The Multifaceted Optimization Algorithm (MOA) is a tool for generating corrected empirical model thermospheric densities during geomagnetic storms. It consists of a suite of Python functions that operate around the Spacecraft Orbit Characterization Kit (SpOCK), an orbital propagator developed by Charles D. Bussy-Virat, PhD, Joel Getchius, and Aaron J. Ridley, PhD at the University of Michigan, and it estimates new densities for the NRLMSISE-00 atmospheric model. MOA generates new model densities by estimating modifications to inputs to the NLRMSISE-00 model that minimize the orbit error between modeled spacecraft in SpOCK, and their actual altitudes as described in publicly-available Two-Line Element Sets (TLEs), made available online via Space-track.org.
MOA consists of three sub-process: (1) The Area Optimization Algorithm (AROPT), (2) the F10.7 Optimization Algorithm (FOPT), and (3) the Ap Optimization Algorithm (APOPT). AROPT computes the contribution to the drag of the modeled spacecraft due to their varying projected area. FOPT estimates modifications to the 10.7 cm solar radio flux in NRLMSISE-00, and APOPT estimates modifications to the Earth's magnetic activity in NRLMSISE-00. MOA finds these modifications across many spacecraft, and the medians of those modifications are then applied in NLRMSISE-00 along the orbit of another satellite to generate new densities for verification. In this instance, modifications are applied along the orbits of the Swarm spacecraft and compared to Swarm GPS-derived densities.
Brandt, D. A., Bussy-Virat, C. D., & Ridley, A. J. (2020). A Simple Method for Correcting Empirical Model Densities During Geomagnetic Storms Using Satellite Orbit Data. Space Weather, 18(12), e2020SW002565. https://doi.org/10.1029/2020SW002565
This data was collected as part of a study to study population dynamics of coastal giant salamanders in Oregon. The study uses genetics to answer questions related to conservation concerns including population connectivity, sensitivity to habitat disturbances (such as logging and fires), and genetic diversity of populations.
Auteri, Giorgia G., M. Raquel Marchán-Rivadeneira, Deanna H. Olson, L. Lacey Knowles. Connectivity in coastal giant salamanders (Dicamptodon tenebrosus) shows no association with land-use, fire frequency, or river drainage but does not offset negative consequences of locally unstable population sizes. PLoS ONE. In review.
Data collected by Mooney aircraft over Houston and Denver in Summer 2020. Flights typically were designed to measure within the boundary layer in a raster pattern perpendicular to wind direction, thus sampling the urban plume repeatedly. Vertical profiles are conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data file contains all merged flight data from each flight day.
Our work seeks to better understand the financial risks to corporate operations as a basis for exploring alternative public-private investment strategies. We applied network analysis to model financial relationships within this sector and its connectedness to primary commodities transported on the Great Lakes. The financial network maps were used to quantitatively analyze the industry risk exposure using corporate financial metrics and to query the financial interdependencies of companies relative to the Great Lakes waterway. Results demonstrate that inventory turnover ratio is a robust proxy to quantify weighted financial risks of water dependency across the entire supply chain network.
All data was manually collected from the Bloomberg Terminal and FactSet which are licensed by the University of Michigan. The SPLC module in the Terminal restricts data download and information must be captured manually. All data was collected from September-November 2018.
Sugrue, Dennis, Abigail Martin, and Peter Adriaens. (under review). “Financial Network Analysis to Inform Infrastructure Investment: Great Lakes Waterway and the Steel Supply Chain.” Journal of Infrastructure Systems, American Society of Civil Engineers.
The data file contains (1) the grayscale images of the nano-tomography experiments that can be segmented into binary images and visualized to show the 3D morphology of three-phase spiral eutectics; and (2) Scanning electron micro of solidified sample.
This is the README for the LunarSynchrotronArray package, maintained by Dr. Alex Hegedus alexhege@umich.edu
This code repository corresponds to the Hegedus et al. 2020 (accepted) Radio Science paper, "Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array". The arxiv link for the paper is https://arxiv.org/abs/1912.04482. The DOI link is https://doi.org/10.1029/2019RS006891
, The Earth's Ionosphere is home to a large population of energetic electrons that live in the balance of many factors including input from the Solar wind, and the influence of the Earth's magnetic field. These energetic electrons emit radio waves as they traverse Earth's magnetosphere, leading to short‐lived, strong radio emissions from local regions, as well as persistent weaker emissions that act as a global signature of the population breakdown of all the energetic electrons. Characterizing this weaker emission (Synchrotron Emission) would lead to a greater understanding of the energetic electron populations on a day to day level. A radio array on the near side of the Moon would always be facing the Earth, and would well suited for measuring its low frequency radio emissions. In this work we simulate such a radio array on the lunar near side, to image this weaker synchrotron emission. The specific geometry and location of the test array were made using the most recent lunar maps made by the Lunar Reconnaissance Orbiter. This array would give us unprecedented day to day knowledge of the electron environment around our planet, providing reports of Earth's strong and weak radio emissions, giving both local and global information.
