1U CubeSatでのバイナリ画像分類用に設計された畳み込みニューラルネットワーク

九州工業大学博士学位論文（要旨）学位記番号：工博甲第510号 学位授与年月日：令和2年12月28

1U CubeSatでのバイナリ画像分類用に設計された畳み込みニューラルネットワーク

As of 2020, more than a thousand CubeSats have been launched into space. The nanosatellite standard allowed launch providers to utilize empty spaces in their rockets while giving educational institutions, research facilities and commercial start-up companies the chance to build, test and operate satellites in orbit. This exponential rise in the number of CubeSats has led to an increasing number of diverse missions. Missions on astrobiology, state-of-art technology demonstration, high revisit-time earth observation and space weather have been implemented. In 2018, NASA’s JPL demonstrated CubeSat’s first use in deep space by launching MarCO A and MarCO B. The CubeSats successfully relayed information received from InSight Mars Lander in Mars to Earth. Increasing complexity in missions, however, require increased access to data. Most CubeSats still rely on extremely low data rates for data transfer. Size, Weight and Power (SWaP) requirements for 1U are stringent and rely on VHF/UHF bands for data transmission. Kyushu Institute of Technology’s BIRDS-3 Project has downlink rate of 4800bps and takes about 2-3 days to reconstruct a 640x480 (VGA) image on the ground. Not only is this process extremely time consuming and manual but it also does not guarantee that the image downlinked is usable. There is a need for automatic selection of quality data and improve the work process. The purpose of this research is to design a state-of-art, novel Convolutional Neural Network (CNN) for automated onboard image classification on CubeSats. The CNN is extremely small, efficient, accurate, and versatile. The CNN is trained on a completely new CubeSat image dataset. The CNN is designed to fulfill SWaP requirements of 1U CubeSat so that it can be scaled to fit in bigger satellites in the future. The CNN is tested on never-before-seen BIRDS-3 CubeSat test dataset and is benchmarked against SVM, AE and DBN. The CNN automatizes images selection on-orbit, prioritizes quality data, and cuts down operation time significantly.九州工業大学博士学位論文 学位記番号：工博甲第510号 学位授与年月日：令和2年12月28日1 Introduction|2 Convolutional Neural Networks|3 Methodology|4 Results|5 Conclusion九州工業大学令和2年

Nepal\u27s Danfe Space Mission: Technology Demonstration Mission on a 3U CubeSat to Mitigate Glacial Lake Outburst Floods

The United Nations Development Program ranks Nepal as the fourth-most country susceptible to climate change. The Global warming has caused more than 40 glacial lakes to potentially burst at any moment. A few Early Warning Systems are in place but are connected by ground cellular networks. A ground-to-space monitoring system instead, can help deter Glacial Lake Outburst Flood reliably. This paper outlines such a system through Nepal\u27s Danfe Space Mission placed on a 3U CubeSat. Danfe demonstrates the first use case of ported PX4 drone Operating System alongside a LoRa integrated STM32 Satellite System-on-Chip. A Ground Sensor Terminal with ultrasonic sensor and LoRa beacons glacier water level data to space. If the demonstration is successful, future satellite constellations for monitoring glaciers can be produced in a significantly shorter period as both hardware and software are drastically simplified. Such constellations can provide near-real time water level data while inciting actions to prevent any impending flash floods

Improvement of Communication System for 1U CubeSat

The Joint Global Multi-Nation Birds Satellite project abbreviated as BIRDS” project started with a first-generation constellation of five 1U satellites in October 2015. Currently, BIRDS project is on its fourth-generation of CubeSat constellation. The 1U CubeSats in each generation follow the heritage of the previous satellites. The Communication subsystem has undergone multiple iterations to improve the performance on orbit. Until the second-generation of satellites (BIRDS2 CubeSat constellation), the two-way communication between the satellite in orbit and the ground station was difficult even though the ground test results seemed promising. BIRDS3 satellites went through major changes and succeeded in establishing a strong link between the satellite and the ground station. This paper describes findings that were made based on the on-orbit test results of the previous satellites that caused difficulty in the communication and some of the major changes that were made on both the BIRDS3 satellite side and the ground station side to improve the communication. BIRDS3 satellites have been operating exceptionally better in the orbit. The orbital link measurement resultis also included in this paper