esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Coupling Coordination Characteristics of ES Supply and Demand  
Using the CCDM, we measured and mapped the CCD of ESSI and ESDI for the 107 counties of Shanxi Province in 2000 and 2020 (Figure 5). The CCD values ranged from 0 to 0.57 in 2000 and from 0 to 0.43 in 2020 (Table 4), indicating a relatively low level of coupling coordination.  
There are four main types of coupling coordination relationships between ESSI and ESDI: extreme incoordination, moderate incoordination, reluctant coordination, and moderate coordination (Figure 5). Most counties belong to the extreme incoordination and moderate incoordination patterns, mainly located in the western and eastern parts of Shanxi. In 2000, the areas with reluctant coordination were primarily found in the Taiyuan Basin in central Shanxi, Linfen Basin, and the Yuncheng Basin in the southeastern parts. However, in 2020, reluctant coordination areas were sparsely distributed in only a few municipalities. Overall, there was a clear incoordination relationship between ES supply and demand in Shanxi.  
Between 2000 and 2020, the number of counties with extreme incoordination significantly increased from 29.0% to 36.5%. The number of reluctant coordination areas decreased from 34.6% to 24.3%, with fourteen counties transitioning from reluctant coordination to moderate incoordination. This indicated a substantial decline in the coupling coordination between ESSI and ESDI in Shanxi over the 20-year period. The main change characteristics were the negative transitions from relatively high coordination grades to incoordination grades. The most significant changes were observed in the shifts from moderate incoordination to extreme incoordination and from reluctant coordination to moderate incoordination, accounting for 20.31% and 16.28% of the counties, respectively.  
In general, the analysis of coordination between ES supply and demand revealed coexisting states of coordination and incoordination, with incoordination being predominant in most counties. The difference in the spatial polarization of CCD in 2020 was more significant than in 2000. Overall, the coordination relationship between ES supply and demand in Shanxi Province deteriorated between 2000 and 2020.
## 3 Application of Pavement Inspection Indicators to Maintenance Decision-Making
### Grading Analysis of Inspection Indicators  
#### 3.2.1 Clustering Analysis Method  
The K-means clustering analysis method involves selecting K initial cluster centers, where K is the number of desired clusters. The method calculates the distance of each observation to each cluster center, assigning each observation to the nearest cluster. This process is repeated until no data points can be moved, as follows [18]:  
* Initialization: Specify K clusters and randomly select K data points as cluster centers.
* Assign Data Points: Calculate the distance using simple Euclidean distance, assigning each data point to the nearest center.
* Calculate Averages: Recalculate the center point of each cluster.
* Clustering: Assign each point to the K clusters, ensuring each point is in the nearest cluster center.
## 3 Development of SoS Metamodels and Ontologies
### Extending SoS Ontologies Based on Flow
#### 3.3.2 SoS Capability Ontology (SoSCO)  
The UAF defines capabilities as being realized through combinations of ways and means. SoS metamodels frequently refer to capabilities [48; 49; 50], such as the \"simple\" and \"complex\" types that describe capability attributes [40]. However, these terms remain vague and require necessary attributes and relationships to support the SoS strategic architecture model process. We propose that SoS missions are external demands, while capabilities are internal abilities that enable SoS to adapt to the external environment. Complementary capabilities related to each other through relationships can trace the mission, with each capability characterized by its attributes. Figure 5 presents the core terminology of SoSCO.  
