esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast and Interactive Positioning of Proteins within Membranes
## 2 Materials and Methods
### Optimization and Extension of the Implementation
#### 2.3.2 Adding a Rigid Body Positioning Algorithm to BioSpring  
For many applications, the internal conformational flexibility of the protein to be incorporated is not required. Therefore, the computations associated with these degrees of freedom that would be required, for example, in an elastic network representation are unnecessary. We therefore implemented a rigid body model in BioSpring to avoid such unnecessary calculations. A customized rigid body dynamics algorithm is necessary to preserve the structure of the protein and thus keep all internal relative positions constant. Such a procedure allows a drastic reduction in computational cost by reducing the number of degrees of freedom. The minimal implementation required to perform our experiments is to allow a rigid structure whose particles can be controlled individually and whose global translational and rotational motions are computed according to all the forces acting individually on the particles, including the forces derived from the equations of IMPALA. The rotational motions of the rigid body are always performed around its center of mass. The state variables of the rigid body are its position--more precisely, the position of the center of mass--its translational velocity, and its angular velocity defined by the instantaneous axis of rotation, whose norm and direction indicate the amplitude and direction of the direction of rotation. The angular velocity is calculated directly from the torque acting on the entire protein. During the interactive simulation, the position of the center of mass, the angular velocity, and the torque may be displayed in the form of lines updated according to the data received from BioSpring.  
Our implementation is minimal and lacks some features typically found in other rigid body implementations, as they are not currently needed for our purposes. The functions needed to calculate the protein inertia matrix are implemented, but are not called in the rigid body force integration routine. The reason for this is that the complexity of the implementation is high, and for insertion into implicit membranes, it is quite sufficient to consider the rigid protein as an object with uniformly distributed mass. We have also simplified the rotation calculations, which are not performed with quaternion multiplication as originally implemented. At each iteration, we sum all the forces acting on the particles and derive the torque, angular velocity, and translational velocity of the entire protein. Both velocities are then used to calculate the velocities of the individual particles as the sum of the translational velocity and the local angular velocity with respect to the center of mass. Thus, the velocities are solved explicitly in a full timestep. So far, we have not gone the route of nested timesteps as in the Verlet or Leapfrog algorithms, which are widely used (and required) for other types of biomolecular simulations.
# SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
## 3 Results
### Performance Evaluation  
In order to verify the validity of SA-SVM locomotion pattern recognition model, confusion matrix is used to further evaluate the model performance based on the same locomotion data set. Here is an abbreviation for several patterns of movement, such as SDt: stairs descent; SAt: stairs ascent; LG: level ground walking. All subjects were asked to walk on different terrain at a natural walking speed. Tables 5 and 6 shows two confusion matrices.  
Figure 11: SA-SVM prediction effect.  
Figure 12: SA-SVM with FSM prediction effect.  
Tables 5-7 respectively report the confusion matrices of the SVM classifier, the SA-SVM combined model classifier and the SA-SVM combined model classifier with FSM on the test dataset. The elements of the confusion matrix are expressed in the form of mean error and standard error. As can be seen from Table 5, the SVM classifier has good test accuracy for each action. This shows that SVM can automatically extract valid features for classification. As can be seen from Table 6, the SA-SVM classifier further effectively improved the accuracy compared with Table 5, especially for the locomotion pattern of stairs descent. As can be seen from Table 7, the SA-SVM combined model classifier with FSM has higher identification accuracy. Since the degree of knee joint flexion is similar to that of level ground walking, it is more difficult to identify than other movement patterns. For the overall classification accuracy of all locomotion, the average classification accuracy of the SA-SVM combined model with FSM in all data was 97.47% \(\pm\) 1.16%, much higher than the 89.24% \(\pm\) 2.17% of the SVM classifier and the 94.8% \(\pm\) 0.75% of the SA-SVM classifier.
