esa-sceva/satcom-chunk-collection huggingface dataset:
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 1 Introduction
### Calculation Results  
(1) Settlement calculation results  
Figure 7 presents the calculation results of vertical settlements of the whole simulation model. It is shown that the settlement of the bridge causes by the tunnel construction starts from foundation excavation step and slowly increases till the piles foundation underpinning construction is completed. In the schemes of piles foundation underpinning plan, the maximum settlements of the bearing platform are about 5.73 mm and 5.71 mm.  
In Figure 8 it presents the simulation model vertical settlements calculated results on the bridge pier point 1 and point 2. As it is shown that the bridge pier point 1 settlement increases from 3.51 mm to 4.71 mm after pier foundation underpinning constructions completed. Before the shield tunnel crosses through, the bridge pier settlement increases slowly. While the shield machine crossed through the foundation pile area, settlement on bridge pier obviously increases and reaches the maximum value, which is 5.28 mm. When compares with the settlements of point 2, they both show the same tendency.  
Figure 6: Sketch of shield excavation loads.  
Figure 8: Vertical displacement of the existed pile cap.  
Figure 7: Vertical displacement of the numerical model.  
In Figure 9, it presents the underpinning bearing platform settlements calculation results. As it shown the four measuring points settlement results were similar, the maximum settlements were about 6.81 mm.  
(2) Stress calculating results  
As it presented in Figure 10, it shows the stress calculation result of the numerical model during the whole shield tunnel construction.  
From the calculation result it can be seen that the underpinning platform concrete maximum tensile and compressive stress occurs at the foundation completed stage. After the prestressing system is established, the maximum tensile stress of the Sections 1-1 and 2-2 in the calculation model sharply reduces from the highest 1.12 MPa to the lowest 0.184 MPa due to the prestressing effect at the bottom of the bearing platform. Obviously, the prestressing system is conducive to control the bearing platform structure stress and prevent the underpinning bearing platform from cracking destruction under the superstructure loads. By comparing the bearing platform tensile and compressive stress calculation results that shown in Figure 10a,b the stress changing tendency are highly similar. The calculated maximal principle compressive stress of the bearing platform in Sections 3-3 and 4-4 were reduced from \(-6.89\) MPa to \(-5.10\) MPa.  
Furthermore, the calculated results also shows that the shield tunnel construction will not cause significantly stress variations of the underpinning bearing platform. While the shield tunnel crosses through the original bridge piles foundation area, the bearing platform  
Figure 9: Vertical displacement of the underpinning pile cap.
## 4 Results and Discussions  
### Steady-State Operation  
A plot of Y versus t for No. 1 showing the variation in measured data with time is shown in Figure 5, wherein Y is defined as the ratio of the measured value to the initial value or the setting value. The figure shows that CO\({}_{2}\) concentration, pH, liquid temperature, gas temperature at the inlet, and gas temperature at the top of the scrubber were maintained constant after 30 min. This variation showed that the system reached a steady-state condition. Due to this, the measured data could be used to evaluate the values of indexes. Calculated data for \(E_{F}\), \(R_{A}\), \(K_{G}a\), and \(\phi\) are listed in Table 4. The range of data was 56.58-100.0%, 4.42 \(\times\) 10\({}^{-4}\)-18.95 \(\times\) 10\({}^{-4}\) mol s\({}^{-1}\) L\({}^{-1}\), 0.1195-0.9139 s\({}^{-1}\), and 0.0433-0.2923 mol-CO\({}_{2}\) mol-solvent\({}^{-1}\) L\({}^{-1}\) for \(E_{F}\), \(R_{A}\), \(K_{G}a\), and \(\phi\), respectively. The steady-state \(pH\) values were 9.60-11.42, depending on the operating conditions. In addition, \(\gamma\)-values were in the range of 0.2942-2.3056. All data were analyzed to search further for the optimum mixed amine.  
Figure 4: Experimental device for CO\({}_{2}\) capture test.
