esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Harmonic Wavelets
Wavelet decomposition [14]:  
\[f(x)=\sum\limits_{j=-\infty}^{\infty}\sum\limits_{k=-\infty}^{\infty}a_{jk} \psi(2^{j}x-k)=\sum\limits_{k=-\infty}^{\infty}a_{\phi k}\phi(x-k)+\sum \limits_{j=0}^{\infty}\sum\limits_{k=-\infty}^{\infty}a_{jk}\psi(2^{j}x-k) \tag{5}\]  
Wavelet decomposition using harmonic wavelets [18]:  
\[ \begin{array}{l}f(x)=\sum\limits_{j=-\infty}^{\infty}\sum\limits_{k=-\infty} ^{\infty}\left[a_{jk}\psi\left(2^{j}x-k\right)=\overline{a}_{jk}\overline{ \psi}\left(2^{j}x-k\right)\right]\\ =\sum\limits_{k=-\infty}^{\infty}\left[a_{\phi k}\phi(x-k)=\overline{a}_{\phi k }\overline{\phi}(x-k)\right]+\sum\limits_{j=0}^{\infty}\sum\limits_{k=-\infty }^{\infty}\left[a_{jk}\psi\left(2^{j}x-k\right)+\widetilde{a}_{jk}\overline{ \psi}\left(2^{j}x-k\right)\right]\\ a_{jk}=2^{j}\int\limits_{-\infty}^{\infty}f(x)\overline{\psi}(2^{j}x-k)dx \end{array}  \tag{6}\]  
Calculations with the last two formulae are inefficient. Fast decomposition can be implemented in the following way:  
\[a_{jk}=2^{j}\int\limits_{-\infty}^{\infty}F(\omega)\frac{1}{2\pi}2^{-j}\mathrm{e }^{\frac{\mathrm{i}ak}{2^{j}}}d\omega=\frac{1}{2\pi}\int\limits_{2\pi 2^{j}}^{4\pi 2^{j} }F(\omega)\mathrm{e}^{\frac{\mathrm{i}ak}{2^{j}}}d\omega\approx\int\limits_{2\pi 2 ^{j}}^{4\pi 2^{j}}F(\omega)\mathrm{e}^{\frac{\mathrm{i}ak}{2^{j}}}d\omega \tag{7}\]  
The substitution is of the following form:  
\[ \begin{array}{c}n=2^{j}+s\\ F_{2^{j}+s}=2\pi F\Big{[}\omega=2\pi\Big{(}2^{j}+s\Big{)}\Big{]}\end{array}  \tag{8}\]  
We can show that:  
\[a_{jk}=\sum_{s=0}^{2^{j}-1}F_{2^{j}+s}\mathrm{e}^{\frac{2\pi\mathrm{i}ak}{2^{j} }}k=0\dots 2^{j}-1;j=0\dots n-1. \tag{9}\]  
\[ \begin{array}{c}\overline{a}_{jk}=\sum_{s=0}^{2^{j}-1}F_{N-(2^{j}+s)}\mathrm{ e}^{\frac{2\pi\mathrm{i}ak}{2^{j}}}k=0\dots 2^{j}-1;\ j=0\dots n-1.\\ \overline{a}_{jk}=\overline{a}_{jk}\end{array}  \tag{10}\]  
Thus, the algorithm for computing wavelet coefficients of the octave harmonic wavelet transform [19] of a continuous-time function _f(x)_ can be written in the following way:  
1. The original function _f(x)_ is represented by discrete-time samples: _f(n)_, _n = 0... N-1_, where \(N\) is of degree 2 (if necessary, we use zero-padding). 2. We calculate the discrete Fourier transform using the fast Fourier transform to obtain a set of complex numbers _f(n)_, _n = 0... N-1_--Fourier coefficients (DFT coefficients). 3. Octave blocks \(F\) are processed using the discrete Fourier transform (DFT) to obtain coefficients: \(a_{jk}=a_{2^{j}+k}\). The calculation results for the coefficients are given in Table 1.  
