esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[22] took Longlingong Tunnel as an example; the bottom axial length of the karst cave is 171 m, the width is 65 m, and the distance to the ground surface is 110 m. The roof of the karst cave collapsed to the surface. Finally, the crossing mode of backfilling the cavity was determined and the treatment mode of dynamic compaction plus grouting was adopted to improve the foundation. Generally, large karst caves are mainly dry. Common crossing methods include route diversion, subgrade backfilling and excavation and bridge construction. The specific method to be used should be determined according to the actual situation and cost. However, the number of projects crossing large karst caves with water are few, and there are few studies that have been made on this subject worldwide. Therefore, it is necessary to study the safety of passing through large karst caves with water. \begin{table}
\begin{tabular}{c c} \hline
**Unfavorable Geological Phenomena of Karst** & **Social and Economic Losses** \\ \hline Foundation collapse of the Lizhan railway & Economic loss reached 1 million yuan \\ Large water and mud innyh in Yesanguan & 10 deaths and the economic loss was more than \\ Tunnel of Yivan Railway & 13 million yuan \\ Water and mud innyh and surface collapse in & Construction period delayed by half a year and \\ the Hejiazhai Tunnel & the economic loss was 10 million yuan \\ Water innyh in the Maluqing Tunnel & 11 deaths \\ Water and mud innyh in the & Economic loss exceeds 10 million yuan \\ Dayaoshan Tunnel & \\ \hline \end{tabular}
\end{table} 
Table 1: Social and economic losses caused by karst adverse geological disasters in recent years. This paper takes the Yujingshan Tunnel as an example to study the impact of rising groundwater on the project. Firstly, an actual investigation of the surrounding rock properties and groundwater conditions of the Yujingshan Tunnel was carried out; the main parameters of the backfill body include: elastic modulus, density, cohesion, internal friction angle, porosity and softening coefficient of water to surrounding rock. Orthogonal experiments were used to design a test group, and the six parameters of backfill layer settlement, tunnel vault settlement, convergence on both sides and the internal force and bending moment of the initial support were used as evaluation indicators. The finite element software FLAC3D was used for a simulation, and the values of the evaluation indicators in each experimental group were extracted to calculate the means and standard deviations. Then, the range standardization process was performed, and the means and standard deviations for the experimental group were normalized to the [0,1]. \(d_{ECULID}^{ik}\) is the ECULID distance between \(\left(x_{ik}^{*},\sigma_{ik}^{*}\right)\) and the origin of coordinates. The backfill parameter corresponding to the minimum value of \(d_{ECULID}^{ik}\) is the optimal parameter. Comparisons with on-site monitoring were made to verify the correctness of the method described in this article. This study provides a reference for engineering design and construction in the future.
# Parameters Adjustment Optimization for Prepreg of Copper Clad Laminate Based on Virtual Metrology
## 3 Virtual Metrology Modeling
### Support Vector Regression (SVR)  
SVR was proposed by [PERSON] [23]. Different from general regression, of which the goal is to find a function that can minimize the sum of square error, SVR allows the error to be smaller than \(\varepsilon\)[24]. Figure 2 shows the difference between general regression and SVR.  
For a dataset of I input-target pair, \(\{(x_{1},\,y_{1}),\,\ldots\,(x_{\rm I},\,y_{\rm I})\}\), Equation (2) is the corresponding linear function.  
\[f(x)=\langle w,x\rangle+b \tag{2}\]  
In Equation (2), \(\langle w,x\rangle\) denotes the dot product. SVR tries to minimize the margin and keep all data lying inside [25]. The margin can be calculated by Equations (3)-(5).  
\[\text{Minimize}\ \frac{1}{2}\|w\|^{2} \tag{3}\]  
Subject to:  
\[y_{i}-\langle w,x\rangle-b\leq\epsilon \tag{4}\]  
\[\langle w,x\rangle+b-y_{i}\leq\epsilon \tag{5}\]  
However, it may not possible that all the pairs can be kept inside the margin when the margin becomes smaller. SVR gives penalties to the pairs outside the margin by slack variables \(\xi_{i}^{+}\) and \(\xi_{i}^{-}\). Then Equations (3)-(5) are rewritten as Equations (6)-(9).  
