esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Results and Discussion
### Influence of Pitch on 1 \(\Omega\) Vibration  
Figures 17-22 show the relationship between the pitch and the amplitude and phase of the lateral, longitudinal, and vertical longitudinal 1 \(\Omega\) vibration components at different speeds.  
Figure 16: Vertical polar diagram of counterweight.  
Figure 17: Lateral amplitude diagram of pitch.  
Figure 15: Vertical amplitude diagram of counterweight.  
Figure 19: Longitudinal amplitude diagram of pitch.  
Figure 20: Longitudinal polar diagram of pitch.  
Figure 18: Lateral polar diagram of pitch.  
Figure 21: Vertical amplitude diagram of pitch.  
It can be seen from Figures 17-22 that the vibration amplitude increases with the pitch. The unbalanced pitch has a great influence on the vertical vibration by the rotor speed. The unbalanced pitch has little influence on the lateral and longitudinal vibration by the rotor speed. The change in pitch has an obvious effect on the phase change. With the change in pitch, the phase first increases and then decreases.
## 3 Results and Discussion
### Physicochemical Parameters Variation during Desalting Process  
#### 3.2.1 pH Value Variation  
Based on Figure 2A, it is possible to observe that the pH of the cod samples increased during the desalting process at both temperatures. Initially, salted cod showed a pH of 6.06 \(\pm\) 0.06 that increased (\(p\) < 0.05) over desalting time to values of 6.53 \(\pm\) 0.04 for the process at 20 \({}^{\circ}\)C and it was maintained (\(p\) > 0.05) thereafter at similar values. At 4 \({}^{\circ}\)C, the pH increased at a slower rate and to a lesser extent to a maximum value of about 6.25 after 16 h. These pH values are like values reported in the literature [26; 27]. These results may be explained by microbial growth, since the main fish-contaminant microorganisms are able to produce alkaline compounds (e.g., TMA), resulting in an increase in the pH value [28]. It is also important to note that the observed pH increment was more pronounced when desalting was performed at 20 \({}^{\circ}\)C, where microbial growth is expected to be more considerable.  
Figure 1: Water ((**A**). g H\({}_{2}\)O/g IS) and salt content ((**B**). g NaCl/g IS) in cod fish during desalting. Different letters (a–e) denote significant differences (\(p\) < 0.05) between desalting times.
## 4 Discussion
### Multifractal Analysis  
While DFA only estimates the monofractal structure of the time series, which can be determined from only one value of the Hurst exponent for the entire series, MFDFA allows the analysis of the multifractal structure of the time series by determining multiple Hurst exponents. Based on the results obtained from the analysis using the MFDFA method, the following conclusions can be drawn:  
* The fluctuation function Fq(s) shows the degree of variability in the data at different scales s (segment size) and instantaneous values of q. The graphs of the fluctuation function at different values of q for the two studied interval series (rest and stress) show linear dependencies, which is evidence of fractal behaviour. The graphs in Figure 10A are of different slope, indicating that the interval series at rest have a multifractal behaviour, while at stress the graphs (Figure 11A) are parallel, i.e., the slope of the fluctuation functions is constant and the signal is monofractal;
* The generalized Hurst exponent (Hq) is a measure of correlations in signals. Under stress, Hq values are lower, suggesting a more pronounced anticorrelation. At rest, Hq values are higher, which is evidence of greater stability in cardiac activity. From the graphs of Figures 10B and 11B, it can be seen that, at rest, the value of the exponent H changes for different values of the parameter q, which is indicative of a multifractal behaviour. Under stress, the dependence of H on q is nearly constant, suggesting that the signal is monofractal. Similar results were reported in publication [47; 48];
* The multifractal scaling exponent \(\tau(\text{q})\) exhibits more nonlinearity (Figure 10C) at rest than at stress (Figure 11C) where the dependence of this exponent on the parameter q is linear;
* The multifractal spectrum f(\(\alpha\)) is broader at rest, which may be an indication of greater heart rate variability. Conversely, in a stress state, the spectrum is narrower, reflecting lower multifractal complexity and heart rate variability. Similar results were reported in publication [51].  
Based on the obtained results, it can be summarized that the DFA and MFDFA methods can successfully evaluate and analyze the impact of stress on cardiac dynamics, revealing key changes in the short-term and long-term structure of HRV.
# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 2 OPF Problem Formulation
### Solution Method  
In order to solve OPF problems, the AGTLBO algorithm is used. The following sections detail the execution of the proposed AGTLBO for OPF problems:  
* Step 1: Initialize AGTLBO using the parameters from the under-study test system. The maximum number of iterations and population (_Npop_) for the 30-bus system have been chosen 500 and 25, respectively, 1000 and 40 for the 57-bus system, and 5000 and 60 for the 118-bus system.
* Step 2: Using Equations (21)-(24), generate the starting population or students depending on the number of populations \(X_{i}=X_{j}^{\min}+rand*(X_{j}^{\max}-(X_{j}^{\min})=\left[P_{GZ}:P_{GNG},\;V_{ G1}:V_{GNG},T_{1}:T_{NT},\;Q_{Ci}:Q_{CNC}\right]\) for \(i=1\): _Npop_, the lowest and maximum values of control variables \(X_{j}^{\min}\) and \(X_{j}^{\max}\) (\(j=1:D\)), system constraints, and OPF constraint constraints using Equations (4)-(10).
* Step 3: Using the system and OPF problem constraints, determine the under-study objective function of the initially generated solutions based on (11) and (12) and save them as initial answers of the initial population.
* Step 4 (Adaptive Gaussian Teaching phase): Enhance the answers from the previous step based on (21), apply the system and OPF problem constraints, and calculate their under-study objective function based on (11) and (12). Then, during the subsequent iteration, choose the best answer (new student) from the current and prior solutions provided.
* Step 5: Compare the best student solution (new student) to the previous step's greatest global solution (teacher). If the new student solution is superior to the best global (teacher) solution, the former takes precedence; otherwise, the old (teacher) solution is retained in the algorithm memory.
* Step 6 (Adaptive Gaussian Learning phase): Based on (24) and the system and optimal OPF problem constraints, enhance the previous step solutions and construct their under-study objective function based on (11) and (12). Then, during the subsequent iteration, choose the student's answer from among the current and previously generated solutions.
* Step 7: Compare the student solution to the previous step's teacher solution. If the student solution is superior to the teacher solution, the student solution takes precedence over the teacher solution; otherwise, the prior student solution is stored in the algorithm memory.
* Step 8: Has the algorithm termination requirement been met? Proceed to Step 4 and continue the optimization process if yes; if not, proceed to Step 3.
## 2 Related Work
### Experiment Setup for the Investigation of the Impact of Attenuation on the Packet Error Rate
#### 3.1.2 Packet Error Rate Measurement of SPIRIT1 Transceiver
Meanwhile, in the model, the change started from \(-107.82\) dBm. Figure 14 shows the estimated modeling function for the PER in AS3933 WuRx according to the RSSI. The calculation result showed a \(-59.55\) dBm sensitivity and an around \(3.01\) dB transition interval. Compared with the demonstrated measurement shown in Section 3.1.1, the rise in the PER started at \(-58\) dBm; meanwhile, in the model, the change started from \(-58.5\) dBm. The modeling purpose was to mimic real WuRx behavior. The fact is that the curves were not as smooth as in Figure 14 for the AS3933 and as in Figure 13 for the SPIRIT1 transceiver. The proposed solution was to add a noise figure to the curves of both the SPIRIT1 transceiver and AS3933, a distribution that minimized the OLS. A quantile\(-\)quantile plot is a visual tool that helps with evaluating if a set of data plausibly come from some theoretical distribution such as a normal or exponential distribution. In this study, we ran a Q-Q plot for both SPIRIT1 and WuRx radios that took our residuals and compared them with the estimated curves. Figure 14: Estimation of the PER curve according to the RSSI in AS3933 WuRX. Figure 13: Estimation of the PER curve according to the RSSI in the SPIRIT1 transceiver. We noticed that in Figures 15 and 16, the samples are consistently on the edges of the graph, and the data set is not intensive when we considered the transition interval of the radios. For that purpose, the chosen distribution for this study was the Weibull distribution due to its ability to provide reasonably accurate analysis estimation, even for a small set of data. The Weibull distribution is a two-parameter family of continuous probability distributions over a set of positive real numbers. Depending on its two parameters, it resembles a normal distribution or an asymmetric distribution, such as an exponential distribution. The Weibull function is written as follows:  
\[f(x;\lambda,k)= \begin{cases}\frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k -1}e^{-(x/\lambda)^{k}}&x\geq 0\\ 0&x<0\end{cases}  \tag{6}\]  
The square distances of the nonlinear function of the PER were extracted, and the Weibull function was applied over the noise figure using \(k\lambda\) and a multiplication factor \(m\) as input parameters for both the WuRx based on the AS3933 and SPIRIT1 main transceiver. Figure 16: Q-Q plot of the WuRx. Figure 15: Q-Q plot of the SPIRIT1 transceiver. The model was used as a filter to minimize the residuals through multiple stages after applying the \(erf(x)\) function, as shown in the block diagram in Figure 17.  
