esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The dynamic sealing evaluation of caprocks is significant for the the sealing safety of UGSs in depleted gas reservoirs. The sealing performance of caprocks is a comprehensive evaluation process with complex multifactor indicators, and some main conclusions of are as follows:  
1. Two main factors affect the sealing performance of the caprock: the thickness and mechanical properties. Microscopically, the pore structure, permeability, and breakthrough pressure also affect the sealing performance of the caprock.
2. Using the AHP method, the weights of six parameters for caprock's sealing is obtained. The breakthrough pressure of the caprock and permeability are the main factors with the weights of 0.4291 and 0.2157, respectively.
3. Using the fuzzy comprehensive evaluation method (FCEM), the sealing performance of the caprocks in three planned UGSs was evaluated. Zhujiadun gas storage, which has the relative evaluation score of 0.731, implies that the caprocks have good sealing performance.  
In the early stage of planning and rebuilding underground gas storages, using this method can provide a general assessment on the sealing ability of caprocks. However,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Levels** & **Values** & **Sealing Performance** \\ \hline Good & \(8<M\leq 10\) & The caprock has good sealing performance and is very suitable for building the gas storage. \\ Generally good & \(7<M\leq 8\) & The caprock has better sealing performance and is suitable for building the gas storage \\ Medium & \(5<M\leq 7\) & The caprock sealing property is general, which needs further demonstration. \\ Bad & \(M\leq 5\) & Not suitable for building the gas storage \\ \hline \hline \end{tabular}
\end{table}
Table 9: The comprehensive values of caprock sealing performance [52].  
some assumptions have been made in the evaluation, and six parameters are considered in the evaluation system. It is necessary to update the evaluation model when the database is renewed.  
S.B.: Data curation, Writing--Original Draft. H.L.: Conceptualization, Methodology, Writing--Reviewing and Editing. X.W.: Data Validation, Writing--Reviewing and Editing. X.S.: Data curation. H.M.: Supervision. Y.S.: Basic geological data collection. H.T.: Data collection. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the CAS Pioneer Hundred Talents Program (Y826031C01) and the National Natural Science Foundation (U22A20166).  
Not applicable.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# Mesostructural Model for the Fatigue Analysis of Open-Cell Metal Foams
## 1 Introduction
These results extend the understanding of metallic foams beyond conventional loading scenarios, emphasizing their potential in high-performance, protective applications where extreme impact resistance is required. This study aims to propose a mesostructural model to assess the fatigue behavior of open-cell metal foams subjected to cyclic loading conditions. The model leverages the concept of progressive collapse, where ligaments within the foam structure are sequentially removed as they fail under cyclic loads. It integrates a finite element model to accurately capture the stress distribution in each ligament, a fatigue analysis model based on the Weibull distribution to predict fatigue life, an equivalent number of cycles model to account for the cumulative effects of prior loading, and a failure criterion model that considers the gradual deterioration of mechanical properties over time. By incorporating these elements, the model provides a detailed representation of the complex fatigue behavior of open-cell metal foams. The accuracy and reliability of the model are validated against experimental data, underscoring its potential utility in the design and analysis of engineering structures that utilize these materials. The paper is structured as follows: Section 2 presents the metal foam considered for this research; Section 3 presents the proposed mesostructural model as well as all the models and approaches needed to implement the model; Section 4 presents a model application and discusses the performance of the model in comparison with experimental data; and, finally, Section 5 presents the conclusions of the research.
## 1 Introduction
### Contributions and Impacts of This Study  
The important results of this study are as follows:  
1. A GA that optimizes the location and capacity of RPG units with an objective function (which minimizes variation in voltage, the installation cost of RPG units, and losses), equal and unequal constraints, and varying demands is proposed.
2. The proposed GA has an integrated bus impedance matrix power flow calculation method that models tap-changing transformers.
