esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Crack Identification with Responses Modulated by Nonlinear Energy Sink
## 2 Dynamic Equation of a Breathing Cracked Beam-NES System  
A simply supported beam with a breathing crack is shown in Figure 1. The length, width, and height of the beam are \(L\), \(b\), and \(h\) respectively. The crack depth is \(a\) and the distance of the crack from the left end of the beam is \(L_{o}\), defining the crack position ratio as \(\beta=L_{o}/L\) and the depth ratio as \(\xi=a/h\).  
The natural frequency of a perfect simply supported beam is given by the following:  
\[p_{n}=\frac{n^{2}\pi^{2}}{L^{2}}\sqrt{\frac{EI}{\rho A}}\;(n=1,\;2,\;3,\cdots) \tag{1}\]  
where \(EI\) and \(\rho\) are flexural rigidity and density of the beam, respectively, and \(A\) is the sectional area. Thus, the stiffness of the simply supported beam is given by \(k_{c}=mp_{1}^{2}\).  
The flexibility change of the simply supported beam due to a crack is [36]  
\[\Delta C=\frac{18\pi L_{0}^{2}(1-\
u^{2})}{Ebh^{2}}g(\xi) \tag{2}\]  
where \(g(\xi)\) is a function of crack depth ratio, given by the following:  
\[\begin{array}{c}g(\xi)=19.6\xi_{0}^{10}-40.7556\xi_{0}^{9}+47.1063\xi^{8}-33.0351\xi^{7}+20.2948\xi^{6}\\ -9.9736\xi^{5}+4.5948\xi^{4}-1.04533\xi^{3}+0.6272\xi^{2}\end{array} \tag{3}\]  
Therefore, the stiffness of the cracked beam can be calculated as follows:  
\[k_{o}=\frac{1}{C_{o}}=\frac{1}{\Delta C+\frac{1}{E_{c}}} \tag{4}\]  
Figure 1: A simply supported beam with a breathing crack.  
The dynamic equation of the simply supported beam with a breathing crack after discretizing is obtained as follows [37,38]:  
\[m\ddot{u}+c\dot{u}+[k_{o}+k_{\Delta C}(1+\cos\omega t)]u=f \tag{5}\]  
where \(m\) is the mass of the beam, \(c\) is the damping coefficient, \(k_{o}\) is the stiffness of the beam with an open crack, \(k_{\Delta C}=(k_{c}-k_{o})/2\) is the change range of equivalent stiffness, \(f\) is the exciting force, and \(\omega\) is the exciting frequency. Figure 2 shows the cracked beam attached to an NES.  
The distance between the force point and the left end of the beam is \(L_{1}\) and the NES is placed at \(L_{2}\) from the left end of the beam. The dynamic equation is obtained as follows:  
\[\begin{split} m_{1}\ddot{u}+c\dot{u}&+[k_{o}+k_{ \Delta C}(1+\cos\omega t)]u+\sqrt{2}k\sin(\pi L_{2})\Big{[}\sqrt{2}u\sin(\pi L _{2})-v\Big{]}^{3}\\ +&\sqrt{2}\gamma\sin(\pi L_{2})\Big{[}\sqrt{2}\dot {u}\sin(\pi L_{2})-\dot{v}\Big{]}=\sqrt{2}f\sin(\pi L_{1})\end{split} \tag{6}\]  
\[m_{2}\ddot{v}+k\Big{[}v-\sqrt{2}u\sin(\pi L_{2})\Big{]}^{3}+\gamma\Big{[}\dot {v}-\sqrt{2}\dot{u}\sin(\pi L_{2})\Big{]}=0 \tag{7}\]  
where the mass of the beam is represented by \(m_{1}\) that equals \(m\) in Equation (5), \(m_{2}\) is the mass of the NES, \(\gamma\) is the damping coefficient of the NES, \(k\) is the stiffness coefficient of the NES, and \(v\) is the displacement of the NES.
