bluuebunny/crossref_metadata_2025_split huggingface dataset:
The economic and environmental viability of green and autonomous ships in inland shipping ecosystems
Authors: Kloch Kristoffer , Normann Kristiansen Jimmi 
Published: October 2024

Abstract
This paper explores the potential of using green, autonomous ships in revitalising inland shipping in Europe against the backdrop of declining market share and the dominance of “economy-of-scale” in waterborne freight transportation. It assesses the economic and environmental viability of converting freight from road- to waterborne modalities in broader business ecosystems, specifically along the Rotterdam-Ghent corridor. The analysis leverages operational and commercial insights from logistics firms, ports and terminal operators, combined with data on European goods flows by road, and accounts for operational, financial and environmental variables including realistic scenario building and ecosystem implications. Findings indicate that inland shipping in general and green, autonomous shipping in particular offer both economically and environmentally viable alternatives to road transport. The study calls for further research into green, autonomous ships from an ecosystem perspective as a potential solution to current challenges in sustainable freight transportation.
Software Architecture Design of ASV Rybitwa: Development of an Autonomous Surface Vehicle for Dynamic Navigation and Task Execution
Authors: Rusiecki Igor , Ujazdowski Tomasz , Wilk Jakub , Sobolewski Patryk , Pyskovatskyi Serhii , Skierkowski Marcel , Lisowski Tomasz , Sieklicki Wiktor 
Published: October 2024

Abstract
This paper introduces the software design of an Autonomous Surface Vehicle (ASV) named ASV Rybitwa. It was designed as an easily transportable platform capable of autonomous navigation and performing tasks during the RoboBoat 2024 competition, with emphasis on its modularity, generality and extendibility. The paper presents a software architecture for a small autonomous surface vehicle using novel tools that provide easy system integration and expansion. To meet such requirements, a generic control box connecting an autopilot (PX4) and a onboard computer (NVIDIA Jetson Nano) was developed so as to host the Robot Operating System 2 (ROS2) and to interact with sensors and actuators. Computer vision from three Oak-D cameras, equipped with AI algorithm support (YOLOv8), was used for navigation and obstacle avoidance. The decision-making system was designed using behavioural trees and computer vision, allowing the vehicle’s capabilities to be adapted to the needs of the current tasks and mission. In addition, an Omni X propulsion system was proposed, providing full holonomy and enhancing dynamic positioning and navigation capabilities in complex navigational conditions. In addition this paper describes lessons learned from ASV Rybitwa’s real-world testing during the aforementioned competition.
GreenHopper: The Danish spearhead towards autonomous waterborne mobility
Authors: Blanke Mogens , Nicholas Hansen P. , Dittmann Kjeld , Enevoldsen Thomas T. , Dagdilelis Dimitrios , Schöller Frederik E.T.S. , Plenge-Feidenhans’l Martin K. , Becktor Jonathan , Papageorgiou Dimitrios , Galeazzi Roberto 
Published: October 2024

Abstract
GreenHopper is the first Danish zero-emission ferry developed as a test platform for autonomous waterborne navigation technologies. The paper presents technology development within the innovation project ShippingLab Autonomy, which led to the commissioning of GreenHopper at Limfjorden (DK) in December 2022. The technology research resulted in a holistic system architecture for surface vessel autonomy, based on distribution of functionality and responsibility on software modules, similar to the structure observed in the International Maritime Organization (IMO) Seafarers Training Certification and Watch-keeping (STCW) regulatory framework. The paper shows how this approach results in an architecture that supports safe behaviours of individual modules and of autonomous navigation at a system level. The paper presents the individual modules, specific features and benefits. Elements of the regulatory framework are highlighted to poise technology approval by maritime authorities. The paper reflects on lessons learned, discusses continued technology validation in dedicated operational scenarios.
Mitigating the impact of accidental ammonia release during bunkering operation
Authors: Ng Clara , Ming Liu 
Published: October 2024

