esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationships between Anthropometric and Physical Performance Parameters
For example, a reduction of 1\(\%\) body fat induced a decrease in time in the agility T-half test of 0.09 s (Figure 1), an extension of the model to the \(\beta\)-coefficient for 15 m sprint was observed. Figure 1: Relationship between body fat and agility depending on the group. Please note that one dot can represent several subjects. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & & & \multicolumn{3}{c}{**Nonstandardized**} \\  & & & & \multicolumn{3}{c}{**Regression Coefficient \(\beta\)**} \\ \cline{4-7}
**Parameters** & & \(r^{2}\) & **Change** & \multicolumn{3}{c}{**Body Height (cm) (kg) (\%)**} & \multicolumn{3}{c}{**Body Fat**} \\ \hline CMJ (cm) & Step 1 & 0.18 & & 0.22 & \(-0.15\) & \\  & Step 2 & 0.22 & 0.04 & 0.16 & \(-0.10\) & \(-0.28\) \\ SJ (cm) & Step 1 & 0.05 & & 0.14 & \(-0.02\) & \\  & Step 2 & 0.09 & 0.04 & 0.07 & 0.05 & \(-0.35\) \\ Medicine ball & Step 1 & 0.29 & & 0.07 & 0.02 & \\ overhead throw (m) & Step 2 & **0.57** & 0.28 & 0.02 & 0.07 & \(-0.23\) \\  & Step 1 & 0.29 & & 16.9 & \(-8.64\) & \\ Yo-Yo IRI distance (m) & Step 2 & 0.44 & 0.15 & 10.1 & \(-2.53\) & \(-32.3\) \\  & Step 1 & 0.01 & & 0.01 & 0.01 & \\
15 m sprint (s) & Step 2 & 0.39 & 0.38 & \(-0.01\) & 0.01 & \(-0.07\) \\  & Step 1 & 0.02 & & 0.01 & \(-0.01\) & \\  & Step 2 & 0.30 & 0.28 & \(-0.01\) & 0.01 & \(-0.06\) \\ Agility T-half (s) & Step 1 & 0.01 & & 0 & \(-0.01\) & \\  & Step 2 & **0.54** & 0.53 & \(-0.02\) & 0.01 & \(-0.09\) \\ Jump shot (m/s) & Step 1 & 0.30 & & 0.18 & \(-0.21\) & \\  & Step 2 & **0.61** & 0.31 & 0.03 & \(-0.08\) & \(-0.69\) \\ Running shot (m/s) & Step 1 & **0.52** & & 0.03 & \(-0.17\) & \\  & Step 2 & **0.65** & 0.13 & 0.02 & \(-0.09\) & \(-0.41\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the linear regression analyses for the anthropometric variables predicting the performance parameters. Body height and body mass were entered as predictors in step 1, while the percentage of body fat (%) was added in step 2. Nonstandardized regression coefficients (\(\beta\)) are given for each predictor. \(r^{2}\) > 0.5 marked in bold. SJ: squat jump, CMJ: countermovement jump, Yo-Yo IRI: yo-yo intermittent recovery test 1.  
of the Yo-Yo IR1 distance by 32.3 m (Figure 2), and increased medicine ball throw by 23 cm (Figure 3). The same reduction in %BF was related to decreased sprint performance (15 m sprint: 0.07 s, 30 m sprint: 0.06 s). This result corresponded to the results of the variance analysis (Table 2) for the parameter 15 m sprint. %BF had no effect on the sprint performances, and neither did the BMI categorization.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Performance Analysis of the Proposed PRNG-CS
#### 3.2.1 Histogram and Chi-Square Test  
A histogram describes the distribution of numerical data in the form of a graph. It forms an estimation of the probability distribution of a random or pseudorandom variable.  
The distribution uniformity of the generated sequences was examined. A uniform distribution over the whole phase space must be provided by the proposed PRNG-CS. Figure 6 shows an example of a histogram of a produced sequence, formed by 3,125,100 samples, in which the first 100 samples were not used (the transient regime).  
