# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 2 State of the Art
### Satellite Operations and Management Systems
#### 2.2.1 Operations in Sporadic Connectivity Constellations  
Space operations management encompasses a broad spectrum of activities crucial for the successful planning and execution of satellite missions. As the complexity and scope of space missions continue to evolve, adherence to established standards, recommendations, and best practices becomes paramount to ensure mission success, data integrity, and interoperability across international space entities. The CCSDS is a cornerstone in establishing such standards and guidelines. Formed by leading space agencies and private companies globally, the CCSDS has been instrumental in developing frameworks that streamline space data and information systems. One of its directives is promoting efficient task-scheduling approaches tailored for satellite operations and missions [39]. Traditionally, space missions were usually operated by space agencies. They developed in-house management tools over the years out of the need to optimize the mission's resources. The ESA started an initiative known as APSI [40] to improve the agency's operations by providing an AI development framework for planning and scheduling. NASA has produced several mission planning tools as well: a framework for developing planning and scheduling applications denominated ASPEN [41], a tool for constrained-based temporal planning named EUROPA [42], and a planning toolkit called SPIFe [43]. The GSOC developed the PINTA [44] and PLATO [45] twin tools. The former is an interactive visualization software, and the latter is a mission management library for the automation of satellite operations. Finally, the STScI developed a general planning framework for the Hubble Space Telescope denominated SPIKE [46]. All the mentioned frameworks are specifically tailored for the space missions and methodologies of the different space agencies. Furthermore, most of them were released and abandoned because funding for that project was interrupted, which led to open code that was heavily outdated. In the early 20 th century, there was a boom in commercial satellite constellations for telecommunications, and several commercial mission planning tools were developed to manage satellite operations. The company SHAI developed the D-SpaCPlanS tool for the Iridium constellation [47]. Another example is the planning system used for EUTESAT's satellites [48] or the Hifly, COTS software developed by the company GMV [49]. Similarly, EO operations management is the scheduling for the ground segment of NASA's CYGNSS mission for hurricane detection using FlexPlan, the general mission planning system from GMV. All the tools mentioned are proprietary and non-extendable, and specific software details are unavailable to the general public. Previous authors have presented a survey of satellite operation strategies [50]. Their survey concludes that most approaches use mathematical programming and are solved mainly with heuristics and meta-heuristics. In [51], a task-scheduling model is presented for constrained energy resources in nanosat constellations. This formulation is extended in [52] by applying continuous time methodology to the potential windows for task scheduling. The classical problem of DADSSP for EO missions is presented in [53], in which the mathematical formulation of these types of scheduling problems is described. An example of a linear programming approach for an EO mission is presented in [54] and solved using a MILP solver, which produced better results than the conventional greedy algorithm. In [55], authors describe a clustering algorithm to improve the scheduling efficiency of on-demand tasks with sequencing constraints. The satellite range scheduling problem for ground contacts is described in [56]. Even though centralized methods are most widely used, another way to tackle operations management of a constellation is through a distributed or hybrid approach. These try to overcome the scalability limitations present in centralized systems.