esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sound Speed Profile Data  
The main way to obtain SSP samples of the South China Sea currently is through Argo float data. The Argo program began in 1999, which, from 2007, formed an observation network composed of 3000 floats, each with the ability to collect ocean temperature-salinity profiles, providing a basis for the calculation of SSPs. As of 2020, the number of Argo floats increased to nearly 4000, and the coverage has expanded to high latitudes, marginal seas, and deep-sea areas.  
The submarine structure of the South China Sea is complex, with widely distributed continental shelves and slopes. The central sea basin is a typical oceanic crust expansion area, with a water depth of more than 4000 m. At the same time, there are many groups of seamount chains and fracture zones, which affect the distribution of seawater chemical properties and heat flux in some areas. The South China Sea also has complex seawater dynamic characteristics. The monsoon-driven surface circulation system affects the temperature and salinity distributions. Moreover, mesoscale eddies significantly change the local temperature-salinity structure through vertical shear and horizontal convergence-divergence. In general, the submarine landform structure and complex hydrodynamic characteristics of the South China Sea have a significant impact on acoustic stability.  
Combining the special geomorphic characteristics and seawater dynamic characteristics of the South China Sea with the Argo float data, we selected data spanning 12\({}^{\circ}\) N to 20\({}^{\circ}\) N and 110\({}^{\circ}\) E to 120\({}^{\circ}\) E from 2009 to 2017 as the training sample for the EOF. Since the number of Argo float samples decreases sharply when the depth exceeds 1000 m, and several factors adversely affecting acoustic stability mainly exist at depths exceeding 1000 m, the research depth studied in this paper is 0-1000 m. Considering the common profile depth intervals used in oceanographic research, adopting a recognized sampling interval is conducive to comparison with other methods. Referring to the World Ocean Atlas (2023; WOA) standard, linear interpolation was performed on the 0-1000 m depth data with a basic sampling interval of 5 m. After the above processing, a total of 3881 SSPs were obtained, of which 3758 were used for training and 123 were used for verification after SSP inversion.  
In the process of carrying out EOF analysis of the SSPs, the temperature-salinity profile WOA data from 2005-2008 were used to calculate annually averaged SSPs. The SSP calculation method was the same as that used for the Argo data, and the SSPs were calculated using the sound speed empirical formula with the temperature and salinity profiles as variables.
## 3 Systems and Methods of Compression Measurement
The three-dimensional body scanning technology, used to capture anthropometric dimensions, has become a common tool for research, design and production [9; 69; 70; 71]. Three-dimensional scanning allows for designing compression products and evaluating individual dimensions of the human limbs, which leads to a more accurate measurement of limb volume and more suitably designed compression products. Digitalisation of the design process ensures the creation of compression products of a given shape with an increased level of comfort and functionality due to their correspondence to the shape and size of the individual patient's limb with its characteristics of stump formation and the ability to ensure the distribution of pressure created by the product throughout the limb.
## 4 Multi-Objective Optimization Framework
### Hybrid-Driven Position Updating Strategy  
The core evolutionary mechanism integrates the hierarchical pursuit strategy from GWO [46] and the spiral encircling tactics from the Whale Optimization Algorithm (WOA) [47] to establish a dynamic hybrid update equation. The hybrid coefficient \(\eta(t)\) dynamically decays with iterations, prioritizing global exploration in early phases and  
Figure 5: The flowchart of the optimization algorithm.  
shifting toward local exploitation in later stages. The GWO component incorporates an enhanced \(\alpha\)-wolf guidance mechanism with adaptive parameters to balance exploration intensity, while the WOA component introduces an exponentially decaying spiral factor combined with cosine fluctuation terms to strengthen local optimum escape capability. This hybrid strategy demonstrates accelerated convergence rates compared to single-mechanism algorithms, particularly excelling in high-dimensional optimization problems. \(\alpha(t)\) denotes the adaptive parameter, \(X_{\text{GWO}}\) and \(X_{\text{WOA}}\) represent position updates from GWO and WOA respectively, \(X_{\text{old}}\) indicates the original individual position, and \(X_{\text{DMOA}}\) corresponds to the updated position in the DMOA. The adaptive learning rates \(\alpha_{0}\) and \(\beta\) are set to 0.5 and 0.9 respectively, with \(T\) denoting the total iteration count. The governing Equations (35)-(37) are formulated as follows.  