3. Case studies performed with IEEE 14-, 30-, and 57-bus test systems demonstrate the validity of the proposed GA.  
Since the case studies included buses, lines, loads, generators, shunt capacitors, tap-changing transformers, P-Q, P-V, and slack buses, the results show that the proposed power-flow calculation method can be used to analyze many different power system configurations. This study integrates the bus impedance power-flow calculation into a GA. As a result, the proposed hybrid GA can be also used for operating, planning, or upgrading transmission systems by optimally allocating RPG units. In particular, PV and WTGs able to control reactive power or with the capability of Volt/Var control management can be optimally allocated by the proposed hybrid GA.
# Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles
## 5 Analysis  
Photogrammetry provides the first quantitative measurements of the shape profile of PAMs, and opens the door for new and improved modeling efforts. As a first step towards analysis of these shape profiles, each shape profile--which is comprised of thousands of data points--must be defined in simpler terms. One method could be to define each shape profile by a single value such as the average or maximum diameter of the profile. Defining the profile by a single diameter value assumes a cylindrical profile, and enables the measured data to be used with current modeling frameworks that have an input of a single approximated cylindrical value. Now, if a more detailed analysis of the shape profile is desired, each profile dataset can be defined by a fit equation. A Fourier series fit equation is presented as a good candidate for accurate representation of the PAM's shape profile.  
The shape profile data can also be compared to the cylindrical diameters that have been assumed in previous work. Prior to the quantitative analysis methodology presented here, the cylindrical diameter could only be estimated based on approximations derived from the resting (uncontracted) geometry of the PAM. We will term these the _estimated cylindrical shape profiles_. The accuracy of these shape profile estimates has gone relatively unchecked. Using the measured shape profile data, we now have the ability to test the accuracy of these estimated cylindrical approximation shape profiles.  
Finally, analysis of the measured shape profile provides the unique opportunity to define the internal volume of PAMs. Using an assumption of axisymmetry along the longitudinal axis of the PAM, two-dimensional photogrammetry measurements can be used to approximate the internal volume through a non-intrusive method that is much simpler than previous methods that have required costly and intrusive measurement setups.
## 1 Introduction
Combining image features within an ensemble framework enhances predictive performance by leveraging their complementary strengths, offering a comprehensive approach to analyzing behavioral data. This research contributes several key findings in the domain of predicting student academic success. 1. This study introduces a novel feature integration approach by combining high-dimensional image features extracted by a CNN from student data transformed into images using methods such as Pixel Representation (PR), Sine Wave Transformation (SWT), Recurrence Plot (RP), and Gramian Angular Field (GAF) with low-dimensional numerical features extracted by an FCN from the original data. The feature-level fusion mechanism leverages concatenated features from both networks, enabling the model to automatically learn optimal combinations of complementary information from heterogeneous data sources for improved classification accuracy. 2. A feature-level fusion mechanism is introduced, where the output feature vectors from the CNN and FCN are concatenated to establish a unified feature structure. Instead of direct weighting, this method empowers the ensemble framework to learn an optimal combination of features through end-to-end training, capturing complex temporal and nonlinear relationships from image data while preserving essential numerical details from data, leveraging the complementary strengths of both networks and improving classification robustness. 3. To benchmark the proposed approach, a range of machine learning techniques, such as Stochastic Gradient Descent (SGD), Gradient Boosting (GB), Decision Tree (DT), Extra Tree (ET), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), AdaBoost, and Random Forest (RF) are employed for performance comparison. Meanwhile, several deep learning methods, consisting of a Convolutional Neural Network (CNN), Fully Connected Network (FCN), MobileNet, and EfficientNetB4, are used for performance comparison. The evaluation metrics enable a thorough evaluation of the proposed framework against cutting-edge methods. The structure of this study is presented as detailed below: Section 2 introduces a comprehensive survey of existing work employing machine learning, deep neural networks combined with feature transformation, and ensemble learning to predict student academic performance, highlighting current research and key investigation results in the domain. Section 3 introduces the data collection utilized within this research, details the proposed methodology, and describes the feature transformation techniques, machine learning classifiers, and deep neural networks applied, along with the evaluation metrics employed for analysis. Section 4 focuses on the experimental results and their interpretation, emphasizing the significance of the findings in achieving the research objectives. In the final section, Section 5 offers a conclusion by highlighting the major contributions and their implications, while also proposing potential future research directions and areas for further exploration.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 6 Conclusions  
In this paper, a hierarchical optimization method is proposed to optimize the energy transaction of a MMG in two stages. Firstly, the EMS of each MG determines the scheduling of ESS in the next hour  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PAR** & **PV Utilization Ratio** \\ \hline Original method & 3.1596 & 85.25\% \\ Proposed method & 2.6992 & 98.07\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of pea-to-average ratio (PAR) and PV utilization ratio.  