## 6 Analysis
### DR Complexity Analysis  
One could assume that a potential advantage of introducing composite features into the terminal set could be the generation of smaller expressions. To analyse this, Figure 5 presents a scatter plot in which the fitness of the DR (x-axis) is plotted against its size (y-axis) for each terminal set used. The figure illustrates that the solutions are very widely scattered and only a very small number of rules below size 30 were obtained. Furthermore, the smaller rules usually did not perform well, often reaching a Twt value of over 16. It seems that the generated DRs started to perform well at around size 35. However, the best overall rule had a size of more than 50, and some other well-performing rules also had sizes of more than 50 nodes. To determine whether there was a correlation between the rule size and its performance, [PERSON]'s rho test was used. Since values between \(-0.15\) and \(0.1\) were obtained for the results of all phases, we can conclude that there was no obvious correlation between these two measures. Although it may seem counterintuitive as to why smaller expressions were not obtained for composite terminal nodes, as more complex expressions should be represented by a smaller number of nodes, this was most likely due to the bloating of GP, and could be mitigated by using different mechanisms to control the bloating [62; 63].
## 2 Materials and Methods
### Data Preprocessing  
After assembling the dataset, data preprocessing was required before testing the ML models. As previously stated, the dataset consists of both numerical and categorical features. To use the categorical features as inputs, they were encoded into numerical values using the one-hot encoding technique, where each categorical feature was represented by 0 for its absence and 1 for its presence [29]. For example, if glucose was used in a sample, it would be represented by 1, while all other carbon sources would be represented by 0.  
Additionally, the dataset contained some missing values that were not due to the different experimental durations but rather to normal experimental unexpected issues. Therefore, value imputation was necessary for the analysis, as most models cannot handle missing data. To select the most appropriate imputation method, 5% of the known values of the dataset were randomly removed, and then several methods were used to predict the removed values. These methods included simple and commonly used techniques, such as the mean and median, as well as machine learning algorithms like k-Nearest Neighbor, RFs, Decision Trees (DTs), Bayesian Ridge, and SVMs. The performance of each method was assessed by comparing the mean squared error (MSE) [30]. Analyzing the results obtained using the different methods, the k-Nearest Neighbor regressor with two neighbors achieved the lowest MSE (42.16). Therefore, it was selected for imputing the true missing values in the dataset.  
After completing this step, the data were scaled, as not all of the features had the same order of magnitude. Scaling the data ensures that each variable contributes equally to the analysis. The z-score method was applied, which standardizes each feature to have a mean of zero and a standard deviation of one [31]. After scaling, 80% of the data (37 samples) were used for training and the remaining 20% for testing (10 samples) the classifiers.
# A Next-Generation Core Network Architecture for Mobile Networks
## 5 Basic Operations
### UE Attachment and SIP Registration  
Generally speaking, in order to get Internet access, a UE needs to be attached to and authenticated with the cellular network. For VoLTE service, it also needs to be successfully registered with the IMS (i.e., with its SIP Registrar).
## 5 Key 6G Technologies
### Improved Wireless Interface
#### 5.2.3 Revolutionizing Access: NOMA  
The transition from OFDMA in LTE to more optimized OFDM in 5G NR demonstrates the move toward orthogonal multiple access (OMA) approaches. With 6G anticipating a connection density increase of tens of times compared to 5G, non-orthogonal multiple access (NOMA) is a potential random access method, addressing requirements such as low latency, low costs, massive connectivity, high reliability, and high throughput in diverse cases [25; 33].  
First introduced by NTT DOCOMO in [118], NOMA fundamentally differs from OMA by encouraging multiple terminals to share the same radio resources in frequency, time, and/or code domains. NOMA introduces interference information at the transmitter, utilizing a successive interference cancellation receiver at the receiving end. Expected benefits include enhanced spectrum efficiency, increased system capacity, reduced latency, and reduced reliance on accurate channel state information (CSI) and feedback quality, albeit at the cost of added complexity [119; 120].  
