esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissimilar Rotary Friction Welding of Inconel 718 to F22 Using Inconel 625 Interlayer
## 4 Conclusions  
Inconel 718/F22 rotary friction welded joints with and without Inconel 625 interlayer were successfully produced in this study. Microstructural and mechanical properties investigation of the weldments was carried out and analysed. The results of the study are summarised as follows:  
1. Weldments were free of weld defects such as porosity or cracks, as evidenced by optical and electron microscopic investigations.
2. Higher hardness was observed at the weld interface of the Inconel 718/F22 rotary friction welded joints, while welded joints with Inconel 625 interlayer did not show such a hardness jump.
3. Tensile samples failed in the F22 base metal for welds prepared with and without interlayer. Inconel 718/F22 rotary friction welded joints prepared using Inconel 625 interlayer showed higher ductility compared to the joints without an interlayer, while both the joints showed similar strength values.
4. Inconel 718/F22 rotary friction welded joints displayed lower impact toughness values compared to the base metals and weld joints prepared using Inconel 625 interlayer. Carbon migration from the F22 to the Inconel 718 at the weld interface, because of the mechanical intermixing and diffusion, resulted in the formation of carbide phases in the Inconel 718 and lowered the toughness of the weld joints.  
Figure 10: Room temperature tensile fracture morphology of (**a**) 718 base metal, (**b**) F22 base metal, (**c**) 718-F22 joint, and (**d**) 718-F22 joint with 625 interlayer.  
**Author Contributions:** Conceptualisation, A.U.R., Y.U., A.M.A.-S. and S.A.; methodology, A.U.R., N.K.B., M.K.T., S.A. and Y.U.; formal analysis, A.U.R., N.K.B., M.K.T., Y.U., A.M.A.-S. and S.A.; investigation, A.U.R., N.K.B., M.K.T., S.A. and Y.U.; resources, A.U.R., Y.U., A.M.A.-S. and S.A.; data curation, A.U.R., N.K.B., S.A. and M.K.T.; writing--original draft preparation, A.U.R., N.K.B., S.A. and M.K.T.; writing--review and editing, A.U.R., N.K.B., M.K.T., Y.U., A.M.A.-S. and S.A.; visualisation, A.U.R., N.K.B., S.A. and M.K.T.; supervision, A.U.R., N.K.B. and M.K.T.; project administration, A.U.R. and Y.U.; funding acquisition, A.U.R., Y.U., A.M.A.-S. and S.A. All authors have read and agreed to the published version of the manuscript.  
National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award number 15-ADV3717-02.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within this article.  
**Acknowledgments:** This project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award number 15-ADV3717-02.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 4 Experiment Results and Discussion
### Data Split  
In our dataset, each command is recorded by the same contributor 10 times, resulting in significant similarity and dependence between samples. Consequently, using classical data-splitting methods (like random splitting) could lead to a high overfitting rate, as the training and validation sets might end up containing very similar samples.  
To ensure that all recordings from the same contributor are within the same subset, and to measure the model's ability to generalize to new individuals outside the dataset, we used the Stratified Group K-Fold technique available in the scikit-learn library for train-test splitting.  
In the Stratified Group K-Fold method, the dataset is split into K folds. Each fold contains groups of samples. One fold is used for validation, and the remaining K-1 folds are used for training. This process is repeated K times so that each fold serves as the validation set once. The overall accuracy is measured by calculating the average accuracy obtained across all the folds. In our case, we have split the dataset into five folds. In each iteration, four folds are used for training (representing 80% of data), and one fold (representing 20% of data) is used for validation.
## 5 Experimental and Evaluation Results
### Power Consumption of CNNs  
The average power consumption (in Watt) during inference while utilizing ResNet15v2, MobileNetv2, VGG19, NASNetMobile, and ThermalAttackNet on ondemand governor is 10.69, 9.56, 10.67, 8.79, and 7.63, respectively. Given the fact that ThermalAttackNet is fraction of a size of popular CNNs (see Table 1) while being able to predict close to other popular CNNs (see Table 3), utilizing ThermalAttackNet for such an attack on the device is more power efficient.
