esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computational Methods

Table 1: Three material properties used in the model. Figure 2: Mesh system of the model. \[\frac{\partial\mathbf{E}}{\partial t}-\frac{1}{2}\Big{[}\
abla\mathbf{v}-(\
abla \mathbf{v})^{\mathrm{T}}\Big{]}=0 \tag{5}\]  
\[\mathbf{S}=\mathbf{C}\ :\mathbf{E} \tag{6}\]  
where \(\mathbf{v}\) is velocity; \(\rho\) is density; \(\mathbf{S}\) is stress tensor; \(\mathbf{E}\) is strain tensor; \(\mathbf{C}\) is elastic tensor (or stiffness tensor); \(\mathbf{F_{v}}\) is volumetric force. Ultrasound is a mechanical wave. The propagation of ultrasound is the vibration of the medium that produces fluctuations. The propagation of sound waves is the propagation of energy. Hence, the process of ultrasound propagation follows [PERSON]'s second law, the conservation of energy and the law of conservation of momentum. Integrating the equation of state and the small amplitude approximation, we can represent the sound field in a solid elastic medium by the following linear equation:  
\[\rho\
abla^{2}u_{i}=C_{ijkl}u_{k,jl}+f_{i} \tag{7}\]  
where \(\rho\) is media density; \(u\) is mass displacement; \(C\) is elasticity factor;\(f\) is external force; \(\
abla^{2}\) is the Laplace operator. Equation (7) is the active acoustic wave equation. If there is no external force, the equation can be expressed as:  
\[\rho\
abla^{2}u_{i}=C_{ijkl}u_{k,jl} \tag{8}\]  
Equation (8) is also called the Helmholtz equation and is mainly used to calculate the propagation of sound waves in a semi-infinite space. The equation for the sound field of an isotropic solid medium can be expressed as:  
\[(\lambda+2\mu)\
abla u-\mu\
abla^{2}u=\rho\frac{\partial^{2}u}{\partial t^{2}} \tag{9}\]  
where \(\lambda\) is Lame constant; \(\mu\) is Lame constant. Next, consider how to apply the excitation signal. In fact, the pulse signal was emitted by the piezoelectric ultrasonic probe and acted on the waveguide rod, so the Gaussian pulse signal was loaded to the upper end of the rod in the form of a load. Figure 3 shows the Gaussian pulse excitation signal with the functional expression in the following equation:  
\[y(x)=A\times\cos(2\pi f_{0}t)\times exp\Big{[}-2\pi^{2}(t-1)^{2}\Big{]} \tag{10}\]  
where \(A\) is the signal amplitude; \(f_{0}\) is the center frequency of the ultrasonic pulse signal, which is taken as 2.25 MHz. Figure 3: Excitation signal used in the model.
## 4 Discussion
### Is the Linear Characteristic Equation Valid for Data Interpretation?  
The secant shear modulus curve, when computed with the optimal \(\alpha\) parameter, deviates somewhat from the experimental results obtained through the conventional procedure, which relies on the linear characteristic equation. The conventional RC test provides both the resonance frequency and damping ratio as a function of the shear strain, with the shear modulus then calculated from the resonance frequency using the characteristic equation for the linear vibration of a column-mass system. Indeed, the hysteretic nonlinear property of the vibration problem, as manifested by the strong strain-dependency, is ignored during the data interpretation process. Therefore, these deviations indicate how much error the use of a linear characteristic equation can introduce into the shear modulus. As an alternative means of describing the amplitude-dependent frequency shift, a nonlinear characteristic equation can be derived by incorporating Equation (1) into the moment balance equation together with the relevant boundary conditions, yet a nonlinear vibration analysis is quite challenging. Given that the resonance frequency shift is inherently related to the increase in the damping ratio, it is recommended that the strain-dependent shear stiffness be estimated using Equation (4), with the optimal nonlinear parameter defined by the measured damping curve.
