Vehículo Aéreo No Tripulado (VANT) una Herramienta para la Conservación de las Áreas Silvestres Protegidas del Paraguay (ASPs)

La falta de recursos humanos y logísticos dificultan en gran medida las actividades de monitoreo de Áreas Silvestres Protegidas tanto en áreas públicas como privadas. La utilización de vehículos aéreos no tripulados (VANT) como herramientas para el monitoreo podría complementar el trabajo de los guardaparques en las labores de patrullaje, especialmente en áreas remotas o de difícil acceso.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI

An Overview of the BIRDS-4 Satellite Project and the First Satellite of Paraguay

The Joint Global Multi-National Birds or BIRDS program, is a multinational small satellite project led by Kyushu Institute of Technology (Japan). The BIRDS program gives to non-space faring nations the opportunity to design, integrate, build, test, launch, and operate their country’s first satellite. This paper focuses on BIRDS-4, a constellation of three 1U CubeSats belonging to Paraguay (GuaraniSat-1: Paraguay’s first satellite), Philippines (Maya-2), and Japan (Tsuru). BIRDS-4 members are graduate students enrolled in Space Engineering International Course (SEIC) at Kyushu Institute of Technology. This constellation will execute nine missions such as Earth Imaging, Total Ionization Dose measurements, evaluation of Perovskite solar cell performance in space, and the use of “Satellite Structure as Antenna” for CW transmission. More importantly, the satellite will conduct a Store-and-Forward mission to test the technical viability of the chosen hardware, such that if proven successful, will be used for future satellite missions to gather data in remote areas. The satellites were launched on February 22, 2021 and deployed from the International Space Station on March 14, 2021.This paper describes the background, missions, stakeholders, lessons learned, and initial operational results after the deployment from ISS. Finally, this paper shall discuss the significance of this satellite for Paraguay, which has been a non-space-faring nation up until now

Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator’s experience. Therefore, telemetry data analysis is less reliable, considering the data’s complexity. This paper presents a Machine Learning (ML) approach to detecting anomalies in solar panel systems. The main challenge inherited from CubeSat is its capability to perform onboard inference of the ML model. Nowadays, several simple yet powerful ML algorithms for performing anomaly detection are available. This study investigates five ML algorithm candidates, considering classification score, execution time, model size, and power consumption in a constrained computational environment. The pre-processing stage introduces the windowed averaging technique besides standardization and principal component analysis. Furthermore, the paper features the background, bus system, and initial operational data of BIRDS-4, a constellation made of three 1U CubeSats released from the International Space Station in March 2021, with a ML model proposal for future satellite missions

Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator’s experience. Therefore, telemetry data analysis is less reliable, considering the data’s complexity. This paper presents a Machine Learning (ML) approach to detecting anomalies in solar panel systems. The main challenge inherited from CubeSat is its capability to perform onboard inference of the ML model. Nowadays, several simple yet powerful ML algorithms for performing anomaly detection are available. This study investigates five ML algorithm candidates, considering classification score, execution time, model size, and power consumption in a constrained computational environment. The pre-processing stage introduces the windowed averaging technique besides standardization and principal component analysis. Furthermore, the paper features the background, bus system, and initial operational data of BIRDS-4, a constellation made of three 1U CubeSats released from the International Space Station in March 2021, with a ML model proposal for future satellite missions