esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Combination Framework for the Differential Evolution Algorithm
## 2 Related Works
### Related Works on Parameter Control Strategy for DE
They then use the success rate to modify the selection probability of each discrete parameter value to guide the individual's parameter allocation. Test feedback strategies based on the individual evaluation are considered individual-level strategies. Each individual is evaluated and the parameter value of the individual is adjusted independently. A typical strategy is proposed by jDE [21]. An algorithm of ensemble of mutation strategies and parameters in DE ( EPSDE) [22] presents a discretized version of this type of parameter strategy. The concept behind these methods is that good parameter values survive and propagate, while poor ones are discarded. There are also some studies use a metaheuristic optimization algorithm to optimize the parameters of DE, such as [34; 35]. In addition to \(F\) and \(CR\), several studies have introduced other parameters in the first category. In JADE, a new mutation operation is proposed [13], and a new parameter p is introduced. JADE uses a fixed value for p, but in jSO [20], p is changed according to generation. Some studies utilize the neighborhood relationship to select parents; neighbor size may thus be a new parameter [36; 37; 38]. Reference [38] uses four neighborhood topologies, where the related parameters are fixed. The relationship between DE performance and the second category of parameters has not been deeply studied. Some studies relate \(NP\) to the problem dimension \(D\), but the advices on the value of \(NP\) differ greatly from \(1D\) to \(40D\)[39]. Some studies set \(NP\) as fixed and independent of \(D\); \(50\) and \(100\) are primarily used in many DE variants [13; 14; 21]. Recently, several strategies with a variable population size have been proposed, and the concept of a gradual decrease in population size is referenced in some studies. dynNP-DE [40] reduces the population size by half for every \(25\%\) of function evaluations. L-SHADE provides a linear population size reduction strategy [18] that changes the \(NP\) every generation. Another proposal that changes the \(NP\) adaptively according to improvements to the best solution is adopted in several studies, which alternately increase or decrease the \(NP\)[41; 42]. The third category of parameters does not share common parameters; different high-level strategies may use different parameters [15; 17; 22; 26; 27].
## 3 Methods
### Complex Parameter Wald Test  
In this section, we present a novel detection theorem referred to as the Complex Wald test, which is developed in parallel with [20], addressing the general hypothesis testing problem involving complex-valued parameters. Traditionally, this approach required concatenating the real and imaginary parts of the complex-valued data to create an augmented real vector for conducting Wald test computations in the real-valued domain. However, the derived Complex Wald test enables direct computation with complex-valued quantities. Moreover, when the Fisher Information Matrix (FIM) of the unknown parameters exhibits a specific structure, the Complex Wald Test simplifies to a more streamlined form.  
