Research on agile space emergency launching mission planning simulation and verification method

Space emergency launching is to send a satellite into space by using a rapid responsive solid rocket in the bounded time to implement the emergency Earth observation mission.The key and difficult points mainly include the business process construction of launching mission planning,validation of the effectiveness of the launching scheme,etc.This paper pro-poses the agile space emergency launching mission planning simulation and verification method,which systematically con-structs the overall technical framework of space emergency launching mission planning with multi-field area,multi-platform and multi-task parallel under the constraint of resource schedul-ing for the first time.It supports flexible reconstruction of mis-sion planning processes such as launching target planning,tra-jectory planning,path planning,action planning and launching time analysis,and can realize on-demand assembly of operation links under different mission scenarios and different plan condi-tions,so as to quickly modify and generate launching schemes.It supports the fast solution of rocket trajectory data and the accurate analysis of multi-point salvo time window recheck and can realize the fast conflict resolution of launching missions in the dimensions of launching position and launching window sequence.It supports lightweight scenario design,modular flexi-ble simulation,based on launching style,launching platform,launching rules,etc.,can realize the independent mapping of mission planning results to two-dimensional and three-dimen-sional visual simulation models,so as to achieve a smooth con-nection between mission planning and simulation.

Enhancing Accumulation and Penetration Efficiency of Next-Generation Antibiotics to Mitigate Antibiotic Resistance in Pseudomonas aeruginosa PAO1

This study explores the efficacy of advanced antibiotic compounds against P. aeruginosa, focusing on Antibiotic B, an enhanced derivative of Ceftriaxone. The study measured the intracellular uptake of Antibiotic B and introduced a novel adjuvant, Influximax, which augmented its antibacterial activity. Results showed a diminished potential for resistance emergence with Antibiotic B, particularly when used in combination with Influximax. The study suggests that optimizing antibiotic delivery into bacterial cells and leveraging syner-gistic adjuvant combinations can enhance drug resistance combat. .

Energy efficiency maximization for buffer-aided multi-UAV relaying communications

This paper studies a multiple unmanned aerial vehicle(UAV)relaying communication system,where multiple UAV re-lays assist the blocked communication between a group of ground users(GUs)and a base station(BS).Since the UAVs only have limited-energy in practice,our design aims to maximize the energy efficiency(EE)through jointly designing the communica-tion scheduling,the transmit power allocation,as well as UAV trajectory under the buffer constraint over a given flight period.Actually,the formulated fractional optimization problem is diffi-cult to be solved in general because of non-convexity.To re-solve this difficulty,an efficient iterative algorithm is proposed based on the block coordinate descent(BCD)and successive convex approximation(SCA)techniques,as well as the Dinkel-bach’s algorithm.Specifically,the optimization variables of the formulated problem are divided into three blocks and we alter-nately optimize each block of the variables over iteration.Numeri-cal results verify the convergence of the proposed iterative al-gorithm and show that the proposed designs achieve significant EE gain,which outperform other benchmark schemes.

A High-order Internal Model Based Iterative Learning Control Scheme for Discrete Linear Time-varying Systems

In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iterationvarying desired trajectories. A high-order internal model(HOIM) is utilized to describe the variation of desired trajectories in the iteration domain. In the sequel, the HOIM is incorporated into the design of learning gains. The learning convergence in the iteration axis can be guaranteed with rigorous proof. The simulation results with permanent magnet linear motors(PMLM) demonstrate that the proposed HOIM based approach yields good performance and achieves perfect tracking.

Trajectory Optimization for Cellular-Connected UAV Under Outage Duration Constraint

Enabling cellular access for unmanned aerial vehicles(UAVs)is a practically appealing solution to realize their high-quality communications with the ground for ensuring safe and efficient operations.In this paper,we study the trajectory design for a cellular-connected UAV that needs to fly from given initial to final locations,while communicating with the ground base stations(GBSs)subject to a minimum signal-to-noise ratio(SNR)requirement along its flight.However,due to various practical considerations such as GBSs’locations and coverage range as well as UAV’s trajectory and mobility constraints,the SNR target may not be met at certain time periods during the flight,each termed as an outage duration.In this paper,we first propose a general outage cost function in terms of outage durations in the flight,which includes the two commonly used metrics,namely total outage duration and maximum outage duration as special cases.Based on it,we formulate a UAV trajectory optimization problem to minimize its mission completion time,subject to a constraint on the maximum tolerable outage cost.To tackle this challenging(non-convex)optimization problem,we first transform it into a tractable form and thereby reveal some useful properties of the optimal trajectory solution.Based on these properties,we further simplify the problem and propose efficient algorithms to check its feasibility and obtain optimal as well as low-complexity suboptimal solutions for it by leveraging graph theory and convex optimization techniques.Numerical results show that our proposed trajectory designs outperform that by the conventional method of dynamic programming,in terms of both performance and complexity.