Further, consider two approaches to classifying bio-acoustic signals. We have used real hydroacoustic signals of whales from the database [20].
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 8 Latency and Handover Analysis for LTE-U  
The performance gains in terms of throughput and coverage from the operation of LTE-U SC's in the unlicensed spectrum have been discussed in the previous sections. However, network latency is another key network performance indicator that must be studied for LTE-U. In this direction, the average number of collisions and latency analysis for WLAN and CSAT based LTE-U has been presented in [88]. It has been shown in [88] that an increase in the CSAT duty cycle results in increased WLAN latency. Buffer occupancy and delay CDF's under low, medium, and high load conditions for LAA-LBT based LTE-U have been studied in [68]. The delay and throughput performance of WLAN in the presence of dynamic contention window adjusting LAA-LBT have been analyzed in [3; 71]. It has been shown that dynamic contention window adjustment in LAA-LBT results in fair coexistence with WLAN. In [89], the ratio of latency to total channel occupancy time has been studied. It has been shown that, from a delay perspective, FBE performs better than LBE in LTE-U. Furthermore, it has been shown that appropriately tuning the duty cycle in CSAT can result in better delay performance.  
Given LTE-U based SCs, another key aspect of the network is the frequent handover in the presence of mobile users. A modified version of the LENA LTE module in the ns-3 simulator is used in [87] to evaluate the TCP's performance for LTE-U handover scenarios. The results in [87] indicate that a new TCP mechanism is needed for coexistence between LTE-U and LTE. In [90], the effect of handover trigger in LTE-U due to the unavailability of unlicensed band in the SC has been considered. Furthermore, a new handover scheme has been proposed and analyzed for LAA  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Indoor/Outdoor** & **Coordinated/Distributed** & **Comment** & **References** \\ \hline \multirow{4}{*}{Simulation} & Indoor & Distributed & WaltSystem & [86] \\ \cline{3-4}  & & & MATLAB & [3; 60] \\ \cline{3-4}  & & Coordinated & MATLAB & [7; 80] \\ \cline{3-4}  & & & MATLAB & [4; 60; 80] \\ \cline{3-4}  & & & NS & [87] \\ \cline{3-4}  & & Both & WinProp & [57; 80] \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Hardware \\ implementations \\ \end{tabular} } & Experimental evaluations using USRP-based SDR on the ORBIT testbed & [39] \\ \cline{2-4}  & NTT DOCOMO and Huawei’s field trials for LTE-U & [53] \\ \cline{2-4}  & & UE of HUAWEI used as U-LTE terminal & [78] \\ \hline \hline \end{tabular}
\end{table}
Table 3: A summary of evaluation environments considered for LTE-U performance analysis.  
networks. Hence, more research is required in handover analysis for LTE-U. Next, we discuss the operation in the unlicensed spectrum of the upcoming 5G new radio.
###### Abstract  
Many applications of Internet of Things (IoT) have been implemented based on unreliable wireless or mobile networks like the delay tolerant network (DTN). Therefore, it is an important issue for IoT applications to achieve efficient data transmission in DTN. In order to improve delivery rate and optimize delivery delay with low overhead in DTN for IoT applications, we propose a new routing protocol, called Scheduling-Probabilistic Routing Protocol using History of Encounters and Transitivity (PROPHET). In this protocol, we calculate the delivery predictability according to the encountering frequency among nodes. Two scheduling mechanisms are proposed to extend the traditional PROPHET protocol and improve performance in both storage and transmission in DTN. In order to evaluate the proposed routing protocol, we perform simulations and compare it with other routing protocols in an Opportunistic Network Environment (ONE) simulator. The results demonstrate that the proposed Scheduling-PROPHET can achieve better performances in several key aspects compared with the existing protocols.  
