esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of the Integrated Improved Algorithm
### Igoo-iapf Algorithm Description
#### 3.3.1 Improvement of Distance Control Parameters  
From Equation (22), it can be observed that the parameter A balances the exploration and exploitation capabilities of the GWO. When \(|A|>1\), the wolf population tends to expand the search range to find more suitable prey, which corresponds to the algorithm's global search capability. When \(|A|<1\), the wolf population tends to narrow down the search range, surrounding the prey from various directions and launching attacks, corresponding to the algorithm's local search capability. The value of A is influenced by the parameter \(a\), which linearly decreases from 2 to 0 during the iteration process. However, the problem solved by the grey wolf optimizer is a nonlinear optimization process, and linearly decreasing \(a\) cannot fully represent this process. Therefore, it is necessary to redesign the parameter \(a\).  
This study proposes a dynamically changing parameter \(a\) based on the distance ratio. During the iteration process, the distances \(d_{i}\) and the average distance \(d_{a}\) between all wolf individuals and the \(\alpha\) wolf (the best individual) are calculated. The ratio \(d_{i-site}\) of the distance \(d_{i}\) to \(d_{a}\) represents the relative proximity of an individual's current position. A larger value of \(d_{i-site}\) indicates a greater relative distance to the individual's position, and a larger \(\alpha\) value is assigned to wolves with relatively distant positions to enhance global search capability. Conversely, a smaller value of \(d_{i-site}\) indicates a closer relative position of the individual, and a smaller \(\alpha\) value is assigned to wolves with relatively close positions to enhance local search capability. Different values of \(\alpha\) are assigned based on the different \(d_{i-site}\) values of each individual in each iteration, aiming to balance the local search and global search capabilities of the algorithm.  
\[d_{i}=\sqrt{\sum\limits_{d=1}^{D}{{{(X_{i}^{d}-X_{a}^{d})}^{2}}}} \tag{33}\]  
\[d_{a}=\frac{\sum\limits_{i}^{N}{{d_{i}}}}{N} \tag{34}\]  
\[d_{i-site}=\frac{d_{i}}{d_{a}} \tag{35}\]  
\[a=2-2\times\left(\frac{t}{t_{\max}}\right)^{\lambda\times d_{i-site}} \tag{36}\]  
In the equation, \(d_{i}\) represents the distance between the \(i\) wolf and the \(\alpha\) wolf. \(X_{i}\) denotes the position of the \(i\) wolf, while \(X_{a}\) represents the position of the \(\alpha\) wolf. \(d_{a}\) represents the average distance in the current iteration, and \(d_{i-site}\) denotes the relative distance of the \(i\) wolf. \(t_{\max}\) corresponds to the maximum number of iterations, and \(\lambda\) is a positive constant.
## 2 System Reliability Evaluation Methods
### System Reliability Evaluation Method Based on the Modified AHP
#### 2.2.2 Calculation of Reliability of Each Slab Segment  
For small-to-medium-span RC hollow slab girder bridges, the failure mode of the slab segment is generally the bending failure and the shear failure. In this paper, we use the bending failure mode as the failure mode of the system, and analyzed the system reliability index under the bending failure mode. The system reliability evaluation process under the shear failure mode is basically similar and will not be described in detail in this paper. Therefore, according to the limit state function of the bending failure of each slab segment, the reliability index \(\beta\) is calculated by FOSM. Then, the reliability indexes of all slab segments are represented as a row vector, \(\mathbf{\beta}=(\beta_{1},\beta_{2}\cdots\beta_{n})\).  
Figure 1: Flow chart of system reliability evaluation.  
Figure 2: Hierarchical model of system reliability evaluation.
## 2 Materials and Methods
### Steps of Weather Disaster Emergency Response Capability Feature Extraction
#### 2.4.1 Feature Data Processing  
(1) Processing of missing features  
The filling of weather disaster emergency information is nonmandatory in nature, and some data have missing problems. To address the problem of missing capability features, first set a threshold value, and if the percentage of missing features of a feature exceeds this threshold value, delete this feature; if the threshold value is not exceeded, use the KNN algorithm to find the k samples closest to the sample where the missing value is located and take the average of their corresponding features to fill the missing features. Usually, this step is based on the 80/20 rule, and the deletion threshold of the missing features is set to 80% [24].  
