esa-sceva/satcom-chunk-collection huggingface dataset:
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 3 Methodology of the Proposed Bangla Alphabet Detection Algorithm
### Mathematical Formulation  
The object detection problem in our proposed framework is formulated as a regression task, where the goal is to predict both the location and category of objects within an input image \(I\). The image is divided into an \(S\times S\) grid, and each grid cell \((i,j)\) is responsible for detecting objects whose centers fall within that cell.  
For each grid cell, the model predicts a bounding box and an associated confidence score. The bounding box is parameterized by its center coordinates \((x,y)\), width \(w\), and height \(h\). At the same time, the confidence score reflects the probability of object presence and the quality of the predicted bounding box:  
\[\hat{B}_{i,j}=(x_{i,j},y_{i,j},w_{i,j},h_{i,j}),\quad C_{i,j}=P_{i,j}\cdot \text{IoU}(B_{i,j},\text{GT})\]  
Here,  
* \(\hat{B}_{i,j}\) is the predicted bounding box for cell \((i,j)\).
* \(P_{i,j}\) is the predicted probability that an object exists in a cell \((i,j)\).
* \(\text{IoU}(B_{i,j},\text{GT})\) is the Intersection over Union between the predicted box \(B_{i,j}\) and the ground-truth (GT) box.  
**Loss Function:** The total loss function used to train the model combines both localization and classification components:  
\[L=L_{bbox}+L_{class}\]  
where  
* \(L_{bbox}\) is the localization loss, typically calculated using Mean Squared Error (MSE), which penalizes deviations in the predicted bounding box coordinates.
* \(L_{class}\) is the classification loss, typically computed using Binary Cross-Entropy (BCE), which penalizes incorrect class predictions.  
Figure 1: Framework of the improved YOLOv11 integrated with ResNet50 as the backbone.  
**Residual Learning in ResNet50:** ResNet50 improves feature extraction through residual learning. Each residual block is formulated  
\[F(x)=H(x)+x\]  
where  
* \(x\) is the input to the residual block.
* \(H(x)\) represents the transformation learned by a series of convolutional layers.
* \(F(x)\) is the output after adding the residual connection.  
This identity mapping ensures better gradient flow and stabilizes training in deeper networks, making it especially effective for capturing the fine-grained structures present in Bangla characters.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 2 Improved Double-Ended Traveling Wave Localization Method Based on Line Mode Component  
When a short circuit fault occurs, the fault point generates a fault-traveling wave signal that flows throughout the entire line. Since there is coupling between the three-phase lines, it is necessary to first decouple the three-phase transmission lines before performing the ranging calculation to eliminate the influence of inter-line coupling.
# Automatic Test Data Generation Using the Activity Diagram and Search-Based Technique
###### Abstract  
In software testing, generating test data is quite expensive and time-consuming. The manual generation of an appropriately large set of test data to satisfy a specified coverage criterion carries a high cost and requires significant human effort. Currently, test automation has come at the cost of low quality. In this paper, we are motivated to propose a model-based approach utilizing the activity diagram of the system under test as a test base, focusing on its data flow aspect. The technique is incorporated with a search-based optimization heuristic to fully automate the test data generation process and deliver test cases with more improved quality. Our experimental investigation used three open-source software systems to assess and compare the proposed technique with two alternative approaches. The experimental results indicate the improved fault-detection performance of the proposed technique, which was 11.1% better than DFAAD and 38.4% better than EvoSuite, although the techniques did not differ significantly in terms of statement and branch coverage. The proposed technique was able to detect more computation-related faults and tends to have better fault detection capability as the system complexity increases.  
automatic test data generation; activity diagram; genetic algorithm; model-based testing; search-based testing +
Footnote †: journal: Applied Sciences
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 2 Materials and Methods
### Methods
#### 2.3.2 Main Downscaling Steps at the Annual Scale  
RF was used to downscale the TMPA and IMERG data over the TP at an annual scale. The main steps in the downscaling process were as follows:  
1. Sum the TMPA and IMERG monthly precipitation to annual precipitation (\(P_{TMPA}^{0.25^{\circ}}\)\(P_{IMERG}^{0.1^{\circ}}\)).
2.