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
###### Abstract
The Long Short-Term Memory (LSTM) model adopts a gating mechanism controlling the contents of an internal memory cell so that it is capable of learning a better and more complex representation of long-term dependencies in the input sequential data. Consequently, LSTM networks work well for feature learning over time series data. Some researches employ it to learn the complex spatial dependencies for scene labeling and action recognition [40; 41; 42]. Inspired by this idea, in this paper we proposed a stacked Convolutional Neural Networks and Long Short-Term Memory (SCNN-LSTM) for finger-vein texture segmentation by combining the CNN model and LSTM model. Compared to existing segmentation-based methods, our approach not only predicts the probability of a pixel based only on its pixels and their correlations in a local region, but it does so by relying also on the spatial dependencies in its neighboring contexts, through a feature representation learned by LSTM from a large sequence training set. The main paper contributions are summarized as follows:  
(1) We proposed a stacked Convolutional Neural Network and Long Short-Term Memory model to automatically learn features from raw pixels for finger-vein verification. First, the vein and background pixels are automatically labeled based on several baselines. For each labeled pixel, we generated four sequences along different directions. As a result, there are various sequence-based training sets, on which several SCNN-LSTMs are independently trained to form a complementary and an over-complete representation. Secondly, for a testing image, the probability of each sequence being to vein pattern is predicted and the scores from patch-based sequences are conjointly input to P-SVM to segment the vein patterns. As the CNN model has the capacity for representation of vein texture features in a local region (i.e., patch) and the LSTM model captures the spatial dependencies among the closed regions, the proposed SCNN-LSTM model is capable of predicting the probability of belonging to a vein pattern. The rigorous experimental results on a public finger-vein database imply that the proposed approach is able to extract vein pattern, which results in a significant improvement for finger-vein verification accuracy. (2) This paper investigates a new approach to encode the finger-vein for verification. Generally, the existing finger-vein segmentation approaches encode an image to extract binary vein patterns based on one or more thresholds, which are not related to verification error reduction. Different from them, an effective supervised scheme is employed to automatically select the threshold for vein pattern encoding. We search for a robust threshold to encode image by maximizing the inter-class distance and minimizing intra-class distance, which is not based on human domain knowledge. So the proposed scheme directly targets biometrics verification performance instead of human perception. We analyze the experimental results and estimate the verification performance.
# Light-Induced Ring Pattern in a Dye-Doped Nematic Liquid Crystal
## 3 Mathematical Modeling for Photo-Isomerization in Dye-Doped Liquid Crystals
### Adiabatic Elimination and Effective Model  
To determine the dynamics described by Equation (5), one can consider the adiabatic elimination of the _cis_-state concentration [36]. Indeed, by assuming that the temporal evolution of the _cis_-state concentration is rapid compared to the dynamics of the order parameter--i.e., \(\lambda\gg 1\)--and by using Neumann series, one can approach, in a dominant order, the _cis_ concentration by  
\[C\simeq C_{0}(I)-\alpha S+\frac{D-\alpha\delta}{\lambda}\
abla^{2}S. \tag{7}\]  
Introducing this expression in the equation for the order parameter S, at a dominant order, we obtain  
\[\partial_{t}S = -[A+\beta C_{0}(I)]S+(B-\alpha\beta)S^{2}-S^{3}+(1-D\alpha)\
abla ^{2}S \tag{8}\] \[+ \frac{D(D-\alpha\delta)}{\lambda}\
abla^{4}S+\frac{\beta}{\lambda }(D-\delta\alpha)S\
abla^{2}S+D\
abla^{2}C_{0}(I).\]  
Renormalizing the space \(\vec{r}=\vec{r}^{\prime}[\lambda/D(\delta\alpha-D)]^{1/4}\), the effective model reads  
\[\partial_{t}S = -\tilde{A}S+\tilde{B}S^{2}-S^{3}-\
u\
abla^{2}S-\
abla^{4}S+bS \
abla^{2}S+\tilde{\eta}, \tag{9}\]  
where  
\[\tilde{A}(\vec{r}^{\prime}) \equiv A+\beta C_{0}(I(\vec{r}^{\prime})), \tag{10}\] \[\tilde{B} \equiv (B-\alpha\beta),\] (11) \[\
u \equiv D\alpha-1\sqrt{\frac{\lambda}{D(\delta\alpha-D)}},\] (12) \[b \equiv \frac{\beta(D-\delta\alpha)}{\sqrt{\lambda D(\delta\alpha-D)}},\] (13) \[\tilde{\eta}(\vec{r}^{\prime}) \equiv D\sqrt{\frac{\lambda}{D(\delta\alpha-D)}}\
abla^{2}C_{0}(I). \tag{14}\]  
The model in Equation (9) corresponds to a non variational Swift-Hohenberg-type equation [37; 38]. This model has been used to study patterns [39; 40], localized, stationary [37; 41], and propagative structures [42; 43], and spatiotemporal chaotic extended [40] and localized structures [44]. These phenomena have been studied in different contexts ranging from physics and chemistry to biology. The physical origin of the formation of spatial structures is due to the anti-diffusion coefficient (\(\
u>0\)), which represents the different scales of the transport processes of the _cis_ order and state parameter, which introduces an intrinsic characteristic scale: the Turing mechanism [45]. Namely, by having two transport processes with different scales, the system cannot propagate the order parameter and the _cis_-state homogeneously; thus, it self-organizes, forming patterns.
