Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, Multi-GSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://github.com/ljl5261/MMM-RS.

Dynamic performance analysis and controller design of wireless power transfer (WPT) systems have always been tough problems because the existing dynamic models of WPT systems have disadvantages, such as high order, incompleteness, and lack of intuition. In this article, a dynamic modeling method based on a singular perturbation theory for a high-order WPT system with a Cuk-based bridgeless rectifier at the receiving side is proposed, which overcomes the disadvantages of the dynamic models mentioned above. In terms of time-scale separation characteristics brought by singular perturbation, a reduced-order model can be derived from the original high-order WPT system model. The proposed model is able to completely present the dynamic behaviors of the whole WPT system precisely. Also, a closed-loop control design method based on the reduced-order model is presented, which greatly simplifies the design process. The experimental results show the great effectiveness of the proposed model for transient state observation, and the output voltage or current of the closed-loop WPT system remains stable despite significant disturbances in load or transfer distance.