esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm
## 2 Preliminaries  
### Association Rule Learning  
Association rule mining was introduced in 1993 [22]. Transactional datasets were the main object of analysis, with the aim of identifying patterns in product groupings and forming associations. The method was supported by the a priori algorithm, which was capable of finding frequent patterns and extracting association rules from transactional datasets.  
Association rule notation can be expressed as \(X\to Y\), where \(X\) is the left-hand side (LHS) and \(Y\) is the right-hand side (RHS). Both \(X\) and \(Y\) are item sets that meet the condition \(X\cap Y=\emptyset\). The subsets \(X\) and \(Y\) are formally derived from an initial population of items \(A\), where \(A=\{A_{1},A_{2},\ldots,A_{m}\}\), with \(X\subset A\) and \(Y\subset A\), and \(A_{j}\) is the \(j\)th item of the subset; from \(A\), we can get some sets of \(k\)_-items_, so \(X\) and \(Y\) can be found frequently in the database--either individually or associated with a rule--and are thus called frequent item sets [23].  
Numerical association rules are expressed as \(C_{1}\to C_{2}\), but they are composed of numerical attributes on both the left and right sides of the rule. \(C_{1}\) and \(C_{2}\) are single or non-single expressions that define the LHS and RHS, respectively. The numerical association rules can be composed of more than one attribute at each side of the rule, and in that case, the logical conjunction connector is used. In classification rules, the RHS is generally conclusive, and an expression with a discrete attribute is used.  
Association rule mining is a process that obtains a large number of rules, not all of which conform to the user's requirements. The selection of rules is supported by quality metrics, and the best known are support and confidence. These are objective measures based on probability.  
The support measures are defined as follows: \(Supp(A\to B)=\frac{|A\cup B|}{|N|}\), where \(|A\cup B|\) is the number of instances that contain the elements \(A\) or \(B\), while \(|N|\) is the total value of the rows in the dataset. Confidence estimates the ratio of instances containing elements of the rule to the total number of rows that contain the elements from only the LHS. The expression is \(Conf(A\to B)=\frac{|A\cup B|}{|A|}\), where \(|A\cup pB|\) is the number of rows containing the elements \(A\) or \(B\), and \(|A|\) is the total number of instances in the LHS.
## 2 Materials and Methods
### JWL Equation of State for Isentropic Expansion  
Detonation consists of a chemical transformation of the energetic material into products, accompanied by an extremely rapid transition of its potential energy into mechanical work. All this is caused by the compression and movement of the primary material or its products of decomposition.  
To address issues involving the interaction of the explosion with the surrounding environment, such as the dispersion of detonation products into the air, underwater or ground explosions, the reflection of detonation waves from barriers, mass ejection, shell fragmentation, and cumulative jet formation, it is essential to understand the equation of state for the detonation products of condensed explosives. For these hydrodynamic flow scenarios, it is often sufficient to use a simplified equation of state for detonation products, which expresses the internal energy as a function of pressure and specific volume, excluding temperature.  
A commonly used equation of this type is the one proposed by [PERSON], [PERSON], and [PERSON] (JWL) [1]:  
\[p_{\text{JWL}}=A\bigg{(}1-\frac{\omega}{R_{1}\
u}\bigg{)}\text{e}^{-\text{R}_{ 1}\
u}+B\bigg{(}1-\frac{\omega}{R_{2}\
u}\bigg{)}\text{e}^{-\text{R}_{2}\
u}+ \frac{\omega E_{0}}{\
u}, \tag{27}\]  
where  
* \(p\) is the pressure measured in \([\text{GPa}]\);
* \(\
u=\frac{p}{v_{0}}\) is the dimensionless volume, with
* \(v\) is the reaction products mixture volume measured in \([\text{m}^{3}/\text{Kg}\) of energetic compound];
* \(v_{0}=\frac{1}{\rho_{0}}\) is the initial volume of the unreacted energetic compound with initial density \(\rho_{0}\);
* \(A,B,\omega,R_{1},R_{2}\) are JWL coefficients that need to be determined by an NLLS algorithm.
* \(E_{0}\) is the energy of the products released to the surroundings, as described in Section 2.3.  
