Global Navigation Satellite System (GNSS) timing is a main service function. Each GNSS has its own time performance specification. However, a uniform timing performance assessment methodology and its outcomes do not exist. Firstly, the timing performance specifications of each GNSS are analyzed. Then, time transfer accuracy is considered as the key GNSS timing performance indicator. Secondly, an assessment method for the Coordinated Universal Time (UTC) published by the Bureau International des Poids et Mesures (BIPM) and UTC kept by the National Time Service Center of China (UTC(NTSC)) is proposed. Thirdly, the uncertainty budget of the assessment method is given. The timing performances of BDS, GPS, GLONASS, and Galileo are assessed and compared. The results show that the time transfer accuracy of BDS, GPS, GLONASS, and Galileo was 13.8 ns, 4.5 ns, 16.8 ns, and 4.2 ns, respectively, in 2021, meeting their performance requirements specified by GNSS (30 ns or 40 ns). Meanwhile, the assessment results of GPS and Galileo are much better than requirements, while the assessment results of BDS and GLONASS show fixed time offset and can still be improved further. If the local reference time of GNSS users can be connected with UTC, this assessment method can be used.

Due to the occlusion of the moon, an annular solar eclipse will have an effect on the ionosphere above the earth. The change of the ionosphere, for the low-frequency time-code signal that relies on it as a reflection medium for long-distance propagation, the signal field strength, and other parameters will also produce corresponding changes, which will affect the normal operation of the low-frequency time-code time service system. This paper selects the solar eclipse that occurred in China on 21 June 2020, and uses the existing measurement equipment to carry out experimental research on the low-frequency time-code signal. We measured and analyzed the signal field strength from 20 June 2020 to 23 June 2020, and combined solar activity data, ionospheric data, and geomagnetic data, and attempted to explore the reasons and rules of the change of signal parameters. The results showed that the field strength of the low-frequency time-code signal changed dramatically within a short time period, the max growth value can reach up to 17 dBμV/m and the variation trend yielded ‘three mutations’. This change in signal field strength is probably due to the occurrence of a solar eclipse that has an effect on the ionosphere. When the signal propagation conditions change, the signal strength will also change accordingly.

In the process of traditional web server elastic performance test, the performance of tracking data is poor, which leads to excessive server jitter and poor accuracy of elastic test results. Therefore, this paper studies the network server elastic performance test method in cloud computing environment. In this method, cloud monitoring technology is used to track the operation data of Web server. According to the multi platform call mode of Web server, a statistical regression model is established to determine the elasticity measurement index. The load balancing algorithm is used to test the server load balancing to obtain the elasticity value of Web server. Experimental results: in the whole running period of Web server, the jitter times of the proposed method are 15.6066 times, 16.5600 times and 16.5733 times lower than those of the three traditional methods, respectively. It can be seen that the new test method can accurately track the response ability of the server and obtain more accurate elasticity value.

Chinese area position system (CAPS) can provide positioning, time and velocity service to China region users by renting several commercial communication satellites. We studied a real-time common-view time transfer method in this paper using communication satellites of CAPS. This method can fully use CAPS resources of the communication satellites and orbit determination stations to broadcast the information of timing bias mode parameters from the ground base stations. The timing bias data information can be demodulated and calculated by users from the navigation message by receiving the satellite signal in one-way, so the common-view time transfer process are implemented. This method can provide time service with the accuracy better than 5 ns, which is a kind of high accuracy time transfer method at present. Users can obtain this kind of time services in real-time and only need to receive CAPS time service signal in one-way reception way and without the process of past-data exchange. This paper provides detailed information on the principle of the common-view time transfer method based on communication satellites. Moreover, the experiment platform has been set up to test the performance of this new method. The long baseline experimental results of Xi’an-Kashi and Xi’an-Sanya indicated that the accuracy of CAPS single-satellite is better than that of GPS single-satellite common-view time transfer. The common-view time transfer based on communication satellite can provide time service to users with the accuracy better than 5 ns.

The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions.