esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Soil-Bentonite Vertical Cut-Off Wall Service Performance and Improvement Measures
### Dry-Wet Cycles  
Seasonal rainfall and temperature fluctuations cause soil-bentonite cut-off walls to repeatedly undergo dry-wet cycles during long-term operation. Dry-wet cycles refer to the process of testing materials under alternating dry and wet conditions. Dry-wet cycles make soil structure more uniform and create cracks, increasing permeability. They also reduce water-holding capacity in the low-suction range and lower cohesion and friction angle of bentonite-modified clay [110]. These effects impair the long-term stability and barrier effectiveness of soil-bentonite cut-off walls. With divalent cations (e.g., Ca\({}^{2+}\)) in the permeate, cation exchange occurs in bentonite. They replace monovalent cations, compressing the bentonite bilayer and reducing hydration and swelling. This creates more connected pores or cracks, increasing fluid flow and permeability. Cation exchange also adjusts osmotic pressure to aid fluid passage through bentonite clay [67]. When cation exchange interacts with dry-wet cycles, bentonite may develop severe cracks, as shown in Figure 10. This interaction enhances hydraulic conductivity, thereby undermining the effectiveness of the cut-off wall [111].  
[PERSON] and [PERSON] [112] found that the hydraulic conductivity of natural sodium-based bentonite increased by three orders of magnitude when exposed to a 0.0125 mol/L CaCl\({}_{2}\) solution after undergoing six dry and wet cycles indoors, indicating a significant decrease in impermeability performance. A study by [PERSON] et al. [83] demonstrated HYPER Clay, especially HC + 8%, outperforms natural sodium bentonite in swelling ability, self-healing capacity, and maintaining low hydraulic conductivity in seawater conditions. After three wet-dry cycles in seawater, the performance of natural sodium bentonite and HYPER Clay can be summarized as Table 6 follows:  
Figure 10: Erosion and dissolution of bentonite in repeated wet and dry cycles.  
In terms of modifications for wet and dry cycling, [PERSON] et al. [113] found that their self-developed polymer-modified bentonite clay exhibited greater resistance to the effects of wet and dry cycling and swelling compared to unmodified bentonite, as shown in Figure 11. The polymer was incorporated into the interlayer structure of montmorillonite and grafted onto its surface, allowing the modified bentonite clay to more effectively seal inter-particle voids, significantly reducing its permeability.
## 2 Process Design
### Pellet Scale  
The pellet size and pellet porosity are the main properties that affect the removal process on the scale of the pellet. Pellet porosity directly affects the intra-pellet diffusion [45]. In order to improve the intra-pellet diffusion, it is more favorable to make pellets with high porosity without sacrificing the mechanical durability. Pellet size also affects the bed porosity and the pressure drop as described by Equations (3) and (4), respectively. Moreover, the pellet size affects the characteristic time of diffusion which means that smaller pellets require shorter diffusion time [46]. However, small pellets in a packed bed lead to high pressure drop. Hence, finding the optimum size of the pellets, that are not too small to cause high pressure drop and not too large that lead to poor diffusion through the pellet, turns into an optimization problem with the three main controlling factors, namely, characteristic time of diffusion through the pellet, bed porosity and bed pressure drop.
## 5 Results  
The simulations and experimental results will be assessed in this section. We will compare the control strategy with STA and the BSTA controller. Both controllers have the same values of the parameters \(k_{1}\) and \(k_{2}\), obtained according to the stability proof from Section 3, whereby the BSTA controller deploys the BF adaptation algorithm described in Section 4. The implementation strategy for STA is simpler, but BSTA achieves good chattering alleviation, where the performance and accuracy of the closed loop remain unchanged. For the experimental results, the positioning system was used with the given parameters. All the parameters are taken from the nameplate of the positioning system manufacturer. Used parameters for the positioning system are presented in Table 1.  