Visually, we observe that the generated sequence is nearly uniformly distributed.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Size**} & \multicolumn{2}{c}{**HD (\%)**} & \\ \cline{3-5}  & & \(r=1\) & \(r=2\) & \(r=3\) \\ \hline Airplane & \(512\times 512\times 3\) & 50.0010 & 49.9974 & 49.9998 \\ Black & \(256\times 256\times 1\) & 50.0027 & 50.0050 & 49.9977 \\ Bridge & \(512\times 512\times 1\) & 50.0039 & 49.9962 & 50.0021 \\ Cameraman & \(256\times 256\times 1\) & 49.9936 & 50.0018 & 50.0000 \\ Flowers & \(256\times 256\times 3\) & 49.9993 & 50.0034 & 49.9955 \\ Goldhill & \(512\times 512\times 3\) & 49.9999 & 49.9988 & 49.9996 \\ Kiel & \(512\times 512\times 1\) & 50.0010 & 49.9989 & 50.0036 \\ Lena & \(512\times 512\times 3\) & 50.0001 & 49.9997 & 49.9972 \\ Sailboat & \(512\times 512\times 3\) & 49.9961 & 50.0050 & 50.0000 \\ White & \(256\times 256\times 1\) & 50.0107 & 49.9948 & 50.0009 \\ \hline \hline \end{tabular}
\end{table}
Table 3: HD versus the round times in the encryption process.  
Then, to assert the uniformity of this sequence, we applied the chi-square test. The experimental chi-square \(\chi^{2}\) value was given by:  
\[\chi^{2}_{exp}=\sum_{i=0}^{N_{c}-1}\frac{(O_{i}-E_{i})^{2}}{E_{i}} \tag{28}\]  
where \(N_{c}=1000\), \(O_{i}\), and \(E_{i}=Ns/Nc\) are the number of classes, the number of calculated samples in the \(i\)th class \(E_{i}\), and the anticipated number of samples of a uniform distribution. Table 4 shows the experimental and theoretical values for the chi-square values obtained. The theoretical value of the chi-square was greater than the obtained experimental one, which proved that the sequence generated by the proposed PRNG-CS was uniform.  
Note that this test was repeated on one hundred various sequences, each was generated using a different secret key. All the sequences passed the chi-square test.
###### Abstract  
In order to solve the problem of the low end positioning accuracy of large hydraulic rock drilling robotic arms due to machining error and the working environment, this paper proposes an end positioning error compensation method based on an Improved Secretary Bird Optimization Algorithm (ISBOA) optimized Back Propagation (BP) neural network. Firstly, the good point set strategy is used to initialize the secretary bird population position to make the initial population distribution more uniform and accelerate the convergence speed of the algorithm. Then, the ISBOA is used to optimize the initial weights and biases of the BP neural network, which effectively overcomes the defect of the BP neural network falling into a local optimum. Finally, by establishing the mapping relationship between the joint value of the robot arm and the end positioning error, the error compensation is realized to improve the positioning accuracy of the rock drilling robot arm. The experimental results show that the average positioning error of the rock drilling robotic arm is reduced from 187.972 mm to 28.317 mm, and the positioning accuracy is improved by 84.94%, which meets the engineering requirements.  
rock drilling robotic arm; secretary bird optimization algorithm; good point set; BP neural network; error compensation +
Footnote †: journal: Applied  
Article  
Research on Positioning Error Compensation of Rock Drilling Manipulator Based on ISBOA-BP Neural Network
## 5 Numerical Results and Discussion
### Network Settings
#### 5.1.2 Dual-Slot Network Settings  
For the dual-slot ISAL networks, we present four networks with different topologies in the area of \(200~{}\text{m}\times 200~{}\text{m}\), as shown in Figure 8. The solid red circles represent anchors whose positions remain unchanged within two time slots; the solid green triangles and the solid black pentagram, respectively, represent radars and target in slot 1; and the hollow green triangles and the hollow black pentagram, respectively, represent radars and target in slot 2. Within each time slot, all four networks contain two anchors, two radars, and one target.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 3 Proposed Methodology
### Hybrid Fusion Mechanism
#### 3.2.3 Adaptive Aggregation Pooling and Max Pooling  
To reduce computational complexity while preserving key information, this study employs an adaptive aggregation pooling strategy that combines average pooling and max pooling. Average pooling computes the mean value over the pooled area, thereby preserving background information, while max pooling selects the maximum value, primarily retaining texture features of objects. By combining these two approaches, the model maintains useful information while effectively downscaling feature maps.  
