esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Vibration Signal Data Processing and Feature Enhancement
### Wavelet Decomposition Denoising
The formula for hard thresholding is as follows:  
\[w_{thr}= \begin{cases}w&|w|\geq thr\\ 0&|w|\leq thr\end{cases}  \tag{11}\]  
The soft threshold formula is as follows:  
\[w_{thr}= \begin{cases}[sgn(w)](|w|-thr)&|w|\geq thr\\ 0&|w|\leq thr\end{cases}  \tag{12}\]  
where \(thr\) is the given threshold and \(w\) is the wavelet coefficient. Comparing the hard threshold and soft threshold denoising methods, the points \(\left|\,w\,\right|\leq\,thr\) are directly set to zero in the hard threshold, which makes it easy to cause the discontinuity of the wavelet coefficients, while the soft threshold denoising method eliminates this discontinuity and ensures the smoothness of the reconstructed signal. Therefore, the soft threshold denoising method is chosen in this paper. (3) Reconstructing the signal: using the threshold wavelet coefficients, the inverse wavelet transform is performed to reconstruct the signal after noise reduction. In this paper, the Symlet-5 wavelet is used to perform 5-level wavelet noise reduction processing on the CWRU signal, and the noise signal is suppressed or eliminated by the soft threshold function. The signal after the noise reduction process is as Figure 6.  
Figure 4: Approximate coefficient decomposition of vibration signals. Figure 5: Detail coefficient decomposition of vibration signals. In Figure 6, the orange signal is the denoised signal and the blue signal is the original signal. The signal can be seen after soft threshold filtering removes most of the noise components in the signal while preserving the vibration frequency of the signal as well as the amplitude and other important features. In addition, wavelet threshold denoising smooths the signal, making the fluctuations of the signal smoother, resulting in the denoised signal compared to the original signal in some moments of the vibration amplitude being smaller, but on the whole, it can still reflect the vibration trend and changes to facilitate the identification of subsequent features. The denoised 12 kHz, 1772 r/ min drive end bearing fault signals are similarly categorized and numbered and divided into denoised sample sets. The sample set is described in Table 4.
## 5 Numerical Simulation
### Stress Ranges of Suspenders Resulting from Combined Wind and Traffic Loads  
In this section, the stress ranges of suspenders under combined wind and traffic loads are investigated. The aforementioned discussions show that the stress responses of long suspenders, i.e., S1 and S2 were similar, while the stress response of short suspenders, i.e., S19 and S36, were similar. Therefore, the stress responses of long suspender S1 and short suspender S36 under combined wind and traffic loads are presented for demonstration purpose, as shown in Figure 10. It is shown in Figure 10a,b that, for both suspenders S1 and S36, the traffic load generally controls the mean trend of the stress response, while the wind load generally contributes to the fluctuation of the stress response. Consequently, as the wind speed increases, the fluctuation of the stress response increases remarkably. Therefore, it is clearly shown in Figure 10 that the stress responses of the suspenders under combined wind and traffic loads were larger than those under either individual wind load or individual traffic load. Considering that both the wind and traffic loads exist on the bridge during its life time, it is of paramount importance to account for both wind and traffic loads for the fatigue reliability analysis of suspenders.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Traffic Condition**} & \multicolumn{3}{c}{**Equivalent Stress Range Values (MPa)**} & \multicolumn{3}{c}{**Numbers of Stress Cycles**} \\ \cline{2-9}  & **S1** & **S2** & **S19** & **S36** & **S1** & **S2** & **S19** & **S36** \\ \hline Free-flow & 12.15 & 11.87 & 13.83 & 14.77 & 4896 & 4608 & 8352 & 9072 \\ Busy-flow & 12.53 & 12.05 & 13.89 & 14.87 & 7776 & 6624 & 15,984 & 18,576 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Stress ranges and number of stress cycles due to traffic load in one day.  
Figure 10: Segment of stress response time histories of representative suspenders under combined wind and traffic load (**a**) suspender S1 and (**b**) suspender S36.  
