CaltechDATA: Combined
https://feeds.library.caltech.edu/people/Dabiri-J-O/combined_data.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenFri, 06 Sep 2024 18:54:59 -0700Supplementary Information and Data for "Magnetic signature of vertically migrating aggregations in the ocean"
https://data.caltech.edu/records/0yha9-5qe05
Year: 2023
DOI: 10.22002/0yha9-5qe05https://data.caltech.edu/records/0yha9-5qe05Visual anemometry measurements of eight vegetation species
https://data.caltech.edu/records/crb7k-1gj48
Year: 2023
DOI: 10.22002/crb7k-1gj48
<p>This repository consists of four files, namely, three data files and a README file:</p><ol><li>Records.csv</li><li>NormalizationCoefficients.csv</li><li>NormalizedRecords.csv</li><li>README.txt</li></ol><p>Eight vegetation species were tested in the Center for Autonomous Systems and Technologies (CAST) facility at Caltech and exposed to wind speeds up to 15 m/s. Wind speeds were recorded using a two-component sonic anemometer (Campbell Scientific WindSonic4) sampling at 4 Hz. Vegetation motion was simultaneously recorded using a 1520 x 2704 CMOS camera (GoPro Hero7) sampling at 60 Hz. The vegetation tested were as follows:</p><ul><li>Vegetation 1 <i>Muhlenbergia emersleyi </i> (Bullgrass)</li><li>Vegetation 2 <i>Cinnamomum camphora </i>(Camphor tree)</li><li>Vegetation 3 <i>Prosopis alba thornless</i> (Mesquite tree)</li><li>Vegetation 4 <i>Quercus agrifolia</i> (Oak tree)</li><li>Vegetation 5 <i>Olea europaea </i>(Olive tree)</li><li>Vegetation 6 <i>Melaleuca quinquenervia</i> (Paperbark tree)</li><li>Vegetation 7 <i>Chinus mole </i>(Pepper tree)</li><li>Vegetation 8 Pinus radiata (Pine tree)</li></ul><p>The data in this repository were binned into 1 m/s bins +/-0.5 m/s in the range of 0-10 m/s. Replicates of each configuration were spaced two weeks apart to assess variability over multiple weeks and are referred to as Round 1 or Round 2 in the repository.</p><p>The vegetation motion estimates were computed with PIVlab (Thielicke & Sonntag 2021). First, we manually selected 320 x 320 pixel bounding boxes of each vegetation to ensure the tunnel floor in the background is excluded from further processing. Then, we computed the displacement vector fields using two passes in the PIVlab algorithm: the first pass consisted of 64 x 64 pixel interrogation windows and the second pass consisted of 32 x 32 pixel interrogation windows, both with 50% overlap. To compare with pointwise wind speed records collected using the sonic anemometer, the vegetation displacement fields were spatially averaged. Temporal kinematics of the wind and vegetation speeds of each bin were fitted to a Weibull distribution separately. The maximum likelihood estimates and confidence intervals of the scale (C1) and shape (C2) factors of both wind and vegetation speeds are provided in this dataset. Each test set is defined to consist of all binned vegetation scale factors (canopy_C1) and their corresponding wind scale factors (wind_C1), separately for each round.</p><p>The observed relationships between the vegetation displacement scale factors versus the wind scale factors resemble the shape of sigmoid logistic curves. Each test set was separately fitted to a sigmoid curve, canopy_C1=a/(1+exp(-(wind_C1-x0)/b)), where a, b, and x0 are the fitted coefficients computed using the Levenberg-Marquardt nonlinear least squares method. Here, a determines the scale of the higher wind asymptote, while x0 and b determine the center and range, respectively, of the region of highest correlation between vegetation speeds and wind speed. The fitted coefficients are later used to normalize each of the vegetation species to a universal curve that determines the relationship to wind speeds. The non-dimensional wind scale factor is computed using b and x0 from the sigmoid curve fitting: tilde{wind_C1}=(wind_C1*b)+x0. Similarly, the non-dimensional vegetation speed scale factor is computed using a from the sigmoid curve fitting: tilde{canopy_C1}=canopy_C1/a.</p><p> </p><p>References</p><p>Thielicke, W., & Sonntag, R. (2021). Particle Image Velocimetry for MATLAB: Accuracy and enhanced algorithms in PIVlab. Journal of Open Research Software, 9. Ubiquity Press, Ltd. Retrieved from https://doi.org/10.5334%2Fjors.334</p>https://data.caltech.edu/records/crb7k-1gj48Electromechanical enhancement of live jellyfish for ocean exploration dataset
https://data.caltech.edu/records/c5cyj-mev09
Year: 2023
DOI: 10.22002/c5cyj-mev09https://data.caltech.edu/records/c5cyj-mev09Caltech Autonomous Reinforcement Learning robot
https://data.caltech.edu/records/b291n-nx970
Year: 2024
DOI: 10.22002/b291n-nx970https://data.caltech.edu/records/b291n-nx970MATLAB linear stability codes for J. O. Dabiri and A. Leonard, Journal of Fluid Mechanics, 2024
https://data.caltech.edu/records/4sm2w-br988
Year: 2024
DOI: 10.22002/4sm2w-br988
<p>These MATLAB scripts enable calculation of linear stability of plane Couette flow, plane Poiseuille flow, and Hagen-Poiseuille pipe flow with user-defined boundary conditions. See Dabiri and Leonard (Journal of Fluid Mechanics, 2024) for background and technical details. </p>https://data.caltech.edu/records/4sm2w-br988