(2) Data dimensionless processing  
Since the composition of the capability features of the construction weather disaster emergency subject is more complex, and the dimensional differences between the capability features are large, the unprocessed features are often not comparable with each other. In order to be able to reflect the actual situation accurately and effectively, the sample is dimensionlessly processed to ensure that the different features have equivalence and homoscedasticity. In this paper, min-max normalization (normalization) is used, and the specific process is as follows: set \(x_{max}\) and \(x_{min}\) be the attributes \(x\) of the maximum and minimum values; then, the original value \(x\) can be mapped to the value in the interval \([0,1]\) by min-max normalization \(\hat{x}\), which is given by  
\[\hat{x}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{25}\]
## 4 Results and Discussion
### Mobility: A Kubo-Greenwood Study
First, the electron's Ph limited mobility was reduced in the [100] orientation due to the higher m\({}_{eff}\) in comparison to m\({}_{bulk}\) for the dominant valley (\(\Delta_{y}\)) [19]. This reduction was caused when m\({}_{eff}\) was considered instead of m\({}_{bulk}\), which was less pronounced when SR was introduced in the total mobility (Ph+SR), because this mechanism mainly depended on the electrostatic force normal to the Si-SiO\({}_{2}\) interface. Second, two scenarios could be distinguished as a function of the diameter in the [110] orientation for both Ph and Ph+SR cases. On the one hand, the electron mobility increased when m\({}_{eff}\) was taken into account for the devices with the smallest cross-sections (3 and 4 nm devices). The reason was similar but opposite compared to the one for the [100] orientation: the lower transport m\({}_{eff}\) in comparison to m\({}_{bulk}\) for the dominant valley (\(\Delta_{z}\)) reduced the scattering rate and, thus, enhanced the mobility. On the other hand, for square nanowires with diameter bigger than 5 nm, this difference was significantly reduced and thus the influence of the other valleys, which presented higher m\({}_{eff}\), had more importance. Third, there was a mismatch in the transport m\({}_{eff}\) tendency between 5 and 6 nm devices for the [111] orientation (as shown in Table 1). Accordingly, even though the impact of m\({}_{eff}\) was quite low for smaller devices in this orientation, the mobility for  
Figure 3: Electron mobility as a function of the diameter for a square nanowire with m\({}_{bulk}\) and m\({}_{eff}\) masses and a sheet density of \(3\times 10^{12}\) cm\({}^{-2}\), considering: (**a**) [100] channel orientation; (**b**) [110] channel orientation; and (**c**) [111] channel orientation. The total phonon (Ph) and the combined total phonon and surface roughness (Ph+SR) scattering effects are considered. \begin{table}
\begin{tabular}{c c c} \hline
**Optical Phonon Type** & **D\({}_{OP}\) (eV/m)** & **Energy (eV)** \\ \hline g-type, TA & \(5\times 10^{9}\) & 0.01206 \\ \hline g-type, LA & \(8\times 10^{9}\) & 0.01853 \\ \hline g-type, LO & \(3\times 10^{10}\) & 0.063 \\ \hline f-type, TA & \(1.5\times 10^{9}\) & 0.01896 \\ \hline f-type, LA & \(3.4\times 10^{9}\) & 0.0474 \\ \hline f-type, TO & \(4\times 10^{10}\) & 0.059 \\ \hline \end{tabular}
\end{table} 
Table 5: Deformation potential (D\({}_{OP}\)) and energy considered for the different branches in the optical scattering mechanism. both Ph and Ph+SR cases decreased following the aforementioned tendency for diameters higher than 6 nm.
