Search

The financial viability of a rainwater harvesting (RWH) system is highly determined by regional conditions such as rainfall pattern and water prices. Successful implementation of rainwater harvesting systems depends largely on the identification of suitable sites. This paper presents the water saving potential and economic effectiveness of rainwater harvesting systems across eight major cities in China, using a daily water balance model. Results show six to 81 days (or 1.6% to 22.2%) of dependability can be achieved by using a rainwater harvesting system over these cities. The annual water saving efficiency ranges from 10% to 37% and the benefit–cost ratio varies between 0.45 and 1.20 across the studied cities. South China achieves the maximum annual water saving and the highest benefit–cost ratio, while southeast China has the most regular profile of precipitation use. Northwest China was found to be the region with the worst performance, both in yearly water saving and in regularity of rainwater use on a yearly scale. It was also found that the RWH system is not financially feasible in the northeast, southwest and central plains due to the benefit–cost ratios being smaller than 1.0. HIGHLIGHTS RWH systems have the best water saving and financial fits for southern China.; The northwest is ranked as having the lowest annual water saving.; Southeast China (represented by Hangzhou) has the most regular profile of rainwater use.;

Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use of their respective advantages and effectively mitigates the limitations of GNSS positioning. However, the GNSS/INS integration faces significant challenges in complex and harsh urban environments. In recent years, the geometry between the user and the satellite has been effectively improved with the advent of lower-orbits and faster-speed Low Earth Orbit (LEO) satellites. This enhancement provides more observation data, opening up new possibilities and opportunities for high-precision positioning. Meanwhile, in contrast to the traditional extended Kalman filter (EKF) approach, the performance of the LEO-enhanced GNSS/INS tightly coupled integration (TCI) can be significantly improved by employing the factor graph optimization (FGO) method with multiple iterations to achieve stable estimation. In this study, LEO data and the FGO method were employed to enhance the GNSS/INS TCI. To validate the effectiveness of the method, vehicle data and simulated LEO observations were subjected to thorough analysis. The results suggest that the integration of LEO data significantly enhances the positioning accuracy and convergence speed of the GNSS/INS TCI. In contrast to the FGO GNSS/INS TCI without LEO enhancement, the average enhancement effect of the LEO is 22.16%, 7.58%, and 10.13% in the north, east, and vertical directions, respectively. Furthermore, the average root mean square error (RMSE) of the LEO-enhanced FGO GNSS/INS TCI is 0.63 m, 1.21 m, and 0.85 m in the north, east, and vertical directions, respectively, representing an average improvement of 41.91%, 13.66%, and 2.52% over the traditional EKF method. Meanwhile, the simulation results demonstrate that LEO data and the FGO method effectively enhance the positioning and convergence performance of GNSS/INS TCI in GNSS-challenged environments (tall buildings, viaducts, underground tunnels, and wooded areas). [ABSTRACT FROM AUTHOR]

Battery swapping stations effectively address the challenges of long charging times, lack of charging stations, and safety hazards for electric two-wheelers. With the rapid development of shared electric bicycles and takeaways, the scale of electric two-wheeler users is expanding while generating a huge demand for battery swapping. The research on the planning and operation of battery swapping stations (BSSs) for electric two-wheelers has yet to be widely discussed. This study developed a data-driven optimization model based on machine learning algorithms using Beijing's battery swapping stations and point of interest (POI) dataset. First, through the spatial features of BSS analyzed by ArcGIS, we found that the coverage of BSSs was mainly concentrated within the fifth ring road, and the utilization rate was unbalanced. Then, on a 3000m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. The final stacking model was constructed by strengthening three single models with an accuracy of 86.21%. Compared with the original BSSs layout, the machine-learning algorithm proposed in this study can cover more factors and avoid the subjectivity of site selection. Finally, the queuing model for BSSs based on the Monte Carlo simulation was proposed. Through two scenarios, we found that the key parameters m (the number of charging slots) and λ (the user arrival rate) were influential to the outputs of service capability., Author(s): Yu Feng [1]; Xiaochun Lu (corresponding author) [1] 1. Introduction Under the dual constraints of resources and the environment, promoting low-carbon travel is the key to achieving sustainable development [...]