Figure 12: Comparison of net energy of MMGs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Utility/CNY** & **Reference [16]** & **Reference [17]** & **Proposed** \\ \hline EMO & 234.2 & 207.6 & 277.8 \\ MG1 & 4792.3 & 4796.4 & 4823.0 \\ MG2 & 5897.2 & 5952.0 & 5973.8 \\ MG3 & 6897.8 & 6946.4 & 7011.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons with other methods.  
by adopting rolling optimization, and decides its participating role in energy trading market. Secondly, according to forecast information and the energy trading roles collected from MGs, the EMO optimizes the internal prices of next hour based on Stackelberg game theory. The simulation results show that the utility of both the EMO and MGO are increased by using the proposed method. In addition, the net load curve and utilization ratio of PV energy in the whole MMG system are both improved.  
**Author Contributions:** Formal analysis, C.H., W.S. and B.X.; Funding acquisition, Q.W.; Methodology, T.R. and G.L.; Writing--original draft, T.R.  
**Funding:** This work was supported by the National Key R&D Program of China (Nos. 2016 YFB0900400)  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dynamics Modelling and Simulation for Deployment Characteristics of Mesh Reflector Antennas
## 1 Introduction
Based on the ANCF beam element, [PERSON] investigated the dynamic characteristics of a deployable truss under the action of heat flow [23]. [PERSON] et al. [24] discussed the asynchronous deployment of a hoop truss considering the role of torsional spring. Furthermore, discretizing the mesh reflector with an ANCF element, [PERSON] [25] researched the asynchronous deployment of a hoop truss antenna with a degenerative driving force applied on the inclined rod. An optimal deployment based on fifth-order polynomial programming using ANCF was presented in [26]. In addition, a strategy of parallel computation [27] was introduced to enhance the computation efficiency involving ANCF-based dynamic analysis. A variable-length element is developed by the arbitrary Lagrangian-Euler description to investigate the deployment phenomenon of asynchronism of the hoop truss antenna [28]. From a practical perspective for application in engineering, the deployed angle is approximated to a certain value with the action of the torsional spring. However, the action of the torsional spring is less considered in the dynamic analysis of the deployable truss antenna using the ANCF description of flexible components, which leads to deviation from practical application in engineering. Therefore, in this work, a mechanics model of the torsional spring is incorporated into the dynamic governing equations derived by the ANCF method to investigate the deployment dynamics of the hoop truss antenna. Figure 1: Deployable truss antenna. This paper is organized as follows. In Section 2, the dynamic equations of the truss antenna are developed based on the ANCF element. Section 3 establishes an equivalent mechanic model of the torsional spring considering the effect of the torsional spring. In Section 4, the dynamic simulation of the deployment of the truss antenna is presented and we conclude the presented work in Section 5.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 4 Method Implementation and Validation
### Preliminary Results  
Prior to the analysis of consolidated results, some results of stego signals are shown for different \(BH\) values. Figure 3 shows a host signal, a secret signal, and the six stego signals obtained for the six \(BH\) values analysed.  
