# Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem
## 1 Introduction  
This paper offers a collection of instances of the OINTS (Offline Nanosatellite Task Scheduling) optimization problem, which aims to schedule the activation and deactivation times of payloads on a nanosatellite. Nanosatellites are revolutionizing the space industry by providing a cost-effective and versatile platform for various space applications, from remote sensing and earth observation to communication and technology demonstrations [1, 2, 3]. They are a vigorous area of research, and more recent methodologies address mission planning optimization, which requires real and repeatable input data for scientific exploration and benchmarks [4, 5, 6, 7, 8]. In order to improve power management, guarantee Quality of Service (QoS), and gather more data throughout the satellite mission lifetime, these studies have investigated several strategies to improve the scheduling of jobs for nanosatellites. The solutions discussed aim at effective resource management while taking into account variables such as available power, task characteristics, and satellite resources. In [5], a battery model and fuzzy constraints have been added to a mathematical integer programming approach to maximize the number of jobs that can be completed in space while promoting energy-efficient management and extending battery life. For large cases of the problem, a branch-and-price method using dynamic programming has been presented as part of a solution technique that uses the Dantzig-Wolfe decomposition [4]. In [8], time synchronization, mission quality of service, and shared resource management have been included in a comparison ofcontinuous-time versus discrete-time approaches. These methods provide decision makers more freedom and alternatives when it comes to planning the work that needs to be carried out by nanosatellites. The instances used in these studies were created expressly to benchmark the performance of various solution methodologies to the OINTS problem. Nevertheless, the methodology for creating such instances was never published before, which is the intent of this paper. The instances are modeled after the FloripaSat-I mission, a CubeSat that has been validated in orbit [9]. This spacecraft format has recently gained significant market share given its cost-effective and simpler deployment [10]. Based on the orbital parameters of the FloripaSat-I mission, realistic input data for power harvesting calculations were used to construct the examples presented in this paper. Furthermore, by using the generation procedure based on random data described here, the authors could scale the difficulty and create instances on demand. It is necessary to emphasize that sharing examples for a particular subject is essential for scientific study repeatability. Other researchers can use the same set of instances to evaluate their approaches and compare their results with those reported in the publication if the instances are made publicly available. This enables a fair comparison of various solutions and contributes to establishing a benchmark for the OINTS problem. In addition, having a publicly accessible set of cases for a particular subject might help to improve the field's research by offering a common starting point for new investigations. In order to plan a nanosatellite mission effectively and ensure that the spacecraft can complete its mission objectives, it is crucial to estimate its power budget. The energy input can be computed on a minute-by-minute basis according to orbit and attitude parameters and account for variables such as EPS efficiency, which can impact the amount of available energy for operations. These data are essential for the design phase because they enable engineers to make informed judgments on the amount and type of payloads that can be included on the satellite.