esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measuring the Effect of the DoS Attack on 3G Networks
We realized that blocking unnecessary TCP traffic from the Internet to 3G subscribers' IP subnets decreases not only the resource usage on cellular network devices but also the number of paging attempts on the RNC. However, we did not apply the UDP based access restriction on our Internet router due to the router's limited filtering capability. In order to see the effects of UDP based DoS/DDoS or flooding attacks from the Internet, we performed a UDP based attack at midnight because of the smallest data and voice load on cellular network at that time. The CPU usage and  
Figure 14: RNCs CPU usage during the UDP based attack. Figure 8.0 Circuit switched (CS) paging attempts on the RNC during the UDP based attack. number of PS paging attempts of the RNC was dramatically affected, although the attack was not sophisticated and it was not performed during peak hours. All these results show that mobile cellular networks are more vulnerable against unwanted bulk traffic caused by DoS/DDoS or flooding attacks. As we discussed before, the RNC is one of the most critical device in a mobile network. It is related to both data and voice services. Hence, both voice and data subscribers will be affected if the RNC is out of service due to a CPU overload caused by high traffic resulting from attacks or malicious software [24] such as viruses [25], worms, _etc._
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 2 Methods
### Error Reduction Method by CNN  
The raw-data output of an IMU (with both deterministic and random errors) is fed as input to the CNN. Figure 3 shows a block diagram of the new methodology introduced in this work. The IMU raw data is divided into step pieces by time divisions. At each time division, the IMU output data can be approximated as having a near constant acceleration and angular velocity value, as long as the time divisions are small enough. The data piece from each time division is then fed to a trained CNN. By this strategy, the output data plots can be mapped into different acceleration and angular velocity classes. Finally, the network sends out the filtered acceleration and angular velocity classes and those classes are used to compute the actual system position and orientation information.
## 2 Material and Methods
### Serum Cytokines
#### 2.3.3 Data Processing
After filtering out proteins classified as only by site, contaminants, and reverse hits, group LFQ intensities (LFQ intensities of proteins aggregated across all replicates within a specific experimental group) were log-2-transformed, and the quantitative profiles were filtered from missing values with a minimum valid number of (number of replicates/2) + 1 in at least one group. Missing values in the MaxQuant output (below the DDA intensity thresholds of 300 for MS1 and 200 for MS2) were imputed from a random sampling of the Gaussian distribution with mean 1.8 standard deviations less than the population mean of all unimputed measurements and within \(\pm\)0.3 standard deviations from this mean, as per the previously determined ideal parameters for LFQ based studies [19]. This imputation strategy aligns with the expected behavior of missing values in LFQ experiments, where missing data typically represent low-abundance proteins or peptides below the detection limit. By sampling from a Gaussian distribution centered 1.8 standard deviations below the mean, the imputed values reflected data variability and accounted for lower-intensity missing values. The \(\pm\)0.3 standard deviation range ensured that the imputed values remained within a realistic intensity range, preventing unrealistic outliers. This method, informed by empirical studies that identified this as an effective estimate for missing LFQ data, preserved the overall data distribution and minimized biases from missing data [19]. Log ratios were calculated as the difference in average log 2 LFQ intensity values between the tested conditions. Inter-group changes were evaluated by a two-tails Student's \(t\) test at a significance of 0.05 using a permutation-based False Discovery Rate (FDR) control. This is a robust non-parametric method for controlling FDR that adapts well to datasets with small sample sizes or complex variance structures. By using an FDR threshold (e.g., 5%), permutation-based control ensured that the proportion of false positives among significant findings remained low, providing reliable results for biological interpretation. An average of 170,548 peptides were identified per sample (ranging from 131,780 to 249,508), with 4292 unique peptides common to all samples. The quality of the LC, MS, and data analysis was controlled a posteriori by analyzing the retention time, chromatographic peak width, precursor charge, mass precision, abundance, missed cleavages, and peptide identification per group, across all identified peptides (Figure S1; see Supporting Information). Identified protein groups are listed in Supplementary File S1 (see Supporting Information). A schematic overview of the methodology presented in Sections 2.3.1-2.3.3 is presented in Figure 2, illustrating the sequential steps of the experimental workflow. The process began with protein precipitation from patient serum samples, followed by downstream processing and resulting in the analysis of the acquired data.