\[\text{Minimize}\ \ \frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{l}\bigl{(}\xi_{i}^{+}+ \xi_{i}^{-}\bigr{)} \tag{6}\]  
Subject to:  
\[y_{i}-\langle w,x\rangle-b\leq\epsilon+\xi_{i}^{+} \tag{7}\]  
\[\langle w,x\rangle+b-y_{i}\leq\epsilon+\xi_{i}^{-} \tag{8}\]  
\[\xi_{i}^{+},\xi_{i}^{-}\geq 0 \tag{9}\]  
The constant \(C\) (\(C>0\)) represents the trade-off between the margin and the extent to which deviations larger than \(\epsilon\) are tolerated [26]. If the constant \(C\) equals to zero, the minimizing problem goes back to linear regression, which also means that the rule is too strict [27].
## 1 Introduction
The ability to reduce the computational costs and deploy on resource-constrained devices makes this combination an attractive option for edge computing systems. The following bullet points summarize the key contributions of the research study, which proposes a lightweight and high-accuracy bus detection model:  
* We proposed a lightweight and high-accuracy bus detection model based on an improved YOLOv5 model;
* We integrated the GhostConv and C3 Ghost Modules into the YOLOv5 network to reduce the number of parameters and floating-point operations per second (FLOPs), ensuring the detection accuracy while reducing the model parameters;
* We added the SimSPFF module to replace the SPPF in the YOLOv5 backbone for increased computational efficiency and accurate object detection capabilities;
* We developed a Slim scale detection model by modifying the original YOLOv5 structure to make the model more efficient and faster, which is critical for real-time object detection applications;
* The experimental results showed that the Improved-YOLOv5 outperformed the original YOLOv5 in terms of the precision, recall, and [EMAIL_ADDRESS];
* Further analysis of the model complexity revealed that the Improved-YOLOv5 was more efficient due to fewer FLOPS, fewer parameters, less memory usage, and faster inference time capabilities;
* The proposed model is smaller and more feasible to implement in resource-constrained mobile devices and a promising option for bus detection systems. The structure of this paper consists of four main sections, with each section covering essential topics related to the proposed model for bus detection. Section 2 elaborates on the principles of YOLOv5 and the Improved-YOLOv5, providing an in-depth discussion of the technical aspects of the proposed model. Moving forward, Section 3 focuses on the model training process and experimental results. The section highlights the various experiments performed to evaluate the proposed model's performance. Finally, in Section 4, the authors conclude the presented work, summarizing the significant findings and contributions of the study. This section highlights the strengths of the proposed model, including its efficiency, accuracy, and feasibility.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 2 Related Work  
There are various approaches to realize indoor localization using a variety of modalities, such as Inertial Measurement Units (IMU), image analysis, and wireless communication. We briefly describe them.
# Legal, Economic and Cyber Security Framework Considerations for Drone Usage
## 6 Discussions
### User Modified or Created Software  
In certain cases, the drone producer may allow users to modify the existing software or add their own software within the drone. In such cases, the liability can shift, as there are multiple entities/persons that can generate vulnerabilities within the drone that can result in a cyber-attack.  
The angles mentioned in the previous use cases are valid for this one as well. This use case is closely related to the one for identifying and remediating vulnerabilities. For this use case, modifying existing software or creating new software for a drone may, on the one hand, generate risks associated with the existing software and, on the other hand, can include in itself vulnerabilities that can be exploited by threat actors.  
The liability aspects are much clearer under existing legislation, as the liability pertains to the entity creating software. However, in terms of preventive security measures, proper analysis of this new software (changes to software) is essential in order not to extend the vulnerability landscape of the drone. Even if such a measure is not directly related to the actions of the drone producers, from a public safety perspective, it is mandatory to limit the applications used in production to those created by certified developers and analyzed prior to entrance into production.  
Proposals in this respect include development by a developer certified for drone software creation. Furthermore, vulnerability analysis and penetration testing of the new application and the drone where the new application is deployed before the new application are used in production and periodically afterwards.  
In cases where the user makes changes to the drone software, respondents primarily view the producer/authorities (60%) and the user (40%) as being liable for any cyber-attacks caused by such changes, invalidating H4, which stated that drone users are considered liable. In this case, the respondents considered that the authorities (e.g., through the certification body) and the producer are best placed to prevent vulnerabilities from being included in the drone software, which validates H3. Thus, their view correlates the liability with the entities that can properly implement preventive measures, even if there is no legal requirement in this respect.
# A P2P Scheme for Debating and Voting with Unconditional Flexibility
## 1 Introduction
Nevertheless, the ballot frame must be defined at the beginning of the voting session and cannot be altered during the process. Another area of research for e-voting is block-chain [23]. All methods taking this approach achieve partial decentralization in comparison to classic methods. Specifically, the bulletin board is substituted by the block-chain, a sequence of blocks where all public data is stored [24]. Even the processes involved can be established in the block-chain by the use of smart contracts [25]. Nevertheless, there are always certain functions that must be carried out by a third party or in a centralized manner. In [26], registration servers, authentication servers and a web server are needed. In [27], voters use a network of \"Bootnodes\" and \"District nodes\" that a third party must set up. The work published in [28] would seem to be the most decentralized scheme, but it requires a census authority to assign a secret number to each voter, which implies that this entity can reveal the vote of any participant (limited privacy). In other block-chain methods the problem stems from the homomorphic encryption and its inherent inflexibility [29]. All of these methods improve decentralization, but none of them achieves the flexibility of enabling variable-length ballots and the submission of new proposals or candidates for voters during the election process. The purpose of this work is to provide flexibility to decentralized e-voting systems. The outcome is a new boardroom e-voting scheme free of message constraints. It is based on anonymous keys, as in the centralized method [30], but is fully distributed and retains the advantage of flexibility. The method works as an encryption layer that guarantees anonymity while preventing double voting or access to unregistered users. Thus, this layer provides the common e-voting security features to the application layer on which any debate software can sit. Therefore, it is not only suitable for voting on a list of candidates, but also for negotiating the minutes of meetings, collaborating on budgets, sharing documents, and so on. The drawback, as occurs with other decentralized e-voting schemes, is the computational cost, which increases with the number of participants. In summary, the main contributions of this paper are  
1. A new boardroom e-voting scheme with unconditional flexibility, suitable for any P2P debate tool. 2. A security study that determines its strength. 3. A list of features with the degree of compliance in each of them. 4. Experiments that measure the computational cost for different scenarios. The paper is organized as follows. Section 2 explains key aspects required to understand the new scheme. This is detailed in Section 3. Section 4 analyzes its main features. Section 5 presents and discuss experimental results. Section 6 closes with the conclusions.
## 3 Results and Discussion
### Texture Analysis
#### 3.3.1. Pectin Concentration  
With increasing pectin content (statistical reference letter: a-b-c), the hardness increased as well. In samples with higher gluten contents, however, the relative influence of pectin was reduced, yet it was still visible and significant. The same trend appeared for tensile strength as well as springiness. Cohesiveness, on the other hand, decreased with increasing pectin concentration, hinting towards a less elastic matrix.  
This can be attributed to a combination of three simultaneously occurring effects, namely, (a) an increase in gel strength due to the low-ester pectin-calcium gel due to increased pectin concentration, (b) interactions of low-ester pectin with the gluten network and (c) the slightly higher binder-to-extrudate ratio. Calcium-induced gels with low-ester pertains are known to form stronger yet more brittle gels with increasing pectin concentrations [70; 71], which is in line with these findings.  
Figure 6: Texture profile analysis of plant-based salami analog samples, all with identical base masses, varying only in binder composition (pectin/gluten), with (a) hardness, (b) springiness and (c) cohesiveness measured via double compression test and (d) tensile strength measured via rupture test. Samples were dried to equal dry matter (DM) contents of 55% and 65% and ripened for 21 days at 15 \({}^{\circ}\)C. Statistical differences are indicated by reference letters a-b-c (pectin concentration), K-L (dry matter content) and X-Y (gluten concentration).  