Specifically, given an input feature map \(F\), the average pooling and maximum pooling operations are defined as  
\[F_{a}=\text{AvgPooling}(F,S),\quad F_{m}=\text{MaxPooling}(F,S) \tag{8}\]  
where \(S\) represents the scaling factor of the feature map. The pooled feature maps are then fused based on a learnable weight parameter:  
\[F_{o}=\lambda\cdot F_{a}+(1-\lambda)\cdot F_{m} \tag{9}\]  
where \(\lambda\) is a trainable parameter, which is initialized to 0.5 at the start of training. This adaptive fusion strategy allows the model to reduce spatial dimensionality while preserving critical information, thereby enhancing the performance of subsequent processing modules.
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 2 Preliminaries  
### Quality of Service (QoS)  
QoS was firstly defined by ITU (International Telecommunication Union) in 1994 as a comprehensive measurement of a service's performance in mobile telecommunication, computer networks, or other digital signal transmission systems. QoS mechanism provides different priorities to different applications, network sessions, or users to guarantee a certain level of performance to a data flow. Most commercial multimedia service providers have applied QoS-oriented optimizing method on effective data flow delivering through resource (bandwidth, throughput, etc.)-limited network, especially for real-time multimedia applications such as IPTV [25], VoIP, online meeting, and multiplayer online games.  
QoS mechanism generally uses numeric scores to quantify the network transmission quality of services, which is referred as QoS score. QoS score is usually valued by some related aspects of the network service, like transmission delay, packet loss rate, throughput, network jitter, resource availability, etc. [26]. Typically, a general assessment model is designed as  
\[QoS=\sum_{k=1}^{N}\omega_{k}\cdot f_{k}(x_{k}) \tag{1}\]  
where \(x_{k}\) is a network influencing factor, \(N\) denotes the number of network influencing factors, \(\omega_{k}\) is their corresponding weight, and \(f_{k}(\cdot)\) represents a mapping function.  
In the literature, the mapping function is a linear function; however, the linear mapping method ignores the \"marginal effect\" of some network parameters, which, in fact, cannot fully describe the relation between the network parameters and QoS score; for example, an extreme case, assuming that a certain kind of service is less sensitive to the network transmission delay, namely, it has a relatively small weight in influencing QoS score, and we further assume the network packet loss rate is 100%, which means packet loss will happen with a probability of 100%, with the throughput rate of 100 Mbps. It is clear that the aforementioned network will fail to deliver any packet, and theoretically the QoS scores will rise with the timeline, but unfortunately, a linear mapping function cannot indicate such trend.
# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product Group** & **Algorithms** & **Accuracy** & **Precision** & **Recall** & **F-1 Score** \\ \hline Skin Make-Up & K-NN & 0.83 & 0.83 & 0.83 & 0.82 \\  & SVM & 0.93 & 0.93 & 0.93 & 0.92 \\  & DT & 0.88 & 0.88 & 0.88 & 0.88 \\  & RF & 0.92 & 0.91 & 0.92 & 0.91 \\  & LR & 0.93 & 0.92 & 0.93 & 0.92 \\ Eye Make-Up & K-NN & 0.86 & 0.85 & 0.86 & 0.84 \\  & SVM & 0.94 & 0.94 & 0.94 & 0.93 \\  & DT & 0.91 & 0.91 & 0.91 & 0.91 \\  & RF & 0.93 & 0.93 & 0.93 & 0.93 \\  & LR & 0.94 & 0.94 & 0.94 & 0.93 \\ Lip Make-Up & K-NN & 0.85 & 0.83 & 0.85 & 0.83 \\  & SVM & 0.93 & 0.92 & 0.93 & 0.91 \\  & DT & 0.89 & 0.89 & 0.89 & 0.89 \\  & RF & 0.92 & 0.92 & 0.92 & 0.91 \\  & LR & 0.93 & 0.92 & 0.93 & 0.91 \\ Skin Care & K-NN & 0.87 & 0.85 & 0.87 & 0.82 \\  & SVM & 0.94 & 0.94 & 0.94 & 0.85 \\  & DT & 0.90 & 0.90 & 0.90 & 0.94 \\  & RF & 0.93 & 0.93 & 0.93 & 0.90 \\  & LR & 0.94 & 