## 6 Space-Segment Optimization Aspects
### Optimization Metrics Related to the Space Segment  
The space-segment optimization, as mentioned above, includes all optimization steps that take place within the satellite constellation, and such optimization steps can happen in multiple layers. An example analogy is the network optimization in [15]. However, in the broadest meaning of the term, the space-segment optimization refers to the parameters of the constellation, see, e.g., Table 5 (and thus named 'constellation optimization'). The aim of constellation optimization is to distribute multiple satellites with similar types of functions into similar or complementary orbits in order to accomplish specific tasks under shared control. It is possible to classify the optimization objectives that fall into the chosen application area into the following three categories: (i) coverage, (ii) cost, and (iii) performance (see also the discussion in Section 3). As the space segment is the first LEO segment for which we discuss specifics, we will provide the commonly used metrics following this categorization. For the ground and user segments, our focus will be solely on presenting the metrics rather than where they fit in the categorization. The coverage metric, which refers to the ground coverage of the satellites from the perspective of the space segment, enables whether or not the target application (e.g., communication, positioning, or sensing) is able to operate in a stable manner. It is traditionally calculated geometrically, and ensuring its continuity is optimized via secondary metrics, such as minimizing the satellite revisit time or maximizing daily visibility time, with a focus on local or global maximal coverage, depending on the application goals. We have already provided an example model for ground coverage calculation in Equation (1). The choice of a suitable performance metric is very important and, naturally, application-dependent. For the space segment, some of the commonly known metrics are the Dilution of Precision (DOP)-based metrics, such as GDOP [139] and Time Dilution of Precision (TDOP) [140], and the system level metrics, such as \(C/N_{0}\), SNR and Signal-to-Interference-plus-Noise Ratio (SINR). It should be noted that such metrics are not exclusive to the space segment. In order to avoid repetition, we divide the metrics between the three LEO segments (see Figure 1) when a particular metric is used in multiple segments. For example, as DOP metrics, such as GDOP and TDOP, are widely used in user-segment optimization, we will describe those metrics in Section 8, while the other metrics such as SNR, SINR, and \(C/N_{0}\), used widely for all three segments, are presented here. The most basic definition of SNR is the ratio of signal power to noise power, often expressed in decibels, as seen in Equation (10). Here, \(P\). denotes the power of the signal or noise in watts, measured at equivalent times and within the same system bandwidth. \[\mathrm{SNR}_{dB}=10\log_{10}(\frac{P_{signal}}{P_{noise}}) \tag{10}\]SINR is a very similar metric to SNR. It is often expressed in decibels (dB), as in Equation (11). Similarly to Equation (10), \(P\). denotes power in watts. \[\mathrm{SINR}_{dB}=10\log_{10}(\frac{P_{signal}}{P_{interference}+P_{noise}}) \tag{11}\]  
\(C/N_{0}\), while close to SNR, is a much more commonly used metric for applications regarding LEO constellations. It is usually expressed in decibel-Hertz and refers to the ratio of the carrier power \(P_{carrier}\) to the noise power \(P_{noise}\) per unit bandwidth. It is calculated as in Equation (12), where notation from Equations (10) and (11) carries over in terms of units and definitions. \[C/N_{0 dB-Hz}=10\log_{10}(\frac{P_{carrier}}{P_{noise}}) \tag{12}\]  
Minimizing the costs is a logical objective for any commercial service; for a straightforward example, using a minimal number of satellites operating at low altitudes can increase the real-life feasibility of applications, as those parameters directly reduce the manufacturing and launch costs.