# Diffusion and Consolidation of Slag-Based Geopolymer for Concrete Payment Rehabilitation
## 3 Results and Discussion
### Microscopic Test  
#### 3.5.1 Microscopic Morphology and Reactants of Geopolymer Slurry  
The microstructure and reactants of the slag-based geopolymer were observed under \(100\times\), \(500\times\), \(1000\times\), and \(2000\times\) magnification, as shown in Figure 14. In Figure 14a, under \(100\times\) magnification, due to the slightly higher water content of the geopolymer slurry, the hydration and dissolution reaction were promoted, but the pores of the structure increased and the density of the structure decreased. In Figure 14b, under \(500\times\) magnification, it could be seen that the slag-based geopolymer had a little shrinkage, and shrinkage cracks appeared locally on the surface of the geopolymer, but the size was not large. In Figure 14c, under \(1000\times\) magnification, the reactants of the geopolymer material were mainly gel-phase material, but, due to the slightly higher water content, the density of the local geopolymer was slightly lower. In Figure 14d, under \(2000\times\) magnification, the polymerization reaction of the geopolymer was relatively complete, and no unreacted sheet-like calcium hydroxide crystals were observed.  
The types and contents of elements in the geopolymer reactants were analyzed using EDS by selecting geopolymer samples from the consolidated core area. The SEM images and EDS spectra under \(1000\times\) magnification are shown in Figures 15-17. It could be seen that a large number of gel-phase products were generated, mainly C-S-H gel. There were micro-cracks on the surface of the slag-base geopolymer locally, but, as the particle size and porosity of the aggregate increased, the consolidation of the geopolymer slurry became better. From the EDS spectrum, it could be seen that the main elements in the hydration products of the slag-based geopolymer were Ca, Si, Al, Mg, C, O, etc.  
Figure 14: The reactants and microstructure of slag-based geopolymer. (**a**) \(100\times\); (**b**) \(500\times\); (**c**) \(1000\times\); and (**d**) \(2000\times\).
## 5 Result and Discussion  
Predictions of dog sounds were made on the basis of context for the Rajapalayam and Kombai dogs. The classification and prediction of the dog sounds using the Q-learning algorithm and k-nearest neighbor produced improved results compared to the existing methods. This method consists of predictor variables such as energy, loudness, band density, energy diff, density diff, pitch min, pitch max, and pitch slope for the determination of dog contexts and sex factors. The prediction levels of the dogs during different scenarios were analyzed. The prediction accuracies for the dogs using the k-nearest neighbor model were 92% in female dogs and 85.7% in male dogs. Table 4 shows the prediction results of dog sounds based on the proposed approach.  
The confusion matrix describes the performance of the predicted sound when it is given as an input. Correct and incorrect values are presented in the confusion matrix, which summarizes the prediction results of the dog sounds. Two different dog species were taken for the study of sounds in five different scenarios. These predictions have improved results, because the machine learning features were updated based on changes in the environment. The confusion matrix of dog sound prediction is shown in Table 5.  
The prediction analysis made of the sounds of the two dog species chosen for study and the results are presented in Table 4. The proposed approach classifies the dog sounds in relation to the sound produced by individuals based on the context in which it was made. The prediction level varies because the clarity of the dog sounds depends on the surrounding environment. The proposed approach also helps us to explore the characteristics of the dog sounds.  
A comparison was made to analyze the predictions of the dog sounds based on the training given to the dogs in the different contexts. Results are described in Figure 8, which clearly shows the improved performance in the dog sounds through training based on different contexts. The machine learning approach has helped to classify different types of sound, and with further training improved performance was achieved.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Prediction (\%)**} \\ \cline{2-3}  & **Rajapalayam Dog** & **Kombai Dog** \\ \hline Stranger Walk & 87.00 & 88.42 \\ Stranger Run & 87.85 & 85.98 \\ Play with Ball & 81.53 & 82.08 \\ Play with Owner & 83.95 & 85.52 \\ Alone & 83.63 & 85.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall prediction level.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Predicted Actual**} & **Actual Sounds** & **Stranger Walk** & **Stranger Run** & **Play with Ball** & **Play with Owner** & **Alone** & **Prediction (\%)** \\ \hline Stranger Walk & 200 & 171 & 21 & 3 & 0 & 0 & 97.50 \\ Stranger Run & 220 & 20 & 186 & 8 & 0 & 0 & 97.27 \\ Play with Ball & 140 & 0 & 9 & 108 & 15 & 0 & 94.28 \\ Play with Owner & 160 & 0 & 4 & 23 & 130 & 0 & 98.12 \\ Alone & 120 & 0 & 0 & 10 & 7 & 95 & 93.33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix of the dog sounds predicted.
