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361 to 370 of 390 Results

01_README.txt Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets Plain Text - 4.0 KB - MD5: 42b31aefa61f60e8884c48f5c0188f41 Description of the dataset.
Fig2.txt Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets Plain Text - 119.6 KB - MD5: dd350a7493ec7b1e9ef46ec96b525ac3 The file ‘Fig2.txt’ contains the energy of the three spin-wave bands of the kagome antiferromagnet when it is in the (+)- and (-)-chiral state as a function of the momentum along the x- and y-directions, which is plotted in Figure 2 in the paper.
Fig3b.txt Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets Plain Text - 20.2 KB - MD5: 40c1bef3acb089db169d1c0813b8e896 The file ‘Fig3b.txt’ contains the z-component of the spin current in the (+) chiral state, the z-component of the spin current in the (-)-chiral state, as well as the y-component of the spin current in the (+)-chiral state, normalized by the constant y-component of the spin current in the (-)-chiral state, as a function of the applied magnetic fiel...
UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis Apr 10, 2025 Dalal, Anurag, 2025, "UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis", https://doi.org/10.18710/WSU7I6, DataverseNO, V2 The dataset comprises three dynamic scenes characterized by both simple and complex lighting conditions. The quantity of cameras ranges from 4 to 512, including 4, 6, 8, 10, 12, 14, 16, 32, 64, 128, 256, and 512. The point clouds are randomly generated.
00_ReadMe.txt Apr 10, 2025 - UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis Plain Text - 7.3 KB - MD5: ae749f5cee22247c02c451b3fd89ac74 Description of the deposited data set.
UnrealGaussianStat.zip Jan 30, 2025 - UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis ZIP Archive - 915.9 MB - MD5: 391a39f3be89c4031b9ddab3ff38a6e3 Not available until 2026-01-30 Zipped files containing data for 3 unreal engine synthetic scenes.
ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium Apr 10, 2025 Dalal, Anurag, 2025, "ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium", https://doi.org/10.18710/8AS0US, DataverseNO, V1 We create a synthetic dataset using Unreal Engine 5 to evaluate 3D reconstruction under scattering media like fog and underwater conditions. It includes two scenes—an outdoor foggy environment and a realistic underwater setting—with images captured from a hemispherical camera layout. Each scene provides separate training and evaluation views, and C...
00_ReadMe.txt Apr 10, 2025 - ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium Plain Text - 4.5 KB - MD5: 9088cc5401acd3191ed2e6f059728370 Description of the deposited data set.
ScatteringSplatting.zip Apr 10, 2025 - ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium ZIP Archive - 61.2 MB - MD5: 6fd0f091d7a865c8c03f9b5b7eb3e81e Not available until 2026-01-30 This is the zip file containing the images and COLMAP data for the two synthetic scenes.
Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Feb 24, 2026 Mathew, Manuel Sathyajith; Kandukuri, Surya Teja; Omlin, Christian, 2026, "Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.", https://doi.org/10.18710/LNXUIR, DataverseNO, V1 The supplementary materials provide the complete codebase for the Soft Ordering 1-D Convolutional Neural Network (1-D CNN) model for estimating the capacity factor of wind farms, as presented in "Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation" (PHME 2024). This research was...

01_README.txt

Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets

Plain Text - 4.0 KB -

Description of the dataset.

Fig2.txt

Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets

Plain Text - 119.6 KB -

The file ‘Fig2.txt’ contains the energy of the three spin-wave bands of the kagome antiferromagnet when it is in the (+)- and (-)-chiral state as a function of the momentum along the x- and y-directions, which is plotted in Figure 2 in the paper.

Fig3b.txt

Jan 6, 2026 - Replication Data for: Spin-Seebeck Signatures of Spin Chirality in Kagome Antiferromagnets

Plain Text - 20.2 KB -

The file ‘Fig3b.txt’ contains the z-component of the spin current in the (+) chiral state, the z-component of the spin current in the (-)-chiral state, as well as the y-component of the spin current in the (+)-chiral state, normalized by the constant y-component of the spin current in the (-)-chiral state, as a function of the applied magnetic fiel...

UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis

Apr 10, 2025

Dalal, Anurag, 2025, "UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis", https://doi.org/10.18710/WSU7I6, DataverseNO, V2

The dataset comprises three dynamic scenes characterized by both simple and complex lighting conditions. The quantity of cameras ranges from 4 to 512, including 4, 6, 8, 10, 12, 14, 16, 32, 64, 128, 256, and 512. The point clouds are randomly generated.

00_ReadMe.txt

Apr 10, 2025 - UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis

Plain Text - 7.3 KB -

Description of the deposited data set.

UnrealGaussianStat.zip

Jan 30, 2025 - UnrealGaussianStat: Synthetic dataset for statistical analysis on Novel View Synthesis

ZIP Archive - 915.9 MB -

Not available until 2026-01-30

Zipped files containing data for 3 unreal engine synthetic scenes.

ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium

Apr 10, 2025

Dalal, Anurag, 2025, "ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium", https://doi.org/10.18710/8AS0US, DataverseNO, V1

We create a synthetic dataset using Unreal Engine 5 to evaluate 3D reconstruction under scattering media like fog and underwater conditions. It includes two scenes—an outdoor foggy environment and a realistic underwater setting—with images captured from a hemispherical camera layout. Each scene provides separate training and evaluation views, and C...

00_ReadMe.txt

Apr 10, 2025 - ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium

Plain Text - 4.5 KB -

Description of the deposited data set.

ScatteringSplatting.zip

Apr 10, 2025 - ScatteringSplatting Dataset: Synthetic dataset for 3D reconstruction in scattering medium

ZIP Archive - 61.2 MB -

Not available until 2026-01-30

This is the zip file containing the images and COLMAP data for the two synthetic scenes.

Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.

Feb 24, 2026

Mathew, Manuel Sathyajith; Kandukuri, Surya Teja; Omlin, Christian, 2026, "Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.", https://doi.org/10.18710/LNXUIR, DataverseNO, V1

The supplementary materials provide the complete codebase for the Soft Ordering 1-D Convolutional Neural Network (1-D CNN) model for estimating the capacity factor of wind farms, as presented in "Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation" (PHME 2024). This research was...

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