Suppose \(\mathbf{x}=\left[\mathbf{u}^{T}\ \mathbf{v}^{T}\right]^{T}\) with \(\mathbf{x}\in\mathbb{R}^{2N\times 1}\), \(\mathbf{u}\in\mathbb{R}^{N\times 1}\) and \(\mathbf{v}\in\mathbb{R}^{N\times 1}\), which is formed from the observed complex-valued vector \(\tilde{\mathbf{x}}=\mathbf{u}+j\mathbf{v}\), where \(\tilde{\mathbf{x}}\in\mathbb{C}^{N\times 1}\). Let \(\boldsymbol{\xi}=[\boldsymbol{\alpha}^{T}\ \boldsymbol{\beta}^{T}]^{T}\), formed from the unknown complex-valued parameter vector \(\tilde{\boldsymbol{\theta}}=\boldsymbol{\alpha}+j\boldsymbol{\beta}\), where \(\boldsymbol{\xi}\in\mathbb{R}^{2p\times 1}\), \(\boldsymbol{\alpha}\in\mathbb{R}^{p\times 1}\), \(\boldsymbol{\beta}\in\mathbb{R}^{p\times 1}\) and \(\tilde{\boldsymbol{\theta}}\in\mathbb{C}^{p\times 1}\). We denote the probability density function (PDF) of the data as \(p_{\mathbf{x}}(\mathbf{x};\boldsymbol{\xi})\). Then, we have the PDF \(p_{\mathbf{x}}(\tilde{\mathbf{x}};\boldsymbol{\tilde{\theta}})\equiv p_{ \mathbf{x}}(\mathbf{x};\boldsymbol{\xi})\equiv p_{\mathbf{u},\mathbf{v}}( \mathbf{u},\mathbf{v};\boldsymbol{\alpha},\boldsymbol{\beta})\). The real Wald test without nuisance parameters (parameters that are unknown yet of no interest) [28] is  
\[T_{\mathrm{W}}(\mathbf{x})=(\boldsymbol{\xi}-\boldsymbol{\xi}_{0})^{T}\mathbf{ I}(\boldsymbol{\xi})(\boldsymbol{\xi}-\boldsymbol{\xi}_{0})\big{|}_{\boldsymbol{ \xi}-\boldsymbol{\tilde{\theta}}_{1}}, \tag{25}\]  
where \(\boldsymbol{\tilde{\xi}}_{1}\) is the MLE of \(\boldsymbol{\xi}\) under \(\mathcal{H}_{1}\), and \(\mathbf{I}(\boldsymbol{\xi})\) is Fisher Information Matrix (FIM) of \(\boldsymbol{\xi}\) and can be partitioned as  
\[\mathbf{I}(\boldsymbol{\xi})=\left[\begin{array}{cc}\mathbf{I}_{\alpha \alpha}&\mathbf{I}_{\alpha\beta}\\ \mathbf{I}_{\beta\alpha}&\mathbf{I}_{\beta\beta}\end{array}\right] \tag{26}\]  
where \(\mathbf{I}_{\alpha\alpha},\mathbf{I}_{\beta\beta},\mathbf{I}_{\beta\alpha}, \mathbf{I}_{\alpha\beta}\in\mathbb{R}^{p\times p}\), \(\mathbf{I}_{\alpha\alpha}^{T}=\mathbf{I}_{\alpha\alpha}\), \(\mathbf{I}_{\beta\beta}^{T}=\mathbf{I}_{\beta\beta}\), and \(\mathbf{I}_{\alpha\beta}^{T}=\mathbf{I}_{\beta\alpha}\). We next derive the complex Wald test for the unknown parameter \(\tilde{\boldsymbol{\theta}}\) by carrying out mathematical operations with respect to complex-valued quantities.
## 3 The Recursive Estimation Algorithm
### Persistency of Excitation  
To avoid practical parameter identifiability issues using the RLS algorithm, it is recommended to use an input signal with a sufficiently rich frequency bandwidth as discussed in [25; 26]. While using a pseudo-random binary signal (PRBS) with limited amplitude and sufficient signal to noise ratio is generally advised for linear systems, amplitude-modulated pseudo-random binary signals and multi-levels pseudo-random signals (MPRS) have been shown to be more appropriate for nonlinear system identification [33; 34].  
A PRBS is a periodic and deterministic signal with white-noise-like properties [35]. This signal is generated by adding a sequence of shift register specific outputs in a \(XOR\) gate in closed-loop. The sequence length is computed as \(L=2^{N}-1\), where \(N\) is the number of shift registers in the sequence. The sequence duration is obtained as \(L\,T_{\textit{prbs}}\) with \(T_{\textit{prbs}}=p\,T_{s}\), the PRBS sampling period expressed as a multiple \(p\in\mathbb{N}^{+}\) of the process sampling time \(T_{s}\). Further details about PRBS generation can be found in [31; 35].  
