The practical application of lithium (Li) metal anodes is limited by various issues, especially the uncontrolled growth of dendrites. Herein, we report single-cluster Au uniformly dispersed on activated carbon cloth as a model to control directed Li deposition and suppress Li dendrite growth. Single-cluster Au can induce Li plating with zero nucleation overpotential, due to an optimized electron field distribution, as validated by DFT calculations. Stable and reversible Li metal anodes were achieved by virtue of single-cluster Au, exhibiting excellent cycling stability for more than 900 cycles with almost 100% coulombic efficiency and deeply cyclable behavior at a high areal capacity of up to 20 mA h cm⁻². Benefiting from guided Li deposition and maximum Li-utilization, we found that Li metal batteries with high-loading cathodes also deliver an outstanding electrochemical performance, showing promise for future rechargeable Li metal battery technologies.

The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning the statistical or spectral distributions of data from diverse scenes. However, many previous studies have overlooked the potential benefits of incorporating spatial topological information from hyperspectral imagery, which could provide a more accurate representation of the inherent data structure in HSIs. To overcome this issue, we introduce an innovative approach for cross-scene HSI classification, founded on hierarchical prototype graph alignment. Specifically, this method leverages prototypes as representative embedded representations of all samples within the same class. By employing multiple graph convolution and pooling operations, multi-scale domain alignment is attained. Beyond statistical distribution alignment, we integrate graph matching to effectively reconcile semantic and topological information. Experimental results on several datasets achieve significantly improved accuracy and generalization capabilities for cross-scene HSI classification tasks.

This article presents a newly invented emergency positioning system deployed on a satellite communication on-the-move (SOTM) platform, which can extend the SOTM communication function to the positioning function. Inherent to the SOTM system's high reliability, this emergency positioning system can effectively estimate the vehicle's position by fusing a geosynchronous orbit (GEO) satellite Doppler frequency observation and inertial navigation system (INS) of a SOTM-mounted moving vehicle when the global navigation satellite system (GNSS) is unavailable. It is a great challenge to use GEO Doppler frequency measurement with weak observability to correct the INS's cumulative error to estimate the position of the vehicle. In this article, the traditional inertial measurement unit (IMU) pre-integration model is improved to meet the needs of large-scale and high-precision navigation. In addition, we use graph optimization (GO) to estimate all states in the trajectory, which can effectively improve the observability of the error and reduce the nonlinear influence. We evaluated the system on a Ku-band SOTM platform, and the experimental results indicate that the system can achieve a convergent positioning accuracy of tens of meters. This method can provide emergency positioning in the case of GNSS interference.

A L-band solid-state power amplifier (SSPA) for future payloads has been implemented using GaN Technology in this paper.The main performance of the GaN SSPA is superior to the GaAs SSPA.This paper propose device level redundancy of GaN along with various techniques such as gain compensation,Automatic level control and different parameter telemetry.The output power of SSPA is greater than 46dBm(20W), and efficiency achieves 50% at L band with an associated power gain of 59dB over the temperature range from -40 to +70 degC.The SSPA has the dimensions of 200mm× 130mm×54mm with a weight of 1.5kg.At the same time, the thermal design and Simulation of SSPA are carried out, and the reliability test is completed.

Hyperspectral anomaly detection (HAD) refers to a method of identifying abnormal targets through the differences in spectral separabilities of anomaly versus background clutter. It plays a significant role in fields such as commercial agriculture, for instance, in pest and disease monitoring and environmental monitoring. Collaborative representation detector (CRD) is a classic hyperspectral anomaly detection method. However, by constructing a sliding dual window, it leads to a high computational complexity and thus takes a relatively long time. In response to the deficiencies existing in that CRD method, we propose a method that first extracts extended morphological profiles (EMP) and then uses the obtained feature images to construct K-means CRD (EMPKCRD). This method performs window reconstruction on complex hyperspectral background pixels through the K-means clustering algorithm to separate abnormal pixels with similar features and obtain the background dictionary matrix. The method leverages the observation that background pixels can be effectively approximated by a linear combination of their spatially adjacent pixels, whereas anomalous pixels, due to their distinct nature, cannot be similarly reconstructed from their local neighborhood. This fundamental disparity in reconstructibility is then exploited to separate anomalies from the background. Then, anomaly detection can be carried out on this matrix faster, avoiding the high computational complexity caused by the use of a sliding dual window. Through comparative simulation experiments with seven widely used algorithms at present on three real-world datasets, the empirical evaluations validate that this method has excellent performance while exhibiting a favorable balance between detection precision and operational speed.