In applications, Equation (27) is usually expanded in a Taylor series around the isentrope expansion curve on the pressure-volume plane as follows:  
\[p_{\text{JWL,is}}=A\text{e}^{-\text{R}_{1}\
u}+B\text{e}^{-\text{R}_{2}\
u}+ \frac{C}{\
u^{\omega+1}}. \tag{28}\]  
For this reason, \(p_{\text{JWL,is}}\) from (28) is often referred to as an isentropic (or adiabatic) expansion curve (for detonation products).  
The contribution of individual terms of Equation (28) to the total pressure can be seen in Figure 1.  
For large dimensionless volumes, it is  
\[p_{\rm JWL,is}\sim\frac{C}{v^{\omega+1}},\qquad v\to+\infty\]  
with the exponential contribution terms being nearly zero; for large volumes, the JWL expression converges to the Poisson adiabatic Equation [19] for an ideal gas with an exponent \(\omega+1\).  
For a volume ratio \(v\) greater than 7, the JWL isentrope and the Poisson adiabatic equation are equivalent [20]. This consideration is later used in reducing the number of independent JWL coefficients to be determined.
## 3 Cylindrical Cavity Expansion in the SCC Soil
### Initial Values at the Elastoplastic Boundary  
To determine the initial values \(\sigma^{\prime}{}_{r}(\xi_{p})\), \(\sigma^{\prime}{}_{\theta}(\xi_{p})\), and \(\sigma^{\prime}{}_{z}(\xi_{p})\), prior knowledge of the initial stress state and the degree of over-consolidation of structured soils is needed. As shown in Figure 4, (\(p_{0}\), \(q_{0}\)) and (\(p_{0}\), \(q_{p}\)) denote the initial stress point and initial yield stress point, respectively. The reference point is the size of a virtual yield surface passing through the point (\(p_{0}\), \(q_{0}\)). The isotropic over-consolidation ratio of the reconstituted soils, \(R\), is defined as by Chen and Abousleiman [22], which is different from the conventional over-consolidation ratio, \(OCR=\sigma^{\prime}{}_{v,max}/\sigma^{\prime}{}_{v}\)[6]. For structured soils, a higher stress level is required to reach the initial yield surface than that for the corresponding reconstituted soils due to \(p^{\prime}_{s}>p^{\prime}_{C}\), which means that the structured soils exhibit partial 'false' over-consolidated behavior [34]. To denote the degree of structure, the initial structural ratio, \(R_{s}\), is defined as follows:  
\[R_{s}=\frac{p^{\prime}_{s}}{p^{\prime}_{C}} \tag{39}\]It can be seen that \(R_{\rm s}\) is greater than 1 for structured soils. When the soil is in a reconstituted state, \(p_{\rm s}^{\prime}\) equals \(p_{\rm C}^{\prime}\); therefore, \(R_{\rm s}=1\). The stronger the initial structure, the greater \(R_{\rm s}\) for the soils.  
It should be mentioned that the stress components at the elastoplastic boundary \(r_{p}\) (corresponding to \(\zeta=\xi_{p}\)) satisfy both the elastic solutions given by Equations (13)-(15) and the yield function given by Equation (2). Combining this with the definition of \(R_{\rm s}\), \(\sigma^{\prime}r(\xi_{p})\), \(\sigma^{\prime}\theta(\xi_{p})\), and \(\sigma^{\prime}z(\xi_{p})\) can be easily obtained following the method of Chen and Abousileiman [22]:  
\[\sigma^{\prime}_{r}(\xi_{p})=\sigma^{\prime}_{h}+\sqrt{{\sigma^{\prime}_{h}}^{ 2}-(4{\sigma^{\prime}_{h}}^{2}+{\sigma^{\prime}_{z}}^{2}-2\sigma^{\prime}_{h} \sigma^{\prime}_{z}-q_{p}^{2})/3} \tag{40}\]  
\[\sigma^{\prime}_{r}(\xi_{p})=\sigma^{\prime}_{h}-\sqrt{{\sigma^{\prime}_{h}}^ {2}-(4{\sigma^{\prime}_{h}}^{2}+{\sigma^{\prime}_{z}}^{2}-2\sigma^{\prime}_{h} \sigma^{\prime}_{z}-q_{p}^{2})/3} \tag{41}\]  
\[\sigma^{\prime}_{z}(\xi_{p})=\sigma^{\prime}_{v} \tag{42}\]  
with  
\[\xi_{p}=\frac{\sigma^{\prime}_{r}(\xi_{p})-\sigma^{\prime}_{h}}{2G_{0}} \tag{43}\]  
\[q_{p}=Mp^{\prime}_{0}\sqrt{RR_{\rm s}\left[1+\frac{1}{M^{2}}\left(\frac{q_{0} }{p^{\prime}_{0}}\right)^{2}\right]-1} \tag{44}\]  
The initial values determined from Equations (40)-(44) are sufficient to solve the system of Equation (38a-c). Note that these equations are related to the auxiliary variable \(\xi\); therefore, the obtained stress components vary with the \(\xi\) instead of the radial position \(r\).
