esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modified Functional Observer with Unknown Input
### Stability Analysis in the Frequency Domain
For a well-designed optimal tracker (Equation (40)), the control law (to be shown in Equation (75)) guarantees the stability of the control system under the following conditions: (i) \(A-L_{c}C\) is stable; (ii) if matrix \(\mathcal{L}\in\Re^{\kappa\times n}\) exists, such that Theorem 1 holds for the functional observer presented in Equations (13a) and (13b); and (iii) if the low-pass filter \(G_{\text{dd}}(s)\approx I_{m}\), such that \(\underset{\omega\in\Omega}{\max}\abs{G_{\text{yd}}(j\omega)}_{\infty}<1\), where \(\Omega\equiv[0,\omega_{c})\). The proof of this theorem is similar to the proof of Theorem 4.4 in [4]. Design Procedure of the Unknown Input Linear Functional Observer for the Unknown Perturbed Singular Systems  
Considering the unknown minimum-phase non-square continuous-time singular system with more control input channels than output channels, this section proposes a procedure for designing a high-performance optimal analog state estimate tracker and an EID estimator for the system with unknown mismatched input and output disturbances. First, we apply the off-line OKID method [17] to determine the parameters \((G_{\text{dd}},\ H_{\text{dd}},\ C_{\text{dd}},\ D_{\text{ok}},\ L_{\text{ok}})\) of the discrete-time mathematical model of this unknown system. Then, we carry out the discrete-to-continuous model conversion to obtain the continuous-time model that will be used to construct the analog unknown input functional observer to estimate and compensate the net EID. Lastly, we determine the proposed robust observer-based optimal LQAT for the unknown singular system. Three advanced techniques are now briefly described to achieve this goal.
## 9 Performance Evaluators
### Relation between the Number of Reference Nodes and Estimation Errors  
In Figures 7-9 the relation between the number of reference nodes and estimation error is given to see the performance of the proposed algorithm. In our simulation, we have added an anchor node one by one on the vertices of the cube as shown by the figures. Initially, we have taken 4 number of reference nodes and gradually increase these reference nodes up to 8. According to Figures 7-9, it is clear that by increasing the number of reference nodes all the considered algorithms are getting better in terms of both localization and synchronization but \(E^{2}JSL\) gives better accuracy as compared to other previously developed methods.  
Figure 6: Relationship between number of messages and clock skew RMSE (ppm).  
Figure 7: Relationship between the number of reference nodes and Localization RMSE (m).
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 3 Datasets and Data Preprocessing
### 5G Traffic Dataset  
We utilized the 5G Traffic dataset presented in [26] for the training of our user traffic prediction module. User traffic was collected via a Samsung Galaxy A90 5G mobile terminal in South Korea (Samsung Electronics, Suwon, Republic of Korea) across various applications such as live streaming (e.g., Naver Now), stored streaming (e.g., YouTube, Netflix), video conferencing (e.g., Zoom), the metaverse (e.g., Roblox), online gaming (e.g., Battlegrounds), and game streaming (e.g., GeForce Now) platforms. Thus, the dataset contains video traffic that imposes significant strain on 5G networks. More specifically, the dataset includes the time when the user started the 5G connection, their Internet Protocol (IP), the IP of the destination (the server to which the user is connected), the protocol used for the connection, and the duration and information regarding the connection. The dataset contains video traffic data with a total length of 328 h collected over a period of 6 months (from May to October 2022).  
To preprocess the 5G dataset, we first stored the provided CSV files in an SQL database. The initial step was optimized indexing and batch query execution, reducing memory requirements during the learning stages of the model. Due to the temporal indexing, the time series can be optimally segregated, which significantly improves performance in the training and testing stages. This use of databases significantly reduces the preprocessing time and assists with the batch processing of very large datasets. The data changed from the initial form of user connection length entries to network traffic in the form of user traffic per time step. The data from this table were used to train our 5G Traffic prediction module.  
