esa-sceva/satcom-chunk-collection huggingface dataset:
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 3 Method
### Feature Pyramid Network  
The resolution of the five features of \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), and \(C_{5}\) that were extracted by the ResNet50 network from the bottom-up gradually decreased, but the perceptual field gradually increased, and the level of the semantic information extracted gradually increased. The top-down and horizontally connected process of the feature pyramid network ensured that the semantic information of the higher-level features could be utilized, while the detailed information could be located by using the bottom-level features. The top-down process of the feature pyramid was to first up-sample the more abstract and semantically stronger higher-level feature maps. Then, we fused the results of the up-sampling with the bottom-up feature maps of the same size generated by the ResNet50 network through a horizontally connected process. This process was iterative until the final feature map with the desired resolution was generated.  
The initial iteration needed to start with a \(1\times 1\) convolution for feature \(C_{5}\) with a size of \(1/32\) of the original image to ensure a fixed feature dimension, and the result was noted as \(O_{5}\). The detailed calculation is shown in Equation (1):  
\[O_{5}=conv1(C_{5}). \tag{1}\]  
This step was not required for the other layer iterations. The iterative process was up-sampling with a ratio of 2 for \(O_{i+1}\) at first to ensure the same size as the previous layer of features. At the same time, the features \(C_{i}\) were convolved \(1\times 1\) once for dimensional transformation. Then, the two features were summed by element to obtain the feature \(O_{i}\). The detailed calculation is shown in Equation (2):  
\[O_{i}=up2(O_{i+1})\oplus conv1(C_{i}), \tag{2}\]  
Figure 1: Framework of scene text detection based on multi-headed self-attention.  
where \(conv1\) denotes \(1\times 1\) convolution; \(i\) = 2, 3, 4; \(up2\) denotes up-sampling with a ratio of 2; \(\oplus\) denotes summation by elements.  
Finally, in order to reduce the overlap effect of up-sampling, a 3 \(\times\) 3 convolution was attached to \(O_{2}\), \(O_{3}\), \(O_{4}\), and \(C_{5}\). In order to obtain the same size of the feature map where all were 1/4 of the original size, a corresponding number of up-sampling repeats were added after convolution to obtain the four final feature maps for \(P_{2}\), \(P_{3}\), \(P_{4}\), and \(P_{5}\). The details were calculated with Equations (3)-(6):  
\[P_{2}=conv3(O_{2}), \tag{3}\]  
\[P_{3}=up2(conv3(O_{3})), \tag{4}\]  
\[P_{4}=up4(conv3(O_{4})), \tag{5}\]  
\[P_{5}=up8(conv3(C_{5})). \tag{6}\]
### Contributions  
The main motivation behind this work is that the traditional DL models are unable to predict CVD at early stages. To overcome the flaws of existing models, we present Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net) by combining different DL models. Class imbalance is one of the leading issues in the dataset. During classification, if data are not balanced, the model's accuracy tends to be very low. Data balancing is achieved using oversampling and undersampling. In oversampling, minority class instances are increased to make them equal to majority class instances. In undersampling, majority class instances are reduced, which can lead to information loss. Many balancing techniques have been proposed to address data imbalance issue. The contributions made in this paper are mentioned below.  
* For data balancing, we used ProWRAS technique to improve model accuracy.
* We proposed two models, DB-Net and DVR-Net, using DL architectures to efficiently predict CVD.
* For validating our proposed model results, we implemented 10-Fold Cross Validation (10-FCV)
* To identify feature's contribution in DB-Net and DVR-Net, we used an eXplainable Artificial Intelligence (XAI) technique, SHapley Additive exPlanations (SHAP).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Description** \\ \hline ANN & Artificial Neural Network \\ \hline AI & Artificial Intelligence \\ \hline CNN & Convolutional Neural Network \\ \hline DL & Deep Learning \\ \hline DT & Decision Tree \\ \hline DenseNet & Densely Connected Convolutional Network \\ \hline DBN & Deep Belief Network \\ \hline DB-Net & Dense Belief Network \\ \hline DNN & Deep Neural Network \\ \hline DVR-Net & Deep Vanilla Recurrent Network \\ \hline FF & Feature Fusion \\ \hline GNB & Gaussian Naive Bayes \\ \hline IoT & Internet of Things \\ \hline KNN & K-Nearest Neighbor \\ \hline LASSO & Least Absolute Shrinkage Selection Operator \\ \hline LR & Logistic Regression \\ \hline ML & Machine Learning \\ \hline NLP & Natural Language Processing \\ \hline NN & Neural Network \\ \hline ProWRAS & Proximity Weighted Random Affine Shadowsampling \\ \hline RF & Random Forest \\ \hline RBM & Restricted Boltzmann Machine \\ \hline RNN & Recurrent Neural Network \\ \hline SVM & Support Vector Machine \\ \hline VRNN & Vanilla Recurrent Neural Network \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of abbreviations.  