Where \(s(t)\) is the original signal; \(s1(t)\) is the signal after applying the error function and obtaining the sensitivity and the transition interval; \(o1(t)\) is the residual output from the original signal; \(o(t)\) is the output signal with the noise distribution. This function was implemented, and the results are shown after the simulation in the OMNeT++ discrete event simulator in Section 4.1.
# A Hybrid Watermarking Approach for DICOM Images Security
## 5 Conclusions  
Attacks on medical systems are increasing. It is essential to protect medical data exchanged or accessed to provide healthcare. Digital watermarking is a promising method to secure medical images and patient data. A watermarking method aiming at filling some gaps in existing watermarking solutions has been proposed in this paper. Unlike most existing solutions, it considers most domain-specific constraints of the medical domain (DICOM format commonly used for medical image transfer, timing constraints of medical diagnosis, no modification of the ROI, confidentiality of patient information). The proposed solution provides strong security of DICOM medical images by combining a zero-watermarking in ROI and a non-zero-watermarking in RONI with the insertion of several copies of the watermark for more robustness, by using and ensuring the confidentiality of patient data available in the header of the DICOM image. The proposed method has been applied to various DICOM images. Several performance parameters have been calculated (SSIM, PSNR, BER, NC) or measured (Time spent for watermarking embedding/extraction) to evaluate the proposed solution. The results analysis shows a good performance of the proposed approach. The average SSIM, PSNR, NC, and BER are respectively 0.98, 71 dB, 0.98, and 0.0054. Watermarking embedding/extraction time does not exceed 12s, which meets real-time requirements for medical diagnosis. Comparison of the proposed method to related methods separating ROI and RONI parts reveals its good results. Future works include investigating adaptive approaches to deal with the variable size of the RONI part of the medical image.  
Conceptualization, M.T., L.N., A.C.P. and F.B.; Methodology, M.T., L.N., A.C.P. and F.B.; Software, M.T.; Supervision, L.N., A.C.P. and F.B.; Validation, L.N. and A.C.P.; Formal analysis, M.T., L.N. and A.C.P.; Visualization, L.N. and A.C.P.; Writing--original draft preparation, M.T., L.N. and A.C.P.; Writing--review and editing, L.N. and A.C.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by University of Brest in France and ENIT (National Engineering School of Tunis) in Tunisia. The APC was funded by University of Brest.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues.  
The authors declare no conflict of interest.
# Pruning Optimization over Threshold-Based Historical Continuous Query
## 5 Experiment
### Performance of Different Time Ranges  
Figure 9 shows the query performance of the three different methods at different time ranges. It can be seen from Figure 9a that as the query time range increases, the number of candidates involved also increases, and all three methods need to access more nodes to obtain candidates. Figure 9b shows that the change in selectivity is proportional to the change in the time range, this is because the increase in the number of candidate segments will naturally increase the selectivity when the data size is constant. As shown in Figure 9c,d, since all three methods require more time to generate and process the candidate segments, their pruning latency and query latency increase with the increase of time range. Among the three methods, BFT has the best query performance. In all cases, BFT takes less than eight seconds to complete the query process, while the time spent by CDQLTD gradually increases from 34 s to 134 s. The reason for this difference in performance has been explained in Section 5.2, it will not be described in detail here.
