esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Calculation of Working Behavior of Bag-Grouting-Pile Foundation  
We verified the subsidence-control effect of the bag-grouting-pile foundation and the interaction of pile and soil through a field test. However, the design failed as the cloth's drainage effect could not be tested. In addition, the bag-grouting-pile foundation in the test section can effectively strengthen deep soft-soil foundation with an interlayer of hard materials. But in terms of design, we need to further analyze and verify the possibility of optimization. The bearing capacity of the composite foundation is characterized by the composite modulus and load-sharing ratio. In the compound modulus calculation formula, the main parameters are pile length, pile diameter, and pile spacing. The main material of the cloth bags is nylon fiber. When the cloth bag wraps the pile, it has the function of a plastic drainage board for the surrounding soil. So, the parameters to be optimized are pile length, pile diameter and drainage effect. Therefore, this paper used numerical calculations to discuss these problems. The paper found that under plane strain conditions, the pile element is equal to the strip sheet pile with the width of the pile diameter [17; 18; 19]. The equivalent formula of pile modulus is calculated as shown in Equation (1). \(E_{p}\) represents the actual pile modulus. \(E_{s}\) represents the deformation modulus of soil between piles. \(S\) represents pile spacing. \(D\) represents pile diameter.  
\[E_{sp} = \frac{\pi d}{4s}E_{p}+\bigg{(}1-\frac{\pi d}{4s}\bigg{)}E_{s} \tag{1}\]
# Calibration Method of Projectional Geometry for X-ray C-arm Fluoroscopy Using Sinogram Data
## 6 Conclusions Furthermore, Future Work  
This study aims to create a calibration method that can be used in the intervention theater immediately prior to a vascular intervention procedure. Furthermore, it aims to improve the accuracy of the calibration method using sinogram data acquired during the CBCT imaging process. In other words, the goal of this study is to create a calibration method that ensures the safety of the vascular intervention procedure and executes within the acceptable computational time for the image-guided vascular intervention room without exposing the patient to any additional radiation.  
The proposed method is a phantom-based calibration technique that uses sinogram data to compute ten geometric parameters of the imaging device. By comparing and evaluating the calibration accuracy across various angular ranges and interval conditions, the optimal angle range and interval are proposed. Consequently, the optimal calibration conditions considering the calculation time and accuracy were found to be an angular range from \(-40\) to \(40\) degrees, with an optimal angular interval of \(1.6\) degrees. Therefore, a calibration method with an operation time of \(2.92\) s and a mean error of \(0.36\) mm could be achieved, thus satisfying our goal of below 1 mm accuracy within a minute computational time. We plan to conduct additional research in the near future to apply this calibration method to a robotic system for vascular intervention.  
[PERSON] contributed to the development of the calibration algorithm, conducted the experiments, acquired the data, analyzed the data and wrote the paper; [PERSON] contributed to the paper review and editing, provided supervision, project administration, review, and editing support. All authors contributed to the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2020 Research Fund of the University of Ulsan.  
Not applicable.  
The authors declare no conflict of interest.