Furthermore, several studies report that low-ester pectin interacts with gluten networks when applied in combination, with higher pectin contents also leading to increased pectin-gluten interactions [42], where network formation develops progressively over time, promoting stronger dough structures [72]. The hardness and elasticity of gluten networks can be attributed to the formation of disulfide and hydrogen bonds [73], with pectin addition causing increased aggregation of gluten through intermolecular hydrogen bonds [42], conversion of sulfhydryl groups (-SH) to disulfide bonds (-S-S-) [41], electrostatic interaction [74] or the formation of random coils [65], all of which contribute to strengthened gluten networks, though they do not necessarily influence the cohesiveness [75]. Hence, we concluded that the observed reduced cohesiveness could predominantly be caused by the calcium-induced pectin gel, whereas increased hardness and tensile strength may be caused by both effects. In the studied system, the overall texture is influenced by both components, the gluten as well as the pectin networks, as well as interactions between the two.
## 3 Materials and Methods
### Geodetic Analysis
#### 3.5.1 GNSS 2003-2020  
The available GNSS data were collected at the permanent station PACA, located in Palma Campania (Figure 1A). The whole data set encompasses 17.3 years, between 2003 and 2020 (Table 2). The GNSS data are a time series of 3D (north, east, up) site positions, produced by the Nevada Geodetic Laboratory [61] as daily solutions obtained using GIPSY-OASIS II software in Precise Point Positioning (PPP) mode. The solid earth and pole tide contributions were reduced according to the \"IERS 2010 Conventions\" [62], whereas ocean tide loading accounted for the FES2004 model [63]. The daily GNSS solutions referred to the IGS14 realisation of the International Terrestrial Reference Frame 2014 [64]. North, east, and up CGNSS components were analysed. Since the horizontal components displayed negligible rates after removal of the Eurasia plate movement, for this study we focused only on the vertical (up) component of the ground deformation.
## 5 Applications and Discussions
### Chaotic Time Series Prediction  
#### 5.1.1 Description of the Equation  
The benchmark problem of model identification, predicting the time series generated by the chaotic Mackey-Glass differential delay equation, can be expressed as follows [23; 24; 25; 26; 27; 28; 29]:  
\[\dot{x}(t)=\frac{ax(t-\tau)}{1+x^{10}(t-\tau)}-bx(t) \tag{23}\]  
The task of this example is to predict the output of some fixed steps later with past states of \(x\), the value of the signal 24 steps ahead \(x(t+24)\) is predicted based on the current signal value. Consider the Mackey-Glass problem as a process plant, parameters \(a\), \(b\) and \(\tau\) will vary in different working condition points. Assume there are three working conditions for the process, the parameter values of the plant are outlined in Table 1, where \(x(0)\) is the initial value of the nonlinear system.
## 3 Results and Discussion
### Coanda Effect  
Figure 5 shows the position of the maximum dimensionless x-velocities of the jet. This can be used to depict how the jet develops in the test section. It can be clearly observed that the jet moves upward first and then downward. The initial free jet develops into a wall  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Relative ErrorModel** & **AB** & **LB** & **LS** & **YS** & **AKN** & **CHC** \\ \hline Re = 1000 & & 72.33\% & 120.68\% & 37.58\% & 56.61\% & 18.76\% & 32.29\% \\  & & & & & & \\ \hline Re = 1000 & & 23.65\% & 79.48\% & 22.65\% & 101.04\% & 17.86\% & 20.57\% \\  & & & & & & \\ \hline Re = 1000 & & 34.59\% & 84.89\% & 23.12\% & 37.10\% & 34.42\% & 20.48\% \\  & & & & & & \\ \hline Re = 1750 & & 95.07\% & 586.95\% & 50.44\% & 204.50\% & 89.86\% & 41.21\% \\  & & & & & & \\ \hline Re = 1750 & & 9.39\% & 38.21\% & 10.18\% & 25.19\% & 17.30\% & 21.56\% \\  & & & & & & \\ \hline Re = 1750 & & 11.42\% & 30.89\% & 7.62\% & 17.61\% & 13.87\% & 21.99\% \\  & & & & & & \\ \hline Re = 2500 & & 39.40\% & 193.89\% & 35.07\% & 83.45\% & 50.95\% & 26.96\% \\  & & & & & & \\ \hline Re = 2500 & & 36.90\% & 10.31\% & 25.50\% & 15.57\% & 21.43\% \\  & & & & & & \\ \hline Re = 2500 & & & & & & \\ \(\chi\)/L = 0.47 & & & & & & \\ \hline Average & & & & & & \\ relative error & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative error of mean velocity predicted by six low-Reynolds number \(k\)-\(\varepsilon\) models.  
jet because of the Coanda effect. It can be attributed to the lower pressure generated by jet entrainment of surrounding fluid between the wall and the free jet.  