The preprocessing operations that we used on the dataset were typical preprocessing operations suitable for RNN models that we fine-tuned to obtain better results. The first operation was a min-max normalization to the range of 0 to 1. The next operation was a sliding window of size 10 to produce a rolling mean of the values with the aim of capturingthe features of local mean network traffic. A rolling standard deviation was used to help with local value volatility. Moreover first- and a second-order differences were used to help with extracting the \"momentum\" of the values within the rolling window. Finally the raw values as recorded were also used without the previous preprocessing steps as the core dataset of the input in the network. The experiments were conducted with an input of 120 values, each representing the user traffic at the start of a given clock time step, and the prediction outputs the next 60 time steps as 60 values, one for each time step in the future.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 2 Related Work
### Degradation Learning  
In order to solve the problem of SR methods in real applications, some works have been proposed to model the image degradation. [PERSON] et al. [12] first utilized a GAN-based network to learn degradation from HR to LR in an unsupervised manner. Later, [PERSON] et al. [9] proposed DSGAN to learn image degradation via frequency separation. [PERSON] et al. [11] proposed KernelGAN to estimate the blur kernels of corrupted images and combined ZSSR as K-ZSSR [11] for SR application. However, these methods have problems of convergence and mode collapse, and the losses need to be carefully fine-tuned. More importantly, both of them try to learn deterministic mapping from HR to LR, ignoring the stochastic nature of image degradation.
## 3 Results
### Application of NMuMG EMP Classifier to Other Lines  
The above analyses demonstrate how DHC can be applied to monitor both the kinetics and degree of morphological changes associated with EMP within a single transitioning cell line. We next sought to determine whether the \"EMP classifier\" trained on NMuMG DHC-derived features generated a useful metric when analyzing other cell lines. To challenge the classifier, we obtained four additional human and mouse cell lines of established EMP. We used the human renal proximal tubule epithelial cells (RPTE), mouse fibroblasts (L929, mesenchymal), adult human dermal fibroblasts (HDF, mesenchymal), and highly malignant HER2 overexpressing human breast cancer cells (JIMT-1, mixed epithelial and mesenchymal). The five cell lines differed substantially in their morphologies (Figure 4). The application  
Figure 3: An “EMP score” generated from four morphological features chosen from correlation matrixes (Figure 2) distinguishes between epithelial and mesenchymal NMuMG cells. The treatments were initiated at time 0 at the start of time-lapse imaging using a digital holographic microscope. (**a**) Images of cells treated with 0.5 mg/mL TGF\(\beta\) or control were captured every hour for 72 h. The control and treated conditions in each time-point were compared using the two-sample equal variance \(t\)-test. At all time-points after 11 hours (horizontal bar) p values generated from this comparison were less than 0.00005. (**b**) Images of cells treated with indicated conditions were captured every hour for 24 h. inhibitor, TGF\(\beta\)RI. Data from 15 images over 3 experiments and bars show \(\pm\)SD.  
of the four DHC-derived morphological features from images of these cell lines to the NMuMG-trained EMP classifier yielded mixed results (Figure 5). The NMuMG cells scored as highly epithelial, as expected. Similarly, HDF and JIMT-1 scored as highly mesenchymal and intermediate, respectively, as would be expected. However, epithelial RPTE cells scored as highly mesenchymal, whereas L929 mouse fibroblasts scored as highly epithelial, affirming that a single morphology-based EMP classifier cannot be universally applied across lines.  
Figure 4: Representative digital holographic images show the five cell lines used in the NMuMG trained EMP classifier. The cells were seeded one day prior to start of the time-lapse imaging. Images were acquired at time-point: (**a**) 48 h, (**b**) 24 h, (**c**) 24 h, (**d**) 24 h, and (**e**) 48 h of the respective time-lapses. The scale bars are 200 \(\upmu\)m.  
Figure 5: EMP scores of established cell lines using the NMuMG-trained classifier. Each dot represents the EMP score calculated from the average of all cells in an image. Fields were taken over 3 experiments.
## 2 Materials and Methods
### Artificial Neural Network
#### 2.4.2 Inputs and Outputs  
The input vector \(\mathbf{U}\) to the MLP NN will be formed by vectors of parameters \(\mathbf{p_{N}}\), \(\mathbf{p_{L}}\), \(\mathbf{p_{T}}\) obtained by the parametrization of the state responses \(N(t),L(t),T(t)\) respectively, the initial state vector \(x(0)\) and the administered leading dose size \(D\) as  
\[\mathbf{U}=\left(\begin{array}{c}\mathbf{p_{N}}\\ \mathbf{p_{L}}\\ \mathbf{p_{T}}\\ x(0)\\ D\end{array}\right). \tag{19}\]  
Figure 1: Structure of the considered multilayer perceptron artificial neural network.  
If \(n_{p}\) is the dimension of \(\mathbf{p}_{N}\), \(\mathbf{p}_{L}\), \(\mathbf{p}_{T}\) according to (14), then \(\mathbf{U}\in\mathbb{R}^{3n_{p}+5}\).  
