esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussions
### TW-SO Strategy for Provisioning of Multi-Security-Level Services  
We assume that the unit key generation rate of each time-slot is set to a fixed value. To verify the performance of TW-SO strategy, different key-updating periods \(T\), i.e., 50, 70, 90, 110, 130 timeslots) were configured for service requests with different security levels. Four wavelengths were assigned to QKCh, and the QKCh of each wavelength was divided into 200 fixed-size timeslots. The number of timeslots required for the request \(t\) was randomly generated from [5,10], and the update time window \(TW_{u}\) was set to 3 timeslots. The key success rate (KSR) and key-updating delay (KUD) were evaluated for provisioning of multi-security-level services in the simulation. Non-TW-SO (N-TW-SO) strategy was used as the baseline to compare the performance of TW-SO strategy.  
The results of KSR and KUD are illustrated in Figure 5a,b, respectively. From those two figures we can clearly see that, for N-TW-SO strategy, the KSR decreased as the security level increased, but there was no difference in KUD among different security levels. The reason is that for a higher security level request, smaller T should be configured, which then leads to more consumption of wavelength-timeslots resources, thus resulting in lower KSR finally. However, for KUD, they keep the same values since there are no priority considerations on security level under N-TW-SO strategy. Obviously, this cannot hit the mark of providing multi-level secure transmission over QKD optical networks. In contrast, for TW-SO strategy, higher security level requests can obtain higher KSR and lower KUD. That means that the TW-SO strategy can not only meet the requirements of multi-level secure data provisioning, but also reduce the blocking probability brought by the frequently key-updating process.  
Figure 5: (**a**) Key success rate (KSR) vs. traffic load; (**b**) Key-updating delay (KUD) vs. traffic load.
## 3 Influence Metrics
### Quality of Service Metrics Analysis
#### 4.3.4 Packet Latency  
Based on Figure 10, it becomes apparent that our algorithms achieve a decreased average packet delay in comparison to the original Spray and Wait protocol, with an average reduction of approximately 2% with respect to S&W and more than 10% with respect to Epidemic or Prophets v1 and v2 protocols. By integrating equivalent buffer sizes, minimizing overhead, and intelligent packet forwarding selection (unlike S&W, which disseminates packets to all encountered nodes), our approach facilitates expedited packet delivery to their intended destination.
###### Abstract  
Comparing the spatial differences in the energy intensity of the Group of Twenty (G20) countries and identifying the factors that influence these differences can help the G20 countries formulate targeted policies to achieve energy conservation goals. This study analyzes the spatial differences in the G20 countries' energy intensity at the aggregate and sectoral levels based on an input-output framework and reveals its driving factors by employing multiplicative structural decomposition analysis, obtaining the sectoral energy intensity, input structure, and final demand structure effects. The results show that: (1) the gap in aggregate energy intensity among the G20 countries tended to converge from 2000 to 2014 with the reducing energy intensity in Russia, India, China, and South Korea having great potential to reduce global energy consumption and improve global energy efficiency; (2) in 2014, the main driving forces for above-average energy intensity was the sectoral energy intensity effect in India, South Korea, and Canada, the input structure effect in Russia and China, and the final demand structure effect in Indonesia; (3) using the average of the G20 countries as a reference, the energy reduction potential of China, Russia, India, South Korea, Indonesia, and Canada is 62.75, 31.94, 21.24, 7.67, 1.47, and 0.81 exajoules (EJ), respectively. The embodied energy consumption decline in these countries was equivalent to 21.78% of the G20's total energy consumption in 2014; and (4) the most important factor of the high embodied energy intensity of key sectors in India and South Korea is the sectoral energy intensity effect, while for Russia and China, it is the input structure effect.  
energy intensity; input-output analysis; multiplicative SDA; G20 countries +
Footnote †: journal: Applied Sciences
## 5 Implementation and Evaluation
### Performance Evaluation  
Optimization--Iteration Curve. In order to analyze the superiority of the initial solution of GPSA and subsequent iterations compared with the other two algorithms, a single time slice was taken to plot the optimization indicator \(\Phi\) curve with respect to the number of iterations \(t\), as shown in the following Figure 6.  
