esa-sceva/satcom-chunk-collection huggingface dataset:
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
###### Abstract
As a result, as far as we are aware, our study is unique in the way that it investigates availability and dependability in the context of streaming video-on-demand using Petri nets and a sensitivity analysis. Stochastic Petri nets (SPNs) are a widely used approach for modeling complex systems with various features, including parallelism and concurrency. SPN models have been applied to represent and to evaluate various types of systems, such as VANETs, hospital networks, cloud data processing, mobile devices, and more [13; 14; 15; 16; 17; 18]. SPN models can be used to evaluate the performance and availability of computer systems, which is referred to as performability. In this article, we introduce SPN models for evaluating the performance of an architecture that provides video-on-demand to a specific group of users. Four models with different redundancy techniques were proposed, and a sensitivity analysis was conducted to identify the parameters with the greatest impacts on the system. The main contributions of this work are:  
* **Base model using SPN. ** A base SPN model was developed using components from a video streaming architecture for the analysis. This model is used to predict the system's response time and availability. * **A sensitivity analysis of the base model. ** A sensitivity analysis of the base model revealed which components have the greatest impact on the availability of the video streaming system. The Front-End, for example, had the greatest influence on the system's response time, while the Back-End had the greatest influence on the availability metric. * **Three extended SPN models to assess the availability capability of a video streaming system. ** Three extended SPN models were developed to assess the availability capability of a video streaming system. These models can be used to build a video-on-demand streaming business, and are based on the base model. The first model is the Cold-Standby model, the second is the Warm-Standby model, and the third is the Hot-Standby model. These models are designed to enhance and to increase the uptime of the service by incorporating redundancy in the components with the greatest impact. The rest of this paper is organized as follows. Section 2 describes the methodology of the work, outlining the main stages of its development. Section 3 presents the evaluated architecture and describes the main components of its structure. Section 4 presents the SPN models. Section 5 presents the results obtained from the models. Section 6 presents related works. Finally, in Section 7, we present the conclusion of the work and the final remarks.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 4 Experimental Setup
### PCB Prototypes  
To validate and compare the proposed balancing topologies, two cell-balancing prototypes were designed and fabricated. Figure 6 presents the PCBs of the two implementations, and Figure 7 shows the setup for the tests performed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Cell 1** & **Cell 2** & **Cell 3** & **Cell 4** & **Unit** \\ \hline L & 335.549 & 329.070 & 344.549 & 357.609 & nH \\ R\({}_{\rm S}\) & 26.822 & 27.634 & 28.308 & 26.497 & m\(\Omega\) \\ R\({}_{1}\) & 8.173 & 3.290 & 8.021 & 3.397 & m\(\Omega\) \\ R\({}_{2}\) & 0.832 & 0.976 & 0.855 & 0.986 & m\(\Omega\) \\ T\({}_{\rm CPE1}\) & 0.268 & 1.763 & 0.238 & 1.630 & - \\ P\({}_{\rm CPE1}\) & 3.331 & 8.449 & 3.102 & 8.261 & - \\ T\({}_{\rm CPE2}\) & 0.936 & 0.845 & 0.981 & 0.852 & - \\ P\({}_{\rm CPE2}\) & 1.912 & 0.241 & 1.727 & 0.242 & - \\ \(\sigma_{\rm W1}\) & 1.898 & 1.785 & 1.835 & 1.797 & mS-s\({}^{0.5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fitted parameters of the EIS measurements.  
Figure 5: EIS measurement for each cell.
## 4 Experimental Results and Analysis
### Discussion
#### 4.5.3 Datasets Divided by Different Proportions  
To verify the feature extraction ability of the model, a set of comparative experiments was set up, and the training data and test data were divided on the dataset with ratios of 1:1, 4:1, 7:3, 3:2, and 9:1. To ensure the reliability of the experimental results, the average results after 10 tests were adopted, and the results are shown in Table 7.  
It can be seen from Table 7 that the higher the proportion of training data, the better the diagnosis result of the model. When the training data and test data are divided by 9:1, the diagnostic rate of the model is 99.98%. The experimental results show that the greater the number of training samples, the more specific fault features included in the samples, and the better the model diagnostic performance training. It is proven that this paper can achieve good results by dividing training samples and test samples in a ratio of 9:1.  