Keywords:PROPHET routing; delay tolerant network; scheduling mechanism; ONE simulator; Internet of Things +
Footnote †: journal: Accepted: 4 January 2019; Published: 10 January 2019
## 4 Results and Discussion
### The Influence of the Perturbed Viscosity on Dynamic Coefficients  
Figure 4 illustrates the dimensionless equivalent dynamic coefficients with the change of \(\epsilon^{\prime}\) for different assembled clearance \(C_{b}\). The difference between results obtained by the model considering turbulence and the pure laminar model is compared, and the influence of perturbed viscosity was also investigated.  
For either the model considering turbulence or the pure laminar model, each dimensionless stiffness or damping coefficient considering perturbed viscosity is larger than that without perturbed viscosity. Especially the stiffness coefficients and damping coefficients \(C_{xx}\) for \(C_{b}\) = 109.5 \(\upmu\)m at \(\epsilon^{\prime}\) larger than 0.7 (see Figure 4a,c,e), the influence of perturbed viscosity is notably obvious. A reason is that the smaller oil film thickness of the 3# and 4# loaded pads enhances the pressure-viscous effect.  
Figure 3: Distributions of the local Reynolds number of each pad of tilting pad bearing at different assembled eccentricity ratios and assembled clearances.  
For \(C_{b}=109.5\) um, with the increase of \(\varepsilon^{\prime}\), the difference between results obtained by the model considering turbulence and the pure laminar model increases first and then declines after about \(\varepsilon^{\prime}=0.4\). Such is corresponding to the local Reynolds number (see Figure 3a) and the turbulence influence on the tilting angle of the 1\(\#\) top pad (see Figure 2a).  
Figure 4: Changes of the dimensionless equivalent dynamic coefficients with assembled eccentricity ratio at different assembled clearance.  
For \(C_{b}=143.5\)\(\upmu\)m, both the dimensionless equivalent stiffness coefficients and the damping coefficients considering turbulence are prominently larger than that obtained by the pure laminar model at \(\varepsilon^{\prime}=0.1\), 0.2, and 0.3, which reason is that the upper reaches of the oil film of 3# and 4# pads are in a turbulent state. When the \(\varepsilon^{\prime}\) is larger than 0.4, the difference between results obtained by the two models becomes small and decreases gently with \(\varepsilon^{\prime}\). A reason is that the flow state in the whole oil film of the 1# top pad is turbulent (see Figure 3b) and the influence of turbulence on the tilting angle of the 1# pad is weak (see Figure 2b). The turbulence has nearly no effect on dimensionless dynamic coefficients at large \(\varepsilon^{\prime}\) though the oil film of 2# and 5# pads contains both laminar and turbulent flow because the oil film thickness of the 3# and 4# loaded pads is small and the viscous effect is dominant.
## 3 Results  
Statistical analysis revealed a statistically significant change in three out of 16 examined parameters for different sequence lengths. The significantly changing parameters were _Amplitude Variability Index (AVI)_ (_p_ < 0.001), _Relative Average Perturbation_\({}_{\text{Bielamowicz}}\) (_RAP\({}_{B}\)_) (_p_ < 0.001) and _Amplitude Perturbation Quotient_-3% (_APQ3_) (_p_ = 0.017). Post hoc tests disclosed that _AVI_ changed between almost all different pairings of sequence lengths. The only not statistically significantly different pairings were between 5 and 10 and between 10 and 20 cycles. _RAP\({}_{B}\)_ changed statistically significantly until 20 consecutive cycles were reached and  
Figure 4: For each parameter, five sets of values for different sequence lengths were calculated. The sets range from 5 consecutive cycles (set number 1) to 100 consecutive cycles (set number 5) and contain 20 values each. Then the depicted statistical analysis workflow was performed for each parameter. _APQ3_ changed statistically significantly until 10 consecutive cycles were reached. Statistical _p_-values of all parameters can be seen in Table S1 in the supplementary information. This table contains the p-values for all Friedman and GLM tests and all performed post hoc tests. Additionally, descriptive values, i.e., group means, standard deviation, maximum and minimum values for period, amplitude and energy perturbation parameters for all sequence lengths are represented in Appendix A in Tables A1-A3. Last in Table 2 a summary of all observed statistically significant changes and also systematic in- or decreases for all parameters is given. In addition to the statistically significant changes, visual subjectively observable trends were found. As depicted in Figure 5, for the Period Perturbation Measures (PPM) the descriptive values i.e., group mean, standard deviation, maximum and minimum of most parameters increased or decreased consistently up to certain sequence lengths. To give a visual impression for parameter behavior in this figure, the descriptive values were normalized to their maximum values for a better comparability. The same standardization was applied to the data depicted in Figures 6 and 7.
