esa-sceva/satcom-chunk-collection huggingface dataset:
# Directed Clustering of Multivariate Data Based on Linear or Quadratic Latent Variable Models
## 2 Motivating Application: Speed-Flow Data
For there to be a break in the monotony of traffic density along the latent variable describing the curved data shape, a line through the origin would need to intersect that curve twice. When considering Figure 1 (left), it looks unlikely that this property is strongly violated here, and as we will see in Section 5, it is certainly not violated for the sequence of ordered (directed) cluster centers identified by our methodology. The data in Figure 1 (right) show a pattern that differs from the typically reported behavior. This dataset was collected from 0:00 to 19:59 on the southbound California Freeway SR57-S, with 240 observations in total, as presented in [24]. Just like the previous dataset, it was originally extracted from the PeMS 7.3 database. As explained in [24], the unusual pattern is likely explained by the presence of heavy vehicles (vehicles with a larger-than-standard gross vehicle weight rating, such as trucks or buses) on a slow lane, where increased speed is counterproductive as it leads to larger stopping distances and, hence, reduces flow. Imagine a latent variable tracing a curvilinear path from the top left to the bottom right of this data cloud. By reasoning similar to the previous example, the assumption that traffic density increases monotonically with respect to the latent variable is plausible. The right panel in Figure 1 also gives the \(K\)-means clustering for this dataset. To summarize, achieving a directed clustering of the data with respect to the latent variable simultaneously results in a directed clustering with respect to traffic density, which is immediately and intrinsically meaningful and interpretable. According to [25], density is \"the primary factor to determine the level of service on a freeway\". So, the algorithmic determination of cluster membership ranked by traffic density enables an automatic assessment of the road's operating condition (congested, free-flow, etc.) with immediate relevance for traffic control strategies and intelligent transportation systems. On the contrary, a plain establishment of cluster membership, such as through \(K\)-means as in Figure 1, does not allow for identifying the operating condition on the road without additional information about the meaning of these clusters. Of course, in other applications, it may even be useful to carry out directed clustering along a latent variable that has no such physical meeting. But if it has, as in the present application, the case for such a method is particularly compelling. We provide the required methodology for directed clustering along curvilinear data structures in the following sections and will return to this example in Section 6.
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 6 Technical/Critical Analysis and Recommendations for Deployment
### Recommendation for Deployment  
Transitioning from simulation to real-world deployment of the Q-RPL protocol requires careful consideration to ensure it adapts effectively to actual network environments. The following is a structured approach for deploying this protocol:  
* **Initial testbed trials:** Begin with small-scale experiments on actual hardware to understand how the protocol performs outside of simulation. This step is crucial for identifying any unforeseen issues that were not apparent during the simulation study.
* **Adaptation to hardware constraints:** This step may be necessary to ensure that the algorithm can operate efficiently without overwhelming device capabilities, maintaining optimal performance even within resource constraints. This step is important if initial evaluations indicate that the current learning algorithm exceeds the device's operational limits.
* **Incremental deployment:** Gradually increases the scale of deployment while continuously monitoring system performance. This step allows for adjusting strategies in response to real-world challenges and complexities as they arise.
* **Performance monitoring and optimization:** Continuously collect and analyze performance data to optimize the protocol settings and adjustments.
## 3 Optimization Algorithms
### Salp Swarm Algorithm (SSA)  
Salps belong to Salpidae group, which are look like jellyfishes, with a body shape like transparent barrel [78]. Usually, they tend to create a chain to have fast coordination for finding food. SSA is inspired by their swarm intelligence. The salp chain has a leader, which is the salp at the front, and followers, which are the rest of the group. The leader is responsible for exploration and the followers handle the exploitation phase. Each salp has a position, which is defined in a search space with \(n\) dimensions, where \(n\) is the decision variables' number in the optimization problem. The following equation updates the leader position.  
