CDAs catalyze the release of acetate from N-acetylglucosamine units of chitins and chitosans (forward mode) but in vitro, they are also able to catalyze the reverse reaction, thus Nacetylating chitosan polymers and oligomers (reverse mode)  19. They are often found in multigene families suggesting different physiological functions such as the production of cell wall chitosan or the deacetylation of oligomers released from the cell wall  2, 3, 20.
Crystal structure and multiple structure alignment To further elucidate the substrate binding site, the X-ray crystal structure of AngCDA was determined at 1. 81 Å resolution. The enzyme crystallized in the space group P432 with one monomer per asymmetric unit. This oligomeric state correlates with the results obtained by gel filtration and DLS measurements (28. 6 ± 6. 5 kDa). AngCDA adopts a distorted ( α / β )8 fold, as typically found in CE4 enzymes. An intramolecular disulfide bridge (C36 -C226), which tethers the N-and C-terminal ends, stabilizes the structure as already observed in the crystal structure of chitin deacetylases from the fungal pathogens Colletotrichum lindemuthianum and Aspergillus nidulans (pdb entries 2IW0 and 2Y8U) 32, 34. The conserved Asp-His-His triad (N ε 2-H97, N ε 2-H101 and O δ 1-D48 atoms) plus a malonate ion (O6 and O7 atoms) from the crystallization solution and a water molecule coordinate a zinc(II) ion to form an octahedral coordination geometry with a metal-ligand distance of 2.
Sequence analysis The AngCDA sequence (UniProt ID: A2QZC8) was analyzed using several online tools including the conserved domain databank  38, 39  for domain prediction, SignalP-5. 0  40 for signal peptide prediction, PredGPI  41  and NetGPI-1. 1  42  for GPI anchor prediction and TMHMM  43  for transmembrane region prediction.
Ligand generation and docking The 3D structures of chitotetraose (A4) and chitopentaose (A5) where created using the carbohydrate builder from GLYCAM Web 49. To create ligands with deacetylated units, the acetyl group was removed using the builder function in PyMOL  45  and the residues and atom names were adjusted to fit the GLYCAM names  50. For docking, the ligands (A4, A5, DAAA, ADAA, AADA and AAAD) and the receptor (AngCDA crystal structure) were prepared using the prepare_ligand4. py and prepare_receptor4. py scripts included in the AutoDockTools  51. The histidine protonation was set to HID for the metal coordinating His97 and His101. The charge of the zinc ion was set to +2. Docking was performed using AutoDock VinaCarb v1. 0 with the default parameters for chi_coeff and chi_cutoff suggested by the authors 52, 53.
Molecular dynamics simulations All simulations where run with the GROMACS 2019 package  54, 55, 56, 57, 58, 59, 60, 61  using the Amber force field ff14SB  62  for the protein, the compatible GLYCAM06 force field  50, 63  for the ligands and TIP3P as the water model  64. The histidine protonation was set to HID for the metal coordinating His97 and His101 and to HIP for the catalytic His195. Since the GLYCAM06 force field is not available in the GROMACS package, the ligands were first prepared using the LEaP program included in AmberTools20  65  and then converted into GROMACS format using the GLYCAM06 compatible ACPYPE  66, 67. Water molecules were added in a dodecahedron-shaped box with 10 Å distance to each side. The charge was neutralized with sodium ions before running the energy minimization using steepest descent.
J o u r n a l P r e -p r o o f For further purification before crystallization, a size-exclusion chromatography was performed at 20 C on a Superdex 75 16/600 column (GE Healthcare, Boston, Massachusetts, USA) equilibrated with 50 mM Tris -HCl pH 8 and 100 mM NaCl. Peak fractions corresponding to the AngCDA monomer were pooled and concentrated with an Amicon Ultra-15 centrifugal filter to 4. 57 mg/ml before the crystallization screening. Crystals of AngCDA were found in sitting-drop vapor-diffusion experiments at 20 C. Drops were prepared by mixing 0. 2 μ l of AngCDA and 0. 1 μ l of reservoir containing 1. 4 M sodium malonate and 0. 1 M Bis-Tris propane pH 7. Crystals were cryo-protected by soaking in 10 % (v/v) glycerol in crystallization buffer, before flash-freezing in liquid nitrogen. Diffraction data were collected at 100 K at microfocus beamline Proxima-2A (Soleil, France).
The collected images were processed using XDS  73  and scaled in the CCP4 suite program AIMLESS  74. MOLREP  75  was used for molecular replacement using Aspergillus nidulans chitin deacetylase (AnCDA, 2Y8U)  34. The structure was rebuilt with Coot  76  and refined with PHENIX  77. The final model was evaluated with MolProbity  78. All values obtained and generated are shown in table 1.
Acknowledgment A special thanks goes to  Mario Sergio Valdes  who helped a lot with the binding energy calculation using his GROMACS implementation (gmx_MMPBSA) of MMPBSA. pyfrom the AmberTools. We thank  David Thieker  for providing missing force field parameters for the substrates used for the simulations via the glycam mailing list. The authors appreciated the access to the CristalO platform ( FR2424 ,  Station Biologique de Roscoff ), which is part of the  Biogenouest core facility  network. The authors thank  Dr. Elizabeth Ficko-Blean  and the staff for beam time and help during data collection on beamline Proxima-2A (Soleil, France). GM is grateful for the support of the Alexander von Humboldt foundation during his stay at the  University of Münster. Furthermore, we would like to thank  Rita Weyer  and  Margareta Hellmann  for their valuable feedback on this manuscript.
Funding: This work was supported by the  French government program  " Investissements d'Avenir" 276 (LABEX INTERACTIFS , reference  ANR-11-LABX-0017-01 ). 277 Acknowledgments: This work pertains to  French government program  " Investissements d'Avenir" 278 (EUR INTREE , reference  ANR-18-EURE-0010 ). 279.
2 eV in the XANES region, and a constant k step of k = 0. 035 Å À1 in the EXAFS region. Integration time was 1 s/point in the XANES region, while it increased quadratically from 1 to 4 s/point in the EXAFS region, globally resulting in ca. 25 min/scan. To characterize each sample, we acquired four or three consecutive XAS scans for catalysts with Zn 1 and 10 wt%, respectively. After energy alignment and normalization to the edge-jump using the Athena software from the Demeter package  [51] , the four mx(E) curves were averaged, after checking for signal reproducibility. The corresponding k 2 -weighted v(k) functions were Fourier transformed (FT) in the range Dk = 2. 3-11. 0 Å À1. To assist the interpretation of the XAS results, we also collected Zn K-edge XAS data for selected model systems.