3. Establish the random forest model \(RF_{TMPA}\) between land surface variables at 0.25\({}^{\circ}\) and \(P_{TMPA}^{0.25^{\circ}}\), and establish the random forest model \(RF_{IMEGR}\) between land surface variables at 0.1\({}^{\circ}\) and \(P_{IMERG}^{0.1^{\circ}}\).
4. Estimate annual precipitation at 0.25\({}^{\circ}\) using \(RF_{TMPA}\), and estimate annual precipitation at 0.1\({}^{\circ}\) using \(RF_{IMERG}\).
5. Map the residuals at 0.25\({}^{\circ}\) (\(P_{resi}^{0.25^{\circ}}\)) by computing the difference between \(P_{TMPA}^{0.25^{\circ}}\) and the estimated annual precipitation at 0.25\({}^{\circ}\). Similarly, map the residuals at 0.1\({}^{\circ}\) (\(P_{resi}^{0.1^{\circ}}\)) by computing the difference between \(P_{IMERG}^{0.1^{\circ}}\) and the estimated annual precipitation at 0.1\({}^{\circ}\).
6. Obtain the residuals (\(P_{resi}^{0.25^{\circ}}\) and \(P_{resi}^{0.1^{\circ}}\)) at 1 km from those at 0.25\({}^{\circ}\) using a simple spline tension interpolator. As the residual data were regularly spaced, the spline interpolation was typically used for this type of data.
7. Generate the annual precipitation estimates, termed as the downscaled results before residual corrections, at 1 km using \(RF_{TMPA}\) and \(RF_{IMEGR}\), respectively, in combination with the land surface variables.
8. Add the residuals at 1 km obtained at step (6) and the downscaled results before residual corrections at 1 km obtained at step (7) to finally obtain the downscaled results after residual corrections.  
The steps used for downscaling are shown in Figure 2.
## 1 Introduction
Increasing the radius between actuator beams and the anchors or central shaft can significantly reduce the stress concentration. Figure 2 shows a comparison of the distribution of the equivalent von Mises stress between a geometric arrangement with a sharp connection of the arms and one with rounded corners (transition radius is twice larger than the width of the arm) in the actuator for the same boundary conditions. This example shows that the maximal value of the equivalent stress is significantly reduced from 982 MPa to 476 MPa. The proposed method of optimal design allows for both, finding the arbitrary shape of the arms and precise selection of the radius at the rounded corners, taking into account the optimal selection of the pre-bending angle as well. As mentioned previously, the result of the multiobjective optimization is expressed in not one, but a set of optimal solutions. When a large number of criteria is considered, the process of selection of a particular compromise-solution could be problematic. The alternative method, compared to the traditional one, concerning the representation and selection-compromise-solutions, is proposed and demonstrated in the paper. The proposed method is aided by self-organizing maps (SOMs), which is one type of artificial neural network (ANN). The rest of the paper is organized as follows. In Section 2, the model of the chevron type actuator is described, including the governing equations of the problem considered, the numerical model developed, and its verification. Section 3 contains the formulation of the multiobjective optimization problem, including the definition of the formulated criteria and SOMs, whereas in Section 4 the system of optimization build by means of IMGAMO algorithm is presented. Section 5 presents details of the optimization model of the electrothermal microacuator. On the basis of the research carried out, in Section 6, results and discussion are presented. The work is concluded in Section 7 with final remarks. Appendix A shows the details of IMGAMO algorithm, whereas Appendix B illustrates its effectiveness in comparative tests with other algorithms for several benchmarks.
## 4 Implementation and Evaluation
### Implementation and Deployments  
Since the existing commercial sensor nodes could not meet the requirements (Table 1) described in Section 1, we implemented and manufactured each type of node, from enclosure and hardware platform to software, following the system architecture and design as in the previous section. The deployments for the evaluation consist of two phases. For the basic functional evaluation such as time synchronization, and reliability, the indoor deployment was done. As the second phase, to evaluate the accuracy of PES, it was deployed in the official ITS performance evaluation center in Korea which was located in a four-lane road and equipped with authorized facilities like high accurate VDSs, video recorder and tracker, and so on. To record different directions at the same time, two sets of PES were deployed. Each set of PES included four T-Sensor nodes for two for each lane, and two T-Sensor nodes in a lane were separated with 3 m to align with the distance of two reference laser detectors in the same lane (Figure 12). The detailed evaluation results for the two phased deployments are analyzed in the following sub-sections.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 4 Simulation and Performance Analysis Results  
In order to examine the performance of the UBSI resource allocation protocol for a UCWN, we performed system level simulations (SLSs). This SLS includes all the details of the UBSI resource allocation protocol described in Section 3. In this section, we describe the SLSs in detail and the simulation results for the different simulation parameters.