From the graph, it can be seen that the initial solution of GPSA is far superior to the other two algorithms, equivalent to approximately 300 iterations of MIX-SA and more iterations of DLS-SA to obtain the solution. At the same time, the initial solution of GPSA is not inferior in convergence speed to the other two algorithms, indicating that the initial solution obtained through GPSA has better guidance for convergence to a more optimal solution.  
Efficiency Comparison. In order to compare the actual efficiency of the algorithm operation, the results of testing each algorithm under different data are shown in Tables 3-6. It can be seen that GPSA has a huge impact on optimizing runtime. In the borderless weight model, the optimization rate of GPSA exceeds 92%, and the number of iterations also decreases to less than 10%. This verifies that GPSA outperforms other algorithms in terms of performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Range** & **Description** \\ \hline \(\mathcal{N}\) & \(48/66/108\) & The number of satellites. \\ \(\mathcal{W}\) & \(1/0\) & The edges are weighted or unweighted. \\ \(\mathcal{S}\) & \([1,100]\) & Number of time slices to be processed. \\ \(Max_{edge}\) & \([\mathcal{N}-1,\frac{\mathcal{N}(\mathcal{N}-1)}{2}]\) & Restriction of sum of links. \\ \(Max_{agror}\) & \([3,5]\) & Restriction of degree for each satellite. \\ \(\rho\) & Depends on the distribution of the data & Exit threshold for simulated annealing. \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of Parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **GPSA** & **MIX-SA** & **DLS-SA** \\ \hline Iterations Count \(\mathcal{K}_{SA}\) & **51,370** & 73,645 & 199,869 \\ Runtime \(\mathcal{T}_{run}\) & **4.997** & 11.673 & 19.164 \\ Optimization Indicator \(\Phi\) & **1.1384** & 1.1458 & 1.1616 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Efficiency Comparison with weighted Globalstar \(\rho=1.16\) (Sum in 100 Time Slices).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **GPSA** & **MIX-SA** & **DLS-SA** \\ \hline Iterations Count \(\mathcal{K}_{SA}\) & **5998** & 6953 & 17,605 \\ Runtime \(\mathcal{T}_{run}\) & **2.436** & 2.562 & 4.164 \\ Optimization Indicator \(\Phi\) & **1.0282** & 1.0299 & 1.0298 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Efficiency Comparison with weighted Iridium \(\rho=1.03\) (Sum in 100 Time Slices).  
Figure 6: Curve of optimization indicator. (**a**) \(\Phi-t\) curve of unweighted iridium. (**b**) \(\Phi-t\) curve of weighted iridium. (**c**) \(\Phi-t\) curve of unweighted Globalstar. (**d**) \(\Phi-t\) curve of weighted Globalstar.
## 4 Sensor Packet Loss Compensation
### Practical Industrial Process Model  
Although there is a near-infinite amount of possible configurations of industrial process plant, it is well known that a large majority may be described (for small perturbations around an operating point) by low-order transfer function models incorporating time delays [27; 29]. In addition, well-known estimates have suggested that over 90% of industrial control system implementations worldwide are of the Proportional Integral Derivative (PID)-type; the algorithm is the standard tool for industrial automation [27]. A representative model commonly employed for PID control designs is the \"Second Order Plus Dead-Time\" (SOPDT) Model represented by Equation (11):  
\[G(s)=\frac{K\omega_{n}^{\ \ 2}e^{-\gamma\phi}}{s^{2}+2\zeta\omega_{n}s+ \omega_{n}^{\ \ 2}} \tag{11}\]  
This model requires only four parameters, namely the static gain \(K\), natural frequency \(\omega_{n}\), damping ratio \(\zeta\) and time delay \(\theta\). Assuming the sample time does not exactly divide the delay, digitization of (11) results in the CARIMA model represented by Equation (12):  
\[(1+a_{1}z^{-1}+a_{2}z^{-2})y(k)=z^{-d}(b_{0}+b_{1}z^{-1}+b_{2}z^{-2})u(k)+ \frac{\upsilon(k)}{\Delta} \tag{12}\]  
where \(d=\lfloor\theta/t_{s}\rfloor\) and the \(A\) and \(B\) parameters can be obtained via the \(Z\)-transform. Note that the assumption of fractional delay also allows the possibility of the model given by Equation (11) including a (possibly non-minimum phase) zero. In situations where \(\theta\) (and hence \(d\) and \(B\)) may also be time-varying, then the 3-term \(B\) polynomial in (12) may be expanded to cover all possible values of delay within a specified bound. A drawback in this situation is the potentially large number of additional parameters that require identification by RLS [27]. Under the assumption that PID control is applied to the process and the sample rate is selected to be approximately equal to one sixth of the 10%-90% open-loop rise time of the process [15, 29], then an upper bound of 8 parameters for \(B\) may be determined. This follows because PID control is seriously degraded by systems having a time delay larger than approximately twice the dominant time constant of the process. If the delay is larger than this, then the degradation of employing PID control will be commensurate (or worse) with that of experiencing long burst errors. Including the two parameters required for the process poles \(a_{1}\) and \(a_{2}\) gives a total of 10 parameters to cover a large majority of industrial processes under PID control. In order to prevent covariance wind-up when insufficient excitation is present in input signals during recursive estimation (as detailed above), a simple conditional updating scheme with low CPU overhead can be employed as an alternative to a variable forgetting factor. In this scheme, when \(\lambda<1\) is employed the parameter updates occur only when the inequality \(x^{T}(k)\,P(k)\,x(k)>2(1-\lambda)\) is satisfied: satisfaction of this condition ensures that the input vector is sufficiently exciting [27].
