esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Clusters: Content Analysis
### Cluster 3: Green and Low-Carbon Maritime Development
#### 4.3.2 Subcluster 2: Quantitative Modeling, Prediction, and Emission Reduction Analysis  
This subcluster is dedicated to studies employing quantitative methods to (1) predict, (2) analyze, and (3) reduce emissions in maritime operations. Two articles in this group leverage modeling approaches, multi-trade analysis, and empirical evaluations to understand fuel consumption patterns and assess the effectiveness of emission reduction strategies.  
[PERSON] et al. (2020) develop a two-stage model for a dry bulk ship using a random forest regressor, leveraging ship noon reports combined with weather forecast data, to predict fuel consumption, followed by a speed optimization model that minimizes fuel use while ensuring timely arrival, offering a more interpretable and efficient solution compared to traditional ANN-based models [47]. Computational experiments using two 8-day voyage reports show that the proposed model can reduce fuel consumption by 2-7%, leading to significant reductions in CO\({}_{2}\) emissions. The analysis confirms that ship sailing speed is the dominant factor influencing fuel consumption, followed by total carried cargo, while combined wind, wave, and swell conditions also have a notable impact, and current type has minimal influence. By integrating machine learning with optimization, the model provides a pioneering, non-analytical approach to better understand and manage ship fuel consumption, enabling shipping companies to more precisely plan daily sailing speeds and improve overall energy efficiency.  
[PERSON] et al. (2019) develop an empirical tool to assess the progress toward carbon-clean maritime supply chains [38]. The study's multi-trade analysis of container shipping reveals that annual CO\({}_{2}\) emissions decreased by approximately 33% from 2007 to 2016. This 33% reduction is primarily driven by a 53% improvement in CO\({}_{2}\) fuel efficiency--resulting from a 35% decrease in commercial speed and a 34% benefit from increased vessel size--and a 21% reduction in the average distance traveled due to a new network design. These improvements are partially offset by an 81% increase in the deployed fleet capacity, which stems from a 6% rise in the number of vessels combined with a 71% increase in the average ship size. Trade-specific variations were observed for the Asia-North America route, where a 50% reduction in emission intensity was mostly due to a decreased commercial speed (\(-\)47%), whereas for Asia-South America trade, a 44% improvement was mainly attributed to increased vessel size.  
Key considerations within this subcluster include an emphasis on advanced modeling techniques that offer detailed, data-driven insights and the evaluation of emission reduction strategies across different segments of maritime shipping to inform decision making and policy.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 3 Wind-Driven Optimization Algorithm
### Derivation of Update Equations
Hence, the velocity update equation can be obtained as Equation (32):  
\[u_{\text{new}}=(1-\gamma)u_{\text{cur}}-g\;{\mathbf{x}}_{\text{cur}}+RT\bigg{|} \frac{1}{k}-1\bigg{|}\big{(}{\mathbf{x}}_{\text{opt}}-{\mathbf{x}}_{\text{cur}}\big{)} +\frac{c{\mathbf{u}}_{\text{cur}}^{\text{other~{}dim}}}{k} \tag{32}\]  
where the recommended value ranges of correlation coefficients \(\gamma,g,c\), and \(RT\) are shown in Table 1[45]. Then, the position update equation of air particles is derived, and the relationship between position and velocity is as Equation (33):  
\[{\mathbf{x}}_{new}={\mathbf{x}}_{cur}+u_{new}\times t \tag{33}\]  
where \({\mathbf{x}}_{new}\) is the update speed of air particles. Let \(t=1\); the equation can be simplified as in Equation (34):  
\[{\mathbf{x}}_{new}={\mathbf{x}}_{cur}+{\mathbf{u}}_{new} \tag{34}\]  
Each updated air particle position is limited to the range of \([-1,1]\) due to the theoretical limit of the WDO algorithm. Meanwhile, its velocity must be limited to prevent the air particle position from moving too much. The limited update speed should be within the range of \([-u_{max},u_{max}]\), where \(-u_{max}\) and \(u_{max}\) are upper and lower bounds of air particle velocity. The WDO algorithm takes the state of air particles as the research object and is an optimization algorithm established in a one-dimensional coordinate system, but the algorithm can map one-dimensional problems into N-dimensions to solve multi-dimensional optimization problems.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 5 Simulation
### Performance Results and Analysis of Algorithms in Dynamic Task Handling Scenarios
#### 5.2.1 Variation in Number of Task Points  
To observe the impact of varying the number of task points on algorithm performance, the number of task points was varied within the range of \([1000,3000]\), while other parameters were kept at their default values.  
