This submission contains the files for reproducing the waves only and sphere test article simulations for the experimental setup (three different wave conditions) associated with the Teamer Request for Technical Support 9 (RFTS 9) fluid dynamics simulations to support the Ocean Energy Systems Energy Technology Collaboration Program (OES) Task 10 Wave Energy Converters Modeling Verification and Validation effort. The 'data' directory contains the experimental validation data, the paddle input signal data, and representative output data from the OpenFOAM simulations. The 'images' directory contains images of representative results for the three test cases, and were generated using the python .py files located in the main directory. The 'openFoamCaseFiles' directory contains cleaned OpenFOAM case files for the three test conditions. Refer to additional README files contained within the directories for additional details. This project is part of the TEAMER RFTS 9 (request for technical support) program: Numerical Modeling of WECs to support OES task 10.

Total and liquid precipitation datasets created with the method described in Bias and trend correction of precipitation datasets to force ocean models (Dussin, JTECH, in revision).

This dataset is about book subjects. It has 1 row and is filtered where the books is Numerical mathematics : exercises in computing with a desk calculator. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This data set consists of three-dimensional meteorological analyses for the entire cold season 2002-2003 for the three CLPX Meteorological Study Areas (MSAs) in northern Colorado (North Park, Fraser and Rabbit Ears) using high-resolution (500 m horizontal grid spacing).

Abstract: This code has been used for the numerical experiments in the thesis "Numerical homogenization of time-dependent Maxwell's equations with dispersion effects" by Jan Philip Freese, see https://www.doi.org/10.5445/IR/1000129214. TechnicalRemarks: # Readme This code was used for the numerical experiments of the PhD thesis "Numerical homogenization of time-dependent Maxwell's equations with dispersion effects" by P. Freese (cf. Section 7.2, Section 7.3) https://www.doi.org/10.5445/IR/1000129214. The computations are done in C++ using the Finite Element library deal.II. Requirements

Field data used to support numerical simulations of variably-saturated flow focused on variability in soil-water retention properties for the U.S. Geological Survey Bay Area Landslide Type (BALT) Site #1 in the East Bay region of California, USA

Only Number

## Overview

Only Number is a dataset for object detection tasks - it contains Numbers annotations for 450 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This paper introduces a comprehensive theoretical analysis of the Birch-Swinnerton-Dyer Conjecture, a longstanding and profound challenge in modern number theory. Our approach is built upon a novel method of stable numerical integration and Fourier transformation, specifically tailored to analyze elliptic curves under extreme mathematical and computational conditions.

The method developed aims to ensure the stability of numerical computations, even when dealing with highly complex parameters and challenging computational environments. By maintaining computational stability, this approach allows for precise analysis of elliptic curves, revealing deeper insights into their properties without compromising the accuracy of the results.

TechnicalRemarks: This code is used for the numerical experiment in Section 6 of the paper "A unified error analysis for nonlinear wave-type equations with application to acoustic boundary conditions" by Jan Leibold. The computations are done in C++ using the Finite Element library deal.II; the plots then are generated with Matlab. To use this code, deal.II (release 9.3.2) has to be installed, cf. https://www.dealii.org/9.3.0 In order to compile the program, open a terminal session in this folder and call "cmake -DDEAL_II_DIR=/path/to/deal.II .". Next, call "make release" and then "make" to compile the files. Finally, run the program with "make run". This performs the computations and generates the file "results" containing the results of the numerical experiments. After that, the plots can be generated with the Matlab Script 'plot_figures.m'.

The numerical model documented here is a regional coupled sea ice - ocean model based on the Massachusetts Institute of Technology General Circulation Model code - MITgcm (for details we refer to: http://mitgcm.org/public/r2_manual/latest/online_documents) with a model domain covering the Arctic Ocean, Nordic Seas and northern North Atlantic. The horizontal resolution is 1/4 degree (approx. 28 km) on a rotated grid with the grid equator passing through the geographical North Pole. The sea ice model is a dynamic-thermodynamic sea-ice model with a viscous-plastic rheology and has a landfast ice parametrization as described by Itkin et al [2015, see bellow], where more details about the model set-up can be found. The model is forced by the atmospheric reanalysis -- The Climate Forecast System Reanalysis from 1979 to 2010 and then from 2011 to 2014 with the NCEP Climate Forecast System Version 2. The model output provided here contains sea ice simulations used by Itkin and Krumpen, [2017, see bellow]. The control run (CTRL) is forced by the CFSR and CSFv2. In the climatological run (CLIM) the May-December fields are replaced by the climatology (1979-2013). On 1. January each year the run is restarted from CTRL. Initial years 1979-1991 are regarded as spin up are not included into the data set here.

