Nanosatellites are proliferating as low-cost dedicated sensing systems with lean development cycles. Kyushu Institute of Technology and collaborators have launched a joint venture for a nanosatellite mission, VERTECS. The primary mission is to elucidate the formation history of stars by observing the optical-wavelength cosmic background radiation. The VERTECS satellite will be equipped with a small-aperture telescope and a high-precision attitude control system to capture the cosmic data for analysis on the ground. However, nanosatellites are limited by their onboard memory resources and downlink speed capabilities. Additionally, due to a limited number of ground stations, the satellite mission will face issues meeting the required data budget for mission success. To alleviate this issue, we propose an on-orbit system to autonomously classify and then compress desirable image data for data downlink prioritization and optimization. The system comprises a prototype Camera Controller Board (CCB) which carries a Raspberry Pi Compute Module 4 which is used for classification and compression. The system uses a lightweight Convolutional Neural Network (CNN) model to classify and determine the desirability of captured image data. The model is designed to be lean and robust to reduce the computational and memory load on the satellite. The model is trained and tested on a novel star field dataset consisting of data captured by the Sloan Digital Sky Survey (SDSS). The dataset is meant to simulate the expected data produced by the 6U satellite. The compression step implements GZip, RICE or HCOMPRESS compression, which are standards for astronomical data. Preliminary testing on the proposed CNN model results in a classification accuracy of about 100\% on the star field dataset, with compression ratios of 3.99, 5.16 and 5.43 for GZip, RICE and HCOMPRESS that were achieved on tested FITS image data.

翻译：纳米卫星作为具有精简开发周期的低成本专用传感系统正在快速普及。九州工业大学及其合作者联合发起了一项名为VERTECS的纳米卫星任务。该任务的主要目标是通过观测光学波段宇宙背景辐射来阐明恒星形成历史。VERTECS卫星将配备小口径望远镜和高精度姿态控制系统，以采集宇宙数据供地面分析。然而，纳米卫星受限于其星载存储资源和下行链路传输能力。此外，由于地面站数量有限，卫星任务将面临难以满足任务成功所需数据预算的问题。为缓解此问题，我们提出一种在轨系统，能够对目标图像数据进行自主分类与压缩，从而实现下行链路数据的优先级排序与优化。该系统包含一个搭载树莓派计算模块4的原型相机控制板，用于执行分类与压缩任务。系统采用轻量级卷积神经网络模型对采集的图像数据进行分类与价值判定。该模型设计精简且鲁棒，以降低卫星的计算与存储负荷。模型使用基于斯隆数字巡天数据构建的新型星场数据集进行训练与测试，该数据集旨在模拟6U卫星预期产生的数据。压缩环节采用GZip、RICE或HCOMPRESS压缩算法，这些均为天文数据标准压缩方法。对所提CNN模型的初步测试显示：在星场数据集上分类准确率约达100%，对测试FITS图像数据分别实现3.99、5.16和5.43的GZip、RICE与HCOMPRESS压缩比。