As shown in Figure 5, as the number of task points increased, all four algorithms exhibited a linear growth trend, with the Greedy Algorithm's curve being steeper. This indicated that the effectiveness of the Greedy Algorithm diminished with the increase in task points, making it unsuitable for handling large-scale data collection tasks. The dynamic KGTSA proposed in this paper showed a slight reduction in time cost compared to the static SOA. This was because the dynamic adjustments quickly reallocated UAVs to more suitable routes, enhancing operational efficiency, which demonstrated the effectiveness of dynamic optimization. Additionally, the time cost for the KGTSA was significantly lower compared to the dynamic HGA. This was due to the superior solutions for static problem-solving and dynamic optimization designed in this study.  
As shown in Figure 6, as the number of task points increased, the vehicle travel distance did not show a direct correlation. The vehicle travel distance was primarily related to the number of docking anchor points, their positions, and the sequence of visits. In other words, an increase in the number of task points did not necessarily lead to an increase in docking anchor points, as the choice of docking anchor points was more influenced by their positions rather than the number of task points. This study used the K-means algorithm for clustering, where the number of centroids was determined by the area size and the maximum flight distance of the UAVs and was independent of the number of task points. To better optimize the total time cost, a higher number of centroids, i.e., docking anchor points, were set. Thus, the vehicle travel distances for the KGTSA and SOA were slightly higher than those for the other two algorithms.  
Figure 5: System time cost.  
As shown in Figure 7, the UAV flight distance increased with the number of task points, with the growth trends for the SOA and KGTSA being relatively gradual. Since time cost was the primary optimization objective of the dynamic KGTSA, when UAV speeds fluctuated, the KGTSA adjusted the route scheduling based on the current status, such as the remaining endurance of the UAVs, to minimize the total task execution time at the current anchor point. However, UAVs still needed to visit all points, so this adjustment did not significantly affect the total route distance.
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 3 Practical Implementation and Case Study
### Digital Twin Application for Monitoring and Stability Assessment
#### 3.2.2. Location and Geometric Design of Underground Cavern  
The location/layout of the power house complex was chosen on the basis of the large caverns being located in the best available rock masses and orientated as favorably as possible with respect to the geological structure and the assumed orientation of principal stresses. At the time of tender design, very little information on ground conditions was available to the designers. Based on the geological data from the detailed feasibility investigation, a BIM-based 3D geological model is developed to provide the first basis for the location and geometric design of the underground cavern based on the most favorable conditions. The BIM-based geological model is shown in Figure 6a. Considering the rock mass properties and parameters, it is decided to place the main part of the cavern within the competent SS-1 sandstone. So, based on a BIM-based geological model, the optimization of location and orientation of the underground powerhouse cavern was performed; see Figure 6b. It was conducted by moving the powerhouse 92 m eastwards to place the main part of the cavern within the competent SS-1 sandstone and rotating the orientation of the cavern axis by 10\({}^{\circ}\) counter-clockwise from the tender design for an optimal cavern axis orientation of 080\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Rock Type**} & \multicolumn{3}{c}{**Properties of Intact Rock**} & \multicolumn{3}{c}{**Rock Mass Parameters for Powerhouse**} \\  & \multirow{2}{*}{\begin{tabular}{c} **UCS** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **GSI** \\ **(EI)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Elastic** \\ **Modulus** \\ **(EI) (GPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Material** \\ **Constant** \\ **(mi)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cohesion** \\ **(c) (MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Young’s** \\ **Modulus (Emr)** \\ **(MPa)** \\ \end{tabular} } \\ \hline
