Gravity measurements have important applications in geophysics, resource exploration, geodesy, and inertial navigation. The range of classical gravimetry includes laser interferometer (LI)-based absolute gravimeters, spring relative gravimeters, superconducting gravimeters, airborne/marine gravimeters, micro-electromechanical-system (MEMS) gravimeters, as well as gravity satellites and satellite altimetry. Atomic gravimetry is a new absolute gravity measurement technology based on atom interferometers (AIs) and features zero drift, long-term stability, long-term continuous measurements, and high precision. Atomic gravimetry has been used to measure static, marine, and airborne gravity; gravity gradient; as well as acceleration to test the weak equivalence principle at the China Space Station. In this paper, classical gravimetry is introduced, and the research progress on static and airborne/marine atomic gravimeters, space AIs, and atomic gravity gradiometers is reviewed. In addition, classical and atomic gravimetry are compared. Future atomic gravimetry development trends are also discussed with the aim of jointly promoting the further development of gravity measurement technologies alongside classical gravimetry.

A stable and reliable power system is crucial for human daily lives and economic stability. Power load forecasting is the foundation of dynamically balancing between the power supply and demand sides. However, with the popularity of renewable energy sources and electric vehicles, it still struggles to achieve accurate power load forecasting due to the complex patterns and dynamics of load data. To mitigate these issues, this paper proposes a new hybrid model based on a sample convolution and integration network (SCINet) and a long short-term memory network (LSTM) for short-term power load forecasting. Specifically, a feed-forward network (FFN) is first used to enhance the nonlinear representation of the load data to highlight the complex temporal dynamics. The SCINet is then employed to iteratively extract and exchange information about load data at multiple temporal resolutions, capturing the long-term dependencies hidden in the deeper layers. Finally, the LSTM networks are performed to further strengthen the extraction of temporal dependencies. The principal contributions of the proposed model can be summarized as follows: (1) The SCINet with binary tree structure effectively extracts both local and global features, proving advantageous for capturing complex temporal patterns and dynamics; (2) Integrating LSTM into the SCINet-based framework mitigates information loss resulting from interactive downsampling, thereby enhancing the extraction of temporal dependencies; and (3) FNN layers are strategically designed to enhance the nonlinear representations prior to feeding the load data fed into the SCINet and LSTM. Three real-world datasets are used to validate the effectiveness and generalization of the proposed model. Experimental results show that the proposed model has superior performance in terms of evaluation metrics compared with other baseline models.

The application of new energy systems for industrial production to advance air pollution prevention and control has become an irreversible trend. This development includes hybrid systems consisting of reversible solid oxide cells (RSOC) and a Li-ion battery; however, at present the energy dispatching of such systems has an unstable factor in the form of poor heat/electricity/gas controllability. Therefore, the system studied in this paper uses the Li-ion battery as the energy supply/storage case, and uses the RSOC to supply power for the Li-ion battery charge or the Li-ion battery supply power to the RSOC for hydrogen production by water electrolysis. In this hybrid system, Li-ion battery thermoelectric safety and RSOC hydrogen production stability are extremely important. However, system operation involves the switching of multiple operating conditions, and the internal thermoelectric fluctuation mechanism is not yet clear. Therefore, in this paper we propose a separate control with a dual mode for hybrid systems. Active disturbance rejection control (ADRC) with a simple structure is used to achieve Li-ion battery module thermoelectric safety and control the hydrogen production/consumption of the RSOC module in the hybrid system. The results show that the required Li-ion battery thermoelectric safety and RSOC hydrogen consumption/production requirements can be met using the proposed controller, leading to a hybrid system with high stability control.

The prediction of stack output power in solid oxide fuel cell (SOFC) systems is a key technology that urgently needs improvement, which will promote SOFC systems towards high-power multi-stack applications. The accuracy of power prediction directly determines the control effect and working condition recognition accuracy of the SOFC system controller. In order to achieve this goal, a genetic algorithm back propagation (GA-BP) neural network is constructed to predict output power in the SOFC system. By testing 40 sets of sample data collected from the experimental platform, it is found that the GA-BP method overcomes the limitation of the traditional back propagation (BP) method—falling into local optima. Further analysis shows that the average relative error of GA-BP has decreased to 1%. The reduction of the relative error improves the accuracy of the prediction results and the average prediction accuracy. Compared with the long short-term memory (LSTM) and BP algorithm, the GA-BP prediction model significantly reduces the relative error of power output prediction, which provides a solid foundation for multi-stack SOFC systems.

Systematic error suppression and test data processing are very important in improving the accuracy and sensitivity of the atom interferometer (AI)-based weak-equivalence-principle (WEP) test in space. Here we present a spectrum correlation method to investigate the test data of the AI-based WEP test in space by analyzing the characteristics of systematic errors and noises. The power spectrum of the Eötvös coefficient η, systematic errors, and noises in AI-based WEP test in space are analyzed and calculated in detail. By using the method, the WEP violation signal is modulated from direct current (DC) frequency band to alternating current (AC) frequency band. We find that the signal can be effectively extracted and the influence of systematic errors can be greatly suppressed by analyzing the power spectrum of the test data when the spacecraft is in an inertial pointing mode. Furthermore, the relation between the Eötvös coefficient η and the number of measurements is obtained under certain simulated parameters. This method will be useful for both isotopic and nonisotopic AI-based WEP tests in space.