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Data supporting the publication "Single-step Phase Identification and Phase Locking for Coherent Beam Combination using Deep Learning"

Data supporting the publication "Single-step Phase Identification and Phase Locking for Coherent Beam Combination using Deep Learning"
Data supporting the publication "Single-step Phase Identification and Phase Locking for Coherent Beam Combination using Deep Learning"
This dataset is supporting the publication "Simultaneous Phase Correction and Beam Shaping for Coherent Beam Combination using Deep Learning" The data is collated in 2 main folders. One for Figurative Data and one for Numerical Data. 1. Figurative Data The figure files used in the publication can be found in the directory named 'root/dataset/Figurative Data/'. The figures referenced in the publication as Figure 1. to Figure 6. correspond to the following filenames: 'Figure 1.jpg', 'Figure 2.png', 'Figure 3.png', 'Figure 4.png', 'Figure 5.png', and 'Figure 6.png'. The graphics denoted as Figure Appendix 1 and Figure Appendix 2, cited within the supplementary materials of the scholarly publication, are accessible under the file name 'Figure Appendix 1.png' and 'Figure Appendix 2.png', respectively. 2. Numerical Data a). The numerical data for Fig 3 a) can be found in the directory named 'root/dataset/Numerical Data/Fig 3 a/', which contains 1000 npz files. Each npz file is named after the Unix time when the corresponding measurement was made. These npz files can typically be opened using the numpy library in Python. Each npz file contains three ndarray objects: · ['arr_0']: This array, with the shape (6,), represents a set of six randomly generated target phase values. · ['arr_1']: This array, with the shape (1, 6), denotes the model predictions made by the neural network trained with the camera A dataset. · ['arr_2']: This array, with the shape (1, 6), represents the model predictions made by the neural network trained with the camera B dataset. b). The numerical data for Fig 5 c) can be found in the directory named "root/dataset/Numerical Data/Fig 5 c/", which contains one npy file. This file can be opened using the numpy library in Python. It contains a numerical array of shape (40, 50), where: · The first index represents 40 different sigmas controlling the randomness of the phase fluctuation. · The second index represents 50 timesteps for each sigma value. The data is accessible via CC BY license
University of Southampton
Xie, Yunhui
c30c579e-365e-4b11-b50c-89f12a7ca807
Chernikov, Fedor
a5a56a14-d8cf-4a11-8946-dbb145dbda91
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Liu, Yuchen
1efd4b12-3f11-4eb1-abea-0f5b40a1a9f1
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michalis
1840a474-dd50-4a55-ab74-6f086aa3f701
Xie, Yunhui
c30c579e-365e-4b11-b50c-89f12a7ca807
Chernikov, Fedor
a5a56a14-d8cf-4a11-8946-dbb145dbda91
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Liu, Yuchen
1efd4b12-3f11-4eb1-abea-0f5b40a1a9f1
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michalis
1840a474-dd50-4a55-ab74-6f086aa3f701

Xie, Yunhui (2024) Data supporting the publication "Single-step Phase Identification and Phase Locking for Coherent Beam Combination using Deep Learning". University of Southampton doi:10.5258/SOTON/D2974 [Dataset]

Record type: Dataset

Abstract

This dataset is supporting the publication "Simultaneous Phase Correction and Beam Shaping for Coherent Beam Combination using Deep Learning" The data is collated in 2 main folders. One for Figurative Data and one for Numerical Data. 1. Figurative Data The figure files used in the publication can be found in the directory named 'root/dataset/Figurative Data/'. The figures referenced in the publication as Figure 1. to Figure 6. correspond to the following filenames: 'Figure 1.jpg', 'Figure 2.png', 'Figure 3.png', 'Figure 4.png', 'Figure 5.png', and 'Figure 6.png'. The graphics denoted as Figure Appendix 1 and Figure Appendix 2, cited within the supplementary materials of the scholarly publication, are accessible under the file name 'Figure Appendix 1.png' and 'Figure Appendix 2.png', respectively. 2. Numerical Data a). The numerical data for Fig 3 a) can be found in the directory named 'root/dataset/Numerical Data/Fig 3 a/', which contains 1000 npz files. Each npz file is named after the Unix time when the corresponding measurement was made. These npz files can typically be opened using the numpy library in Python. Each npz file contains three ndarray objects: · ['arr_0']: This array, with the shape (6,), represents a set of six randomly generated target phase values. · ['arr_1']: This array, with the shape (1, 6), denotes the model predictions made by the neural network trained with the camera A dataset. · ['arr_2']: This array, with the shape (1, 6), represents the model predictions made by the neural network trained with the camera B dataset. b). The numerical data for Fig 5 c) can be found in the directory named "root/dataset/Numerical Data/Fig 5 c/", which contains one npy file. This file can be opened using the numpy library in Python. It contains a numerical array of shape (40, 50), where: · The first index represents 40 different sigmas controlling the randomness of the phase fluctuation. · The second index represents 50 timesteps for each sigma value. The data is accessible via CC BY license

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More information

Published date: 2024

Identifiers

Local EPrints ID: 488592
URI: http://eprints.soton.ac.uk/id/eprint/488592
PURE UUID: 09e3eb22-8cb3-428b-b9a1-00e7d1047d33
ORCID for Yunhui Xie: ORCID iD orcid.org/0000-0002-8841-7235
ORCID for Ben Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for Yuchen Liu: ORCID iD orcid.org/0009-0008-3636-1779
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for James A. Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Michalis Zervas: ORCID iD orcid.org/0000-0002-0651-4059

Catalogue record

Date deposited: 27 Mar 2024 17:49
Last modified: 05 Sep 2024 02:04

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Contributors

Creator: Yunhui Xie ORCID iD
Contributor: Fedor Chernikov
Contributor: Ben Mills ORCID iD
Contributor: Yuchen Liu ORCID iD
Contributor: Matthew Praeger ORCID iD
Contributor: James A. Grant-Jacob ORCID iD
Funder: Michalis Zervas ORCID iD

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