## 2 Results and Discussion
### Onion and Lemon Primary Metabolite Profiling Using GC-MS  
Although UPLC-QTOFMS revealed the variation in organic acids, viz. citric acid and its methyl derivative, as a major contributor to the segregation of pickled lemon from fresh ones, such a platform is not optimized for primary metabolite profiling [48]. To provide a more sensitive analysis of primary  
Figure 3: OPLS-DA score plot derived from modeling primary and secondary metabolites analyzed using UPLC-QTOFMS. Metabolites were derived fresh and pickled lemon fruit (**A**) and onion bulb (**C**), each modeled one at a time. The respective S-plots, (**B**) and (**D**), show the covariance p[1] against the correlation p(cor)[1] of the variables of the discriminating component of the OPLS-DA model. Cut-off values of \(p<0.05\) were used; selected variables are highlighted in the \(S\)-plot and identifications are discussed in the text.  
metabolites, viz. sugars and amino acids, compared to reversed-phase UPLC-QTOFMS, GC-MS was adopted for primary metabolite profiling in extracts prepared from fresh and pickled tissues. GC-MS analysis detected 52 primary metabolites belonging to organic acids, amino acids, fatty acids, sugars, and inorganic and nitrogenous compounds (Figure 4, Table S1). Primary metabolite analysis revealed a remarkable decrease in the total amount of organic acids, amino acids, and inorganic and nitrogenous compounds in the pickled tissue of both onion and lemon. In contrast, sugars and fatty acids were the major primary metabolites in pickled samples. Particularly in onion, fresh tissue was rich in malic acid (12.6%), proline (7%), fructofuranose (27%), and mannopyranose (11.6%). On the other hand, pickled onion showed dominancy in sugars, viz. maltose (16.3%) and sucrose (38%). In lemon, fresh tissue was also predominated by organic acids, viz. citric acid (37%), and sugar, viz. sucrose (28%), accounting for the sour taste of lemon concurrent with an abundance of sugar alcohol, viz. myo-inositol (21.9%) and fructofuranose (47%). A decrease in citric acid and its methyl ester levels in pickling can explain the less sour taste observed in pickled lemon and is in agreement with the UPLC-QTOFMS results. However, citric acid is considered a natural preservative that aids in smooth digestion and helps dissolve kidney stones [49]. Also of interest is the different sugar profile in the pickled product being more dominated by sugar alcohols, viz. myo-inositol, and monosaccharides, viz. fructose, compared to disaccharides in the fresh product, viz. sucrose. A decrease in disaccharide levels upon pickling in lemon could be attributed to the chemical hydrolysis of sucrose upon storage and especially due to the acidic nature of lemon, as an such effect was not observed in case of pickling _Allium_ bulb. Sugar alcohols exhibit higher thermal stability and do not undergo any Milliard reaction compared to sugars [50].  
Figure 4: GC-MS chromatogram of silylated primary metabolites analyzed from fresh and pickled onion bulb and lemon fruit.
###### Abstract  
This article reports on the characterization of the \"as-fabricated\" state of Inconel 718 samples fabricated using laser directed energy deposition (DED). Laser-DED is known to produce complex metastable microstructures that can significantly influence the baseline ultrasonic response compared to conventional processing methods. The present work uses three parameters to characterize the samples: (a) ultrasonic velocity, (b) an attenuation coefficient, and (c) a backscatter coefficient. The baseline ultrasonic response from the DED sample was compared against the ultrasonic properties of conventional IN718 samples reported in the literature. The results suggest that strong grain boundary scattering from large macrograins can lead to attenuation and backscatter values that are significantly higher than conventional samples. Additionally, the results including velocities, attenuation and backscatter coefficients were found to be dependent on the fabrication direction, with the build direction being different from the transverse directions. Finally, destructive analysis was used to develop conjectures to explain the experimentally observed ultrasonic response.  