## 1 Introduction
* The proposed approach is evaluated in terms of four metrics, which are achievable data rate, packet delivery ratio, packet loss rate, and average end-to-end delay under different channel conditions, such as vehicle transmission power (\(P_{V}\)), required QoS, different \(SINR_{th}\) values, path loss, and different transmission distances. Through these findings, the entire network can be optimized under different environmental conditions. The following sections of this article are organized as follows. The proposed model, the optimization problem formulation, the dataset generation, and the deep learning algorithm of the proposed model are presented in Section 2. Assessing the deep learning model, simulation of the analytical, deep learning results, NS2 simulation results, and their discussions are provided in Section 3. Finally, the conclusion of this article is presented in Section 4.
## 3 Research Design
### Proposed Algorithm
#### 3.1.2 Calculation of the Shortest Distance from a Point to a Line Segment  
To determine the position of a fixation point within the stimulus image (i.e., the visual content presented to the participant), it is first necessary to calculate its distance from the image boundary. In this study, the term \"reference image\" refers to the same stimulus image used in the eye-tracking experiment, which serves as the coordinate basis for fixation localization and transformation.  
Let \(\text{P}(\text{x}_{0},\text{y}_{0})\) be the fixation point in the image, as shown in Figure 1a. The shortest distance d from P to the line segment AB can be determined according to the following three possible cases:  
* The distance between point P and endpoint A, \(\mid\)AP\(\mid\);
* The distance between point P and endpoint B, \(\mid\)BP\(\mid\);
* The distance between projection point H of P on AB and P, \(\mid\)PH\(\mid\).  
If the projection point \(H\) of \(P\) lies on the line segment \(AB\), the shortest distance is \(\mid PH\mid\), which can be calculated using the following formula:  
\[\mid PH\mid\ =\ \frac{\mid AP\times AB\mid}{\mid AB\mid}\]  
(where \(\times\) represents the vector cross-product). If \(H\) does not lie on \(\overline{AB}\), then the shortest distance \(d\) is the smaller value of \(\mid AP\) or \(\mid BP\).  
Figure 1: (a) An illustration of distance calculation from a point to a line segment. (b) An illustration of fixation point coordinate transformation.
# Analytical Hierarchy Process for Construction Safety Management and Resource Allocation
## 4 Results and Discussion
### Case Study 1--Cost Analysis
#### 4.1.4 Case Study 1--AHP for Decision-Making  
The three alternatives suggest allocating different Safety Investment Ratios (\(SIR\)) of the total project scope:  
Alternative A: Optimize the safety budget allocated in the project.  
\[SIR(\text{A})=1\% \tag{16}\]  
Alternative B: Upgrade the safety budget allocated in the project.  
\[SIR(\text{B})=1.07\% \tag{17}\]  
Alternative C: Stick to the safety budget allocated in the project.  