\[X_{\text{new}} =\eta(t)\times\left(X_{\text{GWO}}+\alpha(t)\times\left(X_{\text{ WOA}}-X_{\text{old}}\right)\right) \tag{35}\] \[\quad+\left(1-\eta(t)\right)\times X_{\text{DMOA}}\]  
\[\eta(t)=0.9\times\left(1-\frac{t}{T}\right) \tag{36}\]  
\[\alpha(t)=\alpha_{0}\times\beta^{t} \tag{37}\]
## 2 Literature Review
### Digital Twin Application
#### 2.2.3 Services for Digital Twins  
There is a need for a platform executing services based on data acquisition and modeling, and such a platform can use the platform of platforms (PoPs) structure, which employs interoperation. The PoPs structure enables the development and operation of all digital twins fitting the purpose of a digital twin, thus being efficient from the perspectives of reliability and economics (development period and cost) [20].  
Table 2 presents the digital twin services provided in the construction industry, which are distinguished into general situations and disaster and emergency situations. General services include maintenance efficiency improvement, process optimization, cause analysis of system operation, and multi-party decision-making [19; 25]. Disaster and emergency situations are classified as predictive maintenance and proactive control, in which proactive control is related to more sudden changes in states and emergency responses.  
Research is being conducted on how to respond to disasters at common construction sites, such as how to assess the risk of construction sites based on BIM [32], how to prevent geothermal disasters in underground spaces [33], and how to automatically extract risk  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **Major Services** \\ \hline \multirow{6}{*}{General situations} & Maintenance efficiency improvement \\ \cline{2-3}  & Process optimization \\ \cline{2-3}  & Cause analysis \\ \cline{2-3}  & Multi-party decision-making \\ \hline \multirow{2}{*}{Disaster and abnormal situations} & Predictive maintenance \\ \cline{2-3}  & Proactive control \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distinction between service types using a digital twin.  
factors using deep learning [34]. In particular, safety accidents in an underground utility tunnel can escalate to hybrid disasters since a disaster happening in one lifeline can spread to other lifelines due to the dependence between facilities, thereby halting all city functions and hampering the everyday life of citizens, which eventually causes social confusion and foundation collapse; ultimately, infrastructure destruction, financial damage, and causalities can occur as a result [35].
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
###### Abstract  
To accurately describe the deformation characteristics of clay under long-term cyclic load, based on fractional calculus theory, elastoplastic theory and the basic element model, a variable-order fractional dynamic model designed to predict accumulative strain of clay was exhibited. Firstly, the cyclic load was separated into static and alternating load in accordance with cyclic load characteristics, and the strain of clay under static and alternating load was analyzed. Then, on the basis of the variable-order Abel dashpot model, rheological theory and elastoplastic theory, the expressions of the rheological constitutive model and strain response were both obtained. Finally, in combination with the undrained dynamic triaxial testing of Zhan Jiang clay and Tian Jin soft clay, a series of analyses was carried out on the effectiveness and parameter sensitivity of the model when subjected to long-term cyclic loading. By comparing the dynamic constitutive model with pre-existing models, the superiority of the dynamic constitutive model is revealed. The results show that the dynamic constitutive model can characterize properly the deformation characteristics of clay under the action of long-term cyclic loading, especially in its accelerating stage. The parameter sensitivity of the model exhibits a growing trend with the increment of loading duration.  