\begin{tabular}{c} **Sandstone** \\ **(SS-1)** \\ \end{tabular} & \multirow{2}{*}{80–92} & \multirow{2}{*}{60–68} & \multirow{2}{*}{30–34} & \multirow{2}{*}{16–18} & \multirow{2}{*}{2.5–3.1} & \multirow{2}{*}{49–52} & \multirow{2}{*}{19,500–21,000} \\ \hline
## 3 Results
### Spatiotemporal Variability
#### 3.1.2 Seasonal Pattern of Precipitation in Nepal
In general, all four datasets generally exhibited high correlations (R > 0.80), which indicate that the seasonal precipitation dynamics can be captured across the country by all four datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Regions** & **Datasets** & **Mean (mm/day)** & **MB (mm/day)** & **RRMSE** & **R** \\ \hline \multirow{4}{*}{Western region} & IMERG-UC & 3.86 & \(\mathbf{-0.03}\) & 0.50 & 0.92 \\  & IMERG-C & 3.52 & \(-0.31\) & **0.14** & **1.00** \\  & GSMaP-MVK & 3.51 & \(-0.32\) & 0.82 & 0.81 \\  & GSMaP-Gauge & 2.97 & \(-0.86\) & 0.40 & 0.98 \\  & IMERG-UC & 4.57 & \(-0.93\) & 0.48 & 0.95 \\ Central region & IMERG-C & 4.02 & \(-1.48\) & 0.43 & **1.00** \\  & GSMaP-MVK & 3.92 & \(-2.58\) & 0.84 & 0.80 \\  & GSMaP-Gauge & 4.69 & \(\mathbf{-0.82}\) & **0.33** & 0.98 \\  & IMERG-UC & 4.75 & \(-0.47\) & 0.41 & 0.94 \\ Eastern & IMERG-C & 4.73 & \(\mathbf{-0.46}\) & **0.18** & **1.00** \\ region & GSMaP-MVK & 3.71 & \(-1.50\) & 0.71 & 0.83 \\  & GSMaP-Gauge & 3.64 & \(-1.56\) & 0.48 & 0.99 \\  & IMERG-UC & 4.31 & \(\mathbf{-0.47}\) & 0.44 & 0.95 \\ Whole & IMERG-C & 3.93 & \(-0.86\) & **0.28** & **1.00** \\ country & GSMaP-MVK & 3.73 & \(-1.05\) & 0.76 & 0.81 \\  & GSMaP-Gauge & 3.86 & \(-0.92\) & 0.36 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical metrics in the western, central, and eastern regions, as well as in the whole study region, derived from the regional monthly mean precipitation (mm/day) from 2015 to 2016. Bold font indicates the best performance for a given metric.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 7 Literature Review of Problem Solution Techniques
### Heuristic and Meta-Heuristic-Based Solutions  
[PERSON] et al. [53] formulate the scheduling problem as a mapping between edge nodes (e.g., sensors) and fog nodes to ensure a high quality of service (QoS) regarding latency and response time. A genetic algorithm is proposed to find the sub-optimal solution. The limitation of the genetic algorithm is that it may not converge to the optimal solution every time. The analysis conducted by the authors did not include considerations for energy consumption and cost concerns.  
[PERSON] et al. [52] propose an ILP formulation for the scheduling problem to minimize latency in IoT applications operating in a hybrid fog-cloud computing environment. The optimization problem was solved using a genetic algorithm accounting for various request characteristics, including priority, processing time, and location. A drawback of the solution is that it does not facilitate pre-emptive scheduling.  
[PERSON] et al. [34] formulate the task scheduling problem as a nondeterministic polynomial problem solved by means of a population-based meta-heuristic algorithm, which minimizes the total execution time of tasks while fulfilling the Quality of Service (QoS) requirements of Cyber-Physical System applications.  
[PERSON] et al. [38] address the task-scheduling problem for the deployment of bag-of-tasks applications on the Cloud-Fog environment. The proposed solution is the Evolutionary Task Scheduling algorithm, which uses an evolutionary approach. Experimental results demonstrate that the algorithm balanced completion time and financial cost for a set of tasks in the Cloud-Fog system. However, the paper does not address several critical issues, including resource utilization, load balancing, and energy efficiency.  