In this work, it is proposed to generate MPRS by adding three PRBS with different bandwith (sampling time) and shift register sequences inducing a longer period and both amplitude and holding time modulations. Furthermore, this approach allows maximal amplitude and minimal holding time specifications. For DISO processes identification,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Regression** & **Equation** & **Parameters Equal to 0** & **Parameter Subscript** & \(\mathbf{\Delta}\)**Length** \\
**Model** & & **or Signals to be Omitted** & **Changes** & **of \(\mathbf{\theta_{s}}\)** \\ \hline \multirow{2}{*}{\(RM_{2p1z}\)} & (18) & \(\theta_{s11},\theta_{s12},\theta_{s13},\theta_{s14}\) & \multirow{2}{*}{\(\theta_{s15}\to\theta_{s11}\)} & \multirow{2}{*}{\(15\to 11\)} \\  & & \(u_{t}^{2}(k-1)\), \(u_{t}(k-1)\), \(i\in\{1,2\}\) & & \\ \hline \multirow{2}{*}{\(RM_{2p0z}\)} & (18) & \(\theta_{s7},\theta_{s8},\theta_{s9},\theta_{s10},\theta_{s11},\theta_{s12}, \theta_{s13},\theta_{s14}\) & \multirow{2}{*}{\(\theta_{s15}\to\theta_{s7}\)} & \multirow{2}{*}{\(15\to 7\)} \\  & & \(u_{t}^{2}(k-j)\), \(u_{t}(k-j)\), \(i\in\{1,2\}\) & & \\ \hline \end{tabular}
\end{table}
Table 1: Model configurations and corresponding simplifications.  
using such design procedure may also help reducing spectral overlap between inputs when required.
## 3 Results and Discussion  
### Evaluation of the Performance of the SPME-IMS Interface  
Differences in the design of the interface, including inlet structure, tube length and inner radius, have an important influence on its temperature distribution. The traditional completely open gas inlet structure-based interface as reported by [PERSON] [21], has many problems, such as the depth of the needle may differ in different operations and the time for temperature equilibration is much longer. The interface described in our work adopted a semi-enclosed structure with a chromatography septum gas inlet. Such a design could avoid human disturbance factors caused by different needle shapes and effectively reduce the intake air temperature equilibrium time. To realize the temperature distribution of the interface, a finite element analysis method was adopted for the self-made and the published interfaces using the SST module in the ANSYS Workbench software. The comparison was done under the same inlet carrier gas temperature, different inlet velocity setting, internal tube length, inner radius and carrier gas introduction method, respectively. The result is shown in Figure 2. The interface in our work could achieve temperature balance at a distance of 2 cm from the inlet end compared to 5.1 cm in the other setup. Therefore, the interface designed in this paper can reach temperature balance of the whole internal cavity in a short time, and more quickly realize a uniform internal cavity temperature.  
Figure 2: Temperature distribution of the interface designed in our work (**A**) and the published work (**B**). Blue or red colors are presented for the distribution of the different temperature.
# Advances in Permeation of Solutes into Hair: Influencing Factors and Theoretical Models
## 3 Main Factors That Affect the Permeation and Absorption of Solutes in Hair
### Solutes  
#### 3.2.1 Size and Shape of Solute Molecules  
The size and shape of solute molecules affect their permeation in hair [28; 44; 47; 48]. Assuming that the shape of the solute molecule is spherical, [PERSON] [44] determined that the maximum diameter of a molecule that can penetrate into hair is 1.48 nm. [PERSON] et al. [47] studied the penetrating distance of hair dyes of different sizes. The critical dimension of molecules that can enter hair was calculated to be 1 nm using the longest diagonal of the molecular minimum projected area as a measure of the size and shape of the molecule. However, the study had a single object of interest; the staining time was too long; and the permeation of cationic, anionic, and neutral molecules was not discussed separately. [PERSON] et al. [28] considered the effect of the solute charge characteristics on the permeation and absorption of solutes based on the model of [PERSON] et al. [47]. They found that the critical sizes for the permeation of anionic, cationic, and neutral solutes into hair are approximately 1.2-1.3, 1.4, and 0.95 nm, respectively.