\[SIR(\text{C})=0.5\% \tag{18}\]  
According to [PERSON] et al. (2018), the Total Safety Cost Ratio (\(TSCR\)) for each alternative can be calculated as a function of the \(SIR\) as follows:  
\[y=164.95x^{2}-3.2435x+0.0348 \tag{19}\]  
Consequently, and based on the following:  
\[TSCR=SIR+TCAR \tag{20}\]  
The Total Cost of Accidents Ratio (\(TCAR\)) can be calculated for each alternative:  
Alternative A \(TSCR(A)=y(1.0\%)=1.89\%\to TCAR(\text{A})=1.89\%-1.0\%=0.89\%\) (21)  
Alternative B \(TSCR(B)=y(1.07\%)=1.90\%\to TCAR(\text{B})=1.90\%-1.07\%=0.83\%\) (22)  
Alternative C \(TSCR(C)=y(0.5\%)=2.27\%\to TCAR(\text{C})=2.27\%-0.5\%=1.77\%\) (23)  
Using an AHP decision model (delineated in Figure 6), the best alternative is selected based on two criteria: safety cost and safety risk. The \(SIR\) values represent the safety resource allocation criterion, and the \(TCAR\) values represent the safety risk criterion. The alternative with the minimum cost and minimum risk is selected.  
The relative importance of the three alternatives based on both criteria, cost and risk, is shown through the following comparison matrices:  
\[A_{C}=\begin{bmatrix}1&0.93&2\\ 1.07&1&2.14\\ 0.5&0.47&1\end{bmatrix}\quad A_{R}=\begin{bmatrix}1&1.07&0.5\\ 0.93&1&0.47\\ 1.99&2.13&1\end{bmatrix} \tag{24}\]  
Its column's sum normalizes each matrix:  
\[\text{column sum}\ AC=\begin{pmatrix}2.57&2.40&5.14\end{pmatrix}\quad\text{ column sum}\ AR=\begin{pmatrix}3.92&4.20&1.97\end{pmatrix} \tag{25}\]  
As follows:  
\[N_{C}=\begin{bmatrix}0.39&0.39&0.39\\ 0.42&0.42&0.42\\ 0.19&0.19&0.19\end{bmatrix}\quad N_{R}=\begin{bmatrix}0.26&0.26&0.26\\ 0.24&0.24&0.24\\ 0.51&0.51&0.51\end{bmatrix} \tag{26}\]  
The row averages of each matrix are the scores of each alternative according to each criterion:  
\[W_{C}=\begin{pmatrix}WCA\\ WCB\\ WCC\end{pmatrix}=\begin{pmatrix}0.39\\ 0.42\\ 0.19\end{pmatrix}\quad W_{R}=\begin{pmatrix}WRA\\ WRB\\ WRC\end{pmatrix}=\begin{pmatrix}0.26\\ 0.24\\ 0.51\end{pmatrix} \tag{27}\]
## 3 Results  
### Simulation Result  
This study investigated the electroporation effect on cells under nsPEFs with a pulse width of 5 ns and an electric field strength of 50 kV/cm. The pore density on the cell membrane and nuclear membrane was analyzed, and the simulation results, as shown in Figure 4, indicated that the pore density on the nuclear membrane increased first under nsPEFs, and the pore density on the nuclear membrane was consistently higher than that on the cell membrane. The final average pore density on the nuclear membrane reached 7.12 \(\times\) 10\({}^{15}\) m\({}^{-2}\), which was higher than the average pore density on the cell membrane of 3.5071 \(\times\) 10\({}^{15}\) m\({}^{-2}\). The pore area ratio on the nuclear membrane (84%) was also higher than that on the cell membrane (77%). The electroporation pores were mainly distributed at both ends of the cell membrane and the nuclear membrane near the electrode. The maximum pore density on the nuclear membrane was 1.6245 \(\times\) 10\({}^{16}\) m\({}^{-2}\), which was also higher than the maximum pore density on the cell membrane of 8.4693 \(\times\) 10\({}^{15}\) m\({}^{-2}\).  
Figure 5 shows the Maxwell stress on the cell membrane and nuclear membrane under nsPEFs. As can be seen from the figure, the force on the cell membrane and nuclear membrane corresponds to the spatial distribution of pore density, and the stress on the nuclear membrane is significantly greater than the electric field force on the cell membrane. The stress direction mainly focuses on the outward stretching of the nuclear membrane along the electric field direction, and the maximum value of the Maxwell stress in this direction reaches 2.6828 \(\times\) 10\({}^{4}\) N/m, inducing a strain trend that stretches the nucleus along the electric field direction.