## 3 Experimental Results
### Seismic Soil-Foundation-Structure Interaction (SFSI)
Aspect ratio values  
Figure 4: Ratio of response spectra (RRS) of S1 structure considering foundation input motion (RRS\({}_{\text{FIM}}\)) (**a**) and inertia interaction (RRS\({}_{\text{II}}\)) with artificial input motion (**b**). \(f_{n}\) is fixed-base frequency of S1 structure. Solid circles indicate peak RRS of each case. for S1, S2, and S3 structure were 3.2, 3.17, and 3.62, respectively. Previous studies [13; 41] have reported that a structure with a higher aspect ratio value has a larger increment in period lengthening when soil stiffness decreases. Therefore, S3 structure with the highest aspect ratio value exhibited the largest decrement in \(f_{flx}\), when the input intensity increased or when the soil stiffness decreased. Relatively lower reductions in \(f_{flx}\) were observed for S1 and S2 structures because of lower aspect ratio. Figure 5: RRS\({}_{\text{II}}\) of S1, S2, and S3 structures with artificial input motion at L\({}_{\text{-}}\)Input intensity. \(f_{n}\) and solid circles indicate are fixed-base frequency and peak RRS of each structure, respectively. Figure 6: Variation of flexible-base frequency (\(f_{flx}\)) of S1, S2, and S3 structure with level of input excitation. \(f_{n}\) indicates fixed-base frequency of each structure. Average value from two input motions was plotted. Error bars indicate variations of two motions.
## 3 Non-Thermal Technologies in Food Processing
### Ozonation
#### 3.3.2 Application of Ozone in Juices and Beverages  
Consumers' demands for fresh juices are growing without all these negative effects that thermal pasteurization has on juices. However, ozone leaves no residue when applied to juices, and a concentration limit of 0.4 mgL\({}^{-1}\) has been set. Factors related to the effectiveness of ozone against pathogenic microorganisms are the pH of the juice, the concentration, the temperature, and the additives as well as the exposure time and the ozone concentration [164]. The ozonation of juices usually takes place in cylinders with the formation of bubbles [180].  
[PERSON] et al. reported enzyme peroxidase (POD) and polyphenoxidase (PPO) activities which were reduced due to treatments with gaseous ozone in peach juice. The researchers exposed peach juice to ozone with different concentrations. Maximum enzyme activities reductions after 12 min of treatment were achieved through the effect of higher ozone concentration [180].  
Low concentrations of ozone have been found not to completely inactivate pathogenic microorganisms in juices, whereas high concentrations of ozone degrade some of their quality characteristics such as phenolic content and color. [PERSON] et al. reported that a combination of low ozone concentration (24 mg L\({}^{-1}\)) with high hydrostatic pressure (179-321 MPa, 5 min) was efficient for the stabilization of refrigerated pitaya juice [152].
## 2 Fundamentals
### Strategic Production Network Design  
Strategic network design addresses the two main tasks of network configuration and network coordination [9]. According to [PERSON] [10], network configuration refers to the distribution of a company's value creation process across different locations. Network coordination deals with the timing and content of processes between different locations [10; 11]. The two main tasks result in two planning levels integrated into the strategic network design [12]. Firstly, strategic structural decisions (configuration), such as decisions on plant locations and new products, are made on a strategic planning level [12]. Secondly, operational decisions, e.g., concerning the flow of goods within the network (coordination), are subject to an operational planning level within strategic network design [12]. A common approach to strategic network design presented by [12] is shown in Figure 2. This process can be linked to important interdependencies between planning decisions in strategic network design [2].  
The top management makes strategic structural decisions, such as whether or not to open a new plant, to generate different manageable scenarios within the first step of the strategic network design process. These decisions set the framing conditions for network planners to create alternative network solutions for each scenario based on operational decisions such as buffer sizes for internal customer-supply relationships. Next, investments as a consequence of strategic structural decisions and other financial variables are used to evaluate all alternative solutions. Then, industry best practices are applied to benchmark each alternative solution regarding suitable performance indicators. Finally, the decision for an alternative solution can be made based on the results of the previous steps and qualitative objectives such as flexibility or customer service [12].  
The data-farming-based planning approach shown in Figure 1 addresses steps two and three of the strategic network design process and assumes a set of scenarios as input data. Currently, alternative solutions are generated in the industry based on subjective and mostly unsystematic decisions by network planners. Using a data-farming-based approach opens up the solution space for alternative solutions based on operational decisions to enable systematic, objective decisions [2].
# In Vitro Evaluation of the Strength of Dentin Replacement in Complex Posterior Tooth Restoration  
[PERSON]  
1 Department of Conservative Dentstry, Faculty of Dentstry, Hasanuddin University,  
Makassar 90245, Indonesia; [EMAIL_ADDRESS] (F.R.); jektijuni@unhas.acid (J.N.); [EMAIL_ADDRESS] (C.A.R.)  