To see model generalizability, we performed cross-dataset evaluation.  
In Section 2, the literature on CVD prediction is discussed. In Section 3, we discuss the DL techniques used in the proposed model. In Section 4, an overview of the proposed model is discussed. The results are presented in Section 5. In Section 6, cross-dataset evaluation is discussed. Finally, the work is concluded in Section 7.
## 6 Experimental Results and Discussion
### Experimental Damage Observations and Crack Patterns
#### 6.1.3 Specimen S5  
Photographs of crack patterns in specimen S5 at selected drift levels are provided in Figure 12. The first flexural cracks appeared during the first downward-loading cycle at 0.1% drift and were located at the beam slab, specifically within the region reinforced with CFRP strips. During subsequent loading cycles, these initially short flexural cracks expanded progressively toward the web of the beam but did not affect the CFRP laminates. Cracks originating at the bottom face of the beam were difficult to discern because they developed along the saw-cut grooves. During the first cycle at 0.4% drift, long flexural-shear cracks developed outside of the FRP-reinforced region while loading in both directions. First yield of the main longitudinal reinforcement occurred at 0.4% drift next to the support. First yield of the transverse reinforcement within the end-yielding region was detected at 1.2% drift at the third hoop from the support. At 2.9% drift, the initial flexural cracks along the crack-control joints between the first two CFRP strips started widening (Figure 12a). Partial delamination of the second and third strips from the fixed-end support occurred during the second cycle at 2.9% drift (Figure 12b). The specimen's peak capacity was reached during the next displacement level at 3.4% drift. Strength loss due to hinging started during the first cycle at 3.8% drift and was characterized by excessive widening of multiple vertical cracks at the beam-column interface and between the CFRP strips. Severe concrete crushing of the core throughout the sections at the two vertical cracks closer to the support was observed. Sudden fracture of the second-next CFRP strip adjacent to the support occurred at the beginning of the second cycle at 3.8% drift while loading up, with the strip tearing in half along the beam's softit and each half fracturing at opposite web corners (Figure 12c,d). During the next half-cycle downward (3.8% drift), one of the spliced CFRP anchors of the strip closest to the support fractured at one of the exit holes above the slab (Figure 12e), which resulted in a notable loss of strength in the downward-loading direction of 20% between the two cycles at 3.8% and an additional 17% between the second cycle at 3.8% and the first at 4.5%. Fracture of both anchors of the first CFRP strip adjacent to the support at opposite exit holes and complete detachment of the strip occurred during the first cycle at 4.5% drift (Figure 12g), accompanied by extensive damage at the hinging zone, which continued during the next two cycles at 4.5% and 5.3% drift, with massive spalling of core concrete pieces and buckling of the longitudinal reinforcement (Figure 12f,h,i).  
Figure 12: Cont.
## 3 Results and Discussion
The lowest values of RMSE are the most frequent for Equation (3). MABE ranges from 0.31 MJ-m\({}^{-2}\) to 0.87 MJ-m\({}^{-2}\). The best agreement between the calculated and measured values was observed for Equation (3), and the maximal difference between MABE for the analyzed models does not exceed 0.13 MJ-m\({}^{-2}\). Except for Wieluń and Wlodawa stations, similar values of MABE were obtained for Equations (1) and (3). Figure 2 shows the compatibility of the measured and calculated values of long-term monthly average daily solar radiation. An underestimation is clear for Lesko, Torun, and Leba, while at the Wieluń and Lodz stations, an overestimation of the model is observed (Figure 2). MAPE ranges from 4.16% to 8.71% (Model (1)), from 5.68% to 14.20% (Model (2)), and from 4.02% to 9.15% (Model (3)). The highest values of MAPE were obtained for Equation (2), whereas they were similar for Equations (1) and (3). Additionally, model coefficients for Poland as a whole were calculated for Equation (1). The results are presented in Tables 2 and 3. RMSE for this equation varies from 0.54 to 1.29 MJ-m\({}^{-2}\), while MABE and MAPE vary from 0.46 to 1.02 MJ-m\({}^{-2}\) and 5.9%-11.4% respectively.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods  
This study focuses on a challenging road segment of the X412 County Road located in Nanjing Province, China. Through comprehensive data collection and systematic processing procedures, this study establishes a robust foundation for analyzing traffic patterns and safety implications along this geometrically complex road section, ultimately supporting evidence-based decision-making for safety improvements.  