dynamic constitutive model; variable-order Abel dashpot; parameter sensitivity; element model; deformation characteristics
# Sensory Panel Performance Evaluation--Comprehensive Review of Practical Approaches
## 4 Methods, Models and Frameworks in Sensory Panel Evaluation
### Indicators and Effects of Individual Assessor and Panel Performance
In general, scale effects are usually solved in practice by product-specific training of the assessors. In particular, the scale anchoring method is used with reference samples and/or reference materials, to calibrate the panel with appropriate reference standards. It is appropriate to use an unstructured scale with values fixed at both ends [47; 48]. Selected and expert sensory assessors are able to make consistent and repeatable assessments of various products, thus an appropriate sensory panel will provide results that are accurate, discriminating and precise. Ideal group performance can be achieved if the products are differentiated by each assessor (large between product variability) and the same results are obtained several times (small within-assessor variability). However, there should be agreement between assessors regarding the sensory property within a given  
Figure 1: Individual differences in scale usage (based on [13]). tolerance (small between assessor variability). The performance of selected and expert assessors should be regularly monitored. They should use the scale correctly (location and range), score the same product consistently in different replicates (reliability/repeatability), attribute intensity evaluation in agreement with other assessors (validity/consistency) and discriminate among samples (sensitivity) [14; 49; 50]  
According to the requirements of guidelines for monitoring the performance of a quantitative sensory panel its analysis between the individual assessor and the sensory panel, the average of the entire panel is used as a reference by standard. It recommends the determination of [PERSON]'s correlation coefficient, slope and intercept of the regression line fitted to the individual panelist's points [44]. Because the panel average also contains the data of the panelist for whom the comparison is performed, the panel average recommended by ISO distorts the results; therefore, for a correct calculation, the value of the panelist to be examined has to be excluded when calculating the average. After the proposed correction, the correlation coefficient always decreases--except that all assessors give the same score. However, the value of slope and intercept depends on the panelist's rating. If the conditions of the parametric tests are not met, instead of [PERSON]'s correlation it is advisable to calculate the non-parametric Spearman rho correlation. The published research results give sufficient evidence to incorporate international sensory standards into a future revision [51].
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
## 4 Results and Discussions
### Environmental Benefits of PV Systems  
One advantage of PV technology is reducing GHG emissions. The annual rate of GHG emissions savings can be calculated using Formula (4). In 2021, the CO\({}_{2}\) emissions density in electricity production in the KSA was 0.569 kgCO\({}_{2}\) per kWh. Table 7 shows the number of cells installed in each system in the two scenarios: zero energy and zero bills at the end of the system's life. In addition to the installed module's number, the energy produced and the CO\({}_{2}\) that can be saved are also included. It is noted that a high amount of CO\({}_{2}\) can be saved in the three schools during the life of the systems.  
Suppose the electricity generated from PV systems and CO\({}_{2}\) saved are obtained by multiplying the number of public schools, 24,075, [1], by the median characteristic value for each school. Table 8 shows the average electricity generated and CO\({}_{2}\) saved from installing PV systems on each school's roof and the Kingdom's in two scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Zero-Energy Scenario**} & \multicolumn{3}{c}{**Zero-Bill Scenario**} \\ \hline  & \begin{tabular}{c} **Installed** \\ **Modules** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **PV-Generated** \\ **Electricity (MWh)** \\ \end{tabular} & \begin{tabular}{c} **Reduction of** \\ **CO\({}_{2}\) (Tonne)** \\ \end{tabular} & \begin{tabular}{c} **Installed** \\ **Modules** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **PV-Generated** \\ **Electricity (MWh)** \\ \end{tabular} &
\begin{tabular}{c} **Reduction of** \\ **CO\({}_{2}\) (Tonne)** \\ \end{tabular} \\ \hline School 1 & 116 & 2855.1 & 1624.5 & 166 & 4085.7 & 2324.8 \\ \hline School 2 & 374 & 9411.5 & 5355.1 & 622 & 15,652.3 & 8906.1 \\ \hline School 3 & 222 & 5586.5 & 3178.7 & 436 & 10,971.7 & 6242.9 \\ \hline Total & 712 & 17,853.1 & 10,158.4 & 1224 & 30,709.7 & 17,473.8 \\ \hline Average per school & 238 & 5951.0 & 3386.1 & 408 & 10,236.6 & 5824.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The sum of PV-generated electricity and reduction of CO\({}_{2}\) during the 25 years with zero-energy scenario and zero-bill scenario.  