Abstract
Ammonia is a carbon free energy carrier that is gaining popularity as the next generation marine fuel. However, the adoption of ammonia by ships will not be feasible without solving the challenges during its bunkering operation, of which the utmost is the harmful effect from ammonia’s toxicity. A potential release of ammonia during bunkering operation will cause harmful impacts of various levels depending on its initial physical states, rate of release, total released inventory, local geometries, terrain and weather conditions. Without any mitigation measures in place, the released ammonia will undergo a complicated dispersion, dilution and uplifting pathway, affecting potential sensitive receptors in the surrounding. The knowledge and experience learnt from chemical, fertilizer and refrigeration use of ammonia provide valuable references for ammonia handling at sea and port. Mitigation for released ammonia can follow a two-pronged strategy: 1) the preventive strategy to reduce the likelihood of accidental release and 2) the protective strategy to reduce the release severity. This translates into mitigation technologies developed for pre-release scenarios and post-release scenarios. The pre-release mitigation considers inherent safer engineering design and good management, while the post-release mitigation focuses on early vapor detection and warning, containment, suppression, counter measures and emergency response. Nevertheless, there is a lack of understanding and use cases on how to extend the findings to ammonia bunkering operation, where the contact between ammonia and the sea environment, in particular sea water surface and column, is inevitable. The review provides a consolidated view on the applicability of land-based post-release mitigations such as physical containment, use of barriers, foams, dilutions and other chemical approaches to mitigation the consequences of accidental ammonia release from future ammonia bunkering operation.
Scenarios of Multiple Vessels Encounter for Safety Evaluation for Automatic Collision Avoidance Algorithm using AIS Data
Authors: Minami Makiko , Sato Keiji , Sawada Ryohei 
Published: October 2024

Abstract
Interest in Maritime Autonomous Surface Ships (MASS) is growing, and efforts are underway in Japan to realize them. One of the key issues in realizing autonomous operation is automatic collision avoidance. Simulation is effective in developing and evaluating such algorithms. This paper describes the generation of a set of simulation scenarios that consider the actual sea traffic using AIS data, which is traffic flow data in real seas. We assumed that the comprehensiveness of the scenarios could be ensured by generating scenarios by combining typical encounter patterns and collecting difficult situations that are encountered in real situations. First, the characteristics of the target sea area were identified from the AIS data, then difficult traffic situations were extracted from the data, and then a set of scenarios were generated based on them. Considering the presence of manned ships in the test area, we conducted some tests of the generated scenarios not only using fast-time simulations but also using a ship-handling simulator and conducted visual checks from own ship and the target ship.
BEYOND – Building an Experience to Yield Optimal Nautical Displays – Measuring Nautical Situation Awareness
Authors: Schwarze Anastasia , Bhattacharya Sharmistha , Grundmann Robert , Sundar Mahadevan Hari , Hochgeschurz Stephanie , Motz Florian 
Published: October 2024

Abstract
The growing digitisation and integration of ship operations, navigation, communication, safety equipment and systems have expanded the functional scope of modern navigation systems. However, this has also increased the workload for the crew, making it more challenging to cultivate situation awareness and make appropriate decisions, particularly in critical situations. The research project, BEYOND – Building an Experience to Yield Optimal Nautical Displays, aims to develop and validate a test methodology for evaluating maritime Human-Machine Interfaces (HMI).
Besides system usability and user experience, maritime HMIs are generally evaluated in terms of the situation awareness acquired during navigation. For this purpose, the established Situation Awareness Global Assessment Technique (SAGAT) was adapted such that participants experienced a series of simulated navigation scenarios and were interrupted at unexpected intervals to answer situation awareness questions. This paper addresses the challenge of formulating the appropriate situation awareness questions and their associated frameworks. An assembly of questions reflecting the knowledge of a navigator was created, taking into account different categories of information, including the own ship, the route taken, and the surrounding area. The number of questions needed to capture the necessary knowledge comprehensively was also defined. Additionally, it was crucial to determine the appropriate answer options for each question. Besides graphically presented multiple-choice questions, where the answer had to be selected based on a screenshot of the environment, there are also alphanumeric questions where the participant had to retrieve knowledge from the situation and enter numerical values. For instance, this included information about the course, navigation hazards, etc. A major challenge in assessing these numerical values was determining the acceptable range, i.e., the thresholds that define an answer as correct or incorrect.
This paper outlines the BEYOND methodology developed for the comprehensive testing of maritime HMIs, focusing primarily on situation awareness assessment. It describes the steps taken to compile a list of situation awareness questions and their corresponding accuracy thresholds based on the findings from expert interviews. The paper also discusses the experimental results of a user study conducted to test the overall methodology.
Cyber Security Assessment of An Interoperable Port Call and Voyage Optimization tool
Authors: Hacks Simon , Pahl Julia 
Published: October 2024

Abstract
The MISSION project aims to revolutionize maritime transport by developing a digital tool for real-time optimization of port calls and voyages, thereby reducing fuel consumption, cutting greenhouse gas emissions, and decreasing waiting times through enhanced coordination and information sharing among stakeholders. However, the security of the involved IT systems is critical to ensure safe and reliable operations.
This paper introduces harborLang, a novel threat modeling language tailored for the maritime sector, built on the Meta Attack Language (MAL) framework. harborLang addresses the unique security challenges in maritime transport by enabling the modeling and mitigation of potential threats through detailed attack simulations. By integrating harborLang with the Yet Another Cybersecurity Risk Assessment Framework (YACRAF), the project enhances its risk analysis capabilities, allowing for precise threat scenarios that reflect the maritime environment’s complexities. The combined use of harborLang and YACRAF facilitates comprehensive cybersecurity risk assessments, significantly improving decision-making, operational safety, and the overall cybersecurity posture of maritime and port operations.
The art of orchestrating nautical services in a port call: A literature classification
Authors: Derin Yasemin , Braga Joao , Kastner Marvin , Jahn Carlos 
Published: October 2024

