This data provided evidence of the presence of a lower hybrid drift instability in a magnetic nozzle. It was used in DOI: 10.1063/5.0012668 to estimate the effective electron collision frequency that it induced in the context of cross-field electron transport. It is also used to determine the effective reduction in heat flux resulting from propagation along magnetic field lines in an upcoming work.
Hepner, S., Jorns, B. (2020). Wave-driven non-classical electron transport in a low temperature magnetically expanding plasma. Appl. Phys. Lett, 116(263502). https://doi.org/10.1063/5.0012668
The work guides the processing of CAM6 data for use in machine learning applications. We also provide workflow scripts for training both random forests and neural networks to emulate physic s schemes from the data, as well as analysis scripts written in both Python and NCL in order to process our results.
Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Pre Print]. ESSOAr. https://10.1002/essoar.10512353.1
The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
The data represents weekly output from three 60-year CAM6 model runs. The output includes state (.h0. files) and tendency (.h1. files) fields for three difference model configurations of increasing complexity. State fields include temperature, surface pressure, specific humidity, among others; while tendencies include temperature tendencies, specific humidity tendencies, as well as precipitation rates. Using the state variables at a given time step, machine learning techniques can be trained to predict the following tendency field, which can then be applied to the state variables to provide the state at the next physics time step of the model.
Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Preprint]. ESSOAr. https://10.1002/essoar.10512353.1
UWHandles is a dataset for 6D object pose estimation in underwater fisheye images. It provides 6D pose and 2D bounding box annotations for 3 different graspable handle objects used for ROV manipulation. The dataset consists of 28 image sequences collected in natural seafloor environments with a total of 20,427 annotated frames. and Meta repository for the dataset
https://github.com/gidobot/UWHandles
Billings, G., & Johnson-Roberson, M. (2020). SilhoNet-fisheye: Adaptation of a ROI based object pose estimation network to monocular fisheye images. IEEE Robotics and Automation Letters, 5(3), 4241-4248.
UWslam is a dataset for underwater stereo and hybrid monocular fisheye + stereo SLAM in natural seafloor environments. The dataset includes a spiral survey of a shallow reef captured with a diver operated stereo rig and 4 hybrid image sequences captured with a deep ocean ROV in different deep ocean environments. Ground truth pose estimates for the spiral stereo trajectory were obtained by processing the image sequence through COLMAP. Ground truth pose estimates for the hybrid sequences were obtained by distributing fiducials on the seafloor before capturing an image sequence and processing the image sequences with the ROS based TagSLAM package.
G. Billings, R. Camilli and M. Johnson-Roberson, "Hybrid Visual SLAM for Underwater Vehicle Manipulator Systems," in IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6798-6805, July 2022, doi: 10.1109/LRA.2022.3176448.
The data and scripts are meant to show how burster dynamics determine response to a single biphasic stimulus. The files include data which show trends in the propensity of termination for different burster types and the MATLAB scripts used to generate this data. The MATLAB scripts also allow the user to generate their own data sets for alternative bursting paths and stimulus parameter combinations. Furthermore, they allow the user to visually examine the effects of single stimuli in the voltage timeseries and in state space. How the user can access these features of the script is described in the file "ReadMe.pdf."
In this work, we perform Global Sensitivity Analysis (GSA) for the background solar wind in order to quantify contributions from uncertainty of different model parameters to the variability of in-situ solar wind speed and density at 1au, both of which have a major impact on CME propagation and strength. Scripts written in the Julia language are used to build the PCE and calculate the sensitivity results. Data is available in csv, NetCDF and JLD files. A `Project.toml` file is included to activate and install all required dependencies (See README for details).
The goal of the research was to train a surrogate model for the prediction of electric field distribution for a given electrospray emitter geometry design. The surrogate is to be used in reduced-fidelity modeling of electrospray thruster arrays. The code repository is included in the README.txt file.
J.D. Eckels, C.B. Whittaker, B.A. Jorns, A.A. Gorodetsky, B. St. Peter, R.A. Dressler, “Simulation-based surrogate methodology of electric field for electrospray emitter geometry design and uncertainty quantification”, presented at the 37th International Electric Propulsion Conference, Boston, MA USA, June19-23, 2022 Available: https://www.electricrocket.org/IEPC_2022_Papers.html