The research framework is shown in Figure 1. The first part is data acquisition and data preprocessing; the second part is analysis of driving behavior characteristics; the third part is the derivation of FP-Growth mining association rules from composite transaction data; the fourth part is the construction of a traffic conflict prediction model.
# A Mathematical Study on a Fractional-Order SEIR Mpox Model: Analysis and Vaccination Influence
## 4 Stability Analysis
### Boundedness of Solution  
It was reported in [14] that the boundedness of the solution is regarded as a very important trait of this type of population model, with infectious diseases. In the epidemic population models, the solutions must be bounded, as unbounded solutions are physically meaningless. In what follows, we propose to show that the solution given at any specifictime for system (19) is bounded by \(\frac{\Delta}{\mu}\), as assumed previously in (20). For this purpose, we should note that  
\[{}^{C}D_{*}^{\rho}N=^{C}D_{*}^{\rho}S+^{C}D_{*}^{\rho}E+^{C}D_{*}^{\rho}I+^{C}D_{* }^{\rho}R=\Lambda-\mu(S+E+I+R)-\sigma I,\]  
where \(N\) is given in (12). Accordingly, we can have  
\[{}^{C}D_{*}^{\rho}N=\Lambda-\mu N-\sigma I.\]  
Under the assumed conditions in the domain \(\psi\) reported in (20), one could observe that \(\sigma I\geq 0\), which yields the following inequality:  
\[{}^{C}D_{*}^{\rho}N(t)\leq\Lambda-\mu N(t). \tag{23}\]  
In order to solve the above inequality analytically with the initial conditions  
\[N(0)=N_{0}, \tag{24}\]  
we use the so-called Adomian decomposition method (or simply ADM), as it is very suitable in this case. To see this, we first operate \(I^{\rho}\) on both sides of the inequality (23) to obtain  
\[N(t)\leq N_{0}+I^{\rho}\Lambda-\mu I^{\rho}N(t),\]  
or  
\[N(t)\leq N_{0}+\frac{\Lambda}{\Gamma(\rho+1)}t^{\rho}-\mu I^{\rho}N(t), \tag{25}\]  
Now, in view of the ADM, the general solution of inequality (25) might be supposed as \(N(t)\)=\(\sum_{k=0}^{\infty}N_{k}(t)\). This would immediately yield  
\[\sum_{k=0}^{\infty}N_{k}(t)\leq N_{0}+\frac{\Lambda}{\Gamma(\rho+1)}t^{\rho}- \mu I^{\rho}\left(\sum_{k=0}^{\infty}N_{k}(t)\right),\]  
which consequently implies  
\[\begin{split} N_{0}(t)&=N_{0}+\frac{\Lambda}{\Gamma( \rho+1)}t^{\rho},\\ N_{k}(t)&\leq-\mu I^{\rho}\left(\sum_{k=0}^{\infty }N_{k}(t)\right),\;k\geq 1.\end{split} \tag{26}\]  
Thus, based on (26), we can obtain, for instance, \(N_{1}(t)\) as follows:  
\[N_{1}(t)\leq-\mu\bigg{(}\frac{N_{0}}{\Gamma(\rho+1)}t^{\rho}+\frac{\Lambda}{ \Gamma(2\rho+1)}t^{2\rho}\bigg{)}.\]  
In the same way, we can obtain  
\[N_{2}(t)\leq\mu^{2}\bigg{(}\frac{N_{0}}{\Gamma(2\rho+1)}t^{2\rho}+\frac{ \Lambda}{\Gamma(3\rho+1)}t^{3\rho}\bigg{)}.\]  
If we continue in this manner, we obtain  
\[N_{3}(t)\leq-\mu^{3}\bigg{(}\frac{N_{0}}{\Gamma(3\rho+1)}t^{3\rho}+\frac{ \Lambda}{\Gamma(4\rho+1)}t^{4\rho}\bigg{)}.\]Now, due to the solution having the form \(N(t)=\sum_{k=0}^{\infty}N_{k}(t)\), then, with the help of using the Mittag-Leffler function, we can gain  
\[N(t)\leq\bigg{(}\frac{\Lambda}{\mu}-N_{0}\bigg{)}E_{\rho,1}(-\mu t^{\rho}). \tag{27}\]  
Consequently, with the use of our assumptions in (20), we can deduce  
\[N(t)\leq\frac{\Lambda}{\mu}-\bigg{(}\frac{\Lambda}{\mu}-N_{0}\bigg{)}E_{\rho, 1}(-\mu t^{\rho}). \tag{28}\]  
Therefore, the solution of system (19) is bounded by the term \(\frac{\Lambda}{\mu}\) as \(t\to\infty\), and hence this system is well-posed over its domain \(\psi\).