NOMA techniques include code domain (like sparse code multiple access), power domain (like multi-user overlay coding), and interleave-based NOMA [121]. A thorough discussion of the integration of NOMA with cutting-edge wireless technologies, including mmWave, massive MIMO, VLC, energy harvesting, physical layer security, cognitive and collaborative communication, and wireless caching, can be found in [120]. The integration of NOMA with artificial noise to enhance secrecy rates and energy efficiency in massive MIMO-NOMA networks was explored in [122]. Additionally, NOMA-assisted reconfigurable intelligent surface (RIS) frameworks were proposed to enhance deployment efficiency and passive beamforming design, contributing to improved energy efficiency [123]. The fusion of NOMA with ambient backscatter for the development of systems with en -hanced spectral and energy efficiency, along with investigations into system reliability and security, was presented in [124].  
While NOMA has demonstrated significant potential and gained attention from academia and industry [119], its adoption in 5G faced technical challenges and debates. To overcome these obstacles, it will be necessary to investigate low-complexity but high-performing, multi-user interference cancellation algorithms, improve security and trust concerns, and provide a uniform and standard NOMA framework for 6G [120].
# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 3 Methods  
While YOLOv8 demonstrates robust performance in general object detection, its direct application to underwater marine benthos detection yields suboptimal results due to significant blurring and occlusion phenomena in aquatic environments. Persistent issues of false positives and missed detections indicate considerable room for improvement in the model. To address these limitations and enhance detection efficacy in complex underwater environments, we propose the WDS-YOLO algorithm based on YOLOv8n, which incorporates three key enhancements:  
First, the cascaded convolution module WTconv based on wavelet transform was introduced to enhance the receptive field and strengthen the feature extraction capabilities of the backbone network. Second, a Deformable Attention-based SPPF (DASPPF) module was developed to dynamically capture marine benthos objects' geometric variations while suppressing irrelevant features through deformable attention mechanisms. Finally, the original neck structure was re-engineered as SF-PAFPN by integrating the Focus module with the novel CSPOKM module, enabling effective multi-level feature fusion to boost small-object detection. The complete architecture of WDS-YOLO is illustrated in Figure 2.
## 3 Results and Discussion
### Validation of the Regression Model  
The next step involves conducting validation tests to confirm the predictive capability of the equations obtained from the regression analysis [24]. For this purpose, the safety factor values obtained from simulations conducted with inputs other than the material parameters used in the Taguchi analyses are compared with the values predicted by the regression equations. Since AISI 4340 steel is commonly used in autofrettage applications, it was utilized in this validation test [28]. The material properties of AISI 4340 steel are provided in Table 13 [18].  
The safety factors calculated using numerical methods and regression equations are provided in Table 14. When comparing numerical and regression results, it is observed that there is a maximum deviation of 0.0279%. Therefore, it can be concluded that the regression model has a high predictive power.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Designation** & **Yield Strength** & **Tangent Modulus** & **Young’s Modulus** & **Poisson’s Ratio** \\ \hline AISI 4340 & 1200 (MPa) & 1489 (MPa) & 200 (GPa) & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Mechanical Properties of the AISI 4340 Steel [18].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Factor of Safety** & **Factor of Safety** & **Deviation** \\  & **(Numerical)** & **(Regression)** & **\%** \\ \hline Without machining & 1.6057 & 1.6120 & 0.3908 \\ Internal machining & 1.4322 & 1.4326 & 0.0279 \\ External machining & 1.5251 & 1.5310 & 0.3854 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison of the numeric model and regression model.