# Numerical Simulation of Random Cavitation Suppression Based on Variable NACA Airfoils
## 3 Settings for Calculation  
### Flow Field Calculation Settings  
In most of the simulation studies by experts and scholars, the 2D flow mode is mostly used in the researches of NACA airfoils. Compared with 3D simulations, the computational resources required are much lower and the calculation time can be saved. However, there is a significant difference between 2D and 3D simulations for most flow conditions [32]. In the real world, there are also some special turbulences such as flow around a cylinder, a three-dimensional vortex, and so on. There are significant inherent differences between 2D and 3D simulation predictions. After comprehensive consideration, this simulation is based on Ansys 2020R2 for 3D modeling, and the SST cavitation model and k-\(\epsilon\) turbulence model are used to simulate the flow field.  
The calculation domain is shown in Figure 2. The airfoil is located at the center of the upper and lower boundaries, with the head of the airfoil being 3C away from the inlet edge. The upper and lower boundaries are 2.5C, respectively, and the outlet edge is 6C away from the trailing edge. The geometric parameters of airfoil are chord length C = 1 m and incoming flow angle \(\alpha\) = \(6^{\circ}\). The cavitation number is a common standard to measure the severity of airfoil cavitation, and it is also commonly used to identify different cavitation conditions.  
The quality and quantity of the mesh have a significant influence on the calculation results, so the structured mesh was used in the hydrofoil calculation domain, and the mesh around the foil was encrypted. In addition, we carried out a grid-independent analysis and evaluated the cavitation number of airfoils according to Formula (7). The results are shown in Table 1.  
\[\sigma=\frac{p_{\infty}-p_{v}}{0.5\rho U^{2}} \tag{7}\]  
In the formula, \(p_{\infty}\) is the inlet pressure, saturated vapor pressure \(p_{v}\) = 3540 P\({}_{a}\), \(U\) is the velocity of incoming flow at infinity. Therefore, when the boundary conditions are not changed, the cavitation number will not change, and it is used to evaluate the mesh independence.  
It can be seen from Table 1 that the difference between the cavitation number \(\sigma\) and the number from Case A2 to Case A4 is less than 0.2%. Considering the time limitation, the Case A2 mesh is chosen for simulation.
## 4 Proposed Scheme
### User Registration Phase  
As compared to other research studies [38; 39; 40; 42; 44; 45], the user registration phase of our proposed scheme is very simple. A doctor must register themself on the server as a user. Sometimes, doctors need to access real-time patient health information remotely. During the registration, the doctor chooses a unique identity as an \(ID_{D}\) and a strong password as a \(PSW_{D}\). For security reasons, the hash-based identity of the doctor must be  
Figure 2: Flowchart of the proposed secure system.  
generated as \(HID_{D}\) and the hash-based password as \(HPSW_{D}\) by concatenation of identity and password in a hash function, as described in Equation (1) and Equation (2), respectively.  
\[HID_{D}=hash\ (ID_{D}) \tag{1}\]  
\[HPSW_{D}=hash\ (ID_{D}\parallel PSW_{D}) \tag{2}\]  
The server stores the secret credentials of the users along with email addresses and mobile numbers in its database. If the identity of the user is the same as one already saved in a database, then the server rejects it.
## 3 Results
### Evaluations On The Automatically Generated Dataset
#### 3.1.5 The Heterogeneous Dataset
As shown in Figure 12, the orange circle denotes the action, and the attraction list lists the top five actions that are heavily influenced by the specified action. The first bold action in the list is the next action that the particular action will point to and the float numbers in the list are the comprehensive influence factors. There are two orange arrows in Figure 12 pointing at the wrong actions comparing with the initial pattern which means the proposed method needs more adjustment and optimization. The influence threshold \(\beta=0.01\) can be used to filter out the actions with influences below \(\beta\), thus, there are no subsequent actions after a10. The correlation graph can be updated over time and the links between actions will be altered too. The discovery accuracy is used to measure the ability of the system to discover patterns. It can be calculated by \(\delta=\left|\mathrm{D}\right|/\left|\mathrm{E}\right|\times 100\%\) where \(\left|\mathrm{D}\right|\) denotes the correct relations discovered by system, and \(\left|\mathrm{E}\right|\) denotes all the relations the system should correctly discover. The quantitative testing method is used to find out which factors have a potential impact on the accuracy of the proposed method. For the three types of data listed in Table 8, the relationship between the proportion of them in the dataset and the pattern discovery accuracy is tested. The results show that:  
* The number of background sessions has little effect on the discovery accuracy;
* The irrelevant pattern sessions will impact the accuracy depending on the number of common actions sharing the target pattern sessions;  
Figure 11: The dynamic correlation graph based on the analysis results. Figure 12: Intrusion pattern discovery accuracy of the proposed method. With the fixed percentage of noise data, disordered data and incomplete data, the discovery accuracy is mainly impacted by the appearance frequency of pattern sessions. The experimental results are shown in Figure 12. If an intrusion pattern appears above 50 times in the dataset with the three types of data defects, the discovery accuracy can be 91% and remains stable. If the percentage of actions sharing between target pattern sessions and the irrelevant pattern sessions is lower than 40%, the pattern identifying accuracy can be kept above 90%. This is effective for distinguishing the particular intrusion patterns from other similar intrusion patterns.