[PERSON] et al. [26] introduced a population-based meta-heuristic, the Cuckoo Algorithm, to minimize response time and reduce energy consumption costs associated with scheduling in fog networks. Their algorithm has drawbacks, such as neglecting to adjust CPU frequencies for nodes and failing to segment traffic scheduling into multiple requests.  
[PERSON] et al. [68] proposed a meta-heuristic approach that utilizes the genetic optimization algorithm to allocate computation requests from static IoT devices. Their approach's main objective is to minimize missed deadlines and timely fulfillment of requests using a mixed integer programming optimization model. The proposed model considers various delays, including transmission delays, queuing delays at networking devices, and task execution delays at the fog and cloud layers. Their solution was compared with the simulation environment's round-robin and priority scheduling techniques.
# A Novel Data-Aided Frame Synchronization Method Based on Hough Transform for Optical Communications
## 3 HT-Based Frame Synchronization Algorithm
### Hough Transform
The four sets of collinear points on the vertical and horizontal lines in image space corresponds to four points generated by the intersection of two curves in Hough space.
# Research Progress Regarding the Precision of Dosing and Distribution Devices for Fertilizers
## 4 Development Trend  
The future development trend of fertilizer discharges in the precision fertilizer dosing and distribution research can be summarized into the following three aspects.  
First of all, in view of the errors caused by the current idealized calculations and simplified models, future research can be devoted to establishing more accurate measurement methods and mechanical models. In the measurement of particle physical and mechanical properties, more modern measurement tools and technologies, such as advanced sensors and high-precision imaging equipment, can be utilized to improve the accuracy of particle physical and mechanical properties. Simultaneously, for in-depth research on the movement of fertilizer particle groups, complex system modeling and simulation technology can be used to consider more affecting factors, improving the fidelity of mechanical models.  
In addition, considering the numerous varieties of fertilizers with different physical properties, future research should further expand the study of the adaptability of fertilizer discharges for different varieties of fertilizers. Meanwhile, more in-depth research on the materials and processes of fertilizer discharges, especially for flexible blades of spiral fertilizer discharges, can enhance their performance and stability through the exploration of new materials and manufacturing processes.  
Finally, future research on fertilizer discharges can utilize more advanced control systems and real-time monitoring devices with the help of intelligent technology. Using advanced CNC processing technology for manufacturing fertilizer discharges, combined with an intelligent control system, can improve accuracy and stability. In simulations and field trials, by considering factors such as working conditions and terrain and employing more accurate evaluation indicators and real-time monitoring systems, further data on the fertilizer discharge performance can be obtained. These data will facilitate the optimal design of fertilizer discharge devices, providing more reliable support for future research.
# Molecular Dynamics Simulation of Bulk Cu Material under Various Factors
## 3 Results and Discussion
### Influencing Factors
#### 3.2.1 Effect of Atoms Number  
The results showed the influence of the number of atoms on the structural properties, as shown in Figure 2.  
The obtained results show that for the material of Cu\({}_{2916}\) metal, the structure shape is given in Figure 2(a1); the number of structural units was shown by 183 FCC, 1216 HCP, 356 BCC, and 1258 Amor (Figure 2(a2)); the length of the links were r\({}_{\text{Cu-Cu}}\) = 2.475 A, g(r) = 4.86, l = 3.25 nm, and E\({}_{\text{tot}}\) = \(-\)9931 eV (Figure 2(a3)). When the number of atoms increased from N = 2916 (Cu\({}_{2916}\)) to N = 4000 (Cu\({}_{4000}\)), 5324 (Cu\({}_{5324}\)), and 6912 (Cu\({}_{6912}\)) atoms, the structural shape of the material changed with the FCC increase from 183 FCC to 458 FCC, HCP increased from 1216 HCP to 2863 HCP, BCC increased from 356 BCC to 855 BCC, Amor increased from 1258 Amor to 1606, 2162, and 2736 Amor (Figure 2(a2,b2,c2,d2)). r had no change, with r\({}_{\text{Cu-Cu}}\) = 2.475 A; g(r) changed from g(r) = 4.86 to g(r) = 4.80. l increased from l = 3.25 nm to l = 4.33 nm. E\({}_{\text{tot}}\) decreased from E\({}_{\text{tot}}\) = \(-\)9931 eV to E\({}_{\text{tot}}\) = \(-\)23525 eV  
Figure 1: Initial shape (**a**), structure shape (**b**), number of structural units (**c**), and RDF (**d**) of bulk Cu\({}_{2916}\) material at temperature T = 300 K.  