Figure 7: Technical and economic parameters of the investigated schools for zero bill with variable PV tariff for school 3.
## 1 Introduction
The number of unproductive manipulations is an obvious economic and operational burden to the terminal. It also has further consequences in the form of increased intensity of requests to a transshipment system. The greater the unpredictivity, the larger the terminal equipment workload, and the longer the waiting time for executing productive manipulations. \"Unproductivity\" in a container terminal refers to a situation where the inland container terminal is not functioning at its maximum efficiency, resulting in a lower level of productivity. This can occur due to various reasons, such as delays in loading and unloading cargo, equipment breakdowns, inefficient workflow processes, or inadequate staffing [7; 8; 9]. The cause of loading or unloading delays may be the wrong task execution strategy. When a container terminal experiences unproductivity, congestion and delays may occur, which can have a ripple effect on the entire supply chain. For example, delayed loading and unloading of cargo can cause trains to miss their scheduled departure times, resulting in further delays and increased costs for transportation companies and cargo owners [10]. The authors of this paper put forward a hypothesis that there are two main factors impacting the number of unproductive manipulations:  
* adopted operational strategy,
* storage yard occupancy level. A method to avoid unproductivity in the context of container terminals is adopting an appropriate operational strategy and consciously approaching the issue of repeating container operations arising from storage yard occupancy. The objective of the paper is to indicate that the change in storage position occupancy directly influences timely schedule implementation. The analysis is based on the same operational strategy of changing storage yard occupancy. The paper has been developed as follows:  
* the methods for eliminating unproductive manipulations have been discussed,
* previous conclusions associated with the correlation between the number of occupied positions in container yards and the number of unproductive manipulations have been considered,
* the issue related to waiting time in the light of the queuing theory has also been researched. Next, the authors discuss a freight unit handling model with a mathematical description of a selected part of this process. The paper presents computation assumptions arising from actual data and discusses the practical importance of storage position occupancy in the context of the possibility to handle a specific number of freight units. The graph method is used when defining the model, and computations are performed using the mass service theory. The paper ends with conclusions.
## 3 Minimum Expected Delay Based Routing Protocol  
Firstly, we assume initially that all the sensor nodes are randomly deployed in a square area of size M \(\times\) M. The only static sink node is located at the center of the area. All the sensor nodes are homogeneous and have a unique ID number. The maximum transmission range of each node is fixed to \(r\). Moreover, we further assume the mobile sensor network has the following characteristics: (a) The mobility of each sensor node in the given area is assumed to follow the RWP model; (b) The mobile sensor nodes in our model can easily obtain their locations from some attached extra device, for example GPS; (c) All nodes have their clock synchronized by using the NTP or the GPS clock itself [25].  
Based on the assumptions above, we will present the methods to calculate the expiration time of the link which is formed between two nodes whose positions are in the communication scope of each other, as well as the two expected time values when a node meets and departs the sink node. After that, we present the calculation and update mechanism of nodes' MED values. Lastly a detailed description of the MEDR routing algorithm is presented.