[PERSON]  
1 Department of Conservative Dentstry, Faculty of Dentstry, Hasanuddin University,  
Makassar 90245, Indonesia; [EMAIL_ADDRESS] (F.R.); jektijuni@unhas.acid (J.N.); [EMAIL_ADDRESS] (C.A.R.)  
[PERSON]  
1 Department of Conservative Dentstry, Faculty of Dentstry, Hasanuddin University,  
Makassar 90245, Indonesia; [EMAIL_ADDRESS] (F.R.); [EMAIL_ADDRESS] (J.N.); [EMAIL_ADDRESS] (C.A.R.)  
[PERSON]  
1 Department of Conservative Dentstry, Faculty of Dentstry, Hasanuddin University,  
Makassar 90245, Indonesia; [EMAIL_ADDRESS] (F.R.); [EMAIL_ADDRESS] (J.N.); [EMAIL_ADDRESS] (C.A.R.)  
[PERSON]  
2 Faculty of Dentstry, Universitas Muslim Indonesia, Makassar 90231, Indonesia; [EMAIL_ADDRESS] 20 Department of Oral Maxillofacial Surgery, Faculty of Dentstry, Hasanuddin University,  
Makassar 90245, Indonesia; [EMAIL_ADDRESS] 20 Division of Clinical Cardiology and Endondoology, Department of Oral Rehabilitation, School of Dentstry,  
Health Sciences University of Hokkaido, Tobetsu 061-0293, Japan; t-saito@hoku-iryo-u.  
mechanical properties, such as hardness, compressive strength, flexural strength, wear resistance, and tensile strength, which resembles the ability of natural teeth to withstand large masticatory forces. Accordingly, they are resistant to fracture [15; 17]. Moreover, the fracture resistance of composite resin restoration decreases mainly in the complex cavity. Therefore, direct posterior restoration with a complex cavity requires materials that can improve the mechanical properties of composite resins, one of which is dentin replacement [8; 10; 16; 18; 19].  
Dentin replacement is a base material that can be applied to the dental cavity and is followed by the application of the composite resin. This material possesses excellent adhesive properties towards dentin and self-leveling, which optimize the marginal integrity and adaptation of restorative materials [20; 21; 22]. In addition, this material can also function as a stress breaker with its low modulus of elasticity [23; 24]. Currently, dentin replacement is being developed with the addition of fiber to increase its resistance to fractures and strengthen restorations, notably in teeth with complex cavities [8; 25; 26; 27].  
The mechanical properties of the restorative materials can be determined by assessing their tensile strength. Tensile strength is used to evaluate the material's ability to experience plastic deformity when a force is applied [15]. Therefore, the present study aimed to investigate the tensile strength differences in the complex restoration of the posterior tooth with dentin replacement made from fiber and non-fiber materials.
## 2 Materials and Methods
### Cholesky Decomposition  
To ensure realistic interdependencies between patient characteristics--such as the known associations between older age and higher blood pressure or between diabetes, elevated BMI, and cardiovascular risk--the algorithm applies a correlation structure using Cholesky decomposition [14].  
The process begins with a user-defined correlation matrix \(C\), which is symmetric and positive semi-definite. Its diagonal entries are all 1, and off-diagonal entries are pairwise correlation coefficients \(\rho_{i,j}\). The matrix is decomposed as follows:  
\[C=LL^{T}\]  
where \(L\) is a lower triangular matrix. In the context of correlation matrices, which are by definition symmetric and positive semidefinite (and under certain conditions, positive definite), this decomposition allows for the efficient simulation or transformation of multivariate normal distributions. The Cholesky decomposition of \(C\) is computationally efficient and numerically stable, and it guarantees that the resulting transformation preserves the specified correlation structure. Unlike nonlinear latent methods, this linear transformation preserves the exact structure of the specified correlation matrix and can be directly inspected, verified, and updated by the user.  
To introduce correlation into the dataset, the algorithm multiplies each patient's vector of independent standardized normal variables \(Z_{\text{independent}}\ \sim\ N(0,\ I)\) by the matrix \(L\):  
\[Z_{\text{correlated}}=L\cdot Z_{\text{independent}}\]  
\[Z_{\text{correlated}}\ \sim\ N\ (0,\ C)\]  
The result is a vector of correlated Z-scores that reflects the specified relationships among variables. These correlated Z-values are then transformed back to their original scales using the inverse CDFs associated with each parameter, along with their defined means and standard deviations. This step produces the final, correlated parameter values for each synthetic patient.
