esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Combining Knowledge- and Data-Driven Techniques  
The general overview of how knowledge- and data-driven techniques can be combined is provided in Figure 2 and is a schematic representation from the work described in previous research [4]. The events originating from the data stream are being delivered to both the knowledge-driven fault detection component and the data-driven anomaly detection component. Based thus on either predefined rules (in the knowledge-driven component) or a machine learning technique (in the data-driven component), faults and/or anomalies are, respectively, visualized in a dashboard application to the operator. This operator is able to tune the rules within the fault detector manually. However, by providing feedback through the dashboard application, both the knowledge- and data-driven detection models can be updated automatically. In the knowledge-driven model, new rules are automatically derived from confirmed anomalies by analyzing the correlation between such occurring events. In the data-driven model, additional labelled feedback is used to detect similar future unseen events, evaluating to a more general supervised detection approach. In this case, the labelled feedback instantiates a supervised machine learning fault detector.  
The feedback mechanism is standardized for both the data-driven as well as the knowledge-driven detection techniques to prevent the operator from needing to know which detection model provided the information. The next sections will focus individually on each of these more general concepts in more detail, and also provide the technology  
Figure 1: Absolute humidity in the evaluation dataset, shown for the bathroom (**top**) and bedroom (**bottom**).  
which was being used to derive the anomalies and faults for our use case described in Section 3.
# Measurement of Ultra-High Speed by Optical Multistage Cascade Frequency Reduction Technology
## 3 Discussion and Results
### Impact Loading Experiment of Three-Stage Light Gas Gun
The experimental results show that the principle of optical multistage cascade frequency reduction technology is feasible. Figure 10: Spectrum diagram of two kinds of velocity measurement results: (**a**) DISAR, (**b**) first stage, and (**c**) second stage.
## 2 Materials and Methods
### Statistical Analysis  
Intraclass correlation (ICC) is used to analyze test-retest reliability because it takes into account systematic errors between repeated measurements [37]. The ICC form was used by following Shrout and Fleiss reliability coefficients guidelines [38]. Excellent reliability was determined if ICC > 0.75; Adequate reliability was determined if ICC was between 0.40 and 0.74; Poor reliability was determined if ICC < 0.40 [39; 40; 41]. Two-way (direction vs. distance) repeated measures ANOVA was used to analyze the response of proactive balance variables and APA variables in different target directions (0, 45, 90, 135, and 180 degrees) and target distances (60% and 80%). Data were analyzed in the SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA).  
\[ICC(3,1)=\frac{\text{BMS}-\text{EMS}}{\text{BMS}+(k-1)\text{EMS}} \tag{4}\]  
\(k\) number of raters.  
Figure 4: Representative data from two trials with illustration of variables. The black lines are discursion of COP and the green circles are targets. The red “x”s are the center of the targets which are not displayed for subjects. When the COP successfully touch the edge of the target and stay in target for 4 s, the trial is completed and the “path length” is calculated from the total COP discursion from the start point “O” to the point at which the COP touches the edge of the target. \(\theta\) refers to direction error. The anticipatory postural adjustment (APA) is defined as the farthest point in the quadrant opposite to the target. (**a**) An example shows only one phase in APA. (**b**) An example shows two phases in APA. The distance between A1 and A2 is defined as “APA correction”.
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 6 Summary and Prospect
### Prospect  
The model proposed in this paper has improved the prediction accuracy of satellite network traffic to a certain extent, but it still needs further improvement; more network noise will be introduced in the actual satellite network environment, which will affect the accuracy of network traffic prediction to a certain extent. In future work, we can further reduce the noise of network traffic and improve prediction accuracy.  
Conceptualization, Z.L. and W.L.; methodology, Z.L., W.L. and J.F.; software, W.L. and J.Z.; validation, Z.L. and J.F.; formal analysis, W.L.; investigation, J.Z.; resources, Z.L.; data curation, W.L.; writing--original draft preparation, W.L.; writing--review and editing, Z.L.; visualization, J.F.; supervision, W.L.; project administration, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The processed data required to reproduce these findings cannot be shared as the data also forms part of an ongoing study.  
The authors declare no conflict of interest.
## 4 Modeling and Validation
### Individual Modeling
#### 4.1.1 Logistic Regression (LogReg)  
Logistic regression is a classification method that models the probability that a given data point belongs to a specific category using a sigmoid function (unlike linear regression which uses a linear function) [25]. Multinomial logistic regression is a classification method that generalizes logistic regression to a multiclass problem, and it is the method used here. Optimum classification results for the sklearn logistic regression model were achieved by configuring the optimization solver to Limited-memory BFGS (lbfgs), a quasi-Newton method.