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 3 Materials and Methods
### Adaptive Cost Aggregation Module  
As depicted in Figure 2b, the left and right feature maps of size \(\mathbb{R}^{H\times W\times\text{C}}\) are first fed to \(1\times 1\) convolutions to produce a query feature map \(\mathbf{Q}\in\mathbb{R}^{H\times W\times\text{C}}\) and a key feature map \(\mathbf{K}\in\mathbb{R}^{H\times W\times\text{C}}\). Then, matrix multiplication is performed between \(\mathbf{Q}\) and \(\mathbf{K}\) to generate the cost volume \(\mathbf{C}\in\mathbb{R}^{H\times W\times W}\). In our baseline method [21], these cost volumes are directly summed up during the cost aggregation stage. However, this aggregation method overlooks the value differences among elements within the cost volume. Intuitively, capturing stereo consistency in ill-posed areas such as specular highlights and textureless regions of an image is more challenging than in other areas. During the cost aggregation process, a greater focus should be placed on the elements within ill-posed areas. Thus, we construct an adaptive cost aggregation module that leverages the distribution information of the cost volume to assess the value of its elements, and explicitly guide the network to strengthen the cost aggregation for elements of high value, which correspond to those within ill-posed areas. This step is referred to as the adaptive cost aggregation operation (ACAO). Furthermore, the adaptive cost aggregation module incorporates the proposed interlaced group convolution structure (IGCS) and dense connection structure (DCS). They are designed to reduce the computational complexity and parameters while preserving the accuracy of the aggregation process.
# MFC/IMC Control Algorithm for Reduction of Load Torque Disturbance in PMSM Servo Drive Systems
###### Abstract  
The following article presents the hybrid control scheme (MFC/IMC) that involves model following control (MFC) and internal model control (IMC) for velocity control of permanent magnet synchronous motor (PMSM) with the aim of reducing the influence of torque load disturbance. A brief description of the MFC/IMC algorithm is given, its structure is an expansion of a classical cascade control loop; the PMSM model was linearized in order for it to be applicable to the proposed solution. Numerical analysis of sensitivity for both systems (i.e., the MFC/IMC cascade and the classical one) is given with regards to their robustness to torque load disturbance. The behavior of these two control schemes is then analyzed on a laboratory test stand with various load disturbances introduced to the system; the methodology and the test stand itself are also briefly presented in the article. The results presented in the form of performance indices and graphs prove that the developed MFC/IMC method is an improvement over the classical cascade for velocities near the operating point.  
Keywords:load torque in PMSM; MFC/IMC; servo drive velocity control +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Addressing the Unforeseen, Long-Term Byproducts  
The significance of interoperability in the IoT cannot be overstated. This not only ensures that devices from different manufacturers can communicate with each other, but also facilitates the development of scalable, flexible, and more efficient IoT ecosystems. Despite the advances in networking technologies, the coexistence of IPv6-enabled devices--such as those based on 6 LoWPAN--with the prevailing IPv4 infrastructure, presents a complex challenge. For decades, this challenge has been compounded by the fast evolution of IoT devices and the continuous invention of new technologies, techniques, solutions, protocols, and systems [9; 10].  
This study promotes a simple, novel, efficacious approach to address the interoperability challenge between 6 LoWPAN and IPv4 networks through the design of a simple narrowband IoT (NB-IoT) Relay. This Relay performs as a bridge, enabling 6 LoWPAN devices to communicate within legacy IPv4 networks seamlessly; at the same time, the former become visible within the all-IPv4 world. However, the approach of this research is not a tunnel. Tunneling solutions tend to be patches meant to overcome the lack of supporting hardware or compatible software, thereby providing IPv6 talkability to prevent isolation [11; 12]. The Relay we propose, on the other hand, does not masquerade packets to make them compatible, but performs protocol translation for the sake of actual interoperability.  