The reason for providing the information on \(x(0)\) is to ensure parameter estimation under the assumption of an arbitrary initial state. By taking the initial state into account explicitly, the MLP NN will be capable of separating its effect on the state responses from the effect of parametric variations.  
It is important to mention that by providing the dose size \(D\) and the initial state \(x(0)\), which involves the initial drug amount \(u(0)\), the information on the chemotherapy administered within the current and previous dosing periods (cycles) also enters the personalization. A significant (nonzero) dose \(D\) is mandatory to properly capture the effects of chemotherapy. This enables the parameters \(k_{N}\), \(k_{L}\), \(k_{T}\) to be estimated.  
The model personalization itself consists of evaluating vector \(\mathbf{U}\) by the MLP NN to provide vector \(\hat{\mathbf{\xi}}\in\mathbb{R}^{m}\) representing the estimate of \(\mathbf{\xi}\). Subsequently, the estimate of model parameter vector \(\hat{\mathbf{\theta}}\) is determined from \(\hat{\mathbf{\xi}}\) according to (8).  
The MLP NN can be seen as a nonlinear multivariate vector function \(\mathcal{N}:\mathbb{R}^{3n_{p}+5}\rightarrow\mathbb{R}^{m}\)[53].  
\[\hat{\mathbf{\xi}}=\mathcal{N}(\mathbf{U})=\left[W^{o}\text{tanh}\Big{(}W^{h}\mathbf{U}+ \mathbf{b}^{h}\Big{)}+\mathbf{b}^{o}\right], \tag{20}\]  
where \(\text{tanh}(\mathbf{x}):\,\mathbb{R}^{n_{n}}\rightarrow\mathbb{R}^{n_{n}}\) is a vectorized hyperbolic tangent function, \(W^{h}\in\mathbb{R}^{n_{n}\times 3n_{p}+5}\), \(W^{o}\in\mathbb{R}^{m\times n_{n}}\) are the matrices of weights in the hidden and the output layer, respectively, and \(\mathbf{b}^{h}\in\mathbb{R}^{n_{n}}\), \(\mathbf{b}^{o}\in\mathbb{R}^{m}\) are the vectors of biases in the hidden and the output layers, respectively. The number of neurons in the hidden layer \(n_{n}\) was determined based on the heuristic rule of thumb \(m<n_{n}<3n_{p}+5\), which is satisfactory for \(n_{n}=\frac{m+3n_{p}+5}{2}\).
## 4 Results and Discussion  
In order to check the effect of the three conditions (no force feedback, realistic force feedback, and exaggerated force feedback), we performed a one-way Analysis of Variance (ANOVA) on the average of 15 users. The analysis revealed a significant effect in three conditions with \([F(2,15)=39.9\), \(p<0.01]\). The \(p\)-value corresponding to the \(F\)-statistic of the one-way ANOVA is lower than 0.01, which strongly suggests that one or more pairs of treatments are significantly different. Therefore, we can conclude from the ANOVA results that haptic feedback have an effect on operators' performance in terms of handling qualities. ANOVAs do not tell us where the significant difference lies, so we have treatments, for which we shall apply [PERSON]'s HSD (Honest Significant Difference) test to each of the three pairs to pinpoint which of them exhibits a statistically significant difference. Table 2 shows the results of the one-way ANOVA.  
We first found the critical value of the Tukey HSD \(Q\) statistic based on \(k=3\) treatments with \(v=28,602\) degrees of freedom for error with \(\alpha=0.01\) and \(\alpha=0.05\) in the studentized range distribution [28]. The critical values obtained for \(Q\) for \(\alpha=0.01\) and \(\alpha=0.05\) are: \(Q_{critical}^{\alpha=0.01,k=3,v=28062}=4.1208\), and \(Q_{critical}^{\alpha=0.05,k=3,v=28062}=3.3147\). We will check \(Q_{statistic}>Q_{critical}\). The highlighted results (boldface means significant effect) from Tukey HSD are represented in Table 3. On the basis of [PERSON]'s HSD results, we can emphasize that the maximum operator's performance difference is between the scenarios of a no force feedback and exaggerated force feedback.  