## 3 Proposed Approach
### Transfer Functions to Develop Binary Variant of WOA  
WOA is a continuous search algorithm by nature. Therefore, it is not applicable in its original form to deal with FS which is a binary optimization problem. Accordingly, it is imperative to convert WOA to a binary structure by utilizing a binarization scheme. Transfer Function (TF) is deemed as one of the most frequently applied binarization schemes [80,81]. For this purpose, we employed eight different TFs form two well-know groups that are S-shaped and V-shaped [81] (see Figure 10) to develop a binary variant of WOA for the FS problem. In the TF-based binarization scheme, two steps are performed. In the first step, a TF function is employed to convert the real-valued solution \(R^{n}\) into an intermediate normalized solution \(I=(I_{1},I_{2},\ldots,I_{n})\) within [0,1] such that each element in \(I\) represent the probability of transforming the corresponding element in \(R^{n}\) into 0 or 1. In the second step, a binarization rule is used to convert the output of TF into binary. In the literature, the most common binarization rules are called standard method given in Equation (15) and complement method given in Equation (18). Broadly, The standard rule is used with S-shaped TFs while the complement rule is used with V-shaped TFs [82].  
Considering S2 sigmoid function, the probability of updating the generated real-valued solution of WOA into binary is presented in Equation (15).  
\[S(x_{i}^{j}(k))=\frac{1}{1+\exp^{-x_{i}^{j}(k)}} \tag{15}\]  
where \(X_{i}^{j}\) is a variable that represents the \(j\)th element of the \(i\)th real-valued solution \(X\), \(k\) represents the current iteration. The updating process for S-shape group is presented in Equation (16) for the next iteration.  
\[x_{i}^{j}(k+1)=\begin{cases}0&\text{If }rand<S(X_{i}^{j}(k))\\ 1&\text{If }rand\geq S(X_{i}^{j}(k))\end{cases} \tag{16}\]  
where \(X_{i}^{j}(k+1)\) represents the binary value of the corresponding \(X_{i}^{j}\), and the \(S(X_{j}^{i}(k))\) is the probability value that is evaluated based on Equation (15).  
The updating process for V-shape for the forthcoming iteration is presented in Equation (18), which is evaluated based on the probability values that is illustrated in Equation (17) [83]. Table 1 explores the mathematical models for S-shape and V-shape TFs functions.  
\[V(x_{i}^{j}(k))=|\tanh(x_{i}^{j}(k))| \tag{17}\]  
\[x_{i}^{j}(k+1)=\begin{cases}\
eg x_{i}^{j}(k)&r<V(x_{i}^{j}(k))\\ x_{i}^{j}(k)&r\geq V(x_{i}^{j}(k))\end{cases} \tag{18}\]  
where \(\backsim\) is the complement. With the complement binarization rule, the new binary value (\(x_{i}^{j}(k+1)\) is set considering the current binary solution, that is to say, based on the probability value \(V(x_{i}^{j}(k))\), the \(j\)th element is either kept or flipped.
## 8 Results and Discussion
### Example 1  
Consider an example of Equation (1) with \(\mathbf{A}=[a_{ij}]\), \(i,j=1,\ldots,n\); \(a_{ij}=1/(10j)-1/20\), \(i>j\), \(a_{ij}=1/[10(i-j)]-1/20\), \(i<j\), and \(a_{ij}=1\), \(i=j\)[17]. We take \(b_{i}=1\), \(i=1,\ldots,n\) and \(x_{i}^{0}=0,\ i=1,\ldots,n\), and fix \(n=20\).  
With the convergence criterion \(\varepsilon=10^{-12}\) Table 2 compares the number of iterations (NI) for SOR, A2-SOR and A2-AOR, and that obtained in [17] by using Quasi-AOR (QAOR) and Quasi-SOR (QSOR), which are subjected to a loose convergence criterion \(10^{-6}\). In SOR the optimal value of \(w\) is computed from Algorithm 3; through three iterations \(w=0.715496\) is obtained; the spectral radius of the iteration matrix is small with 0.2898. If an ad hoc value \(w=0.5\) is used in SOR, it needs 269 iterations.  
