Search Results (296)

Pottery is one of the most common and abundant types of human remains found in archaeological contexts. The analysis of archaeological pottery involves the reconstruction of pottery vessels from their sherds, which represents a laborious and repetitive task. In this work, we investigate a deep learning-based approach to make that process more efficient, accurate, and fast. In that regard, given a sherd’s digital point cloud in a standard, so-called canonical position, the proposed method predicts the geometric transformation, which moves the sherd to its expected normalized position relative to the vessel’s coordinate system. Among the main components of the proposed method, a pair of deep 1D convolutional neural networks trained to predict the 3D Euclidean transformation parameters stands out. Herein, rotation and translation components are treated as independent problems, so while the first network is dedicated to predicting translation moments, the other infers the rotation parameters. In practical applications, once a vessel’s shape is identified, the networks can be trained to predict the target transformation parameter values. Thus, given a 3D model of a complete vessel, it may be virtually broken down countless times for the production of sufficient data to meet deep neural network training demands. In addition to overcoming the scarcity of real sherd data, given a virtual sherd in its original position, that procedure provides paired canonical and normalized point clouds, as well as the target Euclidean transformation. The herein proposed 1D convolutional neural network architecture, the so-called PotNet, was inspired by the PointNet architecture. While PointNet was motivated by 3D point cloud classification and segmentation applications, PotNet was designed to perform non-linear regressions. The method is able to provide an initial estimate for the correct position of a sherd, reducing the complexity of the problem of fitting candidate pairs of sherds, which could be then carried out by a classical adjustment method like ICP, for instance. Experiments using three distinct real vessels were carried out, and the reported results suggest that the proposed method can be successfully used for aiding pottery reconstruction. Full article

In modern fishery management, fishing vessel trajectory data are used to monitor and analyze fishing vessel activities. However, trajectory data are often of low quality, probably due to environmental factors, equipment failures, signal loss and operation errors, leading to numerous outliers in these data. These outliers not only undermine the credibility of the data but also negatively affect the subsequent data mining and decision-making. In this study, a data cleaning method for the identification of outlier points in fishing vessel trajectories based on the Geohash geocoding algorithm is given, which involves several key steps: obtaining and preprocessing the raw trajectory data; generating the corresponding Geohash codes for each ship position based on its latitude and longitude; calculating the reachable distance considering the time interval between the current point and the following points and their speeds; querying the neighborhood of the current point based on the reachable distance; and obtaining all Geohash codes of the reachable areas of the fishing vessels within the time interval as the reachable range grid set of the current position. The reachable range grid set of the current position is compared with the reachable range grid sets of the previous point identified as normal and the next point in the fishing vessel trajectory. If there is no intersection, it is determined that the current fishing vessel position is an outlier, and this point will be excluded. The method proposed in this study is able to effectively identify outliers in trajectory data, achieving efficient and effective trajectory data cleaning and improving the accuracy and reliability of the data. Full article

Global Navigation Satellite Systems (GNSSs) are the primary source of information for Positioning, Navigation, and Timing (PNT) in the maritime sector; however, they are vulnerable to unintentional or deliberate interference, such as jamming, spoofing, or meaconing. The continuous monitoring of GNSS signals is crucial for vessels and mobile maritime platforms to ensure the integrity, availability, and accuracy of positioning and navigation services. This monitoring is essential for guaranteeing the safety and security of navigation and contributes to the accurate positioning of vessels and platforms involved in hydrographic and oceanographic research. This paper presents the implementation of a complex system for monitoring the quality of signals within the GNSS spectrum at the Maritime Hydrographic Directorate (MHD). The system provides real-time analysis of signal parameters from various GNSSs, enabling alerts in critical situations and generating statistics and reports. It comprises four permanent stations equipped with state-of-the-art GNSS receivers, which integrate a spectrum analyzer and store raw data for post-processing. The system also includes software for monitoring the GNSS spectrum, detecting interference events, and visualizing signal quality data. Implemented using a Docker-based platform to enable efficient management and distribution, the software architecture consists of a reverse proxy, message broker, front-end, authorization service, GNSS orchestrator, and GNSS monitoring module. This system enhances the quality of command, control, communications, and intelligence decisions for planning and execution. It has demonstrated a high success rate in detecting and localizing jamming and spoofing events, thereby improving maritime situational awareness and navigational safety. Future development could involve installing dedicated stations to locate interference sources. Full article