# Time to Capture a Moving Target Travelling along a Circular Trajectory
## 3 Results
### Fit of the Fitts' Model for Moving Targets with Various Speeds
On the right is an example where the coefficient of determination of the linear regression equation is very low (\(K=-10\)). \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**K**}} & \multicolumn{2}{c}{**Linear Regression**} & \multirow{2}{*}{**ID**} & \multirow{2}{*}{**R\({}^{2}\)**} \\  & **Equations** & & \\ \hline Negative infinity & MT = 80.06 + 129.55 \(\times\) ID & \(Log_{2}\)(\(2A/W\)) & 0.9029 \\ \hline \(-200\) & MT = 80.31 + 131.97 \(\times\) ID & \(Log_{2}\)(\(A/(W/2+V/200)\)) & 0.9136 \\ \hline \(-105\) & MT = 83.80 + 131.37 \(\times\) ID & \(Log_{2}\)(\(A/(W/2+V/105)\)) & 0.9172 \\ \hline \(-100\) & MT = 84.32 + 131.36 \(\times\) ID & \(Log_{2}\)(\(A/(W/2+V/100)\)) & 0.9173 \\ \hline \(-95\) & MT = 84.93 + 131.35 \(\times\) ID & \(Log_{2}\)(\(A/(W/2+V/95)\)) & 0.9173 \\ \hline \(-90\) & MT = 85.65 + 131.33 \(\times\) ID & \(Log_{2}\)(\(A/(W/2+V/90)\)) & 0.9172 \\ \hline \(-10\) & MT = 297.05 + 88.13 \(\times\) ID & \(Log_{2}\)(\(A/(W/2+V/10)\)) & 0.6311 \\ \hline \(50\) & MT = 129.62 + 111.03 \(\times\) ID & \(Log_{2}\)(\(A/(W/2-V/50)\)) & 0.7730 \\ \hline \(100\) & MT = 93.64 + 123.04 \(\times\) ID & \(Log_{2}\)(\(A/(W/2-V/100)\)) & 0.8565 \\ \hline \(200\) & MT = 84.28 + 126.95 \(\times\) ID & \(Log_{2}\)(\(A/(W/2-V/200)\)) & 0.8841 \\ \hline \multicolumn{1}{c}{Positive infinity} & MT = 80.06 + 129.55 \(\times\) ID & \(Log_{2}\)(\(2A/W\)) & 0.9029 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variation of linear regression equation and \(R^{2}\) coefficient of determination according to \(K\) value change. Figure 6: Linear regression analysis between target selection times and indexes of difficulty; when the movement speed of the target does not affect the movement time (**left**), when the model fit is the best (**center**), when the model fit is poor (**right**). The coefficient of determination of the linear regression equation was 0.9029 when the traditional Fitts' model, which does not consider the velocity of the moving target, was applied to the collected data. This coefficient is very high enough to validate the fit of the model. However, when the model considering the speed of the moving target was applied, the coefficient of determination of the linear regression equation was further improved (\(R^{2}\) = 0.9173).
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 2 Background
### Handover Decision with 6G  
In 6G networks, handover is significantly different from previous generations (4G, 5G) due to new technologies and improved mechanisms. HO will be managed using AI and machine learning, which will allow devices to predict the optimal moment and target network for connection transfer [34; 35]. AI algorithms will be able to consider factors such as network load, user speed, and signal quality [36]. Handover will cover various heterogeneous networks, including satellite networks, drones, IoT devices, and smart city systems. Furthermore, 6G will also introduce integration with quantum networks and edge computing [37; 38].
# Gemini Surfactant as a Template Agent for the Synthesis of More Eco-Friendly Silica Nanocapsules
## 1 Introduction
Comparing QSB2-12 with its monomeric analogue DTAB, the gemini surfactant is also less toxic than the conventional one (LC\({}_{50}\) for DTAB towards _Daphnia magna_ is 0.35 mg/L [20]). It has been already reported that elongating an alkyl chain leads to an increase in toxicity [22; 23]. As an example, for betainate cationic dimeric surfactants, LC\({}_{50}\) values towards _Daphnia magna_ decreases from 50 to 7.5 mg/L [22] when the number of carbon atoms in the hydrophobic parts increases from 8 to 10. It can be also observed for the above-mentioned monomeric surfactants DTAB (C12) and CTAB (C16). The compound with 12 carbon atoms is classified as very toxic toward _Daphnia magna_[20; 24] whereas the one with 16 carbon atoms as extremely toxic [21]. Nevertheless, when we look at marine or estuarine organisms, not many information is available regarding gemini surfactant toxicity. This represents a serious gap in marine hazard information available, as nanocapsules synthetized using gemini surfactants are often used in maritime infrastructures. One of the few examples is a study where gemini surfactants were shown to be 20-times less toxic when looking at marine microalgae than CTAB [25]. Taking into consideration the relation between surfactant structure, aggregation properties and ecotoxicity, environmentally-friendly silica nanocapsules may be designed. This approach, named safe-by-design (SbD), focuses on setting, at minimum, any sources of possible hazards for the environment and humans. SbD aims at ecologically oriented design since the beginning, meaning that all possible safety issues will be identified and replaced with non-toxic or less toxic alternatives [26; 27]. Therefore, the aim of this study was to develop a safe-by-design approach to silica nanocapsules synthesis. In order to achieve it, CTAB was replaced by the gemini surfactant QSB2-12 as a template for the nanocapsules' synthesis. The nanocontainers based on both cationic surfactantswere fully characterized by scanning electron microscopy (SEM), Fourier-transformed infrared spectroscopy (FTIR), dynamic light scattering (DLS), thermogravimetric analysis (TG/DTA) and N\({}_{2}\) adsorption-desorption isotherms, as well as, in terms of their ecotoxicity towards chosen marine microalgae and crustaceans. The marine environment is the compartment where most of these ENMs will be applied and, therefore, looking at effects to marine biota is crucial and it will help to fill in the gap on the few available information regarding hazard assessment.