\[p_{r}^{1}=\begin{pmatrix}FP_{j}+d_{1}\big{(}(UB_{r}-LB_{r})d_{2}+LB_{r}\big{)}&d_{3 }\geq 0\\ FP_{j}-d_{1}\big{(}(UB_{r}-LB_{r})d_{2}+LB_{r}\big{)}&d_{3}<0\end{pmatrix} \tag{2}\]  
where \(P_{r}^{1}\) is rth dimension of the leader position, \(FP_{j}\) is the position of the food, \(UB_{r}\) and \(LB_{r}\) are the upper and the lower bounds in the \(r\)th dimension, respectively. In addition, \(d_{1}\) balances exploration and exploitation phases; \(d_{2}\) and \(d_{3}\) are random numbers in [0,1]. \(d_{1}\) is calculated as follows:  
\[d_{1}=2e^{-(\frac{4r}{T})^{2}} \tag{3}\]  
where \(T\) is the maximum number of iterations and \(t\) represents the current iteration. The position of the followers are calculated as follows:  
\[V_{r}^{i}=\frac{1}{2}(V_{r}^{i}+V_{r}^{i-1}) \tag{4}\]  
where \(V_{r}^{i}\) is position of the ith salp in the rth dimension. Now, it is possible to simulate the salp swarm. Algorithm 2 illustrates pseudo cod of SSA. This algorithm has been used to train an ANN in a number of previous studies [35; 40; 43; 79].  
```
1:Initiate salp population considering the upper and lower bounds
2:\(i=1\)
3:while (\(i\leq\) number of iterations)
4: Calculate fitness function for each salp
5: Choose the best salp as the food source
6: Determine the value of \(d_{i}\) using Equation (3)
7:for each salp
8:if (\(i=1\))
9: Update the position of the leading salp by Equation (2)
10:else
11: Update the position of the follower salp by Equation (4)
12:end
13:end
14: Correct the positions of the salps considering upper and lower bounds
15:end
16: Return the food source
```  
**Algorithm 2**Pseudocode of SSA.
## 2 Materials and Methods
### Input Selection Technique  
The Chi-Squared Automatic Interaction Detection (CHAID) algorithm creates decision trees employing ChiSquare statistics to establish the optimal divisions [89]. CHAID generates non-binary trees. Some divisions may possess more than two branches that are especially suitable for the examination of complex datasets. CHAID converts continuous inputs into ordinal type employing binning techniques since it handles merely categorical inputs. During the learning process, a heuristic statistical technique is employed to examine the relationship between a set of categorical inputs and the target variable. It offers a tree diagram that shows the kinds of inputs that most significantly affect the value of the target variable. CHAID modelling steps are (1) binning, (2) merging, (3) splitting, and (4) stopping.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Model** & **Model Input Number** & **Site Location** \\ \hline [PERSON] and [PERSON] [69] & ANN & 9 & India \\ [PERSON] et al. [76] & ANFIS & 2 & Turkey \\ [PERSON] and [PERSON] [18] & ANN & 10 & India \\ [PERSON] et al. [19] & ANN & 4 & Iran \\ [PERSON] [77] & ANN, FIS & 2 & Egypt \\ [PERSON] et al. [78] & ANN & 2 & India \\ [PERSON] et al. [70] & FIS & 2 & Turkey \\ [PERSON] et al. [79] & SVM, ANN & 2 & Iran \\ [PERSON] et al. [80] & ANN & 9 & Malaysia \\ [PERSON] et al. [11] & ANN & 3 & Iran \\ [PERSON] et al. [81] & FIS & 6 & Iran \\ [PERSON] et al. [1] & PSO-ANN & 10 & Malaysia \\ [PERSON] et al. [17] & ICA-ANN & 7 & Malaysia \\ [PERSON] [82] & SVM & 12 & Iran \\ [PERSON] et al. [2] & PSO-ANN & 8 & Malaysia \\ [PERSON] et al. [83] & SVM & 2 & Iran \\ [PERSON] et al. [84] & ANFIS & 2 & Malaysia \\ [PERSON] et al. [85] & ANN, ANFIS & 2 & Iran \\ [PERSON] et al. [72] & CEP & 6 & Malaysia \\ [PERSON] et al. [86] & CART & 2 & Iran \\ [PERSON] et al. [87] & PSO-ANFIS & 2 & Malaysia \\ [PERSON] et al. [88] & ICA & 2 & Malaysia \\ [PERSON] et al. [73] & HKM-CA & 4 & Vietnam \\ [PERSON] et al. [74] & SVR-GA & 4 & Vietnam \\ [PERSON] et al. [75] & RF, CART, CHAID & 6 & Malaysia \\ [PERSON] et al. [16] & RF, BN & 5 & Malaysia \\ [PERSON] et al. [5] & FA-ANN & 4 & Malaysia \\ \hline \hline \end{tabular}
\end{table}
Table 1: The most prominent research on the PPV prediction by means of the SC procedures.  
