# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 1 Introduction  
The development of small satellites by non-space-faring nations is significantly driven by the availability of low-cost launch and commercial off-the-shelf (COTS) components. A clear example is the Joint Global Multi-National Birds or BIRDS program, a multinational small satellite research and educational program led by the Kyushu Institute of Technology (Kyutech) in Japan [1]. The BIRDS program allows non-space-faring nations to design, integrate, build, test, launch, and operate their respective first satellites. BIRDS-4 is the fourth iteration of the BIRDS program (previously BIRDS-1, 2, and 3). It is a constellation of three CubeSats: GuaraniSat-1, Maya-2, and Tsuru, deployed into orbit on 14 March 2021 [2]. The satellites use the BIRDS standardized bus, an open-source initiative by Kyutech designed for educational CubeSat projects inherited from the previous generation [3]. The BIRDS program follows the approach of a Lean Satellite concept [3]. The concept relies on utilizing commercially available yet non-space-proven components to obtain effective and efficient development [4]. On the other hand, the mass and size limitations usually indicate only a few or no redundant systems available [5]. The intermittent and short-term communication window limits the data transmission capability, potentially affecting the housekeeping-data-monitoring analysis. Furthermore, the CubeSat system's limitations generally include power generation, telemetry bandwidth, computational power, and memory [6]. The BIRDS team uses conventional threshold values to perform telemetry analysis for the satellite's health monitoring. However, considering the available bandwidth and large volumes of data collected onboard the satellite, it is impossible to download all the dataduring the operational phase. In addition, data monitoring requires a team with sufficient knowledge and experience due to the complex variations in telemetry patterns. According to the BIRDS-3 telemetry data, two satellites revealed power generation loss in one of their panels. The team could not immediately detect the symptoms within 29 (on Raavana) and 72 (on Uguisu) days after being deployed into orbit. Recently, the BIRDS-4 team discovered a critical issue related to the electrical power subsystem of GuaraniSat-1 [2]. The satellite stopped transmitting after three days of operation. Subsequent analysis of the continuous wave (CW) beacon data showed a lower charging capacity than its sister satellite, Tsuru, confirming no power generation on two solar panels. Therefore, there is an urgent need to develop solutions to mitigate the issue from any possible technical approach. One of the possible approaches would be utilizing Machine Learning (ML), considering the substantial amount of data collected by the BIRDS team. The analysis for related research comes from these three main aspects: pre-processing techniques, proximity-based algorithms, and linear models. As a representative case, the work presented by [PERSON] et al. [7] used Long Short-Term Memory (LSTM) and Ensembled One-Class Support Vector Machines to perform anomaly detection. Multiple One-Class Support Vector Machines were used to obtain high-precision and high-recall outputs. The method was evaluated using the telemetry data of the SMAP (Soil Moisture Active Passive) satellite and MSL (Mars Science Laboratory). [PERSON] et al. [8] proposed a cluster-based anomaly threshold determination method in another paper. Experiments were conducted on satellite telemetry data, showing that the proposed method outperformed the autoencoders. Probabilistic clustering approaches, such as the work presented by [PERSON] et al. [9], are also intensely interesting. In this work, the authors proposed a data-driven health monitoring and anomaly detection method for artificial satellites based on probabilistic dimensionality reduction and clustering.