esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate porosity prediction is crucial for evaluating reservoir quality and potential productivity. Traditional laboratory experiments offer reliable porosity measurements but are time-consuming and computationally expensive. Recently, deep learning has been introduced for porosity prediction. However, most existing methods focus on either the relationships among features or deep sequences, neglecting the correlation between them. To better capture these correlations, we propose an adaptive graph convolutional network (GCN) with learning deep sequence and feature correlation graphs. The GCN is applied to well-logging data to capture non-Euclidean characteristics, extracting inherent spatiotemporal relationships. Furthermore, traditional GCNs rely on predefined graphs for feature aggregation, which can vary based on the thresholds for cosine similarity or covariance, potentially affecting accuracy and robustness. To tackle this issue, we introduced an adaptive mechanism for constructing deep sequence and feature correlation graphs, which eliminates the need for predefined thresholds and significantly enhances the accuracy and robustness of predictions. We demonstrated our method on well-logging datasets and comprehensively compared its performance with other deep learning models, showing its superiority in predicting porosity from well-logging data. This work offers geoscientists a more efficient and accurate analytical tool.  
p _applied sciences Article_  
Adaptive Graph Convolutional Network with Deep Sequence and Feature Correlation Learning for Porosity Prediction from Well-Logging Data. _Appl. Sci._**2025**, 15, 4609. [[https://doi.org/10.3390/app15094609](https://doi.org/10.3390/app15094609)]([https://doi.org/10.3390/app15094609](https://doi.org/10.3390/app15094609))  
**Copyright**: (c) 2025 by the authors.  
Lousse MDPL, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# Buckling of Composite Structures with
## 3 A Brief Description of Contact Problems Associated with Local Buckling  
The description of contact problems for composite structures can be divided into two classes:  
* One-dimensional contact problems; the delamination is represented by a single line that separates (or not) the sub-laminates--see Figure 5;
* Two-dimensional contact problems; the delamination is described by a surface--see Figure 6.  
Figure 4: Geometry and location of the embedded delamination.  
The first two modes plotted in Figure 5a,b occur as the upper sub-laminate is thin and the area of the imperfection (delamination) is large. The third mode (Figure 5c) is observed as the delamination has a small area and the imperfection is located deeper in the structure. In the latter case, the delamination can be closed and its effects are connected with contact reactions between upper and lower sub-laminates--see, e.g., Refs. [57; 58].  
The failure mode and loads are functions of the delamination size and form-- see Equation (5).  
For 1D delaminations, Figure 7 shows two characteristic cases: when the delamination length a < a\({}_{\text{crit}}\) is not the critical length and does not cause local sub-laminate stability loss, and the second, when sub-laminate stability loss above the critical length occurs. Therefore, it is obvious that the construction of models characterizing the local loss of stability by the sub-laminate should allow for the determination of both the critical length of a\({}_{\text{crit}}\) delamination as a function of geometrical and material parameters, as well as the assessment (after local buckling) of the degree of decrease in the value of the critical load as a function of the length of the gap a (or the surface area A for two-dimensional delamination)--see Figure 7.  
To evaluate the curve plotted in Figure 8, the imperfection sensitivity method was adopted. In Ref. [59] it is demonstrated that buckling loads can be derived with the aid of the lower bound method, where the form of the imperfection is described by Equation (5). Varying the parameters characterizing the imperfection function, it is possible to find the lower bound corresponding to the curve plotted in Figure 8.  
Figure 5: Schematic view of delaminated plates ([PERSON] et al. [56]) and of axisymmetric spherical shells ([PERSON] [17]). (a) Local mode, (b) mixed mode, (c) global mode.  
Figure 6: Two-dimensional elliptical delaminations (top view).