## 4 Results and Discussion
### Comparison with the Previous and Proposed Sleep Disorders and Sleep Stage Detection Methods  
Performances of the subjects (healthy and bruxism) classification on the EEG (C4-A1 channel) signal in terms of sensitivity, specificity, and accuracy were found to be 0.95, 0.92, and 0.94, respectively. The performance of the sleep stages (w and REM) classification on the ECG (ECG1-ECG2 channel) signal in terms of sensitivity, specificity, and accuracy were found to be 1, 0.86, and 0.95, respectively. Additionally, the performance of the combined (subjects and sleep stages) classification on the EEG (C4-P4 channel) signal in terms of sensitivity, specificity, and accuracy were found to be 0.98, 0.90, and 0.97, respectively. The C4-P4 channel of the combined classification has better performance than the others. Previously, bruxism detection methods were limited, so we compared with sleep disorder and sleep stage detection methods. Patients of bruxism are recognized through various diagnosis methods involving questionnaires, clinical examinations, and various appliances. The questionnaires involve the patient history of tooth mobility and tooth wear, muscle pain, hypersensitivity of teeth and masticatory muscle discomfort, fatigue, or pain [62]. Clinically, [PERSON] et al. [63] detected a bruxism patient through intraoral and extraoral examination. [PERSON] et al. [64] suggested that bruxism is more accurately measured extraorally through masticatory muscle EMG recordings and polysomnography. We compare our proposed method with previously selected sleep disorders and sleep stage methods in terms of author, publication year, subject detection, signal, classifier, sensitivity, specificity, and accuracy, as mentioned in Table 5. The data from sleep disorders and sleep stage detection methods include EEG, EMG, and ECG signals. The classifiers involve decision tree (DT), KNN, SVM RBF kernel, threshold, and the novel HML classifier. Compared with them, our method shows a better performance in detecting bruxism (Figure 10A). Additionally, the proposed HML classifier is also compared with some existing hybrid classifiers, which are reported in Table 6. Our proposed HML classifier achieves satisfactory performance in detecting bruxism (Figure 10B).  
Figure 9: Performance analysis for the combination of subjects (bruxism and healthy) and sleep stages (w and REM) classification using the novel 20-fold HML classifier. (**A**) Comparison between all channels such as EMG1-EMG2, ECG1, ECG2, C4-P4, and C4-A1 channels of the sleep stages classification. In addition, (**B**) highest and mean performance of the combined classification. The C4-P4 channel of the EEG signal has the highest accuracy (97%) in the combined classification.
## 3 Results and Discussion
### Electricity Sales Package Recommendation Analysis
#### 3.2.1 Sample Customer Cluster Analysis
Since residents typically return home around 16:00, the electricity load tends to be higher during this time. According to the dynamic hybrid multi-attribute group decision-making method based on two reference points proposed earlier, with \(l=0.5\) and \(r=0.0001\), the electricity sales packages are ranked in terms of their desirability. By considering the similarity between the target customer and the similar customers, the normalized comprehensive score for each electricity sales package can be obtained for the target customer, as shown in Table 3.  
As shown in Table 3, target customer \(R_{4}\) has the highest satisfaction with \(A_{3}\). In fact, target customer \(R_{4}\) belongs to the residential electricity customer type. Since residential customers are typically away during the day for work and at home in the evening, their electricity usage is concentrated between 16:00 and 22:00. Residential customers usually focus on electricity costs and bill discounts. Therefore, compared to high energy quality value-added services, residential users are more inclined toward energy-saving management services. This result aligns with the theoretical analysis and further demonstrates the accuracy and applicability of the proposed electricity sales package recommendation method for different types of electricity consumers.
## 4 Interface Selection
### Throughput Results  
Figures 7 and 8 show the measured and estimated single-link throughput results for 40 \(MHz\) for _Group A_ and _Group B_. In each Figure, 11_n Mea_. and 11_ac Mea_. represent the measured throughput for the 11n and 11 ac interface; 11_n Est_. and 11_ac Est_. represent the estimated throughput for the 11n and 11 ac interface. For both interfaces, the throughput is estimated using Equation (3) by the _parameter optimization tool_ in Table 2.  