When analysing the graphs in Figure 3, it is noted that distortion of stego signals is minimum in relation to the host signal, and the higher the \(BH\) value, the lower the localised distortion.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 3 Modeling MANETs
### Link Quality Metric (LQM)  
We define a metric to capture the quality of the overall connection in a multi-hop route. This metric is meant to capture the minimum signal strength when considering the entire sequence of hops between the root and the client (known as the \"weakest-link\" hypothesis) relative to the achievable maximum value. The signal strength plateaus at 1 for a distance \(r\) equal or lower than one space unit, i.e., its value is \(\min(1,\frac{1}{r^{2}})\).  
We do this for two reasons: (1) we want to assess the performance of our approach regarding the link quality experienced by the clients (see Equation (2)) in the MANET (cf. Figure 2), and (2) we use this indicator in all nodes that are parent to more than one child, be it because they are a branch in the topology tree or because they serve as an access point for clients. Such nodes will consider the link quality between themselves and all their children to calculate a _stress_ value (cf. Equation (3)) used to decide whether an additional node should be deployed into the MANET (see Section 3.3).  
The normalized LQM for an individual link is defined between any two nodes \(i\) and \(j\) in a path (from the root/data sink to a mobile client) using the distance between them \(r_{i,j}\) (see Equation (4)), with \(i\) and \(j\) being _parent_ node/root and _child_ node/client, respectively:  
\[LQM_{j}=\frac{1}{\max(1,r_{i,j})^{2}} \tag{1}\]  
This assigns each node a value indicating the connection strength to its parent node. Please note that the \(\max(\_,\_)\) in the divisor is simply to ensure that the LQM cannot grow larger than 1, meaning that being closer to the parent does not offer a better LQM value.  
Using these individual link values we can calculate the overall LQM for any route \(LQM_{\text{root},c}\) between the root and a mobile client \(c\) as the worst individual link strength:  
\[LQM_{\text{root},c}=\min(1,LQM_{j})\forall j\in\text{path to }c \tag{2}\]The results shown in Figure 2 on page 8 make use of this metric.  
Figure 2: Evolution of the \(LQM_{\text{root},c}\) variable for all clients \(c\) (cf. Equation (2)). A total of 100 independent simulation runs (500 clients, divided into eight dynamic clusters). **(a)** Clients relocate every 800 time units. **(b)** Clients relocate every 400. The average is an “average of averages”. The standard deviation is the average of the 100 SD values (1 data-point per simulation run and per time unit). The lowest is the average of the 100 worst LQM values experienced by any client \(c\) (idem).
# Directed Path 3-Arc-Connectivity of Cartesian Product Digraphs
###### Abstract  
Let \(D=(V(D),A(D))\) be a digraph of order \(n\) and let \(r\in S\subseteq V(D)\) with \(2\leq|S|\leq n\). A directed \((S,r)\)-Steiner path (or an \((S,r)\)-path for short) is a directed path \(P\) beginning at \(r\) such that \(S\subseteq V(P)\). Arc-disjoint between two \((S,r)\)-paths is characterized by the absence of common arcs. Let \(\lambda_{S,r}^{p}(D)\) be the maximum number of arc-disjoint \((S,r)\)-paths in \(D\). The directed path \(k\)-arc-connectivity of \(D\) is defined as \(\lambda_{S}^{p}(D)=\min\{\lambda_{S,r}^{p}(D)\mid S\subseteq V(D),|S|=k,r\in S\}\). In this paper, we shall investigate the directed path 3-arc-connectivity of Cartesian product \(\lambda_{S}^{p}(G\square H)\) and prove that if \(G\) and \(H\) are two digraphs such that \(\delta^{0}(G)\geq 4\), \(\delta^{0}(H)\geq 4\), and \(\kappa(G)\geq 2\), \(\kappa(H)\geq 2\), then \(\lambda_{S}^{p}(G\square H)\geq\min\{2\kappa(G),2\kappa(H)\}\); moreover, this bound is sharp. We also obtain exact values for \(\lambda_{S}^{p}(G\square H)\) for some digraph classes \(G\) and \(H\), and most of these digraphs are symmetric.  
