The KITSUNE satellite is a 6-unit CubeSat platform with the main mission of 5-m-class Earth observation in low Earth orbit (LEO), and the payload is developed with a 31.4 MP commercial off-the-shelf sensor, customized optics, and a camera controller board. Even though the payload is designed for Earth observation and to capture man-made patterns on the ground as the main mission, a secondary mission is planned for the classification of wildfire images by the convolution neural network (CNN) approach. Therefore, KITSUNE will be the first CubeSat to employ CNN to classify wildfire images in LEO. In this study, a deep-learning approach is utilized onboard the satellite in order to reduce the downlink data by pre-processing instead of the traditional method of performing the image processing at the ground station. The pre-trained CNN models generated in Colab are saved in RPi CM3+, in which, an uplink command will execute the image classification algorithm and append the results on the captured image data. The on-ground testing indicated that it could achieve an overall accuracy of 98% and an F1 score of a 97% success rate in classifying the wildfire events running on the satellite system using the MiniVGGNet network. Meanwhile, the LeNet and ShallowNet models were also compared and implemented on the CubeSat with 95% and 92% F1 scores, respectively. Overall, this study demonstrated the capability of small satellites to perform CNN onboard in orbit. Finally, the KITSUNE satellite is deployed from ISS on March 2022.
Many regions in developing countries do not have any access to communication networks even though the number of devices connected through the Internet of Things (IoT) is increasing significantly. A small satellite platform could provide global network coverage in low Earth orbit to these remote locations at a low cost. This paper describes the overall mission architecture and the implementation of remote IoT using a 1U volume in 6U CubeSat platform named KITSUNE. In KITSUNE, one of the missions is to leverage IoT for building a network of remote ground sensor terminals (GST) in 11 mostly developing countries. This paper evaluates the capacity and coverage of a satellite-based IoT network for providing remote data-collection services to these countries. The amount of data that could be collected from the GSTs and forwarded accurately to the users determines the actual capacity of the Store and Forward (S&F) mission. Therefore, there are several proposed parameters to estimate this capacity in this study. In addition, these parameters are retrieved from the simulations, ground test results, and on-orbit observations with the KITSUNE satellite. The proposed IoT system, which is composed of the GSTs and IoT subsystem onboard KITSUNE satellite, is determined to be capable of providing valuable information from remote locations. In addition, the collected data are achieved and analyzed to monitor sensory data specific to each country, and it could help to generate prediction profiles as well.
this paper introduces a design of a robotic system that can be mounted on a CubeSat. Robotic arms should be small enough to be mounted on a size as 30x10x10 cm or smaller. Meanwhile, robotic arms should provide sufficient space for their actuators, cameras and sensors. The robotic design has one camera on each arm to be used for stereo-vision ability. Velocity and position feedback are desired to be used for controlling the robotic arm's position and minimizing the speed of the arms' motion to reduce the disturbance on attitude control subsystem during arm motions. Each arm provides 5 degrees of freedom. For control design of the system, the Denavit-Hartenberg convention is used for forward and backward kinematics modeling, therefore the position and the orientation of the camera can be calculated after receiving a command from the ground station or ready mission parameters from the memory. By using forward kinematics matrices, the Jacobian matrix will be presented for the system, and it will be used to calculate the inertial tensor matrix which includes the effects of the mass and inertia. After calculating the inertial tensor matrix, Euler-Lagrangian method will be used for dynamic modeling which includes the nonlinear coriolis and centrifugal effects with the inertial forces. The dynamic equation of motion for two arms which includes inertial forces, coriolis and centrifugal effects, friction forces and gravity will be presented for space and the micro gravity environment. Moreover, spin-stabilized satellite motion effect is added to robotic arms' the dynamic equation of the motion. The dynamic equation is used with the proportionalintegral coefficients for modeling the control of the prismatic links' actuators chosen for the arms.
Earth observation (EO) missions remain a challenging task for small satellite platforms due to their demanding requirements and space environment effects. In this study, the camera payload development and mission requirements are presented together with the ground-based testing results for a 6U CubeSat called KITSUNE, operating at low Earth orbit. The major challenge of the payload development is maintaining the focus of the optical system despite the thermal vacuum environment in orbit since the low thermal capacity and rapid temperature variation of CubeSats hinder the camera focus. First, the payload is developed with an objective of a 5-m-class imaging mission, which has a 31.4 MP CMOS sensor and a lens with a 300-mm focal length. Second, polyimide heaters and multilayer insulators are utilized in order to maintain focus during imaging operations. Third, a collimator lens is used to aid in image capture during thermal vacuum tests. These images are analyzed thoroughly using various focus measure operators. The Diagonal Laplacian was found to be the most suitable operator due to the consistency in test results. The results also showed that the heat generated by the camera sensor significantly affects the lens temperature and, ultimately, the target temperature of the lens was defined at −1.8°C. Finally, the test results are discussed, including thermal vacuum, vibration, total ionization dose, and the effect of exposure to direct sunlight on the CMOS sensor.
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