The sub-mission is inherited from the mission to specify the top-level SoS mission breakdown. The trace relationship illustrates what capabilities should have to realize missions. However, when modeling an SoS with AFs, it is crucial to identify the relationships between capabilities and determine the appropriate modeling order. CapabilityRelationship, which encompasses Generalization, Composition, and Dependency, can be used for this purpose. Additionally, the concepts of CapabilityFactor, including TemporalFactor, StructuralFactor, and OperationalFactor, enrich the semantics of the ontology to reason about the general and specialized elements of the generalization relationship. TemporalFactor is a critical component of ensuring the coherence of capabilities with the strategic phase plan of the SoS. Specifically, TemporalFactor considers time-related factors that may influence the performance of constituent systems within an SoS, such as the duration of operations or the frequency of events. StructuralFactor specifies that capabilities are realized through various combinations of realistic entities. StructuralFactor refers to the physical and logical structure of the constituent systems in an SoS, such as the hardware and software components, the communication protocols, and the data formats. OperationalFactor provides information on the purpose of operational activities and physical functions to enrich the context of the above two factors. After establishing the generaliza  
Figure 5: Core terminology of SoSCO.  
tion relationship, the description of capabilities can be complemented by composition and dependency relationships.
## 3 Results and Analysis
### Verification of the Residual Stress Test Results  
The annular and radial stresses on the extruded surface of the lugs along the specified paths were determined experimentally using the X-ray diffraction method. The residual stress distribution pattern of the compression bushing-lug plate after the cold expansion of the compression bushing was obtained using finite element simulation. The comparison curves of the experimental and simulation results for the circumferential and radial stresses are shown in Figure 16. Both material and operating conditions can have a significant effect on the stresses, so the curve in Figure 16 is only applicable to the stress conditions of this study. The four curves in Figure 16 represent the finite element simulation results and experimental measurements of the circumferential and radial stresses, respectively. With the results of circumferential stress, the finite element results are very close to the experimental measurements at a closer distance from the hole wall. When the distance from the hole wall is far, the experimental determination of stress results will have some deviation from the simulation results due to the interference of external factors. However, in determining radial stress, the experimental results are in good agreement with the finite element simulation results, and both results have the phenomenon of reverse yielding in the tested distance limitation range from hole wall. The environmental factors in the experiment are complex, while the simulation is an ideal condition, so the results of the experiment and the simulation cannot be completely consistent [33]. But, the changing trends of the curves are consistent, and the results of the experiment can validate the simulation results.  
Combining the finite element simulation results and experimental measurements of the circumferential and radial stresses, both the circumferential and radial stresses, the finite element simulation results are very close to the results of the X-ray diffraction measurements. Thus, the experimental measurements based on the residual stresses verify the accuracy of the finite element simulation results.  
Figure 16: Comparison between finite element results and experimentally determined residual stresses.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Detection Point** & \(2\theta_{0}\) **(Deg)** & \(\frac{22\theta_{0}}{\delta\sin^{2}\phi}\) & \
\begin{tabular}{c} **Stress Constant** \\ **(MPa/Deg)** \\ \end{tabular} &
\begin{tabular}{c} **Radial Stress (MPa)** \\ \end{tabular} \\ \hline I & \(141.652\) & \(0.678\) & \(-246.836\) & \(-167.355\) \\ II & \(141.421\) & \(1.035\) & \(-248.441\) & \(-257.136\) \\ III & \(141.550\) & \(0.654\) & \(-247.545\) & \(-161.894\) \\ IV & \(141.716\) & \(0.546\) & \(-246.392\) & \(-134.530\) \\ V & \(141.761\) & \(0.359\) & \(-246.080\) & \(-88.343\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main data of radial stress calculation using the X-ray diffraction method.
## 1 Introduction
A SOC estimation method based on the first-order RC equivalent circuit model of lithium-ion batteries was studied in [21; 22; 23]. The studies in [24; 25; 26] explored a SOC estimation method based on the second-order RC equivalent circuit model of lithium-ion batteries. References [26; 27; 28] studied SOH estimation based on second-order RC equivalent circuit models of a lithium-ion battery. Additionally, Reference [29] used the equivalent circuit model of a second-order RC model for lithium-ion battery modeling. It can be seen that the SOC and SOH estimation algorithms based on the equivalent circuit model are the most important class of estimation algorithms in battery management systems. Many scholars have proposed a variety of equivalent circuit models, from simple to complex. However, there are few systematic comparative studies on equivalent circuit models of lithium-ion batteries, and simple models are not able to adequately simulate dynamic characteristics of batteries, while it is difficult to apply complex ones to real-time systems. The choice between these models is a trade-off among modeling complexity, precision, and computational cost. Therefore, the first-order RC model and the second-order RC model commonly used in the literature are studied comparatively in this paper because of their simplicity and relative accuracy. In this paper, firstly, the parameters of the above two models are identified by experimental methods individually, and then the simulation models are built in the Matlab/Simulink environment, and finally the precision of results for the two models is verified and discussed comparatively with the actual data.