## 4 Analyses of the Applications of Digital Twin Models
### Medicine
#### 4.2.2 Benefits and Challenges of Digital Twins in Medicine
Ensuring that medical staff are well-trained and that systems are maintained in optimal conditions requires considerable financial resources, which can be an obstacle for many medical institutions.
## 3 Spectrum Auction Structures
### Beauty Contest  
In an auction process, maximizing revenue or minimizing costs may not be the most important aspect for the auctioneer. The acquisition of goods and services by the government (from war assets to building hospitals) is commonly approached by a beauty contest auction. Theoretically, the winning project would be the one that better serves the auctioneer's needs, with the price relevant, albeit secondary.  
Suppose a purchase auction for pens where two firms dispute the contract. The products are homogeneous, aside from their duration. Firm 1 offers its product at \(p_{1}\), which  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Player 2**} \\ \cline{3-6}  & **Bid** & \$10 & \$20 & \$30 & \$40 & \$50 \\ \hline \multirow{5}{*}{Player 1} & \$10 & 0 & 0 & 0 & 0 & 0 \\ \cline{2-6}  & \$20 & 20 & 0 & 0 & 0 & 0 \\ \cline{1-1} \cline{2-6}  & \$30 & 20 & 10 & 0 & 0 & 0 \\ \cline{1-1} \cline{2-6}  & \$40 & 20 & 10 & 0 & 0 & 0 \\ \cline{1-1} \cline{2-6}  & \$50 & 20 & 10 & 0 & \(-10\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vickrey Auction.  
is capable of writing for 1 Km. On the other hand, Firm 2 charges \(p_{2}\), while the product lasts for 5 Km. Assume \(p_{1}<p_{2}<2p_{1}\).  
A cost-benefit analysis immediately shows that Firm 2's product is a better option. However, in a standard auction, Firm 1 would always be the winner, should price/revenue alone be observed.  
The precept of the beauty contest auction is that the auctioneer clearly defines the requested characteristics, weighs each one, and communicates with all parties involved prior to the dispute. Each competitor presents their product, price, and distinctions to be valued and judged by a committee that awards the best offer as the winner.  
However, this structure is highly susceptible to information asymmetry between the participants and the auctioneer. The committee may not fully understand the process structure of each firm and product, which creates opportunities for firms to mislead the ruling board. Even if the committee has comprehensive technical knowledge, it may not be beneficial to elaborate on every detail, leading to an excessively stringent procedure.  
The subjective nature of committee decisions, combined with information asymmetry, inevitably raises questions about the integrity of the decision-making board.  
A report by the OECD indicates that in Spain, as a result of the 3G spectrum allocation, the unsuccessful firm France Telecom challenged the outcome through the judicial system.  
In Austria, concerns about the potential for collusion led the regulatory body to distance itself from beauty contests. Investigations were warranted in the Netherlands and Italy due to suspected collusive behavior. In addition, the firms were found to have formed alliances to manipulate the process.  
In the observed database, only Chile has held a beauty contest auction in South America or North America in the last 24 years.