The parameters for controllers are selected regarding the conditions of Equations (16) and (27). The unknown disturbance term is assumed to be bounded with the value \(\gamma=18.5\). The control objectives are the complete rejection of the disturbance term, robustness to system parameter variation, smooth controller output and good reference tracking capability, as well as the settling time for the shaft revolution, which need to be less than 4 s. The select controller parameters, with regard to the close-loop requirements, are presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**The System Parameters**} \\ \hline \(J\) & \(6.1\times 10^{-3}\) kgm\({}^{2}\) \\ \(B\) & \(4.2\times 10^{-3}\) Nms \\ \(k_{m}\) & \(89.2\times 10^{-3}\) Nm/A \\ \(k_{e}\) & \(89.2\times 10^{-3}\) Vs/rad \\ \(R\) & \(1.52\)\(\Omega\) \\ \(L\) & \(1.68\times 10^{-3}\) H \\ \(\left|m_{f}\right|\) & \(12.5\times 10^{-3}\) Nm \\ \(i_{max}\) & \(4.5\) A \\ U & \(12\) V \\ \(\omega_{max}\) & \(340\) RPM \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the positioning system.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Controller Parameters**} \\ \hline \(\gamma\) & \(18.5\) \\ \(w\) & \(5\) \\ \(k_{1}\) & \(74.7\) \\ \(k_{2}\) & \(95.2\) \\ \(\varepsilon\) & \(20\) \\ \(\overline{\varepsilon}\) & \(14\) \\ \(\overline{L}\) & \(0.42\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: STA and BSTA controllers’ parameters.  
Figure 3: Closed-loop system with the BSTA controller.  
In order to evaluate the control performance, we have considered three indices, defined as follows,  
\[RMS_{\varphi}=\sqrt{\frac{1}{n_{s}}{\sum_{k=1}^{n_{s}}{\varphi^{2}}}},RMS_{ \sigma}=\sqrt{\frac{1}{n_{s}}{\sum_{k=1}^{n_{s}}{\sigma^{2}}}},RMS_{u}=\sqrt{ \frac{1}{n_{s}}{\sum_{k=1}^{n_{s}}{u^{2}}}}. \tag{29}\]  
where \(n_{s}\), \(RMS_{\varphi}\), \(RMS_{\sigma}\), \(RMS_{u}\) are a number of samples, root mean square value (RMS) for motor angle, \(\sigma\) sliding variable, and STA controller output, respectively.
## 3 Materials and Methods
### Proposed Method
#### 3.3.4 Projection Loss Function
By combining the projection loss function with the symmetric projection space extractor, precise control over the privacy of the generated data can be achieved during the generation process, avoiding the potential privacy leakage issues common in traditional methods, while also ensuring the effectiveness of the generated data. Particularly in the design of the symmetric projection space, the nonlinear transformation of the input data effectively retains the key information, while the projection loss function ensures that the generated data are not only strongly privacy-preserving but also retain the essential features of the original data during inference and training.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 3 Proposed Protocol
### Cluster Formation Phase
#### 3.2.1 Neighbor Discovery  
We offer Algorithm 1, which takes \(Ack\) and \(N\) as inputs and finds neighbors, where \(N=n_{1}\), \(n_{2}\),..., \(n_{x}\) denotes a set of all nodes, and \(Ack\) denotes an acknowledgment received in response to a transmitted Hello packet. Here, \(n\) denotes a single member node, and x represents the number of nodes on the path. The algorithm discovers neighbor nodes and creates a neighboring table (\(NG\)), which is a list of all of a node's neighbors. Neighbors are nodes connected directly to one another or within one hop. Each node broadcasts Hello packets at regular intervals to discover its neighbors.  