This dataset contains the codes and Jupyter notebooks to reproduce the results of the publication Micromagnetic Tomography: Numerical Libraries.

This data is hosted in the Github repository: https://github.com/Micromagnetic-Tomography/paper-2023-mmt-numerical-libraries

The global Distributed Numerical Control (DNC) software market is anticipated to reach a valuation of USD 562.2 million by 2033, expanding at a CAGR of 8.5% during the forecast period of 2023-2033. The rising adoption of automation in manufacturing processes and the increasing demand for enhanced production efficiency are primarily driving the market growth. DNC software enables centralized management and distribution of CNC programs to multiple CNC machines, optimizing production processes and reducing downtime. The market is segmented into different types of DNC software: RS232-based, terminal-based, and network-based. RS232-based DNC software is commonly used in older CNC machines, while terminal-based DNC software provides a user-friendly interface for operators. Network-based DNC software offers advanced features such as remote access and real-time monitoring capabilities, making it suitable for modern manufacturing environments. The market is also segmented by application, with automobile manufacturing and other discrete manufacturing industries being the major users of DNC software. Leading companies in the market include FORCAM, JANUS Engineering, Cadem, Predator Software, Antech Microsystem Private Limited, VEGA, Niha Solutions, Technovision CNC, Greco Systems, Spectrum CNC Technologies, Pengli Technology, Beijing Languang Chuangxin Technology, Extech, and others. The comprehensive report on Distributed Numerical Control (DNC) software provides in-depth insights into market trends, drivers, restraints, key regions and segments, growth catalysts, leading players, and significant developments.

Extracted and Enhanced Dataset of Mediterranean Sea Bathymetry from the GEBCO Dataset for numerical modelling, GEBCO’s current gridded bathymetric data set, the GEBCO_2022 Grid, is a global terrain model for ocean and land, providing elevation data, in meters, on a 15 arc-second interval grid. It is accompanied by a Type Identifier (TID) Grid that gives information on the types of source data that the GEBCO_2022 Grid is based on.

This is the numerical data set belonging to the Nureth conference paper entitled: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'.

The files 'Stefan_singlePhase_Tfield.dat' and 'Stefan_singlePhase_interface.dat' represent the raw data belonging to figure 1 in the paper and contain the solution to the one-phase Stefan problem for the temperature field and interface position (section 3.1 in the paper). The files 'Stefan_singlePhase_error.dat' and 'Stefan_twoPhase_error.dat' represent the raw data belonging to figure 2 in the paper and contain the L2 norm of the relative difference between the numerical and analytical solution for the single and two phase Stefan problem respectively.

The files 'Gau_1140s_lf_50x50_3D', 'Gau_1140s_lf_100x100_3D', 'Gau_1140s_lf_200x200_3D' feature the raw OpenFOAM(v7) data containing the numerical solution to the liquid fraction of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at 1140s of simulation time. These data were used for the mesh convergence study (figure 3, section 3.2).

The files 'Gau_120s_U_200x200_3D', 'Gau_360s_U_200x200_3D', 'Gau_750s_U_200x200_3D', 'Gau_1140s_U_200x200_3D' feature the raw OpenFOAM(v7) data containing the 3-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at respectively 120s, 360s, 750s and 1140s of simulation time. These data underly the velocity colours shown in figure 4 and figure 6 (section 3.2).

Likewise, the files 'Gau_120s_U_200x200_2D' and 'Gau_360s_U_200x200_2D' feature the raw OpenFOAM(v7) data containing the 2-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem. These data underly the velocity colours shown in figure 5 (section 3.2).

This dataset contains the numerical data accompanying the "Fragility of the magnetic order in the prototypical altermagnet RuO2" paper.
Computations were performed using VASP 6.3.2 and WIEN2k 21.1 packages.