## 3 Results and Discussion
### Influence of Culture on Gazing Patterns  
Eye-tracking variables such as fixation duration (FD), fixation count (FC), first fixation duration (FFD) and time to first fixation (TTFF) were measured to capture visual attention following the approach of [28,29], who demonstrated that these metrics are critical in predicting food choices based on visual attention. The FD showed notable differences based on the country, (\(t(1717)=-3.7\), \(p<0.001\)), suggesting that cultural or geographical factors between Hungary (M = 1.75 s, SD = 1.06 s) and China (M = 1.56 s, SD = 1.04 s) influence the total time participants fixated on the apples. Significant differences were found for the FFD between participants from the two countries (\(t(1717)=-2.3\), \(p=0.020\)); again, participants from Hungary (M = 0.40 s, SD = 0.21) required more time compared to the Chinese participants (M = 0.36 s, SD = 0.18). The TTFF and FC did not show significant effect results.  
These findings are in line with those reported by [30], who studied the influence of color on product preference using eye-tracking technology, highlighting the role of eye movements in cultural differences observed in the TFD and FFD. However, our results diverge in aspects like the FC and TTFF, which could potentially be due to the different nature of our stimuli--the defectiveness of apples--in contrast to the color-focused stimuli in their study.  
Additionally, our study relates to the research presented in [31], which focused on consumer barriers to purchasing suboptimal food. This study highlighted the importance of product appearance and consumer attitudes, paralleling our findings on the variability in FFD across cultures. Furthermore, our research aligns with the findings from the [15], emphasizing how product attributes and cultural factors significantly influence consumer attention and decision making in a retail context. Reflecting on the cultural impact on visual attention patterns, our results also resonate with the findings of [18]. Their study on the influence of colors on product preference using eye-tracking technology highlighted the mediating role of eye movements, similar to the cultural differences we observed in TFD and FFD. Yet, our findings slightly diverge in aspects like the TFC and TTFF, which could potentially be due to the specific nature of our stimuli--the defectiveness of apples--in contrast to the color-focused stimuli in their study. This discrepancy might be attributed to the distinct nature of the stimuli used in our study compared to theirs.
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 4 Design and Deployment
### Node Technology
#### 4.2.2 Mobile Nodes (Livestock)  
Our livestock nodes were designed to inform the environmental scientists of the location of animals and to correlate this with rainfall events that could lead to over-land flow causing contamination of the nearby river. We again based the design of this node on Arduino technology, this time using an Arduino Pro Mini as the main board, connected to a GPS module, a 3-axis accelerometer, and an Xbee radio module. Our choice of Arduino in this case was entirely down to cost: Commercial off-the-shelf solutions for animal tracking cost over \(\varepsilon 1000\) per unit, whereas our solution costs just \(\varepsilon 100\). However, due to the need to minimise the scale of our unit to make it sufficiently lightweight to attach to an animal, we needed to hand-craft the design of this node much more closely, involving a detailed electronic design of the board connecting the individual components together and also 3-D printing of the enclosure.
# A Project Scheduling Game Equilibrium Problem Based on Dynamic Resource Supply
## 4 Algorithm Design
### Gads
#### 4.1.2 Decoding Rules  
Herein, a hybrid schedule generation scheme (HSGS) [32] was used as the decoding rule. The earliest start time of an activity can be determined when the predecessors of the activity have been completed and resource requirements have been met. HSGS is used to determine the completion time of each activity in turn and then calculate the total duration of the entire project.  