# Model Updating of Bridges Using Measured Influence Lines
## 2 Materials and Methods
### Measurements
#### 2.2.2 Extraction of the Strain Influence Line (IL) from the Strain Measurements
As this technique can be applied to random traffic, monitoring its shape through time and detection allows for possible changes in bridge behaviour [16]. Since the axle loads of the vehicles from random traffic are unknown, the absolute strain IL value cannot be calculated. Instead, the strain IL is normalised so that the integral between points LS and RS is equal to 1, and the \"raw\" axle loads, calculated from the equation, adapt to this. Finally, one or more calibration vehicles with known axle loads cross the bridge several times. The ratio between raw and actual loads is calculated for each run, and the mean value of all results is taken as a calibration factor that converts raw into actual loads. Calculating the strain IL with known axle loads, as in this study, allows for avoiding normalisation, as the actual influence of a unit axle on the bridge is needed. The known axle loads of the calibration vehicles were taken, and the strain IL height adapted itself during calculations. Strain ILs were extracted separately for vehicle type and load (V1F, V1E, V2F, and V2E) and for vehicle speed (30, 70, and 90 km/h). Overall, 12 strain ILs were generated for each sensor. Figure 9 shows the strain ILs for sensors E_MG1 (a) and E_MG2, with blue,  
Figure 8: Hypothetical three-span bridge strain influence line (IL), approximated with cubic splines (adapted from [16]). The ordinates of the IL are obtained with cubic spline interpolation between the white (LB, LS, M, RS, RB), grey and black (A, B, C, D, E, F) control points. red, magenta, and green colours denoting V1F, V1E, V2F, and V2E vehicles, respectively. The mean (\(\mu\)) and mean \(\pm\) standard deviation (\(\mu\pm\sigma\)) values are shown with dashed and grey lines, respectively. Similarly, Figure 10 shows the generated strain ILs for sensors E_MG1 (Figure 9(a)) and E_MG2 (Figure 9(b)), with red, green, and blue colours denoting 30, 70, and 90 km/h speeds, respectively. Figures 9 and 10 indicate that vehicle type, load, and speed have a minimal impact (less than \(\pm\)5% deviation of the maximum strain IL values, compared with the maximum values of the mean strain IL) on the strain IL's shape and magnitude. Consequently, the mean values of E_MG1 and E_MG2 strain ILs were used for the FEMU.
## 4 Experimental Study
### Benchmark and Comparison Methodology
#### 4.2.5 Quality Indicators  
To measure the quality of the Pareto front approximation yielded by multi-objective metaheuristics, two aspects are usually taken into account: minimizing the distance of the obtained front to the Pareto front of the problem (convergence) and maximizing the extension of solutions on the front so that the distribution is as uniform as possible (diversity). There are different ways to measure these properties, so quality indicators have been defined for that purpose. In our experimental study, we used the additive epsilon (EP) [106], the normalized hypervolume (NHV) [107], and the inverted generational distance plus (IGD+) [108]. The EP measures convergence, while the NHV and IGD+ give a measure of both convergence and diversity; in all of them, the lower the indicator value, the better it is. They are briefly described next:  
* The EP is a measure of the absolute deviation needed to translate each solution in a Pareto front approximation in such a way that it weakly dominates the front used as a reference (it can be the true Pareto front or a subset of it);
* The NHV is computed as 1.0 minus the ratio between the hypervolume of the front to be evaluated and the hypervolume of the reference front. The hypervolume metric calculates the volume (in the space of objectives) covered by members of a given set of non-dominated solutions with respect to a reference point;
* The IGD+ evaluates the convergence performance and the distribution performance of the algorithm by calculating the sum of the minimum distances between each point (individual) on the reference front and the front of solutions obtained by the algorithm.  
Computing the quality indicators requires obtaining a reference front. As the Pareto front of the considered problems is unknown, we constructed, for each problem, a reference front by taking the non-dominated solutions of all the runs of all the algorithms per problem.
# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 2 Materials and Methods
### Feature Extraction and Data Augmentation
Furthermore, since we aimed to use only real data, the dataset of acquired videos presented some limitations, such as a high data dimensionality, a low quantity of data and imbalanced data due to the limited number of subjects participating in the experiment (13 patients with neurodegenerative diseases and 18 healthy subjects). In order to enrich the dataset and to face in part the aforementioned limitations, the SMOTE (synthetic minority oversampling technique) data oversampling approach was applied to the training set [40]. The SMOTE technique is a popular oversampling method that improves random oversampling, creating synthetic new instances that balance the minority class samples augmenting the available dataset.