```
0:\(Ack\), \(N\)
0:\(NG\)
1. Begin:
2. Flush \(NG_{s}\)
3. \(i\)\(\leftarrow\)0
4. \(S\) broadcast \(Hello\) packets
5. Repeat:
6. for Ack received from node \(n_{i}\)
7. \(S\) updates the \(NG_{s}\)
8. \(NG_{s}\) (\(i\), 1)\(\leftarrow\)NID\({}_{ui}\)
9. \(NG_{s}\) (\(i\), 2)\(\leftarrow\)CNP\({}_{ui}\)
10. \(NG_{s}\) (\(i\), 3)\(\leftarrow\)\(\tau_{ui}\)
11. incrementi
12. until: All the received \(Acks\) are processed or each cluster
13. Return \(NG\)
14.end
```  
**Algorithm 1**Neighbor Discovery.  
When S obtains an Ack from another node in reply to its broadcasted Hello packet, it adds this node to its \(NG\). In \(NG\), information about nearby nodes is maintained in the form of current node position (\(CNP\)), node identity (NID), and the time stamp (\(\tau\)) of the last correctly received \(Ack\). Here, \(\tau\) is used to determine the freshness of the received Ack packet and to remove any older information. For node localization, we suppose every node is fitted with a Global Positioning System (GPS). Algorithm 1 produces \(NG\) as a result. The algorithm's complexity is on average \(\Theta\)(n). The output of the algorithm is \(NG\) for the neighbors at each time period.
## 1 Introduction
[6] proposed a high-order modulation technology based on a combination switch. The technology uses the characteristics of ordinary LED lamps with multiple lamp beads to control the light beads on the LED lamp by grouping, forming a high-order modulation signal, thereby improving the communication ability. In addition, using the multi-lamp collaborative transmission is also an effective way to improve the performance of OCC systems. [PERSON] et al. [20; 21] improved the communication capacity and reliability of the camera simultaneously through the coordinated transmission of multiple lamps by considering the characteristics of indoor multi-lamp lighting. The above work has improved the communication capacity and reliability of OCC to a certain extent. However, the above research has not considered the performance bottleneck of RoI extraction during the decoding process, which makes it difficult to further improve the communication capacity. The traditional RoI extraction method first performs a grayscale conversion operation on the entire image received by the camera, followed by Gaussian blur and threshold discrimination to obtain the outline of the RoI. The decoder then extracts a row or column of pixels from within the contour to form a grayscale vector for subsequent demodulation and decoding to enable data communication. However, the realization of the Gaussian blur step needs to be filtered by building the corresponding weight matrix, which makes the entire Gaussian blur process very time consuming [30]. In addition, along with the rise of deep learning, some works are being carried out using the YOLO [31] algorithm to train models to extract the parts of interest through supervised training for RoI extraction in object recognition. There are also transfg [32] models based on the transformer that integrate all of the transformer's raw attention weights into the attention graph to guide the network to efficiently and accurately select differentiated blocks of images and compute the relationships between them. However, a common problem with all these deep learning algorithms is that they take a long time to load the model and recognize the object, but RoI extraction in OCC does not require so much fine detection to figure out the outline of an LED transmitter. To cope with these issues, we propose a lightweight grid virtual division scheme based on pixel grayscale values, which extracts RoI quickly while maintaining low computational complexity. In particular, the proposed novel scheme randomly samples a number of pixels within the received image instead of the traversal of each pixel, thereby greatly improving the extraction efficiency. Extensive experiments on iOS smartphones strongly confirm the effectiveness of the proposed scheme. The decoding delay by the iPhone 8 Plus is 70% lower than those provided by the baseline under a transmission frequency of 5 kHz.
## 2 Wireless Communication for Maritime Applications
### Communication between Sensors and Relays
#### 2.1.5 LoRaWAN  
LoRaWAN is a Low-Power WAN (LPWAN) that is optimized for low power consumption and designed to support networks of millions of devices. It supports low-cost bidirectional communication. Devices using LoRaWAN have power consumptions low enough to be used in conjunction with power harvesting technologies [18].  
LoRaWAN Class A nodes achieve maximum ranges of up to 15 km or a maximum data rate of 21.9 kbps, or a balance of each as required [19]. For the maximum range, a data rate of 250 bps can be achieved [20].  