The t-SNE visualization technique was used to map the high-dimensional features of the whole connection layer into two-dimensional features for visualization to demonstrate the feature extraction capability of the proposed method. As shown in Figure 21a, the data in the input layer are in a disordered state, and the features of the fully connected layer of the proposed method after processing are shown in Figure 21d. It can be seen that the proposed method gathers fault samples of the same type together well and effectively separates fault samples of different types. The results show that the proposed method has a better fault feature identification capability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Load** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline
1 HP & 99.91 & 99.93 & 99.92 & 99.91 \\
2 HP & 99.83 & 99.87 & 99.84 & 99.84 \\
3 HP & 99.61 & 99.27 & 99.18 & 99.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The diagnosis result evaluation of different functioning mode.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Serial Number** & **Ratio of Training Set to Test Set** & **Diagnostic Accuracy \%** \\ \hline
1 & 1:1 & 99.73 \\
2 & 3:2 & 99.83 \\
3 & 7:3 & 99.87 \\
4 & 4:1 & 99.94 \\
5 & 9:1 & 99.98 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results of different scales of dividing the dataset.  
Figure 20: The confusion matrix of IGVMD–1 DCNN.
## 4 Comprehensive Controller Considering Traffic Information
### Access to the Traffic Condition and the Road Grade  
The World Light Test Procedure (WLTP) driving cycle is chosen as a test driving cycle and the information of the traffic condition and the road grade is added into the WLTP driving cycle [31].  
After the driver sets the driving route, the real-time traffic condition can be obtained by means of the vehicle navigation map. Take Google maps as an example, the real-time traffic condition will be displayed after setting the driving route. Red represents traffic congestion and the average vehicle speed is less than 10 km/h. The moderate traffic fluency is denoted by yellow and the average vehicle speed is at 10 -40 km/h. Green presents a good traffic condition and the average vehicle speed is greater than 40 km/h. According to Google maps, the traffic condition of the WLTP driving cycle is shown in Figure 3.  
Similarly, GIS can provide the altitude of the real road and the road grade, \(i\), can be calculated by Equation (10). The diagram of the road grade calculation is depicted in Figure 4.  
\[i=\tan\left(\sin^{-1}\frac{A_{2}-A_{1}}{L}\right)\times 100\% \tag{10}\]  
where \(A_{1}\) is the altitude of the current position of the vehicle, and \(A_{2}\) is the altitude of the next sampling point. \(L\) represents the driving distance between the current position of the vehicle and the sampling point.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operating Condition** & **Switch Condition** & **Power Allocation** \\ \hline Driving: battery and UC & \(P_{min}<P_{dem}\) and \(SOC_{acr,min}<SOC_{acr}\) & \(P_{act}=aP_{dem}+b\) \\  & \(P_{bat}=P_{dem}-P_{ac}\) \\ \hline Driving: battery & \(P_{min}\leq P_{dem}\) and \(SOC_{ac}\leq SOC_{acr,min}\) & \(P_{bat}=P_{dem}\) \\  & \(P_{bat}=0\) \\ \hline Driving: battery & \(0\leq P_{dem}\leq P_{min}\) & \(P_{bat}=P_{dem}\) \\  & \(P_{bat}=0\) \\ \hline Braking: UC & \(P_{dem}<0\) and \(SOC_{acr}<SOC_{acr,max}\) & \(P_{bat}=P_{dem}\) \\  & \(P_{bat}=0\) \\ \hline Braking: neither of them & \(P_{dem}<0\) and \(SOC_{acr,max}\leq SOC_{ac}\) & \(P_{bat}=0\) \\  & \(P_{bat}=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Adaptive rule-based controller. SOC—state of change; UC—ultra-capacitor.  
During the process of adding the road grade into the WLTP driving cycle, the road grade, the vehicle speed, and the length of the slope should follow the relevant rules [31]. Figure 5 describes the added information of the road grade in the WLTP driving cycle.