## 4 Case Study
### Model Results
#### 4.4.2 Analyzing Sparse Road Network Resilience
(3) Remaining Capacity  
The resilience levels of the road network are simulated under three scenarios, i.e., complete blockage, partial blockage, and two-way traffic, caused by a landslide event (Figure 20). In the traffic disruption scenario, the landslide causes the road segment to be entirely impassable, leading to a total loss of network performance and very poor overall resilience (0.4142). Sparse road networks, lacking alternative routes, experience a significant decline in traffic capacity when a critical segment is fully blocked, often resulting in complete network \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disaster Type** & **Survivability** & **Evacuability** & **Recoverability** & **Adaptability** \\ \hline \multicolumn{5}{c}{Duration of disaster} \\ Collapse (42 h) & 0.65 & 0.89 & 0.0036 & 0.35 \\ Debris flow (14 h) & 0.20 & 0.78 & 0.047 & 0.80 \\ Landslide (6 h) & 0.45 & 0.73 & 0.075 & 0.55 \\ Subsidence (10 h) & 0.35 & 0.62 & 0.04 & 0.65 \\ Water damage (69 h) & 0.50 & 0.55 & 0.0069 & 0.50 \\ \hline \multicolumn{5}{c}{Acceptable service level} \\ Traffic disruption & 0.0000 & 0.6864 & 0 & 0 \\ Half-way traffic & 0.4500 & 0.7273 & 0.0008 & 0.9500 \\ Two-way traffic & 0.4500 & 0.7273 & 0.075 & 0.5500 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Resilience assessment indicators. Figure 20: Road network resilience with different residual capacities. failure. This highlights the vulnerability of sparse road networks to complete blockages and underscores the need for optimized emergency response and recovery measures. In the half-way traffic scenario, the network performance declines but still provides partial service, with higher evacuability and adaptability, resulting in the highest overall resilience (0.7294). This indicates that partial blockage is the most resilient scenario among the three. Although partial blockage reduces the network's traffic capacity, it maintains a certain level of service. In the two-way traffic scenario, the landslide has the least impact on the road segment's traffic capacity, with performance only slightly declining before quickly returning to near-normal levels. This scenario demonstrates good adaptability (0.95) and recovery (0.075), leading to high overall resilience (0.6712). Ensuring two-way traffic in sparse road networks significantly enhances post-disaster resilience.
## 5 Experiments and Results  
This section elaborates the experimental workflow that is conducted for the purpose of performance assessment along with the description of data corpora and evaluation metrics as well as the discussion of obtained results. As illustrated in Figure 4, the experimental workflow is conducted through several tasks, including a collection of data, construction of data into training corpus and testing corpus, implementation of the benchmarking machine learners as well as the proposed machine learner (HML). In addition, the experimental workflow pursues the implementation task of the benchmarking anti-ransomware tools and the proposed ransomware multi-tiered streaming analytics model. Then, the evaluation task is carried out to qualify the overall experimental results and to justify the asserted findings.
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 1 Introduction
High routing reliability under jamming: The proposed jamming factor, utilizing jamming strength data acquired by sensor nodes, guides the selection of information relay nodes, avoiding those within affected areas. 3. Adaptability and robustness: The addition of a local maintenance mechanism to JRP enhances network adaptability, while the global maintenance mechanism ensures network robustness. The integration of both mechanisms improves WSN reliability in the presence of jamming. The paper's remaining sections are structured as follows: Section 2 describes the research background and related work. In Section 3, the authors illustrate the cross-domain multi-agent modeling approach and present the JRP design, the auxiliary maintenance mechanism, and simulation parameter configurations. Section 4 discusses the reliability and performance impact of various jamming scenarios on WSNs. Finally, conclusions and future prospects are drawn in Section 5.
## 5 Experimental Verification and Analysis
### Convergence Ability with Initial SOC Error  
Because it is difficult to determine the initial SOC value precisely in practical applications, it is important and indispensable for the algorithms to have the ability to correct the uncertainty brought by the initial SOC error. Therefore, the convergence rate with initial SOC error is adopted as another  
Figure 12: Experimental terminal voltage results using VB-ADEKF in the UDDS test.  
Figure 13: SOC estimation results using VB-ADEKF and DEKF in the UDDS test.  
indicator for evaluating the SOC estimation algorithms. The true initial SOC value is 1.0. Figures 14-16 present the estimation results of VB-ADEKF and DEKF algorithms for different initial SOC values from 30% to 90% under the above three tests, respectively. Overall, the convergence time increases as the initial SOC error rises for both VB-ADEKF and DEKF. But the growth rates are very different for the two filters under different tests.  