## 4 Experimental Results
### Overall Evaluation Indicators  
In order to evaluate the effectiveness of various neural network models in the classification of steel wire rope damage faults, relying solely on the confusion matrix to evaluate the model quality is relatively singular. Therefore, this paper introduces the overall classification accuracy (OA coefficient) and Kappa coefficient evaluation indicators to judge the classification accuracy. The OA coefficient represents the value obtained by comparing the number of correctly classified pixels to the total number of labeled pixels. The Kappa coefficient is based on the confusion matrix and is used to measure the consistency between evaluators. Its value range is in the interval [-1,1], and the closer the value is to 1, the higher the consistency of the evaluation. The OA coefficient and Kappa coefficient calculation formula based on a confusion matrix are as follows:  
\[OA=\frac{\sum\limits_{i=1}^{n}h_{ii}}{\sum\limits_{i=1}^{n}N_{i}} \tag{4}\]  
where \(N\) is the number of categories of the image target, \(N_{i}\) is the number of \(i-th\) class pixels, and \(h_{ii}\) is the number of correctly classified \(i-th\) class pixels.  
\[M=\left(\begin{array}{ccc}m_{11}&\dots&m_{1N_{t}}\\ \vdots&\ddots&\vdots\\ m_{N_{t}1}&\dots&m_{N_{t}N_{t}}\end{array}\right) \tag{5}\]  
\[Kappa=\frac{N\sum\limits_{i=1}^{N}m_{ii}-\sum\limits_{i=1}^{N}m_{i}+m_{+i}}{N ^{2}-\sum\limits_{i=1}^{N}m_{i}+m_{+i}} \tag{6}\]  
where the \(M\) represents the confusion matrix and \(m_{ij}\) represents the number of pixels in class \(i\) that have been misclassified into class \(j\). If the values of \(i\) and \(j\) are the same, they represent the number of correctly classified samples, while if \(i\) and \(j\) are different, they represent the number of misclassified samples. The calculation method of the Kappa  
Figure 13: The confusion matrix based on convolutional neural network.  
coefficient is shown in Equation (6), where \(N\) is the number of pixels tested in the total training sample and \"\(+\)\" refers to the sum that can be performed on rows or columns.  
The classification results of the four neural network models are shown in Table 2, where the horizontal axis represents the four prebuilt neural network models, and the vertical axis represents the evaluation indicators using OA and Kappa coefficients. Figure 14 presents the accuracy of four neural network classification results in a more intuitive manner using a bar chart format. From Figure 13, it can be seen that the residual network has higher OA and Kappa coefficients, which are more suitable for wire rope fault classification and have achieved good classification results.
## 2 Theoretical Strength of Plate-to-RHS Connections  
Figure 2 shows the plate-to-RHS connections with or without chord face stiffener. According to the CIDECT Design Guide 3 [4], the governing design limit state of the unstiffened and stiffened connections is chord face plastification; however, the distribution of yield lines on the chord face differs as follows [8]. For the connection without stiffener (see Figure 2a), yield lines occur at the center of the chord face nearby the longitudinal plate; on the other hand, for the connection with stiffener (see Figure 2b), yield lines on the chord face develop mainly along the perimeter of the chord face stiffener. Thus, the difference in the distribution of yield lines on the chord face needs to be accounted for in estimating the connection strength. Note that, according to AISC 360-10 [2] and CIDECT design guide 3, the plate-to-RHS truss connections should satisfy structural limits on material properties (e.g., \(F_{y}\) and \(F_{u}/F_{y}\)) and geometrical parameters (e.g., \(h/b\), \(h/t\), \(b/t\), \(l_{p}/b\), etc.). The limits of applicability are presented in Table 1.  
