With the rapid development of mobile communication technology and the explosion of data traffic, high capacity communication with high data transmission rate is urgently needed in densely populated areas. Since multibeam antennas are able to increase the communication capacity and support a high data transmission rate, they have attracted a lot of research interest and have been actively investigated for base station applications. In addition, since multi-beam antennas based on Butler matrix (MABBMs) have the advantages of high gain, easy design and low profile, they are suitable for base station applications. The purposes of this paper is to provide an overview of the existing MABBMs. The specifications, principles of operation, design method and implementation of MABBMs are presented. The challenge of MABBMs for 3G/LTE/5G/B5G base station applications is discussed in the end.

Smart refueling can reduce costs and lower the possibility of an emergency. Refueling intelligence can only be obtained by mining historical refueling behaviors from big data; however, without devices, such as fuel tank cursors, and cooperation from drivers, these behaviors are hard to detect. Thus, detecting refueling behaviors from big data derived from easy-to-approach trajectories is one of the most efficient retrieve evidences for research of refueling behaviors. In this paper, we describe a complete procedure for detecting refueling behavior in big data derived from freight trajectories. This procedure involves the integration of spatial data mining and machine-learning techniques. The key part of the methodology is a pattern detector that extends the naive Bayes classifier. By drawing on the spatial and temporal characteristics of freight trajectories, refueling behaviors can be identified with high accuracy. Further, we present a refueling prediction and recommendation system to show how our refueling detector can be used practically in big data. Our experiments on real trajectories show that our refueling detector is accurate, and the system performs well.