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
## 4 System Implementation
### Multilayer Curved Wall Full System Experiment
A sampling rate of 60 kS/s and a moving average of four samples of unwanted noise present after the envelope detector is effectively removed was used, making it possible to demodulate signals with poor signal-to-noise ratio. Furthermore, the use of Manchester coding favors the digital demodulation inside the MCU by using edge transition instead of level voltage to demodulate each bit. The black signal in Figure 9c is the reconstructed sensor data measured at the on-output pin of the microcontroller after the enveloped signal has been processed. The digital signal has a speed of 115 kbps in the universal asynchronous receiver/transmitter (UART) protocol, and it can be connected to any computer or data analyzer using UART-USB converters. Figure 9: Oscilloscope signals showing (**a**) the enveloped signal after the envelope circuit detector in purple (500 mV/div), (**b**) the inside voltage signal that reaches the inside transducers in cyan (5 V/div) and (**c**) the demodulated signal processed inside a MCU and transmitted to an output pin (5 V/div).
## 2 Case Study  
### Basic Characteristics of the Dawuchang Landslide  
The Dawuchang landslide is located in Group 1 of Kaixia Village, Daxi Township, Wushan County, on the left bank of Cuokai Gorge, a tributary of the Yangtze River. It is a water-related landslide, with geographic coordinates of 109\({}^{\circ}\)41\({}^{\prime}\)23.19\({}^{\prime\prime}\) E and 30\({}^{\circ}\)58\({}^{\prime}\)59.19\({}^{\prime\prime}\) N, approximately 1.8 km from the river mouth. The left front boundary of the landslide is characterized by a microtopographic ridge, while the right boundary follows a natural erosion gully. The rear boundary is marked by a steep slope where bedrock transitions into fragmented stone soil, with an elevation ranging from 355 to 365 m. The front edge lies within the submerged zone of the Yangtze River water level. Based on cross-sectional measurements and previous data, the potential shear outlet is estimated to be at an elevation of approximately 130 m. A secondary shear outlet is located in the central part of the landslide at an elevation of 240-250 m. In plain view, the landslide exhibits a tongue-shaped morphology, extending approximately 860 m longitudinally and 330-370 m transversely. The rear scrap elevation ranges from 355 to 365 m, with a total area of approximately 30.1 \(\times\) 10\({}^{4}\) m\({}^{2}\). The average landslide thickness is 57 m, resulting in a total volume of approximately 1710 \(\times\) 10\({}^{4}\) m\({}^{3}\). The primary sliding direction is 65\({}^{\circ}\). This landslide is classified as an extremely large soil-rock mixed landslide, primarily induced by rainfall and reservoir water fluctuations. Currently, it is in a slow-creep deformation stage (Figure 1).  
Figure 1: Layout of monitoring sites for the Dawuchang landslide.