## 4 Experimental Analysis
### Experimental Results and Analysis
#### 4.3.5 Ablation Experiments  
1. Comparison of accuracy under different layer feature fusion structures  
To illustrate the role of multi-scale feature fusion, experiments were conducted on networks with different feature fusion structures. Table 6 presents the accuracy of the networks without feature fusion scales R1, R2, R3, and R4, respectively. During testing, the connections of the corresponding scales in the concat layer in Figure 6 were selectively disconnected.  
From Table 6, it can be observed that compared to models with full-scale feature fusion, the absence of any feature fusion structure leads to a certain degree of reduction in rejoining accuracy. This indicates that each intermediate feature layer contributes to the conjunction accuracy. Therefore, the superiority of the multi-scale fusion approach proposed in this paper has been validated.  
2. Validation of SPADE to guide edge contours  
To validate the effectiveness of SPADE guidance, we compared networks utilizing the SPADE model for contour guidance with the original backbone feature extraction network without the SPADE model. As shown in Table 7, compared to the network without the SPADE model, the proposed MFS-GC model in this study preserves the contour features of the bone stick fragment images, resulting in an improvement of 6.0% in rejoining accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Fusion Architecture** & **Accuracy** & **Missed Detection** & **Time** \\ \hline Without R1 & 91.63 & 8.37 & 32.91 \\ Without R2 & 87.46 & 12.54 & 27.31 \\ Without R3 & 81.33 & 18.67 & 22.68 \\ Without R4 & 74.02 & 25.98 & 19.34 \\ Only scale4 & 69.96 & 30.04 & 10.66 \\ MFS-GC & 95.50 & 4.50 & 35.63 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison under different feature fusion structures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy (\%)** & **Precision** & **Time (s)** \\ \hline Literature [28] & 76.4 & 0.16 & 44.86 \\ Literature [29] & 83.6 & 0.224 & 23.7 \\ Literature [12] & 89.8 & 0.289 & 42.62 \\ MFS-GC & 95.5 & 0.356 & 34.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different literature algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Top-T** & **Literature [28]** & **Literature [29]** & **Literature [12]** & **MFS-GC** \\ \hline Top-1 & 31.5 & 38.0 & 53.5 & 64.0 \\ Top-5 & 48.5 & 55.5 & 69.0 & 75.5 \\ Top-10 & 69.0 & 72.5 & 80.5 & 89.0 \\ Top-15 & 76.4 & 83.6 & 89.8 & 95.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of accuracy at different Top-T (%).
## 5 The Query Management Scheme
### The Query Transmission Phase (QTP)  
When users send queries to the QM, the QM sequentially assigns an ID to each query and goes through the network dictionary to determine which nodes to forward the queries to. The QM generates a collection of queries targeted to each sensor node (called a \"query set\") and routes these query sets to each of the target nodes. Queries are collected as much as each node can transmit at a time. If there aren't as many queries, the QM can periodically formulate a query set and transmit it to the network. In Figure 5, \(S_{i}\), \(D_{i}\), \(T_{i}\) (i = 1, 2,..., N) denote the query's start time, deadline and response period.
## 1 Introduction
[PERSON] [42] proposed a multi-factor relationship, in which the normalized net value of the corrected first pressure reading (\(p_{o}-u_{o}\))/\(\sigma^{{}^{\prime}}v\) and the normalized net value of the corrected second pressure reading (\(p_{1}-u_{o}\))/\(\sigma^{{}^{\prime}}v\) were used as independent variables. It should be noted that the regional geotechnical conditions could have substantial influence on the empirical relationships and the values of empirical coefficients [43; 44; 45; 46; 47; 48]. It is important to point out, that the shear wave velocity \(V_{s}\) as an independent variable was not used so far to evaluate the undrained shear strength from the dilatometer test. This paper presents the results of seismic dilatometer tests SDMT and laboratory tests of preconsolidated cohesive soils (Pleistocene clays and Pliocene clays). The undrained shear strength in cohesive soils is evaluated based on measurements obtained from the dilatometer test using single- and multi-factor empirical relationships presented in the literature. However, the empirical relationships sometimes show relatively high values of the maximum relative error max \(RE\). In this study a method for evaluating the undrained shear strength of preconsolidated cohesive soils using statistical analysis based on data obtained from a seismic dilatometer test is presented. The presented multi-factor empirical relationship predicts the normalized undrained shear strength based on two independent variables: The normalized difference between the corrected second pressure reading and the corrected first pressure reading (\(p_{1}-p_{o}\))/\(\sigma^{{}^{\prime}}v\) and the normalized shear wave velocity \(V_{s}\)/100. The proposed multi-factor empirical relationship provides a more reliable prediction of the undrained shear strength in comparison to the empirical relationships presented in the literature, with a max \(RE\) at about \(\pm\)20% and the mean relative error \(RE\) at about 8%.