Specifically, from the results of the convergence time shown in Table 3, it can be seen that the convergence time of VB-ADEKF is stabilized about 10 s into the constant current discharge test when the initial SOC value is larger than 50%, and increased to more than 100 s when the initial SOC values are 40% and 30%. But the convergence time of DEKF, which is between 300 s and 500 s, is much longer than VB-ADEKF. It shows that VB-ADEKF can quickly converge to the true SOC values without resulting in the accumulation of errors caused by the initial error of the SOC. In the pulse current discharge test, the convergence rates are comparable for VB-ADEKF and DEKF in the case of small initial SOC errors, for example, 10% or 20%. However, when the initial SOC error is relatively small, the convergence rates are comparable for VB-ADEKF and DEKF in the case of small initial SOC errors, for example, 10% or 20%.  
Figure 15: SOC estimation results of the two filters with different initial SOC values in the pulse current discharge test.  
large, the convergence time of VB-ADEKF becomes much smaller than that of DEKF. In the UDDS test, VB-ADEKF only exhibits a slightly increasing trend with the increase of the initial SOC error. However, DEKF converges slower than VB-ADEKF, in the meantime, with a larger SOC estimation error. Its convergence time goes up quickly as the initial SOC error increases. This implies that the initial SOC error has a noticeable impact on the performance of DEKF. But, from the overall perspective, VB-ADEKF is not very sensitive to the initial SOC error. It shows that the proposed VB-ADEKF has better robustness for initial SOC errors than the traditional DEKF.  
Figure 16: SOC estimation results of the two filters with different initial SOC values in the UDDS test.
## 4 Proposed Architecture
### OpenPose Module  
The OpenPose module integrates the OpenPose library, a real-time multiperson key point detection tool. It extracts 25 key points with x and y coordinates. For this study, we utilize only a subset of four key points: the neck, left shoulder, right shoulder, and hip. This selection primarily serves two purposes.  
Firstly, these key points demonstrate minimal data loss across various datasets, ensuring robustness in data processing. An example from the MCFD dataset illustrates this particular selection, as shown in Figure 3, where these key points show significantly fewer missing values than others. Similar results have been demonstrated in other datasets as well. This preservation makes the subset not just reliable but a robust choice for training the network and improving model performance.  
Figure 3: This figure demonstrates the total count of missing values for each key point from MCFD dataset, with the \(Y\)-axis displaying the missing data count and the \(X\)-axis listing the key points. The prefixes L and R indicate left and right sides, respectively.  
Figure 2: Overall pipeline of our proposed architecture, demonstrating **(1)** Gaussian module, **(2)** OpenPose module, **(3)** STFT module, **(4)** classification module, and **(5)** GradCAM module.  
Secondly, by focusing on this subset, the network achieves improved cost-effectiveness and precision due to a substantial reduction in the calculations required. Following the selection of this subset, the velocities of these four key points are computed along the \(X\) and \(Y\) axes using the formula below:  
\[v_{i}=\frac{k_{i}-k_{f-j}}{j} \tag{16}\]  
where \(v_{i}\) represents the velocity at frame \(i\), \(k\) denotes the key point, and \(j\) is the number of frames over which the velocity is calculated.  
Reducing the number of key points from twenty-five to four significantly decreases the amount of data that need to be processed. This streamlined approach enhances the computational efficiency and reduces the processing time required for key point extraction and subsequent analysis. By focusing on the most relevant key points, the system can quickly and accurately detect falls without being bogged down by unnecessary calculations associated with less critical key points.  
This optimization is particularly advantageous in real-time applications where rapid response times are essential. For instance, in a healthcare setting where real-time monitoring is crucial, the ability to quickly process and analyze video data can make a significant difference in the timely detection of falls and the provision of immediate assistance. Moreover, the reduced computational load translates to lower power consumption and resource usage, making the system not just efficient but also practical and scalable for widespread deployment.