It contains the data and gnuplot scripts used to plot Figures 2-6, i.e the dependence of the total energy and local magnetization at the Ru site as a function of the effective Hubbard U parameter for RuO2, the magnetic anisotropy as a function of angle, projected non-magnetic density of states onto Ru d-orbitals, spin-polarized total density of states for majority spin as a function of the effective U parameter and the dependence of local atomic magnetization on hole doping and the effective U parameter.

To produce the figure one simply runs gnuplot script.gnu command with script.gnu a placeholder for a relevant script name.
In the case of VASP computations, the total energy is reported in OUTCAR file at the line containing the free energy TOTEN = token, the local magnetization in the block after the magnetization token, the effective U parameter is Ueff = U - J, where U and J are defined by LDAUU and LDAUJ tokens in the INCAR file and the hole doping is defined by altering the number of electrons in the system using NELECT token in the INCAR file.

The density of states is calculated using WIEN2k and is stored in case.dos?ev* files with case being a filename, ? be replaced with a number and * be either empty, up or dn.

The following POTCARs were used for VASP calculations:
md5sum name and location
91f4c7aab413b97880dee28c8d363ff5 potpaw_PBE.52/Ru_sv/POTCAR
38ce74bba1194bccd788f22d688bfc65 potpaw_PBE.52/O/POTCAR

Computation results of the numerical experiments in the PhD Thesis:

S. W. R. Werner; Structure-Preserving Model Reduction for Mechanical Systems.

The results are ordered with respect to the used benchmark example. The corresponding MATLAB codes for the computations can be found here.

Figures and plotting scripts.Figure 16, 17, 19, 20 of the paper: "Inexact Krylov iterations and relaxation strategies with fast-multipole boundary element method"Submitted for peer review.Fig. 16: (EthrocyteConvergence.pdf)Observed convergence for Stokes flow around red blood cells, with respect to the extrapolated value of the drag coefficient, using Richardson extrapolation.Fig. 17: (EthrocyteSingleCellIterations.pdf)Number of iterations needed for the system to converge for increasingly refined surface meshes on one red blood cell; p = 16, target residual 10^-5.Fig. 19: (EthrocyteMultipleCellIterations.pdf)Number of iterations needed to converge to a desired residual of 10^-5 for systems with multiple red blood cells, discretized with different mesh sizes (p = 16).Fig. 20: (EthrocyteMultipleCellSpeedup.pdf)Speed-ups of the relaxation strategy with several red blood cells in uniform Stokes flow, using different mesh sizes on each cell. The abscissa value corresponds to the total number of panels (all cells).

The Computer Numerical Control (CNC) software market is experiencing robust growth, driven by increasing automation in manufacturing and the rising adoption of advanced machining technologies. The market size in 2025 is estimated at $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 7% projected from 2025 to 2033. This growth is fueled by several key factors: the expanding adoption of Industry 4.0 principles, the increasing demand for precision and efficiency in manufacturing processes, and the growing complexity of parts requiring CNC machining. Furthermore, the rising need for sophisticated CAM (Computer-Aided Manufacturing) software capable of handling intricate designs and materials is significantly boosting market expansion. The integration of AI and machine learning capabilities within CNC software is also adding to its capabilities and appeal, leading to further market expansion. Significant growth is anticipated across various segments, including 2D and 3D CNC software solutions, with 3D CNC software exhibiting faster growth due to its ability to handle complex geometries and multi-axis machining. The market is highly competitive, with several established players and emerging innovative startups vying for market share. Companies like Hypertherm, Renishaw, and SigmaTEK Systems are major players, constantly innovating and expanding their product portfolios to meet evolving industry demands. The geographical distribution of the market is expected to remain diverse, with North America and Europe holding a significant share. However, regions like Asia-Pacific are showing impressive growth potential, primarily driven by rapid industrialization and expanding manufacturing sectors in countries such as China and India. Overall, the CNC software market presents a promising investment opportunity with substantial potential for continued expansion throughout the forecast period.

Board Number

## Overview

Board Number is a dataset for object detection tasks - it contains Board Number annotations for 2,283 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This record contains:

• file Parameters15000withHeaders.txt, listing the combinations of uncertain parameters of the full system model (M0) employed to conduct the Global Sensitivity Analysis,
• file ReadmeParametersGSA.docx, describing the information included in Parameters15000withHeaders.txt.