Our solution is not only simple, hassle-free, and cost-effective, but also offers an accountable method for data communication by translating 6 LoWPAN, IPv6 CoAP messages into standard IPv4 RESTful requests. By facilitating this translation, the relay ensures that 6 LoWPAN devices can be integrated into existing network infrastructures without significant changes or upgrades. This fact is crucial, considering the large number of infrastructures that may be dependent on IoT networks functioning properly, which would otherwise require significant analysis and costs to overhaul.  
Using the NB-IoT offers several advantages such as wide coverage, high power efficiency, and agnostic infrastructures. These matters make the NB-IoT an ideal choice for IoT applications that require little human interaction, as well as those that are expected to be deployed in very heterogeneous and diverse use cases and locations [13; 14]. Furthermore, the integration of the Relay with a Home Assistant (HA) [15; 16], a state-of-the-art, open-source, _home-and-beyond_ automation platform, exemplifies the practical application of the proposed solution, demonstrating its effectiveness in real-world scenarios, as well as our commitment to open sourcing.
## 4 Performance Evaluation
### Feasibility Analysis  
Figure 11: The effect of leaving movement in the Mobile WiMAX environment.  
Figure 12: The feasibility analysis of MRSS in the WLAN environment.  
In this subsection, a random waypoint mobility model was adopted to study the feasibility of MRSS motion detection service in a WLAN and a Mobile WiMAX environment. The WLAN and the Mobile WiMAX environment were configured as shown in Table 2. Figure 12(a) shows the user motion trajectory in a WLAN environment and Figure 13(a) presents the user movement in a Mobile WiMAX environment. The detailed user motion parameters of Figures 12(a) and 13(a) are described in Tables 3 and 4 respectively. According to the analysis in Section 4.2, the distance between an MN and an AP strongly affects the measured RSS. Figure 12(b) plots the measured RSS value from the MN in a WLAN environment. Clearly, when the MN is far away from the AP, the change of RSS is small. For instance, when the MN moves from location C to location D at 2.11 m/s, the change of the measured RSS is not easy to identify.  
Figure 12(c) shows the fluctuation of momentum and the results of user motion identification by using the MRSS method. The symbols on the X-axis indicate the locations presented in Figure 12(b). In Figure 12(c), the momentum value can easily determine the user motion state: stationary, leaving or approaching. Figure 12(d) shows the measured DIF value and the motion detection results of the MACD approach. In Figure 12(d), MACD can detect almost all user motion except when the MN moves from location C to location D. It is because the changes of RSS are small and MACD is not sensitive enough to detect them. Figure 13(b) shows the measured RSS of an MN in the Mobile WiMAX environment. The motion detection results of MRSS and MACD are presented in Figures 13(c) and 13(d) respectively. The simulation results also demonstrate that MRSS can detect all user motion correctly and MRSS is more sensitive than MACD.  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline Start & End & Duration (s) & Velocity (m/s) \\ \hline A & B & 14.9336 & 0.9588 \\ \hline B & B &  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline Start & End & Duration (s) & Velocity (m/s) \\ \hline A & B & 60.7216 & 6.3783 \\ \hline B & B & 20.2616 & 0 \\ \hline B & C & 27.4318 & 27.3668 \\ \hline C & C & 14.9154 & 0 \\ \hline C & D & 235.8984 & 9.955 \\ \hline D & D & 17.4647 & 0 \\ \hline D & E & 144.077 & 20.2115 \\ \hline E & E & 10.3044 & 0 \\ \hline E & F & 12.1474 & 26.9875 \\ \hline F & F & 16.7168 & 0 \\ \hline F & G & 77.5418 & 29.7336 \\ \hline G & G & 23.7228 & 0 \\ \hline G & H & 39.9845 & 21.6865 \\ \hline H & H & 21.2732 & 0 \\ \hline H & I & 77.5386 & 6.7055 \\ \hline \end{tabular}
\end{table}
Table 4: The user motion parameters for feasibility analysis in a WiMAX environment.  
Figure 13: The feasibility analysis of MRSS in the Mobile WiMAX environment.