Table 8 lists the stress ranges and number of stress cycles of four representative suspenders due to combined wind and traffic loads in one day. Based on the results in Table 8, the effective stress range (\(S_{re}^{\star}\)) and the average daily number of cycles (\(N_{avg}^{\star}\)) were then computed using Equations (13) and (14), as tabulated in Table 9. The \(S_{re}^{\star}\) and \(N_{avg}^{\star}\) were further used to predict the fatigue life of the suspenders, as discussed in the subsequent section.
## 5 Comparison and Sensitive Analysis  
### Comparison  
In Figure 7, it can be seen that the product cost increases slowly when the product reliability increases from 0.8500 to 0.9420, and the product cost increases sharply when the product reliability increases from 0.942 to 0.9475. The best reliability is about 0.9475 with the cost increases from 14593.1573 to 63722.7969 and the best cost is 13.1923 with the reliability equal to 0.8500. Therefore, the optimal reliability designed should within [0.8500, 0.9420]. For the sake of observation, part of the Pareto fronts obtained in Figure 7 is magnified, and the multi-objective algorithm for the optimal reactive power (VAR) dispatch is adopted to assess the solutions of the Pareto fronts so that the best compromise solution can be available as shown in Figure 8. Hence, the reliability-based and cost-oriented product optimization scheme for the target CNC machine tool is as follows:  
\(R=0.9090\), \(C=15009.2\) dollars, \(R_{1}=0.9705\), \(R_{2}=0.9846\), \(R_{3}=0.9865\), \(R_{4}=0.9776\), \(R_{5}=0.9863\).  
Based on Figures 7 and 8, it can be concluded that, when the product reliability and cost are selected as two optimization objectives, it is reasonable to improve moderately the product reliability from the minimum value allowed, but it is over-wasteful and meaningless to improve the product reliability to the maximum value possible. This is because the product cost is the nonlinear monotone increasing function of the product reliability, and the high cost is required to achieve high reliability. The economic cost is unlimited high to achieve the ultimate reliability which means quite a lot of money is needed to spend on the product manufacture and installation in engineering practice. In addition, the cost to improve the high product reliability is higher than that to improve the low product reliability at least to the same extent. Hence, the running results are consistent with the actual situation.  
Figure 7: Complete Pareto fronts acquired by culture-based dynamic multi-objective particle swarm optimization (DMOPSO).  
To further discuss the best compromise solution obtained, a single objective optimization model aiming at the minimum cost is built whose corresponding constraints and parameter initialization remain the same. The results of iteration convergence are shown in Figure 9. As shown in Figure 9, the optimization design scheme is as follows:  
\(R\) = 0.8500, \(C\) = 13139.3 dollars, \(R_{1}\) = 0.9571, \(R_{2}\) = 0.9698, \(R_{3}\) = 0.9733, \(R_{4}\) = 0.9650, \(R_{5}\) = 0.9750.  
Therefore, the minimum cost with the reliability \(R\) = 0.8500 calculated with the single objective optimization model is almost the same as the best cost obtained by the proposed multi-objective mathematical model. This further validates the correctness of the proposed method.  
Figure 8: Best compromise solution in partial magnification of the Pareto fronts acquired by Culture-based DMOPSO.  
Figure 9: Pareto fronts acquired by the single objective optimization model.
## 4 The Multi-Resolution Pre-Stack Migration Imaging Method  
In cases where the formation dip is small and the geological structure is simple, the phase axis of the virtual wavefield can intuitively reflect the characteristics of the geological structure. However, for complex geological structures, the phase axis of the virtual wavefield in deeper regions cannot accurately represent the formation morphology, requiring the use of migration imaging methods to spatially position it accurately.
