bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cosmological forecasts from the combination of Stage-IV photometric galaxy surveys and the magnification from forthcoming GW observatories
Authors: Beltrame Matteo , Bonici Marco , Carbone Carmelita 
Published: October 2024

Abstract
In this work we have investigated the synergy between Stage-IV galaxy surveys and future GW observatories for constraining the underlying cosmological model of the Universe, focussing on photometric galaxy clustering, cosmic shear and GW magnification as cosmological probes. We have implemented a Fisher matrix approach for the evaluation of the full 6×2pt statistics composed by the angular power spectra of the single probes together with their combination. For our analysis, we have in particular considered dynamical dark energy and massive neutrino scenarios. We have found that the improvement to galaxy survey performance is below 1%, in the case of ℓ
GW
max=100 and a luminosity distance error of σ

dL
/dL
=10%. However, when extending the analysis to ℓ
GW
max=1000, we find that the GW magnification improves the galaxy survey performance on all the cosmological parameters, reducing their errors by 3%-5%, when σ

dL
/dL
=10%, and by 10%-18% when σ

dL
/dL
=1%, especially for Mν
, w
0 and wa
. However, here our analysis is unavoidably optimistic: a much more detailed and realistic approach will be needed, especially by including systematic effects. But we can conclude that, in the case of future gravitational wave observatories, the inclusion of the gravitational wave magnification can improve Stage-IV galaxy surveys performance on constraining the underlying cosmological model of the Universe.
Cosmic inflation and (g-2)
μ
in minimal gauged Lμ-Lτ
model
Authors: Paul Arnab , Roy Sourov , Saha Abhijit Kumar 
Published: October 2024

Abstract
The minimal U(1)
Lμ
-
Lτ
gauge symmetry extended Standard Model (SM) is a well motivated framework that resolves the discrepancy between the theoretical prediction and experimental observation of muon anomalous magnetic moment. We envisage the possibility of identifying the beyond Standard Model Higgs of U(1)
Lμ
-
Lτ
sector, non-minimally coupled to gravity, as the inflaton in the early universe, while being consistent with the (g-2)μ data. Although the structure seems to be trivial, we observe that taking into consideration of a complete cosmological history starting from inflation through the reheating phase to late-time epoch along with existing constraints on U(1)
Lμ
-
Lτ
gauge symmetry extended Standard Model (SM) is a well motivated framework that resolves the discrepancy between the theoretical prediction and experimental observation of muon anomalous magnetic moment. We envisage the possibility of identifying the beyond Standard Model Higgs of U(1)
Lμ

Lτ
model parameters leave us a small window of allowed reheating temperature. This further results into restriction of (n
s
-r) plane which is far severe than the one in a generic non-minimal quartic inflationary set up.
Self-frequency-doubling Yb:GdCOB laser intracavity-pumped Pr:YAP laser
Authors: Chu Chu , Anh Nguyentuan , Zhang Anfeng , Xia Jing 
Published: October 2024

Abstract
We present for the first time a Pr:YAlO3 (Pr:YAP) laser emitting at 547 nm intracavity-pumped by a self-frequency-doubling Yb3+:Ca4GdO (BO3)3 (Yb:GdCOB) blue laser at 488 nm. We carried out a model to design the system properly, and the laser performance was experimentally investigated. Intracavity sum-frequency mixing at 547 and 488 nm was then realized in a CsLiB6O10 (CLBO) nonlinear crystal to reach the ultraviolet laser at 258 nm. We obtained a continuous wave output power of 575 mW at 258 nm with a pump source, which is a diode-pumped quasi-three-level Sr1-x
La
x

-y
Nd
y
Mg
x
Al12-x
O19 (Nd:ASL) laser emitting 11.2 W at 902 nm.
Enhancing Situational Awareness for Remote Control of Ship Cranes
Authors: Bellingmo P R , Jørgensen U , Bjerkeng M , Johanson K 
Published: October 2024

