esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion  
Within the discussion part of this study, the data obtained as a result of the application of the HS algorithm were interpreted by expressing them as a function of the pile diameter, pile length, and unit pile cost. In total, 42,000 analyses are conducted to evaluate the relationship between design dimensions and cost. In addition to this, 72 finite element analyses are performed to control the acceptableness of the application of the HS algorithm to design the cantilever soldier pile retaining walls. The first discussions are constituted according to the change of excavation depth. Some reference cases are selected that can be representative of the behavior of all the fictionalized cases to interpret the results. Figure 2 reflects the change of the cantilever soldier pile diameter (a), length (b), and unit \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Definition** & **Value** & **Unit** \\ \hline \(h\) & Depth of excavation & 3 to 12 & \(m\) \\ \(f_{g}\) & Yield strength of steel & 420 & \(MPa\) \\ \(f_{c}\) & Compressive strength of concrete & 25 & \(MPa\) \\ \(c_{c}\) & Concrete cover & 30 & \(mm\) \\ \(E_{std}\) & Elasticity modulus of steel & 200 & \(GPa\) \\ \(E_{remote}\) & Elasticity modulus of concrete & 23,5 & \(GPa\) \\ \(\gamma_{std}\) & Unit weight of steel & 7.85 & \(t/nr^{3}\) \\ \(\gamma_{camerte}\) & Unit weight of concrete & 25 & \(k\)N/\(nr^{3}\) \\ C\({}_{c}\) & Cost of concrete per m\({}^{3}\) & 50, 75, 100, 125, 150 & \$ \\ \(C_{s}\) & Cost of steel per ton & 700 & \$ \\ \(q\) & Surcharge load located adjacent the top of the wall & 0–5–10–15–20 & \(kPa\) \\ \(\beta\) & Backfill slope angle & 0 & \(\circ\) \\ \(\phi\) & Shear strength angle & 27 to 38 & \(\circ\) \\ \(\gamma\) & Unit weight of soil & 14 to 20 & \(k\)N/\(nr^{3}\) \\ \(D\) & Diameter of pile & 0.3–2 & \(m\) \\ \(\phi_{p}\) & Diameter of reinforcing bars of soldier pile & 14–40 & - \\ \(n\) & Number of reinforcing bars of soldier pile & 6–20 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The design constants and design variables of cantilever soldier piles. cost (c) based on the increase of the excavation depth. The diameter of soldier pile is represented by \(D\), the length of the pile is \(L\), and the unit cost of the pile is defined by \(C_{t}\) symbols. It is assumed to embed the pile into a sandy soil formation that has a shear strength angle of 30\({}^{\circ}\), while the unit weight is 16 kN/m\({}^{3}\), the coefficient of reaction is 200 MN/m\({}^{3}\), and the surcharge load application is absent.
## 5 Simulation, Experiment, and Comparison
### The Experiment  
#### 5.2.1 The Data Collection by ILC  
Similar to the simulation, the gripper is programmed to repeat the ILC process in Section 3.3 for different closing speeds, desired forces, and object hardness. However, from a practical perspective, it is hard to find many objects with different hardness, so six objects are used as shown in Figure 9. The closing speed \(S_{g}\) varies from 5 to 10 mm/s, with each step being 1. The desired force \(F_{d}\) varies from 1 to 8 N, with each step being 0.5. The initial value of \(B\) is set at 10 N.m/s. The total number of experiments is 540, so the dataset has 540 records. The dataset is analyzed to evaluate the performance based on the parameters of the maximum overshoot \(F_{p}\) and the final force error \(F_{fe}\). Table 3 presents the analysis data from 540 records of the dataset in the simulation. Figure 10 shows the tracking force of several loops in one case of the ILC process with the closing speed of 5 mm/s and the desired force of 4 N (the same case as shown in the simulation).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Max** & **MAE** \\ \hline \(F_{p}\) (N) & 0.2866 & 0.0247 \\ \(F_{fe}\) (N) & 0.0013 & 0.00021 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The quality parameters on the testing data of simulation.  
Figure 8: The force monitoring of some cases in simulation testing.  
Figure 10: The tracking force and \(B\) value through several loops in the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Max** & **MAE** \\ \hline \(F_{p}\) (N) & 0.4827 & 0.1173 \\ \(F_{fe}\) (N) & 0.3980 & 0.1224 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ILC quality parameters in the experiment.  
Figure 9: The objects used in the experiment of ILC.