To obtain more accurate results, we choose 14 different host locations in the experimental field with two different AP positions. Figures 7 and 8 show that _Group A_ is better than _Group B_ for the dual interface, and the measured throughput and the estimated throughput matched well for each host position. In Group A, _Raspberry Pi_ is used for 11n and _Archer T4U_ is 11 ac, and in Group B, they are opposites. The performance of Group A is better than that of Group B, because _Archer T4U_ used the more advanced wireless chipset and a higher-quality antenna than _Raspberry Pi_, which can enhance the throughput performance when _Archer T4U_ is used for the faster communication protocol of 11 ac at 5 GHz. Thus, we should use the built-in wireless network interface card (NIC) of the _Raspberry Pi_ device for 11n, and the _Archer T4U_ wireless NIC adapter is for 11 ac.  
Figure 6: Experiment field.  
Figure 7: Group A throughput results for 40 MHz.
## 3 Generic Model of the 3 RRR PPM
### Kinematic Model of the 3 RRR PPM in Fully Cartesian Coordinates
#### 3.1.1 Constraint Equations and Solution to the Positions Problem  
The manipulator has three degrees of freedom; thus, the dependent coordinates in \(\mathbf{q}\) are related by a set of 15 constraint equations, obtained from the constant distant condition among bodies. The vector of constraint equations, \(\mathbf{\Phi}(\mathbf{q})=0\), is presented in Equation (30).  
Note that the equations in Equation (30) are nonlinear but quadratic, very simple, and easy to differentiate to get the a linear Jacobian matrix. Thus, the solution of the positions problem though numerical iterative procedures could be faster with the aid of symbolic computing software.  
\[\mathbf{\Phi}(\mathbf{q})=\begin{bmatrix}(x_{B1}-x_{A1})^{2}+(y_{B1}-y_{A1})^ {2}-l_{1}^{2}\\ (x_{C1}-x_{B1})^{2}+(y_{C1}-y_{B1})^{2}-l_{2}^{2}\\ (x_{B2}-x_{A2})^{2}+(y_{B2}-y_{A2})^{2}-l_{3}^{2}\\ (x_{C2}-x_{B2})^{2}+(y_{C2}-y_{B2})^{2}-l_{4}^{2}\\ (x_{B3}-x_{A3})^{2}+(y_{B3}-y_{A3})^{2}-l_{5}^{2}\\ (x_{C3}-x_{B3})^{2}+(y_{C3}-y_{B3})^{2}-l_{6}^{2}\\ (x_{C1}-x_{C2})^{2}+(y_{C1}-y_{C2})^{2}-l_{7}^{2}\\ (x_{C2}-x_{B3})^{2}+(y_{C2}-y_{C3})^{2}-l_{7}^{2}\\ (x_{C3}-x_{C1})^{2}+(y_{C3}-y_{C1})^{2}-l_{7}^{2}\\ x_{A1}-\frac{1}{2}l_{0}\\ y_{A1}+\frac{1}{2\sqrt{3}}l_{0}\\ x_{A2}+\frac{1}{2\sqrt{3}}l_{0}\\ x_{A3}\\ y_{A3}-\frac{1}{\sqrt{3}}l_{0}\end{bmatrix}=\mathbf{0} \tag{30}\]  
Figure 3: A model in fully Cartesian coordinates of a 3 RRR PPM with nine “basic points”: \(A_{1},A_{2},A_{3},B_{1},B_{2},B_{3},C_{1},C_{2}\), and \(C_{3}\). Points \(A_{1},A_{2}\), and \(A_{3}\) are fixed. Point \(H\) represents the end-effector and is not part of the “basic points”.
## 6 Experiments
### The Performance Metric
#### 6.1.1 Diebold Mariano test  
A comparison test, the Diebold Mariano (DM) test, was proposed by [PERSON], [PERSON], and [PERSON] [46], which focused on the predictive accuracy and evaluating the forecasting performance between two or more-time series models.  