## 2 Methods and Samples
### IR Spectral Measurements  
The sub-diffraction scattering (s-)SNOM (neaspec GmbH) uses a metalised AFM tip which simultaneously maps the surface relief (topography) by its basic AFM operation and under external IR illumination (broadband laser working by difference-frequency generation, Toptica), acts as a light-concentrating antenna such that the sample is probed with a nanofocused light field. The AFM tapping mode operation (ca. 60 nm amplitude) modulates the near-field interaction between the tip and sample [12]. The measured nano-IR absorbance is proportional to the imaginary part of the scattering coefficient \(\sigma_{n}(\omega)=s(\omega)e^{i\phi(\omega)}\), which relates the light scattered field \(E_{s}(\omega)\), and the incident field \(E_{i}(\omega)\) through the equation \(E_{s}=\sigma_{n}E_{i}\), where \(s(\omega)\) and \(\phi(\omega)\) are the amplitude and phase of the back-scattered spectra [10]. Using the asymmetric Michelson interferometer, the full  
Figure 1: Optical image of longitudinal micro-tome slices of _Bombyx mori_ fibers. Left inset shows schematically the principle of spectrally broadband excitation and detection of scattered light implemented in neaspec tool for detection of nanoscale absorbance and scattering. The excitation light field is p-polarised, \(E_{p}\).  
complex function of the scattered optical signal can be recorded, therefore enabling the simultaneous measurement of both nano-IR absorbance and reflectivity spectra [10]. The Michelson interferometer and a lock-in detection of the signal at higher harmonic of the tapping frequency \(\sim\)250 kHz provides background-free nano-IR spectra and images with maximum resolution imposed by the AFM tip size independent of the laser wavelength [10].  
The nano-FTIR spectra were recorded in \(\sim\)100 s/spectrum with a spectral resolution of 10 cm\({}^{-1}\). Removal of the instrumental response function from the nano-FTIR spectra was done by normalization of the measured spectra to a reference Si signal. Resulting nano-FTIR absorbance and reflectivity spectra are directly correlated with the standard far-field IR spectra [13; 14].
## 3 Results and Discussion
### Infrared and Raman Spectral Analysis
#### 3.7.2 Skeletal Vibrations  
Aromatic C=C stretching vibrations appear in the 1430-1625 cm\({}^{-1}\) region, and the ring stretching vibrations for C-C are expected to appear in the 1300-1000 cm\({}^{-1}\) region [27, 28, 29]. The ring vibrations can vary due to different substitutions. In our investigation, the band at 1425 cm\({}^{-1}\) in the FT-IR spectrum and the bands at 1579, 1540, 1516, 1495, and 1406 cm\({}^{-1}\) in the Raman spectrum were assigned to C=C stretching vibrations. The band at 1301 cm\({}^{-1}\) was assigned to a C-C vibration. The C-C-C dihedral in-plane and out-of-plane bending vibrations were observed at 996, 980, 965, 936, 910, and 761 cm\({}^{-1}\) and 405, 396, 345, 322, 285, and 160 cm\({}^{-1}\) in the expected region, respectively. The theoretical values exhibited excellent agreement with the experimental ones. As anticipated, the in-plane deformations were observed at higher frequencies than the out-of-plane vibrations.
## 5 Performance Analysis
Finally, in figure 9 we illustrate the connected components related to the connectivity graphs resulting from the various deployment methods. We represent the number of nodes for each connected component. We plot only the \(10\) largest connected components related to each graph. A largest connected component is the one which contains the greatest number of nodes. We note that only the graphs resulting from the Tabu Search approach and the Grid have one connected component. In the case of the Grid,  
Figure 5: Tabu Search: evolution of iterations  
we certainly guarantee the connectivity but the performances in term of detection probabilities are not satisfactory (see Figure 6). The number of sensors deployed in the Grid is slightly lesser than that Tabu Search. The cause is due to the construction of the grid shape. The other methods contain more than one connected component. Which means that the wireless communication network is not connected. For example Diff-Deploy and DDA have \(134\) and \(41\) connected components respectively. Tabu Search method is the only method which ensures the connectivity and produces a better satisfaction rate compared to other methods, while minimizing the number of required sensors. Figure 6: cdf between desired and obtained detection probability considering connectivity  
Figure 7: Tabu Search: Wireless communication network graph, \(R_{c}=3\)