Abstract
Global carbon dioxide emissions are forecast to increase by around 15% by 2050 compared to 2020. In order to move towards a sustainable transition to net zero emissions by 2050, more energy-efficient operations are a prerequisite. In response, terminals are looking for ways to optimise port processes to reduce energy consumption while also keeping waiting time and costs low. This paper focuses on optimisation approaches for the individual actors in the nautical service that can support these objectives. A comprehensive review, classification and discussion of the relevant literature on current methods and procedures for the stakeholders of the nautical service is conducted. In this regard, current challenges and limitations are identified. The results of the classification scheme show that tugboats in particular are examined in more detail. The objective is often to reduce costs and increase safety in the port area. For this purpose, mathematical approaches, or various types of algorithms are usually applied. Future research activities could be based on more detailed analyses of the approaches used in the identified papers.
Coordinate Transformation Techniques for Improved Co-Simulation in the Maritime Industry
Authors: Sadjina S , Wille E , Jørgensen U , Skjong S 
Published: October 2024

Abstract
Co-simulation is the modular and flexible coupling of stand-alone simulation models. It promises to help unlock the maritime industry’s digitalization potential, but the correct and efficient handling of coordinate systems between models is one of the unsolved challenges that stand in the way of wide-spread adoption. Here, we give a brief introduction to the problem and describe what makes it especially difficult for co-simulations. We introduce a few of the most common models and provide a list of coordinate systems and reference frames used in maritime co-simulations. Focusing on hull and gangway models, we explore general solution approaches and provide practical advice for how to handle coordinate systems in co-simulations using a vessel–gangway simulation case for illustration.
Research of Converter Station Intelligent Inspection System Algorithm
Authors: Zhou Fan , Zhang Kaining , Huang Ziqiang , Wu Wei , Liu Hu 
Published: October 2024

Abstract
This article mainly reveals a converter station equipment defect detection system based on the YOLOv5 algorithm. The research encompasses the establishment of a simulated environment for converter station inspection tasks, problem formulation, algorithm design, training process, and experimental evaluation. Key contributions include the enhancement of the YOLOv5 algorithm through improved clustering techniques, dataset preparation, and multi-object detection model training. This highlights the importance of appropriate anchor box initialization in object detection tasks, especially when dealing with small-sized images and varied defect types. In order to verify the correctness of this research, a comparative simulation analysis with mainstream detection algorithms further validates the superiority of the proposed method. The findings of this research contribute valuable insights into the development of AI-driven defect detection systems for power equipment inspection.
Road Traffic Safety Status Analysis and Prediction Based on Dynamic Bayesian Network
Authors: Zhao Pengzhi , Geng Danyang , She Shaoyi , Duan Mengyuan 
Published: October 2024

Abstract
Dynamic Bayesian networks can effectively capture dynamic changes and uncertainty relationships in data. Conventional prediction methods do not consider the temporal characteristics between traffic flow sequences, which affects prediction accuracy. This article proposes a method for analyzing and predicting road traffic safety status based on DBN. Firstly, data matching is performed according to the “case-control” sample structure of the matching formula to minimize the influence of other factors on the modeling of traffic safety status; Secondly, the random forest model is applied to analyze and extract the variable with the highest correlation coefficient as the input variable for the traffic safety status prediction model; Then, a DBN prediction model is established using matched accident traffic flow and non-accident traffic flow sample data; Finally, by analyzing the effectiveness evaluation indicators of the model, multiple prediction results showed that the overall prediction accuracy of the DBN method was over 80%.
Application of Visual Transformer in Low-resolution Thermal Infrared Image Recognition
Authors: Li Shuoda , Wang Xinyao , Li Xicai 
Published: October 2024

Abstract
Addressing the challenges of inadequate accuracy and limited robustness exhibited by current lightweight object detection networks specifically tailored for low-resolution thermal infrared face detection scenarios, this paper delves into developing an ultra-lightweight thermal infrared face detection algorithm that leverages visual attention mechanisms. To ascertain the optimal neural network complexity, a series of comparative experiments are meticulously conducted. With Yolo-FastestDet serving as the benchmark, this study endeavors to compress the backbone network, striking a delicate balance between network depth and detection speed. Additionally, to bolster the network’s capacity for profound feature extraction and precise discrimination of target edges and small objects, a Transformer-Encoder-based visual attention module is seamlessly integrated. Consequently, a lightweight face detection algorithm, enriched with attention mechanisms, is formulated. Furthermore, to mitigate the scarcity of low-resolution infrared face image data, a self-constructed visible-infrared face dataset is employed for training and evaluation purposes. The experimental outcomes reveal that the proposed algorithm attains an impressive mAP@0.5 score of 0.953 on the test dataset while satisfying the stringent real-time detection criterion of 30 frames per second (FPS) when deployed on an embedded Raspberry Pi CPU.
NEAT Based Approach for Cognitive Covert Communication Assisted by UAV-IRS
Authors: Cai Yuxuan , Liao Xiaomin , Lu Yaobin , Han Yang , Wang Yulai 
Published: October 2024