###### Abstract  
To resolve the critical issues of severe deformation, structural failure, and maintenance difficulties in the advanced reuse zone of gob-side-entry retaining roadways under pillarless mining conditions in ultra-long fully mechanized top-coalving isolated mining faces, this study proposes a surrounding rock control technology incorporating pressure relief through roof cutting. Taking the 3203 ultra-long isolated mining face at Nanyang Coal Industry as the engineering case, an integrated methodology combining laboratory experiments, theoretical analysis, numerical simulations, and industrial-scale field trials was implemented. The deformation and failure mechanism of flexible formwork walls in gob-side-entry retaining and the fundamental principles of pressure relief via roof cutting were systematically examined. The vertical stress variations in the advanced reuse zone of the retained roadway before and after roof cutting were investigated, with specific focus on the strata pressure behavior of roadways and face-end hydraulic supports on both the wide coal-pillar side and the pillarless side following roof cutting. The key findings are as follows: 1 Blast-induced roof cutting reduces the cantilever beam length adjacent to the flexible formwork wall, thereby decreasing the load per unit area on the flexible concrete wall. This reduction consequently alleviates lateral abutment stress and loading in the floor leave-affected zone, achieving effective control of roadway surrounding rock stability. 2 Compared with non-roof cutting, the plastic zone damage area of surrounding rock in the gob-side entry retained by flexible formwork concrete wall is significantly reduced after roof cutting, and the vertical stress on the flexible formwork wall is also significantly decreased. 3 Distinct differences exist in the distribution patterns and magnitudes of working resistance for face-end hydraulic supports between the wide coal-pillar side and the pillarless gob-side-entry retaining side after roof cutting. As the interval resistance increases, the average working resistance of hydraulic supports on the wide pillar side demonstrates uniform distribution, whereas the pillarless side exhibits a declining frequency trend in average working resistance, with an average reduction of 30% compared to non-cutting conditions. 4 After roof cutting, the surrounding rock deformation control effectiveness of the track gateway on the gob-side-entry retaining side is comparable to that of the haulage gateway on the 50 m wide coal-pillar side, ensuring safe mining of the working face.  
gob-side-entry retaining with flexible formwork concrete wall; ultra-long isolated mining face; roof-cutting pressure relief; floor heave failure mechanism; working resistance
## 3 Materials and Methods
### LULC Classification and Change Detection
After classification, the LULC maps in 2009 and 2019 were assessed, in terms of thematic accuracy, based on reference information from very high spatial resolution images from Google Earth in 2009/2010 and a field survey in 2020, respectively. The number of sample points used for the accuracy assessment was 788, based on multinomial distribution, and sample points were allocated using the stratified random sampling technique suggested by [33]. Final LULC maps in 2009 and 2019 were further used to detect LULC change, using a post-classification comparison algorithm to describe from-to change information between 2009 and 2019, as suggested by [34; 35].
## 3 Analysis and Results
### Accuracy  
#### 3.1.1 Ultrasonic Tomography  
To compare the accuracy of the measurements from both procedures (automatic and manual), a reference stone bock--ZX section, 250 mm width and 250 mm height--was considered, with a limited number of veins with different propagation velocities in known locations (Figure 14). Applying both procedures on this reference section, the accuracy of the results can be comparatively evaluated.  
The used criterium to compare both procedures considers the following: (i) each pixel of the grid represents a tomography; (ii) a pixel whose dimension is at least 85% inside the area of a vein is accepted as a detected vein.  
Regarding the manual procedure, two cases were considered: (1) 10 \(\times\) 10 grid (Figure 15), which is a regular measuring grid achievable by a skilled operator, and (2) the best manual case scenario of a 15 \(\times\) 15 grid (Figure 16), which is considered as the highest accuracy that can be performed by manual measurements.  
Figure 14: Reference ZX section of the stone block.  
Figure 13: CSV standard file.  
Regarding the new developed automatic procedure, it is considered a 41 \(\times\) 21 grid (Figure 17), whose accuracy is allowed by the prototype setup.  
Table 5 shows the comparative accuracy in detecting stone veins by three significant grid dimensions (automatic and manual measuring procedures).  
Analyzing the numerical results, as shown in Table 5, it is possible to detect a major difference between the automatic and manual procedures. The manual procedures show a very low capacity of veins detection when compared with the automatic procedure. In fact, both manual grids (10 \(\times\) 10 and 15 \(\times\) 15) detect at the highest detection capacity only 53.2% of the target area, while the automatic procedure (grid 41 \(\times\) 21) arrives to a detection rate of 82.0%. The proposed automatic measurement procedure can easily achieve much  
Figure 16: Grid 15 \(\times\) 15 (manual best case).  