0.93 & 0.94 & 0.92 \\ Hair Care & K-NN & 0.72 & 0.91 & 0.72 & 0.93 \\  & SVM & 0.95 & 0.95 & 0.95 & 0.79 \\  & DT & 0.92 & 0.91 & 0.92 & 0.94 \\  & RF & 0.94 & 0.94 & 0.94 & 0.91 \\  & LR & 0.94 & 0.94 & 0.94 & 0.93 \\ Body Care & K-NN & 0.78 & 0.92 & 0.78 & 0.94 \\  & SVM & 0.95 & 0.95 & 0.95 & 0.84 \\  & DT & 0.93 & 0.92 & 0.93 & 0.95 \\  & RF & 0.95 & 0.94 & 0.95 & 0.93 \\  & LR & 0.95 & 0.95 & 0.95 & 0.94 \\ Perfumery & K-NN & 0.85 & 0.84 & 0.85 & 0.95 \\  & SVM & 0.93 & 0.93 & 0.93 & 0.82 \\  & DT & 0.89 & 0.88 & 0.89 & 0.92 \\  & RF & 0.92 & 0.91 & 0.92 & 0.89 \\  & LR & 0.93 & 0.93 & 0.93 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Proposed model performance metric results of classifiers. LR is another algorithm with the highest classification performance. LR is an effective method for solving binary or multiple classification problems and has computational efficiency due to its simple implementation [65]. The computational efficiency success of the LR algorithm also manifested itself in the model used in the study. Among all algorithms, K-NN gave the lowest scores in classification performance. Although K-NN is an important algorithm for density estimation, its efficiency level is low, and its performance varies according to the data size [66]. The high-dimensional and complex structure of all product groups caused the K-NN algorithm to reveal its weakness. The complexity and variety of the data show that the K-NN algorithm has difficulty learning non-linear boundaries. In this sense, SVM and LR were more successful in classifying big data sentiment labels than the K-NN algorithm.
# A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
## 4 Results and Discussion
### Image Metric Comparison of the Proposed DCT/IDCT Architectures
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{5}{c}{**PSNR (dB)**} \\ \cline{2-6}  & **Shen’s Method [9]** & **HEVC [26]** & **High Per [29]** & **Imp. Loeffler [25]** & **Proposed** \\ \hline Cameraman & 33.785 & 37.893 & 33.98 & 38.124 & 40.476 \\ Apple & 32.654 & 34.912 & 33.46 & 35.485 & 38.394 \\ Bird & 36.297 & 39.098 & 37.23 & 39.768 & 40.511 \\ Lena & 36.784 & 39.164 & 36.89 & 39.567 & 40.523 \\ Average & 34.88 & 37.766 & 35.39 & 38.236 & 39.976 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: PSNR comparison.
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
## 5 Fractional-Order LQR Method for Output Feedback Control  
### Necessity of Fractional-Order State Observer  
The fractional-order LQR control is a kind of state feedback control. Therefore, estimation of the states is an important factor in state feedback control. Numerical simulation of the control may be conducted under the condition that all of the states of the system are assumed to be detected. However, such a condition is not effective in many real cases. Even though sensors are present in the controlled system, obtaining all of the states of the system in most cases is difficult. Furthermore, the viscoelastic damper system, as the controlled object in the present study, contains a fractional-order derivative term that cannot be detected with ordinary sensors in general. Therefore, in order to carry out fractional-order LQR control in practice, it is necessary to estimate the fractional-order derivative states based on information about the observable states.  
Accordingly, a scheme that enables estimation of the remaining states based on the input signals and the output signals measured by the sensors is called a state observer. The state observer can be designed as long as the system is observable. If all of the states ofstates and the actual states are called estimation errors. When the estimation errors become zero, the estimated states match the actual states. Therefore, the state observer must be constructed so that the estimation errors converge to zero.  