Abstract
To address the security vulnerabilities inherent in cognitive radio technology amidst extensive control information and data exchanges, this paper introduces the use of unmanned aerial vehicle equipped with intelligent reflecting surfaces (UAV-IRS) as relay nodes to facilitate covert communications for cognitive users. Given the complexities and dynamics of the electromagnetic environment, which involve multiple interconnected and constantly changing communication resources, as well as high-dimensional data, we propose the NeuroEvolution of Augmenting Topologies based Resource Optimisation Algorithm Method (NEAT-ROAM). This advanced algorithm continuously optimizes and adapts the network structure in response to the external environment, ensuring robust and secure communication in dynamic and challenging scenarios. The algorithm not only provides superior covert communication performance but also exhibits fast convergence, which can be applied to the problem of assisting covert communication under mobile conditions using UAV-IRS.
Stability analysis of SnO2 surfaces using First-Principles computational methods
Authors: Mamani F Y , Huallpa D L , Carazas K A , Vizcardo M A 
Published: October 2024

Abstract
This study focuses on investigating the structural and electronic properties of the most stable faces of SnO2 using the Quantum Espresso software. Structural properties were found applying the generalized gradient approximation (GGA-PBE) with ultrasoft pseudopotentials and plane wave basis sets. Methodology involved determining the cutoff of the set of plane waves, selecting appropriate k-points to represent the first Brillouin zone, and optimized the lattice parameters (a and c). Models were generated for each studied surface, with the most optimal being the one with the lowest energy. These results are based on the periodicity of infinite sets of surfaces, increasing the gaps of the vacuum layers. As a result of the calculations, a discrepancy of less than 1% was observed in the lattice parameters compared to previous publications. Furthermore, it was found that the (110) is the most stable surface, in agreement with results published using VASP.
Use of satellite data to determine the cloud optical depths present during overirradiance conditions
Authors: Zamalloa-Jara M A , Grieseler R , Töfflinger J A 
Published: October 2024

Abstract
Overirradiance conditions can negatively impact the operation of photovoltaic systems if no protective measures have been implemented, leading to potential damages and economic losses in photovoltaic generation plants. Current simulation models attempt to understand the mechanism of overirradiance conditions. However, their observations still differ significantly from experimental ones, emphasizing the need to better understand the two main hypotheses that account for overirradiance events: reflection at the edges of thick clouds and Mie scattering in thin clouds. This paper studies the qualitative correlation between the global tilted irradiance measured by a spectroradiometer on the surface and the optical depth of the clouds measured by the GOES-16 satellite to shed more light on this phenomenon. Our results show a good qualitative correlation between the global tilted irradiance and the optical depth of the clouds present during overirradiance events. We also show that all overirradiance conditions occurred when thick clouds were present. These results indicate that the overirradiance events analyzed have been produced predominantly by reflections at the edges of thick clouds, supporting the hypothesis that the increase in global irradiance is mainly due to a substantial increase in direct irradiance.
Advancing Education in Challenging Times: A Review of the XVI International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2024)
Authors: Kiv A E , Semerikov S O , Striuk A M , Osadchyi V V , Vakaliuk T A , Nechypurenko P P , Bondarenko O V , Mintii I S , Malchenko S L 
Published: October 2024

AbstractThis paper presents a comprehensive review of the XVI International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2024), held from May 15-17, 2024, at Kryvyi Rih State Pedagogical University, Ukraine. The conference, organized in a hybrid format, brought together over 100 attendees from 9 countries, showcasing cutting-edge research and innovations in educational practices amid global challenges. The review synthesizes key themes and findings from 37 selected papers across various domains, including Mathematics Education, Science Education, Computer Science Education, and Educational Technology. Notable trends include the increased integration of artificial intelligence and digital technologies in education, the development of 21st-century skills, and adaptive strategies for education in crises. Particular attention is given to studies addressing the unique educational challenges faced in Ukraine, demonstrating the resilience and innovation of educators and researchers in conflict-affected regions. The paper highlights significant contributions in areas such as online learning environments, visual thinking in mathematics, AI applications in mental health education, and the design of university digital ecosystems.