The results of the ANOVA and [PERSON]'s HSD suggest that the operator's performance in the altitude hold task varied for different testing scenarios with a maximum difference between the no force feedback and exaggerated force feedback conditions, as presented in Table 3. We analyzed the mission data of all the operators, as shown in Figure 8, for three force conditions, i.e., no force feedback (force vector (0, 0, 0)), realistic force feedback (force vector (0, \(y\), 0)), and exaggerated force feedback (force vector (0, 2\(y\), 0)). We divided the graph on the basis of time intervals and altitude values, as discussed in Section 3 to analyze the operator's performance with respect to rise time, peak time, settling time, overshoot, and time elapsed in the tolerance band, which gave us an insight into our system's stability and performance.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 5 Influence of Design Parameters on EMC
### Conductor Placement and Impregnation  
The placement of the conductors in the slot is very important to model the CM current and voltage, as the skin and proximity effects have a great impact on the voltage distribution of the windings and the insulation stress of the conductors [39; 99]. The further the winding conductors are from the rotor, the lower winding-to-rotor capacitance will be, and the lower the bearing currents will be [53].  
Concerning to the orientation of the conductors with respect to the leakage flux lines, in [66; 99] it is found that the lowest copper losses are generated when the strands are aligned in the direction of the leakage flux lines. Then, a bit higher losses occur if strands are arranged in compact bundles and significantly higher losses occur when the strands are aligned in perpendicular to the flux lines. The voltage level also influences the copper loss. For the same power, the lower is the voltage, the higher is the AC loss due to the increased cross-section of the conductor to carry higher currents.  
Moreover, it is found that lower capacitance is obtained if the conductors are located near the middle of the slot instead of near the tooth edge, as shown in Figure 10. Equally, the slot shape may also affect winding-to-rotor capacitance and the shaft voltage, as it directly affects to the conductor position [12; 57].  
Moreover, it is found that a small quantity of impregnation decreases the stray capacitance, but this may affect to the thermal and mechanical performance as shown in Figure 10, so an equilibrium must be found [57]. [PERSON] et al. make an analysis to see the influence of the insulation in the whole capacitance matrix of a slot. The capacitive coupling is proportional to the amount of dielectric material in the slot.  
Taking this to a more practical approach, in [44] an optimization algorithm is proposed to improve the EMC behavior of DC machines modifying the winding insulation thickness, material and the placement of the conductors, obtaining close results. This must be the objective when designing electrical motors for EMC optimization, but a broader analysis must be made to consider all influencing parameters.  
Figure 10: Winding position and impregnation.
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 4 Proposed Algorithm for Dynamic UE Scenario
### The Proposed DDPG-Based Algorithm  
Note that value-based algorithms (i.e., Q learning) train discrete action spaces only; however, in this work, as the action space is continuous, these algorithms cannot be applied. Moreover, although policy-based algorithms (i.e., the policy gradient) can handle continuous or discrete action spaces, they still have limited convergence performance. Thus, to overcome these limitations, the actor-critic (DDPG) learning algorithm is adopted in this work to handle the designed optimization problem efficiently with continuous spaces.  
As a model-free, online, off-policy reinforcement learning approach, the DDPG algorithm learns and trains two main deep neural networks, namely the actor-network and the critic network, as shown in Figure 3a. Specifically, the actor-network is a deterministic policy network, \(\mu(s;\theta_{\mu})\), trained to choose the optimal action for a current state with the parameters \(\theta_{\mu}\). The critic network \(Q(s,a;\theta_{q})\) evaluates the performance of the actor's output by the estimated Q-value. Therein, the network parameters \(\theta_{\mu},\theta_{q}\) are updated during training by a deterministic policy gradient algorithm and gradients obtained from the temporal difference (i.e., TD) error signal, respectively. Since the DDPG aims to approach the optimal output Q-value without knowing the actual target value, it may cause divergence in the Q-network. To tackle this issue, two copies of networks are created for both the actor and critic networks, respectively, which are called target networks and responsible for target values dented as \(\mu^{\prime}(s;\theta_{\mu^{\prime}})\), and \(Q^{\prime}(s,a;\theta_{q^{\prime}})\), respectively. The target networks share an identical structure with the original training networks, but the parameters are different. Additionally, the DDPG algorithm connects with a replay memory to store all trained experiences in the form \(\{s_{t},a_{t},r_{t},s_{t+1}\}\). This constructed memory is accessed during network updates by a random mini-batch that helps to eliminate the correlation between samples. Moreover, to guarantee exploration performance with the learning deterministic policy, the DDPG employs an exploration policy on the selected actions during the training process. Generally, to construct the exploration policy \(\widetilde{\mu}\), the noise sampled from a noise function \(\mathcal{N}\) is added to a chosen action, as follows:  
\[\widetilde{\mu}(s_{t})\ =\mu(s_{t};\theta_{\mu}\ )+\mathcal{N}_{t}. \tag{21}\]
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 4 Results and Analysis  
This section presents the distribution of the collected dataset. The Kolmogorov-Smirnov test statistic was used to quantitatively expand upon the hypothesis of the proposed framework and predict the likely zone classes from the dataset where the highest accuracy improvement would be expected. The later subsections provide results of the cascaded neural network cross-entropy for different hyperparameters and give the accuracy improvement results for the RSSI-and-device-pose framework.