additive manufacturing; ultrasonics; nondestructive evaluation; Inconel 718; material characterization; ultrasonic velocity; ultrasonic attenuation +
Footnote †: journal: Applied Sciences
# Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions
## 6 Experimentation and Results
### Results and Discussion
After a detailed analysis of each group of strategies, we obtain the following consideration for both studies:  
When applying Strategy 3 (using _Fuzzy Normalized Used Bandwidth_ as a cost function), we observe that the value of the GMDR surpasses its crisp equivalents (strategies 1 and 2), although in a small percentage. We find that confidence intervals are not overlapping. From the statistical point of view, this means that the results are distinguishable so that the resulting comparison has statistical validation. Similarly, with Strategy 5 (Fuzzy Shortest-Widest), the value of the GMDR surpasses its crisp equivalent (Strategy 4), but their confidence intervals are overlapped. Thus, we must consider both results as equal from a statistical point of view. Strategies 6 and 7 (Widest-Shortest and Fuzzy Widest-Shortest, respectively) have the same GMDR values with practically equal confidence intervals. We also observe that strategies 6 and 7 are superior to the previous ones (in the order of 10%). Our hypothesis in this regard is the advantage of these strategies working in a network close to saturation. Since these strategies search for paths with the minimum number of hops, they contribute to a lesser flow of information through the network. Therefore, Strategies 6 and 7 are benefited by the low consumption of resources when compared to Strategies 1 to 5. Finally, as the main result of this study, we verify that Strategy 8 (proposed for the first time in this work), achieves a performance which is widely superior to all the previous ones. In particular, the value of GMDR is close to 1. Therefore, the obtained results can be summarized with the following two statements:  
1. Fuzzy versions of the classical strategies are more efficient (or practically equal) than their corresponding crisp versions, although in a very narrow margin. Thus, we can say that our fuzzy modeling of a communications network correctly incorporates the uncertainty in the measurement variables, being competitive. 2. Strategy 8, proposed by us, is the most efficient one with a clear advantage over the rest of the strategies. Specifically, it leads to a GMDR close to 1. This advantage comes from the definition of a new fuzzy cost function that incorporates our empirical knowledge about the effects of measurement uncertainty.
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 2 Related Works
Meanwhile, in software implementations, SIMON, SPECK, CHAM, and PRIDE exhibit high throughput with low memory requirements, ensuring excellent efficiency. [PERSON] et al. have examined the FPGA implementation of the PRIDE and PRESENT lightweight block ciphers [26]. Their study focused on evaluating throughput, energy consumption, and power efficiency in FPGA implementations. The study has analyzed various design parameters on FPGAs and has compared PRIDE's performance against PRESENT, offering insights into its efficiency and reliability in resource-constrained IoT environments. [PERSON] et al. have examined the role of lightweight cryptography in addressing security and privacy challenges in IoT applications [27]. Their study has analyzed five lightweight block ciphers (PRESENT, CLEFIA, PICCOLO, PRINCE and LBLOCK) in a controlled test environment to evaluate their effectiveness in securing IoT data. [PERSON] et al. have analyzed the security challenges associated with resource-constrained IoT devices [28]. Their research has evaluated and compared well-known cryptographic algorithms based on implementation cost, hardware and software efficiency, and resistance to attacks, providing a comprehensive analysis of their suitability for constrained environments by conducting a thorough literature review on existing lightweight cryptographic techniques and their applications in IoT security. As previous studies show, researchers have mostly focused on developing new algorithms or testing and improving the algorithms developed in the literature according to various criteria. Certainly, the development of new algorithms that are robust to attack is very valuable. However, it is important for the industry to use applicable algorithms defined by standards. For this reason, studies should be carried out on algorithms that are determined by ISO standards and announced to the public. When reviewing the literature, it can be seen that these studies are very limited. With this motivation, this study analyses the algorithms within the scope of ISO/IEC 29192-2.
## 3 Results and Discussion
### Analytical Results
#### 3.1.3 Influence of Elastic Parameters on Vertical Strain Distributions  
The analytical vertical strains as computed from Equation (4) are independent of the orientation \(\theta\). For the investigated cases including various plane inclinations, the vertical strains do not have a dependency on the ratio \(\
u/\
u^{\prime}\) since varying ratios only correspond to a change in [PERSON]'s ratio \(\
u\). However, this parameter is not part of the vertical strain equation. In contrast, the transverse Poisson's ratio \(\
u^{\prime}\), which actually affects the vertical strains, remains unchanged for all considered parameter sets.  
For samples with horizontal isotropy planes, i.e., \(\beta=0^{\circ}\), the vertical strains are received from \(\varepsilon_{z}=(1/E^{\prime})\cdot\sigma_{z}\) and are therefore only dependent on the elastic parameter \(E^{\prime}\), which is fixed in this study. The resulting strain distribution for this case can be seen in Figure 9a.  
For isotropy plane inclinations of \(\beta=90^{\circ}\), the vertical strains can be computed from \(\varepsilon_{z}=(1/E)\cdot\sigma_{z}\) and are only influenced by the Young's modulus \(E\). Accordingly, only the chosen ratios for \(E/E^{\prime}\) have an effect on the vertical strains for this plane inclination (see Figure 12a).  