# Crasworthiness Analysis and Multi-Objective Optimization for Concave I-Shaped Honeycomb Structure
## 2 Related Theories on the Concave I-Shaped Honeycomb Structure
### Comparison of Analytical and Simulation Solutions  
Finite element software is used to evaluate the concave I-shaped honeycomb structure, and the findings are compared to the analytical solution in Section 2.1. \(S\), \(h\), and \(b\) are used to examine the laws of the impact of geometrical parameters on the equivalent Poisson's ratio. Figure 7 displays the outcome.  
The analytical solution expands as \(S\) rises, and as \(S\) rises higher, the tendency of its growth slows. The error between them becomes reduced as \(S\) increases, but as \(S\) continues to increase, the error between the analytical solution and the simulated solution becomes worse.  
The analytical solution becomes smaller as \(h\) rises. The same is true for the simulated solution, where there is little difference between it and the analytical solution, and the outcomes are consistent with those of the analytical equation.  
For parameter \(b\), the simulated and analytical solutions fall faster at first, but as \(b\) increases, they level off.  
Figure 7: Comparison of simulated and analytical solutions when loaded in the y-direction: (**a**) effect of cellular length \(S\) on Poisson’s ratio; (**b**) effect of vertical short cellular height \(h\) on Poisson’s ratio; (**c**) effect of horizontal short cellular length \(b\) on Poisson’s ratio.  
There are two basic categories of causes for these inaccuracies in the analytical and simulation solutions. One is that the theoretical derivation uses the energy method, which leads to certain mistakes in the findings since tensile and shear force effects are not taken into account during theoretical computation. The second is that because the simulation model's segments are all horizontal or vertical, the force acting on some of them is smaller during theoretical analysis, resulting in smaller segmental integration results. However, because the segments are elastic during the simulation process, they will bend and deform, causing the simulation solution in some cases to be larger than the analytical solution.  
From the discussion above, it is clear that when \(b\) and \(h\) take particular values, an increase in \(S\) will also cause the structure to change from a negative Poisson's ratio to a positive Poisson's ratio. When \(S\) is taken as a constant value, an increase in \(b\) and \(h\) can increase the visibility of the structure's negative Poisson's ratio characteristic. By altering the dimensional parameters of the cellular element structure, it is possible to change the Poisson's ratio of the concave I-shaped structure, and the findings provide some theoretical support for additional research.
## 2 Issues in Topology Design
### Hierarchical Structure  
The first issue to consider in designing the proposed logical topology is the structure of the topology, _i.e._, whether the topology should have a hierarchical-structured or not. A hierarchical structure has many advantages over a non-hierarchical structure. For example, a hierarchical network structure can reduce the length of time for transmitting messages between two distant nodes in a sensor network. The structure requires that some capable sensors act as local leaders/clusterheads to interface with the outside world. Additionally, the grouping/clustering of sensors can also aggregate and process data locally to reduce communication load in the network. However, this solution may not be energy efficient. It is well known that, given two nodes, the radio transmission power required at the transmitter end is exponentially proportional to the distance from the receiver [26]. For hierarchical structures, leaders/clusterheads need to use exponentially more power to relay messages because they decrease the number of intermediate nodes, and consequently have to deal with longer distances. Although a hierarchical structure is not theoretically energy efficient, it is an advantageous choice for a large-scaled dense sensor networks for several reasons. The exponential effect is not significant as the distances within a dense environment are limited. Furthermore, by careful rotation of leaders/clusterheads, a balanced energy dissipation state can be achieved, where some sensors can afford to consume more energy. In contrast, multi-hop communication without a hierarchical structure consumes energy among all participant sensors in an unplanned way, resulting in faster energy exhaustion of sensors with lower energy capacity. With local leaders/clusterheads taking more responsibility, energy can be saved for energy-constrained sensors, which extends the lifetime of the overall network. For this reason, we choose hierarchical structure for the proposed topology.
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 2 Materials and Methods
### Calculation of Dynamic Loss of Interference  
In the process of the high-speed operation of the motor, both the magnet sheath and the magnet are affected by the centrifugal force. The inner diameter of the sheath and the diameter of the magnet become larger. However, for the sheath there is a thin-walled part, and the stiffness is less than that of the magnet; the increase in the inner diameter of the sheath will be higher than the diameter of the magnet. Therefore, when the motor rotates, the actual interference is less than the static interference. Based on this phenomenon, the amount of interference lost \(\delta_{a}\) should be calculated to avoid failure.  