###### Abstract
Therefore, it is necessary to apply an appropriate AKF algorithm to enhance the carrier tracking's performance under weak signals and high dynamic conditions. In this research, we propose an AKF carrier tracking approach for weak signals and high dynamic conditions. The adaptive factors are analyzed and designed to adjust the process noise covariance matrix \(\mathbf{Q}_{k}\) adaptively, which can compensate for modeling errors due to the inaccurate \(\mathbf{Q}_{k}\) matrix. Moreover, this algorithm introduces the chi-square method to detect the system's stability. The adaptive factor is used only when the system is not steady. Based on the GPS L1 software receiver experiment results, the proposed AKF tracking shows higher robustness of tracking performance under signal fading and high dynamic conditions. The other sections are structured as follows. Section 2 illustrates the model of KF-based carrier tracking. Section 3 introduces the proposed AKF carrier tracking loop. Then, the experiment results and analyses are demonstrated in Section 4 and the proposed AKF tracking's performance is validated. Finally, Section 5 draws conclusions.
# LogInjector: Detecting Web Application Log Injection Vulnerabilities
## 6 Evaluation
### Effectiveness
#### 6.2.1 Known Vulnerabilities Verification  
In order to verify the effectiveness of LogInjector, we first use LogInjector to test web applications with known log injection vulnerabilities. The web applications, version numbers, and vulnerability numbers corresponding to these known vulnerabilities are shown in Table 5. The experimental results show that LogInjector can detect all the vulnerabilities in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Web Application** & **Version** & **ID** & **Web Application** & **Version** \\ \hline
1 & Contao [3] & 4.9.5 & 8 & easy-digital-downloads [38] & 2.11.5 \\
2 & SuiteCRM [39] & 7.11.19 & 9 & ZenCart [40] & 1.5.5e \\
3 & webmin [4] & 1.941 & 10 & AbanteCart [41] & 1.3.2 \\
4 & PHFPusion [1] & 9.03.20 & 11 & MaxSite CMS [42] & v108 \\
5 & DSGVO All in one for WP [43] & 3.9 & 12 & SuiteCRM [39] & 7.11.23 \\
6 & WP Photo Album Plus [44] & 8.0.9 & 13 & osCommerce [45] & 2.3.4.1 \\
7 & BWP Google XML Sitemaps [46] & 1.4.1 & 14 & CE Phoenix [47] & 1.0.8.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Web Application** & \
\begin{tabular}{c} **Detectable** \\ **by LogInjector** \\ \end{tabular} &
\begin{tabular}{c} **Vulnerability** \\ **Type** \\ \end{tabular} & **VUL-ID** \\ \hline Contao 4.9.5 & Yes & XSS & CVE-2021-35210 \\ \hline SuiteCRM 7.11.19 & Yes & RCE & CVE-2021-42840 \\ \hline webmin 1.941 & Yes & XSS & CVE-2020-8820 \\ \hline PHFPusion 9.03.20 & Yes & XSS & CVE-2020-17449 \\ \hline ZenCart 1.5.5e & Yes & RCE & CVE-2017-11675 \\ \hline DSGVO All in one for WP 3.9 & Yes & XSS & CVE-2021-24294 \\ \hline WP Photo Album Plus 8.0.9 & Yes & XSS & CVE-2021-25115 \\ \hline BWP Google XML Sitemaps 1.4.1 & Yes & XSS & CVE-2022-0230 \\ \hline Easy-digital-downloads 2.11.5 & Yes & XSS & CVE-2022-0706 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of public vulnerabilities.  
LogInjector can accurately identify that SuiteCRM and webmin use files to record logs, and the other seven web applications use databases to record logs. By constructing a specific input to the web form, LogInjector can discover the log-injectable interfaces of the web application in Table 5. Combining the dynamic crawler and the static features of the view-logs interfaces, LogInjector can identify the view-logs interfaces of the web application in Table 5 and further use the dynamic testing method based on feedback-guided mutation to bypass the input-filter checks of these web applications.