# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 3 Findings  
This section summarizes the findings on the suitability of the proposed on-line learning to adapt in different scenarios, on tuning of the parameters, as well as the overall energy savings and the parameters tradeoffs. For evaluation of the transmission scheduling, the goal is to (1) demonstrate the ability of the algorithm to learn patterns behind transmission success probabilities, and (2) to make apparent how the performance gets affected by the main variables, such as the overpass duration, angle and the RF noise. Simulations were conducted for the proposed algorithm under all three preference models in Table 5. The power data published by Swarm, as per Table 1 forms the basis of the transmitter energy consumption. The effects of random noise were expressed in two ways: noise contained in a single bucket, or across all buckets from Table 4, to model the remote and populated locations, respectively. Figures 7-12 show the transmission success and and average time to transmit for three preference models, as training epochs(i.e., the number of times that the learning steps are applied) progress, all parameterized by the discount factor \(\lambda\). For their evaluation, notable is the response to overall RF noise distribution and transmission difficulty. We observe that wider RF noise distributions negatively impact the success, as well as when it is relatively hard (or easy) to transmit, there is less room for the algorithm to make huge improvements in success rate. For example, when there are few good passes, the algorithm often has to make a choice between a mediocre and a bad pass. This is seen in the lower (but still significant) improvement for the first preference model, and lower TX success rates, as there is simply no room for much improvement. Figure 7: Simulated results for **preference model 1** and random noise within **1 bucket**. Figure 8: Simulated results for **preference model 1** and random noise across **all buckets**. Figure 9: Simulated results for **preference model 2** and random noise within **1 bucket**. Figure 11: Simulated results for **preference model 3** and random noise within **1 bucket**. Figure 10: Simulated results for **preference model 2** and random noise across **all buckets**. Second to note is the difference in response to discount factors \(\lambda\). In the first preference model, less likely to succeed, differing discount factors made little difference to \(r_{attempt}\), as the penalty for long wait for a decent pass outweigh the cost of other poorer options. For the other two preference models, however, the discount factors closer to \(1\) did see significantly higher average times to attempt transmission. For certain applications, an average time to transmit of 24 h may be unacceptable. If it is necessary to keep \(r_{attempt}\) higher, using a lower value of \(t_{max}\) or a value of \(\lambda\) closer to 0 would lower the average time to transmit. A lower value of \(t_{max}\) in particular forces the algorithm to only consider satellite passes within a more constrained time frame. Regarding noise distribution, Figures 7-12 also compare two noise models, where the RF noise values were drawn uniformly from all noise buckets or from one bucket only. This second model was explored as remote sites have largely consistent RF background noise levels. The primary impact is that the algorithm consistently learns faster and converges to higher success rates when exposed to RF noise associated with remote locations. For example, the algorithm learned in 1000 epochs for preference model 2 and fully random noise what it learned in under 300 epochs for same-bucket noise. The most permissive conceptual preference model 3, Figures 11 and 12 shows that a high baseline success rate is rapidly improved through fewer epochs of training, especially as RF noise is constrained in a single bucket, as the expectation for remote, wilderness areas noise is not to occupy all buckets.