esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Optimization of the Joint
### Effect of the Structural Parameters  
#### 3.6.1 Effect of the Structural Parameters  
The structural parameters of the tapered thread include the taper, pitch, lead, helix angle, thread angle, and thread height. The strength of the thread is mainly affected by the taper, thread angle and thread height. The pitch and lead mainly affect the self-locking effect of the thread. Therefore, the effects of three parameters were analyzed: taper, thread height and thread angle. To further explore the degrees of influence of the three parameters on the equivalent stress, an orthogonal analysis was performed for each parameter with the equivalent stress concentration factor as the evaluation standard. The stress concentration factor is defined as  
\[K=\frac{M_{s}}{A_{s}} \tag{1}\]  
where \(K\) is the stress concentration factor; \(M_{s}\) is the maximum equivalent stress, MPa; and \(A_{s}\) is the average equivalent stress, MPa.  
The maximum equivalent stress and equivalent stress concentration factor under different structural parameters are shown in Table 1. The data in Table 1 were obtained by extracting data from the stress path.  
The data in Table 1 show that the maximum stress in the thread is the smallest when the taper is 1:32, and the stress concentration factor is smaller. Therefore, the optimization scheme (i.e., the settings that produce the lowest stress concentration factor) uses a taper of 1:32, a thread height of 2.44 mm, and a thread angle of 15 degrees.  
Figure 15: Relationship between the displacement and interference of each surface with respect to the pretightening angle.
# A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
## 2 Reservoir-Scheduling Problem
### Long-Term Hydropower-Generation Scheduling (LHGS) Model  
The LHGS model is used to find a set of water releases or storage volumes that maximizes the generating benefit for cascade hydropower system. The objective formula of hydropower optimization problem is given as follows:  
\[E=\text{max}\!\sum_{t=1}^{T}\sum_{i=1}^{N}k_{i}Q_{i,t}H_{i,t}\Delta t \tag{1}\]  
where \(E\) is the total energy production of the cascade hydropower system; \(T\) is the count of periods; \(N\) is the number of reservoirs; \(k_{i}\) is the output coefficient of reservoir \(i\); \(Q_{i,t}\) is the generation flow which outflows through hydropower units of the reservoir \(i\) in time \(t\); \(H_{i,t}\) is the net water head of hydropower reservoir \(i\) in time \(t\); and \(\Delta t\) is the length of time interval.
## 2 The Lattice Discrete Element Method (LDEM)
Finally, the strains \(\varepsilon_{p}\) and \(\varepsilon_{r}\) are related to another material parameter, the characteristic length \(d_{eq}\), by means of the following expressions:  
\[\varepsilon_{p}=\sqrt{\frac{G_{f}}{E\,d_{eq}}}, \tag{3}\]  
\[\varepsilon_{r}=\varepsilon_{p}d_{eq}\Big{(}\frac{A_{i}^{*}}{A_{i}}\Big{)} \left(\frac{2}{L_{i}}\right), \tag{4}\]  
where the subindex \(i\) identifies the type of element referenced (diagonal or normal), whereas \(d_{eq}\) is an upper limit for a given structure's characteristic length: once that length becomes larger than \(d_{eq}\), the structure presents unstable fracture propagation. Such a length can be determined through the so-called Carpinteri number [43]  
\[s=\frac{\sqrt{\frac{G_{f}E}{d}}}{\sigma_{p}}, \tag{5}\]  
where \(d\) represents the characteristic length of the structure and \(\sigma_{p}\) is the failure stress. As shown in [44], for the LDEM constitutive law, considering that \(\sigma_{p}=E\varepsilon_{p}\), and combining Equations (3) and (5), one can write  
\[s=\sqrt{\frac{d_{eq}}{d}}. \tag{6}\]  
In the LDEM, \(G_{f}\) is a random parameter given by a Weibull probability distribution  
\[p(G_{f})=1-\exp\left[-\left(\frac{G_{f}}{\beta}\right)^{\gamma}\right] \tag{7}\]  
with \(\beta\) and \(\gamma\) denoting the scale and shape parameters, respectively. Such parameters are determined by the coefficient of variation \(CV_{G_{f}}\), defined as the ratio between the standard deviation \(s(G_{f})\) and the mean value \(\mu(G_{f})\) of the specific fracture energy \(G_{f}\) related to the size \(L\). The spatial correlation for \(G_{f}\) is defined through the correlation lengths \(L_{cx}\), \(L_{cy}\), and \(L_{cz}\) along with the directions \(x\), \(y\), and \(z\), respectively. For a detailed explanation of these parameters and their application in the context of LDEM, refer to [45; 46; 47]. Since this work's intended application presents relevant compression effects, the proposed LDEM model must be improved by inclusion of an internal-friction dissipation mechanism. To that end, the constitutive law given in Figure 1b is modified here, following the peridynamics considerations proposed by [48]. Such a modification relies on changing the failure strain to also take into account the compression level of the bars connected to the element under analysis. Then,  
\[\varepsilon_{r}=k_{rt}\varepsilon_{p} \tag{8}\]  
where  
\[EA_{d} = \frac{A_{n}2\sqrt{3}}{3}, \tag{1}\]0  
In Equation (9), \(n_{bc}\) represents the number of bars linked to the analyzed element under compression strain. The scalar parameter \(k_{rt}\) rules the increase in dissipated energy due to internal friction, being activated when the analyzed element is solicited by traction and the bars connected to it are compressed. An exhaustive explanation of the Lattice Discrete Element Method (LDEM) used in the present paper and the complete deduction of Equations (1)-(9) appear in [40; 44; 46; 47; 49].