Boosting procedures were introduced by [PERSON] and [PERSON] [90], who utilized resampling and merging algorithms to develop the weights of misclassified examples. In this study, we utilized boosted-CHAID technique for input selection since a single tree may not show the importance of ranking variables, and they could be completely masked by other related inputs.  
Figure 1: A systematic flowchart of this study.
## 4 Measurement Processing  
To ensure comparability and minimize variations, all data points recorded are filtered by the following rules:  
* wind speed < 5 m s\({}^{-1}\)
* speed at P-P' \(\pm\)1 km h\({}^{-1}\) of target speed
* SPL right \(\leq\) 1 dB(A)  
To generate curves for the rolling noise, the original least square method is applied. For the acceleration noise, a weighted constrained regression is applied. The weighting of points is used to decrease the impact of data points clustered at the same x-axis section. This is necessary because only the average acceleration between the entry and exit point of the test track is given when performing the measurements. That acceleration is often slightly different from the local acceleration at the centerline of the track. Therefore, weighting the data points differently is reasonable. All speed measurements are smoothed with a Savgol filter [20] before being used for acceleration calculation. The filter window is thereby set to 11 data points, so that every point is smoothed with data from before and after one meter. The order of polynomial used for fitting is set to one. Following, the mathematical methods  
Figure 3: Principal sketch measurement track.  
used for regression are described. In addition, a method to estimate a density probability of a data set is explained. Thereby, matrices are marked as bold capital letters, vectors as bold lower case letters and estimated values are marked through a hat.
## 4 Results
### Tractive Current Type
#### 4.1.3 One Bouncing Segment-One Suspension Segment-One Transitional Zone Pattern  
The curves of this pattern mainly develop the bouncing components, the transitional components and the suspension components (Figure 2c,d). The bouncing component contents are between 60% and 80%, and the particles are mainly composed of coarse  
Figure 2: Grain size curves of tractive current type. (**a**) Well M16, 4041.80 m, tri\(-\)segment pattern, subaqueous distributary channel microfacies of shallow\(-\)water delta inner front; (**b**) Well QS402, 3979.33 m, bi\(-\)segment pattern, subaqueous distributary channel microfacies of shallow\(-\)water delta outer front; (**c**) Well QS201, 4018.70 m; one bouncing segment\(-\)one suspension segment\(-\)one transitional zone pattern, estuary bar microfacies of shallow\(-\)water delta outer front; (**d**) Well QS4, 4003.50 m, one bouncing segment\(-\)one suspension segment\(-\)one transitional zone pattern, subaqueous distributary channel microfacies of shallow\(-\)water delta outer front; (**e**) Well QS201, 4017.50 m, bi\(-\)bouncing segment-one suspension segment\(-\)one transitional zone pattern, estuary bar microfacies of shallow\(-\)water delta outer front; (**f**) well M16, 4044.10 m, one bouncing segment\(-\)one suspension segment\(-\)one complex transitional zone pattern, subaqueous distributary channel microfacies of shallow\(-\)water delta inner front.  
debris particles. The gradients of the bouncing segment concentrated at 60-70\({}^{\circ}\), which has good sorting. The contents of the transitional component range from 15% to 25%, and the gradients of the transitional segment are about 30\({}^{\circ}\) to 40\({}^{\circ}\), which means poor sorting. The cut-off values of the transitional segment range from 1 \(\varphi\) to 3 \(\varphi\), and 3-4 \(\varphi\) with the suspension segment. The particle size span is large, and the transport mode is between progressive suspension transport and bouncing transport [11]. There is a much lower content of suspended components, mostly below 10%, and the gradients of the suspension segment are about 10\({}^{\circ}\) to 15\({}^{\circ}\), which also means poor sorting. The curves of this pattern reflect the hydrodynamic characteristics of slow current speed and reduced energy in the process of forward transport of a stable river current into the lake basin in the dry season [11]. The transitional zone represents the environment with slow terrain slope and turbulent water body in the sedimentary period. In other words, high-density river water quickly settles, and the inclusion of debris particles has no time for sorting, so that the relatively coarse part enters the jumping component and becomes its thin end, while the smaller particles merge into the coarse end of the suspended component; its thick end bends to form a transitional section [18]. In the research area, curves of this pattern mainly occur in the subaqueous distributary channel and estuary bar microfacies of the shallow-water delta outer front.
