Department of Space Science and Engineering

The International Satellite Program in Research and Education’s (INSPIRE) first satellite is an Ionosphere and Solar X-ray observing microsat slated for launch in November of 2019 onboard an ISRO Polar Satellite Launch Vehicle. The microsat has a mission specific structure fitting on a PSLV ring deployer. There are two payloads aboard with two different science objectives. The Compact Ionosphere Probe (CIP) will take in-situ measurements of ion density, composition, temperature, velocity, and electron temperature. The CIP is a smaller version of the Advanced Ionosphere Probe (AIP, both developed in Taiwan) currently operating onboard the FORMOSat-5. This instrument is capable of sampling the ionosphere at 1 and 8 Hz. The second payload is the Dual Aperture X-ray Solar spectrometer (DAXSS). DAXSS is a modified Amptek X123 that will observe Solar X-rays, specifically soft X-rays. Hot plasma in the sun’s corona is best measured in the soft X-rays. Many emission lines for important elem...

The Ionospheric Dynamics Exploration and Attitude Subsystem Satellite (IDEASSat/ INSPIRESat-2) is a three-unit (U) CubeSat developed with the objective of providing in-situ measurements of the Earth’s ionosphere in order to quantify both global scale ionospheric variability and small scale irregularities. The science payload is the Compact Ionospheric Probe (CIP) - an all in one in-situ plasma sensor developed at Taiwan National Central University (NCU), which is the miniaturized version of the larger Advanced Ionospheric Probe (AIP) that is carried and operational aboard the 450 kg FORMOSAT-5 spacecraft. The spacecraft has been developed by NCU in partnership with the International Satellite Program in Research and Education (INSPIRE) consortium, and is funded by the Taiwan National Space Organization (NSPO), Ministry of Science and Technology, and Ministry of Education as part of the first national effort to encourage small satellite development at Taiwan universities. The development of IDEASSat offers students a hands-on opportunity to learn about space science and technology and will work in conjunction with FORMOSAT-5 and INSPIRESat-1 to provide ionospheric observations spanning different altitudes and local times. The IDEASSat spacecraft subsystems are a combination of commercial off the shelf (COTS) and self-developed components designed by NCU in partnership with other INSPIRE member universities. IDEASSat is expected to be launched in late 2020. In this report, we describe the IDEASSat mission and spacecraft design, as well as unique lessons learned as part of the development process.

The conversion of airglow intensity to volume emission rate (VER) is a common method for studying the ionosphere, but the contribution of the intensity conversion process to the uncertainty in estimated electron or ion density is significant. The Abel inversion is a commonly used method for retrieving VERs from vertical profiles of airglow intensities accumulated along the rays horizontally at the tangent point, but it requires that the intensities converge to zero at their uppermost height, which is often not the case due to observational limitations. In this study, we present a method for optimizing the retrieval of VER from satellite-measured airglow intensities using the techniques of deep learning and Abel inversion. This method can be applied to fill in unobserved or discontinuous observations in airglow intensity profiles with the Chapman function, allowing them to be used with the Abel inversion to determine VERs. We validate the method using limb 135.6 nm airglow emission intensity data from the NASA Global-scale Observations of the Limb and Disk (GOLD) mission. Our training process involves using three hidden layers with varying numbers of neurons, and we compare the performance of the best-performing deep learning models to Abel-transformed results from real-time observations. The combination of Abel inversion and deep learning has the potential to optimize the process of converting intensity to VER and improve the capacity for analyzing ionospheric observations.

We present a machine learning (ML) approach for classifying kinematic profiles of elliptical galaxies in the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Previous studies employing ML to classify spectral data of galaxies have provided valuable insights into morphological galaxy classification. This study aims to enhance the understanding of galaxy kinematics by leveraging ML. The kinematics of 2,624 MaNGA elliptical galaxies are investigated using integral field spectroscopy (IFS) by classifying their one-dimensional velocity dispersion (VD) profiles. We utilised a total of 1,266 MaNGA VD profiles and employed a combination of unsupervised and supervised learning techniques. The unsupervised K-means algorithm classifies VD profiles into four categories: flat, decline, ascend, and irregular. A bagged decision trees classifier (TreeBagger) supervised ensemble is trained using visual tags, achieving 100% accuracy on the training set and 88% accuracy on the test set. Our analysis identifies the majority (68%) of MaNGA elliptical galaxies presenting flat VD profiles, which requires further investigation into the implications of the Dark Matter problem.

The coincidental loss of 38 out of 49 SpaceX Starlink satellites during their launch on February 3, 2022, concurrent with two moderate geomagnetic storms, opens a unique window into the study of ionospheric irregularities and their potential impacts on Low Earth Orbit assets. This research provides evidence for the first time on the influence of Prompt Penetration Electric (PPE) fields and Disturbance Dynamo (DD) fields on the GNSS S4 amplitude scintillation indices of this particular geomagnetic storm, using observed variations in the distance of Equatorial Ionospheric Anomaly (EIA) crests and the O/N 2 density ratio. By examining observations from the F7/C2 (FORMOSAT-7/COSMIC-2) mission, the NASA/GSFC's OMNI data set, the TIMED/GUVI (Thermosphere Ionosphere Mesosphere Energetics and Dynamics/Global Ultraviolet Imager) O/N 2 density ratio, and the GIM-TEC (Global Ionosphere Map of Total Electron Content) data, the study uncovers the complex dynamics of storm-induced irregularities and their correlation with suppressed S4 at low latitudes. It reveals the roles of PPE and DD in augmenting and mitigating S4 occurrences, respectively, during different storm phases. These findings contribute to enhancing the understanding of irregularity occurrence rates, scintillation effects, and geomagnetic storms across various longitudinal sectors, thereby providing a case study of changes to the scintillation environment during this moderate but high-profile geomagnetic event.