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- Creator:
- Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
- Description:
- We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
- Keyword:
- neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
- Citation to related publication:
- Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7. https://doi.org/10.3389/fspas.2020.00042
- Discipline:
- Science and Engineering
-
- Creator:
- Ahluwalia, Vinayak S., Steimle, Lauren N., and Denton, Brian T.
- Description:
- This repository includes test instances of infinite-horizon Markov decision processes with multiple models of parameters (i.e., "Multi-model Markov decision processes"). We generated each test instance in the dataset using a Python script. The test instances can be read in using the provided C++ and Python script. See the README for details.
- Keyword:
- Markov decision processes, mixed-integer programming, stochastic programming, and dynamic programming
- Citation to related publication:
- Ahluwalia, Steimle, and Denton. "Policy-based branch-and-bound for infinite-horizon Multi-model Markov decision processes". 2020.
- Discipline:
- Engineering
-
- Creator:
- Nasser, Ahmad and Gumise, Wonder
- Description:
- The work on accelerating authenticated boot for embedded system resulted in designing an algorithm in python to perform the random address generation and cryptographic MAC calculation. The Sampled Boot schemes implemented in this package allow a significant reduction of the time needed to authenticate firmware images during startup, while still retaining a high degree of trust. This is particularly useful for automotive applications in which startup time constraints make secure boot a time prohibitive process. and Citation for this dataset: Nasser, A., Gumise, W. (2019). Authenticated Boot Acceleration Algorithm [Code and data]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/yeh1-1x17
- Keyword:
- Trusted Computing, IOT security, Embedded Security, and Cyber Physical Systems
- Citation to related publication:
- Nasser, A., Gumise, W., and Ma, D., "Accelerated Secure Boot for Real-Time Embedded Safety Systems," SAE Int. J. Transp. Cyber. & Privacy 2(1) : 35-48, 2019, https://doi.org/10.4271/11-02-01-0003
- Discipline:
- Science
-
- Creator:
- Robert Buckley, Grace O'Brien, and Zoe Zhou
- Description:
- The purpose of the research is to better understand and approximate the Thurston Set. This project was computational in nature and Python was used to collect our data. The data set contains encoded itineraries that can be used to compute values that are elements of the Thurston Set. A visual approximation of the Thurston Set can be found here ( https://arxiv.org/abs/1402.2008), on the first page Thurston’s own paper. The data can also be used to study the distribution of superattracting beta values within the interval (1, 2] and to explore an analogous Mandelbrot-Julia Correspondence. This research was conducted through the Lab of Geometry at Michigan under the advisement of Harrison Bray during the Fall semester of 2019. , The Python 3.x scripts in this deposit are the exact versions used to created the *.txt files that are in the zip archive. As the project continues, any expansion to the work, such as further analysis or visualization scripts, will be posted to the project's GitHub https://github.com/Tent-Maps-Team/Thurston-Set. Also, a user can reproduce our results and generate bigger datasets on machines with large amounts of memory. , and The data consists of zipper folders representing tent map itinerary orbit lengths. These orbit files can be used to create visualizations, create and explore conjectures such as refining proposed bounds on the Thurston Set and supporting an analogous Mandelbrot-Julia Correspondence. Within these zipped folders are .txt files in CSV format with the naming structure of xx_y of admissible itineraries up to the length indicated by the folder name where xx is the length of the encoded itineraries included. The txt's have a single column and each line(row) is an array representing an encoding of an itinerary. Some of the txt's have been split into multiple parts (whenever there are more than 200 MB of itinerary data) and these txt's have been numbered using the y after the underscore. As we exclude the degenerate tent map (where β = 1), we cannot have orbit length 1 or 2 and this is why the orbits start with length 3 (i.e. start with 3.zip).
- Keyword:
- Math, mathematics, tent maps, thurston, milnor, Milnor-Thurston, supperattracting, entropy, orbit, and itineraries
- Citation to related publication:
- Buckley R, O’Brien G, Zhou Z (2021). On Itineraries of Tent Maps. Forthcoming.
- Discipline:
- Other
-
- Creator:
- Mukhopadhyay, Agnit, Daniel T Welling, Michael W Liemohn, Aaron J Ridley, Shibaji Chakrabarty, and Brian J Anderson
- Description:
- An updated auroral conductance module is built for global models, using nonlinear regression & empirical adjustments to span extreme events., Expanded dataset raises the ceiling of conductance values, impacting the ionospheric potential dB/dt & dB predictions during extreme events., and Application of the expanded model with empirical adjustments refines the conductance pattern, and improves dB/dt predictions significantly.