It has a star-topology network, with a gateway that is connected to many nodes and concentrates all their activity [19]. It operates in the 868 MHz ISM band and has a maximum radio power of 14 dBm [21].  
The technical specifications provided by the manufacturers and developers of the aforementioned protocols are, naturally, related to terrestrial communications. This environment presents challenges to wireless communications such as signal losses and multipath propagation due to the presence of obstacles. Nevertheless, the ocean environment is even more hostile to wireless communications over long distances. Line-of-sight loss due to the earth's curvature or undulation, signal reflection on the water's surface, and rough  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Attributes** & **Wi-Fi HaLow** & **BLE** & **ZigBee** & **SigFox** & **LoRaWAN** \\ \hline Data Rate 1 & 150 kbps & 125 kbps & 10 kbps & 100 bps & 250 bps \\ Range & 1 km & 100 m & 14.5 km & 10 km & 15 km \\ Battery life [10] & Years & Years & Years & Years & Years \\ Network topology [10] & Star & P2P & Mesh & Star & Star \\ Standard & IEEE 802.11 ah & Bluetooth SIG & IEEE 802.15.4 & Proprietary & Proprietary \\ Subscription-based [10] & No & No & No & Yes & Yes \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Description of wireless communication protocols used for data transmission between sensors and relays.  
Figure 1: Comparison of the performance of each communication protocol in key parameters. Data obtained in Ref [11] 2021, Wi-Fi CERTIFIed HaLowTM.  
weather conditions are some examples of maritime environment hindrances that can cause the technical specifications provided by manufacturers to be inaccurate in the ocean.  
Considering the protocols above and Table 1 and Figure 1, it appears that ZigBee is more suited for applications in the ocean. A mesh-type topology becomes advantageous as it helps guarantee, through multihopping, that the data payloads from all the sensors reach the coordinator or the base station even if two nodes lose lines of sight, which may happen due to the presence of an obstacle or waves. Considering as well that it requires no subscription, network integration is easy, and the cost is low, it is at the very least a technology that must be seriously considered, as long as the achievable data rate is sufficient for the desired application.
## 4 Experiment Settings
### Evaluation of Conformal Prediction
#### 4.4.1 Coverage Metrics  
_Marginal Coverage._ A valid conformal predictor with nominal coverage \(1-\alpha\) should include the correct label for a fraction \(\approx(1-\alpha)\) of test points. We define the empirically observed coverage as:  
\[\text{Coverage}=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}\big{(}y_{i}\in\mathcal{C}( \mathbf{x}_{i})\big{)}, \tag{34}\]  
where \(N\) is the total number of predictions or data points, and \(\mathbb{I}(\cdot)\) is the indicator function that returns \(1\) if the condition is true and \(0\) otherwise. High coverage indicates reliability but, by itself, does not guarantee balanced coverage across classes.  
_Average Coverage Gap._ This metric quantifies the deviation of the observed empirical coverage from the desired nominal coverage of \(1-\alpha\). It measures whether the CP method is under-covering or over-covering on average and is defined as:  
\[\text{AverageCoverageGap}=(1-\alpha)-\text{Coverage}. \tag{35}\]  
A low Average Coverage Gap indicates that the actual coverage closely aligns with the desired coverage, which is especially important in imbalanced settings where achieving the target coverage uniformly across classes can be challenging.
