esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 4 Performance Analysis  
The performance of a CI/CD platform is a critical factor in determining its suitability for modern software development workflows. Organizations rely on CI/CD pipelines to automate builds, run tests, and deploy applications across different environments. Any inefficiencies in these processes can lead to delays, increased costs, and reduced developer productivity. Evaluating the performance of Azure DevOps and GitHub requires a close examination of their efficiency, scalability, and reliability across various projects and organizational needs. These aspects are crucial in selecting a CI/CD solution that aligns with an enterprise's DevOps strategies, cloud infrastructure, and deployment models. Efficiency is measured by how quickly and effectively a platform can execute builds, run automated tests, and deploy applications while maintaining stability and security. Azure DevOps and GitHub offer sophisticated CI/CD mechanisms. They differ in their underlying architectures and execution models, resulting in variations in pipeline execution times, caching mechanisms, and resource utilization [22, 23]. Azure DevOps utilizes Azure Pipelines, which supports both Microsoft-hosted and self-hosted agents, allowing teams to tailor build environments to their specific infrastructure needs. Microsoft-hosted agents offer a preconfigured, cloud-based execution environment that supports multiple operating systems, including Windows, Linux, and macOS. Self-hosted agents allow organizations to fine-tune build performance by running pipelines on their own hardware or cloud infrastructure. This flexibility makes Azure DevOps particularly well suited for large-scale enterprise applications that require fine control over compute resources, build caching, and distributed workloads. GitHub provides both GitHub-hosted and self-hosted runners, enabling teams to strike a balance between managed execution environments and on-premises infrastructure. GitHub Actions' execution model is optimized for smaller, incremental builds, making it more efficient for agile teams and open-source projects that prioritize rapid iteration
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 3 CNN-NLP Architecture Design for LSB Steganography Detection
### Preprocessing  
Each image in the dataset is grayscale and resized. A Laplacian filter is then applied to obtain a residual channel highlighting local intensity differences. The four least significant bit planes (LSBs) are extracted, producing a 5-channel input tensor (1 residual + 4 LSBs) designed to highlight subtle alterations introduced by masking techniques. For this case, the kernel is defined as follows:  
\[K=\frac{1}{8}\begin{bmatrix}-1&-1&-1\\ -1&8&-1\\ -1&-1&-1\end{bmatrix} \tag{1}\]  
Figure 1: Proposed methodology for the development of the CNN-PLN model.  
This filter analyzes each pixel by comparing it to its eight neighbors and highlights points where the intensity changes abruptly, which may indicate the presence of a steganographic pattern. Applying the kernel generates the residual channel presented in Equation (2):  
\[R(x,y)=\frac{1}{8}\bigg{(}-8I(x,y)+\sum_{i=-1}^{1}\sum_{j=-1}^{1}I(x+i,y+j)\bigg{)} \tag{2}\]  
Then, the first four bit planes of each pixel are extracted using Equation (3) to isolate the lowest weighted bits of each intensity value:  
\[LSB_{k}(x,y)=\text{mod}\bigg{(}\left\lfloor\frac{I(x,y)}{2^{k-1}}\right\rfloor,2\bigg{)} \tag{3}\]  
Finally, the residual channel obtained in the previous stage and the four extracted bit planes are combined to form a three-dimensional tensor, where each of the five channels represents a distinct feature of the processed image. The tensor constitutes the direct input to the CNN and is defined as follows:  
\[X(x,y,:)=\bigg{[}\frac{R(x,y)}{255},LSB_{1}(x,y),LSB_{2}(x,y),LSB_{3}(x,y),LSB _{4}(x,y)\bigg{]} \tag{4}\]  
Here, \(X(x,y,:)\) represents the characteristic vector corresponding to the spatial position \((x,y)\) of the input three-dimensional tensor. The \((:)\) operator indicates that all channels at that location are considered. The notation \(LSB_{k}(x,y)\) refers to the \(k\)-th least significant bit extracted from the intensity value of the pixel at position \((x,y)\).