The estimation errors for the fractional-order system asymptotically go to zero by means of the following design of the state observer:  
\[D^{\alpha}\hat{\mathbf{x}}(t)=A\hat{\mathbf{x}}(t)+B\mathbf{u}(t)+\mathbf{H}(y(t)-C\hat{\mathbf{x}}( t)), \tag{32}\]  
where \(\hat{\mathbf{x}}(t)\), \(\mathbf{H}\), and \(\mathbf{C}\) are the estimated state vector, the state observer gain, and the output matrix for the controlled system, respectively. The state observer gain \(\mathbf{H}\) is selected in order to satisfy the following inequality [31]:  
\[|\arg(\lambda_{so,j})|>\frac{\alpha\pi}{2},\ (i=1,\ 2,\ \cdots n), \tag{33}\]  
where \(\lambda_{so,i}\) designates an eigenvalue of the matrix \(\mathbf{A}_{so}=\mathbf{A}-\mathbf{H}\mathbf{C}\).
# The Role of Platelet-Rich Plasma in the Management of Ankle Osteoarthritis: A Systematic Review
## 3 Results
### Adverse Effects
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Authors, Year** & **Sample Size** & **Outcomes** & \multicolumn{6}{c}{**Follow-Ups (Months)**} \\ \hline  & & & **Baseline** & **0.5** & **1** & **1.5** & **3** & **6** & **6.5** & **17.7 (Mean)** \\ \hline  & & VAS & \(4.1\pm 1.7\) & - & \(2.2\pm 1.9\) * & - & \(1.7\pm 1.5\) & \(1.8\pm 1.6\) * & - & - \\  & & ACS & \(4.3\pm 1.7\) & - & \(2.6\pm 2.0\) * & - & \(21.1\pm 1.7\) * & \(2.2\pm 1.7\) * & - & - \\ Sun et al., 2021 & 39 & AOFAS & \(80.3\pm 8.9\) & - & \(87.2\pm 10.3\) * & - & \(916.9\pm 9.1\) * & \(89.7\pm 10.0\) * & - & - \\ Sun et al., 2021 & 39 & SLS test & \(27.5\pm 33.5\) & - & \(41.4\pm 35.8\) * & - & \(43.7\pm 35.1\) * & \(42.8\pm 34.3\) * & - & - \\  & & Analgesic & \(15.1\pm 6.2\) & - & \(6.3\pm 2.5\) * & - & \(5.7\pm 2.4\) * & \(6.5\pm 2.6\) * & - & - \\  & & Satisfaction & - & - & \(70.9\pm 21.2\) & - & \(71.7\pm 20.1\) & \(71.7\pm 21.2\) & - & - \\ \hline  & & VAS & & & & & & & \\  & & ACS & & & & & & & \\ [PERSON] et al., 2021 & 100 & AOFAS & & Intra-group changes between 26-week and baseline measurements in both groups \(p<0.001\) & & & & & \\  & & AAS & & & & & & & \\  & & SF-36 & & & & & & & \\  & & GAS & & & & & & & \\  & & EQ-SD-3L & & & & & & & \\ \hline  & & Overall & & & & & & & \\  & & (\(n=20\)) & & & & & & & \\ [PERSON] et al., & & & & & & & & \\  & & & SAFF-46 & \(64.7\pm 16.9\) & \(61.8\pm 17\) * & \(56.1\pm 19.3\) & \\ [PERSON] et al., & & & & & & & & \\  & & & VAS & \(57.7\pm 16\) & \(22.9\pm 8.9\) * & \(259.3\pm 139\) * & \(43.7\pm 20.1\) & \\
2017 [21] & 20 & Early-stage OA & & & & & & \\  & (\(n=7\)) & & SAFF-Q & \(56.9\pm 14.4\) & \(70.0\pm 13.2\) & \(75.3\pm 116\) & \(66.2\pm 18.8\) & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \[PERSON] et al., & & & & & & & & \\
2017 [20] & 20 & VAS & \(7.8\pm 0.5\) & - & - & - & - & - & \(2.6\pm 2.2\) * \\  & & PADI & \(59.2\pm 3.6\) & - & - & - & - & - & \(80.2\pm 17.3\)* \\ \[PERSON] et al., & & & & & & & & & \\
2013 [22] & 5 & VAS-FA & \(69.6\pm 18\) & \(\dagger\) & - & \(\dagger\) & \(84.5\pm 10.3\) * & \\  & & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & & \\ \hline \end{tabular} Values are mean \(\star\) standard deviation or otherwise indicated. Abbreviations VAS, Visual Analogue Scale; FADL, Post Absolute Desibility Index; VAS-FA, Visual Analogue Scale; FedL and Adike; SF-36, Joint Form; 3-RC, Joint Survey; AOS, Ankle Observhrits Score; AAS, Ankle Activity Score; FAGS, Foot and Ankle Outcome Score; SIS, Single Leg, Stance test, AO/EAS, American Orthopaedic Foot and Ankle Score; CAS, Goal Alignment Scaling EQ-SD-3L, EuroSQ Quality of Life questionnaire. Symbols: \(\dagger\), data not provided \(\star\), significant within-group changes.  
