This paper describes the orbit utilization increase by virtue of side-lobe reduction in a homogeneous geostationary satellite link. The antenna side-lobe envelope may conveniently by expressed by next two cases: the slope of the side-lobe envelope is enlarged from α to α' (Case ), and the side-lobe level is reduced by a uniform factor of β (Case ). If the total interference noise temperature for the system with equal spacing θ is assumed to be equal to that for the system with shortened spacing owing to the side-lobe reduction, the relationship between orbit utilization increase U and antenna gain function can be obtained by a straight forward way. In this paper, U is given for the cases where side-lobes are reduced at, () earth stations, () satellites, and () earth stations and satellites. Numerical examples are given assuming the orbit spacing θ of 3, earth antenna beamwidth of 0.16, and satellite antenna beamwidth of 1.0. It is quantitatively concluded that the side-lobe reduction is very effective for increasing the orbit utilization.

The rise of next-generation logistics systems featuring autonomous vehicles and drones has brought to light the severe problem of Global navigation satellite system (GNSS) location data spoofing. While signal-based anti-spoofing techniques have been studied, they can be challenging to apply to current commercial GNSS modules in many cases. In this study, we explore using multiple sensing devices and machine learning techniques such as decision tree classifiers and Long short-term memory (LSTM) networks for detecting GNSS location data spoofing. We acquire sensing data from six trajectories and generate spoofing data based on the Software-defined radio (SDR) behavior for evaluation. We define multiple features using GNSS, beacons, and Inertial measurement unit (IMU) data and develop models to detect spoofing. Our experimental results indicate that LSTM networks using ten-sequential past data exhibit higher performance, with the accuracy F1 scores above 0.92 using appropriate features including beacons and generalization ability for untrained test data. Additionally, our results suggest that distance from beacons is a valuable metric for detecting GNSS spoofing and demonstrate the potential for beacon installation along future drone highways.