This study evaluates the performance of Precise Point Positioning with Ambiguity Resolution (PPP-AR) using multi-constellation GNSS for kinematic hydrographic surveying, comparing it against the traditional Network RTK (NRTK). Dual-frequency GNSS data were processed using three strategies: CORS-based NRTK corrections, the online CSRS PPP-AR service, and the open-source PRIDE PPP-AR software, using IGS rapid products. Each approach was assessed epoch-by-epoch against a high-precision PPK reference trajectory under various constellation configurations. Results demonstrate that ambiguity-resolved PPP achieves centimeter-level positioning accuracy comparable to NRTK and complies with IHO S-44 standards, providing PPP-AR with rapid orbits is a viable alternative for dynamic marine positioning.
Existing spatial keyword query methods mainly focus on the relevance between interest objects and keywords, while paying relatively limited attention to multiple preference scenarios in road network environments. To overcome the limitations of existing methods in addressing spatial keyword queries that comprehensively consider exclusion keywords and users' multiple preferences in road networks, as well as to protect user privacy, this paper proposes a privacy-preserving spatial keyword query method with exclusion keywords and multiple preferences in road networks. The method uses a DCEIG-tree pruning algorithm to obtain a preference queue, selects candidate groups from feature objects, allocates them via a dominance-counter-based algorithm, scores interest objects by a scoring function, ranks and returns the optimal set, and adopts differential privacy for protection. Theoretical analysis and experimental results verify the method's effectiveness in query efficiency, accuracy, and privacy usability.
No previous study has simultaneously examined the influence of diverse feature types, scales, and thresholds on simplification performance. We compared ten algorithms using six measures, seven datasets, and eleven thresholds. Results revealed that 'Ramer-Douglas-Peucker,' 'Sleeve-fitting,' and 'Before Opening Window' produced the largest changes in angularity and vector displacement, while minimizing percentage changes in coordinates and curvilinear segments. The opposite holds for Triangular Routine, Euclidean Distance, and Perpendicular Distance. Performance varies with feature type, scale, and threshold, revealing anomalous trends requiring further investigation. Statistical validation supports these findings, and guidelines help avoid misleading comparisons of newly developed algorithms.