Current Satellite-Based Augmentation Systems (SBASs) improve the positioning accuracy and integrity of GPS satellites and provide safe civil aviation navigation services for procedures from en-route to LPV-200 precision approach over specific regions. SBAS systems, such as WAAS, EGNOS, GAGAN, and MSAS, already operate. The development of operational SBAS systems is in transition due to the extension of L1 SBAS services to new regions and the improvements expected by the introduction of dual frequency multi-constellation (DFMC) services, which allow the use of more core constellations such as Galileo and the use of ionosphere-free L1/L5 signal combination. The UKSBAS Testbed is a demonstration and feasibility project in the framework of ESA’s Navigation Innovation Support Programme (NAVISP), which is sponsored by the UK’s HMG with the participation of the Department for Transport and the UK Space Agency. UKSBAS Testbed’s main objective is to deliver a new L1 SBAS signal in space (SIS) from May 2022 in the UK region using Viasat’s Inmarsat-3F5 geostationary (GEO) satellite and Goonhilly Earth Station as signal uplink over PRN 158, as well as L1 SBAS and DFMC SBAS services through the Internet. SBAS messages are generated by GMV’s magicSBAS software and fed with data from the Ordnance Survey’s station network. This paper provides an assessment of the performance achieved by the UKSBAS Testbed during the last two years of operations at the SIS and user level, including a number of experimentation campaigns performed in the aviation and maritime domains, comprising ground tests at airports, flight tests on aircraft and sea trials on a vessel. This assessment includes, among others, service availability (e.g., APV-I, LPV-200), protection levels (PL), and position errors (PE) statistics over the service area and in a network of receivers. Full article

Passive acoustic monitoring plays a critical role in the study of marine species, particularly in understanding the behavior of deep-diving endangered species like the Mediterranean sperm whale (Physeter macrocephalus). This paper presents an effective method for tracking sperm whales using synchronized acoustic data from four hydrophones. The tracking method estimates the location of sperm whales by measuring the time difference of arrival of detected clicks. The direction of arrival of the clicks and their reflections on the surface are then reconstructed to determine the position of the whale. The method was used to perform the first acoustic tracking study of sperm whale dives recorded in the Central Mediterranean Sea by the NEMO-O$\nu$DE cabled observatory, deployed at a depth of 2100 m in the Gulf of Catania. The data analyzed in this study were collected in August and October 2005 and include 49 five-minute recordings with the presence of sperm whale clicks. A Monte Carlo simulation revealed an estimated relative error of 2.7% in depth and 1.9% in the horizontal distance for the positioning of clicks. The algorithm successfully reconstructed 64 tracks of diving sperm whales and demonstrated its potential for monitoring within a 12 km radius. Moreover, a simultaneous tracking of a vessel and a sperm whale was performed, illustrating how the method can be used to study potential changes during dives in the presence of vessels. This method offers a reliable, non-invasive approach to studying sperm whale behavior, ecology, and interaction with anthropogenic activities. Full article

During the waiting period before passing through locks, inland vessels typically rely on diesel generators to power their onboard equipment, which leads to air pollution and poses more direct threats to the surrounding residents and ecological environment. To assess the extent to which green and efficient lock passage strategies can reduce air pollution, this study takes the Linked Control Policy for Vessels Passing Through Locks released by the Three Gorges Navigation Authority in China in December 2017 as the research object. It collected air quality monitoring data for six years before and after the policy implementation (2014–2020) and used a Regression Discontinuity model (RD model) to analyze the policy’s effect. The results show that compared to 2014, the average concentration of SO₂ in the air decreased by 67% in 2020, along with NO₂ decreasing by 32%, PM_2.5 by 42%, PM₁₀ by 46%, and the AQI (Air Quality Index) by 27%. The robustness test of the RD model also confirmed the causal relationship between the policy implementation and the improvement in air quality. This research is the first to systematically disclose the environmental benefits of the “soft” management policy of optimizing the lock passage process, uncovering the positive influence of active ship lock passage policy on air quality, and providing a scientific basis for promoting the implementation of policies related to lock management. Full article