Abstract
Cargo handling in ship operations poses risks due to heavy loads, necessitating innovation to mitigate accidents. Automation and remote control offers safer and more efficient operations by moving people from hazardous situations. Smaller and remote ports often rely on geared ships, which are typically manually operated. A first step towards automation is to remotely control these cranes. In remote control, the operator needs to gain situational awareness through sensor inputs. Previous research has shown that there is a need to investigate the sensor configuration and placement to provide situational awareness, particularly considering the placement of cargo onboard. This study seeks to identify needed sensor configurations for situational awareness in remote control of ship cranes using a simulator framework. A modular crane simulator framework has been developed to evaluate situational awareness for different sensor configurations. The sensor configuration has been evaluated with feedback from a crane expert. This research has identified potential sensor configurations for enhancing situational awareness in remote crane operations. The developed simulator framework has demonstrated its flexibility and efficiency as a platform for testing and optimizing cargo handling operations.
Harmonizing Maritime Innovation: Enhancing International and National Standardization in Intelligent Ship Transport Systems
Authors: Yeon Koo Kwi , Jan Rødseth Ørnulf , Lislebø Geir , Herman Ulvensøen Jon 
Published: October 2024

Abstract
The rapid advancement of maritime technologies, particularly in the domain of autonomous ships and maritime intelligent transport systems (MITS), necessitates a robust framework for digital interoperability at both national and international levels. This paper presents an in-depth analysis of the current state of standardization within MITS. It emphasizes the importance of harmonizing efforts to facilitate the integration of innovative technologies into the maritime sector, ensuring safety, efficiency, and environmental sustainability. To gain broader insights, standardization practices from other industries are examined. In conclusion, the paper argues that enhancing collaboration and standardization in MITS is essential for the successful implementation of maritime innovations. It calls for a collective effort from national and international stakeholders to develop a unified framework that supports the growth of autonomous ship technology and the evolution of intelligent maritime transport systems.
A Formalism for Scalable Maritime Traffic Monitoring and Explainable Anomaly Detection and Resolution at Vessel Traffic Services
Authors: Stach T , Koch P , Constapel M 
Published: October 2024

Abstract
Vessel traffic service (VTS) centres around the globe monitor and manage the ship traffic in their areas of responsibility and respond to arising unsafe or inefficient situations. Decision support tools (DST) help vessel traffic service operators (VTSO) to enhance their situational awareness and decision-making process, thus to recognise and assess dangerous situations. However, current DST have unsophisticated features and require manual configuration by the operator. Firstly, triggers for alerts are mostly based on the creation of geographical polygons for spatial-dependent assignment of rules, thresholds, ranges and limits for specific features. Secondly, calculations for the detection of safety-critical situations rely on measurements based on the closest point of approach (CPA) without taking contextual information into account. Thirdly, trajectory predictions are performed considering a constant velocity model (CVM) which does not depict reality. Due to these and other peculiarities, current DST are prone to false alerts. This results into a high workload where unsafe situations may be overlooked. In this paper, we propose a hierarchical formalism as foundation for an anomaly detection and resolution which generates less false alerts. The formalism consists of three hierarchical layers which represent, objects, measurements and situations. The formalism ensures adaptability and extensibility to various VTS areas and traffic patterns. Moreover, due to the given structure, the presented formalism is easily implementable, adaptable and scalable taking the currently available technological capabilities at VTS centres into account. We demonstrate the feasibility of this formalism by implementing a proof-of-concept and assessing it with realistic scenarios. Our implementation utilizes a rule-based system as central framework and it is configured through statistical methods, e.g. machine learning. Given this hybrid approach, the implementation provides reasonable and explainable results. This enables comprehensibility and verifiability by authorities and operators which is crucial for acceptance.
Digital twins for resource optimization in multi-purpose ports: A design approach for data-driven decision making
Authors: Karlsson M , Liu M , Li H , Haraldson S , Lind M , Mishra M , Sandberg J , Lind K 
Published: October 2024

