Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Arctic-specific limitations, including a limited number of visible satellites with unfavorable distribution, and the fact that during Arctic navigation, Global Navigation Satellite System (GNSS) signals suffer from significant multipath interference caused by sea ice, icebergs, ship superstructures, and low-elevation satellite signals reflected off the sea surface, have rendered conventional positioning solutions inadequate for maritime navigation safety requirements. To address these challenges, this paper implements a vector tracking loop (VTL) architecture incorporating forward–backward Kalman filtering to improve the estimation accuracy of carrier and code phase errors while proposing a scalar sequential integrity monitoring algorithm that enables identification and exclusion of faulty satellite signals to further enhance position estimation accuracy. The faulty satellite signals refer to satellite signals containing significant deviations that cannot be corrected by conventional models. The experiment uses a navigation scheme with commercial receivers as a reference. To verify the effectiveness of the proposed method at different latitudes, it was tested with real-world data from the Arctic; two sets of tests were conducted at latitudes between 70 and 80◦ and above 80◦. The results show that the optimized navigation method improved positioning accuracy by 65.9% and 56.8% compared with existing methods in the two test groups, respectively, effectively enhancing positioning accuracy in the Arctic environment....
We present an improved algorithm based on the POlarization LIdar PHOtometer Networking (POLIPHON) method to retrieve cloud condensation nuclei (CCN) concentration profiles from spaceborne lidar observations. Our previous paper, which was the first study to demonstrate the feasibility of using measurements from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) to retrieve CCN is revisited. Our results focus on the Evaluation of CALIPSO’s Aerosol Classification scheme over Eastern Mediterranean (ACEMED) research campaign that took place over Thessaloniki, Greece, in September 2011. We compare our results with our earlier retrievals, discussing the critical changes that have been made and the importance of using the proper conversions factors. We also demonstrate the use of conversion factors acquired based on CALIPSO aerosol typing for CCN retrievals. The analysis highlights the strong influence of smoke on CCN concentrations and shows that the assumed aging state of the smoke can significantly alter the retrieval outcome....
Focused on urban experiments such as electric vehicle deployments and the development of requisite urban technologies and infrastructures, this contribution argues that such developments become sites where place- based, relationally constructed futures can take shape. We outline a curatorial approach founded in relational geography to inform the construction and navigation of such futures. Drawing parallels with nineteenth- century curatorial approaches centred on exhibits as instruments of power and seduction, a critical perspective on contemporary technology demonstrators foregrounds how urban experiments promote specific futures and implicitly pare down alternatives. A curatorial lens is applied to urban experiments to reveal the tensions and topologies of power as technology developers, communities and local authorities add new elements to urban constellations and in doing so (re)negotiate the meanings and futures associated with urban technologies such as low- carbon vehicles....
Deep learning has recently shown strong potential in crop row detection for navigation line extraction. However, existing approaches often rely on datasetspecific customization and extensive image preprocessing, limiting their practicality in real-world agricultural scenarios. In contrast, human operators can instinctively navigate machinery by simply following the central crop row. Inspired by this observation, we propose a novel strategy that directly extracts the central crop row as the navigation line. To support this paradigm, we introduce a three-class annotation scheme—background, vegetation, and central crop row—where the vegetation class serves as an auxiliary supervisory signal to provide structural constraints and guide accurate localization. A consistent annotation width of crop row is applied across all samples to enable the model to learn invariant structural features. We develop CCRDNet (Central Crop Row Detection Network), which predicts the central row position and subsequently fits the navigation line using the least-squaresmethod. A dataset of 7,367 images comprising eight crop types across diverse environments was collected, yet only 400 images—from two crop types in eight environments—were used for training. Despite the limited supervision, the proposed method achieved a navigation line extraction accuracy of 95.57% with an average angle error of 1.13°. CCRDNet is lightweight, requiring only 0.033M parameters, and operates at 86.76 FPS on an RTX 3060 GPU and 48.78 FPS on a Jetson Orin NX. These results demonstrate that the proposed approach not only simplifies the navigation pipeline but also enables zero-shot generalization across previously unseen environments, fully satisfying the real-time requirements of agricultural machinery....
GNSS navigation can be challenging in urban environments, especially when low-cost devices are adopted. Among the possible solutions, in more recent years, approaches based on Machine Learning became popular. In this work, features based on geometry, satellite visibility and carrier-to-noise ratio are used in combination with k-Nearest Neighbors classifier to distinguish between open-sky and obstructed environments. The purpose of this research is to develop a reliable context classifier, to evaluate its recognition capabilities in static and dynamic environments and to assess its applicability in real-time positioning. Several performance metrics have been used, i.e., accuracy, precision, recall, F1-score, and multiple tests have been carried out to demonstrate the reliability of such algorithm with validation data. More than 98% of classification accuracy for the static tests has been obtained in average, evidencing the detection capabilities of such an algorithm....
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