## 2 Materials and Methods  
### System Overview  
Our proposed system aims to solve assembly tasks with an uncertain goal pose. Figure 2 shows the overall system architecture. There were two control loops. The inner loop was an adaptive compliance controller; we chose to use a parallel position-force controller that was proven to work well for this kind of contact-rich manipulation task [24]. The inner loop ran at a control frequency of 500 Hz, which is the maximum available in Universal Robots e-series robotic arms (Robot details at [[https://www.universal-robots.com/e-series/](https://www.universal-robots.com/e-series/)]([https://www.universal-robots.com/e-series/](https://www.universal-robots.com/e-series/))). Details of the parallel controller are provided in Section 2.2.3. The outer loop was an RL control policy running at 20 Hz that provided subgoal positions and the parameters of the compliance controller. The outer loop's slower control frequency allowed for the policy to process the robot state and compute the next action to be taken by the manipulator, while the inner loop's precise high-frequency control would seek to achieve and maintain the subgoal provided by the policy. Details of the RL algorithm and the policy architecture are provided in Section 2.2. Lastly, the input to the system was estimated target position and orientation for the insertion task.  
Motion commands \(\mathbf{x}_{\text{c}}\) sent to the adaptive compliance controller corresponded to the pose of the robot's end effector. The pose was of the form \(\mathbf{x}=[\mathbf{p},\phi]\), where \(\mathbf{p}\in\mathbb{R}^{3}\) is the position vector, and \(\phi\in\mathbb{R}^{4}\) is the orientation vector. The orientation vector was described using Euler parameters (unit quaternions), denoted as \(\phi=\{\eta,\varepsilon\}\), where \(\eta\in\mathbb{R}\) is the scalar part of the quaternion and \(\varepsilon\in\mathbb{R}^{3}\) the vector part.
## 4 Results and Discussions
### Performance Evaluation of ORCL  
In case of ORCL dataset, LSTM performs better than RNN and GRU models. The actual vs. predicted value curves of a one-day interval are shown by Figures 14-16, of a three-day interval are shown by Figures 17-19, and of a five-day interval are shown by Figures 20-22. Tables 7-9 summarize the performance of one-day, three-day, and five-day intervals, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(R^{2}\) & **MAE** & **MAPE** & **RMSE** \\ \hline RNN & 0.94284 & 0.57858 & 2.10638 & 0.78400 \\ \hline LSTM & 0.94093 & 0.58079 & 2.10279 & 0.79704 \\ \hline
**GRU** & 0.96032 & 0.56959 & 2.10013 & 0.78115 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of HMC five-day interval.  
Figure 11: HMC actual vs. RNN predicted for five-day interval.  
Figure 12: HMC actual vs. LSTM predicted for five-day interval.  
Figure 13: HMC actual vs. GRU predicted for five-day interval.  
Figure 16: ORCL actual vs. GRU predicted for one-day interval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** \\ \hline RNN & 0.96749 & 0.64351 & 1.29133 & 0.91327 \\ \hline
**LSTM** & 0.97345 & 0.53824 & 1.10547 & 0.82530 \\ \hline GRU & 0.97295 & 0.55344 & 1.18299 & 0.83296 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of ORCL one-day interval.  
Figure 14: ORCL actual vs. RNN predicted for one-day interval.  
Figure 15: ORCL actual vs. LSTM predicted for one-day interval.  
Figure 19: ORCL actual vs. GRU predicted for three-day interval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** \\ \hline RNN & 0.95136 & 0.73144 & 1.49556 & 1.06560 \\ \hline
**LSTM** & 0.95364 & 0.70202 & 1.41944 & 1.04041 \\ \hline GRU & 0.94486 & 0.83837 & 1.68504 & 1.13464 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison of ORCL three-day interval.  
Figure 17: ORCL actual vs. RNN predicted for three-day interval.  