In A2-SOR, we take \(w=0.715496\) and \(\alpha=1.6;w=0.15\) and \(r=0.759399\) are used in A2-AOR; \(w=0.3\) and \(r=0.9\) are used in AOR and QAOR. In AOR we take \(w=1.5\), and if the optimal \(r=0.641305\) is computed from Algorithm 5, then NI reduces to 83.  
In Table 3, we demonstrate the usefulness of A1-SOR method with \(w=0.5\). A1-SOR with \(\alpha=1.98\) can improve the convergence speed twice than the original SOR.  
In Table 4 the test is for the AOR method with \(w=0.3\) and \(r=0.9\). The convergence is improved by increasing the value of \(\alpha\).  
Comparing Tables 2-4, the Algorithm 2 type iterative schemes are convergent faster than the Algorithm 1 type iterative schemes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\alpha\) & **1** & **1.2** & **1.3** & **1.4** & **1.5** & **1.6** & **1.7** \\ \hline NI & 161 & 133 & 121 & 111 & 103 & 96 & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Example 1, NI obtained by A1-AOR for different values of \(\alpha\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **QAOR** & **QSOR** & **AOR** & **AOR (op. \(r\))** & **SOR (op. \(w\))** & **A2-SOR** & **A2-AOR** \\ \hline NI & 155 & 141 & 161 & 83 & 167 & 72 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example 1, NI for QAOR, QSOR, AOR, SOR, A2-SOR and A2-AOR; op. \(r\) means the optimal value of \(r\), and op. \(w\) means the optimal value of \(w\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\alpha\) & **1** & **1.2** & **1.3** & **1.4** & **1.5** & **1.6** & **1.7** \\ \hline NI & 161 & 133 & 121 & 111 & 103 & 96 & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example 1, NI and spectral radius obtained by A1-SOR for different values of \(\alpha\).  
Tables 2-9 demonstrate that the proposed NSIS and the optimization technique for determining the optima value of parameter can significantly reduce the number of iterations (NI).
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Run-time Analysis  
The time for computing the coefficients of the filtering probabilities for transition parameter variation is recorded for the Coefficient-Matrix-Fill procedure and our method (_SMF_) using the HMM shown in Example (1). It is shown in Figure 3, where the Coefficient-Matrix-Fill procedure is labled as _CMFP_. In [26], the algorithm for the Coefficient-Matrix-Fill procedure is presented. It is shown for an HMM model with \(n=2\) hidden states and \(m=2\) observable symbols. The proposed SMF algorithm has shown a significant improvement in computational time compared to the Coefficient-Matrix-Fill procedure as the length of the observation sequence increases. For example, for an observation sequence length of 1000, the proposed technique takes a mean time of 0.048 s. In comparison, it takes the Coefficient-Matrix-Fill procedure 2.183 seconds. This is an improvement of 98%. The algorithms are implemented in MATLAB on a 64-bit Windows 7 Professional Workstation (Dell Precision T7600 with Intel(R) Xeon CPU E5-2680 0 @ 2.70 GHz dual core processors and 64 GB RAM).  
The computational complexity of our method is linear time, \(O(l)\), where \(l\) is the length of the observation sequence, whereas the Coefficient-Matrix-Fill procedure is quadratic time, \(O(l^{2})\). In our method, the sensitivity function coefficients are computed in _one for loop_ that runs for the length of the observation sequence, as shown in Algorithms 1-6, whereas in the Coefficient-Matrix-Fill procedure _two nested for loops_ are used. It computes the sensitivity coefficients in the forward matrices element-by-element in the _inner loop_, which runs in increasing time up to the maximum of the length of the observation sequence, and the _outer loop_ runs for the length of the observation sequence.