# Video Error-Resilience Research Based on Error-Resilient Screen Content
## 3 Proposed Method
### R-\(\lambda\) Model
#### 3.4.1 Bit Allocation  
(1) Group of Picture (GOP) level bit allocation  
The number of encoded frames is \(N_{code}\), and the number of bits used to encode these frames is \(R_{coded}\). The number of frames in a GOP is \(N_{GOP}\); and \(SW\) is the size of the sliding window for smoothing bit allocation, which is used to make bit consumption change and the quality of encoded pictures gentler. The bit allocation of GOP level is:  
\[T_{GOP}=T_{AVgpic}*N_{GOP} \tag{11}\]  
It is emphasized that the target code rate is achieved after \(SW\). frames. If each frame in the \(SW\) frames can exactly consume \(T_{AVgpic}\) bits, then the above equation can be written as follows:  
\[T_{AVgpic}=T_{AVgpic}+\frac{R_{picAvg}*N_{coded}-R_{coded}}{SW} \tag{12}\]  
(2) Frame-level bit allocation  
Define \(\text{Coded}_{GOP}\) as the consumed bits of the current \(GOP\), and \(\omega\) is the bit allocation weight of each frame, then the bits assigned to the current frame is defined as:  
\[T_{Pic}=\frac{T_{GOP}*\text{Coded}_{GOP}}{\sum\text{NotCodedPictures}\omega_{ i}}*\omega_{CurPic} \tag{13}\]  
(3) Largest Coding Unit (LCU) level bit allocation  
Similar to the frame-level bit allocation strategy, the bits allocated to each LCU are calculated based on:  
\[T_{LCU}=\frac{T_{CurPic}-Bit_{header}-Coded_{pic}}{\sum\text{NotCodedLCUs} \omega_{i}} \tag{14}\]  
where \(Bit_{header}\) is the estimated number of all header information bits, which is estimated from the actual number of header information bits of the previously encoded pictures. \(\omega\) is the bit allocation weight of each LCU.
## 3 Materials and Methods
### Data Preprocessing  
EEG signals are inherently noisy due to various artifacts introduced during recording, such as eye movements, muscle activity, and head movements. Removing this noise, therefore, becomes a crucial step in the analysis. We have cleaned the raw data and applied a band-pass filter to retain frequencies within the 0.5 to 80 Hz range. In this analysis, the rest mode is considered as the low mode, and task execution is considered as the high mode. The initial data collection time was 3 min, but the first 15 s and the last 15 s were removed by the STEW data team to mitigate inter-task effects. Ratings for subjects 5, 24, and 42 were unavailable and were provided using an average method. Following segmentation, the EEG data were annotated with subjective workload ratings assigned by the participants, categorized into three levels: low (ratings 1-3), average (ratings 4-6), and high (ratings 7-9). For classification purposes, the EEG data from low- and high-workload subjects were merged into two files, each containing 921,600 rows and 15 columns. The first 14 columns represent Emotiv 14 channels EEG data, while the last column represents workload ratings considered as labels. Figure 2(a),b showcases the workload ratings assigned by the participants in both low and high modes.  
Figure 3: (a,b) Participants’ workload ratings in low and high modes.  