- Keyword:
- Space Weather Forecasting, Extreme Weather, Ionosphere, Magnetosphere, MI Coupling, Ionospheric Conductance, Auroral Conductance, Aurora, SWMF, SWPC, Nonlinear Regression, and dB/dt
- Citation to related publication:
- Mukhopadhyay, A., Welling, D. T., Liemohn, M. W., Ridley, A. J., Chakraborty, S., & Anderson, B. J. (2020). Conductance Model for Extreme Events: Impact of Auroral Conductance on Space Weather Forecasts. Space Weather, 18(11), e2020SW002551. https://doi.org/10.1029/2020SW002551
- Discipline:
- Engineering and Science
-
- Creator:
- Malhotra, Garima and Ridley, Aaron
- Description:
- This research aims to understand the importance of lower thermospheric atomic oxygen on the upper thermosphere. O number densities between 95-100 km from WACCM-X are much closer to the observations from SABER instrument on TIMED satellite as compared to those from MSIS. We show in this study that the correction of the lower boundary atomic oxygen yields better agreement between GITM and GUVI O/N2 in the upper thermosphere .
- Keyword:
- Lower Thermosphere Atomic Oxygen, Thermospheric Dynamics, Thermospheric composition and mixing, Lower-Upper Thermosphere Vertical Coupling, GITM - WACCMX coupling, and Global Ionosphere Thermosphere Model
- Citation to related publication:
- Malhotra, G., Ridley, A. J., Marsh, D. R., Wu, C., Paxton, L. J., & Mlynczak, M. G. (2020). Impacts of Lower Thermospheric Atomic Oxygen on Thermospheric Dynamics and Composition Using the Global Ionosphere Thermosphere Model. Journal of Geophysical Research: Space Physics, e2020JA027877. https://doi.org/10.1029/2020JA027877
- Discipline:
- Science
-
- Creator:
- Al Shidi, Q. and Pulkkinen, T.
- Description:
- Provided are the resultant and processed data.
- Keyword:
- space physics, ground magnetometers, magnetosphere, numerical space physics, solar wind, numerical space physics, and ionosphere
- Discipline:
- Science
-
- Creator:
- Iong, Daniel, Chen, Yang, Toth, Gabor, Zou, Shasha, Pulkkinen, Tuija I., Ren, Jiaen, Camporeale, Enrico, and Gombosi, Tamas I. I.
- Description:
- In this work, we trained gradient boosted trees using XGBoost to predict the SYM-H forecasting using different combinations of solar wind and interplanetary magnetic field (IMF) parameters. Data are in csv and Python pickle formats.
- Keyword:
- SYM-H forecasting
- Citation to related publication:
- Iong, D., Y. Chen, G. Toth, S. Zou, T. I. Pulkkinen, J. Ren, E. Camporeale, and T. I. Gombosi, New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines, Space Weather,11, accepted, 2022. https://doi.org/10.1002/essoar.10508063.3
- Discipline:
- Science
-
- Creator:
- Dulka, Eden A
- Description:
- This data is a subset of that originally produced as part of an effort to characterize GnRH neuron activity during prepubertal development in control and PNA mice and investigate the potential influences of sex and PNA treatment on this process (1). It was later used in (2) to further investigate the firing patterns of GnRH neurons in these categories of mice and determine how these patterns might differ based on age and treatment condition. The data files can be opened and examined using Wavemetric's Igor Pro software. Code used to further examine and visualize the data can be found at https://gitlab.com/um-mip/mc-project-code. This research was supported by National Institute of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development R01 HD34860 and P50 HD28934. (1) Dulka EA, Moenter SM. Prepubertal development of gonadotropin-releasing hormone (GnRH) neuron activity is altered by sex, age and prenatal androgen exposure. Endocrinology 2017; 158:3941-3953 (2) Penix JJ, DeFazio RA, Dulka EA, Schnell S, Moenter SM. Firing patterns of gonadotropin-releasing hormone (GnRH) neurons are sculpted by their biology. Pending.
- Keyword:
- action potential, Monte Carlo, polycystic ovary syndrome, puberty, and androgen
- Citation to related publication:
- Dulka EA, Moenter SM. Prepubertal development of gonadotropin-releasing hormone neuron activity is altered by sex, age and prenatal androgen exposure. Endocrinology 2017; 158:3943-3953. https://dx.doi.org/10.1210%2Fen.2017-00768 and Penix JJ, DeFazio RA, Dulka EA, Schnell S, Moenter SM. Firing patterns of gonadotropin-releasing hormone (GnRH) neurons are sculpted by their biology. Pending.
- Discipline:
- Health Sciences
-
- Creator:
- Agnit Mukhopadhyay
- Description:
- Conducting quantitative metrics-based performance analysis of first-principles-based global magnetosphere models is an essential step in understanding their capabilities and limitations, and providing scope for improvements in order to enhance their space weather prediction capabilities for a range of solar conditions. In this study, a detailed comparison of the performance of three global magnetohydrodynamic (MHD) models in predicting the Earth’s magnetopause location and ionospheric cross polar cap potential (CPCP) has been presented. Using the Community Coordinated Modeling Center’s Run-on-Request system and extensive database on results from various magnetospheric scenarios simulated for a variety of solar wind conditions, the aforementioned model predictions have been compared for magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimmilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, we have quantified the models’ reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations, and identified salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities.
- Citation to related publication:
- Mukhopadhyay, A., Jia, X., Welling, D. T., & Liemohn, M. W. (2021). Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Predictions. Frontiers in Astronomy and Space Sciences, 8. https://doi.org/10.3389/fspas.2021.637197
- Discipline:
- Engineering and Science
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