esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical and Experimental Verification
### Numerical Verification

Table 2: Estimated embedment depth of the anchor bolt in numerical verification. Figure 8 plots the variation of the estimation according to the value of the regularization factor of Equation (6) for case E100C100. The regularization factor of 0 corresponds to solving Equation (5) that does not apply regularization. The regularization factor of 0.0001 appears to have a very weak regularization effect and produces solutions with a dispersion comparable to the non-application of regularization but the complete removal of outliers is noteworthy. The quasi-absence of improvement in the dispersion indicated that the value of the factor was too small. Increasing the value of the regularization factor led to the gradual reduction in the instability of the solution and clear convergence but at the expense of a clearly slower convergence to the solution. Consequently, the accuracy of the estimation of the embedded depth of the anchor bolt can be improved by selecting a large regularization factor when there is sufficient space for displacing the ultrasonic receiver and perform measurement until large \(d_{i}\). Moreover, when it is difficult to secure a sufficiently large \(d_{i}\), a relatively smaller regularization factor shall be used to verify the convergence to the solution. Figure 7: Variation in transferred distance of ultrasonic pulse through the embedded anchor bolt according to the error of ultrasonic velocity in the anchor bolt and concrete: (**a**) error of ultrasonic velocity in the anchor; and (**b**) error of ultrasonic velocity in concrete. An appropriate regularization factor can thus be selected in view of the circumstances and the comparison presented in Figure 8. If it is difficult to determine the convergence value because of excessive dispersion, the regularization factor shall be increased. On the other hand, the regularization factor shall be decreased appropriately and stepwise when the convergence value cannot be distinguished due to excessively slow convergence.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 5 Progress of CO2 Storage in China
### Development Status of CO2 Geological Storage in China
#### 5.1.4 Co\({}_{2}\) Storage in Salt Caverns in China  
Salt caverns are artificial cavities constructed by dissolving salt deposits using water in deep underground salt formations [168]. The low permeability and visco-plastic properties of rock salt [179] allow salt caverns for the safe containment and storage of large volumes of liquids and gases under high pressure. Therefore, salt caverns have been widely used to store natural gas [180] and hydrogen [181]. The extensive successful track records of salt caverns for natural gas storage suggest that they may serve as a potential option for CO\({}_{2}\) geological storage [182].  
China's rock salt has a bedded structure with thin salt layers and interlayers [180], and this brings great challenges for salt cavern construction. Currently, CO\({}_{2}\) storage in salt caverns in China is still in the research stage. [PERSON] et al. carried out the safety and applicability evaluation of CO\({}_{2}\) storage in typical bedded rock salt caverns of China [179] by using numerical simulation to preliminary determine the appropriate cavern pressure, volume variation, displacement and other parameters, so as to provide theoretical support for subsequent practical applications.
## 4 Parameter Setting and Analysis
### Residual Threshold  
Experimentation and adjustments are commonly used to determine the residual thresholds. The residual threshold was calculated based on the noise level of the road surface sample data. To estimate this threshold, we randomly selected 50 representative supervoxels that represented the road surface. The noise level in each supervoxel was the square root of the variance in the orthogonal distances from all points within the supervoxels to the fitting plane.  
In this study, we used different residual thresholds for the region growing segmentation and observed the segmentation results. From Figure 13a, it can be observed that for Site 1, the change in accuracy was not significant when the residual varies from 0.01 to 0.05 m. This is because Site 1 data points have a relatively low noise level. For the slope road surface (Figure 13b), there is no difference in accuracy when the residual threshold increases from 0.03 to 0.05 m. However, when the residual threshold decreases from 0.03 m to 0.01 m, the accuracy significantly decreases. This lower accuracy is due to the higher noise level in Site 2 data. After experimenting and discussing different residual thresholds, we finally selected 0.05 m as the residual threshold for this study.  
Finally, the thresholds and input parameters of the road surfaces of the flat and sloped road datasets were extracted experimentally, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Supervoxel** & **Slope** & **Angle** & **Residual** \\  & **Resolution/m** & **Threshold/\%** & **Threshold/\%** & **Threshold/m** \\ \hline Flat road dataset & 0.2 & 5 & 5 & 0.2 \\ Sloped road dataset & 0.2 & 5 & 5 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental parameters.  
Figure 12: Accuracy analysis of four different angle threshold values: (a) Site 1; (b) Site 2.  
Figure 13: Accuracy analysis of four different residual threshold values: (a) Site 1; (b) Site 2.
## 6 Limitations and Future Research Directions  
Our research is not without its limitations. While our participant pool provided a range of insights, it was limited in demographic diversity, potentially impacting the generalizability of the findings. Future research could address a more diverse population to validate the universal applicability of our results. Additionally, our study's scope was limited to comparing two specific types of interaction modalities, and further research could explore additional interfaces, such as voice control or gesture recognition, which may offer other avenues for engaging with AR.
## 4 Discussion
These findings suggested the As exposure dose, even in the control group, was able to trigger methylation reactions. From the results, the As species concentration pattern mirrored the trend observed in the total As concentrations in the liver and in the other investigated organs in this study. The control and the CBZ groups seemed to show low and comparable concentrations of all the studied As species, while the As and As + CBZ groups appearedto show elevated concentrations of all the As species, with the As + CBZ group appearing to have accumulated the highest amount of each As species. These findings imply that the concentration of the As species in the liver was dependent on the dose of As exposure. This pattern mirrored the total As concentration in the liver and other organs in this research. This is consistent with the findings of [PERSON] et al. [66], who also observed dose-dependent concentrations of As species in various investigated organs in mice exposed to As. These results also reinforce the already made point that CBZ may have enhanced the intake of As in the As + CBZ group. Even though we anticipated the predominant form of As exposure in this study was As(V), it is noteworthy that the concentration of As(III) was the highest when As species were compared within individual groups. Even the control group was not an exception. The As + CBZ group had the highest concentration based on intergroup comparisons of As species. It has already been established that the pathway of inorganic As metabolism or biotransformation starts with the reduction of As(V) to As(III) in vivo where anaerobic conditions exist, after which As(III), because of its high affinity for thiol compounds, binds to GSH and other thiol compounds inside the cells [80; 81]. The liver has high levels of GSH, especially in mice. Thus, this leads to high quantities of As(III)-(GS) complexes, which serve as the substrate for As3 MT [80; 82; 83]. This probably explains the reason why the concentration of As(III) was the highest among the As species within the individual groups, even in the control group. The type of As species that accumulates in a target organ is presumed to be the dominant cause of its toxicity [79]. Therefore, in this current research, As(III) may be the As species predominantly associated with the highest As toxicity among the four As species determined in the liver of animals exposed to very high As doses (As and As + CBZ groups).
## 2 Materials and Methods
### Vision System  
In order to achieve the morphometric and thermal characterization of plant roots and leaves, a vision system based on three video cameras was designed (Figure 4). The model mainly consists of a Medium-Density Fibreboard (MDF) wooden structure with the capability of leaf and root image capturing. Three cameras were installed covering the light spectrum: visible (VIS), near Infrared (NIR) and far infrared (FIR). The VIS spectrum images were captured with a GoPro Hero4 camera (GoPro, Inc., San Mateo, CA, USA) having a spectral range of 400 to 700 nm. The NIR spectrum images were obtained with an Allied Vision's Bigeye G-132 NIR Cool camera (Allied Vision Technologies, Stadtroda, Germany) with: Sony ICX285 EXview HAD CCD sensor, 1280 \(\times\) 1024 pixels resolution, extended sensitivity ranging from 350 nm up to 1000 nm, Peltier cooling, stabilized at \(-\)20 \({}^{\circ}\)C and 0 \({}^{\circ}\)C to 35 \({}^{\circ}\)C operating temperature. The FIR spectrum images were captured with an FLIR E4 infrared camera (Teledyne Flir, Wilsonville, OR, USA) with: infrared range of 7500-13,000 nm, operating temperature range of \(-\)20 to 250 \({}^{\circ}\)C with an accuracy of \(\pm\)2% for room temperature of 10 to 35 \({}^{\circ}\)C, 80 \(\times\) 60 pixels IR resolution, 45 \(\times\) 34 field of view, 0.5 m minimum focal length and thermal sensitivity of 0.15 \({}^{\circ}\)C.  