## 4 Results and Discussion  
### First Part: Factorial Design Multivariate Statistical Analysis  
The effect of the plasticization parameters, screw rpm, barrel temperature, and back pressure, with moisture effect, are shown in Figures 2b and 3. Figure 2b shows the experiment run number versus measured air bubble area. The number of visible air bubbles in the product part was counted and saved in an Excel sheet. Figure 3 shows the experiment run number versus counted air bubble number.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Factor Levels**} \\ \hline
**Exp Run** & **Barrel Temp** & **Screw Rotation** & **Back Pressure** \\ \hline A14 & 300 & 100 & 10 \\ A15 & 300 & 150 & 10 \\ A16 & 300 & 50 & 15 \\ A17 & 300 & 100 & 15 \\ A18 & 300 & 150 & 15 \\ A19 & 280 & 50 & 5 \\ A20 & 280 & 100 & 5 \\ A21 & 280 & 150 & 5 \\ A22 & 280 & 50 & 10 \\ A23 & 280 & 100 & 10 \\ A24 & 280 & 150 & 10 \\ A25 & 280 & 50 & 15 \\ A26 & 280 & 100 & 15 \\ A27 & 280 & 150 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Moisture Content** & **Screw Rotation** & **Back Pressure** \\  & **(ppm)** & **Barrel Temp (\({}^{\circ}\)C)** & **(rpm)** & **(MPa)** \\ \hline B1 & 100 & 150 & 5 \\ B2 & 600 & 280 & 150 & 5 \\ B3 & 600 & 50 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Plasticization parameter table of the third stage experimental group.  
The experiment run versus measured value figure shows that for a few experimental runs, the air bubble defect appeared, and for the remainder of the experimental runs, parameter factor combinations and levels were good enough to produce product parts without any air bubble defects. From this, we can assert that some parameter factor combinations and levels have a significant effect on the moisture content. To find out the influence of moisture content with the parameter factor and levels, the measured output response values of air bubble area and air bubble numbers were copied into Minitab software to analyze the output response with multivariate statistics. This software was used for factorial regression analysis on the measured output response value and to estimate the interaction and main effect strength with the output response. From Figures 2b and 3, it was observed that for a few of the experimental runs, the two output responses, the number of air bubbles and the air bubble area, seemed to increase, which shows that there was a strong correlation between the air bubble area and the number of air bubbles that appeared. The output response variables were generated during the plasticization process, and it appeared that the plasticization parameter factor levels in the experimental run played a key role in determining and controlling the air bubble defect in the final product part. As a result, we can say that the parameter factor levels can be used to control air bubble defects because the rest of the machine parameter settings were constant variables, as shown in Table 2.
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 3 Results and Discussion
### Macroscopic Flow Characteristics and EOR Effect  
Figure 5 shows the differential pressure varied with the injection volume, and, the results of core-flooding experiment were shown in Table 10 (E\({}_{0}\) is the EOR rate of water flooding and E\({}_{\rm c}\) is the EOR rate of chemical flooding).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Temperature** & **Concentration of XC** & **V\({}_{0}\)** & **V\({}_{t}\)** & **t\({}_{1/2}\)** & **t\({}_{\rm D}\)** & **FCI** \\ (\({}^{\circ}\)**C**) & **(g/L)** & **(mL)** & **(mL)** & **(min)** & **(s)** & **(mL\(\cdot\)min)** \\ \hline \multirow{4}{*}{25} & 0.05 & 535 & 5 & 278 & 631 & 111,547.5 \\  & 0.075 & 510 & 5 & 263 & 672 & 100,597.5 \\  & 0.1 & 460 & 0 & 242 & 714 & 83,490 \\  & 0.125 & 435 & 0 & 233 & 747 & 76,016.25 \\ \hline \multirow{4}{*}{45} & 0.05 & 540 & 5 & 292 & 609 & 118,260 \\  & 0.075 & 525 & 0 & 281 & 681 & 110,643.75 \\  & 0.1 & 490 & 0 & 264 & 698 & 97,020 \\  & 0.125 & 450 & 0 & 247 & 704 & 83,362.5 \\ \hline \multirow{4}{*}{65} & 0.05 & 495 & 15 & 231 & 519 & 85,758.75 \\  & 0.075 & 505 & 15 & 234 & 545 & 88,627.5 \\  & 0.1 & 455 & 10 & 209 & 571 & 71,321.25 \\  & 0.125 & 435 & 5 & 213 & 574 & 69,491.25 \\ \hline \multirow{4}{*}{85} & 0.05 & 395 & 25 & 168 & 292 & 49,770 \\  & 0.075 & 420 & 25 & 184 & 341 & 57,960 \\ \cline{1-1}  & 0.1 & 415 & 15 & 181 & 392 & 56,336.25 \\ \cline{1-1}  & 0.125 & 405 & 15 & 190 & 403 & 57,712.5 \\ \hline \end{tabular}
\end{table}
Table 9: Foaming properties varied with the XC concentration at different temperature.  