## 3 Methodology
### SIR Spreading Dynamics  
The SIR (Susceptible, Infected, Recovered) spreading dynamics model is a widely used methodology for assessing the node importance within a network based on their spreading ability. The predicted ranking of nodes based on their spreading ability can be obtained using this model. With the SIR model, each node in a network is categorized into one of three distinct states: _Susceptible_\((S)\), _Infected_\((I)\), or _Recovered_\((R)\). _Susceptible_\((S)\) nodes represent individuals who have not yet contracted the infection but remain vulnerable to transmission. _Infected_\((I)\) nodes constitute those individuals who have acquired the infection and possess the capacity to transmit the disease to susceptible individuals within their network. _Recovered_\((R)\) nodes denote individuals who were formerly infected but have subsequently recovered from the disease and are no longer capable of transmission.  
The fundamental principle underlying this method involves these steps: (1) One node is selected as an infected node, while all other nodes are initially classified as susceptible nodes. (2) At each time step, every infected node has the probability \(\beta\) of transmitting the infection to its neighbor nodes. (3) The infected nodes transition to the recovered state with probability \(\lambda\) (which is set to 1 in this study). Different infection probabilities (\(\beta\)) are applied for this experiment. We set them as \(\beta\in[0.5\beta_{c},1.5\beta_{c}]\). \(\beta_{c}\) is the spreading threshold of the SIR model for each network, defined as the following equation.  
\[\beta_{c}=\frac{\langle k\rangle}{\langle k^{2}\rangle-\langle k\rangle} \tag{13}\]  
where \(\langle k\rangle\) denotes the average degree of the network, \(\langle k^{2}\rangle\) represents the second-order average degree of the network. This process terminates when the network no longer contains any infected nodes.  
Based on this model, the influence of a given node \(i\) can be estimated as \(F(i)=N_{r}/N\), where \(N_{r}\) represents the total number of recovered nodes in the network upon reaching the steady state of the SIR spreading dynamics process. To achieve a more accurate approximation of the true ranking of node spreading ability, the final spreading influence for each initially infected node is determined by calculating the statistical average over 1000 iterations, \(N=1000\). The ranking results of network nodes' SIR spreading abilities are obtained through the implementation of the Epidemic on Networks (EoN) module [31].
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 3 Adaptive Downward/Upward Protocol
### Overview of RRD+ Mechanism
#### 3.1.3 Rank Updating  
Rank mechanism, which is proposed by RPL, is also an important part of RRD+. The Rank of a node is a value that defines the position of the node with respect to the sink in terms of routing metrics. The Rank of the sink node is \(ROOT\_RANK\) and \(MinHopRankIncrease\) is the minimum increase of the Rank between a node and any of its parent nodes. The rank value is proportional to the increase of the metric contained in control messages; therefore, the Rank of a node is calculated as shown in Equation (1).  
\[Rank=ROOT\_RANK+axMinHopRankIncrease \tag{1}\]  
where \(a\) is a value included in control messages that come from lower rank nodes.  
A node is not allowed to send data packets to neighbors with higher or equal Ranks, which is an effective way to avoid loops in the network. In mobility scenarios, the position of a node frequently changes. The original Rank mechanism does not offer methods to update the Rank in a timely manner. This causes loops when a parent node becomes a descendant node. RRD+ monitors link existence and movement direction to allow nodes to update their Ranks in a timely manner. The goal is to update the Rank of a node when it is about to lose its link with its current parent node based on the link quality-monitoring mechanism.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 6 Sleep Pattern Extraction and Sleep Quality Analysis
### Daily Sleep Profile and Sleep Quality Score
#### 6.4.1 Daily Sleep Profile  
A night sleep is considered to be any sleep event that started before midnight of the given day and ended after, or started before 5 am of the next day. Note, a daily sleep profile can contain multiple night sleep events, as derived according to the policy in Section 6.2, i.e., if there are breaks that last more than an hour at nighttime. Any other sleep events that occur in the given day are considered to be naps.  
Aiming at providing an effective, yet concise representation, we illustrate the users' daily sleep profile with sleep indicators (three for night sleeps, and two for naps), according to the guidance provided in [16]. Specifically, night sleep profiles are composed of duration, number of awakenings/breaks, as well as sleep efficiency; nap profiles are composed of the number of naps and total nap duration. Figure 6 shows an example of a daily profile of the user shown in Figure 3.  