Figure 11: Distributions of radial strains \(\varepsilon_{r}\) for samples with dip angles \(\beta=45^{\circ}\), ratios \(E/E^{\prime}\) of 1.0, 1.5 and a shear modulus \(G^{\prime}=G^{\prime}_{\text{s}\text{r}}\) acc. to Equation (5) displayed within a range of \(\theta=0^{\circ}\) to \(360^{\circ}\) and plotted for various ratios \(\
u/\
u^{\prime}\).  
When the plane inclination \(\beta\) is equal to \(45^{\circ}\), the shear modulus \(G^{\prime}\) has an influence on the developing vertical strains \(\varepsilon_{z}\), which can be seen from Figure 12b,c. The curves in Figure 12b are plotted using a constant value for the shear modulus \(G^{\prime}=G^{\prime}_{sv,Erat=2}\) computed at ratio \(E/E^{\prime}=2.0\). Inserting the expression for \(G^{\prime}_{sv}\), following Equation (5) with \(E=2E^{\prime}\), into Equation (4) results in a vertical strain value for samples with \(\beta=45^{\circ}\) of \(\varepsilon_{z}=[(2E^{\prime}+5E)/(8E^{\prime})]\cdot\sigma_{z}\). No direct dependency on the Poisson's ratios \(\
u\) and \(\
u^{\prime}\) is given.  
When the shear modulus \(G^{\prime}\) is chosen as \(G^{\prime}_{sv}\) acc. to Equation (5) considering the corresponding ratio \(E/E^{\prime}\) (see Figure 12c), the vertical strain value for samples with isotropy plane inclination \(\beta=45^{\circ}\) computes as \(\varepsilon_{z}=[(E^{\prime}+E)/(2E^{\prime})]\cdot\sigma_{z}\). Thus, differences in the results from Figure 12b,c occur; however, this is only for the curves with \(E/E^{\prime}\
eq 2.0\).
## 2 Related Work
### Recreating Real-World Scenes from Photographs
#### 2.1.2 Strengths of Multi-View Capture  
To recover the geometric information required for comfortable motion parallax from a set of input photographs, one very practical solution is to rely on multi-view capture, rather than capturing a single 360\({}^{\circ}\) image. Indeed, acquiring the scene from multiple different viewpoints enables the recovery of dense depth data through standard 3D reconstruction techniques [1; 7; 14]. For instance, [PERSON] et al. [14] thus demonstrate a method for transforming sets of casually captured photographs into complete and realistic 3D scenes that can be viewed with motion parallax in VR. Furthermore, this strength of multi-view capture has encouraged multiple research efforts towards developing custom inside-out multi-camera rigs designed specifically for recreating 3D scenes for VR. Such systems are typically composed of several tens of outward-facing cameras arranged on a sphere [15] or rotating arc [1], and are all the more practical that they enable capturing the entire set of photographs at the click of a button (rather than having to move a single camera to multiple viewpoints).  
Additionally, beyond the practical aspect of being able to more easily reconstruct 3D geometry from the captured photographs, multi-view acquisition systems are also interesting in that they enable the recovery of complex visual effects such as reflections or transparency [1; 2; 3; 4; 6]. Indeed, a number of view-dependent effects become particularly visible when capturing a scene from multiple viewpoints, such as specular highlights on a shiny  
Figure 1: Rendered views from our recreated viewpoint in the MINES ParisTech Mineralogy Museum, with view-dependent reflections visible on the windows and minerals in real-time VR.  
object or reflections on a mirror. Because multi-view setups enable capturing these effects, they can thus be coupled with dedicated image-based rendering algorithms to effectively replicate these captured view-dependent effects in VR. For instance, [PERSON] et al. [1] present a system for capturing and rendering light field image datasets at VR-compatible speeds, demonstrating its view-dependent capabilities on several sample scenes and sharing the results by way of a complementary VR application. Similarly, [PERSON] and [PERSON] [6] present an open-source toolkit and interface for transforming photographs into virtual environments that can be rendered with view-dependent highlights in VR. As another example, [PERSON] et al. [5] also demonstrate that these approaches can also be extended to dynamic scenes, using a hemispherical setup to capture light field videos that they then render using a deep learning network.
