Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
Potholes threaten public safety and automated vehicles (AVs) safe navigation by increasing accident risks and maintenance costs. Traditional pavement inspection methods, which rely on human assessment, are inefficient for rapid pothole detection and reporting due to potholes’ random and sudden occurring. Advancements in Artificial Intelligence (AI) now enable automated pothole detection using image-based object recognition, providing innovative solutions to enhance road safety and assist agencies in prioritizing maintenance. This paper proposes a novel approach that evaluates the integration of 3 state-of-the-art AI models (YOLOv8n, YOLOv11n, and YOLOv12n) with an ADAS-like camera, GNSS receiver, and Robot Operating System (ROS) to detect potholes in uncontrolled real-life scenarios, including different weather/lighting conditions and different route types, and generate ready-to-use data in a real-time manner. Tested on real-world road data, the algorithm achieved an average precision of 84% and 84% in recall, demonstrating its effectiveness, stable, and high performance for real-life applications. The results highlight its potential to improve road safety, allow vehicles to detect potholes through ADAS, support infrastructure maintenance, and optimize resource allocation....
We compare the observational properties of rotation-powered binary millisecond pulsars (BMSPs) in the Galactic Field with various companion types. First, BMSPs with diverse companion types exhibit different properties in the relation of binary orbital period versus companion mass, and in the spin period distribution of neutron stars (NSs), etc., implying multiple origins of BMSPs. Second, BMSPs with companions of CO/ONeMg white dwarfs (CO-BMSPs) show fewer sources than those with companions of Helium white dwarfs (He- BMSPs), which may result from the different evolutionary histories or accretion efficiencies in their progenitors. Third, BMSPs with main-sequence companions (MS-BMSPs) and ultralight companions or planets (UL-BMSPs) are mostly eclipsing sources that are detected in both radio and γ-ray bands (i.e., radio+γ sources), implying that they may be younger systems and share a faster average spin period and higher average accretion rate than CO-BMSPs/He-BMSPs. We propose that the predecessors of MS-BMSPs may share a short binary orbital distance with low-mass companion stars of Mc ∼ 0.5–0.8M, which induces an efficient binary accretion process, and ultimately leaves a BMSP with a main-sequence companion due to the low efficiency of its hydrogen burning. Lastly, radio+γ He-BMSPs share a faster average spin period of NSs than radio-only He-BMSPs. Meanwhile, these two groups of sources share similar companion mass distributions, implying the γ-ray evaporation effect may not obviously strip the companion mass of He-BMSPs during ∼0.3 Gyr, which may be due to the strong gravitational potential energy of the white dwarf companions....
This study evaluates the positional accuracy of Global Navigation Satellite Systems (GNSS) and Unmanned Aerial vehicle (UAV)-based LiDAR systems in terrain modeling, using a total station as a reference. The research was conducted over 17 Ground Control Points (GCPs), with measurements obtained using a CHCNAV i50 GNSS receiver and a DJI Zenmuse L1 Light Detection and Ranging (LiDAR) sensor mounted on a UAV. Accuracy was assessed for horizontal (X, Y) and vertical (Z) components by comparing the results against total station data. Errors were quantified using statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and RMS at 1σ. GNSS exhibited superior horizontal accuracy with an RMS 1σ of 1.1 cm, while LiDAR achieved 1.7 cm. In contrast, GNSS outperformed LiDAR in vertical precision, achieving a 1σ RMS of 6.4 cm compared to 6.6 cm for LiDAR. These findings align with manufacturer specifications and international standards such as those of the American Society for Photogrammetry and Remote Sensing (ASPRS). The results highlight that GNSS is preferable for applications requiring high horizontal precision, while LiDAR is better suited for vertical modeling and terrain analysis. The combination of both systems may offer enhanced results for comprehensive geospatial surveys. Overall, both technologies demonstrated sub-decimetric accuracy suitable for precision agriculture, civil engineering, and environmental monitoring....
Global Navigation Satellite Systems (GNSSs) have become a critical service in modern society, and this has increased the need for GNSS situational awareness. On top of this, the GNSS field is rapidly changing. Increased signal interference has been observed in the last few decades, requiring more prominent GNSS services, in addition to flexibility and adaptability from GNSS monitoring systems. With the emergence of new Galileo features, such as Open Service Navigation Message Authentication (OSNMA) and the High Accuracy Service (HAS), monitoring systems have the opportunity to leverage these new services to enhance GNSS situational awareness. The Finnish Geospatial Research Institute (FGI) has developed an open GNSS situational awareness service called GNSSFinland, which monitors signal quality, detects potential interference, and informs users of the expected level of performance of different services around 47 stations in the Finnish Continuously Operating Reference Station (CORS) network (FinnRef). Recently GNSSFinland’s capabilities have been extended to monitor and leverage OSNMA and the HAS around FinnRef stations. Due to the novelty of both OSNMA and the HAS, custom software solutions are needed to integrate these services into GNSS-Finland. We give an overview of GNSS-Finland and its flexible architecture, present the integration of the new Galileo services into GNSS-Finland, and finally discuss how these new services can be leveraged from a monitoring system point of view....
With the renewed interest in the Moon, manifested by the growing number of planned missions for the past decade, space agencies are investing in reliable and dedicated lunar communication and navigation systems and services, such as the Moonlight programme of the European Space Agency (ESA), to provide support to all types of lunar users (i.e., surface users, landers and orbiters). In the context of lunar human and robotic exploration, one of the critical phases will be the landing of spacecraft on lunar soil. This type of operation is far from trivial, as shown by the recent crashes such as the one of the Luna25 lander from the Russian Space Agency. A reliable method to position a lander during its descent could be provided by a dedicated lunar navigation system. This paper will focus on what the achievable positioning accuracy is for a lander landing on the Moon’s South Pole using dedicated satellite-based navigation services such as Moonlight. It will be shown that using the LCNS constellation and the altimeter can achieve a sub 50 m accuracy with a 99% confidence interval....
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