Figure 3 shows the longitudinal plate-to-RHS K-connection investigated in this study. At the connection, concrete-filled composite branch members are joined with the longitudinal plate by bolting. Thus, when designing the truss connection, in addition to the wall plastification of the chord member, failure modes that may occur at the bolted joint of the branch members (i.e., bolt shear yield, hole bearing, connection slip, gross area yield, net area rupture, block shear rupture, etc.) need to be accounted for.  
Figure 2: Details of longitudinal plate to rectangular hollow section (RHS) connections: (**a**) Unstiffened; (**b**) Stiffened.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 3 Sensor Node Design  
The proposed node model is depicted in Figure 1. The sensor node can transmit data to an access point using the LoRa/LoRaWAN radio module. To carry out its different operations, the sensor needs an embedded energy source [22]. This source is a battery in this study. In this paper, we consider a connected sensor to measure the acceleration values. Internal memory integrated into the microcontroller is enough for this type of application and we don't consider external memory.  
The three main units of the sensor are the sensor unit, the processing unit and the radio module. Each block is briefly described in the following paragraphs.
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 4 Flight Control System Design
### Angular Rate and Attitude Controller
The desired rotational rates can be achieved using Equation (22), which gives the following [36]:  
\[ \begin{bmatrix}p_{c}\\ q_{c}\end{bmatrix} = \begin{bmatrix}1&\sin\phi\tan\theta\\ 0&\cos\phi\end{bmatrix} ^{-1}\bigg{\{} \begin{bmatrix}v_{\phi}\\ v_{\theta}\end{bmatrix} - \begin{bmatrix}\tan\theta/\cos\phi\\ -\tan\phi\end{bmatrix} r\bigg{\}} \tag{23}\]  
where \(v_{\phi}\) and \(v_{\theta}\) are the desired dynamic responses for roll and pitch attitude, which are calculated using the following equation:  
\[ \begin{bmatrix}v_{f}\\ v_{\theta}\end{bmatrix} = \begin{bmatrix}k_{f}&0\\ 0&k_{\theta}\end{bmatrix}\begin{bmatrix}f_{c}-f\\ \theta_{c}-\theta\end{bmatrix}\begin{bmatrix}p_{c}\\ q_{c}\end{bmatrix}  \tag{24}\]  
The slow outer attitude control loop [37] is governed by an NDI-based control law, which is a model-based strategy for controlling nonlinear dynamic systems. The input-output linearization method, based on NDI control theory [38], is utilized due to the precise measurement of the aircraft's kinematic parameters, using an inertial navigation system (INS). This enables the design of a simple linear controller for the resulting system, where the command attitude angles [\(\phi_{\text{c}}\), \(\theta_{\text{c}}\)] and proportional control gains [\(k_{\phi}\), \(k_{\theta}\)] are utilized. To achieve coordinated flight, it is necessary to regulate the sideslip angle, \(\beta\), instead of the yaw angle \(\psi\), particularly for tailless aircraft. The desired yaw angular rate can be obtained by using the following equation, as proposed in Reference [39]:  
\[r_{c}=-k_{\beta}(\beta_{c}-\beta)+\frac{g\sin\phi\cos\theta}{V} \tag{25}\]  
where \(\beta_{c}\) is the command sideslip angle, which is set to zero throughout the short-landing process, and \(k_{\beta}\) is the control gain. The complete diagram for the attitude and angular rate control loop is illustrated in Figure 14.
## 4 Parameter Sensitivity Analysis
### Circumferential Grout Filling Phase
#### 4.1.1 Yield Shear Stress \(\tau_{0}\)  
The variation range of \(\tau_{0}\) is 100 to 700 Pa [40]. The variation of the grout pressure \(P\) with the yield shear stress \(\tau_{0}\) is shown in Figure 10.  