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 4 Performance and Evaluation  
In this section, we objectively evaluate the proposed personalized HRTF estimation method. To avoid confusion, the performance of the proposed method is compared with other HRTF estimation methods: (1) an HRTF estimation method based on our model but using accurate pinna measurements instead of ear images (using 27 parameters), called \"Full-measurements HRTF\"; (2) an HRTF estimation method using average HRTF of 32 subjects, called \"Average HRTF\"; (3) Towards Fast And Convenient End-To-End HRTF [38], called \"TFACE HRTF\"; and (4) the proposed method is called \"Proposed HRTF\".  
All methods are implemented using Pytorch version 2.4.0 and Python version 3.8.0. The CIPIC dataset includes HRTF (left ear) of 32 subjects, 17 head and torso measurements, and left ear images of subjects. Due to the small amount of data in the dataset, to avoid the problem of over-fitting, this paper divides the data into a training set and test set through \"leave-one-out validation\", and then conducts 32 cross-validation rounds and takes the average of the validation results.
## 7 Open Issues and Research Challenges
### Security  
UAVs operate over an open wireless channel, which any malicious node may attack. A malicious user can either change the UAV path or hijack the whole UAV. So, the security of UAVs is of utmost importance in such a critical COVID-19 outbreak.
# Voice Communication Systems Impact on Navigating Officers
## 3 Results
### Mobile Phone Calls  
A mobile phone call is an incoming call via a ship's official mobile phone stationed on a bridge during watch keeping. These calls can be made during coastal navigation, i.e., when the ship sails in areas where the mobile signal is available (approximately within 30 km from the shore-based mobile signal antenna).  
The number of these calls was estimated on the basis of the seafarers' answers in the survey. The collected data (statistical sample) have the following parameters: Range=1410 min (30-1440 min), Mean = 376.4, Median = 174, Standard deviation = 456.3 and 90% confidence interval for the mean value [277.96, 474.84]. The data are congruent with the lognormal distribution. By rejection of outliners,  
Figure 3: Satellite call process times [min].  
beyond three standard deviations the initial distribution parameters are \(\mu_{l}\) = 370.14 and \(\sigma_{l}\) = 567.63 (Figure 4), as per the following expressions:  
\[\mu=\ln\left(\frac{\mu_{l}^{2}}{\sqrt{\sigma_{l}^{2}+\mu_{l}^{2}}}\right)=\ln \left(\frac{370.14^{2}}{\sqrt{567.63^{2}+370.14^{2}}}\right)=5.31 \tag{5}\]  
\[\sigma = \sqrt{\ln\left(\frac{(\sigma_{l}^{2}+\mu_{l}^{2})}{\mu_{l}^{2}} \right)}=\sqrt{\ln\left(\frac{567.63^{2}+370.14^{2}}{370.14^{2}}\right)}=1.1 \tag{6}\] \[f(x) = \frac{1}{\sigma x\sqrt{2\pi}}e^{-\left(\ln\left(x\right)-\mu \right)^{2}/(2\sigma^{2})}\ =\frac{1}{1.1t\sqrt{2\pi}}e^{-\left(\ln\left(t\right)-5.31\right)^{2}/2.42}\] (7) \[x>0;\ \mu_{l},\ \sigma_{l}\ e^{\ R}\]  
The average time between two consecutive calls may be estimated at one call every 370.14 min or 6.17 h, i.e., approximately four calls a day. According to the participants' answers in the interviews, a satellite and a ship's mobile phone calls are, in most cases, of the same nature, that is, business related. Therefore, the average process time (conversation time) via a ship's mobile phone is estimated to be the same as in the case of satellite phone calls.  
Considering the frequency of incoming calls and their duration, it is estimated that officers of the watch spend approximately 12.76 min talking on the official mobile phone per day. Since satellite and mobile phone calls are not received very often, by analyzing them together as the same source, it can be estimated that an officer receives six calls per day or one per watch.
# Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System
## 3 Methodology  
### Data Set Description  
This section examines the effectiveness of the proposed Harris Hawks sparse auto-encoder networks (HHSAE-ASR) framework. The jim-schwoebel voice datasets applied on our experiments [56]. The dataset consists of several voice datasets that are widely used to investigate the effectiveness of the introduced system.