Search Results (3)

Satellite technology has advanced with rising demand from the service sector, but increased accessibility also raises risks to the orbital environment. Space debris in low-Earth orbit (LEO) poses a major threat to satellite operations and access to space. Potential solutions for debris removal include using an onboard propulsion module to deorbit a satellite or employing a robotic arm on a “chaser” satellite to capture and remove debris. This study examines active debris removal from LEO at 2000 km altitude, focusing on a target debris weight of 100 kg and a chaser-satellite mass of 100 kg. The mission’s velocity change was calculated using the Hohmann transfer for different trajectories, and propellant requirements were derived using Tsiolkovsky’s rocket equation: ΔV = Isp × g0 × ln(mf/mi). Several scenarios were considered to assess the mission’s feasibility with respect to debris removal. Full article

The limestone islands of Malta face high levels of water stress due to low rainfall over a small land area and a high population density. We investigate an innovative, cost-effective approach to groundwater monitoring in an island environment by computing auto- and cross-correlations of ambient seismic noise recorded on short-period and broadband seismic stations. While borehole readings give accurate site-specific water level data of the groundwater across the islands, this technique provides a more regional approach to quantitative groundwater monitoring. We perform the moving window cross-spectral method to determine temporal changes in seismic velocity (δv/v). Comparison of the δv/v with groundwater levels from boreholes and precipitation shows comparable patterns. We find that the variations of the δv/v from auto-correlations are more pronounced than the cross-correlation, and that short-period seismic stations are also sensitive. The δv/v signal deteriorates at longer interstation distances, presumably because paths traverse complex geology. We conclude that changes in the groundwater level found beneath very small islands, even as small as 3 km², can be detected seismically. Low-cost, easy-to-deploy seismic stations can thus act as an additional tool for groundwater monitoring, especially in places with limited natural water reservoirs, like rivers and lakes, and infrastructure. Full article

Pre-crash injury risk (IR) assessment is essential for guiding efforts toward active vehicle safety. This work aims to conduct crash severity assessment using pre-crash information and establish the intrinsic mechanism of IR with proper interpretation methods. The impulse–momentum theory is used to propose novel a priori formulations of several severity indicators, including velocity change (ΔV), energy equivalent speed (EES), crash momentum index (CMI), and crash severity index (CSI). Six IR models based on different machine learning methods were applied to a fusion dataset containing 24,082 vehicle-level samples. Prediction results indicate that the pre-crash indicators (PCIs) are more influential than the commonly used basic crash information because the average accuracy of six models can be improved by 14.35% after utilizing PCIs. Furthermore, the features’ importance and their marginal effects are interpreted based on parameter estimation, Shapley additive explanation value, and partial dependence. The ΔV, EES, and CMI are identified as the determinant indicators of the potential IR, and their partial distributions are significantly influenced by the crash type and impact position. Based on partial dependence probabilities, the study establishes decision thresholds for PCIs for each severity category for different impact positions, which can serve as a useful reference for developing targeted safety strategies. These results suggest that the proposed method can effectively improve pre-crash IR assessment, which can be readily transferred to safety-related modeling in an active traffic management system. Full article