## 3 Results  
We used time, frequency, and recurrence quantification analysis to monitor the condition before, during, and after the HIMS application in the chest area. Results from the time-domain analysis provided us with 7 standard parameters used for the HRV analysis. Frequency analysis delivered additional 4 parameters relating to standard bands used in the HRV frequency analysis. The RQA delivered a total of 11 parameters relating to the structures occurring in the recurrence plot, and which describe the dynamics of the system.  
To get statistics, we compared all the obtained parameters to monitor statistically significant differences in the state before, during, and after the HIMS application, see Table 1. Distribution of individual parameters among 3 datasets depicted in Figures 4-6.  
Figure 4: Distribution of the time-domain HRV parameters before (A) during (B) and after (C) the HIMS application with highlighted outliers (red crosses). Symbol * means \(p<0.05\) vs. group A, \(\dagger\) means \(p<0.05\) vs. group B, \(\ddagger\) means \(p<0.05\) vs. group C.  
Figure 5: Distribution of the frequency-domain HRV parameters before (A) during (B) and after (C) the HIMS application with highlighted outliers (red crosses). Symbol * means \(p<0.05\) vs. group A, \(\dagger\) means \(p<0.05\) vs. group B, \(\ddagger\) means \(p<0.05\) vs. group C.  
Figure 6: Distribution of the recurrence quantification analysis parameters before (A) during (B) and after (C) the HIMS application with highlighted outliers (red crosses). Symbol * means \(p<0.05\) vs. group A, \(\dagger\) means \(p<0.05\) vs. group B, \(\ddagger\) means \(p<0.05\) vs. group C.  
The results show that in the case of time-domain analysis there was a significant change between the state before and after the application of HIMS in 2 parameters (SDHR, NN50), and then also between the state during and after the HIMS application in 5 parameters (SDNN, SDHR, NN50, pNN50, RMSSD). The distribution of the individual RQA parameters is shown in Figure 4.  
Frequency analysis showed only one statistically significant transition, and that was in the low-frequency band between the state during and after the HIMS application. The distribution of the individual RQA parameters is depicted in the Figure 5.  
The RQA showed statistically significant transitions between 4 parameters. The transition between the state before and during the HIMS application proved to be significant for 4 parameters--RR, TND, L, and ENT. A statistically significant difference between the before and after HIMS application was observed in the RR parameter. A significant difference between the state during and after the HIMS application was observed in the L parameter. The distribution of individual RQA parameters is shown in Figure 6.
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 3 Materials and Methods
### Signal Quality Indexes
It measures how frequently the signal alternates from positive to negative values and vice versa, indicating the number of times the signal crosses zero relative to the total number of samples [38; 39]. It measures the frequency of sign changes in the signal and can reflect its regularity and stability. A high zero-crossing rate typically indicates a noisy or artifact-laden signal, while a lower rate suggests a smoother, more stable signal. This analysis is particularly valuable in distinguishing between meaningful physiological variations and undesirable noise or artifacts, thus enhancing the quality of signal interpretation. \[Z_{SQI}=\frac{1}{N}\sum_{n=1}^{N}\mathds{I}\{y<0\} \tag{4}\]  
where \(y\) is the filtered PPG signal of length \(N\), and the indicator function \(\mathds{I}\{A\}\) is '1' if its argument \(A\) is true, and '0' otherwise. \(E_{\text{SQI}}\): Entropy is used to quantify the deviation of the signal probability density function (PDF) from a normal distribution, providing a measure of uncertainty and disorder within the PPG signal [38; 39]. High entropy values indicate increased randomness and complexity, reflecting less predictability and uniformity. This highlights diverse physiological states and how different cardiovascular systems respond to variations in CF, emphasizing significant variability in signal responses among individuals. \[E_{SQI}=-\sum_{n=1}^{N}x[n]^{2}\log_{e}(x[n]^{2}) \tag{5}\]  
where \(x\) signal is the raw PPG signal, and \(N\) is the number of data points. \(P_{\text{SQI}}\): The PI is a suitable benchmark selected for its ability to evaluate the quality of the PPG signal in terms of peripheral blood flow. The usefulness of \(P_{\text{SQI}}\) lies in its ability to determine whether the PPG signal is clear and stable enough to provide an accurate PI measurement [39]. Variations in the PI value reflect changes in peripheral circulation, potentially indicating physiological responses or alterations in blood flow dynamics. A higher PI value suggests increased blood flow or stronger signal detection. Conversely, lower PI values may indicate reduced perfusion, signalling issues like decreased blood volume or vasoconstriction. The normal value of peripheral PI has been suggested to range between 0.2 and 20% [60]. \[P_{SQI}=[(y_{max}-y_{min})/||\overline{x}||]\times 100 \tag{6}\]  
where \(y_{max}\) and \(y_{min}\) are the maximum and minimum values of the AC component in a specific range of the signal, and \(||\overline{x}||\) is the absolute value of the statistical mean of the raw signal in that interval.