## 1 Introduction
### Mechanical Metamaterials and Their Applications  
A mechanical metamaterial is a material created artificially with specific characteristics adjusted by the designer to suit particular tasks. The mechanical properties of metamaterials come from their microstructural geometries rather than their material constitution [19]. Mechanical metamaterials are often porous and formed from the repetition of the basic unit known as the unit cell. Their porosity may be encountered in 2D or 3D. The effective mechanical properties of metamaterials are tailored via the controlled distribution of matter within a structure. These controlled arrangements often result in periodic structures and can be defined through effective or apparent properties, which depend on the topology, volume fraction, and base material. The field of mechanical metamaterials has also benefited from the growth of additive manufacturing technologies, as these allow the fabrication of porous structures with complex geometries [20]. In engineering, the three relevant elastic metamaterial properties are stiffness, rigidity, and compressibility [19]. Mechanical metamaterials can be found in various engineering applications, including biomedicine and the aerospace and automobile industries.  
In biomedicine, for example, the use of mechanical metamaterials as bone implants has been explored, along with the understanding of the effects of bone formation during early osteointegration [21]. In addition, biomedical applications such as new techniques forthe development of tissue engineering scaffolds for cell culture studies [22] and the implementation of hybrid meta-biomaterials for tissue regeneration [23] have been investigated.  
In the aerospace and automobile industries, many applications have been developed over the years to innovate the design of specific components. For example, for aerospace engineering, auxetic (negative Poisson's ratio) composites and materials have been tested due to their high shear modulus and damping resistance [24]. The term auxetic was first used by [PERSON] et al. [25] in 1991, and it comes from the Greek word _auxetickos_, which means something that tends to increase [26]. Other topologies that have been shown to have auxetic properties are those based on chirality or achirality. They were used by [PERSON] et al., who proposed a method for the implementation of chiral topologies in wing airfoils [27]. Another example is the one proposed by [PERSON] et al., where the use of hierarchical truss elements as mechanical metamaterials was explored to develop lightweight aerospace components [28]. In the field of motor vehicles, [PERSON] et al. worked with auxetic structures in vibration-damping bodies for Rolls-Royce automobiles [29].
###### Abstract  
_Osmanthus fragrans_ flowers have long been used as raw materials in food, tea, beverage, and perfume industries due to their attractive and strong fragrance. The P450 superfamily proteins have been reported to widely participate in the synthesis of plant floral volatile organic compounds (VOCs). To investigate the potential functions of P450 superfamily proteins in the fragrance synthesis of \(O\). _fragrans_, we investigated the P450 superfamily genome wide. A total of 276 _P450_ genes were identified belonging to 40 families. The RNA-seq data suggested that many _OfcYP_ genes were preferentially expressed in the flower or other organs, and some were also induced by multiple abiotic stresses. The expression patterns of seven flower-preferentially expressed _OfcYP_s during the five different flower aroma content stages were further explored using quantitative real-time PCR, showing that the CYP94C subfamily member _OfcYP142_ had the highest positive correlation with finalool synthesis gene _OfcYP142_. The transient expression of _OfcYP142_ in \(O\). _fragrans_ petals suggested that _OfcYP142_ can increase the content of linalool, an important VOC of the \(O\). _fragrans_ floral aroma, and a similar result was also obtained in flowers of _OfcYP142_ transgenic tobacco. Combined with RNA-seq data of the transiently transformed \(O\). _fragrans_ petals, we found that the biosynthesis pathway of secondary metabolites was significantly enriched, and many 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway genes were also upregulated. This evidence indicated that the OfcYP proteins may play critical roles in the flower development and abiotic response of \(O\). _fragrans_, and that _OfcYP142_ can participate in linalool synthesis. This study provides valuable information about the functions of _P450_ genes and a valuable guide for studying further functions of _OfcYP_s in promoting fragrance biosynthesis of ornamental plants.  
