esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analyses of Volatiles Extracted from Cream and Sour Cream Samples  
Volatiles were extracted by SPME using a carboxene/polydimethylsiloxane fiber (Supelco, Bellefonte, PA, USA) mounted in a CTC CombiPal autosampler (Agilent Technologies, Santa Clara, CA, USA). For the analysis of each volatile, 5 g of a sample was placed into 20-mL vials, spiked with 1 \(\upmu\)g of an internal standard (naphthalene d8), and sealed with polytetrafluoroethylene/silicon septa caps. To avoid the influence of temperature on the formation of volatiles, the compounds were extracted without additional heating from the headspace at a stable room temperature of 21 \({}^{\circ}\)C for 30 min. The identification of the compounds was performed using a GCxGC-TOF-MS system with a ZOEX cryogenic (N2) modulator (Pegasus IV, LECO, St. Joseph, IL, USA) which was equipped with an SLB-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.5 \(\upmu\)m) column (Supelco Bellefonte, PA, USA) coupled to a Supelcowax-10 (1.1 m \(\times\) 0.2 mm \(\times\) 0.2 \(\upmu\)m) column (Supelco Bellefonte, PA, USA) and operated in a multidimensional chromatography mode. The temperature of the injector was set to 250 \({}^{\circ}\)C. During the injection, the fiber was exposed for 5 min in the splitless mode (1-min purge time). The conditions used for chromatography were as follows: helium flow, 0.8 mL/min; initial oven temperature, 40 \({}^{\circ}\)C (for 1 min) which was then increased to 180 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min and then further to 250 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min. For the two-dimensional analysis, the modulation time was optimized and set to 4 s and mass spectra were collected at a rate of 150 spectra/s. The transfer line was kept at 280 \({}^{\circ}\)C and the ion source was heated to 250 \({}^{\circ}\)C. Volatiles were identified by comparing the RI and the mass spectra of the elutingcompounds with those of the NIST 05 library match. A mixture of \(n\)-alkanes (C6-C20), dissolved in pentane which was obtained from Supelco (Bellefonte, PA, USA), was used for determining the RI of the volatiles. Calculations were carried out using Chroma TOF software (version 3.34). The semiquantification of compounds, which is represented by the average relative peak area of three replicates (arbitrary units presented as the mean value of three determinations), was achieved using the characteristic ions listed in Table 4. The results do not correspond to the absolute amount of a compound present in the samples, but they were calculated and used only for evaluating the differences between the analyzed samples.
## 4 Results
### Parameter Fitting  
Nine parameters were adjusted using the fitting procedure described in Section 3.6. The quasi-static tensile experiments in rolling and transversal directions, and an additional experiment at high strain rate in the SHTB setup, all of them described in Section 2.3, were used for the fitting. The obtained parameters are listed in Table 4 and simulated tensile curves are compared with the experimental data in Figure 5. Using a single set of fitting parameters for the LT and HT materials, the model is capable of reproducing the experimental results in different directions and strain rates. The difference between the predicted  
Figure 4: Crystallographic textures of the bainite and martensite phases calculated from EBSD experiments for the LT and HT steels. The \(\varphi_{2}=45^{\circ}\) section of the ODF is shown for the bainitic and martensitic phases of each steel. The ODFs were calculated using MTM-FHM software [62] (with \(L_{\text{max}}=22\) and a Gaussian spread of \(7^{\circ}\)). Intensity levels are displayed over the contour lines.  