## 2 Experimental Setup  
To meet the requirement of a femtosecond streak camera, a sweep circuit was designed, with the core component of GaAs PCSSs. The schematic diagram of a lateral GaAs PCSS for the experiment is shown in Figure 1. The bottom copper board is part of the transmission line, and the Al\({}_{2}\)O\({}_{3}\) insulation layer is located between the copper board and GaAs substrate. The nominal dark resistance of the GaAs wafer is larger than 5 \(\times\) 10\({}^{7}\)\(\Omega\)-cm, and the nominal electron mobility is higher than 5000 cm\({}^{2}\)/(V\(\cdot\)s). The two Au/Ge/Ni electrodes form ohmic contact with the GaAs substrate. The size of each electrode is 6 mm \(\times\) 3 mm, and the electrodes are rounded with the corner radius of 1.1 mm in order to uniform the electric field distribution. The gap between the two electrodes is 3.5 mm. Multi-layer transparent dielectrics used as the passivation and insulation protection materials are deposited and coated on the surface of the GaAs PCSS. The switch is connected with an external circuit using coaxial transmission lines.  
The schematic diagram and the photo of bilateral sweep circuit based on the GaAs PCSSs are shown in Figure 2a,b, respectively. The capacitances of filter capacitors C\({}_{1}\), C\({}_{2}\), C\({}_{5}\) and C\({}_{6}\) are 10 nf. The capacitances of energy storage capacitors C\({}_{3}\) and C\({}_{4}\) are 33 pf. The resistances of current-limiting resistors R\({}_{1}\), R\({}_{2}\), R\({}_{3}\) and R\({}_{4}\) are 470 k\(\Omega\). In order to improve the circuit's bandwidth, these capacitors and resistors are the surface-mounted components with small parasitic parameters and high voltage capacity (3000 V).  
The experimental setup is shown in Figure 3. The sweep circuitwas connected with a 50 \(\Omega\) coaxial line by sub-miniature-A (SMA) connectors. The output voltage was attenuated by 60-dB attenuators (BW-N20W50+ and BW-N40W50+, Mini circuits, NY, US) and recorded by an oscilloscope (OSC) (LeCroy WaveMaster 806 Zi-A, 4x40 GS/s, Teledyne LeCroy, NY, US) with a bandwidth of 6 GHz.  
Figure 1: Schematic diagram of a lateral PCSS.  
Figure 2: (**a**) Schematic diagram of the sweep circuit; (**b**) The photo of the sweep circuit.
# Development of Automatic Crack Growth Simulation Program Based on Finite Element Analysis
## 4 Verification of AI-FEM
### Comparison of SCC Growth with the ASME Method
#### 4.1.2 SCC Growth Law  
The SCC growth rate curve for Alloy 182 in the pressurized water reactor (PWR) environment is presented in ASME code, Sec. XI, App. C was applied and is shown in Figure 10. The SCC growth curve for Alloy 182 is a function of the SIF and temperature, and the relation is as follows in Equation (2):  
\[\frac{da}{dt}=\exp\left[-\frac{Q_{g}}{R_{g}}\left(\frac{1}{T_{abs}}-\frac{1}{T _{ref}}\right)\right]\omega(K_{I}-K_{Ith})^{\eta} \tag{2}\]  
where \(K_{I}\) is the mode I SIF and \(K_{Ith}\) is the threshold value of the SIF. \(Q_{g}\), \(R_{g}\), \(T_{abs}\), and \(T_{ref}\) are thermal activation energy for SCC growth, universal gas constant, absolute metal operating temperature, and absolute reference temperature for SCC, respectively. \(\omega\) and \(\eta\) are the SCC growth factors. The values of each parameter are shown in Table 6. Also, the elastic modulus was considered to be 200 GPa, and the Poisson's ratio was considered to be 0.3.  
Figure 10: SCC growth rate curve for Alloy 182 presented in ASME code, Sec. XI.  
Figure 9: Schematics of pipe with circumferential semi-elliptical inner surface crack.
## 3 Macro-Control and Micro-Autonomy Pathfinding Strategy  
### Overall Design of Macro-Control and Micro-Autonomy Pathfinding System  
Centralized planning strategies require the pre-computation of a conflict-free path for each AGV before execution. This approach shows a lack of adaptability. As the number of AGVs increases, the solution space expands exponentially, making centralized methods computationally expensive and slow in large-scale deployments. Centralized planning struggles to handle unexpected obstacles or dynamic environmental changes, requiring frequent re-planning.  