## 1 Introduction
Further, PM allows the uniform distribution of carbon-based nanoparticles in the matrix material [17]. Powder metallurgy encompassing critical stages such as powder compaction and sintering, offers significant potential for fabricating high-performance composites. Among the key processing parameters, compaction pressure, sintering temperature, and sintering time are crucial in determining the microstructure and, consequently, the mechanical properties of the final composite [18]. Compaction pressure affects the densification and bonding between powder particles while sintering temperature and time control grain growth, diffusion, and porosity elimination [19]. Comparing the role of processing parameters with other approaches such as laser powder bed fusion (LPBF), the overall characteristics of the material directly correlate [20]. Therefore, it is essential to optimize these parameters to achieve the desired balance of mechanical strength, hardness, and durability. The Taguchi method has been widely employed in recent years as an effective statistical approach for optimizing powder metallurgy parameters. [PERSON] et al. [21] demonstrated the use of the Taguchi method to optimize compaction pressure, sintering time, and sintering temperature to enhance the microhardness of Al-CNT 2 composites. Similarly, [PERSON] et al. [22] applied the Taguchi approach to identify optimal conditions for the densification and mechanical strength of steel-based composites, revealing the significance of compaction pressure and sintering temperature. [PERSON] et al. [23] applied Taguchi optimization in hybrid metal matrix composites and found that properly tuned sintering time and temperature significantly improved tensile properties. The objective of this study is the optimization of the processing parameters for AZ31 alloy reinforced with 1.75 wt.% of graphene nanoplatelets (GNP), focusing on the optimal GNP weight fraction. Using the Taguchi method with an L9 orthogonal array, the effects of compaction pressure, sintering temperature, and sintering time on microhardness and compressive strength are analyzed through signal-to-noise (SN) ratios and ANOVA. This study also aims to develop predictive regression models to establish quantitative relationships between process parameters and mechanical properties, ensuring accurate and reliable optimization.
## 3 Methodology
### Phase 2: Optimal Architecture Synthesis  
#### 3.2.1 Component Selection Methodology  
The synthesis of the optimal architecture follows a systematic selection process based on three empirical criteria derived from the results of the ablation study. The criticality criterion identifies components that cause performance degradation exceeding 10% when removed, indicating essential architectural elements. The consistency criterion evaluates components that show beneficial effects in all three FN datasets, ensuring generalizability. The efficiency criterion assesses the performance-to-parameter ratio, prioritizing components that provide substantial improvements without excessive computational overhead. Components that meet all three criteria are designated as core elements for the optimal architecture.
## 3 Results  
### Statistical Analysis and Fitting of Model of Encapsulation Efficiency  
The Box-Behnken design is a common RSM design. When compared with the central composite design, another common RSM design, Box-Behnken requires a smaller number of runs and fewer levels of variable [34]. The Box-Behnken design is also useful when researchers wish to avoid unreasonable results, as it does not consider factors at extreme high/low levels [35; 36]. Consequently, Box-Behnken was chosen in this study. In the experiment, 46 experiments were conducted, including 40 combinations and 6 center points. The experimental design and related results are shown in Table 2.  
The responses of the Box-Behnken design in EE ranged from 51.06% (Run 4) to 98.25% (Run 26). For water absorption, they ranged from 53.1% (Run 38) to 239.5% (Run 3). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multirow{2}{*}{**Symbols**} & \multicolumn{3}{c}{**Levels**} \\  & & \(-\)**1** & **0** & **+1** \\ \hline SA concentration (\%) & X\({}_{1}\) & 1 & 2 & 3 \\ CMS concentration (\%) & X\({}_{2}\) & 1 & 3 & 5 \\ Urea concentration (\%) & X\({}_{3}\) & 10 & 20 & 30 \\ CaCl\({}_{2}\) concentration (\%) & X\({}_{4}\) & 1 & 2 & 3 \\ PDA concentration (\%) & X\({}_{5}\) & 0.4 & 0.5 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Coded and actual levels of independent variables used in the RSM design. The results of ANOVA of the experimental data on encapsulation efficiency are shown in Table 3. In Table 3, sodium alginate (SA) is factor A, carboxymethyl starch sodium (CMS) is factor B, urea is factor C, calcium chloride (CaCl\({}_{2}\)) is factor D, and polydopamine (PDA) is factor E. The f-value is 3.03, and the \(p\)-value is 0.0048 (\(p<0.05\)), indicating that the model is significant. The \(p\)-value of lack of fit is 0.0656 (\(p>0.05\)), which indicates that the model has a high degree of fitting with the experimental data. By comparing the effects of various primary factors on EE, the order of influence is D \(>\) C \(>\) A \(>\) E \(>\) B. Among these, item
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 3 Computer Simulation  
In order to verify our method, we carried out computer simulations. The simulated camera had the following parameters: \(f_{x}\) = \(f_{y}\) = 1700.00 pixels, \(u_{0}\) = 600.00 pixels, \(v_{0}\) = 500.00 pixels and \(\alpha\) = 0. The size of a square of the simulated checkerboard is 14.175 \(\times\) 14.175 mm. The simulated rotation center in the world coordinate system is \(O_{r}\) = (-80.00, -50.00, 900.00) mm. The simulated camera is placed at Cr = (-80.00, -33.31, 852.87) mm, Cg = (-80.00, -5.26, 852.85) mm and Cb = (-80.00, -33.37, 821.75) mm in the world coordinate system and 50.00 mm, 65.00 mm and 80.00 mm away from the rotation center, respectively, as shown in Figure 5.  