Figure 3: Distribution of grain sizes (equivalent diameter), aspect ratio (of the equivalent ellipsoid axes) and orientation (of the equivalent ellipsoid, not crystallographic) calculated from EBSD experiments for the LT and HT steels.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & **Phase** & \(\mathbf{v_{f}}\) (\%) & \(\mathbf{d}\) (\(\mathbf{\mu}\)m) & **A.R. (-)** & \(\mathbf{\rho}\) (\(\mathbf{m^{-2}}\)) & \(\mathbf{W_{\text{C}}}\) (\%) & \(\mathbf{v_{c}}\) (\%) & \(\mathbf{d_{c}}\) (\(\mathbf{\mu}\)m) \\ \hline HT & Bainite & 91.8 & 1.44 & 0.42 & \(1.45\cdot 10^{14}\) & 0.03 & 3.8 & 0.07 \\  & Martensite (LCM) & 4.8 & 0.50 & 0.44 & \(1.72\cdot 10^{14}\) & 0.28 & 1.8 & 0.07 \\  & Martensite (MA) & 3.4 & 0.70 & 0.44 & \(1.72\cdot 10^{14}\) & 0.44 & 0 & \(-\) \\ \hline LT & Bainite & 91.4 & 1.17 & 0.42 & \(1.56\cdot 10^{14}\) & 0.03 & 3.3 & 0.07 \\  & Martensite (LCM) & 6.2 & 0.47 & 0.44 & \(1.88\cdot 10^{14}\) & 0.28 & 1.5 & 0.07 \\  & Martensite (MA) & 2.4 & 0.60 & 0.44 & \(1.88\cdot 10^{14}\) & 0.44 & 0 & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Microstructural parameters for bainite, low carbon martensite and martensite-austenite islands: \(v_{f}\) is the volume fraction of the phase, \(d\) is the size (grain diameter for bainite and lath width for martensite), A.R. is the aspect ratio, \(\rho\) is the dislocation density, \(W_{\text{C}}\) is the carbon content (in weight percent) and \(v_{c}\) and \(d_{c}\) are, respectively, the volume fraction and size of carbides.  
curve and the experimental data points is of the same magnitude as the experimental error in most cases, and even significantly lower for the dynamic experiments, with relatively high experimental scatter. Taking into account the magnitude of the experimental error, a good fitting is obtained. The \(R^{2}\) value resulting from the combination of all experiments (the six simulations) is of more than 0.94.
## 5 Data Acquisition and Processing
### Data Processing
#### 5.2.3 Inversion of HVSR and Dispersion Curves  
The inversion process was carried out using the Dinver software from the Sesarray package [45]. This software uses the neighborhood algorithm to estimate the shear-wave velocity profiles from the HVSR and dispersion curves [44]. The geotechnical model built from the gathered geotechnical data (Table 1) was used as an input parameterization for the process. However, the number of layers was not constant; it varied from one site to another depending on the lithological aspect at each measurement site.  
The HVSR curves were inverted entirely (0.2-20 Hz). The maximum number of iterations was fixed to 350 iterations, and 100 models were generated at each iteration. Only models with acceptable misfit values were considered. The threshold for an acceptable misfit value was 0.45 for curves with one frequency peak, and 0.6 for curves with two frequency peaks. For curves with multiple frequency peaks, misfit values under 0.8 were considered.  
For the array measurements, the dispersion curves were not inverted entirely; the curves were cut and considered only within the theoretical wavenumber limits (K\({}_{\rm min}/2\), K\({}_{\rm max}\)). For each dispersion curve, the corresponding HVSR curve was added in order to perform a joint inversion. Both curves were equally weighted.
## 1 Introduction
This cut-off frequency was chosen to meet typical requirements in biomedical signal processing applications, particularly for electrocardiogram (ECG) and electromyogram (EMG) signals, where most of the relevant frequency content lies below 150 Hz. For ECG signals, in particular, the majority of the signal energy is concentrated below 100-150 Hz. The selection of a 110 Hz cut-off frequency therefore provides a balance between preserving the desired signal bandwidth and attenuating high-frequency noise. The proposed filter achieves a dynamic range of 57.2 dB with a total harmonic distortion (THD) of 1%.