Given this 5-dimensional representation of the daily sleep profile, we can use the methodology applied in Section 6.3.2 to extract daily sleep patterns, analogous to the sleep pattern extraction from sleep events. Figure 7 illustrates the four dominating daily sleep patterns extracted from this user. The first cluster refers to days, where only night sleep was detected. The last two clusters show days, where the user had both day and night sleep. More specifically, the last cluster depicts days with a larger number of naps and longer nap durations than cluster 2, which indicate a lower sleep quality according to [16]. The second cluster indicates days without any sleep onset. Those days can easily be identified in Figure 3 and are due to holidays or hospitalizations.  
From this analysis, caregivers can be informed about the daily sleep patterns of a patient. For this specific user, with a monitoring period of 502 days, 156 days with daily sleeps with both night sleep and naps during the day, 270 days with daily sleep without a nap, and 63 days without sleep-onset were identified. The remaining 13 days do not correspond to any of these daily sleep patterns, due to their dissimilarity to the clusters in the feature space. Figure 8 shows the clustered daily profile samples in the t-SNE space. Again, we can see that the extracted daily sleep profile patterns align well with the densely distributed groups that formed in the t-SNE space.  
Figure 6: Daily sleep profile composed of nightly sleeps and naps for a single user.
# Easy Fabrication of Ultrafiltration Membrane via Polyethersulfone-Fumed Silica
## 2 Materials and Methods
### Pore Characterization  
To assess the membranes' surface porosity, the dry membranes were first cut to a specific size and subsequently immersed in glycerol for 1 day. The glycerol was carefully removed to acquire the wet membrane weight (\(W_{w}\)). The wet membrane was later placed in a desiccator and allowed to dry for 24 h to obtain the dry membrane weight (\(W_{d}\)). The membrane surface porosity was analyzed to assess the impact of incorporating funnel silica on membrane pore size. This analysis was conducted using a gravimetric method, which involves the following equation [25]:  
\[\epsilon(\%)=\frac{W_{w}-W_{d}}{\rho H_{2}O\times A\times L}\times 100 \tag{4}\]  
Figure 1: Configuration of the water flux test in a dead-end cell unit system.  
where \(A\) is the effective area of the membrane, \(L\) is the thickness of the membrane, and \(\rho H_{2}O\) is the density of the water. The average pore size (\(r_{m}\)) of the prepared membranes can be determined using the following the Guerout-Elford-Ferry Equation, based on the porosity and water flux data [26]:  
\[r_{m}=\sqrt{\frac{(2.9-1.75\varepsilon)8\mu_{H_{2}O}\times L\times Q_{H_{2}O}}{ \varepsilon\times A\times\Delta P}} \tag{5}\]  
where \(\varepsilon\) is the membrane porosity, \(\mu_{H_{2}O}\) is the dynamic viscosity of water at room temperature, \(L\) is the membrane thickness, \(Q_{H_{2}O}\) is the volume of water passing through the membrane per unit time, \(A\) is the active area of the membrane for filtration, and \(\Delta P\) is the transmembrane pressure.  
In ultrafiltration, the molecular weight cut-off (MWCO) is determined by filtering a range of molecules with known molecular weights, which are known to have a linear correlation with the size of the membrane pores [27]. According to a slit sieve model developed by [PERSON], the MWCO of a membrane is established by finding an inert solute with the smallest molecular weight that achieves at least 80% solute rejection during ultrafiltration experiments [28]. In this work, BSA (66.5 kDa), pepsin (40 kDa), and lysozyme (14.3 kDa) were selected for rejection studies using PES/FS blended membranes.