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 6 Evaluation of Authentication Accuracy
### Experimental Results  
Figure 7a shows the relationship between the number of EM traces and the number of distinguishable provers when the previous authentication scheme was used with Dataset A. In the case of \(n=1171\), i.e., using \(1171\) EM traces, \(M=2^{50}\), which indicates that \(2^{50}\) provers were distinguishable from the previous authentication scheme. Contrary to the above result, the results obtained with the proposed scheme using Dataset B show that when \(n=908\), i.e., \(908\)-round AES, \(2^{50}\) was obtained (see Figure 7b) which means that false errors do not occur even if \(10\) million provers are authenticated twice a day for \(100\) years. Furthermore, it should be noted that the authentication time was \(0.58\) s. In addition, Figure 7c shows that \(M=2^{70}\) was obtained with \(759\)-rounds of AES. The summaries of these figures are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Leakage Model** & \(\mu_{1}\) & \(\mu_{2}\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\alpha\) & \(h\) \\ \hline A & HD Model & 0.57 & 0.00 & 0.98 & 1.00 & 0.20 & 0.29 \\ B & HD Model & 0.541 & 0.00 & 1.05 & 1.00 & 0.19 & 0.27 \\ B & XOR Model & 0.718 & 0.00 & 1.06 & 1.06 & 0.25 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimentally obtained parameters: mean values \(\mu_{1}\) and \(\mu_{2}\) and constants of proportionality \(\beta_{1}\) and \(\beta_{2}\); \(\alpha\), and \(h\).
# X-ray Image Enhancement Based on Adaptive Gradient Domain Guided Image Filtering
## 2 Related Works  
### Gradient Domain Guided Image Filtering  
The gradient domain guided image filter is proposed by incorporating an explicit first-order edge-aware constraint, and it overcomes the disadvantage of halo artifacts in the guided image filter [25]. This method is widely used in image enhancement, image fusion, and image defogging, etc. In the GDGIF method, we suppose the guidance image, input image and output image as \(G\), \(X\) and \(\hat{Z}\), respectively [25]. The \(\hat{Z}\) is defined as follows:  
\[\hat{Z}(p)=a_{p_{1}}G(p)+b_{p_{1}}\ \forall p\in\Omega_{\xi_{1}}(p_{1}) \tag{1}\]  
where \(a_{p_{1}}\) and \(b_{p_{1}}\) are constants in the window \(\Omega_{\xi_{1}}(p_{1})\), the corresponding values can be calculated by minimizing the cost function \(E\big{(}a_{p_{1}},b_{p_{1}}\big{)}\), and it is computed by:  
\[E=\sum_{p\in\Omega_{\xi_{1}}(p_{1})}\bigg{[}\big{(}a_{p_{1}}G(p)+b_{p_{1}}-X(p )\big{)}^{2}+\frac{\lambda}{\hat{\Gamma}_{G}(p_{1})}\big{(}a_{p_{1}}-\gamma_{p _{1}}\big{)}^{2}\bigg{]} \tag{2}\]  
\[\gamma_{p_{1}}=1-\frac{1}{1+e^{\eta(\chi(p_{1})-\mu_{\chi,\infty})}} \tag{3}\]  
\[\eta=\frac{4}{\mu_{\chi,\infty}-\min(\chi(p))} \tag{4}\]where \(\hat{\Gamma}_{G}(p_{1})\) depicts an edge-aware weighting, and it is computed by the following:  
\[\hat{\Gamma}_{G}(p_{1})=\frac{1}{N}\sum_{p=1}^{N}\frac{\chi(p_{1})+\varepsilon}{ \chi(p)+\varepsilon} \tag{5}\]  
where \(\chi(p_{1})\) is defined as \(\sigma_{G,1}(p_{1})\sigma_{G,\xi_{1}}(p_{1})\).  
