An air traffic network is a special and complex Spatio-temporal network. What makes it unique is that multi-data sources-including airports, airlines and air routes-spatial dependence and strong temporal dependence in a dynamic environment. In this paper, we use big service to predict the flight departure delay time in air traffic networks. In the local services layer, we use graph sequences to model the Spatiotemporal network from multi-data sources, what is, using graphs to model the spatial dependence, and using sequences to model the temporal dependence. In the domain-oriented services layer, we use graph neural network to embed the graph sequence. We validate the method on an air Spatiotemporal network. Then, we use the embedding to estimate the departure delay time of the flight based on real-time conditions. In the demand-oriented services layer, we design a weighted cross entropy loss function and use a special evaluation to predict the flight departure delay time by the embedding in the domain-oriented services layer. Evaluated through a series of experiments on a real-world data set, we show that the method produces an effective result on the Spatio-temporal network which is substantially better than state-of-the-art alternative task: flight delay estimation. And it performs well in predicting the departure delay time with a total accuracy of 0.87.

In this paper, a novel wideband circularly polarized slot antenna is presented. The slot antenna with four microstrip line feeds orientated to have phases of 0deg, 90deg, 180deg and 270deg, using a feed network comprising a pair of broadband 900 hybrid was found to deliver a measured impedance bandwidth of 77.8% from 1.02 to 2.32 GHz for SWR3dBi. The simulated impedance bandwidth is 89.1% from 1.02 to 2.66 GHz for SWR3dBi. Good agreement is observed between simulation and measurement.

Functional refinement is beneficial to object-oriented (OO) software development, especially for problems with more complex functions. However, the use of functional refinement in OO software development has not received much attention. This paper proposes an enhanced data flow diagram (DFD), called data flow net (DF net), for specifying use-cases through functional decomposition. It proposes a novel approach to complement existing OO software development methods with functional decomposition for realizing use-cases, especially those with more complex functions. In the requirements analysis stage, the proposed approach realizes use-cases through functional refinement and specifies them in DF nets. In the design and implementation stages, it transforms the DF nets systematically and precisely into OO design and implementation. The approach is amenable to automation and a prototype has been developed to support the transformation process. In the development of an OO system, it is seamless to realize some of the use-cases using the proposed approach and the remaining use-cases in the same target system using any existing OO software development methods.

In this paper, the preprocessing method for improving the quality of interferograms is described The method consists of three processing steps - azimuth filtering, range filtering and oversampling. It is shown that the first two steps are effective to eliminate the decorrelation caused by spectrum misalignment in azimuth and range and the oversampling can eliminate the error introduced by the aliasing because of convolution in frequency domain. The validity of the proposed method is verified by ERS SLC data.