connectivity; directed path \(k\)-connectivity; Cartesian product +
Footnote †: journal: Journal of Computer Science
## 2 Experiments
### Experimental Results
#### 2.5.2 Branch Crack Growth Rate  
In the mixed mode I/III loading, the cracks grown may branch to form torsional facets. In such a case, the cracks form perpendicularly to the maximum principal stress and the equivalent SIF range has been proposed as follows [15]:  
\[\Delta K_{\rm t}=0.8\Delta K_{\rm I}+0.5\big{[}0.16\Delta K_{\rm I}^{2}+4 \Delta K_{\rm III}^{2}\big{]}^{0.5} \tag{9}\]  
Figure 8: Coplanar crack growth rates against \(\Delta K_{\rm s}\) for RF.  
under the condition of \(\
u\) = 0.3. The combined growth data obtained from RP4 and RP5 as a function of \(\Delta K_{\rm t}\) are plotted in Figure 10. When \(\Delta K_{\rm III}/\Delta K_{\rm I}\) was large (i.e., in the RP5 case), the growth rates went down. This happened because Equation (9) does not consider the attenuation of \(\Delta K_{\rm III}\) due to the crack face contact. This attenuation was considered in the previously performed mixed mode I/III test with \(R_{\rm I}=R_{\rm III}=0.05\) and the equivalent SIF range (\(\Delta K_{\rm eq}\)) that was weighted to \(\Delta K_{\rm III}\), as in the following equation [16], was proposed:  
\[\Delta K_{\rm eq}=\left\{\Delta K_{\rm I}^{2}+0.2\Delta K_{\rm III}^{2}\right\} ^{0.5} \tag{10}\]  
When the growth rates were plotted against \(\Delta K_{\rm eq}\), a good correlation was obtained even when \(\Delta K_{\rm III}/\Delta K_{\rm I}\) and \(\delta\) were extensively changed. Therefore, also in this study, the growth rates for RP and RF were plotted against \(\Delta K_{\rm eq}\), and the results for RP are shown in Figure 11, revealing a fairly good correlation. The growth rate in RP was expressed via the Paris-type law as follows:  
\[\mathrm{d}a/\mathrm{d}N=1.84\times 10^{-12}(\Delta K_{\rm eq})^{3.29}\ (R^{2}=0.960) \tag{11}\]  
Although not shown, the growth law for RF was  
\[\mathrm{d}a/\mathrm{d}N=4.66\times 10^{-13}(\Delta K_{\rm eq})^{3.71}\ (R^{2}=0.987) \tag{12}\]  
As mentioned previously, coplanar growth was considered to occur in RF1 and RF2 when \(\delta\) was \(30^{\circ}\), and the growth rates were plotted with \(\Delta K_{\rm s}\). However, the correlation was poor in these cases. This was probably due to the effect of small factory-roof fractures that formed on the fracture surfaces under the initial conditions. Although the coplanar growth occurred when \(\delta\) was \(30^{\circ}\) for both specimens, an effect of the contact at the factory-roof fracture surfaces on the growth rates was observed throughout the experiments. Therefore, all data were correlated with \(\Delta K_{\rm eq}\), and the results are shown in Figure 12. A better correlation was obtained compared with the case of plotting with \(\Delta K_{\rm s}\). The growth law was as follows:  
\[\mathrm{d}a/\mathrm{d}N=4.51\times 10^{-11}(\Delta K_{\rm eq})^{2.60}\ (R^{2}=0.823) \tag{13}\]  
Figure 10: Branch crack growth rates against \(\Delta K_{\rm t}\) for RP.