, This set of codes should guide you through making the figures in the paper, as well as hopefully being accessible enough for changing the code for your own array. I would encourage you to please reach out to collaborate if that is the case! Requirements:
, and
CASA 4.7.1 (or greater?) built on python 2.7
Example link for Red Hat 7
https://casa.nrao.edu/download/distro/casa/release/el7/casa-release-4.7.1-el7.tar.gz
Users may follow this guide to download and install the correct version of CASA for their system
https://casa.nrao.edu/casadocs/casa-5.5.0/introduction/obtaining-and-installing
CASA executables should be fairly straightforward to extract from the untarred files.
gcc 4.8.5 or above (or below?)
GCC installation instructions can be found here: https://gcc.gnu.org/install/
SPICE (I use cspice here)
https://naif.jpl.nasa.gov/naif/toolkit_C.html
As seen in lunar_furnsh.txt which loads the SPICE kernels, you also must download
KERNELS_TO_LOAD = ( '/home/alexhege/SPICE/LunarEph/moon_pa_de421_1900-2050.bpc'
'/home/alexhege/SPICE/LunarEph/moon_080317.tf'
'/home/alexhege/SPICE/LunarEph/moon_assoc_me.tf'
'/home/alexhege/SPICE/LunarEph/pck00010.tpc'
'/home/alexhege/SPICE/LunarEph/naif0008.tls'
'/home/alexhege/SPICE/LunarEph/de430.bsp' )
All of which can be found at
https://naif.jpl.nasa.gov/pub/naif/generic_kernels/
SLDEM2015_128_60S_60N_000_360_FLOAT.IMG for the lunar surface data by LRO LOLA found at
http://imbrium.mit.edu/DATA/SLDEM2015/GLOBAL/FLOAT_IMG/
Citation to related publication:
Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2019). Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array. https://arxiv.org/abs/1912.04482 and Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2020). Radio Science. https://doi.org/10.1029/2019RS006891
Multi-satellite tracking of solar wind dynamic pressure pulse observations through the Earth's magnetosphere enables us to distinguish local changes with propagation signatures.
Vidal-Luengo, S. E., & Moldwin, M. B. (2021). Global magnetosphere response to solar wind dynamic pressure pulses during northward IMF using the heliophysics system observatory. Journal of Geophysical Research: Space Physics, 126, e2020JA028587. https://doi.org/10.1029/2020JA028587
The research that produced this data focused on conducting a statistical comparison between horizontal winds modeled with GITM and those derived from the accelerometer aboard the GOCE satellite. The winds from GITM and GOCE were compared by constructing their respective probability densities under different levels of geomagnetic activity, and by distributing them as a function of geomagnetic activity, magnetic latitude, magnetic local time, day-of-the-year, and solar radio flux.
There are three experimental outputs from Seq-Scope. (1) High definition map coordinate identifier (HDMI) sequence, tile and spatial coordinate information from 1st-Seq, (2) HDMI sequence, coupled with cDNA sequence from 2nd-Seq, and (3) Histological image obtained from Hematoxylin and Eosin (H&E) staining of the tissue slice. (1) and (2) were uploaded to GEO ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186601). (3) is deposited here. In addition, this deposit includes the processed RDS (single R object) data files.
Citation to related publication:
Do TH, Ma F, Andrade PR, Teles R, de Andrade Silva BJ, Hu C, Espinoza A, Hsu JE, Cho CS, Kim M, Xi J, Xing X, Plazyo O, Tsoi LC, Cheng C, Kim J, Bryson BD, O'Neill AM, Colonna M, Gudjonsson JE, Klechevsky E, Lee JH, Gallo RL, Bloom BR, Pellegrini M, Modlin RL. TREM2 macrophages induced by human lipids drive inflammation in acne lesions. Sci Immunol. 2022 Jul 22;7(73):eabo2787. doi: 10.1126/sciimmunol.abo2787. Epub 2022 Jul 22. PMID: 35867799; PMCID: PMC9400695.
This publication contains anonymized SPECT/CT scans of two patients. Patient scans were taken at 4 different time points in the week following a therapeutic dose of Lu-177 DOTATATE. Both the SPECT and the co-registered CT are provided. All images are in DICOM format.