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Demodulation is a key technology for the enhanced Loran (eLoran) system to achieve positioning and timing, and it affects the final performance of the system. Based on the traditional matched correlation algorithm, this paper proposes a new matched correlation demodulation method with notch processing. Furthermore, by combining it with the pattern modulation of the eLoran system, the matched correlation integrate notch demodulation method is further modified to improve demodulation performance. Firstly, the data link of the eLoran system is introduced in detail, including the encoding and modulation processes, the influencing factors of received signals, and the evaluation methods in the demodulation process. Secondly, on the basis of the principle of the matched correlation (MC) demodulation algorithm, a matched correlation demodulation algorithm integrating notch processing (MC-NF) and a demodulation correlation algorithm combined with modulation patterns (PMC-NF) are proposed. And, an analysis of the key factors affecting demodulation performance is given. Next, the demodulation performance of the mentioned algorithms under the conditions of random noise, skywave, and in-band continuous wave interference is calculated in detail. A large number of experimental results show that notch processing performs excellently in suppressing random noise and in-band continuous wave interference, and it can greatly improve the demodulation performance of the traditional matched correlation algorithm. Moreover, PMC-NF is superior to MC-NF; approximately 2.8 dB at decoding the critical point.

Recently, there are many efforts attempting to learn useful policies for continuous control in visual reinforcement learning (RL). In this scenario, it is important to learn a generalizable policy, as the testing environment may differ from the training environment, e.g., there exist distractors during deployment. Many practical algorithms are proposed to handle this problem. However, to the best of our knowledge, none of them provide a theoretical understanding of what affects the generalization gap and why their proposed methods work. In this paper, we bridge this issue by theoretically answering the key factors that contribute to the generalization gap when the testing environment has distractors. Our theories indicate that minimizing the representation distance between training and testing environments, which aligns with human intuition, is the most critical for the benefit of reducing the generalization gap. Our theoretical results are supported by the empirical evidence in the DMControl Generalization Benchmark (DMC-GB).

The Connectivity Augmentation Problem (CAP) together with a well-known special case thereof known as the Tree Augmentation Problem (TAP) are among the most basic Network Design problems. There has been a surge of interest recently to find approximation algorithms with guarantees below 2 for both TAP and CAP, culminating in the currently best approximation factor for both problems of 1.393 through quite sophisticated techniques. We present a new and arguably simple matching-based method for the well-known special case of leaf-to-leaf instances. Combining our work with prior techniques, we readily obtain a [Formula omitted]-approximation for Leaf-to-Leaf CAP by returning the better of our solution and one of an existing method. Prior to our work, a [Formula omitted]-guarantee was only known for Leaf-to-Leaf TAP instances on trees of height 2. Moreover, when combining our technique with a recently introduced stack analysis approach, which is part of the above-mentioned 1.393-approximation, we can further improve the approximation factor to 1.29, obtaining for the first time a factor below [Formula omitted] for a nontrivial class of TAP/CAP instances.