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Single-image super-resolution (SISR) has experienced vigorous growth with the rapid development of deep learning. However, handling arbitrary scales ( e.g., integers, non-integers, or asymmetric) using a single model remains a challenging task. Existing super-resolution (SR) networks commonly employ static convolutions during feature extraction, which cannot effectively perceive changes in scales. Moreover, these continuous-scale upsampling modules only utilize the scale factors, without considering the diversity of local features. To activate more information for better reconstruction, two plug-in and compatible modules for fixed-scale networks are designed to perform arbitrary-scale SR tasks. Firstly, we design a Scale-aware Local Feature Adaptation Module (SLFAM), which adaptively adjusts the attention weights of dynamic filters based on the local features and scales. It enables the network to possess stronger representation capabilities. Then we propose a Local Feature Adaptation Upsampling Module (LFAUM), which combines scales and local features to perform arbitrary-scale reconstruction. It allows the upsampling to adapt to local structures. Besides, deformable convolution is utilized letting more information to be activated in the reconstruction, enabling the network to better adapt to the texture features. Extensive experiments on various benchmark datasets demonstrate that integrating the proposed modules into a fixed-scale SR network enables it to achieve satisfactory results with non-integer or asymmetric scales while maintaining advanced performance with integer scales.

Mobile satellite networks are now being utilized to provide complementary service coverage to their terrestrial counterparts. The migration from voice-dominated service delivery to a data-centric environment implies that the choice of one particular access network over another will need to take into account many different criteria, which previously need not have been considered. For this reason, it is important that a suitable decision making algorithm is designed to ensure that all of the different characteristics of the system are taken into account when making the segment selection for a particular service delivery. In this paper, a segment selection algorithm based on the fuzzy multiple objective decision making algorithm is presented. This algorithm is analyzed to show how the overall performance of a multi-access segment network can be improved by varying the relative importance of certain service and network characteristics.