Research on Pulsar Time Steered Atomic Time Algorithm Based on DPLL

In today’s society,there is a wide demand for high-precision and high-stability time service in the fields of electric power,communication,transportation and finance.At present,the time standard in various countries is mainly based on atomic clocks,but the frequency drift of atomic clocks will affect the long-term stability performance.Compared with atomic clocks,millisecond pulsars have better long-term stability and can complement with the excellent short-term stability of atomic clocks.In order to improve the long-term stability of the atomic timescale,and then improve the timing accuracy,this paper proposes an algorithm for steering the atomic clock ensemble(ACE)by ensemble pulsar time(EPT)based on digital phase locked loop(DPLL).First,the ACE and EPT are generated by the ALGOS algorithm,then the ACE is steered by EPT based on DPLL to calibrate the long-term frequency drift of the atomic clock,so that the generated steered atomic time follows both the short-term stability characteristics of ACE and the long-term stability characteristics of EPT,and finally,the steered atomic time is used to calibrate the local cesium clock.The experimental results show that the long-term stability of atomic time after steering is improved by 2 orders of magnitude compared with that before steering,and the daily drift of a local cesium clock after calibration is less than 9.47 ns in 3 yr,3 orders of magnitude higher than that before calibration on accuracy.

Effect of Strategy-Homogeneity on the Prisoner’s Dilemma Game in a Square Lattice

We investigate the effect of strategy-homogeneity on the prisoner’s dilemma game in a square lattice. Strategy-homogeneity means that the population contains at least one connected group in which individuals maintain the same strategy at each iteration and may update according to updating rule at next iteration. The simulation results show that the introduction of strategy-homogeneity increases the cooperation in the evolutionary stable state. For any value of temptation to defect, the density of cooperators in equilibrium state increases firstly and then decreases as the level of strategy-homogeneity increases constantly, and there exists an appropriate level of strategy-homogeneity, maximizing the density of cooperators. The results may be favorable for comprehending cooperative behaviors in societies composed of connected groups with coherent strategy.

Multi-Objective Deep Reinforcement Learning Based Time-Frequency Resource Allocation for Multi-Beam Satellite Communications

Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO).

Empowering highway network:Optimal deployment and strategy for dynamic wireless charging lanes

Amid escalating energy crises and environmental pressures,electric vehicles(EVs)have emerged as an effective measure to reduce reliance on fossil fuels,combat climate change,uphold sustainable energy and environmental development,and strive towards carbon peaking and neutrality goals.This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes(DWCLs)and EV charging strategy joint optimization in highway networks.Taking into account established charging resources in highway service areas(HSAs),the nonlinear charging characteristics of EV batteries,and the traffic capacity constraints of DWCLs.The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs.By ensuring that EVs maintain an acceptable state of charge(SoC),the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment,thus reducing the construction cost of wireless charging facilities and user charging expenses.The efficacy and universality of the model are demonstrated using the classical Nguyen-Dupius network as a numerical example and a real-world highway network in Guangdong Province,China.Finally,a sensitivity analysis is conducted to corroborate the stability of the model.The results show that the operating speed of EVs on DWCLs has the largest impact on total cost,while battery capacity has the smallest.This comprehensive study offers vital insights into the strategic deployment of DWCLs,promoting the sustainable and efficient use of EVs in highway networks.

Mars orbit insertion via ballistic capture and aerobraking

A novel Mars orbit insertion strategy that combines ballistic capture and aerobraking is presented.Mars ballistic capture orbits that neglect the aerodynamics are first generated,and are distilled from properly computed stable and unstable sets by using a pre-established method.A small periapsis maneuver is implemented at the first close encounter to better submit a post-capture orbit to the aerobraking process.An adhoc patching point marks the transition from ballistic capture to aerobraking,from which an exponential model simulating the Martian atmosphere and a box-wing satellite configuration are considered.A series of apoapsis trim maneuvers are then computed by targeting a prescribed pericenter dynamic pressure.The aerobraking duration is then estimated using a simplified two-body model.Yaw angle tuning cancels the inclination deflections owing to out-of-plane perturbation from the Sun.A philosophy combining in-plane and out-of-plane dynamics is proposed to simultaneously achieve the required semi-major axis and inclination.Numerical simulations indicate that the developed method is more efficient in terms of the fuel consumption,insertion safety,and flexibility when compared with other state-of-the-art insertion strategies.