Figure 18: ORCL actual vs. LSTM predicted for three-day interval.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 6 Simulation
### Simulation Results
#### 6.2.1 Impact of The Number of Nodes  
The results for different numbers of nodes are shown in Figure 3. Figure 3a shows that when increasing the number of nodes, the delivery rate of AR-RT increases significantly, while that of the other algorithms does not fluctuate much, which is because the higher the number of nodes, the more accurate AR-RT is in determining the social attributes among the nodes. It is worth noting that after the number of nodes exceeds 300, the delivery rate of Bubble Rap starts to decrease due to the increase in complexity of calculating the node centrality with the number of nodes, resulting in inaccurate results. Figure 3b shows that as the number of nodes increases, the average delay of all four algorithms decreases, with AR-RT having the smallest average delay, which is due to the fact that AR-RT is more efficient in forwarding the propagation phase in the final Relation Tree, reducing the overall delay. Figure 3c shows that the network overhead of Epidemic and Prophet is increasing significantly as the number of nodes increases, due to the uncapped replication of these two algorithms, and the network overhead of these two algorithms in a high node density scenario can have a serious impact on the network. The network overhead of AR-RT increases slightly but is within an acceptable range, and the network overhead of Bubble Rap remains stable and is always at the lowest. Figure 3d shows that the average hop count of Epidemic and Prophet increases significantly as the number of nodes increases, reflecting the inefficient forwarding of these two algorithms. The average hop counts of the remaining three algorithms remain basically stable, among which AR-RT performs better due to the high forwarding efficiency caused by using different forwarding methods based on the Relation Tree.  
Figure 3: Comparison of the results (95% CI) of various algorithms under different numbers of nodes. Subfigures (**a–d**) compare the performance of the five algorithms with the variation of the four parameters, delivery rate (**a**), average delay (**b**), overhead (**c**) and average hop count (**d**), with the number of nodes, respectively.
# Inductive Thermography as Non-Destructive Testing for Railway Rails
## 4 Reference Rail Piece with Artificial Cracks  
### Vertical EDM-Cuts with Different Depths  
To verify the simulation results, a reference rail piece (RP01) with artificial cracks was created. In an intact piece of a rail with a length of 10 cm, several cuts at the gauge corner were manufactured by the technique of electrical discharge-machining (EDM). On one side the cuts were perpendicular to the surface, as is shown in Figure 4a.  
The resulting phase image of the thermographic measurement, as well as a photo of the vertical cuts at the gauge corner, can be seen in Figure 4. It noteworthy that as the cuts were created in the curved region of the rail, the deeper the cracks are, the longer they are at the surface. In the phase image the following features can be recognized:  
* The edges of the cuts have higher phase value than the sound part. This is caused by the selective heating of the cuts by the induced eddy currents.
* At the tips of the cuts an even higher phase value, a kind of 'hot spot', can be recognized.
* At both sides of the cuts darker regions with low phase values can be observed, which are symmetrical. The heat accumulation causes slower cooling rates, which further result in lower phase values [15]. Since the EDM-cuts are perpendicular to the surface, the heat accumulation on both flanks of the cut are identical, resulting a symmetrical pattern.
* With increasing defect depth, the width of the darker regions and the phase difference also increases. The deeper the crack, the larger the phase contrast around it. Therefore, with a phase image it is possible to estimate the depth of a vertical crack [15].
* Real cracks have only several \(\upmu\)m width, but the EDM-cuts have a width of 0.3 mm, therefore the cut itself is also visible as a dark line in the phase image.  
Figure 3: Left side: geometrical model of the rail in the head check region; mid: temperature distribution after 0.1 s inductive heating pulse; right side: half of the same model in order to show the temperature distribution below the surface.  
Figure 4d shows the corresponding simulation results for the artificial cracks with depths of between 1.5 and 0.5 mm. The model was used as presented in Figure 3, by varying the crack depth. The width of the cut is not resolved in the simulation, as the goal is to model realistic cracks with very small openings. In this way in the simulation results the dark line along the cut is not visible, but the phase distribution around the crack, caused by the perturbation of the eddy current and heat flow due the crack, is in very good agreement with the experimental results.
