Abstract The astronomical observation records in Chinese ancient documents, including historical books and local chronicles from ancient time, are of significant importance for astronomical research. Observation records of solar and lunar eclipses, as well as lunar occultations in historical literature, can be used to study secular change in the Earth’s variable rotation. The Local Chronicle of Jingzhou Prefecture in the Qing Dynasty recorded a total solar eclipse that occurred on A.D. 1641 November 3. In the local chronicles of Zhijiang County and Yidu County under Jingzhou Prefecture, namely, Zhijiang County History and Yidu County History , there were also textual records about the total solar eclipse. Combined with modern astronomical planetary ephemeris, the authors calculate solar eclipse path of this solar eclipse and study the secular change in the Earth’s variable rotation. In the end of this work, observable time of the solar eclipse in Shaoxing Prefecture at that time is analyzed.
Abstract With the rapid development of various software for processing data, it is essential to determine the most efficient and dependable use of existing processing software for Global Navigation Satellite System (GNSS) post-processing. This study compares different software packages used for post-processing GNSS measurements in a static mode. The research involved collecting data from dual-frequency GLONASS/GPS receivers at 15 IGS stations, recorded with a 30-second observation interval. The data sets were processed using industry-standard software packages, such as GNSS Solutions, Waypoint GrafNav, Magnet Office Tools, Leica Geo Office, Justin, and Trimble Business Center. All data sets were processed under standardized conditions, and positioning performance was evaluated using a PPP-derived reference solution. Statistical metrics, including mean error, standard deviation, and root mean square error, were calculated for horizontal and vertical components. The results confirm that Waypoint GrafNav and Magnet Office Tools provided better results for horizontal coordinate determination, while GNSS Solutions performed better in establishing point heights. This study underscores the importance of choosing the right software and processing methodology to attain an accuracy of centimeters when making multi-GNSS static measurements. Such insights have practical implications for engineers and surveyors in making informed decisions on the choice of post-processing software.
Modern marine gravity surveys are very costly and time-consuming. In the 1960s to 1980s, many marine gravity measurements were carried out in the southern and eastern Baltic Sea, mainly with the support of the research infrastructure of the Soviet Union. During the period of independence from the Soviet Union and transformation of the political system, much of these older data remain forgotten or not fully used. Now, it is scientifically and economically justified to reactivate these historical data so that they can supplement the modern surveys. This study presents the historical Polish marine gravity data, which were measured during four campaigns: Zaria, Jan Turlejski, Petrobaltic, and Ustka-Rozewie in 1971-1981 in the southern Baltic Sea. The datasets were transformed to modern geodetic and gravity reference frames, taking into account the available documentation. Next, a simple procedure was proposed for the determination of grid resolution, and the gridded 1 '& times;1 ' free-air and Bouguer anomalies were calculated for each survey. The resulting gridded anomalies were validated by three global geopotential models: EGM2008, EIGEN6c4, and GECO. Additionally, depths measured during the survey were validated using the ETOPO 2022 bathymetric model.
This paper proposes a novel framework for detecting and mitigating multipath interference in Global Navigation Satellite Systems (GNSS), a common issue caused by reflections of signal off surfaces such as buildings and the ground. To address this challenge, the study integrates Differential GNSS (DGNSS) techniques with advanced machine learning models, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Random Forest-to automatically detect and exclude satellites affected by multipath. The methodology involves synchronized GNSS data collection from a stationary base station and a mobile rover using high-precision u-blox ZED-F9P receivers with polarized antennas, DGNSS corrections via single and double differencing, and feature vector construction from both corrected and raw observation data. Signal quality labels ("Clean" or "Noise") are derived through skyplot analysis and environmental modeling. Three classification approaches are explored: direct classification using DGNSS-derived vectors (Approach 1), image-based classification (Approach 2), and classification using combined feature vectors (Approach 3). Experimental evaluation using 2,312 labeled samples shows that ensemble learning significantly outperforms single-model classifiers for multipath detection. Random Forest achieves the highest performance across all approaches, reaching up to 99.75% accuracy, while CNN outperforms traditional methods, reaching up to 86.77% accuracy in image-based classification in Approach 2. The findings demonstrate the effectiveness of the framework in identifying and excluding compromised satellite signals, which has the potential to enhance the accuracy of GNSS positioning, with potential for real-world application in complex urban environments.
The Indian Regional Navigation Satellite System (IRNSS), or NavIC, provides regional satellite positioning services across India and parts of Southeast Asia. In this study, we develop and evaluate a software-defined receiver (SDR) enhanced with deep learning techniques to acquire the IRNSS Standard Positioning Service (SPS) L5-band signal. The SDR architecture incorporates data-driven improvements in acquisition decision-making while retaining compatibility with the IRNSS signal structure as specified in the official ICD. Field experiments were conducted in Hanoi, Vietnam, a location situated at the fringe of NavIC's primary service area. Signal data were collected using a low-cost RF front-end connected to a rooftop-mounted antenna. Experimental results demonstrate that the proposed SDR is capable of reliably acquiring and tracking up to four IRNSS satellites under nominal conditions. The average C/N0 ranged from 30 to 42 dB-Hz, and successful position solutions were obtained with a horizontal accuracy of approximately 25 meters. Additionally, the deep learning-based acquisition module improved robustness in low-SNR scenarios. This work represents the first implementation of a learning-aided IRNSS receiver validated in Vietnam and offers insights into extending NavIC-based positioning services to broader Southeast Asian regions.