Figure 4: Model of the multispectral vision system.  
The near-infrared image capturing required the NIR camera to be plugged into a computer and a light source, for this case a 7.5 W white light lamp was used [50; 51; 52].
# Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link
## 2 Overview of Orbit Determination Algorithms
Currently, the developments for autonomous orbit determination, time synchronization, and autonomous operation and management are being widely investigated. More progress on designing an efficient distributed data processing algorithm, however, needs to be made.
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 3 Theoretical Basis
### Density Peak Clustering Algorithm  
The full name of the clustering algorithm for density peaks is Clustering by Fast Search and Find of Density Peaks (DPC). This algorithm, proposed in _Science_ in 2014 [7], can automatically discover cluster centers and is noted for its simplicity and efficiency, requiring no iterative calculations. It is particularly suitable for processing large-scale and high-dimensional data.  
* The algorithm operates based on two underlying assumptions: (1) The local density of the cluster center (density peak point) exceeds that of its neighboring data points within a defined locality. (2) The distance separating the cluster center from other cluster centers is notably substantial. Two critical parameters play a pivotal role in this algorithm: local density (\(\rho\)) and the distance (\(\delta\)) between data points and their respective local density extreme points. The definitions are as follows: \[\rho_{i}=\sum_{i\
eq j}\chi(d_{ij}-d_{c})\] (1) \[\chi(x)=\begin{cases}&1,\,x<=0\\ &0,\,x=>0\end{cases}\] (2)In this context, \(\chi(x)\) represents a logical decision function, \(d_{ij}\) signifies the Euclidean distance between data points \(x_{i}\) and \(x_{j}\), and \(d_{c}\) denotes the truncation distance, used to calculate \(\delta_{i}\), which is chosen as the 1% to 2% quantile value of the sorted \(d_{ij}\) distances. The meaning of \(\rho(i)\) is as follows: with \(x_{i}\) as the center and \(d_{c}\) as the radius, it determines whether \(x_{j}\) lies within the circle, where the local density of \(x_{j}\) is defined as the count of points within this circle. When there are data points with a higher density around a given data point, the minimum distance from each data point to other points with a higher local density than itself is denoted as \(\delta_{i}\). For data points with the highest local density, \(\delta_{i}\) values are determined as follows:  
\[\delta_{i}=\left\{\begin{matrix}\underset{j\
eq i_{j}>\rho_{i}}{\min}\big{(}d _{ij}\big{)},&if\exists j\ s.t.\rho_{j}>\rho_{i}\\ \underset{j}{max}\big{(}d_{ij}\big{)},&otherwise\end{matrix}\right. \tag{3}\]  
The DPC algorithm sorts instances by their \(\rho\) value and \(\delta\), and then identifies the top c instances as cluster centers. The decision function is as follows:  
\[score(x_{i})=\delta_{i}\rho_{i} \tag{4}\]  
The data source for Figure 1 is referenced in [7]. The left image shows the shape distribution of the data, and the right image shows the decision graph of the data. The labels \"1\", \"2\", and \"3\" on the points in the right image indicate the data points with the top three scores according to Equation (4).
## 3 Results
As a result, the relationship between rupture force and diameter is highlighted by Equation (5):  
\[R\text{{{{  
* Comparative analysis: The RFD provides a direct and easy-to-understand metric when comparing different populations. It allows, indeed, quick assessment of which compositional population is stronger or more resistant to rupture. An example illustrating the significance of \(RFD\) can be demonstrated through the influence of wall thickness. As the capsule consists of a core/wall structure, the wall thickness plays a crucial role in determining the overall strength of the capsule. \(RFD\), being a holistic material characteristic property, considers the contribution of the wall thickness to the capsule's overall strength. This allows \(RFD\) to effectively capture the variations in wall thickness within the population of capsules. In summary, \(RFD\) comprehensively characterizes the material's behavior under the incipient loading limit relative to the diameter. In Table 5, the \(RFD\) is reported for each sample along with the standard error associated with the calculation of \(RFD\) via the regression slope between rupture force and diameter. The capsule batches were analyzed for their performance regarding _Encapsulation Efficiency_ and _On-Fabric delivery_. The results are reported in Table 6.  