## 3 Result and Discussion  
### Statistical Analysis  
We performed a logistic regression analysis on the dataset using Python's statistics models [44] and the smf.logit function. The model predicts the likelihood of having learning difficulties based on several independent variables, including categorical and numerical ones. After fitting the model, the model summary provides us with coefficients, \(p\)-values, OR, and CI. The interpretation of results involves understanding the impact of each independent variable on the likelihood of having learning difficulties. Table 5 displays each variable's \(p\)-value, OR, and 95% CI. Below is a detailed analysis of all variables to understand their impact:  
* Took away privileges: Children who had privileges taken away are 1.490 times more likely to have learning difficulties compared to those who did not. This association is statistically significant (\(p<0.001\)), with a confidence interval of 1.284 to 1.728, indicating a strong effect;
* Shook child: The odds of having learning difficulties are 1.260 times higher for children who were shaken compared to those who were not. This finding is statistically significant (\(p=0.002\)), with a confidence interval of 1.086 to 1.462;
* Beat child as hard as one could: Children who were severely physically punished (beaten as hard as one could) have 2.265 times higher odds of learning difficulties compared to those who were not. This effect is highly statistically significant (\(p<0.001\)), with a confidence interval of 1.776 to 2.888;
* Sex: Male children are 1.215 times more likely to have learning difficulties compared to female children. This effect is statistically significant (\(p=0.009\)), with a confidence interval of 1.049 to 1.406;
* Wealth index and mother's education: Children from the poorest households are 2.612 times more likely to have learning difficulties compared to the richest households. The odds decrease with increasing wealth, remaining significant in the second andmiddle quintiles but not in the fourth. Lower maternal education also increases the odds of learning difficulties, though not all levels are statistically significant;
* Called child dumb/lazy: The odds of learning difficulties for children who were called dumb or lazy are 1.077 times higher compared to those who were not, but this effect is not statistically significant (\(p\) = 0.376), with a confidence interval of 0.913 to 1.271;
* Weight-for-age percentile: The weight-for-age percentile does not show a statistically significant association with learning difficulties (OR = 1.000, \(p\) = 0.963), suggesting it has a negligible impact;
* Child's weight: Lower child weight is associated with slightly reduced odds of learning difficulties (OR = 0.954), and this effect is statistically significant (\(p\) = 0.040).
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 5 Conclusions and Future Work  
The VQoSRR is a QoS and QoE-aware SDN framework for video streaming that adjusts network resources and service characteristics based on the user's needs. Because the proposed method combines the reservation mechanism with QoS-based routing, this allows the determining of which network path has adequate resources to support the requested QoS, as well as providing reserving and requesting network resources at the same time. Due to this, it is suitable for use with various applications, such as IPTV services and video surveillance in smart city environments.  
We used SSIM, VMAF, and MOS QoE measurements for SD and HD videos to evaluate the proposed framework's performance. The experiments proved that VQoSRR enhanced the user perception of quality and allowed fine control over routes and resources.  
From the results of our proposal, the video quality improved. For instance, for SSIM measurements, when we used VQoS-RR, the SSIM quality value increased from 0.91 to 0.95 for SD videos, whereas for HD videos, it increased from 0.92 to 0.96 (Figure 11). Likewise, in the case of MOS measurements, the VQoSRR increased the average MOS by 36.0% for SD and 56% for HD (Figure 13). We also observed that subjective MOS reflected the differences in results more clearly than the objective SSIM for comparing results obtained with the proposed model.  
Additionally, we noticed from the testing results of the DR-RA algorithm that the HD achieved the highest SSIM at 7 s, while the SD had the best result at 5 s, which indicates that DR-RA should be executed at longer intervals for HD than SD in order to shift the flow to a new path.  
Our experiment did not explore all possible video durations; therefore, long-duration videos may require further analyses to identify the most appropriate execution interval of the algorithm for the different video resolutions. Further, we will investigate the scalability issues in a large-scale network environment in future work. Additionally, we will integrate the proposed model with the multicast transmission. This technique of using video streaming would improve the tools and applications used for smart cities such as surveillance systems for hospitals and civil defense organizations.  
Methodology, M.O.E.; validation, M.O.E., and J.L.; investigation, M.O.E., A.A., N.A., J.L.; writing--original draft preparation, M.O.E.; writing--review and editing, A.A., N.A.  
and J.L.; supervision, J.L.; funding acquisition, A.A. and N.A. All authors have read and agreed to the published version of the manuscript.  
