The Evaluation of Nanosatellite Missions: Statistical Analysis of Missions for Payloads

CubeSats have become a dominant platform for conducting cost-effective space missions in recent years. This study presents a comprehensive statistical analysis of 2806 CubeSat-class missions launched between 1998 and 2024. The missions are classified into five primary categories – communication, Earth observation, science, education, and technology demonstration-based on their payload functions. Each satellite was further evaluated by form factor and operational status to determine mission success rates. The results show that Earth observation accounts for 39% of all CubeSat missions, with the 3U form factor being the most commonly used and most successful in this domain. Educational missions are significantly underrepresented in launch frequency. Scientific missions, though more frequent, remain below Earth observation and communication in terms of mission volume. A matrix linking mission types, payload categories, form factors, and success outcomes is presented to guide future CubeSat design strategies. A detailed case study of Automatic Dependent Surveillance – Broadcast (ADS-B) payloads was conducted, revealing a 96.9% success rate across over 90 missions. Most of these satellites maintained operations for more than 1000 days, highlighting the reliability and efficiency of ADS-B as a cross-functional payload applicable to communication, Earth observation, and technology demonstration. The findings in this paper provide actionable insights for satellite developers, researchers, and mission planners. By correlating payload types with form factor performance and mission outcomes, this work offers a data-driven framework for optimizing CubeSat mission design and payload integration.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3607970
Primary Topic
Spacecraft Design and Technology
Type
article
Field-Weighted Citation Impact
0.00
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article

The Evaluation of Nanosatellite Missions: Statistical Analysis of Missions for Payloads

Amirkhan Temirbayev, Nursultan Meirambekuly, Peng Li, А. N. Beisen et al.
IEEE Access
Spacecraft Design and Technology
article

The Evaluation of Nanosatellite Missions: Statistical Analysis of Missions for Payloads

Amirkhan Temirbayev, Nursultan Meirambekuly, Peng Li, А. N. Beisen, Sabyrzhan Orynbassar, Nursultan Uzbekov, Zhanzira Geroyeva, Lazzat Abdizhalilova, Darkhan Tokmyrzayev
article en

Abstract

CubeSats have become a dominant platform for conducting cost-effective space missions in recent years. This study presents a comprehensive statistical analysis of 2806 CubeSat-class missions launched between 1998 and 2024. The missions are classified into five primary categories – communication, Earth observation, science, education, and technology demonstration-based on their payload functions. Each satellite was further evaluated by form factor and operational status to determine mission success rates. The results show that Earth observation accounts for 39% of all CubeSat missions, with the 3U form factor being the most commonly used and most successful in this domain. Educational missions are significantly underrepresented in launch frequency. Scientific missions, though more frequent, remain below Earth observation and communication in terms of mission volume. A matrix linking mission types, payload categories, form factors, and success outcomes is presented to guide future CubeSat design strategies. A detailed case study of Automatic Dependent Surveillance – Broadcast (ADS-B) payloads was conducted, revealing a 96.9% success rate across over 90 missions. Most of these satellites maintained operations for more than 1000 days, highlighting the reliability and efficiency of ADS-B as a cross-functional payload applicable to communication, Earth observation, and technology demonstration. The findings in this paper provide actionable insights for satellite developers, researchers, and mission planners. By correlating payload types with form factor performance and mission outcomes, this work offers a data-driven framework for optimizing CubeSat mission design and payload integration.

IEEE AccessVol. 13
Northwestern Polytechnical University (CN), Al-Farabi Kazakh National University (KZ)
Openalex Percentile: Top 67%
Spacecraft Design and Technology
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