Figure 2: Overview of STEW dataset.
## 11 Beyond the Standard Cavity Design
### Optimization of Laser Parameters: Compactness, Optical Elements, Stability and Pulse Duration  
Various parameters of operation can be optimized using advanced cavity designs. Investigation of two- and three-mirror cavity configurations [86] demonstrated highly compact cavities, in which the Kerr efficiency (Equation (14)) was found to be maximal in a three-mirror configuration. Optimization guidelines for the crystal length were given in [87] considering the estimated Rayleigh range of the laser mode in the crystal. Another novel cavity design was demonstrated in [88] by replacing the flat end mirror in the long arm with a curved one. The result is a fundamentally different diagram of the stability zones (compared to the conventional diagram shown in Figure 3) with a considerable improvement in the laser intensity stability. Pulse duration can also be minimized by maximizing the spectral broadening of the pulse (by SPM; see Section 4) inside the Ti:sapphire crystal. It was found that to maximize SPM, it is important to consider the physical arrangement of the elements in the cavity in order to achieve a symmetric dispersion distribution. When both cavity arms are compensated for dispersion independently [89], the nonlinear response can be twice as strong as the standard dispersion compensation, since it maximizes the nonlinear response in both forward and backward propagation through the crystal. Thus, for maximal spectral broadening, it is beneficial to consider either a four-prism sequence in a ring cavity configuration or to include a prism pair/chirped mirrors in the short arm, also. Spectral broadening can also be achieved using an additional Kerr medium [90], thus enhancing the intra-cavity nonlinearity. This results in a mode-locked operation that can be sustained with lower pulse energy. This effect is of great importance for frequency comb-based measurements, where a higher repetition rate is preferred, which inherently requires a lower pulse energy. Usually, the additional Kerr medium is a Brewster-cut window placed in an additional focus in the cavity. However, due to the beam expansion in the tangential dimension upon refraction into the Brewster window, this configuration does not fully exploit the Kerr nonlinearity of the material. Recently, we presented a novel cavity design [52], illustrated in Figure 11b, with an additional planar-cut (non-Brewster) and anti-reflection (AR) coated Kerr medium in normal incidence, which allows full exploitation of the nonlinear response, due to the tighter, astigmatic-free focusing compared to a Brewster-cut window. In addition, another interesting phenomenon was observed: with sufficient enhancement of the nonlinearity, the ML threshold can be lowered all the way down to the CW threshold, as illustrated in Figure 11d. When the two thresholds are equal, ML can be obtained directly from zero CW oscillation.
## 6 Experiment Evaluation
### Performance Testing  
In order to better present the effectiveness and practicability of the IAP system, the performance tests are carried out on three actual phones, including processing time, memory occupation, CPU occupation and stability. The specific data are shown in Table 5.  
In Table 5, the processing time of three actual phones is less than 1 s, which can give a better user experience. Then, we used the Android Monitor to record the memory occupation and CPU occupation of the application on three phones, all of which had small memory occupation and CPU occupation. Finally, we used the Monkey tool to simulate the pseudo-random user time flow, and conducted the stability test by generating 3000 random user operations. From the data in Table 5, during the entire stability testing process, the application did not crash. This indicates the robustness of the designed application. In addition, we observed minimal power consumption throughout the testing process.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 3 Results and Discussion
### Effects of MGWP on Workability  
The workability of the mortar was assessed by measuring its consistency through the flow table method, in accordance with ASTM C1437. Figure 6 displays the test results, which indicate that the flow of the mortar decreases as the proportion of MGWP used to replace OPC increases. According to ASTM C1437, the initial flow of the mortar should be \(110\pm 5\%\). As such, only the fresh mortar mixes containing 5% and 10% MGWP met this requirement, while those with 15% MGWP and higher failed to comply with the standard. This can be attributed to the fact that increasing the proportion of MGWP leads to higher fineness, resulting in increased heat of hydration and water demand for full hydration, which can diminish workability. Furthermore, the higher surface area of the MGWP can absorb more water, further decreasing workability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Category**} & \multirow{2}{*}{**Properties**} & **Test** & \multirow{2}{*}{**Examined Samples**} & \multirow{2}{*}{**Curing Ages**} \\  & & **Standards** & & \\ \hline Fresh & Workability & ASTM C1437 & All & – \\ \hline \multirow{4}{*}{Hardened} & Water absorption & ASTM C642 [31] & & \\  & Compression strength & ASTM C109 [34] & & \\  & Homogeneity & ASTM C597 [33] & & \\  & Sulfate attack resistance & ASTM C1012/C1012M [35] & & \\  & Bulk density & ASTM C29/C29M [25] & & \\ \hline \multirow{2}{*}{Microstructure} & Thermal decomposition & – & MGWP-0, MGWP-10, & 28 days \\  & Mineralogical & – & MGWP-30 & 7 and 28 days \\ \cline{1-1}  & composition & – & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The workability, hardened, and microstructural property test results of the mortars.