## 4 Discussion
### Phases
#### 4.2.4 \(\sigma\)-Phase  
As reported in Section 3.4, on some occasions, small amounts of \(\sigma\)-phase were found in weld passes that did not reach the critical temperature range (750 \({}^{\circ}\)C-1073 \({}^{\circ}\)C). In other cases, the time registered went from 1 s in GR to 16 s in GH, times that are on the lower end of times reported in the literature [25] or predicted by thermodynamics. JMatPro predicted for this experiment a TTT curve with a nose (990 \({}^{\circ}\)C, 34 s to have 1% \(\sigma\)-phase and 5 s to have 0.1% \(\sigma\)-phase)]. The reasons that might explain these current observations could be related to the following:  
1. Locations for thermal recording and for metallographic inspection are different. The coupons for metallographic inspection were extracted from close to the end point of the weld, i.e. close to the downslope, whilst the thermal recording was done at the start of the weld.
2. It has been assumed that the temperatures registered locally and the cooling rates calculated are representative of the entire weld pass. However, in reality, there is a gradient in temperatures within the same weld pass, being the region close to the fusion boundary experiencing higher temperatures than the locations at a distance in the weld pass.
3. The \(\sigma\)-phase could form randomly along the longitudinal weld direction due to local compositional segregation, and the specific section evaluated might contain or might not contain \(\sigma\)-phase.
4. It might be that the kinetics to form \(\sigma\)-phase require longer times at lower temperatures than considered.
# Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization
## 4 Results and Discussion
### Analysis of the Simulated Signal  
#### 4.2.1 Simulation and Comparison  
The result of the signal decomposition using the EMD method is shown in Figure 4a. Signal \(x(t)\) was decomposed into nine IMFs. Therefore, the maximum number of modes for TVF-EMD was set to three. The original CO\({}_{2}\) signal was then processed by the proposed TVF-EMD model, based on the BO algorithm. The combination of the bandwidth threshold \(\xi\), B-spline order \(n\), and the maximum \(CCKur\) of the BO algorithm were \(\xi=0.31\), \(n=7\), and \(CCKur_{max}=0.49\), respectively. Figure 4b shows the signal decomposition results obtained using the optimized TVF-EMD model.  
The Hurst exponent was computed for each IMF. As shown in Figure 5, the different values of the Hurst exponent for the seven consecutive IMFs were NaN, 0.37, 0.32, 0.58, 0.72, 0.79, and 1.0, respectively. The first IMF, second IMF, and third IMF had a Hurst exponent value of less than the threshold of 0.5, indicating the weak long-range autocorrelation of residual signals. The fourth through seventh IMFs were reserved for further processing and summed to obtain the noise-reduced signal. To better illustrate the effects of signal noise cancellation by the proposed method, a quantitative comparison of the three denoising methods was conducted, as shown in Figure 6. Conventional noise reduction methods, such as exponential smoothing and the moving average method, were not highly effective in eliminating the impact of shock signals. This comparison verified the superiority of the TVF-EMD method in signal noise cancellation.  
Figure 4: Decomposition results of the CO\({}_{2}\) signal obtained by (**a**) EMD and (**b**) TVF-EMD.  
Figure 5: Hurst exponent values in different modes.
## 4 Discussion
### Baseline  
We first investigated the effects of the COI on steady-state LD signals, usually affected by minimal artifacts. Steady-state signals are represented by baseline signals acquired when participants are lying supine and resting. Although an LD signal recording of at least 20 min is traditionally required to ensure high-quality resolution of the reconstructed WA spectra [7], we decided to focus our study on analysing signals of short duration that are of particular research interest when exploring microcirculatory responses [4; 7]. Moreover, we analysed the baseline and the PORH signals of the same duration, which allowed us to  
Figure 4: Relative power (the ratio between the median power within a given frequency interval and the median power of the overall power spectrum) of the corresponding frequency components of the LDF signal acquired at the solar forearm of eighteen healthy participants without (red) and with the COI correction (black) during the baseline (**A**) and PORH (**B**). Horizontal lines in the box indicate the first quartile, median, and third quartile. The whiskers represent one and a half times the interquartile range. Plus signs represent outliers.  
construct the WA spectra whose lower frequency limit does not differ between these two cases. Otherwise, the normalization would establish a direct relation with the analysed signal length, which we tried to avoid.  