## 3 Results and Discussion
### Influence of Coefficient of Subgrade Reaction  
To consider the influence of the coefficient of subgrade reaction (Winkler spring coefficient), the values of the coefficients were set as 5, 10, 20, 40, 60, 80, and 100 MPa/m, respectively. The relationship curve between the coefficient of subgrade reaction and the stiffness reduction coefficient is shown in Figure 6(a) where, when the coefficient of subgrade reaction increased from 5 MPa/m to 100 MPa/m gradually, the stiffness reduction coefficient of the sidewalls lay between 0.60 and 0.61 with very small variations. The relationship curve between the coefficient of subgrade reaction and the bending moment adjustment coefficients is shown in Figure 6(b) where, when the coefficient of subgrade reaction increased from 5 MPa/m to 100 MPa/m gradually, the bending moment adjustment coefficient of the lower end of the sidewalls varied between 0.10 and 0.11, and the bending moment adjustment coefficient of the upper end of the sidewalls also changed very little, with a variation range between 0.12 and 0.13. The upper end bending moment adjustment coefficient was found to be slightly larger than the corresponding lower end bending moment adjustment coefficient. It can be said that the coefficient of subgrade reaction had little effect on the internal force and deformation of the utility tunnel structure. However, according to a study by [PERSON] et al. (2018), the impact of the coefficient of subgrade reaction on the internal forces and deformation of a circular shield tunnel structure was significant, in which case the increase in the coefficient of subgrade reaction led to a decrease in the bending moment of the segment by up to 93.8%. The main reason for this difference lies in the fact that the deformation of the sidewalls was outwardly  
Figure 6: Influence of aspect ratio of width to height: (**a**) stiffness reduction coefficient and (**b**) bending moment adjustment coefficient.  
convex for the circular shield tunnel structure, making it necessary to consider the foundation resistance of the segments, as mentioned in Section 2.2. However, under the action of earth pressure, the deformation of the sidewalls of the rectangular utility tunnel was inwardly convex, so the foundation resistance of the sidewalls in terms of the coefficient of subgrade reaction had a negligible effect, and in this case, the earth pressure only acted as a lateral load to participate in the analysis of the structure.
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 5 Conclusions  
This paper presents a computational approach for designing an optimized multi-mission CubeSat constellation system. We have two mission objectives: global Earth observation and regional disaster management. The East African region is selected as the regional target area because it is frequently affected by natural disasters. The objective  
Figure 8: Relationship between the number of satellites, Regional ART, and Global ART in the optimal solution.  
Figure 9: CubeSat constellation model for solution number 20.  
functions and optimization parameters are chosen by considering the constellation's requirements and design variables. We propose a modified NSGA-II algorithm to find Pareto optimal solutions.  
The study performs two simulation cases, regional and multi-mission, to see the effect on the constellation parameters' design. The results of the regional simulation case showed a decrease in ART with increasing altitude, but a relative change in ART is small as the number of satellites increases. The second simulation results have shown the capability of CubeSats constellations to perform multiple missions with an acceptable level of performance. The Pareto optimal solution presents all possible feasible solutions. The paper also presents a new mission design approach for the future CubeSat constellation design.  
This study can be further extended by developing the communication architecture and protocol for better regional data collection. Furthermore, the mission operation, control, and economic aspects can be further investigated for CubeSat constellation missions. The other consideration is the deployment of the CubeSat constellation. CubeSats are usually launched as a secondary payload, which may not offer the desired launch date and orbit. Several initiatives for a new micro-launch vehicle have recently been underway to change this issue. However, these initiatives face challenges such as competitive pricing, complexity, and scale-down [27].  
Conceptualization, S.D.M. and H.-D.K.; methodology, S.D.M. and H.-D.K.; software, S.D.M.; validation, S.D.M. and H.-D.K.; formal analysis, S.D.M.; investigation, S.D.M. and H.-D.K.; resources, S.D.M.; data curation, S.D.M.; writing--original draft preparation, S.D.M.; writing--review and editing, S.D.M. and H.-D.K.; visualization, S.D.M.; supervision, H.-D.K.; project administration, S.D.M.; funding acquisition, H.-D.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea funded by the Ministry of Science and ICT under Grant 2022M1A3C2074536, Future Space Education Center.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.