The foam flooding showed an obvious blocking effect, while the ASP flooding had almost no blocking effect. Meanwhile, the target foam system showed better blocking effect in both high permeability group (numbers 1-3) and low permeability group (numbers 4-6) than normal foam system. This results indicated that the Jamin Effect caused by the target foam system is more stable and effective than normal foam system. Which means, the target system could be more stable than normal foam system in porous media at high temperatures [27; 28].  
In high permeability group (numbers 1-3), the EOR rate of target foam flooding was nearly equivalent to ASP flooding, but significantly better than normal foam flooding. Commonly, blocking effect has a weak impact on the EOR rate in high permeability conditions [29], therefore, normal foam system performed a weak EOR effect. However, as the liquid phase had high EOR capacity (having ultra-low oil-liquid IFT), the target foam system showed better EOR rate than other two controlling group. In the low permeability group (numbers 4-6), the EOR effect of both foam flooding increased, but the ASP flooding's EOR effect declined sharply. In low permeability conditions, liquid with low viscosity could escape along the high permeability channels, thus, blocking effect could improve the EOR rate effectively [30; 31].
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Resistance to Statistical Attacks
#### 5.5.3 Information Entropy Analysis  
The information entropy is a measure of the data distribution after encryption, which is defined by Equation (19). If each symbol has almost the same entropy as a random distribution, then the system has high random complexity and each pixel is treated as a random event.  
\[H(S)=\sum_{S}P(S_{i})\log_{2}\frac{1}{P(S_{i})} \tag{19}\]  
Here, \(P(S_{i})\) represents the probability of each symbol \(S_{i}\). According to Equation (19), for a uniform distribution, the ideal entropy for each symbol is 8 for an image in which each pixel consists of 8 bits. Table 6 shows the entropy analysis results for encrypted images. Table 6 shows global entropies of the encrypted images. The results indicates that the entropy values are very close to the ideal value 8 for grayscale images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & \multicolumn{3}{c}{**Orientation**} \\ \hline  & **Horizontal** & **Vertical** & **Diagonal** \\ \hline Lena (plain) & 0.9719 & 0.9850 & 0.9593 \\ Lena (encrypted) & 0.00051 & \(-\)0.0043 & 0.00031 \\ Baboon (plain) & 0.9337 & 0.9123 & 0.8669 \\ Baboon (encrypted) & \(-\)0.00245 & \(-\)0.00314 & 0.00137 \\ Peppers (plain) & 0.9802 & 0.9803 & 0.9706 \\ Peppers (encrypted) & \(-\)0.00178 & 0.00812 & \(-\)0.00031 \\ Airplane (plain) & 0.9676 & 0.9628 & 0.9371 \\ Airplane (encrypted) & 0.00519 & \(-\)0.00362 & \(-\)0.00039 \\ House (plain) & 0.9401 & 0.9372 & 0.8952 \\ House (encrypted) & \(-\)0.00238 & \(-\)0.00497 & 0.00847 \\ Lake (plain) & 0.9761 & 0.9764 & 0.9616 \\ Lake (encrypted) & 0.00874 & 0.00812 & 0.00038 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation coefficients of adjacent pixels.  
Figure 9: (**a**). Distribution of Lena image; (**b**). Distribution of encrypted Lena image.