# Air-Polishing Powders' Effect on the Color of CAD/CAM Restorative Materials
## 1 Introduction
Therefore, aluminum trihydroxide, with an 80-325 \(\upmu\)m particle size, was introduced as an alternative for removing heavy stains and the biofilm layer [24]. However, due to their great particle sizes, both APPs were abrasive and destructive for surfaces of both the enamel and the dental restorations [27]. To overcome the negative effects of these APPs, calcium carbonate, glycine, sodium bicarbonate with reduced particle size, and erythroid-based APPs were introduced [28]. Calcium carbonate, used as an abrasive in dentifrices, exists naturally on rocks, eggshells, pearls, and seashells [24]. In addition, this APP is in the form of small spheres in clusters, while sodium bicarbonate and glycine powders are in the form of grains [29]. Glycine (aminoacetic acid) is the smallest of the non-essential amino acids found in proteins. It has two powder forms with different sizes of grains to be used, either supragingival or subgingival [24]. Furthermore, it is indicated for removing medium-light discolorations along with the APP composed of erythroid, a water-soluble sugar alcohol with very fine, dense particles [30]. There are studies about the efficiency of the APPs in removing discolorations and the staining susceptibility of restorative materials that are exposed to air-polishing and staining solutions afterward [31; 32; 33]. However, to the best of the authors' knowledge, there is no research on how these APPs themselves affect the color stability of indirect restorative materials such as resin matrix ceramics. Dentists need to know how these APPs might affect the color of restorative materials to predict aesthetic outcomes. Therefore, this study aimed to investigate the effect of five different APPs on five different CAD/CAM restorative materials regarding color change. As a result, it is hypothesized that (1) applying different APPs does not affect the color parameters of CAD/CAM restorative materials and (2) the color change after air-polishing is not affected by the type of CAD/CAM restorative material.
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
## 5 Experiment
### Performance Analysis  
Figure 11 displays the idle situation of the SCADA server's CPU when the SCADA system was running. The X-axis is time in different periods and the Y-axis shows the idle percentage of the SCADA server's CPU. Three lines in different colors show the idle percentage of the CPU under different circumstances: the blue line is under the condition that the SCADA server runs no service, the red line shows how the SCADA server runs to access one temperature and humidity sensor, and the green line is when the SCADA server runs to access one temperature and humidity sensor after adopting the TEVM mechanism proposed in this paper. According to the line graph, while no great difference can be distinguished between the idle server and the server running without employing an encrypted authentication mechanism, the CPU in the server with the TEVM mechanism has been used a little more frequently. Figure 12 presents the assumption that hackers have destroyed the BEMS and controlled it to launch DoS attacks to the SCADA server through LOIC. The X-axis is time in different periods and the Y-axis shows the idle percentage of the SCADA server's CPU. The red line represents the situation when the SCADA server adopted no encrypted authentication, while the blue line represents the SCADA server employing the TEVM mechanism. If no protective mechanism was applied, the SCADA server would be easily disabled by hackers' DoS attacks; however, when the TEVM mechanism was used, DoS attacks were effectively defended.  