\end{table} 
Table 5: Reported outcomes from included studies.
## 7 Conclusions  
1. Based on the thermal analysis of the gas regulator, temperature and heat flux in the regulator operation process have obvious fluctuations in the regulator structure. The effect of temperature and heat flux on the regulator should be considered during the simulation.
2. According to the thermo-hydro-mechanical analysis of the gas regulator, the maximum deformation of the regulator sleeve, valve bore, and outlet valve seat wall occurs when the opening is 20%, and the maximum deformation of each part is 0.032 mm, 0.066 mm, and 0.066 mm, respectively. At the same time, as the opening increases, the deformations of the regulator valve hole and outlet seat wall gradually decrease, and the sleeve deformation tends to fluctuate. When the valve opening is small, the sleeve, valve, and outlet seat wall regulator are subject to greater stress. The maximum stress on each part is 98.9 MPa, 292.1 MPa, and 164.9 MPa, respectively. At the same time, with the increase in the opening of the valve port, the stress on each part decreases significantly. At 20% opening, the working conditions of the regulator are complex; the stress on the sleeve and outlet valve seat wall has a certain rise, and the stress on the valve bore has no significant decrease.
3. To optimize the structure of the gas regulator at the 20% opening, the sleeve wall thickness, valve bore diameter, and outlet seat wall thickness are confirmed as the parameter variables. The equivalent stress is determined as the objective function, and the response surface method is applied to optimize the structure of the gas regulator. Hence, to obtain the optimal objective function and the optimal solution, that is, when the sleeve wall thickness is 7.25 mm, the valve hole diameter is 25 mm, the outlet seat wall thickness is 31.05 mm, and the gas regulator's overall displacement and equivalent stress reach the minimum. Lastly, the maximum equivalent stress is 135.62 MPa.  
[PERSON] and [PERSON] wrote the manuscript [PERSON] was in charge of the whole project; [PERSON] and [PERSON] were in charge of translation. [PERSON] was responsible for reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Xiong'an New Area Science and Technology Innovation Project, National Key R&D Program of China, grant number 2022 XAGG0147 and the Science and Technology Project of the Hebei Education Department, grant number QN2022198.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data is not publicly available.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Flow Rate  
A variety of conditions were trialed to obtain the ideal flow rate of 1-2 mL h\({}^{-1}\) (0.017-0.033 mL min\({}^{-1}\)). The final process was to perform a brief vacuum filtration with just water to flatten the membrane and then to filter the CNT suspension completely under gravity, with no applied vacuum. An example of the flow rates of arc-discharge single-walled CNTs (SWCNTs) is shown in Figure 2a,b. Over time, the flow rate approaches the ideal parameters.
## 4 Results
### GNSS Data Processing: Quality and Reliability
#### 4.2.2 Alert Assessment Based over a Long-Term Period Trend
(**A**) computation of the E, N, and h displacements from the difference with original WGS84 32N coordinates for the GNSS SAS b; (**B**) computation of the deformation calculated from the original distance between the GNSS SAS a and SAS b. The time is expressed in Doy (Day of the year, where 0 = is 1 January 2018). If we focus the analysis on the period of GNSS continues acquisition (Figure 12B), it is possible to see that, if the displacement is averaged on a weekly window, the time series shows low noise (the correlation coefficient, R\({}^{2}\) = 0.86). This sampling interval of seven days is based on an empirical test that, for this rate of deformation, best fit acceptable noise with measure frequency. The results show that the rate of horizontal displacement calculates with linear regression is about 4.6 mm/year, below the GT of 3 mm in 100 days (\(\approx\)11 mm/year).