## 5 LPWAN Application Requirements
### Traffic Requirements  
The inherent communication mechanism of LPWAN networks entails traffic generated by distributed sensors. In addition to the possible presence of traffic created by smartphones or other devices, the LPWAN traffic itself can vary in a wide range of attributes like the number of messages, message size, and reliability requirements. LPWAN technologies have diverse categories of applications with varying requirements. Some of the applications are delay-tolerant (e.g., smart metering), while applications like fire detection, nuclear radiation detection, and home security require prioritized and immediate transmission. In some applications, a priority message scheduling may be required for event-triggered transmissions. With the massive number of active devices, there is a possibility of a service level agreement (SLA) requirement of each application that might not be satisfied. Mechanisms need to be supported for the co-existence of different traffic types, the required quality of service (QoS), and SLA. In LPWAN applications, provision may need to be made for handling multiple classes of end devices based on their communication needs in uplink or downlink. In some applications, device mobility support is needed and is required to be able to be connected anywhere and ensure seamless service on the move.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 3 Results and Discussion
### The Effect of Denoising on the Effect of Clustering Experiments
Combining the tables of unsupervised learning and supervised learning, we can conclude that whether using the clustering method of unsupervised learning or the clustering method of supervised learning, signal denoising can significantly improve classification performance, making the results more accurate and reliable.
# Trends and Opportunities in the Dairy Industry: A2 Milk and Processing Methods
## 3 Important Milk Components
### Fats  
Milk fat is a carrier for fat-soluble vitamins (A, D, E, K), is responsible for a significant portion of milk's energy value (9 kcal/g fat), and one of its fractions is essential fatty acids (EFAs), such as conjugated linoleic acid (CLA), which is a valuable nutrient with health-promoting properties, as well as other short-chain TCs. Fatty acids in milk are diverse and have chain lengths ranging from 4 to more than 20 carbon atoms. The fat fraction in milk is characterized by low levels of mono- and diglycerides.  
Milk fat forms milk fat globules (MFGs) that range from 0.1 mm to more than 22 mm in diameter (usually 4-6 mm). They consist of a non-polar nucleus of triglycerides and cholesterol esters. It maintains stability and is protected from lipase action by a nanometer-thick membrane on its surface--the milk fat globule membrane (MFGM). The MFGM ensures the stability of the milk emulsion and contains various bioactive components such as glycoproteins, phospholipids, sphingolipids, cholesterol, and free fatty acids, which are crucial for brain development [41].  
For the industry, its sensory and technological aspects are also important--fat is known as a flavor carrier, and, similarly, in this case, it imparts flavor to milk and dairy products. It is responsible for the soft, smooth texture of cheeses and the rich flavor of other products. In addition, fat fractions are separated from the milk matrix and used in the preparation of the raw material to increase the nutritional value of the food, as is the case with protein [37; 41].  
The attention paid by the industry and consumers to the value of individual milk components has led to the intensive development of food products that can be used independently. The current focus on milk proteins and, in particular, the distinction between their types (A1/A2 \(\beta\)-casein) allows new products to be designed for people with specific nutritional needs. The fractionation of milk and the use of its components also allows for more precise product design due to the considerable variation in the chemical composition of milk batches depending on the breed of cattle, geographical location, living conditions, and many other factors.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### UOC--Today's Research Challenges
#### 7.2.6 Optical Networking Technologies Supporting 5G/6G Communication Infrastructure  
Several future applications will require the integration of heterogeneous emerging technologies, such as 5G/6G wireless and UOC technology. One of the future applications will even extend to develop a \"smart city\" to improve and modernize everyday life. The smart city concept will support almost everything useful, from street electrical grids to traffic management (surface transport, trains, Ubers, airline landing and departure in a city, etc.). It will therefore require the transmission of a massive amount of data. Without properly integrating UOC with its huge bandwidth capability and advanced wireless technologies, it will be impossible to develop a reliable communication infrastructure to meet network requirements. Advanced optical networking will be required to provide 5G/6G transport networks capable of connecting a huge number of devices for future smart city infrastructure. The successful implementation of optical technologies in 5G/6G core networking, particularly in determining how wireless and optical can coexist, will determine the development of future advanced applications such as smart cities.