# A Parcel Transportation and Delivery Mechanism for an Indoor Omnidirectional Robot
## 3 Design of the Transportation and Delivery Mechanism
### Analysis of the Parcels Received in an Educational Building  
The parcels received in the Polytechnic School of the Universitat de Lleida (Spain) have been analyzed to guide the design of the transportation and delivery mechanism. This small-sized five-story educational building has 174 possible delivery destinations. The parcel analysis was conducted for 4 months by the staff of the building, from July to November 2023, inclusively, with the exception of the holiday period in August. Prior to starting the analysis, the parcels received were pre-screened to define some representative sizes and categories (Table 1): small envelope (SE), large envelope (LE), very small parcel (VSP), small parcel (SP), medium parcel (MP), large parcel (LP), and very large parcel (VLP). During the four months of the analysis, a total of 1070 envelopes and 727 parcels were received and classified into the nearest size category. This was documented by the janitors of the building that filled out a daily questionnaire with the size and classification of the parcels received. Table 1 shows the categories, and the total share of the parcels received [49; 50]. Large envelopes (LEs) were the most frequently received (36%), followed by small envelopes (SEs) and small and medium parcels (SPs and MPs). In general, the share of the parcels received in a specific building depends on the main activities carried out [49; 50], the period of time, the existence of holiday periods, and the existence of special purchase days. The aim of this analysis was to have a picture of the parcels received in a building where a variety of technological activities take place: mathematical analysis, electronic design and implementation, robotics, and mechanical design and implementation. The dimensions of the transportation and delivery mechanism will take the results of this analysis into account to carry as many parcels as possible.  
Figure 2: Top view of the omnidirectional mobile robot and its free navigable area required to move (blue circle and lines): (**a**) passing through a door and (**b**) rotating while moving to approach a table. The green arrows depict the orientation of the frontal part of the robot.
### Regional Structural Geology  
The Beijing-Zhangjiakou high-speed railway is tectonically located in the Zhangjiakou area on the northern margin of North China Craton, at the intersection of the Taihang Mountain uplift belt, Yanshan uplift belt and Shanxi graben, and at the western segment of the Zhangjiakou-Bohai tectonic belt (Zhangbei-Huailai segment). The tectonic environment is complex (Figure 1a). Geomorphologically, the Zhangjiakou area is divided into two completely different geomorphological units, the Bashang Plateau area and the middle and low mountain basins. The main basins include the Yanqing-Fanshan (Yanfan) Basin, Huailai-Zhuolu (Huaizhuo) Basin, Tianzhen-Yanggao Basin, Yangyuan Basin, Yuxian Basin, Huaian Basin and Xuanhua Basin. In the Zhangjiakou area, the NE-NEE and NW-NWW trending faults are relatively developed (Figure 1b). The NE-NEE trending faults are relatively large in scale and long in extension. The main faults include the Huaizhuo Basin northern margin fault (F8), the Yanfan Basin northern margin fault (F9) and the Sunzhuangzi-Wulonggou fault (F12). The distribution of NW-NWW trending faults is discontinuous and hidden. The main faults are the Ximalin fault (F2), Zhangjiakou fault (F3), Tumu-Huangtuyao fault (F10), Xinbaan-Shacheng fault (F11) and Shizhuang fault (F13) [27; 28; 29; 30].  
The Zhangjiakou area is active in earthquakes. The Zhangbei strong earthquake area, located in the north of Zhangbei County, is one of the three strong earthquake areas in North China. There was an M6.3 earthquake in 1998. The seismic statistics since 2010 show that the main earthquakes in Zhangjiakou are small (M < 3.0), and the other earthquakes spread along the main active faults in the region. Among them, there were 387 M1.0-1.9 earthquakes, 240 M2.0-2.9 earthquakes, 186 M3.0-3.9 earthquakes and 28 M4.0-4.9 earthquakes. No earthquakes more than M5.0 were detected. Most of the earthquakes were concentrated in the Zhangbei strong earthquake area, and the rest were distributed along the main active faults. The Beijing-Zhangjiakou high-speed railway (Huailai section) spans the key tectonic site of the intersection of the Yanfan Basin northern margin fault and Tumu-Huangtuyao fault (Figure 1c). This key tectonic site has the strongest activity and the most complex engineering geological conditions since the Cenozoic era. Therefore, we chose this section as the research area to explore its potential impact on high-speed railway subgrade.