## 3 Kinematics Modeling of Self-Insulating Mechanical Arm Joints  
According to the self-insulating mechanical joint design described above, a simplified structure diagram of the entire robotic arm was established, as shown in Figure 4. Since wrist joint part I is exactly the same as shoulder joint part II, and the elbow joint has only one degree of freedom, the shoulder joint is modeled and analyzed separately, as shown in Figure 5.  
The simplified model considers the cables, platforms, and connecting rods while neglecting their masses and diameters. The four cables in the kinematic model are assumed to be massless with negligible diameter, having their lower platform attachment points defined as \(P_{1}\) and \(P_{2}\) and upper platform points as \(Q_{1}\) and \(Q_{2}\). Both the fixed and moving platforms are treated as reference points with negligible thickness.  
The fixed platform's local coordinate system \(O_{1}X_{1}Y_{1}Z_{1}\) has its origin \(O_{1}\) at the platform center, with the \(Y_{1}\)-axis oriented along the \(O_{1}P_{2}\) direction and the \(X_{1}\)-axis perpendicular to \(Y_{1}\). The \(Z_{1}\)-axis follows the right-hand rule. Similarly, the moving platform's local coordinate system \(oxyz\) is defined.  
Figure 3: Bionic self-insulating joint mechanism.  
Dielectric plates are attached to both platforms' surfaces to provide electrical insulation during live-line operations. In the initial configuration, the connection lines between attachment points on the moving and fixed platforms remain perpendicular. The distances from points \(P\) and \(Q\) to their respective platform centers are denoted as \(p\) and \(q\), while \(L\) represents the cable length between corresponding \(P\) and \(Q\) points. Each cable's tension force is represented by \(T\), and its directional unit vector is defined as \(u_{i}\).  
Figure 4: Simplified model of a self-insulating bionic mechanical arm.  
Figure 5: Simplified model of self-insulating joint.  
The model maintains the following essential characteristics: cable-driven actuation through the four tension elements, orthogonal initial alignment of the platform connection lines, and incorporated dielectric insulation through the attached plates. The coordinate system definitions and geometric parameters establish the foundation for subsequent kinematic analysis while preserving the self-insulating properties of the joint mechanism.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 3 Methodology
### Design of the Conv-for-Origin Module  
The purpose of the Conv-for-Origin layer is to embed the position information in the original CSI, to ensure the richness of the features. At the same time, the size of the original CSI matrix can be adjusted to fit the input of the later Dual-Encoder module. The data size is fixed to reduce the feature loss of the data source. Therefore, we align dimensions of the original CSI matrix using convolutional layers. This avoids the loss of features caused by simple size splicing and stacking directly on the source data. After that, the new CSI matrix can be used to obtain the position feature matrix through absolute position encoding, and the obtained position feature matrix can be superimposed with the new CSI matrix, and finally the output of the Conv-for-Origin module can be obtained. Additional design details of the Conv-for-Origin module are presented in the following subsection.
## 2 A Brief Overview of Previous Work on Jaya  
A variation of the standard Jaya algorithm is presented in the multi-team perturbation-guiding Jaya (MTPG-Jaya) [5] where several \"teams\" explore the search space, with the same population being used by each team, while the \"perturbations\" governing the progression of the teams are different. The MTPG-Jaya was applied to the layout optimization problem of a wind farm. The Jaya algorithm was originally designed for continuous (real-valued) optimization, and most of Jaya's applications to date have been in the continuous domain. A binary version of Jaya, however, was proposed in [6], where the authors borrowed (from [7]) the idea of combining particle swarm optimization with angle modulation and adapted that idea for Jaya. The binary Jaya was applied to feature selection in [6]. Modifications to the standard Jaya algorithm include a self-adaptive multi-population-based Jaya algorithm that was applied to entropy generation minimization of a plate-fin heat exchanger [8], a multi-objective Jaya algorithm that was applied to waterjet machining process optimization [9], and a hybrid parallel Jaya algorithm for a multi-core environment [10]. Application areas of the Jaya algorithm have included such diverse fields as pathological brain detection systems [11], flow-shop scheduling [12], maximum power point tracking problems in photovoltaic systems [13], identification and monitoring of electroencephalogram-based brain-computer interface for motor imagery tasks [14], and traffic signal control [15].