## 4 Results and Discussion  
### Dynamic Response of Different Electric Field Intensity in the Accelerating Region  
In the previous sections, the dynamic equations were developed. The adaptive fourth-order Runge-Kutta approach [31] is utilized to solve the dynamic equations. After the particles are charged, they slowly enter the acceleration room, and the initial conditions of the acceleration room are determined. The particle enters the separation room after being accelerated by the electric field, and the initial conditions of the separation room are determined. The adaptive time step is adopted to ensure the correctness of the results. This paper takes the airflow at normal temperature and pressure as the research object. The body parameters and numerical simulation parameters are shown in Table 1.  
Figure 5 depicts the velocity response of the particle of different diameters at different electric field strengths. It can be seen that, the particle velocity increases with the increase of the particle diameter, and increases with the increase of electric field. Moreover, the electric field has a more significant effect on particles with large diameters. When the electric field is 30 kV, the speed of PM\({}_{2.5}\) is \(-0.813\) m/s, and the speed of PM\({}_{10}\) is \(-2.754\) m/s. They can pass through the acceleration area faster. In addition, the particle velocity increases with the increase of the particle diameter and increases with the increase of the electric field strength. When the electric field is 30 kV, the speed of PM\({}_{2.5}\) is \(-3.59\times 10^{-5}\) m/s, and the speed of PM\({}_{10}\) is \(-4.51\times 10^{-5}\) m/s. Comparing Figure 5a,b it can be seen that the speeds in the \(Y\)-axis direction and the \(X\)-axis direction differ greatly. The displacement of particles in the \(Y\)-axis direction is negligible.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Size** \\ \hline vacuum dielectric constant & \(\varepsilon_{0}=8.875\times 10^{-12}\) F/m \\ unit charge & \(q_{\varepsilon}=1.6\times 10^{-19}\) C \\ Boltzmann constant & \(k=1.38\times 10^{-23}\) J/K \\ absolute temperature & \(T=298.15\) K \\ relative dielectric constant & \(\varepsilon=5\) \\ air density & \(\rho_{\text{g}}=1.1691\) kg/m\({}^{3}\) \\ particle density & \(\rho_{\text{p}}=1.5\times 10^{3}\) kg/m\({}^{3}\) \\ dynamic viscosity & \(\mu=1.86\times 10^{-5}\) N\(\cdot\)s/m\({}^{2}\) \\ the initial velocity & V\({}_{0}=0.001\) m/s \\ \hline \end{tabular}
\end{table}
Table 1: Particle separation parameter.
## 3 AQM Framework for Smart City Applications
### Verification and Assessment  
The third AQM component is the verification and assessment phase as shown in Figure 4. In this phase, a process model called Run-Time Verification and Quality Assessment (RVQA) is developed to perform the checking and assessment process. The modeled agent systems from the previous phase incorporate rules to check the reachability of critical states where violations can occur. Next, whenever the critical states are reached, quality assessments are performed to determine the quality statuses. The assessment procedure identifies the state quality conditions and assesses the quality status based on the specified checking rules. The agent quality statuses are used for classifying the agent quality satisfaction and violations. For each agent quality property level, i.e., individual, social, or content level, the total number of the detected quality violations is calculated.
# Towards a Universal Semantic Dictionary
## 5 Experimental Results
### Experimenting with SVD
#### 5.2.3 SVD at the Beginning  
This experiment was carried out applying SVD only once, at the very beginning. This means, in simple terms, that instead of a random initial transformation matrix, the system tried to adjust an orthogonal one. Figure 3 shows that the learning curve is monotone increasing, and owing to the initial SVD it gets fairly high right at the beginning.
## 3 Incident Detector Using Big Data Analytics and Neural Networks
### Other Approaches for Road Accident Detectors
#### 3.1.2 Action Recognition for Accident Detection in Real-Time Vehicle Tracking  
Within the network, dynamic changes in traffic patterns occur throughout the day and on a global scale. Therefore, signal timing parameters need to be continuously monitored depending on the performance and variability demands. Travel times must be computed regularly to do this [119]. AI technologies are being used to create automated vehicles for personal and commercial transportation usage. These vehicles rely on various sensors, including GPS, cameras, radar, actuators (things that put motion into an input signal), control systems, and software [120].  