# Types and Performances of Polymer Gels for Oil-Gas Drilling and Production: A Review
## 1 Introduction
Modified PAM (e.g., hydrophobically modified and amphoteric PAM) or multi-copolymers (e.g., 2-acrylamide-2-methylpropanesulfonic acid (AMPS) copolymerized with acrylamide (AM)) can optimize the temperature resistance, salt resistance, and mechanical properties of the gels. The addition of nanoparticles into the polymer gels can also create larger specific surface areas, resulting in better gel performance in high-temperature and high-salinity environments [20; 21; 22]. This paper systematically reviews research progress on the preparation principle, temperature resistance, salt resistance, and mechanical properties of the ground and in situ crosslinked polymer gels for oil-gas drilling and production engineering. We then comparatively analyze the applicable conditions of the two types of polymer gels. Second, we elaborate upon strategies for increasing the high-temperature resistance, high salt resistance, and gelation mechanical strength properties of polymer gels to improve the polymer structures and types, crosslinkers, and additives.
# Analysis of Cavity Corner Geometry Effect on Recirculation Zone Structure
## 2 Materials and Methods  
### Physical Model and Boundary Conditions  
Four open-type cavities with differently rounded corners were chosen for numerical simulations (Figure 1). Ch1 is the classic case of an open-type cavity with two 90\({}^{\circ}\) steps. Ch2 and Ch4 have fully rounded and half-rounded bottom cavity corners, described as \(R\) = 0.3 mm and \(R\) = 0.15 mm, respectively. Conversely, only the top corners of cavity steps are fully rounded in Ch3, with \(R\) = 0.3 mm. Geometric dimensions given in Figure 1 (Ch1) apply to all investigated cavity cases. All investigated geometries were three-dimensional, with channel width \(b\) = 0.9 mm. Expansion and aspect ratios were fixed at ER = 2 and AR = 3, respectively.  
Numerical simulations performed in this study involve water flow in the Re range of 10 < Re < 1000. The fluid is supposed to be incompressible with constant physical properties as follows: inlet temperature T = 288.16 K; dynamic viscosity \(\mu\) = 1.003-10\({}^{-3}\) kg/m\(\cdot\)s; density \(\rho\) = 998.2 kg/m\({}^{3}\).  
The flow velocity profile is considered flat at the inlet; however, the inlet section is set to be 50 hydraulic diameters long before entering the cavity to ensure fully developed flow. A no-slip condition was assumed for the channel walls. Additionally, constant static pressure was set at the outlet boundary.
# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
## 2 Related Work
aimed to overcome problems such as noise and overexposure that often accompany industrial two-dimensional code recognition; they proposed an image enhancement algorithm based on gamma transform of top-bottom hat transforms to effectively reduce the impact of lighting. According to the characteristics of DM codes, they proposed a fast positioning algorithm for DM codes based on Shi-Tomasi corner point density by cleverly using internal block corner point information to achieve fast positioning [37]. It is not difficult to see from the above research work on DM codes that researchers mainly focus on image preprocessing and detection and positioning of DM codes. For positioned DM codes, researchers generally divide sampling grids based on finder patterns and take central pixels from sampling grids to convert recognized DM code images into data matrices. This paper improves the recognition method for positioned DM codes with the aim of recognizing low-quality DM code images and improving the decoding ability of DM codes in different environments.