The optimal values of \(a_{p_{1}}\) and \(b_{p_{1}}\) can be defined as follows:  
\[a_{p_{1}}=\frac{\mu_{G\Theta\chi,\xi_{1}}(p_{1})-\mu_{G,\xi_{1}}(p_{1})\mu_{X, \xi_{1}}(p_{1})+\frac{\lambda}{\Gamma_{G}(p_{1})}\gamma_{p_{1}}}{\sigma_{G, \xi_{1}}^{2}(p_{1})+\frac{\lambda}{\Gamma_{G}(p_{1})}} \tag{6}\]  
\[b_{p_{1}}=\mu_{X,\xi_{1}}(p_{1})-a_{p_{1}}\mu_{G,\xi_{1}}(p_{1}) \tag{7}\]  
The final value of the output image \(\hat{Z}(p)\) can be calculated by the following:  
\[\hat{Z}(p)=\overline{a}_{p}G(p)+\overline{b}_{p} \tag{8}\]  
where \(\overline{a}_{p}\) and \(\overline{b}_{p}\) represent the mean values of \(a_{p_{1}}\) and \(b_{p_{1}}\) in the window, respectively. The corresponding formulas can be defined as follows:  
\[\overline{a}_{p}=\frac{1}{\left|\Omega_{\xi_{1}}(p)\right|}\sum_{p_{1}\in \Omega_{\xi_{1}}(p)}a_{p_{1}} \tag{9}\]  
\[\overline{b}_{p}=\frac{1}{\left|\Omega_{\xi_{1}}(p)\right|}\sum_{p_{1}\in \Omega_{\xi_{1}}(p)}b_{p_{1}} \tag{10}\]  
where \(\left|\Omega_{\xi_{1}}(p_{1})\right|\) represents the cardinality of \(\Omega_{\xi_{1}}(p_{1})\).
# Advanced Servo Control and AI Integration in 3-DoF Platforms for Enhanced Simulation Interactivity
## 1 Introduction
To address these challenges, iterative learning control (ILC) is often used to improve transient system performance and eliminate periodic nonlinear disturbances. ILC adjusts the compensation amount based on the system's error, without needing to know the various parameters of complex systems beforehand, making it suitable for nonlinear, time-varying, and complexly modeled systems [17]. This paper proposes a novel three-degree-of-freedom platform system for an interactive shooting simulator, combined with iterative learning theory to enhance operational performance. The system dynamically adjusts based on error and is capable of eliminating periodic nonlinear disturbances without measuring complex system parameters. The core control uses a 200 MHz DSP clock, providing participants with an immersive experience of controlling gun turrets and shooting freely, enhanced by the Oculus Quest VR gaming device running Unity-based VR shooting games. To the best of our knowledge, this is the first approach that introduces the concepts discussed in our paper, emphasizing their novelty and originality, as they have not been covered in previous publications [8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. The main contributions of this paper include the following:  
1. Development of an innovative 3-DoF shooting platform integrating VR for immersive simulation training. 2. Implementation of ILC to dynamically adjust system performance, mitigating periodic nonlinear disturbances. 3. Integration of advanced visual effects and interactive elements for realistic and engaging user experiences. 4. Utilization of AI-controlled aircrafts to enhance realism in interactive mechanisms. The paper is structured as follows: Section 2 covers the control system design, including conventional and proposed servo control methods. Section 3 elaborates on the design of AI for enemy aircrafts. Section 4 provides a comprehensive system overview. Section 5 presents experimental setups and results, while Section 6 concludes the paper, highlighting future research directions.
## 1 Introduction
### Problem Context  
In this work, we consider a scenario comprising a single-antenna transmitter and receiver, focusing on the channel estimation problem with fractional delays and Doppler shifts and no prior information about the number of propagation paths [8; 9]. The transmitter sends a pilot symbol in the delay-Doppler (DD) domain, while the receiver processes the received pilot frame to extrapolate the knowledge of the channel parameters.  
The channel estimation problem is often considered in the _integer DD scenario_, which is facilitated by the assumption of having a channel with delays and Doppler shifts that are multiples of delay and Doppler resolutions. In this case, there is no interference between adjacent DD bins, and a simple threshold method can be used to obtain good performances, as shown in [10]. On the other hand, in reality, this assumption may not hold, which motivates the study of _fractional DD scenario_, which is more challenging, since the received replica of the pilot symbol in the DD domain spreads into adjacent bins after 2D sampling of the DD domain [7; 11]. This is the well-known inter-path interference phenomenon. Our focus is on the effects of IPI caused by fractional DDs and IPI cancellation to recover channel state information (CSI) with high accuracy.