# Predicting Online Item-Choice Behavior: A Shape-Restricted Regression Approach
## 4 PV Sequence Model
### Shape-Restricted Optimization Model  
Let \(n_{\mathbf{v}}\) be the number of user-item pairs that have the PV sequence \(\mathbf{v}\in\Gamma\). Moreover, \(q_{\mathbf{v}}\) is the number of choices arising from user-item pairs having \(\mathbf{v}\in\Gamma\) on the base date. Similarly to Equation (1), we can calculate empirical item-choice probabilities as  
\[\hat{x}_{\mathbf{v}}:=\frac{q_{\mathbf{v}}}{n_{\mathbf{v}}}\qquad(\mathbf{v}\in\Gamma). \tag{6}\]  
Our shape-restricted optimization model minimizes the weighted sum of squared errors subject to the monotonicity constraint:  
\[\underset{(x_{\mathbf{v}})_{\mathbf{v}\in\Gamma}}{\text{minimize}} \sum_{\mathbf{v}\in\Gamma}n_{\mathbf{v}}(x_{\mathbf{v}}-\hat{x}_{\mathbf{v}})^{2}\] (7) subject to \[x_{\mathbf{u}}\leq x_{\mathbf{v}}\qquad(\mathbf{u},\mathbf{v}\in\Gamma\text{ with }\mathbf{u}\prec\mathbf{v}), \tag{8}\] \[0\leq x_{\mathbf{v}}\leq 1\quad(\mathbf{v}\in\Gamma), \tag{9}\]  
where \(\mathbf{u}\prec\mathbf{v}\) in Equation (8) is defined by one of the partial orders \(\prec_{\texttt{UM}}\) or \(\prec_{\texttt{US}}\).  
The monotonicity constraint (8) enhances the estimation accuracy of item-choice probabilities. In addition, our shape-restricted optimization model can be used in a post-processing step to improve the prediction performance of other machine learning methods. Specifically, we first compute item-choice probabilities using a machine learning method and then substitute the computed values into \((\hat{x}_{\mathbf{v}})_{\mathbf{v}\in\Gamma}\) to solve the optimization model (7)-(9). Consequently, we can obtain item-choice probabilities corrected by the monotonicity constraint (8). Section 6.4 illustrates the usefulness of this approach.  
However, since \(|\Gamma|=(m+1)^{n}\), it follows that the number of constraints in Equation (8) is \(\mathcal{O}((m+1)^{2n})\), which can be extremely large. When \((n,m)=(5,6)\), for instance, we have \((m+1)^{2n}=282475,249\). The next section describes how we mitigate this difficulty by removing redundant constraints in Equation (8).
## 3 Conceptual Approach  
### Basic Idea  
The proposed approach is based on the extension of PM techniques with several procedures, which are illustrated in Figure 1 and discussed in further detail in this section. Process discovery is followed by complexity and fitness estimation procedures (\"1\" in Figure 1). The complexity level is estimated using structural and representative characteristics of the identified process model. The value of fitness is estimated through a comparison of event log coverage with a particular process model.  
The estimated values are used to perform optimization within a parametric space for a process identification algorithm. In general, the optimization problem is a multi-objective  
Figure 1: General approach for PM procedures extension.  
problem where objectives contradict each other in most cases, and higher complexity corresponds to better fitness.  
To introduce higher-level interpretable states, we propose the procedures for meta-state identification. The meta-states identification (\"2\" in Figure 1) has various sources of knowledge available for use during the identification problem. The primary source is a structural analysis of process maps (\"3\" in Figure 1) with the identification of cyclic states, which are often collapsible to a single meta-state. Second, we can exploit the data available in event logs (event attributes, case parameters, etc.) to identify states of the process. Usually, the data can be analyzed using data mining techniques. Finally, domain knowledge can be applied as well to introduce information on states specific to a particular process or system where the process evolves.  
The meta-states identification could be used extensively for analysis and interpretation of a process model. However, within the presented work, we are focused on the explicit introduction of meta-states into the process model to bring more expressive power with lower model complexity.