Background: The diagnostic performance of preprocedural CT angiography in detecting coronary artery disease (CAD) in patients scheduled for transcatheter aortic valve implantation (TAVI) has been reported. However, data on predictors of diagnostic inaccuracy are sparse. We sought to investigate clinical characteristics and imaging criteria that predict the inaccurate assessment of coronary artery stenosis based on pre-TAVI-CT. Methods: The patient- and vessel-level analysis of all CT datasets from 192 patients (mean age 82.1 ± 4.8 years; 63.5% female) without known CAD or severe renal dysfunction was performed retrospectively in a blinded fashion. Significant CAD was defined as a CAD-RADS™ 2.0 category ≥ 4 by CT. Invasive coronary angiography (ICA) served as the reference standard for relevant CAD (≥70% luminal diameter stenosis or fractional flow reserve ≤ 0.80). Pertinent clinical characteristics and imaging criteria of all true-positive (n = 71), false-positive (n = 30), false-negative (n = 4), and true-negative patient-level CT diagnoses (n = 87) for relevant stenosis according to ICA were assessed. Results: In the univariate per-patient analysis, the following parameters yielded discriminative power (p < 0.10) regarding inaccurate CAD assessment by pre-TAVI-CT: age, atrial fibrillation, scanner generation, and image quality. Factors independently associated with CT diagnostic inaccuracy were determined using multivariable logistic regression analysis: a younger age (odds ratio [OR] 0.87; 95% confidence interval [CI] 0.80 to 0.94; p < 0.01) and insufficient CT image quality (OR 0.6; CI 0.41 to 0.89; p < 0.01). Conclusions: Our results demonstrate younger age and poor CT image quality to predict less accurate CAD assessments by pre-TAVI-CT in comparison with ICA. Knowledge of these predictors may aid in more efficient coronary artery interpretations based on pre-TAVI-CT. Full article

This study focuses on an analysis of the dwell time of vessels in the Port of Sines jurisdiction area, between 2010 and 2023, as an indicator of operational efficiency, with the objective of analyzing the temporal patterns of vessel movements at the Port of Sines, aiming to understand how efficient the operations are. This research will enable the extraction of meaningful patterns from temporal data and the addressing of potential bottlenecks, enabling smother operations and optimized performance. A total of 157,515 records of vessel movements were analyzed using statistical modeling in Python (version 3.11.8). The overall average dwell time calculated for these 13 years was 0.55 days, for a medium number of port calls per year of 2199. This result highlights the operational efficiency of the Port of Sines, although the variability between the different terminals remains significant. The Multipurpose Terminal registered the longest dwell time (1.08 days), especially due to the diversity of cargo handled. In contrast, the Container Terminal had an average dwell time of 0.38 days. Anchoring frequency has emerged as critical for optimization. The implementation of just-in-time principles is proposed as a strategy to reduce anchorage times, enhance coordination and collaboration within the operational chain, and mitigate greenhouse gas (GHG) emissions. Notwithstanding the efficiency attained at the Port of Sines, this study suggests that further enhancement of its operational efficiency is feasible and desirable. This would contribute to the sustainability agenda and reinforce the port’s position in the global trade landscape. Full article