In Figure 7, a multivariate analysis was conducted by generating a scatterplot matrix to assess the correlation between the parameters related to the chemical structures of the monomer mixture employed (\(MW_{c}\), \(HSP\)_distance_), and the parameters related to the performance of the capsules (\(RFD\), _Encapsulation Efficiency_, _On-Fabric delivery_). A logarithm transformation was applied for the _Encapsulation Efficiency_ % values to address the data's non-normality, as explained in the Supporting Information Document. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & **Encapsulation Efficiency [\%]** & **On-Fabric Delivery [nmol/L]** \\ \hline CAP110101 & 94.3 \(\pm\) 0.2 & 307.5 \(\pm\) 45.7 \\ CAP1011101 & 0 & 50.02 \(\pm\) 12 \\ CAP103107101 & 83.8 \(\pm\) 0.3 & 319.6 \(\pm\) 19.5 \\ CAP10510510 & 89.6 \(\pm\) 0.2 & 322.1 \(\pm\) 47.9 \\ CAP1010111 & 95.1 \(\pm\) 0.3 & 201.3 \(\pm\) 51.3 \\ CAP1031010.71 & 94.8 \(\pm\) 0.2 & 248.1 \(\pm\) 49.8 \\ CAP105101051 & 94.3 \(\pm\) 0.3 & 260.7 \(\pm\) 9.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance characterization of the capsules in terms of _Encapsulation Efficiency_ and _On-Fabric delivery_. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & **MW between Cross-Links [g/mol]** & **RFD [N/mm]** \\ \hline CAP110101 & 128 & \(8.5\times 10^{1}\pm 6.1\times 10^{-1}\) \\ CAP1011101 & 172 & \(7.7\times 10^{0}\pm 5.1\times 10^{-1}\) \\ CAP103107101 & 147 & \(6.0\times 10^{1}\pm 4.8\times 10^{-1}\) \\ CAP105105101 & 139 & \(7.5\times 10^{1}\pm 5.2\times 10^{-1}\) \\ CAP1010111 & 105 &
## 2 Materials and Methods
### Genetic Path Planner
#### 2.1.1 Consistency Check  
It is necessary to ensure that each generated path is within the mission area and does not intersect possible obstacles. Therefore, a consistency check is made every time a new gene is created or the content (\(\alpha\) or \(d\)) of an existing one changes. This guarantees that all the locations \(x\) of the nodes composing a path belong to \(X_{free}\). Thanks to the _a priori_ knowledge about obstacles and boundaries of the mission area, such verification results to be a trivial task. If the verification fails, meaning that the position \(x\) resulting from the new generated gene is either outside the target area or inside the obstacle area, then the gene is deleted and replaced with a new one. However, due to the limitation on the standard deviation \(\sigma_{\alpha}\), imposed to obtain routes compliant with the AUV manoeuvrability capabilities, it might happen that no consistent genes could be generated. A similar scenario could occur near the borders of the target and obstacle areas, especially in the corners, where any steering angle \(\alpha\) sampled from the Gaussian distribution will result to be insufficient to generate points in \(X_{free}\), with a consequent impossibility to carry on the algorithm. The strategy adopted by the authors in such circumstances is structured as follows. First, the algorithm attempts to generate a new consistent gene sampling the elements of its tuple from the respective distributions, for a limited number of trials \(C_{g}\). If this first attempt fails, then the new gene is generated adopting an uniform distribution with range \([0^{\circ},360^{\circ})\)  
Figure 2: Flowchart of the Genetic Path Planner.  