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 4 Results
### Severity Classification of Two Hairy Scalp Disorders

Table 6: Performance evaluation metrics for the fine dandruff diagnosis model. Overall, the normal, moderate, and severe categories all displayed relatively high F1-scores (above 80%), signifying the model's robust performance in these areas. In contrast, the mild category, with an F1-score below 80%, showed a comparatively low performance, suggesting that the model has room for improvement in accurately diagnosing mild perifollicular erythema. Building on these findings, the next section proposes enhancements to the perifollicular erythema model to address the identified weaknesses. Specifically, the focus is on increasing the amount of training data for the mild category and refining the model's parameters to improve precision and recall. Additionally, the development of an integrated platform with a web interface is discussed, aiming to provide users with accessible diagnostic tools for perifollicular erythema. This platform will enable users to upload images, receive real-time analysis, and obtain personalized care recommendations, leveraging the strengths of the current model while addressing its limitations. Through these improvements, the proposed system aims to enhance the user experience and diagnostic accuracy in managing perifollicular erythema. Compared to the study conducted by [PERSON] [22], the performance of the model presented in this paper in diagnosing fine dandruff and perifollicular erythema was found to be approximately 2% lower. This slight performance discrepancy can be primarily attributed to the difference in the amount of training data available. Although the current study achieved a level of accuracy similar to that reported in [PERSON]'s research [22], the smaller dataset used in this study most likely impacted the final results. Despite this minor difference, the model developed still demonstrates a high degree of accuracy and reliability. Building on these findings, the next section proposes the development of a comprehensive platform, integrated with a web interface that was specifically designed for scalp diagnosis. The aim of this platform is to provide users with easy access to diagnostic tools, enabling them to upload scalp images, receive an immediate analysis, and obtain personalized recommendations for scalp care. By leveraging the robust model developed in this study, the proposed platform seeks to enhance user experience and accessibility in managing scalp health, offering a practical application for the research outcomes.
# Switched Observer Design for a Class of Non-Linear Systems
## 3 Switched Observer Design
### Stability Regions  
A practical way of determining regions of stability comes from Lyapunov Theory [35]. It is well known that if \(V(\hat{x}(t))\) is a Lyapunov function, such that \(\hat{V}(\hat{x}(t))<0\), \(\forall\hat{x}\in\hat{X}\subset\mathbb{R}^{n_{x}}\) along the trajectories of the system, and \(\hat{X}\) is a set containing the origin in its interior. In addition, to derive computationally tractable solutions, the state error domain \(\hat{X}\) may be constrained to be a given polytopic set as defined below  
\[\hat{X}=\left\{\hat{x}\in\mathbb{R}^{n_{x}}:c_{k}^{T}\hat{x}\leq 1,\;\;k=1,...,n_ {g}\right\} \tag{43}\]  
where \(c_{1},...,c_{n_{g}}\in\mathbb{R}^{n_{x}}\) define the \(n_{g}\) faces of \(\hat{X}\).  
To apply the result presented in [35], we have to determine the largest contour curve of the Lyapunov function  
\[V(x(t))=\min_{i\in\{1,...,n_{p}\}}\hat{x}^{T}(t)P_{i}\hat{x}(t) \tag{44}\]  
Hence, the following developments are instrumental for the derivation of results to determine estimates of the basin of attraction, using ellipsoidal domains contained in the set \(\hat{X}\). With this aim, we can normalize the associated level sets [35]  
\[\hat{\mathcal{X}}=\{\hat{x}\in\mathbb{R}^{n_{x}}:V(x)\leq\eta\},\;\text{with} \;\eta>0, \tag{45}\]  
to be given by ellipsoidal domains \(\hat{\mathcal{X}}_{i}\) defined as follows:  
\[\hat{\mathcal{X}}_{i}=\left\{\hat{x}\in\mathbb{R}^{n_{x}}:\hat{x}^{T}P_{i} \hat{x}\leq 1\right\} \tag{46}\]and seek an LMI formulation for the inclusion problem \(\hat{\mathcal{X}}_{i}\subset\bar{X}\). This inclusion relationship may be rewritten as  
\[\begin{split} 2-c_{k}^{T}x-x^{T}c_{k}\geq 0,\ \ k=1,...,n_{g}\\ \forall\bar{x}:\bar{x}^{T}P_{i}\bar{x}-1\leq 0,\ \ i=1,...,n_{p} \end{split} \tag{47}\]  
then, applying the S-procedure [36] to the last set of inequalities, we obtain  
\[\begin{split}\begin{bmatrix}1\\ -x\end{bmatrix}^{T}\begin{bmatrix}1&c_{k}^{T}\\ c_{k}&P_{i}\end{bmatrix}\begin{bmatrix}1\\ -x\end{bmatrix}\geq 0,\\ i=1,...,n_{p},\ \ k=1,...,n_{g}.\end{split} \tag{48}\]  
The above condition is satisfied if there is a solution feasible for the following LMI:  
\[\begin{split} 1-c_{k}^{T}P_{i}c_{k}\geq 0,\\ i=1,...,n_{p},\ \ k=1,...,n_{g}.\end{split} \tag{49}\]