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Near-space high-altitude balloon-based platforms have a series of advantages and provide superior conditions for optical observation. In order to ensure the stability of the optical axis of the optical detection load and stable tracking of the target, a near-space high-altitude balloon-based high-precision pointing and tracking system was designed, which can compensate for changes in the pitch angle and azimuth angle of the platform during flight. The system includes a primary platform stable pointing system and a secondary precise tracking system. In the finished flight experiment, the primary platform pointing system and secondary precise tracking system on the balloon-based observation platform worked normally, providing a guarantee for the coronagraph’s stable tracking and detection of the sun. The primary platform pointing system can realize ±1° pointing accuracy, and the simulated accuracy of the secondary precise tracking system is 4″, which guaranteed that the coronagraph obtained more than 20,000 images. In subsequent works, we will upgrade and optimize the whole system and conduct our next flight experiment in the future.

To mitigate carbon emissions during the construction of deep foundation pit support in undulating strata, a theoretical calculation approach was utilized to design and compute the foundation pit support for Qingdao’s Metro Line 4. On this basis, the numerical simulation method is used to optimize the design scheme of foundation pit support with the surface deformation and the stress of the support structure. The results of numerical simulation show that the final ground settlement is 5.26 mm, the maximum horizontal displacement is 0.2836 mm, and the corresponding maximum shear force of the retaining pile is 250 kN, which is obviously superior to the ground settlement of 55 mm, 33 mm, and 1341.03 kN in the theoretical design. The use of simulation software before the construction and support of deep foundation pit excavation can reduce resource waste and achieve low-carbon design while ensuring construction safety.

Much research has focused on classification within a closed set of emitters, while emitters outside this closed set are misclassified. This paper proposes an open-set recognition model based on prototypical networks and extreme value theory to solve the problem of specific emitter identification in open-set scenes and further improve the recognition accuracy and robustness. Firstly, a one-dimensional convolutional neural network was designed for recognizing I/Q signals, and a squeeze-and-excitation block with an attention mechanism was added to the network to increase the weights of the feature channels with high efficiency. Meanwhile, the recognition was improved by group convolution and channel shuffle. Then, the network was trained with the joint loss function based on prototype learning to complete the separation of intra-class signals and the aggregation of inter-class signals in the feature space. After the training, the Weibull model was fitted for pre-defined classes by incorporating the extreme value theory. Finally, the classification results were obtained according to the known classes and the Weibull model, effectively completing the open-set recognition. The simulation results showed that the proposed model had a higher recognition performance and robustness compared with other classical models for signals collected from five ZigBee and ten USRP 310 devices.

In the recent decade, many research efforts in robotic society have considered motion planning for maintaining connectivity in networked robotic system (NRS) by exploiting robotic autonomous mobility. On the other hand, cognitive radio (CR) in the communication society aims at fully exploiting the spectrum in a wireless network, while the motion planning is seldom considered, as a wireless device itself may not decide where to go. In this article, joint communication–motion planning (JCMP) is proposed to boost the capability of NRS by exploiting both the adaptive communications and mobility control of autonomous robots. Specifically, we propose a JCMP framework for NRS, which aims at jointly exploiting the degree-of-freedom in mobility, space, time, frequency and power dimensions from both the motion and communication components. Afterward, we design and evaluate JCMP in a conventional and a CR-relay-assisted robot system, which shows the capability of JCMP in improving the performance of NRS. Finally, we summarize the proposed JCMP-enabled NRS framework and provide a series of future research directions.