for the \(\alpha\) angle. That is done with the aim of straying from the borders of the target and obstacle areas, thus increasing the chances to obtain a consistent gene. In the case that also this approach does not provide useful results, after \(C_{u}\) attempts, the last resort is to delete the last \(C_{n}\) nodes (genes) of the path, if any, and replace them with new ones. Deleting the nodes allows to exit from the condition which generates inconsistent genes, but does not guarantee that such condition will not recur. Moreover, the proposed strategy could sacrifice the conformity of the route to the manoeuvrability of the AUV in order to always obtain a viable path and avoid algorithm interruption, even for the aforementioned critical scenarios.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 2 Methodology
### ConvMixer  
Extraction of prominent features is critical in deep learning tasks. Leveraging the success of the ConvMixer architecture in computer vision applications, we adopt it with some modifications to suit our purpose. The fundamental concept of the mixer architecture is to shuffle the data both spatially and channel-wise using depthwise and pointwise convolutions, respectively. Additionally, ConvMixer maintains the input structure throughout the mixer layers.  
Unlike the original ConvMixer, which operates on image data and includes an embedding layer to deal with image patches, we omit the embedding layer as our data can be directly processed in its existing form. However, as mentioned in Section 2.1, for obtaining spectral features the real and imaginary parts of the output of DEFT are treated as separate channels. Hence, the number of input channels becomes twice the number of dependent time series \(N\). Additionally, we use 1D convolutions due to the nature of our data. Furthermore, we use layer normalization, contrary to the original ConvMixer architecture. For input X, the pointwise convolution (PW) is represented as \[PW=LayerNorm(GELU(1D\_PointWiseConv(X)) \tag{6}\]  
and the depthwise convolution (DW) is represented as  
\[DW=LayerNorm(GELU(1D\_DepthWiseConv(X)) \tag{7}\]  
Then, the function \(F\) representing the working of ConvMixer architecture can be represented as  
\[F=[PW(Residual(DW(\mathfrak{F}(WSO))))]*L \tag{8}\]  
where _Residual_ signifies the presence of a residual connection in the block, \(\mathfrak{F}\) represents the DFFT operation, and \(L\) is the number of repetitions of the mixer layer.
## 3 Environment Characterization
### Rural Environment  
The rural environment is categorized into a low-density area. In fact, it refers to an outside area of urban or suburban regions that is primarily characterized by a natural or agricultural landscape. Both wired and wireless communication technologies are used in rural environments to provide access to communication and information resources. Each technology has its own advantages. The best choice depends on the specific needs and circumstances of the utilities.  
PLC technology is a useful technology in rural areas where power lines already exist. It provides a reliable and cost-effective way to transmit data and communication signals over long distances. However, PLC is limited by the quality and availability of the power lines themselves. In some cases, power lines are too old or not well-maintained, which leads to poor signal quality and reduced reliability. Additionally, PLC is subject to interference from other electrical devices, which further degrades the quality of the communicationsignal. On the other hand, the length of the aerial cable, in rural networks, can generally be extended to 1 km when the number of charges is very simple [46].  
RF technology is used to provide communication services in rural areas and is particularly useful in areas where power lines do not exist or are not reliable. Furthermore, RF provides high-speed internet access and other communication services to remote and underserved communities, even in areas where traditional infrastructure is lacking. However, wireless technologies are subject to signal degradation and interference from environmental factors, such as weather, terrain, and foliage. These sparsely populated remotely controlled areas are less likely to cause distortions, which explains the low number of paths.  
Overall, the choice between PLC and RF technologies will depend on the specific needs and conditions of a given rural environment. A careful consideration of factors such as the existing infrastructure, signal quality, and reliability is the best choice for a rural environment. Both technologies may also be used to provide an uninterrupted and reliable communication network. The typical distance between a smart meter and a data concentrator in a rural environment varies depending on various factors, such as the communication technology used, terrain, vegetation, and the presence of obstacles or interference. For a PLC system, the distance can range from a few hundred meters to several kilometers, depending on the quality of the power lines and the presence of electrical devices or interferences.