# Flexural Behavior of a 30-Meter Full-Scale Simply Supported Prestressed Concrete Box Girder
## 3 Full-Scale Destructive Experiment
### Arrangement of Test Instrumentation  
The load, strain of the concrete, displacement, and cracking of the girder were observed after every load step. Two JMZX type load cells were used to measure the load to keep the structure under uniform stress. As illustrated in Figure 4a, resistance strain gauges T1 through T5 for concrete were arranged on the top flange to measure the strain of the compression zone, located at 1/4, 1/2, and 3/4 of the span along the length. Resistance strain gauges W1 through W14 were arranged on the web to measure the strain along the depth of the mid-span section of the girder. DH3816 static strain test system was used for strain data acquisition. As shown in Figure 4b, five JMDL type displacement sensors D1 through D5 were installed at the support point and the quarter points of the girder to observe the deflection. JMCD type crack width meter was used for measuring the crack widths of girder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Case** & **Applied Load** & **Detail** \\ \hline
1 & 0\(\rightarrow\)100 kN\(\rightarrow\)0 & Loading to the self-weight of the girder and then unloading to eliminate the initial deformation and verify working condition of the instruments. \\
2 & 0\(\rightarrow\)133 kN\(\rightarrow\)186\(\rightarrow\)380\(\rightarrow\)0 & Loading to the design serviceability limit state and design ultimate state \\  & & of bearing capacity, then unloading. \\
3 & 0\(\rightarrow\)780 kN\(\rightarrow\)0 & Loading until the first crack occurs, then unloading. \\
4 & 0\(\rightarrow\)1100 kN\(\rightarrow\)0 & Loading until about 60\% of ultimate bending capacity, then unloading. \\
5 & 0\(\rightarrow\)1300 kN\(\rightarrow\)0 & Loading until about 70\% of ultimate bending capacity, then unloading. \\
6 & 0\(\rightarrow\)1500 kN\(\rightarrow\)0 & Loading until about 80\% of ultimate bending capacity, then unloading. \\
7 & 0\(\rightarrow\)1600 kN\(\rightarrow\)0 & Loading until about 85\% of ultimate bending capacity, then unloading. \\
8 & 0\(\rightarrow\)1700 kN\(\rightarrow\)0 & Loading until about 90\% of ultimate bending capacity, then unloading. \\
9 & 0\(\rightarrow\)1800 kN\(\rightarrow\)1872 kN\(\rightarrow\)0 & Loading until the structure is about to fail, then unloading. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Loading procedure.  
Figure 4: Arrangement of test instrumentation (Unit: cm): (**a**) Strain gauges; (**b**) Displacement sensors.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 2 Related Work
### Machine Learning for IoD Security  
The use of machine learning methods to improve IoD security has been recently investigated. [PERSON] et al. (2018) utilized deep-learning methods ([PERSON] et al., 2018) to identify unauthorized drone intrusions into defended airspace and found high accuracy in identifying legitimate versus malicious drone activity; however, their method is not concerned with authentication needs. [PERSON] et al. (2018) created a multi-factor authentication scheme for drones based on machine learning-based behavioral biometrics that examine flight trajectories to authenticate the identity of the drone in real-time, demonstrating the potential for prevention of session hijacking attacks but with a requirement for extensive training data. Likewise, [PERSON] et al. (2018) have designed an intrusion detection system for IoD networks based on ensemble learning methods that dynamically respond to changing threat scenarios. Reinforcement learning ([PERSON] et al., 2018) has also been identified to have potential for enhancing drone security, as seen with [PERSON] et al. (2018), who came up with a reinforcement learning-driven intrusion detection framework for flying ad hoc networks (FANETs) that achieves maximum detection accuracy with low false positives. Their work recognizes the potential of adaptive security mechanisms for drone networks but focuses more on detection than on prevention. Some recent advances involve federated learning for distributed anomaly detection of drone swarms ([PERSON] et al., 2018) and transformer models verifying drone pilots by control signal patterns with 97.8% accuracy ([PERSON] et al., 2018). Our proposed hybrid method is as accurate as that but has much lower computational requirements.