Identifying illegal fishing activities from Automatic Identification System (AIS) data is difficult since AIS messages are broadcast cooperatively, the ship’s master controls the timing, and the content of the transmission and the activities of interest usually occur far away from the shore. This paper presents our work to predict ship types using AIS data from satellites: in such data, there is a pronounced imbalance between the data for different types of ships, the refresh rate is relatively low, and there is a misreporting of information. To mitigate these issues, our prediction algorithm only uses the sequence of ports the ships visited, as inferred from the positions reported in AIS messages. Experiments involving multiple machine learning algorithms showed that such port visits are informative features when inferring ship type. In particular, this was shown to be the case for the fishing vessels, which is the focus of this paper. We then applied a KD-tree to efficiently identify pairs of ships that are close to one another. As this activity is usually dangerous, multiple occurrences of such encounters that are linked to one ship sensibly motivate extra attention. As a result of applying the analysis approach to a month of AIS data related to a large area in Southeast Asia, we identified 17 cases of potentially illegal behaviours. Full article

This study deeply explores the permeation deformation mechanism of limestone cores in deep-water environments. Through a customized test device, the mechanical responses of deep-water rocks under different moisture content conditions are simulated. The device is composed of a pressurization system, a pressure vessel, and a data acquisition system, which can accurately apply high water pressure and monitor the stress and strain changes in the core in real time. The cores used in the experiment were cut to standardized sizes and subjected to strict saturation and drying treatments to ensure the consistency of test conditions and the accuracy of data. The mechanical behaviors of cores under four working conditions of dryness, 50% moisture content, and 100% moisture content are analyzed. Through the cyclic process of pressurization and depressurization, the changes in the strain characteristics of the cores are observed. The research results show that when pressure is applied for the first time, low moisture content cores will absorb water and expand, and the strain will increase and then tend to be stable as the pressure increases. There is no such process for cores with 100% moisture content. Water pressure is positively correlated with the elastic modulus of rocks, negatively correlated with the strain rate during pressurization, and positively correlated during depressurization. Moreover, an increase in moisture content reduces the average strain mutation, reduces the average strain rate amplitude, and increases the elastic modulus. This study provides important theoretical support for the design and construction of deep-water rock engineering and provides a reference for further research in related fields. Full article

The maritime traffic status is monitored through the Automatic Identification System (AIS) installed on vessels. AIS data record the trajectory of each ship. However, due to the short sampling interval of AIS data, there is a significant amount of redundant data, which increases storage space and reduces data processing efficiency. To reduce the redundancy within AIS data, a compression algorithm is necessary to eliminate superfluous points. This paper presents an offline trajectory compression algorithm that leverages geospatial background knowledge. The algorithm employs an adaptive function to preserve points characterized by the highest positional errors and rates of water depth change. It segments trajectories according to their distance from the shoreline, applies varying water depth change rate thresholds depending on geographical location, and determines an optimal distance threshold using the average compression ratio score. To verify the effectiveness of the algorithm, this paper compares it with other algorithms. At the same compression ratio, the proposed algorithm reduces the average water depth error by approximately 99.1% compared to the Douglas–Peucker (DP) algorithm, while also addressing the common problem of compressed trajectories potentially intersecting with obstacles in traditional trajectory compression methods. Full article

This study investigates the spatiotemporal density aggregation and pattern distribution of vessel traffic amidst bustling maritime logistics scenarios. Firstly, a relatively new spatiotemporal segmentation and reconstruction method is proposed for ship AIS trajectories to address trajectory disruptions caused by berthing, anchorage, and other factors. Subsequently, a trajectory filtering algorithm utilizing time window panning is introduced to mitigate position jumps and deviation errors in trajectory points, ensuring that the dynamic trajectory adheres to the spatiotemporal correlations of ship motion. Secondly, to establish a geographical spatial mapping of dynamic trajectories, spatial gridding is applied to maritime traffic areas. By associating the geographical space of traffic activities with the temporal attributes of dynamic trajectories, a dynamic trajectory temporal density model is constructed. Finally, a case study is conducted to evaluate the effectiveness and applicability of the proposed method in identifying spatiotemporal patterns of maritime traffic and spatiotemporal density aggregation states. The results show that the proposed method can identify dynamic trajectory traffic patterns after the application of compression algorithms, providing a novel approach to studying the spatiotemporal aggregation of maritime traffic in the era of big data. Full article

