LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer.

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

Gene disruption and characterization of a class V chitin synthase in Botrytis cinerea.

Cell-wall chitin biosynthesis in the phytopathogenic fungus Botrytis cinerea involves 7 classes of chitin synthases. In this study, we disrupted a gene encoding a chitin synthase with a myosin-like motor domain (BcchsV) through Agrobacterium tumefaciens mediated transformation. The resulting mutant was not significantly affected in either growth characteristics or pathogenicity on tomato leaves. Surprisingly, the BcchsV mutant exhibited a 31% (m/m) increase in its chitin content compared with the wild-type strain. In addition, the BcchsV mutant showed increased sensitivity to Calcofluor White and slightly enhanced tolerance to cell-wall disturbing substances and osmosis regulators, including SDS, sorbitol, and NaCl. These results suggest that Bcchs does not play an essential role in the synthesis of cell-wall chitin in B. cinerea. However, disruption of this gene provoked a compensatory mechanism regulating the cellular response to cell-wall damage.