## 5 Performance Analysis
### CASE 2: MANET Nodes without \(\Delta T_{i}\) Track Records  
The second set of experiments analysed the situation when a new node joins the MANET and requests a route for the first time. The same conditions are employed as in Section 5.1, though now it is assumed that only between three and 15 \(\Delta T_{i}\) values are available for the node requesting the new route, since there is no _a priori_ knowledge about previously measured \(\Delta T_{i}\) values. The corresponding time tampering detection results are displayed in Figure 5.  
The absence of any track record meant that detection performance was not as consistent as CASE 1, though a time tampering detection rate of \(\geq\)80% has still been achieved for all wormhole HC when \(\sigma_{R}\leq 0.2\) and \(\rho_{max}\leq 0.6\). For wormholes \(\geq\)5 hops, at least 68% of tampered \(\Delta T_{i}\) values were correctly detected even when \(\sigma_{R}=0.5\) and \(\rho_{max}=0.9\). The equivalent FP detection rates displayed in Figure 6, were slightly higher than in CASE 1 for \(\rho_{max}\leq 0.6\) for example, and performance was more sensitive to high network traffic load variations (\(\rho_{max}=0.9\)) due to the smaller number of \(\Delta T_{i}\) samples. Nevertheless, even a FP rate of 45% when \(\rho_{max}=0.9\) can still be deemed acceptable as more than half of all possible routes are available.  
The time tampering detection performance is thus less robust in CASE 2 when no \(\Delta T_{i}\) track record is available, though this does represent the worst possible MANET situation, when a new node performs its first route discovery procedure. As a node runs the route discovery procedure more often, the corresponding time tampering detection rate will quickly improve and converge towards the results presented for CASE 1. This infers that to strengthen the time tampering detection performance for new nodes, it is prudent to run a few route discovery procedures before starting to communicate within the network. This could for instance, be accomplished by specifying within the routing protocol that a node is not allowed to communicate within the network until it has collected a minimum of 15 \(\Delta T_{i}\) samples.  
Figure 5: Time tampering detection performance for different wormhole HC under variable network traffic loads (\(\rho_{max}\)) and routing packet service times (\(\sigma_{R}\)) for \(3\leq\Delta T_{i}\) samples \(\leq\)15.  
Figure 6: False positive detection for different wormhole HC under variable network traffic loads (\(\rho_{max}\)) and routing packet service times (\(\sigma_{R}\)) for \(3\leq\Delta T_{i}\) samples \(\leq 15\).
## 1 Introduction
Many studies have investigated thermal inactivation of bacteria, and various models of the bacterial survival curves have been developed [33; 34; 35; 36]. Among these studies, many focused on the inactivation of planktonic bacteria. However, it has been reported that biofilms protected the indwelling microorganisms and it was much harder to inactivate the bacteria in biofilms and inhibit the biofilm formation [37]. The inhibition effect of thermal shock on biofilms might differ from that on planktonic bacteria. Several studies have considered using thermal shock for biofilm inhibition [38; 39; 40], but few have investigated using thermal shock to inhibit biofilms in heat exchangers in the TSSHPS and the inhibition mechanism has not been fully studied. Furthermore, most of the existing studies focused on parameters, such as biofilm weight and the number of live bacteria in the biofilms, not many studies have investigated the thermal resistance caused by the biofilms in the heat exchangers. This study have investigated the biofilm inhibition effect and mechanism of thermal shock. Model sewage was used to form biofilms, both in a rotating coupon device and in a plate heat exchanger, and the biofilms were thermal shocked at regular intervals. Balance weighing, confocal laser scanning microscope (CLSM) picturing and thermal resistance monitoring have been used to analyze the biofilms. The results showed that thermal shock significantly inactivated the bacteria in the biofilms and, thus, inhibited biofilm growth, and the inhibition effect was enhanced as the temperature and exposure time increased. Additionally, thermal shock significantly mitigated the biofilm thermal resistance and could be used as a potential inhibition approach for biofilms in the plate heat exchangers. This study has investigated the biofilm inhibition effect and mechanism of thermal shock. Model sewage was used to form biofilms, both in a rotating coupon device and in a plate heat exchanger, and the biofilms were thermal shocked at regular intervals. Balance weighing, CLSM picturing, and thermal resistance monitoring have been used to analyze the biofilms. The results showed that thermal shock significantly inactivated the bacteria in the biofilms and, thus, inhibited biofilm growth and the inhibition effect was enhanced as the temperature and exposure time increased. Furthermore, thermal shock significantly decreased the biofilm thermal resistance and could be used as a potential inhibition approach for biofilms in the heat exchangers.