## 4 Evaluation Results
### Grout-Injection Material Physical Property Change Measurement Comparison Results
#### 4.2.4 SPRG Grout-Injection Material  
The physical property changes of the three different SPRG grout-injection specimens were mutually compared. The results are shown that the three SPRG products were somewhat susceptible to physical property changes with regards to wet surface adhesion strength and water flow erosion. Product B, in particular, showed a higher stability than the other two products. Refer to the below radar chart in Figure 16.
## 1 Introduction
Multiscale Permutation Entropy (MPE) is a statistical tool for analyzing the complexity and nonlinearity of time series through a multiscale analysis [16; 17]. It reveals dynamic data behavior at different scales and is widely applied in signal processing. An MPE threshold of 0.6 distinguishes between noisy and clean components. IMF components with an MPE greater than 0.6 are considered noisy, while those below 0.6 are considered clean [18]. This paper addresses the challenge of manually selecting K and \(\alpha\) in traditional VMD, which can reduce decomposition effectiveness. Given the large volume of gate vibration data requiring robust optimization, this paper proposes the MVO-VMD-MPE-WTD joint denoising method to improve signal processing quality. The MVO algorithm optimizes the number of modes and penalty factor in VMD, achieving optimal signal decomposition. MPE is then used to assess the noise content of each IMF. Noisy components are denoised using WTD, and finally, the processed components are combined with the clean ones to complete denoising. This approach improves both decomposition accuracy and the robustness and adaptability of the algorithm. This method is applied to denoise measured vibration data from spillway gates in a hydraulic project. Prototype observations and numerical simulations jointly demonstrate the method's effectiveness.
## 4 Experimental Results
### Experimental Setup
Figure 6: Average PSNR versus Lagrange multiplier at different target bitrates for test video sequences: (a) Soccer (640 kbps) and (b) Park_joy (10240 kbps). In addition, we also investigate PSNR variations during video reconstruction. Since high fluctuation of frame PSNR values may cause perceptual annoying to viewers, the fluctuation of PSNR values is one of the vital factors for video coding applications. The standard deviation of PSNR (PSNR STD) is utilized to measure for the smoothness of video quality. The smaller PSNR STD value result in the smoother PSNR variation and hence more consistent video quality over the video frames. Figure 7 illustrates the average PSNR STD values over different target bitrates for the proposed algorithm and the other four methods. Compared to the four methods, the proposed algorithm achieves more stable visual quality with substantially smaller PSNR fluctuations as depicted in Figure 7. From this figure, we can see that the proposed Lagrange multiplier selection algorithm reduces the PSNR standard deviations of all frames by up to 3.58 with respect to RPI-MC-EZBC. As shown in Figure 7, our method generates the minimum PSNR fluctuation over the entire sequence than the other three methods. Hence, we reach the conclusion that the proposed method can be beneficial for controlling the fluctuations of qualities in the reconstructed video and produce more stable visual quality than others.
## 2 Methods
### CMG Base Model
#### 2.1.1 Parameter Settings of the Base Model  
A five-spot well pattern is established using CMG software, as shown in Figure 1. The model has a well in the middle where water and CO\({}_{2}\) can be injected alternatively. There are 23 grids both in the \(X\) and \(Y\) directions, with a grid size of 30 \(\times\) 30 \(\times\) 3 m in the \(X\), \(Y\), and \(Z\) directions, respectively. The model is divided into 8 layers in the \(Z\) direction, and thus the total number of grids is 23 \(\times\) 23 \(\times\) 8 = 4232.  
The key reservoir parameters used in this work are selected based on the geological information of the Tuo 28 block in Shengli Oilfield [33; 34], as shown in Table 1. The oil reservoir depth is 1800 m, and the reservoir pressure is 18 MPa due to the normal formation pressure coefficient of 1.0. The reservoir temperature is 85 \({}^{\circ}\)C. The porosity is 0.24, the initial oil saturation is 0.7934, the crude oil viscosity is 15.4495 cp, and the crude oil density is 760.9 kg/m\({}^{3}\).  