Abstract
Multi-purpose ports’ ef/icient and sustainable operation relies on seamless coordination and decision-making among multiple organizations. This paper underscores the critical importance of forecasting resource and infrastructure utilization for informed operational, tactical, and strategic decision-making. The proposed approach draws on digital twin technology to enable collaborative decision-making by modeling complex port environments to enable shared situational awareness among stakeholders. Illustrated through a collaborative project involving the RISE Research Institutes of Sweden, National University of Singapore, Grieg Connect, Umeå University, Kvarken Ports Umeå, and INAB, we propose a digital twin design to empower the port as a decision-maker in multi-organizational settings to proactively plan and optimize its utilization of present and future resources.
A Modified Three-terms CG Method with the MWWP Line Search and its Application to Image Restoration
Authors: Liang Chunzhao , Yuan Gonglin , Lu Junwei 
Published: October 2024

Abstract
In this article, an innovative method, which integrates the optimized three-term Polak–Ribière–Polyak (PRP) conjugate gradient (CG) method with the improved WWP line search rule, is presented and applied in the field of image restoration. The proposed search direction exhibits sufficient descent and trust region characteristics, maintaining its effectiveness regardless of the specific line search rule employed. We established the global convergence of the presented algorithm and eliminated the dependence on the Lipschitz continuity assumption. The numerical comparisons with the PRP CG method revealed the superior performance of the designed algorithm in the field of image restoration. This research outcome provides strong support for further advancements in the field of image recovery.
ICOPP: A Highway Autonomous Driving Planner Based on Interactive Collaborative Optimization and POMDP
Authors: Zhao Kun , Yin Jialun 
Published: October 2024

Abstract
In this paper, we propose a planner for autonomous vehicles in complex highway scenarios, which can efficiently plan the trajectory of autonomous vehicles. The method aims to improve the intelligence of autonomous driving under the uncertainty of the perception and prediction system. The method employs a POMDP framework to address the challenges brought about by uncertainty. Firstly, this method divides the entire planning cycle into several discrete decision-making processes. In each process, the vehicle’s lateral and longitudinal behaviors are semantically processed to obtain a limited number of decision sequences, thereby addressing the curse of dimensionality in state space and observation space in POMDP problems. In each decision-making process, different simulation scenarios are constructed based on the decision of the self-vehicle and probability distribution of the predicting behaviors of surrounding vehicles. In each scenario, key vehicles are selected for collaborative optimal planning among multiple vehicles, resulting in the optimal drivable trajectories for both the ego vehicle and other vehicles under the given decision. Finally, an evaluation of the policy is conducted in terms of safety, comfort, and efficiency to select the optimal policy. In simulation experiments, the ICOPP demonstrates smoother and more stable autonomous driving performance compared to traditional methods.
A Mathematical-logic Perspective for Analyzing Machine Learning Models
Authors: Li Kechen 
Published: October 2024

Abstract
Machine learning (ML) has achieved remarkable success. There are various types of machine learning methods. However, the previous categorization of these models was simply based on the paradigm of learning. What kind of data and environment can be used for learning becomes the only concern, but whether the trained model is competent for a specific task is less discussed. Guiding ideology of what machine learning models are competent for a given application scenario is urgently needed. To solve this problem, we believe that this requires a logical analysis of the external properties of the learned models. In this paper, we propose a perspective for analyzing machine learning models by applying mathematical logic, which helps determine whether a model is up to a given task. In addition, we construct an analysis case and illustrate that the structural objects can be mapped to the class labels in machine learning. We believe this perspective will open a new field of analyzing machine learning with mathematical logic.
Compensation Algorithm for Wind Turbine Main Shaft Loads Based on LSTM
Authors: Wen Zecheng , Deng Yu , Wu Wei , Wen Hua , Chen Jikang 
Published: October 2024