Figure 13 shows the performance analysis of SCADA server (Control server, CS) accessing a single Internet of Things device. The horizontal axis is the number of times that CS accessed the Internet of Things device, while the vertical axis is the time spent on such access. The blue bar denotes that no encryption verification mechanism is executed, and the orange bar denotes that the complete encryption and verification mechanism is executed. In the experimental analysis, the average access time without the encryption verification mechanism is 105 ms, and the average access time with the complete encryption and verification mechanism is 186 ms. It can be found that the time consumed by executing the complete encryption and verification mechanism is about 1.7 times that of no encryption verification mechanism. However, from the data point of view, it takes a lot of time. In fact, the time consumed in both cases is very short. In addition, in practice, the SCADA system does not have too strict requirements regarding the access time of most Internet of Things devices. Furthermore, we learned from the system integrator that the average time from transmission to response must be controlled within 1 s when integrating this mechanism. Adding to this, according to expert's recommendation, it only requires that a reply is  
Figure 10: SCADA server with TEVM mechanism.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 2 Related Work
IETF has suggested one expired Request For Comments (RFC) draft on a mechanism that supports the integrity of NSH headers [12], but this does not support per-flow encryption nor automation between multiple domains. Therefore, we aim to extend the NSH header integrity check approach by introducing additional upper and lower control plane channels for scaling, automation and encryption. From the encryption perspective, no new protocols have been found for encrypting SFCs. However, associated encryption technologies have research potential. An upcoming technology such as the Software Defined Internet Key Exchange (SD-IKE) [13] opens up new possibilities by running individual encryption per flow that is controlled by an SDN controller. Currently, SD-IKE is specified for the use in a single controller domain only, enabled by OpenFlow (OF). It does not work between two different SDN domains because of a missing control plane protocol and the lack of common SFC flow identities [14]. However, if an SFC-aware control plane protocol between an SDN controller is developed for this technology, it is possible to use one common encryption engine that encrypts every SFC flow individually. Due to the lack of inter-domain communication standards of SD-IKE, the architecture proposed in this article uses an alternative approach. We propose that each VNF is connected to a standalone encryption service that is only used once in one SFC. No further research has been found that discusses per-user encryption of SFCs. Neither have any protocols or related research been found that supports encryption setup or key exchange mechanisms between multi-domain VNFs.
# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 3 Method
### Algorithm Design
\[\mathcal{L}_{\boldsymbol{\Phi}}=\mathbb{E}_{\boldsymbol{x}_{1}\boldsymbol{x}_ {2}}\left[(\parallel\boldsymbol{\Phi}(\boldsymbol{x}_{1}-\boldsymbol{x}_{2}) \parallel_{2}-\parallel\boldsymbol{x}_{1}-\boldsymbol{x}_{2}\parallel_{2})^ {2}\right] \tag{13}\]  
\(\mathcal{L}_{\boldsymbol{\Phi}}\) is the loss of the observation model. \(\boldsymbol{x}_{1},\boldsymbol{x}_{2}\) are any natural vectors that exist. Here \(\boldsymbol{\Phi}\) can be untrainable matrix, trainable matrix and neural network. In the outer loop, the weights of the generation model and the observation model need to be updated. So the outer loop loss function is the joint loss, which is composed of the loss of the generative model and the loss of the observation model. In summary, the observation reconstruction process of E2E_DCSGM is shown in Algorithm 1. ```
Input:\(\boldsymbol{x}\): real samples \(\boldsymbol{x}\)-P(T) N: Outer loop iteration T: Inner loop iteration Training: for i in range N//Outer loop iteration N times \(\boldsymbol{y}=\boldsymbol{\Phi}(\boldsymbol{x})//\boldsymbol{y}\) is obtained by real data \(\boldsymbol{x}\) \(\boldsymbol{z}=L_{2}(\boldsymbol{y})//\text{real data observation vector normalization}\) for j in range T/Inner loop iteration T times \(\hat{\boldsymbol{y}}=\boldsymbol{\Phi}\boldsymbol{G}_{\boldsymbol{\theta}}( \boldsymbol{z})//\hat{\boldsymbol{y}}\) is obtained by generation sample \(\boldsymbol{G}_{\boldsymbol{\theta}}(\boldsymbol{z})\) \(Loss_{inner\_loop}=\mathcal{L}_{G}//\text{calculate the inner loop loss}\) \(\hat{z}\leftarrow\hat{z}-\alpha\frac{\partial Loss_{inner\_loop}}{\partial z }\Big{|}_{z=\hat{z}}//\text{optimize input, the inner loop optimization rate }\alpha\) end for//End the inner loop \(Joint\ loss=Loss_{outer\_loop}=\mathcal{L}_{\boldsymbol{G}}+\mathcal{L}_{ \boldsymbol{\Phi}}//\text{the joint loss of outer loop}\): \(\theta=\theta-\beta\
abla_{\theta}\big{(}Loss_{outer\_loop}\big{)}//\text{optimize model, the outer loop optimization rate }\beta\) end for//End the outer loop Output:\(\boldsymbol{G}_{\boldsymbol{\theta}}\): the generative model \(\boldsymbol{\Phi}\): the observation model \(\boldsymbol{\Phi}\): the observation model \(\hat{x}\): reconstruction samples
```  
**Algorithm 1**The pseudo code of end-to-end deep compressed sensing generative model (E2E_DCSGM).