# Antivirus Evasion Methods in Modern Operating Systems
## 4 Antivirus Evasion Mechanisms
### Signatures  
A vulnerability in antivirus software can lead to the exploitation of the program's flaws by hackers. An example of such a vulnerability is the discovery that poorly-designed signatures in an antivirus program can be used as an attack tool against the user. Researchers have developed a method to obtain the exact virus signatures of a specific antivirus software. The method involves three steps: (1) detecting critical bytes in the malware using feedback from the antivirus, (2) aligning corresponding bytes from samples with the same pattern and combining them into one sequence, and (3) converting the linked sequences into the appropriate format to obtain the original signature. The obtained signatures can be used to implant them in secure data, resulting in the quarantining or deletion of such data, which can lead to significant problems. This vulnerability and its exploitation are detailed in [22].  
Currently, there are numerous scientific works utilizing the detection mechanism of signatures for the aforementioned attacks. An exemplary framework was presented in [23], where the authors attacked operating systems by forcing the antivirus to perform specific actions harmful to the user.
## 4 Results
### Validation on an Air-Piloted Jet Flame (Sandia Flame D)
Figure 5: Comparison between experimental data for Sandia Flame-D and LES hybrid ESF/FPV numerical results for different numbers of SFi at different axial positions, z/d = 7.5; 15 and 30. Radial profiles of mean and rms of (**a**) temperature (**b**) mixture fractio. Figure 6 shows two series of instantaneous scatter plots of the temperature distribution at 3 axial positions. The first series exhibit the calculated instantaneous scatter plots plotted versus the instantaneous mixture fraction quantities, when 8 SFi are used (Figure 6, bottom). The second series present the reference instantaneous scatter plots (Figure 6, top). It turns out that the maximum temperature corresponding to the stochiometric mixture fraction is well predicted for all locations except the first axial position where it is very slightly overestimated with small deviations. Some local extinction values could not be predicted numerically in the region close to the centerline. However, one can note the presence of few unrealistic structures of temperature in the mixture fraction range 0.8 \(<\)\(f\) \(<\) 0.9 for both axial positions z/d = 7.5 and 15. This could shape up due to the standard mixing model used in this work. The Interaction by exchange of mean (IEM) is known, by nature, to be not local in composition space which may lead to unphysical events (see in [70]). As reported in [57] where two different mixing models (IEM and EMST (Euclidean minimum spanning tree)) have been tested on all piloted Sandia flames including the Flame-D, the IEM approach could not capture perfectly the conditional temperature distribution leading to a similar unphysical trend within the same mixture fraction range as observed also in [58]. Another quantity which is essential to evaluate numerical combustion models in predicting the flame characteristics is the probability density function (PDF). Figure 7 displays the PDF of the temperature at 2 axial locations further downstream from the flame nozzle obtained numerically using different SFi numbers in comparison with experimental data. This PDF was calculated for a narrow band of the mixture fraction around the stoichiometry where 0.30 \(<\)\(f\) \(<\) 0.40. The PDFs obtained calculated using 8 SFi are showing better results comparing to other numerical results simulated with less fields, where very similar behavior to the available experimental data is illustrated. In particular, the probability of the presence of temperature ranging from 1650 K to 2150 K is comparable to the measurements at both positions, z/d = 15 and 30. Next, the novel LES hybrid ESF/FPV model was applied to investigate the evolution of oxy-fuel flame characteristics. Figure 6: Instantaneous scatter plots of temperature versus mixture fraction for different axial positions ((**a**) z/d = 0.75, (**b**) z/d = 15 and (**c**) z/d = 30). Comparison between experiment and simulation results; Top: Measurements; Bottom: LES hybrid FESF/FPV with 8 SFi.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 2 Background  
This section summarizes the fundamental concepts for understanding this study, including the general features of UAV-enabled communication systems, 3D path planning algorithms, and the proposed strategies.