Mind-map generation aims to process a document into a hierarchical structure to show its central idea and branches. Such a manner is more conducive to understanding the logic and semantics of the document than plain text. Recently, a state-of-the-art method encodes the sentences of a document sequentially and converts them to a relation graph via sequence-to-graph. Though this method is efficient to generate mind-maps in parallel, its mechanism focuses more on sequential features while hardly capturing structural information. Moreover, it's difficult to model long-range semantic relations. In this work, we propose a coreference-guided mind-map generation network (CMGN) to incorporate external structure knowledge. Specifically, we construct a coreference graph based on the coreference semantic relationship to introduce the graph structure information. Then we employ a coreference graph encoder to mine the potential governing relations between sentences. In order to exclude noise and better utilize the information of the coreference graph, we adopt a graph enhancement module in a contrastive learning manner. Experimental results demonstrate that our model outperforms all the existing methods. The case study further proves that our model can more accurately and concisely reveal the structure and semantics of a document. Code and data are available at https://github.com/Cyno2232/CMGN.

Wireless communications aided by unmanned aerial vehicles (UAVs) have emerged as a promising technology for serving numerous terminal devices of the Internet of Things. To support such a broad range of emerging applications, millimeter-wave (mm-Wave) is an effective solution because it has the characteristics of high transmission efficiency, wide frequency band, and very low latency. Meanwhile, a UAV-assisted communication system also requires a global positioning system reception (1.575 GHz) and ground control unit (2.4 GHz). This article investigates a highly integrated multiband antenna design to operate at the microwave and mm-Wave bands for robust and flexible UAV communications. Each antenna operating band utilizes the technology of a substrate-integrated waveguide cavity-backed antenna. By strategically engineering an mm-Wave antenna into a 2.4 GHz band antenna to share the aperture, besides showing high aperture reuse efficiency with a large frequency ratio, the proposed antenna has also maintained the geometrical advantages of compactness, planar configuration, and ease of integration with other circuits. Furthermore, the proposed antenna can provide flexible radiation patterns with upper/lower-space signal coverage according to the mission demands of the UAV. Experimental results of the prototype show that the antenna can fully cover the 1.575 GHz, 2.4 GHz, and fifth-generation mm-Wave bands of n257, n258, and n261. Multiple bands and stable radiation characteristics indicate the proposed antenna's suitability for deploying a UAV-supported wireless network.