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 3 Methodology
### Optimizing Permit Management Systems Through Rare Event Simulation and Reinforcement Learning  
Integrating rare event simulations, robust evaluations, and reinforcement learning, enhances permit management systems by ensuring adaptability under both standard and exceptional conditions. In key cases--macro-party organization, the agricultural use of wastewater, and a financial audit for municipal water resources management--simulating emergencies, regulatory shifts, and system failures improves decision-making and crisis response.  
For macro-party organization, agents manage security threats, extreme weather, and infrastructure failures, ensuring swift regulatory adjustments. In the agricultural use of wastewater, real-time water quality monitoring, regulatory compliance updates, and emergency infrastructure repairs maintain environmental safety. For financial audits, fraud detection, emergency fund reallocations, and compliance with new financial regulations enhance municipal financial integrity.  
System robustness is evaluated using response time, approval rates, resilience, and risk prediction accuracy for event and agricultural permits, while fraud detection efficiency, regulatory adaptability, and data recovery time measure financial audit performance.  
Reinforcement learning techniques, such as Q-learning, deep Q-networks (DQN), multi-agent reinforcement learning (MARL), and federated learning, enhance agent decision-making, coordination, and adaptability. These approaches enable agents to respond proactively to crises, improving permit issuance efficiency and systemic resilience.  
By leveraging a multi-agent system--including MayorAgent, SecretariatAgent, TreasuryAgent, SecurityAgent, and RegulatoryComplianceAgent--municipalities optimize permit management, ensuring public safety, regulatory compliance, and operational continuity. This AI-driven framework strengthens governmental adaptability and public service efficiency.
## 5 Blast Analysis
### Numerical Results  
The overpressure histories calculated by varying the slope angle of the external tunnel entrance for ratios of the blast pocket, \(L_{b}/L_{w}\), of 0.5, 1.0, 1.5, and 2.0, are shown in Figure 17, where the original model (Arc-90-0) was included for comparison. The overpressures fluctuate with time owing to the repeated reflection and diffraction of the shock waves. The maximum overpressure, \(P_{M}\), was identified among the first few peaks in most cases. They were effectively reduced compared to those of the original one because of the effects of the external entrance slope and blast pocket. Similar observations were made for the overpressure histories calculated by varying the blast-pocket ratio for slope angles, \(\alpha_{s}\), of \(90^{\circ}\), \(75^{\circ}\), \(60^{\circ}\), and \(45^{\circ}\), as shown in Figure 18. The results of the maximum overpressure, \(P_{M}\), for all the parametric cases are summarized in Table 4. Model Arc-60-05 showed the greatest overpressure mitigation; that is, the maximum overpressure, \(P_{M}\), for the model was estimated to be 8.98 kPa and was reduced by 64.5% compared to the original (25.32 kPa). A more detailed discussion of the numerical results is presented in the following section.  
\begin{table}
\begin{tabular}{c c c c} \hline No. & Model & \(P_{M}\) (kPa) & Note \\ \hline
1 & Arc-90-0 & 25.32 & Original \\ \hline
2 & Arc-90-05 & 13.62 & \\ \hline
3 & Arc-90-10 & 16.68 & \\ \hline
4 & Arc-90-15 & 17.96 & \\ \hline
5 & Arc-90-20 & 19.72 & \\ \hline
6 & Arc-75-05 & 10.72 & \\ \hline
7 & Arc-75-10 & 16.10 & \\ \hline
8 & Arc-75-15 & 18.42 & \\ \hline
9 & Arc-75-20 & 17.50 & \\ \hline
10 & Arc-60-05 & 8.98 & \\ \hline
11 & Arc-60-10 & 14.09 & \\ \hline
12 & Arc-60-15 & 17.73 & \\ \hline
13 & Arc-60-20 & 16.71 & \\ \hline
14 & Arc-45-05 & 9.89 & \\ \hline
15 & Arc-45-10 & 15.73 & \\ \hline
16 & Arc-45-15 & 13.09 & \\ \hline
17 & Arc-45-20 & 17.29 & \\ \hline \end{tabular}
\end{table}
Table 4: Results calculated for maximum overpressures, \(P_{M}\).  
Figure 16: Definition of length for blast pocket A.  
Figure 18: _Cont._