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A microwave absorbing patch (MAP) was prepared by compounding millable polyurethane elastomer (MPU) and carbonyl iron powder (CIP). Surfaces of CIP were modified using silane coupling agents (SCA). The effects of different types and amounts of SCA, the amount of vulcanizing agent and the amount of CIP on the microstructure and mechanical properties were investigated. SCA was found to improve the interface compatibility between MPU and CIP and also reinforced the mechanical properties of MAP. The KH550 was the best modifier, and the optimal dosage of KH550 was 2% of the mass of CIP based on a comparison of tensile properties. The optimal dosage of the vulcanizing agent DDPH was 3% of the mass of MPU. However, a large number of CIP could be filled into MPU. The maximum filling content of CIP increased with the increasing Mooney viscosity of MPU, and the maximum filling content of CIP was 1200% of the mass of MPU. The electromagnetic parameters and reflection loss of MPU absorbers and MPU filled with CIP absorbers were measured in the 1-18 GHz frequency range. The absorbers of MPU filled with CIP had excellent microwave absorbing properties compared with the absorbers of MPU. [ABSTRACT FROM AUTHOR]

A high-quality electron beam with a central energy of 0.56 GeV, an energy spread of 1.2% rms, and a divergence of 0.59 mrad rms was produced by means of a 4 cm ablative-capillary-discharge plasma channel driven by a 3.8 J 27 fs laser pulse. This is the first demonstration of electron acceleration with an ablative capillary discharge wherein the capillary is stably operated in vacuum with a simple system triggered by a laser pulse. This result of the generation of a high-quality beam provides the prospects to realize a practical accelerator based on laser-plasma acceleration. [ABSTRACT FROM AUTHOR]

Most of the existing trackers address the visual tracking problem by extracting an appearance template from the first frame, which is used to localize the target in the current frame. Unfortunately, they typically face the model degeneration challenge, which easily results in model drift and target loss. To address this issue, a novel Template Adjustment Siamese Network (TA-Siam) is proposed in this paper. The proposed framework TA-Siam consists of two simple subnetworks: The template adjustment subnetwork for feature extraction and the classification-regression subnetwork for bounding box prediction. The template adjustment module adaptively uses the feature of subsequent frames to adjust the current template. It makes the template adapt to the target appearance variation of long-term sequence and effectively overcomes model drift problem of Siamese networks. In order to reduce classification errors, the rhombus labels are proposed in our TA-Siam. For more efficient learning and faster convergence, our proposed tracker uses a more effective regression loss in the training process. Extensive experiments and comparisons with trackers are conducted on the challenging benchmarks including VOT2016, VOT2018, OTB50, OTB100, GOT-10K, and LaSOT. Our TA-Siam achieves state-of-the-art performance at the speed of 45 FPS.