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.

Fiber nonlinearity is one of the most important limiters of capacity in coherent optical communications. In this paper, we review two nonlinear compensation methods: digital backward propagation (BP) and nonlinear electrical equalizer (NLEE) based on the time-domain Volterra series. These compensation algorithms are implemented in a single-channel 50 Gb/s coherent optical single-carrier frequency-division multiplexed (CO-SCFDM) system transmitting over 10 × 80 km of standard single-mode fiber (SSMF).