It is necessary to have a system for tracking and managing bus transportation that is both time- and money-efficient because of the expanding population and technological sophistication of the world. The growing population has caused poor real-time data analysis of transportation facilities, taxing paper-and-pen methods, inadequate bus transportation corporation facilities, and improper human resources and cost management. As a result, distant users and bus transportation services such as MSTRC require an intelligent system that offers real-time bus locations and information. Figure 5 demonstrates the transport tracking system: vehicle tracking, data storage, GPS satellite, cellular network, and a database [121].
## 3 Results  
There were significant differences between the scanning accuracies of the four types of scanner (\(p\) < 0.001; Figure 4; Table 1). The laboratory scanner (E1) demonstrated a high degree of accuracy, as did the CS3600 (Table 1). There was no significant difference between the i500 and EZIS PO scanners in terms of accuracy (\(p\) > 0.05; Table 1).  
There were significant differences between the accuracies for the premolars and molars in the four types of scanner; indeed, there was a high level of accuracy in the scanning of premolars (\(p\) < 0.001; Figure 5; Table 1). Furthermore, there was an interaction effect between the type of scanner and the type of tooth (\(p\) < 0.001; Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scanner type**} & **Premolar** & **Molar** & \multirow{2}{*}{**t**} & \multirow{2}{*}{\(p\)} \\ \cline{2-2} \cline{5-5}  & **Accuracy (Mean \(\pm\) SD, \(\mu\)m)** & & & \\ \hline
**E1** & 12.8 \(\pm\) 2.0 \({}^{\text{A}}\) & 13.8 \(\pm\) 1.1 \({}^{\text{A}}\) & \(-\)3.964 & \textless{}0.001 \({}^{\text{*}}\) \\ \hline
**CS3600** & 17.5 \(\pm\) 3.6 \({}^{\text{B}}\) & 24.0 \(\pm\) 3.7 \({}^{\text{B}}\) & \(-\)10.963 & \textless{}0.001 \({}^{\text{*}}\) \\ \hline
**i500** & 23.4 \(\pm\) 3.9 \({}^{\text{C}}\) & 32.6 \(\pm\) 4.6 \({}^{\text{C}}\) & \(-\)9.333 & \textless{}0.001 \({}^{\text{*}}\) \\ \hline
**EZIS PO** & 24.2 \(\pm\) 6.2 \({}^{\text{C}}\) & 34.1 \(\pm\) 6.7 \({}^{\text{C}}\) & \(-\)13.08 & \textless{}0.001 \({}^{\text{*}}\) \\ \hline
**F** & 119.3 & 319.8 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of RMS values per abutment type using 4 scanners.  
Figure 4: Comparison of RMS value according to scanner. Different letters (A, B, C) indicate significant differences in the scanner group in the Tukey honestly significant difference test (\(p\) < 0.05).  
The volume and area of 10 abutments and the accuracy of the four types of scanner showed significant positive correlations (\(p\) < 0.001; Table 3). There were weak correlations between the accuracy of the laboratory scanner (E1) and the volume (PCC = 0.300) and area (PCC = 0.349) (\(p\) < 0.001; Table 3). There were moderate correlations between the accuracy of the CS3600 and the volume (PCC = 0.625) and area (PCC = 0.631) (\(p\) < 0.001; Table 3). There were strong correlations between the accuracy of the i500 and the volume (PCC = 0.717) and area (PCC = 0.725) (\(p\) < 0.001; Table 3). There were strong correlations between the accuracy of the EZIS PO and the volume (PCC = 0.743) and area (PCC = 0.742) (\(p\) < 0.001; Table 3).
# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
###### Abstract  
As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.  
federated learning; microservices; fault tolerant system design Article