With the increase in the yield shear stress, the grout pressure decreases significantly. The yield shear stress of the grout reflects the minimum resistance that needs to be overcome when the grout starts to flow. Similar results were found in [PERSON] et al. (2021). The higher the yield shear stress of the grout, the stronger the hindrance to the flow process [31]. A higher yield shear stress causes a greater loss of grout pressure.  
When the yield shear stress increases from 100 Pa to 700 Pa, the grout pressures near the top (\(\theta=5^{\circ}\)), the sides (\(\theta=90^{\circ}\)), and the bottom (\(\theta=175^{\circ}\)) of the tunnel decrease by 16.9 kPa, 22.1 kPa, and 17.7 kPa, respectively. However, the decreasing values of the grout pressure at each position are similar. The grout pressures are decreased by 8.9%, 7.8%, and 4.7%, respectively. So, the variation in yield shear stress \(\tau_{0}\) has the most obvious effect on the area near the top of the tunnel.  
Figure 10: The effect of yield shear stress \(\tau_{0}\) on grout pressure \(P\) ((**a**) tunnel section, (**b**) rectangular coordinate system).
## 4 Proposed HE-DEPSO Algorithm
### Complexity Analysis of the HE-DEPSO Algorithm  
The logical operations of HE-DEPSO are simple, so its computational complexity is low. The analysis uses the big-\(\mathcal{O}\) notation. The initialization of the NP individuals has a complexity of \(\mathcal{O}(NP\cdot D)\). The evaluation of the cost function is \(\mathcal{O}(NP)\), and the sorting of \(NP\) individuals has a complexity of \(\mathcal{O}(NP\cdot logNP)\). Updating the values of each individual depends on mutation and crossover operations, each with a complexity of \(\mathcal{O}(D)\), so the update of the individuals is given by \(\mathcal{O}(NP(1+logNP+2D))\). Thus, the total complexity of HE-DEPSO is \(\mathcal{O}(t_{max}\cdot NP(1+logNP+2D))\). This complexity is similar to the computational cost of recent DE variants (CoDE, SHADE, and DEPSO) proposed for global optimization; therefore, HE-DEPSO has similar execution times to these variations [30; 32; 39].
## 2 Background
### Driving Technologies of Industry 4.0
#### 2.2.7 The Internet of Things (IoT)  
The Internet of Things (IoT) refers to the integration of machines and devices that connect to each other, allowing the exchange of information and representing the interconnection of physical objects through sensors connected to the Internet, which collect and transmit data between devices, reducing the need for human interaction [58; 77; 79]. The IoT transforms common objects into connected devices, encompassing all industrial assets, such as machines, control systems, information systems, and business processes [89].  
Some essential components for the efficient operation of the IoT include barcodes, wireless and infrared sensors, middleware, cloud computing, and radio frequency identification (RFID) [72; 77; 79; 99].
## 2 Materials and Methods
### Background
#### 2.1.4 General Features with Radial Basis Function  
A powerful aspect of AL-IRL is the ability to leverage prior knowledge about the environment to construct a feature space from which the algorithm can learn an appropriate reward function [9; 10; 11; 13]. In continuous spaces, an alternative solution is to discretize the state space into a feature space. Common approaches include uniformly dividing the state space [6; 24] or using techniques like clustering to randomly separate it [12].  
To assign a state to a particular feature, a widely used method is the application of radial basis functions (RBFs) [38]. RBFs are real-valued functions that depend on the distance between an input and a fixed point (usually a center). In this context, RBFs compute the distance between the state and the centers of each feature or cluster. The state is assigned to the feature whose RBF value is the highest, indicating that it is closest to that cluster or division.  
Among the various types of RBFs, the Gaussian function is one of the most commonly used, defined as  
\[\varphi(x)=\exp\left(-\frac{\|x-c\|^{2}}{2\sigma^{2}}\right) \tag{11}\]  
where \(x\) is the input, \(c\) is the center, and \(\sigma\) controls the spread of the function.