Actual values:  
\[\{y_{t};\ t\ =\ 1,\ldots,\ n\ +\ m\} \tag{28}\]  
Two forecasts:  
\[\left\{\hat{y}_{t}^{(1)};\ t\ =\ 1,\ldots,\ n\ +\ m\right\} \tag{29}\]  
\[\left\{\hat{y}_{t}^{(2)};\ t\ =\ 1,\ldots,\ n\ +\ m\right\} \tag{30}\]  
The forecast errors from the two models are:  
\[\left\{\varepsilon_{n+h}^{(1)}=y_{n+h}-\hat{y}_{n+h}^{(1)},\ h=1,2,\ldots,m\right\} \tag{31}\]  
\[\left\{\varepsilon_{n+h}^{(2)}=y_{n+h}-\hat{y}_{n+h}^{(2)},\ h=1,2,\ldots,m\right\} \tag{32}\]  
A suitable loss function, \(L(\varepsilon_{n+h}^{(1)})\) i = 1,2., was applied to measure the accuracy of each forecast. Square error loss and absolute-deviation error loss are the most popular loss functions.  
Square error loss:  
\[L(\varepsilon_{n+h}^{(i)})\ =\ (\varepsilon_{n+h}^{(i)})^{2} \tag{33}\]  
Absolute deviation loss:  
\[L(\varepsilon_{n+h}^{(i)})\ =\ /\varepsilon_{n+h}^{(i)}/ \tag{34}\]  
The DM test statistics estimate the forecasts according to arbitrary loss function \(L(\mathfrak{g})\):  
\[\text{DM}= \tag{35}\]  
where \(S^{2}\) is an estimator of the variance of \(d_{h}\)= \(\ L(\varepsilon_{n+h}^{(1)})\ -\ L(\varepsilon_{n+h}^{(2)})\).  
The null hypothesis is:  
\[\mathbf{H_{0}}\text{: }E(d_{h})\ =\ 0\ \forall\ t \tag{36}\]  
versus the alternative hypothesis, which is:  
\[\mathbf{H_{1}}\text{: }E(d_{h})\ \
eq\ 0 \tag{37}\]  
The null hypothesis means that the two forecasts have the same accuracy. The alternative hypothesis means that the two forecasts have different levels of accuracy. Under the null hypothesis, test statistic \(DM\) is asymptotically \(N\) (0, 1) distributed. The null hypothesis of no difference is rejected if the computed \(DM\) statistic falls outside the range from -\(z_{\alpha/2}\) to \(z_{\alpha/2}\), that is, if  
\[|DM|>\ z_{\alpha/2} \tag{38}\]  
where \(z_{\alpha/2}\) is the upper (or positive) z-value from the standard normal table, corresponding to half of the desired \(\alpha\) level of the test.
## 2 Materials and Methods
### Sample Entropy Analysis  
Entropy is defined as the loss of information in a time series or signal. Over the past 30 years, entropy methods have been increasingly used to quantify the periodicity or regularity of physiological data. The two most commonly used entropy methods for biological data are ApEn and SampEn. According to the literature, SampEn is more reliable for short-gait data sets (\(N<200\)) because SampEn is less sensitive to changes in data length, is more consistent, and does not contain the inherent bias associated with the ApEn algorithm. Hence, SampEn was used as the complexity analysis method in this study. A flowchart of the entropy analysis is presented in Figure 3.  
Figure 3: Flowchart of SampEn calculation. \(N\): the time series length. \(m\): the sequence length to be compared. \(r\): accepting matches tolerance.  
Figure 2: RIP signal processing flowchart.  
The procedure includes the following steps:  
1. Input a time-domain signal \(\mathbf{u}\) with length \(N\).
2. Define the sequence \(\mathbf{x}\)m, which is a vector with length m.
3. Define the distance \(d_{i,j}^{m}\) between \(\mathbf{x}\)m(\(i\)) and \(\mathbf{x}\)m(\(j\)) as the largest difference between these elements for all elements \(i\) and \(j\).
4. Count the number of \(d_{i,j}^{m}\) less than a given threshold r as \(B_{i}^{m}\)(\(r\)).
5. The average value of these counts for all i is calculated as \(B^{m}\)(\(r\)).
6. Similarly, calculate these values for \(m+1\).
7. \(\mathrm{SampEn}(m,r,N)=-\ln\big{[}\frac{B^{m+1}}{B^{m}}(r)\big{]}\).  