Abstract
For wind turbine development, load size is the core and critical factor. Currently, most companies employ Bladed software for load calculations, which enjoys widespread use in engineering applications due to its efficiency and ease of use. However, constrained by the limitations of blade element theory and momentum theory, there still exist discrepancies between the simulation results and actual conditions. This paper aims to combine simulation algorithms with the application of an LSTM (Long Short-Term Memory) neural network to develop a compensation algorithm for the main shaft loads of wind turbines. By doing so, it endeavors to realize real-time monitoring of the main shaft loads, thereby providing a foundation for achieving high-precision control in wind turbine systems.
Rail State Recognition Method Based on a Convolutional Block Attention Module
Authors: Liu Lili , Zuo Jihong , Yang Chuanyin , Tang Yufeng 
Published: October 2024

Abstract
In order to solve the problems of high subjectivity and serious lag in traditional rail surface state recognition based on manual experience, a rail surface state recognition model based on convolution attention is proposed. First, the transfer learning method is used to pre-train the ImageNet data set to obtain the model parameters. Secondly, the 3×3 convolution in the ResNet-50 residual block is replaced with a convolutional block attention module (CBAM) to obtain a new CBAM-ResNet. Finally, the model identification results of the rail surface status are obtained through the Softmax classifier. The results show that this method can effectively identify the rail surface status, and the identification accuracy can reach 99.68%, which is better than other deep neural network models.
An Efficient Numerical Algorithm for Abnormal Integrals with Arbitrary Order Double Bessel Functions of the First Type
Authors: Yang Yonglin , Wang Wenshuai 
Published: October 2024

Abstract
The integrals of the first type of double Bessel functions have a wide range of applications in geological exploration, mechanical and electromagnetic responses, signal processing, scattering, and wetting. In this paper, we develop a linear transformation accelerated convergence algorithm (LTACA) that combines the large argument approximate expression of the Bessel function (LAAEBF) and the integral accumulation to provide an efficient numerical algorithm for abnormal integrals with arbitrary order double Bessel functions of the first type. The effectiveness and high efficiency of the algorithm are verified by numerical examples, and its high accuracy is demonstrated by comparison with the Gaver-Stehfest inverse Laplace transform method (GSILTM). This offers a reliable and efficient computational method for the study of signal processing and mechanical problems.
A Modified Wei-Yao-Liu Stochastic Conjugate Gradient Algorithm in Machine Learning
Authors: Mo Zhongyu , Yuan Gonglin , Deng Wei 
Published: October 2024

Abstract
The Wei-Yao-Liu (WYL) Conjugate Gradient (CG) algorithm exhibits favourable attributes, notably sufficient descent and trust domain characteristics, in the context of solving unconstrained optimization problems. The exploration and enhancement of computational methodologies for stochastic optimization problems have garnered significant attention. In this study, our objective is to improve the Wei-Yao-Liu (WYL) CG algorithm and introduce an effective optimization method tailored for solving stochastic optimization problems. We will present a comprehensive framework for analysing the convergence of this algorithm and, under suitable conditions, establish its sufficient descent properties, trust region features, and global convergence of stationary points. We will also give an improved CG algorithm to calculate a framework for variance reduction. Additionally, we will conduct experimental comparisons to showcase the competitiveness of our proposed algorithm.
A Target Collision Algorithm of Autonomous Mobile Robot for Two-player Competition
Authors: Zhu Qianqian , Wang Ding , Dai Yipeng 
Published: October 2024

Abstract
A variety of robot competitions are held to train and attract students everywhere. This paper studies the algorithm of autonomous robot with two-player competition. The autonomous robots in a two-player competition takes the scoring form of collision and wins first. The impact object is the enemy target. The key algorithms in a two-player competition are target detection, target path decision, and collision target motion control. First, the vehicle target data set was established and enhanced, the lightweight detection algorithm model was screened and trained, the model pruning optimization experiment and the model deployment optimization experiment were carried out, and then the TensorRT framework was used for inter-layer fusion and data quantification processing. After hitting the target path decision algorithm after detecting the target target, it finally integrates the decision in simple and complex situations. The collision target motion control is the scheme of more intelligent and self-adjustable parameters of fuzzy Radial Basis Function (RBF) network Proportional Integral Derivative (PID) controller. Finally, the collision target implementation algorithm is tested in a real environment, and the results show that the task requirements can be met.
Dynamic Surface Tracking Control for Nonlinear Constraints
Authors: Li Wang 
Published: October 2024