Step 1. Let \(A_{n}\) be the set of activities that have been scheduled, and let \(U_{n}\) be the set of activities that have not been scheduled. When initialized, \(A_{n}=\varnothing\) and \(U_{n}=\{1,2,\ldots,N\}\). First, the priority of each activity in \(U_{n}\) is sorted in descending order, and the activity with the highest priority is selected for the arrangement.  
Step 2. Continue to select the highest priority activity from \(U_{n}\) and conduct a timing constraint judgment. If satisfied, proceed to the next step. If not, the next activity is selected for judgment until the activity that meets the conditions is determined.  
Step 3. Conduct resource constraint judgment on the activity to determine whether it can be scheduled in parallel with scheduled activities. If so, proceed to the next step and arrange the activity into \(A_{n}\); if not, go to Step 2.  
Step 4. Update \(A_{n}\) and \(U_{n}\), then repeat from Step 1 until all activities are scheduled, i.e., \(A_{n}=\{1,2,\ldots,N\}\) and \(U_{n}=\varnothing\).
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 4 Case Study
### Experimental Setup  
Infrared thermography (IRT) is a powerful technique for identifying defects within the insulation of steel pipes, with moisture presence being a key indicator of potential corrosion under insulation (CUI). Moisture ingress alters the insulation's heat transfer coefficient, resulting in observable temperature variance in the pipe's surface, crucial for pinpointing defect locations and extents. For this experiment, six different steel pipes of varying sizes and thicknesses were selected to create a diverse set of scenarios. The geometrical properties of each pipe are detailed in Table 2.  
To simulate moisture ingress and enhance realism, each pipe was insulated with fiberglass, selected for its widespread industrial use and favorable thermal properties. The insulation applied at a thickness of 12.7 mm (0.5 inches) was cut to create defects of varying sizes. These sections were saturated with water to 70% of their weight before being reattached to the pipes. An aluminum cladding, 0.76 mm (0.03 inches) thick, enveloped the insulation, mimicking operational insulation systems' protective layer, providing mechanical protection and weather resistance. Figure 3 provides additional clarity on the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Challenge** & **Solution** \\ \hline \hline \multirow{4}{*}{Sequential data processing} & LSTM-RNN \\  & GRU \\  & 3D CNN \\ \hline \multirow{2}{*}{Increasing performance} & Design of neural network architecture \\  & Hyperparameter optimization \\ \hline \multirow{4}{*}{Feature extraction} & Extended Blind End-Member and Abundance \\  & Extraction (EBEAE) \\  & Convolutional Autoencoder \\ \hline \multirow{4}{*}{Increasing the size of the dataset} & Deep Autoencoder (unsupervised learning) \\  & Semi-supervised learning (SSL) \\ \cline{1-1}  & GAN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Challenges researchers face in subsurface defect characterization by machine learning algorithms with thermography and state-of-the-art solutions.  
geometrical positioning and dimensions of these defects relative to the pipes, aiding in the understanding of the simulated defect's types and placements.  
A FLIR T650 Sc thermal camera, with a high resolution of 640 \(\times\) 480 pixels, was used to capture thermal images. In this study, median filtering was primarily used due to its simplicity and effectiveness in reducing noise without significantly affecting the important features of the thermal images. This preprocessing step ensured that the subsequent analysis and defect detection algorithms operated on clearer and more reliable data. Given the predefined locations of the defects, Regions of Interest (ROIs) were established based on these positions for later analysis. Figure 4 shows the defined ROI, based on the position and size of the defect, for pipe number 6. As can be seen, the position of the defect is known in the thermal image, so a circle which circumscribes the square is defined.
# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
## 3 System Model  
The system model is shown in Figure 1. The system includes a CP, an NO, and multiple mobile onboard users; the NO has a set of cache-enabled RSUs and a cache-enabled UAV, where the coverage of the RSUs is \(C_{R}\) and the coverage of the UAV is \(C_{U}\), \(C_{R}>C_{U}\). The CP rents some of the contents to the NO and the NO caches this rented content in the RSUs to save the expensive transmission cost. The roads in the city are two-way lanes with lanes appearing in pairs and there are two road sections, each of length \(L\). An RSU is deployed next to each road section, which can provide services to vehicles on that road section, with the RSU on the left road section denoted as \(R_{l}\) and the RSU on the right road section denoted as \(R_{r}\). A UAV with caching capabilities is deployed over the road sections to assist the RSUs in providing services to vehicle users.
# Assessment and Review of Heavy Metals Pollution in Sediments of the Mediterranean Sea
## 2 Materials and Methods
### Sediment Quality Guidelines--SQGs  
The chemical parameter analyzed was made under rigorous methods and quality assurance measures for credibility and accuracy. Heavy Metals: Arsenic (As), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Nickel (Ni), Lead (Pb), Vanadium (V), and Zinc (Zn) were analyzed using inductively coupled plasma-mass spectrometry (ICP-MS) with Agilent 7500 ce equipment (manufactured by Agilent, Santa Clara, CA, USA, EEUU), following the EPA 6020 method. Mercury (Hg) levels were determined using the EPA 7471 method for solid or semisolid waste. Main Elements of Minerals: Atomic absorption spectrophotometry, with X-ray fluorescence, was employed to assess primary mineral elements. Quality Assurance: Our X-ray laboratory participates in semi-annual intercomparison tests for ongoing quality control, ensuring precision and reliability. For ICP-MS, annual internal control tests involve sample repetitions, with achieved precision ranging from 0.1% to 2.5%, which is within the acceptable relative standard deviation range of \(\leq\)10%.
# Business Process Configuration according to Data Dependency Specification
## 4 Conf-BP Framework: A Configurable System to Create Imperative BP
### Conf-BP Declarative Specification (ConfD-BP). A Formalization of the Language
#### 4.1.2 Sub-Process Relationships  
There exist different relationships between the data inputs and data outputs defined in the _sub-process description_. Those relationships are expressed as constraints, both at activity and at process level, giving rise to the _sub-process relationships_, with the following definitions:  
**Definition 5**.: PRE\((A_{i})\) _is the set of constraints that limits the specific values of the_ DATA_INPUT\((A_{i})\) _that must be satisfied to execute activity \(A_{i}\). Likewise,_ POST\((A_{i})\) _is the set of constraints that limits the specific values of the_ DATA_OUTPUT\((A_{i})\) _that must be satisfied after the execution of activity \(A_{i}\)._  
**Definition 6**.: INPUT_CONSTraint(OUTPUT_CONSTraint) _relates the values of variables of_ PDI\(/\)PDO _with variables of_ DATA_INPUT\(/\)DATA_OUTPUT _can take according to the_ PDI\(/\)PDO _values in each instance, and the possible values between the input and output of the activities._  
**Definition 7**.: OBJECTIVE_FUNCTION\((\)OBJ_FUNC\()\) _is an optimization function defined in terms of the data output of the activities (ADO)._  
The objective of this optimization function is either to maximize or to minimize some of the output data that represents the business product, which constitutes the outcome of the process.  
\[\texttt{OBJ\_FUNC}:f(v\subseteq\texttt{ADO})\to value,\text{ where }f\text{ is }MAX\text{ or }MIN\]  
The result of this optimization problem is a set of input values that satisfies the objective function, the pre and post-conditions of the activities, and also satisfies the input and output constraints.  
The set of input values that optimizes the output is found at runtime. The role of the constraints is to determine the possible values that this input and output data can take. All the constraints (INPUT_CONSTRAINT,OUTPUT_CONSTRAINT, PRE, and POST) are always defined at design time by a business expert who is familiar with the problem, although they are solved for each instance at runtime. Among every possible tuples of solutions that satisfy the constraints, the outcome of the sub-process will be the one which optimizes the variable defined in the optimization function.