For clinical data, m should be set to 2 for the ApEn algorithm. The setting \(m=2\) was also used in the earliest studies reporting SampEn. In this study, the SampEn parameters were as follows: \(N=\) input data, \(m=2\), \(r=0.2\) SD.
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 3 New Flexible Double Weight (FDW) Code Construction  
The FDW code is an improved version of the double weight (DW), MDW, and EDW codes. Unlike the other codes, the amount of weight for FDW is flexible. The amount of weight might be odd (EDW) or even (MDW). The sequence for FDW is nearly identical to that of DW but with significant additions. FDW has a code sequence of 1,2,1,...,1n. The basic user code for FDW is fixed at 2, which is unique because the prior code had a separate basic user code for each weight. Tables 4 and 5 present the fundamental code architecture for odd (\(W=3\)) and even (\(W=4\)) FDW.  
Based on the code sequence in Tables 4 and 5, the code contains cross-correlation, \(\lambda_{c}\) equal to one, and high autocorrelation. The relationship between \(N\), \(K\), and \(W\) is shown in the universal formula below.  
\[N = \left[K+\left[\sin\!\left(\frac{K\pi}{2}\right)\right]^{2}\right]W + \left[-\frac{1}{2}K-1.5-\left[\cos\!\left(\frac{W\pi}{2}\right)\right]^{2} \right]\left[\sin\!\left(\frac{K\pi}{2}\right)\right]^{2} - \frac{1}{2}K\!\left[\cos\!\left(\frac{K\pi}{2}\right)\right]^{2} \tag{3}\]  
The code length may be calculated using Equation (3) by specifying the weight and the number of users. The FDW code structure is based on the DW code structure, which is represented by the letter \(A\). The code is extended as \(B\) for pairing chips (even weight, EW) and without pairing chips (odd weight, OW). For OW (\(W=3\)) and EW (\(W=4\)), Equation (4) displays matrix \(A\), and Equation (5) shows matrix \(B\).  
\[[A]=\begin{bmatrix}1&1&0\\ 0&1\end{bmatrix} \tag{4}\]  
\[[B]=\begin{bmatrix}1&0\\ 0&1\end{bmatrix}_{W=3}OR\ \begin{bmatrix}1&1&0&0\\ 0&0&1&1\end{bmatrix}_{W=4} \tag{5}\]  
\[[B]=\begin{bmatrix}1&0&\cdots&W_{B}\\ 0&1&\cdots&W_{B}\end{bmatrix}_{W=n(odd)}OR\ \begin{bmatrix}1&1&0&0& \cdots&W_{B}\\ 0&0&1&1&\cdots&W_{B}\end{bmatrix}_{W=n(even)} \tag{6}\]  
To design an FDW, matrix \(B\) must have zero cross-correlation properties. For the system to function properly, the cross-correlation properties must be equal to or less than 1. The amount of weight in matrix \(A\), \(W_{A}\), is set at two, but in matrix \(B\), \(W_{B}=W-2\). As demonstrated in the equation, the same approach may be extended to a larger amount of weight, such as 5,6,7,..., \(n\), respectively. Equations (4)-(6) provide a new code in Equation (7) for \(W=3\) and Equation (8) for \(W=4\). A matrix concatenation approach based on the equation below may also be used to create a matrix in Equations (7) and (8).  
\[FDW=|AB|\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \(K_{th}\) & \(C_{10}\) & \(C_{9}\) & \(C_{8}\) & \(C_{7}\) & \(C_{6}\) & \(C_{5}\) & \(C_{4}\) & \(C_{3}\) & \(C_{2}\) & \(C_{1}\) \\ \hline
1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 \\
2 & 0 & 0 & 0 & 0 &\[FDW_{W=3}=\begin{bmatrix}1&1&0&1&0\\ 0&1&1&0&1\end{bmatrix} \tag{7}\]  
\[FDW_{W=4}=\begin{bmatrix}1&1&0&1&1&0\\ 0&1&1&0&0&1\end{bmatrix} \tag{8}\]