Prediction of the spatial configuration of the umbilical cable during deep-sea mining in-situ tests is of great significance because dynamic change may cause the umbilical cable to touch the ground or overturn the mining vehicle. In the present paper, a real-time prediction method for the dynamic spatial configuration of the umbilical cable during the deep-sea mining process is proposed. At first, the environmental information, position and motion of the vessel–umbilical cable–mining system were collected by sensors arranged at different locations. Then, the data were converted and transformed to the local vessel coordinate system. After that, the commercial software OrcaFlex was employed to conduct real-time simulation, in which the spatial configuration of the umbilical cable was predicted by the lumped mass method. Furthermore, the proposed real-time simulation method was employed in a sea trial test of deep-sea mining in an area with a water depth of 1100 m. Comparing the prediction results with the trajectory of the USBL beacon obtained from the monitoring data, the maximum distance of some specific points was close to 5 m, and most of them were less than 3 m. Meanwhile, it could also give the dynamic responses of the deep-sea mining system. For example, the maximum top tension of the umbilical cable was less than 15 kN, which could be used to evaluate the health condition of the system. During the sea trial test, the proposed method played an important role in ensuring the safety of the umbilical cable during wide-range movement of the mining vehicle. With characteristics of good real-time performance, accurate prediction, high reliability and stability, the proposed method could enhance the confidence of engineers for on-site operation as a powerful digital tool for visualization of the subsea working state. Full article

Hydrodynamic noise is induced by hydrodynamic phenomena, such as pressure fluctuations, shear layers, and eddy currents, which have a significant impact on ship performance, pumping equipment efficiency, detection accuracy, and the living environment of marine organisms. Specifically, hydrodynamic noise increases fluid resistance around the hull, reduces speed and fuel efficiency, and affects the stealthiness of military vessels; whereas, in pumping equipment, noise generation is usually accompanied by energy loss and mechanical vibration, resulting in reduced efficiency and accelerated wear and tear of the equipment. Traditional physical experiments, theoretical modeling, and numerical simulation methods occupy a key position in hydrodynamic noise research, but each have their own limitations: physical experiments are limited by experimental conditions, which make it difficult to comprehensively reproduce the characteristics of the complex flow field; theoretical modeling appears to be simplified and idealized to cope with the multiscale noise mechanism; and numerical simulation methods, although accurate, are deficient in the sense that they are computationally expensive and difficult to adapt to complex boundary conditions. In recent years, intelligent algorithms represented by data-driven algorithms and heuristic algorithms have gradually emerged, showing great potential for development in hydrodynamic noise optimization applications. To this end, this paper systematically reviews progress in the application of intelligent algorithms in hydrodynamic noise research, focusing on their advantages in the optimal design of noise sources, noise prediction, and control strategy optimization. Meanwhile, this paper analyzes the problems of data scarcity, computational efficiency, and model interpretability faced in the current research, and looks forward to the possible improvements brought by hybrid methods, including physical information neural networks, in future research directions. It is hoped that this review can provide useful references for theoretical research and practical engineering applications involving hydrodynamic noise, and point the way toward further exploration in related fields. Full article

Increasing vessel traffic in narrow, winding inland waterways has heightened the risk of accidents, driving the need for improved surveillance and management. This study addresses the challenge of real-time processing and synchronization of voluminous video and AIS data for effective waterway management. We developed a surveillance method utilizing smart buoys equipped with sensors and edge computing devices, enabling dynamic spatiotemporal data fusion. The integration of AIS data with advanced computer vision techniques for target detection allows for real-time traffic analysis and provides detailed navigational dynamics of vessels. The method employs an enhanced Long Short-Term Memory network for precise trajectory prediction of AIS data and a single-stage target detection model for video data analysis. Experimental results demonstrate significant improvements in ship detection accuracy and tracking precision, with an average position prediction error of approximately 1.5 m, which outperforms existing methods. Additionally, a novel regional division and a Kalman filter-based method for AIS and video data fusion were proposed, effectively resolving the issues of data sparsity and coordinate transformation robustness under complex waterway conditions. This approach substantially advances the precision and efficiency of waterway monitoring systems, providing a robust theoretical and practical framework for the intelligent supervision of inland waterways. Full article