(Figure 2(a3,b3,c3,d3)). The obtained results showed that there was a close relationship between the atom number and the size and total energy of the system (Figure 3).  
Figure 2: Structure shape (**a1,b1,c1,d1**), number of structural units (**a2,b2,c2,d2**), and radial distribution function (**a3,b3,c3,d3**) of cubic Cu with different atom numbers.  
The results showed that there was a linear relationship between size (I) and the number of atoms (N), described by the expression 1 = 7.52-61.38N\({}^{-1/3}\). Similarly, the total energy of the system (E\({}_{\rm tot}\)) and number of atoms (N) is described by the expression E\({}_{\rm tot}\) = \(-\)13.22-3.402N (Figure 3). The obtained results showed that there was an influence between the values, similar to the results for metal [42; 43; 62] and alloy [44; 45; 46; 47]. In addition, the size increase of the material was confirmed by the increases in enthalpy and entropy [43]. When increasing the atom number, the size of the matter increased by the ratio I\(\sim\)N\({}^{-1/3}\), and the total energy of the system decreased by the ratio E\({}_{\rm tot}\)\(\sim\)N; this phenomenon is caused by the size effect and the surface effect. The results obtained will be the basis for experimentation methods to deploy future applications. To ensure the calculation speed as well as the stability of the structural features, we choose the Cu\({}_{5324}\) material model as the standard material to investigate the influencing factors in the following sections.
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 4 Results and Analysis
### RTD Classification Summary  
The RTD observed in the DroneResponse case study stems from the dependencies between environmental assumptions (A5-A15) and derived requirements (DR1-DR5). Based on the qualitative analysis conducted, the technical debt of requirements caused by environmental assumptions is classified into five categories:  
* _Unvalidated assumption debt_: This type of debt refers to requirements grounded in assumptions that were never verified or tested during development. * _Incomplete assumption debt_: This type arises from the omission or lack of necessary environmental assumptions during the formulation of requirements. * _Outdated assumption debt_: This type refers to the technical debt that accumulates when requirements are not updated to reflect changes in environmental assumptions. * _Misconstrained assumption debt_: This type refers to technical debt arising from requirements that are based on environmental assumptions imposing incorrect or overly rigid constraints. * _Overconstrained assumption debt_: This refers to overly detailed requirements because they are based on rigid assumptions, leaving no flexibility to adapt to changing conditions. This classification establishes a structured framework for better understanding RTD and implementing more effective strategies to identify and mitigate its risks. Table 3 summarizes the five categories of assumption-driven RTD, highlighting their core conditions, how they differ, and examples from the case study. To strengthen the theoretical foundation of this work, we relate the proposed classification of RTD, driven by environmental assumptions, to the RTDQM developed by [PERSON] et al. (2018). RTDQM is a conceptual framework that enables the quantification of RTD based on three main dimensions, as follows: cost, effort, and system impact. Whereas RTQM focuses on quantifying observable consequences, this classification highlights the causal origins rooted in assumption failures. For example, unvalidated assumptions may lead to increased verification costs and rework, while evolving assumptions can cause system instability, thus impacting all three RTQM dimensions. Figure 2 visualizes this relationship, showing how these five assumption-driven RTD categories intersect with RTQM's dimensions through representative RTD items. This integration shows that the proposed classification not only extends RTD literature in a novel direction but also provides a practical foundation for enhancing quantification models like RTQM. Future work will focus on mapping specific instances of the proposed  
Figure 2: Assumption-driven RTD categories mapped to RTDQM dimensions through RTD items.