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{A}\longrightarrow\mathbf{B}\)} & **Without** & \multirow{2}{*}{**WS-UDA**} & \multicolumn{2}{c}{**Distance with**} \\  & **Sequential** & & & **Unseen C and:** \\ \hline \multirow{2}{*}{Dvd \(\longrightarrow\) Books} & \multirow{2}{*}{75.6\%} & \multirow{2}{*}{78.3\%} & **A** (Dvd) & **B** (Books) \\  & & & 0.214 & 0.816 \\  & & & **A** (Dvd) & **B** (Kitchen) \\ \multirow{2}{*}{Dvd \(\longrightarrow\) Kitchen} & \multirow{2}{*}{72.3\%} & \multirow{2}{*}{73.6\%} & **A** (Rooks) & **B** (Kitchen) \\  & & & 0.214 & 0.2 \\ \hline \multirow{2}{*}{Kitchen \(\longrightarrow\) Dvd} & \multirow{2}{*}{82.3\%} & \multirow{2}{*}{84.3\%} & **A** (Kitchen) & **B** (Dvd) \\  & & & 0.9 & 0.657 \\ \multirow{2}{*}{Kitchen \(\longrightarrow\) Books} & \multirow{2}{*}{82.8\%} & \multirow{2}{*}{84.6\%} & **A** (Kitchen) & **B** (Books) \\  & & & 0.9 & 0.816 \\ \hline \multirow{2}{*}{Books \(\longrightarrow\) Dvd} & \multirow{2}{*}{71.3\%} & \multirow{2}{*}{74.19\%} & **A** (Books) & **B** (Dvd) \\  & & & 0.442 & 0.657 \\ \multirow{2}{*}{Books \(\longrightarrow\) Kitchen} & \multirow{2}{*}{72.9\%} & \multirow{2}{*}{73.2\%} & **A** (Books) & **B** (Kitchen) \\  & & & 0.442 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy percentages assessed when tested on the electronics domain, exemplified in the Amazon dataset across various transfer tasks. Another intriguing direction for future research is the exploration of combining WS-UDA with other UDA models to enhance overall adaptation performance. By delving into these research directions, we can advance the field of unsupervised domain adaptation for text classification and improve the performance of weakly supervised UDA methods. This exploration holds the promise of making UDA more effective and versatile across a wide array of applications and domains. Conceptualization, H.B. and N.W. ; methodology, H.B. ; software, H.B. ; validation, H.B., N.W. and M.F. ; formal analysis, H.B. ; investigation, H.B. ; writing--original draft preparation, H.B. ; writing--review and editing, H.B. and N.W. ; supervision, N.W. and M.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are openly available. Amazon data-set in [52], Fdu-MTL data-set in [53] and Spam data-set in [53]. The authors declare no conflict of interest.
## 3 Mission Application and Trajectory Simulations
### Case of Constrained Clock Angle  
The performance of an SFGIS-powered spacecraft in a case where the sail clock angle is constrained to assume only the values included in a discrete set \(\mathcal{I}\) was analyzed considering an Earth-Venus mission scenario with \(a_{c}=0.175\,\mathrm{mm}/\mathrm{s}^{2}\). The results obtained in the same transfer, but with the unconstrained clock angle, summarized in Figures 4 and 5, gave a minimum flight time of 434.8 days.  
In this \(\delta\)-constrained mission scenario, the optimization procedure gave a minimum flight time of \(\Delta t_{\mathcal{D}}=437.7\,\mathrm{days}\) when \(\mathcal{I}=\mathcal{I}_{\mathcal{D}}\), and \(\Delta t_{\mathcal{D}}=441.9\,\mathrm{days}\) when \(\mathcal{I}=\mathcal{I}_{\mathcal{D}}\). Note that in both cases, the optimal flight time was (slightly) higher than the value obtained in the unconstrained case, with \(\Delta t_{\mathcal{D}}<\Delta t_{\mathcal{D}}\) as expected, while the difference was only a few days. According to Figure 13, the optimal control law in the \(\delta\)-constrained mission scenario presented a number of attitude maneuvers in which the direction of the unit vector \(\hat{\mathbf{m}}\) in the RTN frame was suitably changed. The attitude maneuvers were performed at a solar distance \(r\) that could be retrieved from Figure 14. Specifically, there were 11 maneuvers when \(\mathcal{I}=\mathcal{I}_{\mathcal{D}}\) and 6 maneuvers when \(\mathcal{I}=\mathcal{I}_{\mathcal{D}}\). From the point of view of trajectory design, these attitude maneuvers were considered instantaneous, although obviously, the rotation of the SFGIS around the Sun-spacecraft direction required a finite time interval. This analysis is the subject of the next section.
## 1 Introduction
Thirdly, through simulation analysis, we find that the MC\(-\)ODBR\(-\)DCSK system improves the transmission data rate and has a better bit error rate than SR-ODBR-DCSK, ODBR\(-\)DCSK and DCSK systems under specific signal-to-noise ratio.
# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 3 Method  
In this section, we present the methodology used for detecting oil spills with CNN and Transformer models (Figure 2). The flowchart below provides an overview of the steps involved, such as labeling, modeling, optimization, and evaluation.
# Performance Analysis of Interferometer Algorithm under Phase Measurement Error
## 4 Performance Analysis of Phase Comparison Interferometer
In addition, the dependence of the elevation MSE on the true azimuth or the true elevation should be addressed. From (18), the azimuth MSE only depends on the true elevation, not on the true azimuth. From (19), the elevation MSE is also dependent on the true elevation, not on the true azimuth. Although both the azimuth MSE and the elevation MSE are dependent on the true elevation, not on the true azimuth, how the azimuth MSE varies with the increase of the true elevation is different from how the elevation MSE varies with the increase of the true elevation. The azimuth MSE increases with the increase of the true elevation. On the other hand, the elevation MSE decreases with the increase of the true elevation.
###### Abstract  
A simple and practical spray method is employed to prepare a PVDF/PFOTES-SiO2 superhydrophobic composite coating on the AZ31B Mg alloy substrate. The morphology, composition, and water contact angle (CA) were measured by Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscope (FESEM) and contact angle measuring instrument. Hydrophilic nano-SiO2 is modified by PFOTES to obtain hydrophobicity. The influence of the mass of PFOTES-SiO2 to PVDF on the hydrophobic properties was studied. The wear resistance and stability of the composite coating have been investigated by immersion test, cross-cut adhesion test and friction test. Additionally, the corrosion resistance was measured by electrochemical workstation and salt spray corrosion test. The CA of PVDF/PFOTES-SiO2 coating is 161.3\({}^{\circ}\) and the sliding angle (SAs) is less than 2\({}^{\circ}\). After 10\(\times\) the sandpaper friction test, the superhydrophobic contact angle of the coating remained above 155\({}^{\circ}\), and the sliding angle was less than 5\({}^{\circ}\), which indicated that the prepared coating is a strong superhydrophobic coating with good wear resistance. The results of the electrochemical tests show that the superhydrophobic coating improved the anti-corrosion performance of Mg alloy, and the water contact angle is greater than 150\({}^{\circ}\) after 168 h salt spray corrosion test. Due to its excellent superhydrophobicity, wear resistance and anti-corrosion properties, the robust PVDF/PFOTES-SiO2 coating is considered to have great potential for future applications in the automotive and marine industries.  
superhydrophobic; Mg alloy; corrosion resistance; wear resistance 2021  
22 November 2021  
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# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Performance Evaluation Using Different Classifiers
#### 3.5.1 Classification Using Convolutional Neural Network  
In our experiments, we used the CNN architecture having three convolutional layers followed by a batch normalization layer, a RELU layer and one fully connected layer (FC). The input video frames are transformed into grayscale and down-sampled from 640 \(\times\) 480 to 64 \(\times\) 64 resolution to reduce the computational cost. Each convolutional layer has a filter size of 3 \(\times\) 3, and max-pooling was performed on every 2 \(\times\) 2-pixel block. The output is fed to Soft-max layer for classification that helps to determine the classification probabilities used by the final classification layer. We used the learning rate of 0.01 in our experiments as we received the best results on this parameter setting. The details of each layer with the feature maps are as follows:  
* 1 st Convolutional layer (Conv1) with a kernel size of 3 \(\times\) 3 and 64 feature maps.
* 2 nd Convolutional layer (Conv2), kernel size of 3 \(\times\) 3 and 96 feature maps.
* 3 rd Convolutional layer (Conv3), kernel size of 3 \(\times\) 3 and 128 feature maps.
* Fully connected layer, hidden units (4000).
* FC hidden units equal to several classes i.e., 4.
* Soft-max layer provides the classification probabilities.
* Final classification layer.  
The results achieved by the standard CNN architecture for shot classification are presented in Table 2 that shows the classification accuracy, error rate, precision, recall, and F1-score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Videos** & **Accuracy (\%)** & **Error Rate (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Video 1 & 93.99 & 6.01 & 94.5 & 95.46 & 96.39 \\ Video 2 & 94.67 & 5.33 & 93.6 & 97.73 & 94.38 \\ Video 3 & 92.39 & 7.61 & 96.7 & 96.7