Embry-Riddle Aeronautical University Showcase

This archive contains drone-based remote sensing dataset collected during the design, construction, and early performance phase of the Veterans Memorial Park “Smart” Rain Garden in Cape Canaveral, Florida, as funded by the National Science Foundation's CIVIC Innovation Challenge 2.0 program (NSF Grant Award Number 2321162). The dataset includes high-resolution LiDAR point clouds, orthomosaic imagery, and digital elevation models (DEMs) derived from approximately 10,000 aerial photographs acquired between 2023 and 2025 in the City of Cape Canaveral. These products capture fine-scale changes in site topography and vegetation associated with the conversion of a dry retention area into a functioning green stormwater infrastructure (GSI) rain garden system at the City of Cape Canaveral's Veterans Memorial Park. The imagery provides a detailed temporal record of construction progress, vegetation establishment, and erosion dynamics at the site. The LiDAR data achieved ~2 cm vertical accuracy, enabling detailed hydrologic modeling of flow pathways and runoff accumulation within the rain garden site and nearby areas of the stormwater drainage basin. Together, these datasets support research on stormwater infiltration, flood mitigation, and urban ecosystem restoration, and are intended as a transferable resource for GSI planning and performance evaluation in other coastal communities.

These data were generated by a full-wave model that has been referenced in the paper. Data are saved in text format in various files which are listed in the readme document. The columns of data used in the figures are provided in the readme document.

This dataset is the main dataset used by the UA Detection Team here at Embry-Riddle Aeronautical University. It provides the ability to conduct binary drone/no drone classification as well as specific drone sub-type classifcation. Dataset is gathered from field data from drones flown by the Unavail research team. Each clip of audio is fed through YAMNET to generate points in an embedding space. The no_drone directory contains various sounds from ambient noise to cars and jets. drone contains samples from the DJI Matrice M100, the Mavic 3, and the Mavic Mini 2. There are 9108 samples total across all the directories. Each .tfdata segment represents a single second of audio.

The Embry-Riddle Coastline Dataset (ER-Coast) is designed for maritime machine learning research, including sensor fusion. ER-Coast introduces a diverse set of data using multiple sensing modalities. These include three LiDAR, three 8-megapixel color cameras, one 5.4-megapixel high dynamic range camera, two long-wave infrared cameras, and a GPS/IMU inertial navigation system. The captured data includes coastal waterways in the state of Florida during the day and at night. There are 36 separate sequences split across 4 days of collections, totaling over 5 hours of calibrated and timestamped data. A subset of this raw data has been annotated for the tasks of LiDAR semantic segmentation, image semantic segmentation, and image object detection. Intrinsic and extrinsic calibrations are available in the dissertation of https://commons.erau.edu/edt/741/, and the original calibration data has also been provided for users to generate their own calibrations. A paper with a focused discussion of ER-COAST will be released at a later date.

The repository includes TEC data, MAGIC-GEMINI modeled electron densities, and simulated TEC along real LOSs on 27th May 2017 at 19:30 UT. Description of data and simulation output is found in ReadMe.txt

System for Rapid Analysis of Ionospheric Dynamics based on GNSS TEC Signals (S-RAID) performs downloading, parsing, and processing of Global Navigation Satellite System (GNSS) signal measurements and their geometry of observations, calculation of slant and vertical total electron contents (sTEC and vTEC) and subsequent visualization for selected band-passes of fluctuations with periods shorter than two hours. In a routine operation, the System processes 15/30 sec GNSS signal measurements over Continental United States (CONUS) from ~2700 stations. Raw GNSS signal measurements are collected from public archives, including The Crustal Dynamics Data Information System (CDDIS), EarthScope/UNAVCO, National Oceanic and Atmospheric Administration (NOAA), and Scripps Institution of Oceanography's Orbit and Permanent Array Center (SOPAC). The System is oriented towards rapid access to GNSS sTEC/vTEC data for the investigation of traveling ionospheric disturbances (TIDs) of various nature, from large scale TIDs to irregularities of ~10s of km and minutes of periods, in particular those driven by atmospheric acoustic and gravity wave dynamics. The details of data processing methodology are provided the section Steps to Reproduce below, and in (2) and Supporting Information to it. Currently, this archive provides an access to high-resolution visualization of processed vTEC mapped over CONUS for years 01/2017-12/2024. The structure of the archive: /YYYY/MM/DD/animation.mp4, where YYYY - year, MM - month, DD - day of month, animation.mp4 - temporally evolving visualization of processed vTEC (see Steps to Reproduce for the details of data processing methodology). To reference the archive or the methodology for data processing, we suggest to: (1) Cite this archive by its DOI: 10.17632/jbx98yscmd.1, and/or (2) Cite Inchin, P. A., et al. (2023). Multi-layer evolution of acoustic-gravity waves and ionospheric disturbances over the United States after the 2022 Hunga Tonga volcano eruption. AGU Advances, 4. https://doi.org/10.1029/2023AV000870. Being processed in an automated regime, the visualizations may contain errors and bugs and thus should be used with caution as is. If you encounter any issues or wish to obtain data for animation replication, contact Inchin P.A. pinchin@cpi.com, J.B. Snively snivelyj@erau.edu or M.D. Zettergren zettergm@erau.edu. Research is supported by DARPA Cooperative Agreement HR00112120003. This work is approved for public release; distribution is unlimited. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.

This collection hosts the datasets associated with the journal article, Three-Dimensional Numerical Modeling of Coseismic Atmospheric Dynamics and Ionospheric Responses in Slant Total Electron Content Observations. Authors: P.A. Inchin Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA Computational Physics, Inc., Springfield, Virginia, USA Y. Kaneko Department of Geophysics, Kyoto University, Kyoto, Japan A.-A. Gabriel Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA Department of Earth and Environmental Sciences, Ludwig-Maximilians-University, Munich, Germany T. Ulrich Department of Earth and Environmental Sciences, Ludwig-Maximilians-University, Munich, Germany L. Martire Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA A. Komjathy Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA J. Aguilar Guerrero Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA M.D. Zettergren Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA J.B. Snively Center for Space and Atmospheric Research, Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA Correspondence: P.A. Inchin, pinchin@cpi.com The collection includes next datasets: Dataset 1 Title: Sim #1. Electron density 3D fields Description: 3D outputs of electron densities of GEMINI simulation #1 in HDF5 format, geographic coordinates and scripts for reading and plotting of the data. Dataset 2 Title: Sim #1. Vertical fluid velocity 3D fields Description: 3D outputs of vertical fluid velocities of MAGIC simulation #1 in mat format, geographic coordinates and scripts for reading and plotting of the data. Dataset 3 Title: Sim #3. Vertical fluid velocity 3D fields Description: 3D outputs of vertical fluid velocities of MAGIC simulation #3 in mat format, geographic coordinates and scripts for reading and plotting of the data. Dataset 4 Title: DynamicVSKinematic model sTEC comparison Description: The figures illustrating the comparison of sTEC signals from Simulation #1 and Simulation #3 and observations.

Debris propagation states for DRO3 orbit

Explosions dataset that includes the evolution of the state vectors for each fragment and the initial orbits IC of specific L1 and L2 Lyapunov orbits.

Initial conditions include a state vector and period of each orbit in a periodic family in the Earth-Moon CR3BP system.