Figure 17: Grid 41 \(\times\) 21 (automatic).  
Figure 15: Grid 10 \(\times\) 10 (manual—skilled operator).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Procedure** & **Grid** & **Total Vein Area [mm2]** & **Total Accurately Detect Area [mm2]** & **Percentage [\%]** \\ \hline Manual skill & 10 \(\times\) 10 & 5625.0 & 38.5 \\ Manual best & 15 \(\times\) 15 & 14,610.8 & 7777.8 & 53.2 \\ Automatic & 41 \(\times\) 21 & 11,977.4 & 82.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ultrasonic tomography—accuracy results.  
finer grids that will result in much more accurate results. In fact, the smaller veins are not detected by the coarser grid (10 \(\times\) 10) and only barely detected by the 15 \(\times\) 15 grid.
## 3 Results and Discussion
### SO\({}_{2}\) Content  
Sulfur dioxide is the most common additive that is widely used for wine preservation, inhibiting both the enzymatic and non-enzymatic browning during production and storage [7]. It acts as a multifaceted antiseptic and reducing agent that offers protection against oxidation. Temperature has a dual effect on the SO\({}_{2}\) content of wine since it affects both its equilibrium and the kinetics of the oxidative reactions in which it takes part as an inhibitor [16]. Concerning equilibrium, the higher the temperature, the greater the fraction of sulfur dioxide in the most active molecular form. However, as it was shown in this study, oxidation kinetics were accelerated, resulting in higher amounts of reactive oxygen species (ROS).  
As it can be seen in Figure 1, the addition of Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\) resulted in significantly lower total SO\({}_{2}\) contents in the wines at 50 \({}^{\circ}\)C compared with that of the control. Similar observations were made at 35 \({}^{\circ}\)C with the exception of the wines containing low concentrations of Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\), where the differences with the control were not significant. It has been shown that in the presence of catalysts such as metal ions, it binds with dissolved oxygen and thus protects wines from chemical intense oxidations and consequently from losses of their phenolic and volatile contents [7; 33].  
An interesting observation was that the addition of Se in wines resulted in preservation of total SO\({}_{2}\) content. At both temperatures, the samples with Se contained significantly higher amounts of SO\({}_{2}\) than that of the control. However, when Se was added in the presence of Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\), the protection was not adequate to preserve total SO\({}_{2}\) content. Regarding free SO\({}_{2}\) content at 35 \({}^{\circ}\)C, the control and the wines with the addition of Se contained the highest amounts. However, at 50 \({}^{\circ}\)C, all samples contained significantly lower amounts of SO\({}_{2}\) than that of the control, even those with the Se addition. This is in agreement with the findings presented previously in this study, where Se at 35 \({}^{\circ}\)C prevented browning development in wines. Se probably protects wine color by preserving SO\({}_{2}\) content which in turn offers antioxidant protection to wine.  
[PERSON] and [PERSON] [40] reported that the reaction rates of nucleophilic molecules such as SO\({}_{2}\) with quinones are faster than those of electron transfer of flavonoids (where the B ring is implicated) and by this way, sulfur dioxide can prevent the browning due to quinone reactions in wine. It is thus possible that Se offered protection by reducing the electron transfer reactions between quinones and flavonoids, preserving total SO2 content of wines. However, acetaldehyde formation due to ethanol oxidation, which was enhanced by the higher temperature and the presence of metals, probably resulted in binding SO2, thus reducing its free form [41].
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 2 Methods
### Initial Alignment Based on RGB Images
#### 2.1.1 Extraction of Feature Points with SIFT  
SIFT, SURF, and ORB are commonly used algorithms for feature point detection. However, the ORB algorithm detects feature points unevenly, lacks rotation invariance, and is sensitive to noise [25]. The SURF algorithm performs poorly in detecting feature points under lighting changes and deformation [26]. Considering the positive stability of the SIFT algorithm and its ability to adapt to rotation [27], scale changes, and brightness changes [28], our work uses SIFT to extract features from RGB images and perform initial alignment.  