# FPGA Applied to Latency Reduction for the Tactile Internet
## 4 PHANToM Omni Device Model (MD & SD)
### Kinesthetic Feedback Force  
The kinesthetic feedback force allows the environment to be \"felt\", i.e., when the SD comes into physical contact with an object, the MD will receive a counter force. This model can be implemented through the equation  
\[\boldsymbol{\tau}=\mathbf{J}^{\mathsf{T}}\mathbf{F}, \tag{26}\]  
where \(\boldsymbol{\tau}\) defines the torque array that will be applied to each joint (\(\theta_{1}\), \(\theta_{2}\) and \(\theta_{3}\)) of the PHANToM Omni associated with the MD, \(\mathbf{J}^{\mathsf{T}}\) is the transpose of the Jacobian matrix and \(\mathbf{F}\) is the force array resulting from the interaction of SD with ENV. The torque array \(\boldsymbol{\tau}\) can be expressed as  
\[\boldsymbol{\tau}=[\tau_{1},\tau_{2},\tau_{3}]. \tag{27}\]  
The \(\mathbf{J}\) Jacobian matrix incorporates structural information about the handler and it is identified as  
\[\mathbf{J}=\begin{bmatrix}J_{11}&J_{12}&J_{13}\\ J_{21}&J_{22}&J_{23}\\ J_{31}&J_{32}&J_{33}\end{bmatrix}, \tag{28}\]  
where  
\[J_{11}=-\cos(\theta_{1})(L_{2}\sin(\theta_{3})+L_{1}\cos(\theta_{2})), \tag{29}\]  
\[J_{21}=0, \tag{30}\]  
\[J_{31}=-L_{1}\cos(\theta_{2})\sin(\theta_{1})-L_{2}\sin(\theta_{3})\sin( \theta_{1}), \tag{31}\]  
\[J_{12}=L_{1}\sin(\theta_{1})\sin(\theta_{2}), \tag{32}\]  
\[J_{22}=L_{1}\cos(\theta_{2}), \tag{33}\]\[J_{32}=-L_{1}\sin(\theta_{2})\cos(\theta_{1}), \tag{34}\]  
\[J_{13}=-L_{2}\sin(\theta_{1})\cos(\theta_{3}), \tag{35}\]  
\[J_{23}=L_{2}\sin(\theta_{3}), \tag{36}\]  
and  
\[J_{33}=L_{2}\cos(\theta_{3})\cos(\theta_{1}). \tag{37}\]  
The force array \(\mathbf{F}\) is expressed as  
\[\mathbf{F}=\left[F_{x},F_{y},F_{z}\right] \tag{38}\]  
and can be obtained through sensors internal or external to the device. According to Equation (26), the \(\boldsymbol{\tau}\) torque array representing the resulting force at each joint can be defined as  
\[\tau_{1}=J_{11}F_{x}+J_{21}F_{y}+J_{31}F_{z}, \tag{39}\]  
\[\tau_{2}=J_{12}F_{x}+J_{22}F_{y}+J_{32}F_{z}, \tag{40}\]  
and  
\[\tau_{3}=J_{13}F_{x}+J_{23}F_{y}+J_{33}F_{z}. \tag{41}\]
## 2 Materials and Methods
### Specim Preparation  
Eighty sound human maxillary central incisors, extracted for periodontal reasons, were selected for this study. The exclusion criteria included the presence of caries, restoration, root canal treatment (RCT), crack/s, attrition, very long or very short teeth, and/or severe root curve. Teeth were thoroughly cleaned with a brush after extraction, and a scalpel and a periodontal curete were used to remove any remaining hard and soft tissues from the root surfaces. Teeth were subsequently stored in 0.2% sodium azide (Merck KGaA, Frankfurter Str. 250, Darmstadt, Germany) prior to specimen preparation (ISO 28399;2011) [28]. Each tooth was randomly assigned a number (from 1 to 80) and allocated into one of 4 groups (\(n=20\) each). Group 1 teeth were restored with custom-cast non-precious post and cores (CCNPPCs); Group 2 teeth were restored with custom-milled titanium post and cores (CMTPCs); Group 3 teeth were restored with custom-printed titanium post and cores (CPTPCs), and Group 4 teeth were restored with custom-milled zirconia post and cores (CMZPCs).