Researchers Supporting Project number (RSP-2021/295), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
###### Abstract  
Urban planning, noise propagation modelling, viewshed analysis, etc., require determination of routes or supply lines for propagation. A point-to-point routing algorithm is required to determine the best routes for the propagation of noise levels from source to destination. Various optimization algorithms are present in the literature to determine the shortest route, e.g., Dijkstra, Ant-Colony algorithms, etc. However, these algorithms primarily work over 2D maps and multiple routes. The shortest route determination in 3D from unlabeled data (e.g., precise LiDAR terrain point cloud) is very challenging. The prediction of noise data for a place necessitates extraction of all possible principal routes between every source of noise and its destination, e.g., direct route, the route over the top of the building (or obstruction), routes around the sides of the building, and the reflected routes. It is thus required to develop an algorithm that will determine all the possible routes for propagation, using LiDAR data. The algorithm uses the novel cutting plane technique customized to work with LiDAR data to extract all the principal routes between every pair of noise source and destination. Terrain parameters are determined from routes for modeling. The terrain parameters, and noise data when integrated with a sophisticated noise model give an accurate prediction of noise for a place. The novel point-to-point routing algorithm is developed using LiDAR data of the RGIPT campus. All the shortest routes were tested for their spatial accuracy and efficacy to predict the noise levels accurately. Various routes are found to be accurate within \(\pm\)9 cm, while predicted noise levels are found to be accurate within \(\pm\)6 dB at an instantaneous scale. The novel accurate 3D routing algorithm can improve the other urban applications too.  
LiDAR; source; destination; route determination; 3D route; terrain data +
Footnote †: journal:  
0000-0001-9991-53270-4626
## 1 Introduction  
Under long-term weathering, the surrounding rock in a tunnel portal section is relatively broken and has poor stability and blasting has adverse effects on the surrounding rock stability. A detailed analysis of the harmful effects of blasting vibration is crucial to avoid the disaster of engineering instability [1; 2; 3]. Due to the complex environmental influence of the tunnel-slope composition, the signal obtained by the field blasting vibration test contains a lot of noise, which changes the real signal amplitude and spectrum characteristics. The measured blasting vibration signal belongs to the non-stable time domain signal. It is the most effective denoising method to obtain the mode component signal of different frequency domains and then judge the noise component [4; 5].  
The algorithms commonly used for the decomposition of blasting vibration signals include the empirical mode decomposition (EMD) [6], local mean decomposition (LMD) [7], and variational mode decomposition (VMD) [8]. EMD and its improved algorithm can produce more ideal decomposition signals, but there are endpoint effect and modal stacking problems. [PERSON] et al. [9] put forward a signal noise reduction model of complete ensemble empirical mode decomposition (CEEMD) in which low-pass filtering is constructed to effectively remove the mixed noise of the tunnel blasting signal. [PERSON] et al. [10] proposed a smooth denoising model of complete ensemble empirical mode decomposition with adaptive ability (CEEMDAN) to reduce the noise in underwater drilling and blasting vibration signal, and achieve the great denoising effect. The LMD algorithm can adaptively decompose the signal into the product functions of several frequency modulations and envelope signals, which has good results for noise reduction and features the extraction of vibration signals, which is better than the EMD algorithm [7; 11]. The methods of LMD, EMD, and their improved algorithms do not need to select base equations and have self-adaptability, but they will lead to mode mixing when dealing with noise signals.  
Variational mode decomposition algorithm is a non-recursive signal decomposition with extensive adaptability and good robustness, which can solve endpoint distortion and mode confusion [12]. [PERSON] et al. [13] and [PERSON] et al. [14] studied the noise reduction and feature extraction of the blasting vibration signal of the VMD algorithm and verified its effectiveness. However, the mode number \(K\) of this algorithm is difficult to determine, which may lead to large manual intervention errors. The whale optimization algorithm (WOA) is a new type of intelligent optimization algorithm, and the optimization process has the advantages of few parameters, high accuracy, and fast convergence. In this paper, WOA is used to optimize VMD parameters, and is applied with practical engineering methods to verify the noise reduction effect.