Our results revealed that, in baseline LD signals decomposed with WA, there were no statistically significant differences between the results where the COI correction was applied and those without the correction, as can be seen in Figure 3A. This suggests that the COI correction might not have a notable impact on the baseline signals, indicating that edge effects in this context are likely minimal, even when analysing short signals. While this outcome was expected, due to the quasi-periodic nature of baseline signals, it provides an excellent starting point to emphasize the problematic assessment of transient signals.
## 1 Introduction
The frequent topology changes also lead to a high packet loss rate because of the outdated routing table. Additionally, the complex and harsh space environment, including multipath fading, the ionosphere, atmosphere, and solar activity, also severely impact inter-satellite links, potentially leading to the destruction of the links and even the nodes themselves [8]. Additionally, owing to the long-distance transmission between satellites, the delay of transmission between satellites is high [9]. Therefore, this paper proposes a shortest queue length-cluster-based routing protocol (SQL-CBRP) for the satellite self-organizing network scenario. This algorithm is based on a cluster-based network structure, dividing routing into inter-cluster and intra-cluster routing. Intra-cluster routing is forwarded by the cluster head, while inter-cluster routing selects the node with the shortest queue length among neighboring cluster heads as the next hop. This effectively avoids packet loss due to queue overflow (i.e., a new packet arrives when the queue is full) and reduces queuing delay. Furthermore, to prevent network collapse caused by the destruction of the cluster head, a sub-head is established. When the cluster head is destroyed, the sub-head can take over the original cluster head's role in data forwarding and cluster management. Additionally, this paper has constructed a satellite self-organizing network simulation platform based on OMNeT++ and has validated the performances of SQL-CBRP on this platform, comparing it with [PERSON]'s algorithm and the GPSR (Greedy Perimeter Stateless Routing) algorithm. The main works of this paper are as follows:  
1. A novel routing strategy for satellite self-organizing network, shortest queue length-cluster-based routing protocol is proposed, which considers nodes' queue length when making routing decisions;
2. A satellite self-organizing network simulation platform is built based on OMNeT++. In this platform, some functions are designed and implemented, including initial network forming, cluster maintenance, data generation and routing, etc. ;
3. The delay and packet loss performance of the proposed algorithm are tested and compared with GPSR and Dijkstra.
## 3 Results and Discussion
### Confinement Loss  
The loss in optical fibers, especially PCFs, can have different origins including intrinsic material absorption, structural imperfection, micro or macro bending losses or confinement loss [22; 23]. The periodic cladding of PCFs causes a decrease in optical confinement, which is called the confinement  
Figure 4: Dispersion of (**a**) C-PCF and (**b**) E-PCF with different air-hole dimensions as a function excitation wavelength. The C-PCF parameters are: \(d_{c}/\Lambda=0.876\), \(\Lambda=0.867\), and E-PCF parameters are \(b/a=0.79\) and \(a=1\)\(\upmu\)m.  
loss (CL) [23; 24]. The imaginary part of the effective modal refractive index of guided modes \(Im(n_{eff})\) is related to CL through the following equation [23; 24]  
\[CL=\frac{40\pi Im(n_{eff})10^{6}}{\lambda Ln(10)}. \tag{5}\]  
CL is determined by the geometry of the PCF, especially the number (and size) of air holes in the cladding [23]. We investigate the relationship between CL and the number of air-hole rings in the cladding of the PCF with a 1550 nm excitation. As seen in Figure 5, by increasing the number of air-hole rings from one to three, the CL decreases significantly in both E-PCF and C-PCF. In E-PCF, the CL decreases from 0.002 dB/cm to 2.47 \(\times\) 10\({}^{-12}\) dB/cm, which is equal to nearly nine orders of magnitude. We saw the same pattern at different excitation wavelengths and with different air-hole dimensions. However, after \(N=3\), further increases in the number of air-hole rings does not have a significant impact on the magnitude of CL. Therefore, we confer that three rings of air-holes in a hexagonal-lattice PCF (in our excitation wavelengths) is the optimal number of air-holes, as more air-hole rings would increase the fabrication difficulty and fabrication-induced imperfections without any significant gain. However, the total loss in our proposed PCF is strongly dependent on absorption, fabrication-induced losses, and scattering losses of As\({}_{2}\)S\({}_{3}\) and tellurite glass, which is estimated to be less than 1 dB/m at 1550 nm [13; 14]. Excess losses during the fabrication are related to the interface between the capillaries, which is discussed in detail in [25].
# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 4 Task Computation Model
### Cost of UAVs  
Based on the description in the previous subsection, the total latency and the energy consumption of UAV \(n\) for offloading task \(t_{i}\) can be expressed as  
\[T_{n,i}^{al}=T_{n,i}^{pre}+T_{n,i}^{tra} \tag{15}\]  
\[E_{n,i}^{al}=E_{n,i}^{pre}+E_{n,i}^{tra} \tag{16}\]  
By considering the latency, the energy consumption of a UAV for offloading task \(t_{i}\), and the QoS requirements of the task, the cost of UAV \(n\) for offloading task \(t_{i}\) can be expressed as  
\[cos_{n,i}=\eta_{i,1}T_{n,i}^{al}+\eta_{i,2}E_{n,i}^{al} \tag{17}\]  
where \(\eta_{i,1}\) and \(\eta_{i,2}\) represent the weights of task \(t_{i}\) on the energy consumption and transmission delay, respectively, as presented in Algorithm 1.  
Hence, for UAV \(n\), the total overhead of offloading the tasks in the task queue is  
\[cos_{n,i}^{al}=\sum_{i=1}^{I}cos_{n,i}=\sum_{i=1}^{I}\eta_{i,1}T_{n,i}^{al}+ \eta_{i,2}E_{n,i}^{al} \tag{18}\]
## 2 Methodology
### Saliency Detection in Frequency Domain
#### 2.2.2 Redundancy Removal  
Although most of the redundant regions have been obviously suppressed by the operations above, the scattered redundancies still need to be removed. Based on [PERSON]'s matrix decomposition theory [31] that an input matrix can be decomposed into a sparse part and a low-rank part, an image can be represented as the following two parts:  
\[I(x,y)=L(x,y)+M(x,y), \tag{3}\]  
where \(L(x,y)\) is the low-rank part corresponding to the background, and \(M(x,y)\) is the sparse noises corresponding to the salient regions. The decomposition is implemented by Equation (4):  
\[\left\{\begin{array}{c}\min_{L,M}rank(L)\,+\,\|M\|_{0}\\ \,s.\,t.\,P\,=\,L\,+\,M\end{array}\right., \tag{4}\]  
where \(P\) is the input matrix. This is a Non-deterministic Polynomial Hard (NP-Hard) problem, so Equation (4) is optimized by Equation (5):  
\[\left\{\begin{array}{c}\min_{L,M}\|L\|_{*}\,+\,\varepsilon\|M\|_{1}\\ \,s.\,t.\,P\,=\,L\,+\,M\end{array}\right., \tag{5}\]  
where \(\varepsilon\) is a balance coefficient, and we set \(\varepsilon\) as 0.01. When the input matrix \(P\) is an initial saliency map, a global saliency map \(P_{g}=L\) is obtained. However, global decomposition cannot remove all of the redundancies. To this end, local decomposition is performed as follows.  
First, initial saliency map is divided into k blocks of the same size (i.e., \(N_{1}\), \(N_{2}\), \(N_{k}\)). Next, grayscale features from all the blocks are extracted independently and combined into a matrix \(Y\), as shown in Equation (6):  
\[Y\,=\,[y_{1},y_{2},\dots,y_{k}] \tag{6}\]  
where \(y_{i}\) is the column vector obtained by averaging each row of pixels in the \(i^{\text{th}}\) image block \(N_{i}\). Then, \(Y\) is decomposed by Equation (5), and the saliency level \(S\) of \(N_{i}\) is obtained by Equation (7):  
\[S(N_{i})\,=\,norm(Y(:,i))\,=\,\sqrt{\sum_{i=1}^{N}(Y(:,k))^{2}}. \tag{7}\]We have difference strategies to process the blocks according to \(S\), which is a key step of obtaining the local saliency map:  
\[\left\{\begin{array}{ll}\mu_{1}N_{i}&S(N_{i})\leq\sigma_{1}\\ \mu_{1}N_{i}&\sigma_{1}<S(N_{i})\leq\sigma_{2}\\ N_{i}&\text{others}\end{array}\right., \tag{8}\]  
where \(\sigma_{1}\) and \(\sigma_{2}\) are the thresholds, while \(\mu_{1}\) and \(\mu_{2}\) are the suppression coefficients. Experiment results show that the best thresholds are 0.3 and 0.7, and the suppression coefficients are 0.2 and 0.8. Note that when \(S_{i}>\sigma_{2}\), the block \(N_{i}\) will not be processed.