The permeability in the horizontal directions, for each layer from top to bottom, is 10 \(\times\) 10\({}^{-3}\) um\({}^{3}\), 20 \(\times\) 10\({}^{-3}\) um\({}^{3}\), 30 \(\times\) 10\({}^{-3}\) um\({}^{3}\), 40 \(\times\) 10\({}^{-3}\) um\({}^{3}\), 60 \(\times\) 10\({}^{-3}\) um\({}^{3}\), 70 \(\times\) 10\({}^{-3}\) um\({}^{3}\) and 90 \(\times\) 10\({}^{-3}\) um\({}^{3}\), as shown in Table 2, and the average permeability in horizontal directions is 50 \(\times\) 10\({}^{-3}\) um\({}^{3}\). The permeability in the vertical direction is 0.1 times the horizontal permeability (K\({}_{\rm v}\)/K\({}_{\rm h}\) = 0.1) and the average permeability in the vertical direction is 5 \(\times\) 10\({}^{-3}\) um\({}^{3}\), which is a typical non-homogeneous low permeability reservoir with a positive rhythm. The thickness of the whole reservoir is 27 m, with each layer measuring 3 m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial No.** & **Input Parameters** & **Unit** & **Value** \\ \hline
**1** & Average permeability in the X and Y directions & \(\times\)10\({}^{-3}\) um\({}^{3}\) & 50 \\
**2** & Average permeability in the \(Z\) direction & \(\times\)10\({}^{-3}\) um\({}^{3}\) & 5 \\
**3** & Oil layer thickness & m & 27 \\
**4** & Top depth & m & 1800 \\
**5** & Porosity & 1 & 0.24 \\
**6** & Initial oil saturation & 1 & 0.7934 \\
**7** & Crude oil viscosity & cp & 15.4495 \\
**8** & Crude oil density & kg/m\({}^{3}\) & 760.9 \\
**9** & Reservoir temperature & \({}^{\circ}\)C & 85 \\
**10** & Initial reservoir pressure & MPa & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of the model.  
Figure 1: Three-deminsional diagram of the Water Alternating Gas model.
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
###### Abstract
Self-organizing Networks (SONs), for instance, are independent and flexible structures that enable vehicular networks to determine what steps are required to sustain efficiency stability and enhance Quality of Service (QoS) levels. SON technologies are classified as self-configurable, optimized, and healing entities. This technology works when a problem is already generated. AI and ML may be explored as prospective remedies for integrating intellect into SONs. Such solutions would anticipate impending network problems and be capable of handling the intricacies suitably. A vehicular network primarily focuses on efficiency in three key fields: energy, vehicle safety, and traffic. Traffic flow details are the significant application of V2X. These vehicle details can be utilized by vehicular networks to effectively carry out operations including the resolution of congested roads, the optimization of Plug-in Electric Vehicle (PEV) battery usage, the reduction in petroleum intake, and the enhancement of Global Positioning System (GPS)-based services. Various resources, including closed-circuit television (CCTV) recording devices, crowd-sourced databases, and vehicles, are capable of providing traffic flow statistics [14]. Developing traffic flow forecasting systems that are exceptionally precise while utilizing traditional traffic flow analysis techniques presents a significant obstacle. Not only that, but study and testing are constrained to just a small subset of available traffic data. Researchers have a limited amount of freely accessible statistics to work with when developing and testing methods to forecast traffic flows. ML has been extensively used to construct prediction models in various domains like visualization, robotics, healthcare, and Natural Language Processing (NLP). ML techniques employ Supervised and Unsupervised Learning to better manage sparse and dense nodes of vehicles. ML in VANETs includes handling data congestion, driverless transportation facilities, clustering, stability, storing information, and geographical location applications [15; 16; 17]. Nevertheless, there exists significant potential for expansion within the domain of ML emergence techniques to address the issues with vehicle networks.