Keywords:cytochrome P450 monooxygenase; fragrance; linalool; _Osmanthus fragrans_
# Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network
###### Abstract  
The reconfigurable intelligent surfaces (RIS) represent a transformative technology in wireless communication, offering a novel approach to managing and enhancing radio signal propagation. By dynamically adjusting their electromagnetic properties, RIS can significantly improve the performance and efficiency of 5G and beyond communication systems. In this paper, we study a cognitive RIS-aided non-orthogonal multiple access (NOMA) network that serves multiple users and improves spectrum efficiency. Our analysis assumes a secondary network operates under multi-primary user constraints and interference from the primary source. We derive approximation closed-form formulas for outage probability (OP), and system throughput. To obtain further insights, an asymptotic expression for OP is computed by taking into account two power configurations at the source. Additionally, numerical results show the effects of important factors on performance, confirming the accuracy of the theoretical derivation. According to the simulation results, performance by the system under consideration might be improved considerably by combining a RIS and NOMA, particularly when compared to an orthogonal multiple access scheme.  
RIS; NOMA; cognitive radio; performance analysis; throughput  
into the inside of buildings and onto the surface of huge vehicles without requiring changes to the device's hardware and software [11]. In the meantime, RIS can cover a wide area while using little power [12].  
Non-orthogonal multiple access (NOMA) is a technique that facilitates several linked devices and improves spectral efficiency. Multiple user equipments (UEs) can use the same resource without being constrained by conventional orthogonal multiple access (OMA) techniques thanks to the utilization of successive interference cancelation (SIC) at the receiver and superposition coding at the transmitter to cancel out intra-NOMA user interference [13; 14; 15; 16; 17]. Performance benefits are higher with NOMA than with OMA approaches because it enables UEs with poor channel conditions (far) to serve UEs with good channel conditions (near) at a single access point [18; 19; 20]. In addition, to increase spectrum efficiency, cognitive radio (CR) offers secondary users (SU) access to a licensed spectrum of the primary user (PU) [21]. It has drawn notice for helping new applications, especially when incorporating NOMA with CR, which may satisfy the high throughput, enormous connection, and low-latency needs of the 5G network [22; 23; 24]. The goal of cognitive NOMA is to offer more thoughtful spectrum sharing [25]. There are three operating paradigms for CR networks: the interweave mode, the overlay mode, and the underlay mode. Since the frame structure in overlay and underlay modes simply consists of data transmission, PU and SU networks can coexist at the same frequency band [26].
# A Study on the Creep Characteristics of Gassy Clay Mixed with Silt
## 3 Results
### Long-Term Resilience Prediction Model
To validate the efficacy of Equation (9), gassy soil with a gas content of 20.68% was prepared for triaxial creep tests to determine the long-term resilience of the sample. Simultaneously, the long-term resilience was calculated using Equation (9), and the results are detailed in Table 7 and Figure 8.2  
Table 7 and Figure 9 indicate a close proximity between the calculated and measured results of the long-term resilience of gassy soil, affirming the feasibility of employing Equation (9) for calculating the long-term resilience of silt-containing gassy clay. \begin{table}
\begin{tabular}{c c c c} \hline
**Confining Pressure** & **Calculated Strength** & **Measured Strength** & **Relative Error** \\
**(kPa)** & **(kPa)** & **(kPa)** & **(\%)** \\ \hline
100 & 51.83 & 54.13 & 4.27 \\
150 & 76.02 & 73.28 & 3.47 \\
200 & 100.21 & 102.23 & 1.98 \\
250 & 124.40 & 121.47 & 2.41 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison between calculated and measured long-term strength results of gassy soil (at gas content of 20.68%). To further verify whether Equation (9) can be generalized to gassy clay in other regions, we substituted the gas content and confining pressure of gassy clay (from Somerset, UK) in reference [22] into Equation (9) for calculation and compared the calculated results with the measured values in reference [22]. However, in reference [22], the addition of zeolite leads to a decrease in the saturation of gassy clay, and under different confining pressures, the rate at which the saturation of gassy clay decreases with the addition of zeolite is different. To avoid the influence of different saturations on the calculation results of Equation (9), we only selected the gas content corresponding to the same saturation (from 20% to 40%) and substituted it into Equation (9) for calculation. The results are detailed in Table 8.  
