Large-eddy simulations were employed over half-ice and half-water surfaces, with varying surface temperatures, wind speeds, directions, as to test if the atmospheric interaction with the heterogeneous surface can be predicted via a heterogeneity Richardson number. This dataset was used to determine that surface heat fluxes over ice, water, and the aggregate surface seem to be captured reasonably well by the wind direction and the heterogeneity Richardson number, but the mean wind and turbulent kinetic energy (TKE) profiles were not, suggesting that not only the difference in stability between the two surface, but also the individual stabilities over each surface influence the dynamics.
Large-eddy simulations were employed over five different sea ice patterns, with a constant ice fraction, to test if the overlying atmospheric boundary layer (ABL) dynamics and thermodynamics differs. The results of these simulations were used to determine that there were differences in vertical heat flux, momentum flux, and horizontal wind speed, and that more surface information is needed to predict the ABL over the sea ice surface. To see what other surface information is needed, twenty-two landscape metrics were calculated over forty-four different maps at differing resolutions, using the FRAGSTATs program. The results of that analysis are available in a .csv file in this dataset.
The data are 4554 light curves derived from images taken of the globular cluster M4 by the Kepler space telescope during the K2 portion of its mission, specifically during Campaign 2 of that mission, which occurred in 2014. A total of 3856 images were taken over approximately three months at a cadence of approximately half an hour. The purpose of these observations was to find stars and other objects that vary in brightness over time --- variable stars. Also included is a table with associated information for each of the 4554 objects and their light curves.
Canal, G. P.; Ferraro, N. M.; Evans, T. E.; Osborne, T. H.; Menard, J. E.; Ahn, J. -W.; Maingi, R.; Wingen, A.; Ciro, D.; Frerichs, H.; Schmitz, O.; Soukhanovskii, V.; Waters, I.; Sabbagh, S. A.
This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-2, +3] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 150 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-5, +20] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 50 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