Various studies have been carried out dealing with the design [15], control [16; 17; 18] and the structural optimization of CDPRs. In [19], [PERSON] et al. studied the optimal position of the cable exit points allowing minimization of CDPR size for fully constrained and suspended configurations. In this work, the location of the cable exit points was supposed to be fixed, which was an unrealistic assumption. In [20], [PERSON] et al. optimized the CDPR geometry by minimizing the maximum cable tensions. This criterion could not guarantee minimum cable tensions since only the maximum value was optimized. [PERSON] et al. [21] studied the optimal design of a suspended CDPR using first the workspace area, then the global condition index, which describes the robot dexterity, as two separated objective functions and, thus, only one criterion in each optimization was used. [PERSON] et al. [22] studied first, the optimal design of a CDPR taking into consideration the dexterity then the elastic stiffness as criteria, then a multiobjective optimization approach was used mixing the two characteristics. The selection of each criterion weight was chosen in a way that either the system was preferred to be more dexterous or stiffer. The authors chose to give the same importance to the two criteria. Such a choice can be improved by studying the influence of the variation of each coefficient on the objective function and on the criteria. The gaps presented above are taken into consideration in this paper. The goal is to design a planar CDPR for upper limb rehabilitation. The prescribed exercise consists of tracing the number eight shape with the hand. This form was used among others since it involves shoulder and elbow motions [23]. The rehabilitation of the wrist joint was also considered since the hand orientation was not fixed. The cable tensions, the elastic stiffness and the dexterity were selected as the problem criteria. The choice of each criterion weight was justified, and the design parameters were set in a way to obtain a realistic design. This design problem can be formulated as a constrained optimization problem. Thus, this paper aims to design an optimal planar CDPR, based on the above-mentioned criteria, able to mobilize the patient's affected upper limb along the prescribed task. The design optimization problem was formulated after considering the disadvantages of some proposed approaches introduced above. Once the optimal structure was selected, an experimental prototype was developed allowing a check of solution feasibility and reliability. The paper is organized as follows: Section 2 details the experimental protocol followed to record the prescribed task. The planar CDPR model is presented as well as the criteria and the constraints adopted to seek the optimal design. In Section 3, the optimization results, using a multiobjective formulation then an adapted mono-objective formulation, are discussed and the experimental validation using a developed prototype is shown. The last section concludes the paper.
## 4 Conclusions  
Graphene-based flake nanomaterials, GO and rGO, were applied to fabricate an enhanced water treatment membrane, and the GO- and rGO-blended membranes were comparatively evaluated to identify which type of membrane maintained its rejection rate while increasing flux efficiency and exhibiting an antifouling effect. The effect of the graphene nanomaterial contents on the hydrophilic and antifouling properties of the membrane was investigated. Results showed that the GO-blended membrane provided the best performance, as it had hydrophilicity due to its surface functional group, while the PSf membrane had larger pores and a higher permeability than the PVDF membrane. For both PSf and PVDF membranes, the highest permeability was found with the GO-blended membrane when GO was mixed at a rate of 0.10 wt%. Scanning electron microscopy showed a widening of the membrane pores when the graphene materials were blended. However, the pores narrowed and were blocked when the amount of GO exceeded 0.10 wt%. A mixed solution of kaolin and alginate was employed to evaluate the membrane fouling and rejection efficiencies, and the PSf membrane with GO at 0.10 wt% was found to provide the best membrane fouling reduction, pollutant removal rate, and best permeability.  
Conceptualization, methodology and writing--original draft preparation and editing, Y.Y.; data curation and formal analysis, H.K.; writing--review and editing, W.S.Y.; supervision and writing--review, J.-W.K.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT, MSIT) (No. 2019R1G1A1100560).  
The authors declare no conflict of interest.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 2 Applications, Motivations, and Challenges
### Applications of Receiver-Initiated MAC  
In applications such as underwater environment monitoring, offshore structural health monitoring (SHM), and oceanography data collection, environmental or structural information is frequently collected by the sensors in water and then forwarded to the sink nodes deployed on the ocean surface. The aggregated data flow in these applications naturally forms a tree network, where the sink nodes are the roots and the sensors are the leaves. The sensing pattern can either be periodic or have a varying rate depending on the environment (e.g., temperature and salinity, etc.) or human activity (e.g., oil drilling) [16]. When an interesting event is detected, bursty traffic from a short period of intensive observations may also be triggered. In addition, the traffic on a node is a combination of self-generated sensing data and the forwarding packets, resulting in traffic dynamics in both temporal and spatial dimensions. Therefore, we consider the data gathering tree network as an application that is preferable, yet challenging, to the receiver-initiated MAC protocols.