The maximum dimensionless x-velocities predicted by all six models are located below the experimental data. At Re = 1000, the maximum value of y/L appears at 0.27 \(<\) x/L \(<\) 0.33. At Re = 1750, the maximum value of y/L appears at 0.33 \(<\) x/L \(<\) 0.4, and at Re = 1750, the maximum value of y/L appears at 0.4 \(<\) x/L \(<\) 0.47. Behind these positions, it begins to decrease. This phenomenon indicates that the attached wall jet is separating from the wall. Moreover, with the increase in Re, the separation of the wall jet occurs later.  
In Figure 5a, it can be seen that the LB and YS models perform quite poorly for Re = 1000. The maximum value of y/L predicted by the CHC model is still increasing after x/L = 0.53. The trend of the results predicted by the AB and AKN models is in good agreement with the experimental results. In Figure 5b,c, the LB and YS models predict better results for Re = 1750 and Re = 2500 than the result for Re = 1000. By contrast, the CHC model predicts the worst result when Re is high (Re = 2500).  
The LS model always overpredicts the position of separation of the wall jet, and the AB model has the best result in predicting the Coanda effect.
## 3 Results
### Lactic Acid Adsorption Kinetics  
Among the adsorption kinetic models (i.e., pseudo-first order, pseudo-second order and Elovich) investigated, only the pseudo-second order kinetic model followed the experimental results. Figure 4 shows the pseudo-first order, pseudo-second order and Elovich kinetic model plots for lactic acid adsorption onto GAC, RLX425 and AMB400, respectively. The pseudo-first order and Elovich kinetic model showed poor fitting between the experimental and model predicted results (data not shown), and hence these models failed to explain the experimental results.  
The straight-line plot (Figure 4b) between \(t/q_{t}\) versus \(t\) was used to calculate the pseudo-second order kinetic constants, namely \(q_{e}\) and \(K_{2}\) as shown in Table 5. Figure 4b shows a good correlation (\(R^{2}\sim 1\)) between the experimental data and the model predictions for the three adsorbents tested. The initial adsorption rate (\(h_{0}\)) was the highest for AMB400 (142.9 min\({}^{-1}\)), followed by GAC (31.5 min\({}^{-1}\)) and RLX425 (13.6 min\({}^{-1}\)). The \(q_{e}\) value was the highest for GAC (38.2 mg lactic acid/g adsorbent), followed by AMB400 (31.2 mg lactic acid/g adsorbent) and RLX425 (17.2 mg lactic acid/g adsorbent).  
Figure 4: Adsorption kinetics of lactic acid by the three adsorbents GAC, RLX425 and AMB400 at 303.15 K. (**a**) Pseudo-first order; (**b**) pseudo-second order and (**c**) [PERSON] kinetic models at an initial lactic acid concentration of 4.5 g/L and an adsorbent dose of 10% (\(w/v\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Kinetics** & **Coefficients/Constants** & **Units** & **GAC** & **RLX425** & **AMB400** \\ \hline \multirow{3}{*}{Pseudo-Second Order} & \(q_{e}\) & mg/g & 38.2 & 17.2 & 31.2 \\ \cline{2-6}  & \(K_{2}\) & mg/g/min & 0.02 & 0.05 & 0.15 \\ \cline{2-6}  & \(h_{o}\) & min\({}^{-1}\) & 31.5 & 13.6 & 142.9 \\ \cline{2-6}  & \(R^{2}\) & - & 1 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pseudo-second order model kinetic parameters for the three adsorbents (GAC, RLX452 and AMB400) at 303.15 K and with 4.5 g/L of lactic acid in the bulk solution.