# Particle-Velocity-Based Mixed-Source Sound Field Translation for Binaural Reproduction
## 6 Experimental Verification
### Experimental Results  
After removing the scores of the disqualified subjects, we collect the scores of 30 subjects in total for the source localization test, and 29 scores for the basic audio quality test. Box plots showing the results for both tests are given in Figure 12. On each box, the central short red line indicates the median value, and the edges of the box are the 25 th and 75 th percentile of the scores. The extremes of the whiskers correspond to \(q_{3}+1.5(q_{3}-q_{1})\) and \(q_{1}-1.5(q_{3}-q_{1})\), where \(q_{1}\) and \(q_{3}\) are the 25 th and 75 th percentile of the scores, respectively. If a score goes beyond the extremes of the whiskers, it is considered an outlier, indicated by the symbol + in the plots. The symbol \(*\) denotes the mean value, and the v-shaped notches refer to the 95% confidence interval. The endpoints of the notches are calculated by \(q_{2}-1.57(q_{3}-q_{1})/\sqrt{N_{s}}\) and \(q_{2}+1.57(q_{3}-q_{1})/\sqrt{N_{s}}\), where \(q_{2}\) is the median value and \(N_{s}\) is the number of the scores. The intervals can be used to compare the median values. If two intervals do not overlap, their true medians are different with 95% confidence.  
From Figure 12a, we observe that, for the source localization, the closed-form solution of the sound-pressure-based expansion and the least squares solution of the particle-velocity-based mixed-source solution have similar mean values and median values, and their confidence intervals have nearly 100% overlapping. The sparse solutions of both expansions have higher mean values and median values. However, the sparse solution of the particle-velocity-based expansion has a more significant improvement in score and a more centralized score distribution compared to the sparse solution of the sound-pressure-based expansion. Similar results are shown for the basic audio quality test in Figure 12b, where no significant difference is found between the closed-form solution and the least squares solution. For the sparse solutions, the particle-velocity-based expansion is still observed to perform better than the sound-pressure-based expansion; however, both of outperform the closed-form solution and the least squares solution. We also notice that for the sparse solution of the particle-velocity-based expansion the scores in the source localization test are relatively higher than those in the basic audio quality test.  
According to the above experimental results, we conclude that in terms of the perceptual criteria of source localization and audio quality the sparse solutions have better performance than the closed-formed solution and the least squares solution, both of which have similar performance. In the sparse solutions, the particle-velocity-based expansion shows an improvement against the sound-pressure-based expansion, especially for the source localization.
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs
### Near and Onboard Living-Tissue Tools in SC Environment
#### 5.1.2 Onboard Tools Sustainable Design  
As mentioned in Section 3, EMF perturbations of an onboard tool could be circumvented through eco-design either by managing its constituents or via shielding equipment. This could be achieved by avoiding EMF-sensitive matters in tool ingredients. Otherwise, shields should be used. Simple conductor shields could be employed when their heating does not produce a temperature rise in the adjacent living tissues. Otherwise, an amendment of the simple conductive shielding is needed. Thus, the use of multifunctional matched shield constituents permits low-reflectivity shields that lessen the strong field reflection triggered by the high substance conductivity [63; 64; 65; 66; 67; 68]. The outcome of such an eco-design involving RA and OH approaches permits a sustainable onboard tool with diminished adverse EMF effects. Figure 2 clarifies the weight of eco-design comprising RA and OH approaches through the design or shielding of onboard tools, considering the whole biodiversity by supervising hostile urban SC EMF effects.  
Figures 1 and 2 demonstrate the impact of eco-design of an onboard tool on its performance and the reduction of its disturbances due to EMF exposure, which leads to a reduction in unwanted side effects.  
Figure 1: Behavior of the onboard tool in relation to EM radiation, its design, its intended use, and its unsolicited effects on humans and their environment [41].