Satellite navigation systems enable long‐distance and high‐precision time transfers. The authors proposed an undifferenced precise time transfer method with information constraints in this study. A more accurate receiver clock offset can be obtained by constraining the station coordinate parameters and receiver clock offset parameters to achieve a precise time transfer. In this example, the research object is the time transfer link established by the Global Positioning System observation station using a caesium clock, rubidium clock, and hydrogen clock. The clock difference sequence of the time‐transfer link was calculated by combining the observation data and precise products. Following analysis of the examples, the following conclusions were reached. The undifferenced precise time transfer method based on information constraints can effectively be used to smooth the clock difference sequence of the receiver and the clock difference sequence of the time transfer link. To some extent, this method can also be used to reflect the performance of atomic clocks at Global Positioning System stations and is better suited for the precise time transfer of the equal‐clock long‐baseline time transfer link. Simultaneously, the frequency stability of this method is improved significantly in the short term compared to the traditional scheme without any constraints. The improvement of frequency stability is helpful to improve the accuracy of time transfer. [ABSTRACT FROM AUTHOR]

Positioning by means of the Global Positioning System (GPS) is a traditional and widely used method. However, its performance is affected by the user environment, such as multi-path effects and poor anti-interference abilities. Therefore, an Inertial Navigation System (INS) has been integrated with GPS to overcome the disadvantages of GPS positioning. INSs do not rely on any external system information and has strong autonomy and independence from the external environment. However, the performance of GPS/INS is visibly degraded in low-observability GPS environments (tall buildings, viaducts, underground tunnels, woods, etc.). Fortunately, with the emergence of Low-Earth-Orbit (LEO) satellites in recent years, the constellation configuration can be extended with the advantages of lower orbits, greater speeds, and richer geometric structures. LEO improves the geometric structure between users and satellites and provides many more observations. Meanwhile, a robust theory approach is applied that can restrain or remove the impact of low-accuracy observations. In this study, we applied LEO data and a robust theory approach to enhance the GPS/INS tight integration. To verify the effectiveness of this method, a set of vehicles and simulated LEO data were analyzed. The results show that robust Kalman filtering (RKF) provides a visible enhancement in the positioning accuracy of GPS/INS integration. This effectively restrains the mutation error and has a smoothing effect on the positioning results. In addition, the addition of LEO data significantly improves the positioning accuracy of a sole GPS and GPS/INS integration. The GPS/LEO/INS integration has the highest positioning accuracy, with Root-Mean-Square Errors (RMSEs) of the north, east, and vertical positions of 2.38 m, 1.94 m, and 2.49 m, respectively, which corresponds to an improvement of 30.21%, 47.43%, and 34.13% compared to sole GPS-based positioning and 8.60%, 17.24%, and 12.14% when compared to the GPS/INS mode. Simultaneously, the simulation results show that LEO and INSs can improve the positioning performance of GPS under GPS-blocked conditions. [ABSTRACT FROM AUTHOR]

Establishing an ultra-high-precision link for time–frequency comparisons between satellites and ground stations is critically important. This endeavor is fundamental to the advancement of pioneering space science exploration and the development of a robust space-based time–frequency system featuring ultra-high-precision space atomic clocks. In response to the requirements for assessing the long-term stability of high-precision space atomic clocks, we have designed and implemented a satellite–ground microwave time–frequency comparison system and method based on a three-frequency mode. Ground-based experimental results demonstrate that the equipment layer can achieve a satellite–ground time comparison accuracy better than 0.4 ps (RMS), with the equipment delay stability (ADEV) for all three frequencies being better than 8 × 10−18 at 86,400. By leveraging the ground-based experimental results, we constructed a satellite–ground time–frequency comparison simulation and verification platform. This platform realizes ultra-high-precision satellite–ground time–frequency comparison based on the China Space Station (CSS). After correcting various transmission delay errors, the satellite–ground time comparison achieved an accuracy better than 0.8 ps and an ADEV better than 2 × 10−17 at 86,400. This validation of our novel satellite–ground time–frequency comparison system and method, capable of achieving an 10−17 magnitude stability, is not only a significant contribution to the field of space time–frequency systems but also paves the way for future advancements and applications in space science exploration. [ABSTRACT FROM AUTHOR]