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 3 Evolution of Industrial Maintenance
### Predictive Maintenance Tools and Materials
#### 3.2.4 Digital Twin  
A Digital Twin (DT) is a digital or virtual copy of physical assets or products. The term was originally coined by Dr. [PERSON] in 2002 [35]. NASA was one of the first to use the technology for space exploration [36]. DTs connect the real and virtual world by collecting real-time data from the installed sensors. The collected data is either locally decentralized or centrally stored in a cloud. The data is then evaluated and simulated in a virtual copy of assets [37; 38]. After receiving the information from the simulation, the parameters are applied to real assets. The integration of data in real and virtual representation helps in optimizing the performance of real assets.
## 4 Results and Discussion  
### The Aerodynamic Force Coefficients and Pressure under Smooth Condition  
The influence of the nose length of a train on the time-averaged aerodynamic coefficients was investigated. Figure 8 shows the variation curves of the side force coefficient, drag force coefficient, lift coefficient and overturning moment coefficient of the three-section vehicle resistance for different streamlined lengths head. In Figure 8a, as the nose length increases, the drag coefficient of all three trains decreases. The drag coefficient of the three cars decreases by a total of 13.89%, and the drag coefficient of the tail car decreases by 20.43% more than that of the head car decreases by 4.95%, which is contrary to people's intuition awareness, but consistent with the conclusion of [PERSON] et al. [7] and [PERSON] et al. [1]. From Figure 8b, it can be concluded that the longer the length of the front end of the tail car, the smaller the side force coefficient, and the tail car is more sensitive to the effect of crosswind compared to the head car.  
Moreover, it can be seen from Figure 8c,d that the increase in the length of the head car has little effect on both the side force coefficient and the overturning moment coefficient. However, the increase in the length of the tail car has a greater effect on the side force coefficient and the overturning moment coefficient, and the lift and overturning moment coefficients both decrease by 72.35% and 103.63%, respectively.  
Figure 9 shows the comparison of averaged pressure at the S2 plane of 5 m nose length, and it can also be seen that the pressure differences between the simulation and experiment are small. The maximum difference is 6.7% at 120\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **Condition** & \(\mathbf{I_{u}}\)**(\%)** & \(\mathbf{L_{head}}\)**(m)** &
\begin{tabular}{c} **Total** \\ **Numbers** \\ \end{tabular} & \(\mathbf{y^{+}}\) & \(\mathbf{s^{+}}\) & \(\mathbf{l^{+}}\) \\ \hline
1 & & 1 & 5 & 71.62 million & 0.33 & 87.5 & 87.5 \\
2 & Smooth & 1 & 7.5 & 70.97 million & 0.33 & 88 & 88 \\
3 & & 1 & 10 & 63.31 million & 0.33 & 99 & 99 \\
4 & & 5 & 5 & 71.62 million & 0.33 & 87.5 & 87.5 \\
5 & turbulence & 5 & 7.5 & 70.97 million & 0.33 & 88 & 88 \\
6 & & 5 & 10 & 63.31 million & 0.33 & 99 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of simulations performed at a 15\({}^{\circ}\) yaw angle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grids** & \(\overline{C_{D}}\) & \(\overline{C_{S}}\) & \(\overline{C_{L}}\) & \(\overline{C_{Mx}}\) \\ \hline Coarse & 0.1296 & 0.1625 & 0.2506 & 0.0578 \\ Medium & 0.1288 & 0.1681 & 0.2572 & 0.0608 \\ Fine & 0.1283 & 0.1682 & 0.2569 & 0.0597 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration for 7.5 m nose length train’s three types of grids.