Table 8 indicates a close proximity between the calculated and measured results of the long-term resilience of gassy soil, affirming the feasibility of generalizing Equation (9) for calculating the long-term resilience of silt-containing gassy clay in other regions. Comparing Tables 7 and 8 reveals that when using Equation (9) to calculate silt-containing gassy clay from other regions, its relative error increases slightly but is still within an acceptable range overall.
## 4 Numerical Results
### Impact of the ct on \(S_{A}\)*, \(S_{Ar}\)*, \(p\)*, and \(F(T)\)  
In this subsection, we investigate the impact of the cost of carbon emission, \(ct\), on the optimal stock capacity for new products, \(S_{A}\)*, reconditioned products, \(S_{Ar}\)*, returned products percentage,  
\begin{table}
\begin{tabular}{c c c c c} \hline \(q_{l}\) & \(S_{A}\)* & \(S_{Ar}\)* & \(p\)* & \(F(T)\) \\ \hline
100 & 1578 & 517 & 50\% & 6.06718 \(\times\) 10\({}^{10}\) \\
250 & 1401 & 483 & 50\% & 6.06987 \(\times\) 10\({}^{10}\) \\
500 & 1292 & 458 & 50\% & 6.07126 \(\times\) 10\({}^{10}\) \\
750 & 1173 & 431 & 50\% & 6.07297 \(\times\) 10\({}^{10}\) \\
**1000** & **1029** & **411** & **50\%** & **6.07350 \(\times\) 10\({}^{10}\)** \\ \hline \end{tabular}
\end{table}
Table 4: Impact of the limited quantity of carbon emission, \(q_{l}\), on \(S_{A}\)*, \(S_{Ar}\)*, \(p\)*, and \(F(T)\).  
\(p\), and the total profit, \(F(T)\). Thus, we vary the value of \(ct\), and by using the optimization algorithm, we determine the corresponding \(S_{A}\)*, \(S_{Ar}\)*, \(p\)*, and \(F(T)\). To study the influence of the cost of carbon emission, we changed the parameters' values for \(cs_{A}=0.00002\), \(cs_{Ar}=0.00002\), and \(cr=0.0000001\) monetary units. The simulations' results are illustrated in Table 5. In order to improve the visualization of the obtained results in the analyzed part, we add the Figure 8 to the presented Table.  
The first observation in Table 5 is that the total profit obviously decreases when the cost of carbon increases. Indeed, when the cost of carbon emission increases, the system produces less new parts in order to decrease the cost of emissions, and then, optimal stock, \(S_{A}\)*, decreases (see Figure 8). On the other hand, in order to decrease the cost of emissions, the system produces more reconditioned parts instead of new ones. That explain the increase of optimal stock, \(S_{Ar}\)*, when the cost of carbon emission increases (see Figure 8). This study allows a firm manager to propose optimal decisions on the manufacturing, reconditioning, stocking, and quantity of returned worn products when the government increases the carbon emission cost.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 6 Discriminant Telecom Customer Loss Model  
### Empirical Analysis for the Discriminant Model  
#### 6.1.1 Discriminant Attributes  
According to the data, the discriminant analysis revealed an appropriate discriminant model. The model refers to the discrimination between the sample and the parent. First, historical data are established from the samples' discriminant distances. Then, each sample's data are replaced with the discriminant distance to calculate the actual distance.