## 5 Conclusions  
This study investigated the interference between new 5G DL and FSS-ES signals in the frequency range of 3.4-4.2 GHz. Measurements and MC simulations were used for the SEAMCAT interference analysis. No adjacent interference was recorded between the 5G-BS and the FSS-ES because the transmission power level dropped due to filters and masks, which is extremely convenient. Co-channel interference necessitates a protection distance of at least 35 km to work properly. However, the protection distance can be reduced by increasing the elevation angle of 5G-BS. This finding is especially interesting because it allows some scenarios in which the protection distance can be reduced (if no other option is available). Interference models between 5G-BS and FSS-ES involving GBs, antenna configuration, and FSS-ES bandwidths were optimised for potential deployment. Coexistence issues between 5G-BS and FSS-ES can benefit from these interference analysis methods and results. The coexistence effect may be approximated up to an unlimited frequency band distance by using the co-channel interference model. An ANN-LM interference model was presented. An innovative interference model between FSS-ES and 5G-BS based on different distances with highly accurate ANN-LM prediction algorithms and small effort was developed. This interference might be used to deploy and implement 5G-NR and satellite communications. Machine learning can be applied to previously simulated data in new contexts or at other frequencies in the future. The design and development of sophisticated analysis processes can be performed with the assistance of ANNs. Furthermore, to increase comparability and broaden our understanding of interference mitigation techniques in 5G networks, future research should consider incorporating data from a variety of geographical regions to account for regional deployment and environmental differences. It is essential to evaluate a variety of 5G deployment scenarios and the influence of environmental factors on interference patterns. To facilitate such comparisons, standard evaluation metrics should be developed. In addition, longitudinal studies can shed light on performance over the long term. By adhering to these recommendations, researchers can obtain a comprehensive understanding of the efficacy and adaptability of proposed techniques across various network environments and geographic locations.  
[PERSON] and [PERSON] developed the concept for the study, while [PERSON] conducted the research and drafted the initial draught. [PERSON], [PERSON], [PERSON], E.L., F.M.I.N., F.F.F., Y.S.H., M.J.S., F.S., H.A. and [PERSON] all contributed resources. This text's manuscript has been reviewed and accepted by all authors. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge IRENE (PID2020-115323 RB-C33) (MINECO/AEI/FEDER, UE); MFOC: Madrid Flight on Chip--Innovation Cooperative Projects Comunidad of Madrid--HUBS 2018/Madrid Flight on Chip. In addition, this work was partly funded by IGNITE: Interference Modeling for 5G and FSS Coexistence at mmWave with Climate Change Considerations in the Tropical Region (FRGS/1/2021/TKO/UPM/01/1) and BIDANET: Parametric Big Data Analytics over Wireless Networks (UPM.RMC.800-3/3/1/GPB/2021/9696300, Vot No.: 9696300) for the financial assistance in the measurement campaign. This project is a collaboration with Rohde & Schwarz Malaysia for consultations on the measurement analysis. And in part by Grant ICARUS, PID2020-113240 RB-I00 funded by MCIN/AEI/10.13039/501100011033.