###### Abstract  
Load-sharing behavior is very important for power-split gearing systems. Taking the multistage planetary gear train transmission of an Million Watt (MW) wind generator as the investigation object, and based on the gear transmission system of a wind generator in a complex and changing load environment, a random wind model of a wind farm is built by using the two-parameter Weibull distribution. According to the realistic working region of the wind generator, the random wind speed is changed into time-varying input speed of the wind generator gear box. Considering the internal excitation, such as mesh stiffness, mesh damping of gear pairs and the meshing error, a dynamic model for a multistage planetary gear transmission system is built by using the lumped parameter method. The load-sharing coefficients are obtained for each planet gear pair in the same meshing period of the transmission system that is under the interaction of time-varying input speed and internal excitation. It is shown that the degree of load-sharing coefficient fluctuation for each planet gear pair of the first- and second-stage planetary gear train is significantly affected by time-varying input speed. The research results can lay a theoretical foundation for optimization and reliability of planetary gear transmission systems of wind generators.  
wind generator; multistage planetary gear train; random wind speed; load sharing +
Footnote †: journal: Applied Sciences
## 1 Introduction
Due to various restrictions on wireless networks, routing protocols used in networks are also subject to a number of constraints. Many studies have been conducted in this field and two types of topological structures have been proposed, primarily planar topologies and layered topologies. In a planar structure, all nodes in a network are at the same level and have the same routing capabilities, making it simple and efficient in a small network. The problem, however, is that as the network grows, the amount of routing information increases rapidly, and it takes a long time for routing information to reach the final node. For large networks, cluster-based hierarchical routing can be used to resolve the problem. In hierarchical routing, nodes within the network are dynamically configured by being grouped into areas called clusters, and clusters are eventually assembled into base stations. Routing with clustering has the following advantages:  
1. Clusters help maintain a relatively stable network topology. 2. Routing overhead can be significantly reduced by propagating high levels of information through cluster heads. 3. Only CH or intermediate nodes need to maintain path information. 4. Reduce energy consumption across all networks. 5. Improve network scalability. Consequently, the use of hierarchical routing protocols will maximize the energy efficiency. Optimal CH selection and clustering configuration methods are required to ensure equal energy consumption to maximize the network lifetime in a routing protocol. In this paper, we propose a hierarchical routing protocol and utilize fuzzy logic in the method of optimal CH selection and clustering configuration. The rest of this paper is as follows. In Section 2, we introduce the related research. More details of enhanced LEACH-dual fuzzy logic (ELEACH-DFL) with optimal CH selection method and clustering configuration are presented in Section 3. In Section 4, we evaluate the performances of the ELEACH-DFL. Finally, the paper is concluded in Section 5.
# Influence of Stress Anisotropy on Petrophysical Parameters of Deep and Ultradeep Tight Sandstone
## 5 Conclusions  
1. Rock sonic velocity is extremely sensitive to water saturation under overburden pressure. At ultrasonic frequencies, when the water saturation increases from 0% to 80%, the P-wave velocity increased first and then decreased. When the water saturation continued to increase to 100%, the P-wave velocity increased. Saturation was negatively correlated with S-wave velocity and resistivity. This is because the shear modulus will change with the change in water saturation, which will lead to changes in longitudinal and transverse wave velocity.
2. Different minerals have different controlling effects on the velocity ratio of S-waves and P-waves in rocks, and quartz content plays the main controlling role, which was negatively correlated with the ratio, followed by feldspar and clay, which were positively correlated with the ratio.
3. The confining pressure, axial pressure, stress ratio and burial depth were positively correlated with the P-S wave but negatively correlated with the P-S wave ratio. The order of influencing factors were the stress ratio > confining pressure > axial pressure.
4. In general, the experiment demonstrated the influence mechanism of strong stress on reservoir quality in the Tarim study area, but the experiment was not perfect. The rock surrounding the experiment site was not under true triaxial confining pressure. We  
Figure 12: Relationship curves between P-S wave ratio, resistivity and confining pressure.  