Under the condition of high rotation, the formula for calculating the radial displacement \(\delta_{m}\) of the diameter of the magnet is as follows [9]:  
\[\delta_{m}=\frac{2\rho_{m}\omega^{2}(1-v_{m})}{4 gE_{m}}\cdot\left(\frac{d_{m}}{2} \right)^{3}\cdot 10^{-5} \tag{6}\]  
where \(\rho_{m}\) is the density of the magnet, kg\(\cdot\)m\({}^{-3}\), and \(\omega\) is the angular speed of the motor.  
Under the condition of high rotation, the formula for calculating the radial displacement \(\delta_{\mathrm{s}}\) of the inner diameter of the sheath is as follows:  
\[\delta_{\mathrm{s}}=\frac{2\rho_{\mathrm{s}}\omega^{2}(1-v_{s})(3+v_{s})}{8 gE _{s}}\cdot\left(\frac{d_{m}}{2}\right)^{3}\cdot\left(\frac{2}{1-v_{s}}+\frac{2 }{3+v_{s}}\right)\cdot 10^{-5} \tag{7}\]  
where \(\rho_{\mathrm{s}}\) is the density of the magnet, kg\(\cdot\)m\({}^{-3}\).  
The loss of interference in the state of high-speed rotation \(\delta_{\omega}\) is as follows:  
\[\delta_{\omega}=\delta_{\mathrm{s}}-\delta_{m} \tag{8}\]  
According to the above formulas, when the motor is running at high speed, it is necessary to ensure that the residual interference is still greater than the minimum interference \(\delta_{\mathrm{min}}\). Comprehensively speaking, the designed amount of the interference \(\Delta\) can be taken:  
\[\Delta=k(\delta_{\mathrm{min}}+\delta_{\omega}) \tag{9}\]  
For high-speed motors, \(k\) is generally 1.5-2. Its value is taken to be 2 in the calculation in order to leave a larger safety margin.
## 3 Test Results Analysis
### Stress-Strain Analysis under Different Hole Depths  
From Figure 3 (in Figure 3a, the non-hole test block and the test block with drilling depths of 3, 6, 9, 12, and 15 cm are replaced by the numbers of 0, 3, 6, 9, 12, and 15, respectively.) and Table 3, which compared the test blocks without holes, as the drilling depth gradually increases, the peak strength of the test blocks decreases and the strain at the peak stress decreases.  
It can be seen that when the drilling depth increases from 9 cm to 12 cm, the strength increases. According to the source mechanism of the rock burst, the occurrence of the rock burst is caused by the close distance between the free surface and the source. The deeper the borehole is, the closer the free surface is to the source. When the hole depth is greater than the critical value between the two, the stress wave will have an impact on the surrounding rock of the roadway [21]. This is also the reason why the peak strength of the test block does not decrease when the drilling depth is from 9 cm to 12 cm. When the stress peak is 15 cm, the strain value decreases greatly. This is because the hole runs through the whole test block, and the stress at both ends is evenly distributed. Compared with the non-penetrating test block, the bearing capacity of the latter part decreases, resulting in a smaller strain value. Combined with the strain energy curve accumulated before the peak in Figure 3b, it can be seen that with the increase in drilling depth, the peak strength is similar to the strain image change at the peak stress, and the elastic strain energy accumulated before the peak of the test block is reduced.  
Figure 3: (**a**) is the peak strength of the test block under different hole depths, and (**b**) is the stress–strain and pre-peak cumulative strain energy curve.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Drilling Depth/cm** & \
\begin{tabular}{c} **Percent Reduction in** \\ **Peak Stress/\%** \\ \end{tabular} &
\begin{tabular}{c} **Percentage Reduction** \\ **in Strain/\%** \\ \end{tabular} \\ \hline
3 & 1.68 & 30.92 \\
6 & 6.90 & 42.65 \\
9 & 12.33 & 34.03 \\
12 & 11.93 & 32.03 \\
15 & 17.38 & 46.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peak intensity and strain value decrease by percentage at peak intensity.