On the other hand, distributed planning strategies allow AGVs to make decisions independently based on locally available information. Although these methods are highly adaptable and scale linearly with the number of AGVs, they often suffer from lower path quality and convergence issues. Distributed methods may result in suboptimal or inefficient routes. Training RL models for pathfinding may be slow.  
To address these challenges, this paper proposes the following macro-control and micro-autonomy pathfinding strategy, which combines centralized pre-planning with distributed obstacle avoidance and pathfinding:  
1. Macro-Level (Centralized Pre-Planning): The central system pre-plans AGV paths using an modified A* algorithm, ignoring potential dynamic obstacles and AGV conflicts. This pre-planned path serves as a guideline for AGVs.
2. Micro-Level (Distributed Decision Making): Each AGV autonomously decides its next move based on the pre-planned path from the central system, real-time local obstacle detection, and neighboring AGV states (shared via communication).  
This hybrid strategy ensures that AGVs benefit from high-quality initial pathfinding while maintaining the flexibility to adapt to real-time changes. The system response time is very fast and grows linearly with AGV size \(n\). For the pre-planning part, since a single-agent A* algorithm is used, the time complexity is \(O(n)\); The distributed planning part is executed independently and in parallel by each AGV, resulting in a time complexity of O(1).  
The overall framework is shown in Figure 4.
## 3 Data and Methodology
### Modeling Methods
#### 3.4.2 Artificial Neural Network  
The artificial neural network (ANN) is a calculating algorithm that attempts to simulate the method of biological brain neurons to process information [69]. The error backpropagation algorithm (BP) is probably the most popular ANN algorithm [70]. The process of BP ANN includes forward propagation of signal and backward propagation of error. The initial connection weights used to pass the units in previous layer forward to the units in next layer are randomly set, and then recursively adjusted according to the back-propagating error discrepancies between the computed and desired results until the error discrepancies of the network reach a satisfactory threshold or are minimized.
# A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks
## 4 Experiments
### Analysis in 3D-Space
#### 4.3.2 Geometric Transformation  
To formally give a more realistic example, we generated random points in 3D-Space using the domain \(x\in(-1,1)\), \(y\in(-1,1)\), and \(z=(2-x^{2}-y^{2})\), which creates input shapes, shown in Figure 7. The input shapes become clearer as the number of randomly generated points increases, shown in Figure 7. These randomly generated points are then mapped by the function in Equation (9), creating output shapes, shown in Figure 7. The primary objective here is to learn the functional mapping between the input and output shapes using an NN architecture with a limited set of data points, specifically 20 in our context, while ensuring accurate estimates as the number of data points increases. Thus, 20 numbers are uniformly random generated across the \(x\) and \(y\) axes, and this process is repeated 1000 times, segregating the accumulated points into training and testing sets with dimensions of \((800,20,3)\) and \((200,20,3)\), respectively. Furthermore, to analyze the consistency of the estimates quantitatively, additional sets of \(10^{2}\), \(10^{3}\) and \(10^{4}\) data points are generated in the same domain with dimensions of \((100,100,3)\), \((100,1000,3)\) and \((100,10,000\), 3), respectively, similar to the data points generated in 1D space to test the consistency in Section 4.1.  
As in the continuous functional mapping case, two different NN architectures were trained for 1000 epochs on the generated 3D training dataset, using the Adam optimizer and the MSE loss function. The architecture with the GRU layer achieved an MSE of \(1.0\times 10^{-4}\) on the test set, while the ConsRNN-based architecture achieved an MSE of \(3.7\times 10^{-3}\). The trained architectures are also tested on the additionally generated points in the same 3D domain with the same and higher frequencies of \(20\), \(10^{2}\), \(10^{3}\), and \(10^{4}\). The detailed results are shown in Table 1.  
Figure 6: The 3D results with continuous functional mapping. The black lines represent the input paths and the red lines represent the output paths that are expected to be estimated by NN architectures. As can be seen from both the visual results and MSE scores, while the number of increments increases, the GRU estimations deviate from the ground truth in contrast to the ConsRNN-based NN architecture. In the left MSE figure, the change in MSE value is indicated with reference to 20 increment case as a multiple. The ‘x’ values correspond to the MSE value multiples for different numbers of increments.