Abstract
An adaptive control scheme based on dynamic surface control (DSC), and intelligent control techniques is developed for a 3-DOF helicopter with unknown disturbance and asymmetric saturation. The reconstructed dynamic neural network (NN) is established to compensate for nonlinear terms in real-time. A designed smooth nonlinear sigmoid function is used to model saturation nonlinearity. The combination of dynamic surface control with adaptive intelligent control techniques resolves and improves the tracking and robust performance of the system. The stability and ultimately bound of all signals of the controlled system are demonstrated by theoretical analysis. Numerical results prove the feasibility of the presented control scheme.
Partial Discharge Detection Technique Based on Full Convolutional Network with Channel-wise Convolution and Channel Mixing
Authors: Bi Jinjie , Gu Hongyu , Yang Hang , Hao Zhengyang , Huang Shoulin 
Published: October 2024

Abstract
The occurrence of partial discharge (PD) serves as an indication of power equipment failure, which, if not given due attention, may result in severe power accidents. Traditionally, the detection of PD involves applying various pre-processing techniques to the original signal and extracting specialized signal features that are subsequently classified using a classifier. The process is intricate and demands extensive expertise. One drawback of the conventional detection approach lies in its failure to consider the information conveyed between the phases of a three-phase electrical signal during the process of identifying PD in medium voltage three-phase cables. The present study proposes a novel method for detecting PDs, which is based on channel-wise convolution and channel mixing(C2CM) using full convolutional networks. Firstly, a set of pulses for each phase of a three-phase signal is extracted and combined to form a three-phase pulse set, serving as the input to the network. Subsequently, we introduce a FCN-C2CM network designed specifically for PD detection, capable of learning both the time-domain features of the input signal and the phase features of the three-phase signal, thereby enhancing recognition accuracy. We evaluated our approach using online testing tools on publicly available datasets, with experimental results demonstrating its superiority over existing.
Design of Intelligent Distributed Cable Laying System Based on Multi-machine Cooperative Control Strategy
Authors: Li Tiefeng , Gao Jiantong , Xu Mingwei , Li Jialu , Zhou Yuntong , Wang Hao 
Published: October 2024

Abstract
To solve the problems of complicated long-distance cable laying process, low laying efficiency and easy cable damage, an intelligent distributed cable laying system based on a multi-machine coordination strategy is proposed in this paper. Firstly, the multi-machine cooperative control strategy of cable clamping push laying is proposed, which solves the problem of complex and low efficiency of long-distance cable laying. Secondly, the intelligent cable-laying machine is developed to realize the perception and adaptive control of the cable-laying field conditions. Finally, an intelligent distributed cable laying system based on a multi-machine cooperative control strategy is developed. The combination of a multi-machine cooperative control strategy and an intelligent cable laying machine further strengthens the cooperation and stability of the whole system and provides a more intelligent and efficient way for cable laying.
The implementation of STE(A)M education through Scratch projects
Authors: Leshchuk S , Dilna N , Grod I , Radchenko O , Hnoiova T 
Published: October 2024

Abstract
The present research is motivated by the necessity of introducing modern methodologies corresponding to the study’s content into the educational process. The work aims to use Scratch projects for the organization of students’ educational activities, as well as for their creative self-realization. Authors realize STE(A)M-approach in the educational activity by facilities of Scratch-projects. The high-level block-based visual programming language could be an auxiliary technology for a teacher and an independent developing tool for students. The research revealed the main themes that became the basis for the development of the author’s course “Creative Self-Fulfilment in Scratch”. This contributes to the development of students’ creativity. The content of the course is extended with projects in algebra, geometry, physics and musical culture. Each of these projects demonstrates the unity of technology and creativity.