For each position, 20 pairs of external parameter matrices (\(\mathbf{R}\) and \(\mathbf{T}\)) were assigned to simulate the movement of the camera. A total of 60 simulated images \"seen\" by the camera were substituted into [PERSON]'s method to calculate the internal parameters \(\mathbf{A}\) and external parameters \(\mathbf{R}\) and \(\mathbf{T}\), from which the positions of the optical centers were obtained. Then 3.5% Gaussian noise was added to affect the coordinates of each pixel of the images; the same operation is performed to calculate \(\mathbf{A}\), \(\mathbf{R}\) and \(\mathbf{T}\) and the optical centers. The obtained internal parameters in both cases are shown in Table 1. The trajectories of the three groups of the optical centers projected on the \(Y_{w}\)\(O_{w}\)\(Z_{w}\) plane are shown in Figure 6. Figure 6a is the result without noise, and Figure 6b is the result with noise. The fitted circle curves by the LSC fitting method are shown in Figure 7. The fitted centers and radii are shown in Table 2.  
Figure 5: The rotation diagram of the simulated camera placed at positions Cr, Cg and Cb.  
Figure 6: (**a**) Projection of the simulated optical centers on the \(Y_{w}O_{w}Z_{w}\) plane without noise; (**b**) projection of the simulated optical centers on the \(Y_{w}O_{w}Z_{w}\) plane with 3.5% Gaussian noise.  
Figure 7: (**a**) Fitted circular curve without noise; (**b**) fitted circular curve with 3.5% Gaussian noise.  
It can be seen from Table 2 that the three circle centers and radii are the same as the setting values without considering noise. When there is 3.5% Gaussian noise, the average of the three circle centers is \(\overline{O_{r}}=(-49.98,\,900.02)\) mm, and the errors of the fitted center and radius are less than 0.05 mm and 0.19 mm, respectively. These results confirm that the proposed method can be used to locate the rotation center in the rotating optical measurement system.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 3 The Architectural Model
### The Data-Plane--A Hierarchy of SFC Headers  
To be able to isolate and encrypt traffic between different VNFs, an encryption service has to run at all ends of every VNF in the SFC. Furthermore, to ensure compatibility with the SFC specification [15], the encryption services are separated from the VNF and the Service Function Path (SFP) itself. Hence, the proposed architecture introduces additional encryption tunnels in the SFC model, which are identified as inner SFC tunnels, and defined by the SFC header 1 (Figure 6), while SFC header 2 corresponds to the original SFC header in accordance with the SFC specifications [15]. It must be noted that the Virtual Links (VLs) are layered headers and are not implemented as communication tunnels, but they can be perceived as virtual tunnels from an architectural perspective. In practice, the SFC layers are placed below the IP layers in order to enable IPsec encryption. As previously mentioned, this is because encryption of an IP packet with SFC headers inside would hide both SFC header and the classification data [16].  
We define the SFC header 1 to constitute the Encrypted Link (EL) and to let the SFC header 2 define the Virtual Link (VL), while the transport layer is named the Transport Link (TL). All of these packet headers need routing information associated with them, which is defined by a tiered control plane (see next Section). From an OSI-model perspective, we define the two new SFC layers to belong between layer two and layer three (Figure 6). The Transport layer is according to the SFC specification [15] referred to as the layer that transports SFCs. Here, we define the transport layer to be an IPsec tunnel.  
Figure 5: The architectural model.  
The SFC header 1 is always associated with an Encrypted Link that consists of an inner SFC with one hop only. Due to the static nature of the inner SFC header 1, this information is placed as an extension of the original SFC header 2 and not a next-protocol header. For SFCs enabled by NSH headers, this means that one NSH header can contain both SFC 1 and SFC 2 headers. Section 6 shows how the original SFC header of NSH is extended to include inner SFCs identifiers by introducing a new type value of the NSH header.