Figure 13: Correlation curves between P-S wave ratio, resistivity and axial pressure.  
simulated the condition of biaxial compression, not true triaxial confining pressure. At the same time, the influence of underground pore pressure was less considered, so as a next step, we will consider the influence of true triaxial compression and underground pore pressure.  
Conceptualization, H.Z.; Funding acquisition, H.Z.; Investigation, G.Y.; Methodology, H.Z., K.X., B.Z. and C.L.; Project administration, H.Z.; Software, K.X., B.Z., H.W., S.L. and Z.Q.; Validation, Z.W., S.L. and Z.Q.; Visualization, H.W. and S.L.; Writing--original draft, H.Z., K.X., B.Z., G.Y., H.W., Z.W. and C.L. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Major National Science and Technology Project (2016 ZX05051), Major Science and Technology Project of PetroChina Company (2018E-1803).  
Not applicable.  
H.Z., K.X., G.Y., H.W., C.L., S.L. and Z.Q. were employed by Tarim Oilfield Company, PetroChina. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Experimental Facilities  
To validate the availability and correctness of the probe measurement methods, an experiment was performed to compare the local flow parameters obtained by the four-sensor probe and visual measurement, including void fraction, IAC, velocity, chord length and interface direction. An air-water two-phase flow with bubbles approximately the same size injected vertically up in a transparent tube was adopted. There is no doubt that such flow properties can be easily determined by senor probe or visual techniques with high accuracy. Therefore, it was chosen as a validation case for the proposed four-sensor probe measurement method.  
Although the proposed probe and algorithm have not yet been validated, the existing fundamental measurement principles described in Section 2 can also apply to micro and conventional large systems. For fierce two-phase flow scenarios, corrosive fluid, high pressure system, high flow rate and cases of flow experiencing heat and mass transfer, the measurement methods still apply as long as the probe is fixed strongly and prevented from damage. The experiment in this study was only designed for the purpose of primary method validation, considering that it is convenient to be measured by visual technique and easy to be replicated.
## 3 Theoretical Analysis
### The Reason and Way of Simplifying Path Collection  
The critical paths reflect the timing state of the circuit, and the information provided by the critical paths is an important reference for threshold voltage distribution. However, the order of magnitude of the paths in the mesh structure is so large that traversing all critical paths is time-consuming or infeasible. For example, as shown in Figure 3, suppose that the structure in the dotted box is repeated 15 times. Currently, the maximum logic depth (the number of cells on one path) is \((5+15*2)=35\), which is common in VLSI designs. When the structure inside the dashed box is not considered, the paths through \(A\) and \(B\) are 3 respectively. For each repetition of the structure inside the dashed box, the number of paths is multiplied by 2. Thus the number of paths is \((2^{15}*(3+3))=196,608\), while the cell number is \((7+15*3)=52\). It means that the order of magnitude of the paths may be much larger than that of cells.  
The high similarity of these paths means they provide similar information, and access to all of them means redundant information at a high cost. In order to reduce the computational complexity, the proposed method obtains only the worst timing path through each pin. In the case of Figure 3, the maximum number of paths counted in this way is 68, which is the same order of magnitude as the pin. Using simplified path collection lacks completeness, and ways to expand the path collection to compensate for this drawback are described later in Section 3.5.
## 4 Proposed Methodology
### Solution Based on the Recommendation System
#### 4.2.2 Data Processing  
At this stage, as illustrated in the pseudo-code Algorithm 1, after retrieving the necessary data, the RS performs a filtering process to improve efficiency and facilitate subsequent searches. This included removing duplicate entries, handling missing values,  
Figure 9: Detailed architecture of the proposed hybrid recommendation system, where arrows represent the direction of data flow and interactions between the layers.  
and removing outlier data. Custom scripts were written to identify redundant data points and eliminate them. Additionally, outlier detection was performed to ensure that any anomalies in the mobility patterns of the taxis were filtered out. This step was crucial for maintaining the accuracy and reliability of the data before using them for further analysis in the recommendation system.  
```
Input: Raw data retrieved from the dataset Output: Cleaned and processed data function DataProcessing(data) for each user \(u\) in data do if duplicate entries are detected for \(u\)then  Remove duplicates endif if missing or incomplete data is found for \(u\)then  Remove the incomplete entry endif if outliers are detected in \(u\)'s data then  Remove outliers endif endfor return Cleaned data endfunction
```  
**Algorithm 1** Data Processing