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We have studied the laser pulse shape dependence of the conversion efficiency of λ = 1.03 μm laser pulse energy into 13.5 nm extreme ultraviolet (EUV) emission from a Sn laser-produced plasma. Laser pulses of arbitrary temporal shape ranging from hundreds of picoseconds to several nanoseconds were generated using a programmable pulse synthesizer based on a diode-pumped chirped pulse amplification Yb: YAG laser. Measurements show that the conversion efficiency favors the use of nearly square pulses of duration longer than 2 ns, in agreement with hydrodynamic/atomic physics simulations. A 35% increase in conversion efficiency was obtained when Q-switched pulses were substituted by square pulses of a similar duration. Experiments conducted irradiating a Sn target with a sequence of two time-delayed 250 ps pulses showed a 30 percent increase in the EUV yield respect to a single pulse of the same total energy when the pulse separation was optimum at 2.1 ns. This suggests that re-heating of the plasma with delayed laser pulses could be used to improve the EUV yield. The spectroscopic characterization of EUV emission and in-band EUV images that characterize the source size are also presented.

As the resource crisis and environmental pollution become much dangerous, the manufacturing industry must maintain its competitiveness while achieving sustainable development. Remanufacturing to produce products needed for economic growth under the condition of saving resources and energy is an effective way to build a resource-saving and environment-friendly society. The unreasonable disposal of waste auto parts not only wastes a lot of resources but destroys the ecological environment and economic development. Clarifying the influence of influencing factors on purchasing behavior is expected to be widely promoted in the remanufacturing industry and promote the sustainable development of China's remanufacturing industry. In nonparametric regression, how to reduce the dimension of the independent variable vector and extract the sufficient information is a significant problem. Based on the research of the remanufactured automobile engine's product characteristics, combined with slice inverse regression method, this paper identifies the influencing factors of consumers' purchase of remanufactured parts and constructs a theoretical model of consumer acceptance of remanufactured parts

This paper studies the UAV-enabled integrated sensing and communication (ISAC), in which UAVs are dispatched as aerial dual-functional access points (APs) for efficient ISAC. In particular, we consider a scenario with one UAV-AP equipped with a vertically placed uniform linear array (ULA), which sends combined information and sensing signals to communicate with multiple users and sense potential targets at interested areas on the ground simultaneously. Our objective is to jointly design the UAV maneuver with the transmit beamforming for optimizing the communication performance while ensuring the sensing requirements. First, we consider the quasi-stationary UAV scenario, in which the UAV is deployed at an optimizable location over the whole ISAC mission period. In this case, we jointly optimize the UAV deployment location, as well as the transmit information and sensing beamforming to maximize the weighted sum-rate throughput, subject to the sensing beampattern gain requirements and transmit power constraint. Although the above problem is non-convex, we find a high-quality solution by using the techniques of SCA and SDR, together with a 2D location search. Next, we consider the fully mobile UAV scenario, in which the UAV can fly over different locations during the ISAC mission period. In this case, we optimize the UAV flight trajectory, jointly with the transmit beamforming over time, to maximize the average weighted sum-rate throughput, subject to the sensing beampattern gain requirements and transmit power constraints as well as practical flight constraints. While the joint UAV trajectory and beamforming problem is more challenging to solve, we propose an efficient algorithm by adopting the alternating optimization together with SCA. Finally, numerical results are provided to validate the superiority of our proposed designs as compared to various benchmark schemes., Comment: 30 pages, 19 figures

This paper studies downlink communications in multi-unmanned aerial vehicle (UAV) enabled wireless communication systems with co-channel interference, where multiple UAVs are employed as aerial base stations (BSs) to serve mobile users on the ground. Our goal is to maximize the average sum-rate throughput of all users over a finite time horizon via UAV dynamic movement and communication (including user association and power control) co-design. Different from conventional offline designs based on the ideal assumption that channel state information (CSI) is perfectly known ahead of the flight, here we consider more challenging scenarios that the UAV can only obtain the real-time CSI along its flight. To tackle the online movement and communication co-design problem, we propose a deep reinforcement learning (DRL) algorithm based on deep deterministic policy gradient (DDPG), which can handle the problem of our interest with high-dimensional action space. Numerical results show that our proposed algorithm outperforms other benchmark schemes and baseline algorithms significantly.

Large repositories of scientific literature call for the development of robust methods to extract information from scholarly papers. This problem is addressed by the SemEval 2018 Task 7 on extracting and classifying relations found within scientific publications. In this paper, we present a feature-based and a deep learning-based approach to the task and discuss the results of the system runs that we submitted for evaluation.