The SIFT algorithm detects the extremum in the scale space by constructing a Gaussian pyramid, determines feature points from the extremum, and forms a 128-dimensional feature descriptor. To compare the performance of different feature point detection algorithms, feature points were extracted from the RGB scene images corresponding to the experimental point cloud, and the final detection results are shown in Figure 2. The left image in Figure 2 shows the feature point extraction result of ORB on the RGB scene image, with unevenly distributed extracted features. The right image in Figure 2 shows the feature point extraction results of SIFT on the RGB scene image. The SIFT algorithm is sensitive to feature points in the image, and even if the scene object is single, it can obtain a sufficient number of feature points, which is beneficial for subsequent feature matching.
###### Abstract
The contribution of our study is summarized as follows:  
* The proposed method addresses inter-patient variability in ECG signals for the n-BSM-based arrhythmia classification by utilizing adversarial learning. Specifically, an adversarial framework is applied to the pre-training stage of the beat classifier used for PI-BSM generation. Consequently, PI-BSMs derived from ECG signals are able to effectively capture beat-related features while excluding patient-specific features;
* The proposed framework extends the applicability of the BSMs to cross databases by not mandating beat annotations. That is, a beat classifier can be pre-trained on any beat-annotated dataset (referred to as a source dataset), which is then used to derive PI-BSMs from other target datasets without beat annotations. This approach is suitable for real-world scenarios where beat annotations are lacking. Due to the enhanced generalization in the beat-level training phase, the PI-BSM can mitigate individual bias;  
Figure 1: Illustration of (**a**) inter-patient scheme and (**b**) intra-patient scheme. * The proposed method improves the performance of ECG arrhythmia classification in the inter-patient paradigm by using PI-BSMs as the input for a CNN-based rhythm classification model. Cross-validation within the MIT-BIH arrhythmia database (MIT-BIH dataset) showed a 14.27% improvement in the F1-score. When tested on the Chapman-Shaoxing 12-lead ECG database (SPH dataset) in a cross-database scenario, PI-BSM-based classification indicated a 4.97% improvement compared to our previous study;
* The proposed method achieves the most notable improvement in atrial fibrillation (AFib) rhythm, which exhibits the lowest performance in most other inter-patient studies. Utilizing this method demonstrated a 27.70% F1-score improvement in the MIT-BIH dataset cross-validation and a 16.22% increase in F1-score when tested with the SPH dataset. These findings confirm that there is significant variability among patients, particularly in AFib rhythms, and highlight the importance of taking this variability into account in AFib research.
## 2 Proposed Models  
### The Proposed Custom Model  
The proposed architecture has two parallel paths that allows convolutions to the input image with different kernel sizes and numbers of filters. It has a shallow depth, with only three convolutional layers and three fully-connected (FC) layers. Each path includes two convolutional layers each followed by batch normalization and max-pooling layers. The two parallel paths are concatenated to enter a new convolution layer followed by a max-pooling layer. The network is then flattened and followed by three FC layers of 400, 200, and 2 outputs, respectively. Figure 2 shows the proposed architecture and Table 1 summarizes the network structure. The custom model has about 16 million parameters.  
As can be seen in Table 1, the kernel and the number of filters of each convolutional layer in the parallel block varies between the two branches. In one branch, the architecture makes use of a simple technique to reduce dimensionality of the input color image through 1 \(\times\) 1 convolutions, preserving its most outstanding properties. In the other branch, the architecture uses 3 \(\times\) 3 convolutions to extract patterns with a different resolution to those of the first branch. Since shape patterns are not important for this type of classification problem, the network is not very deep.  
The ReLU activation function is applied in all convolutional and FC layers (except the last layer). It was selected because it is an efficient function which has been widely used in CNNs for classification tasks [18]. The output is obtained through Equation (1):  
\[\text{ReLU(x)}=\text{max}(0,\text{x}), \tag{1}\]  
where x is the result of the convolution (or the weighted sum, in the case of FC layers). With the ReLU function application, all values of the feature maps are positive. On the other hand, the output layer uses the softmax function instead of the sigmoid function because the proposed architecture has two units in the output to ensure compatibility with the TensorFlow method to calculate the F1-score (i.e., fta.metrics.F1 Score).  
To evaluate the size of the input image, three different resolutions were selected. Finally, regarding the optimizer, RMSProp, SGD and Adam were selected to analyze their impact on classifier performance.