Orthogonal frequency division multiplexing (OFDM) has been one of the mainstream technologies in the fields of modern wireless communications. However, its own high peak to average power ratio (PAPR) problem can impair the performance of system, which has greatly restricted its wide applications. Partial transmit sequence (PTS) was proposed for improving the PAPR performance of OFDM systems. But its introduction greatly increases the computational complexity of the system. In this paper, a low-complexity PTS scheme is proposed based on the dominant time-domain samples, and two new metrics for choosing these samples are introduced. In addition, to further reduce the computational complexity, grouping method has been involved in proposed scheme. Simulation results indicate that the proposed low-complexity PTS scheme can provide a perfect PAPR reduction performance with more computational complexity savings.

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Scene classification is one of the most important applications of remote sensing. Researchers have proposed various datasets and innovative methods for remote sensing scene classification in recent years. However, most of the existing remote sensing scene datasets are collected uniquely from a single data source: Google Earth. In addition, scenes in different datasets are mainly human-made landscapes with high similarity. The lack of richness and diversity of data sources limits the research and applications of remote sensing classification. This paper describes a large-scale dataset named "NaSC-TG2", which is a novel benchmark dataset for remote sensing natural scene classification built from Tiangong-2 remotely sensed imagery. The goal of this dataset is to expand and enrich the annotation data for advancing remote sensing classification algorithms, especially for the natural scene classification. The dataset contains 20,000 images, which are equally divided into 10 scene classes. The dataset has three primary advantages: 1) It is large-scale, especially in terms of the number of each class, and the numbers of scenes are evenly distributed; 2) It has a large number of intra-class differences and high inter-class similarity. Because all images are carefully selected from different regions and seasons; 3) It offers natural scenes with novel spatial scale and imaging performance compared with other datasets. All images are acquired from the new generation of Wide-band Imaging Spectrometer of Tiangong-2. In addition to RGB images, the corresponding multi-spectral scene images are also provided. This dataset is useful in supporting the development and evaluation of classification algorithms, as demonstrated in the present study.

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