esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Design and Laboratory Testing of Encapsulated PCM Panels for PCM-Air Heat Exchangers
## 2 Methods
### Design Procedure
#### 2.1.2 Numerical Validation  
To ensure that the correct numerical model was selected, a smooth panel section was designed (0.0422 \(\times\) 0.027 \(\times\) 0.575 m) to evaluate the Nusselt number and compare with [PERSON]'s correlation (Equation (1)) for smooth channels. The mesh was adapted through ANSYS Fluent until no changes on the fourth decimal were found on outlet pressure and outlet temperature. Added to that, under-relaxation factors were adjusted to allow convergence. The simulation stopped when the residual achieved \(10^{-5}\) for turbulence, energy and continuity. Realizable \(k-\varepsilon\) turbulence model with scalable near wall treatment functions and SIMPLE method for pressure-velocity coupling was used with second-order upwind scheme for pressure, momentum, turbulence and energy.  
\[\mathbf{Nu_{D}}=\frac{\left(\frac{\mathbf{f}}{\mathbf{g}}\right)(\mathbf{Re_{D }}-\mathbf{1000})\mathbf{Pr}}{\mathbf{1+12.7}\left(\frac{\mathbf{f}}{\mathbf{ g}}\right)^{\frac{1}{2}}\left(\mathbf{Pr^{\frac{3}{2}}-1}\right)} \tag{1}\]  
where \(\mathbf{Re}\) is the Reynolds number, \(\mathbf{Pr}\) is the Prandtl number of the air and \(\mathbf{f}\) is the friction factor calculated by the Petukhov correlation Equation (2):  
\[\mathbf{f}=(\mathbf{0.791n}(\mathbf{Re_{Dh}})-\mathbf{1.64})^{-2} \tag{2}\]  
After several iterations, the final mesh of the panel section had 2,389,901 nodes with results presenting a difference of 5.41% between [PERSON]'s correlation and simulation.  
Moreover, a laboratory test (Figure 5) using the existing panel with 1, 2 and 3 modules measured and analyzed the pressure drop along the channel to validate the CFD numerical model. Measurements were made upstream and downstream of the thermal battery modules using the [PERSON]'s method [24] with the Pitot tube TSI TA465-P.  
Three different Reynolds numbers (10,089, 18,736 and 21,600) were set for a two-module TB and two Reynolds numbers (18,736 and 21,600) for one and three TB modules. For the CFD simulation, the mesh followed the same criteria used to generate the smooth panel in 2D. As the existing panel is not uniform along its width, a section located at the top of the bulges was selected to perform the 2D analysis. Table 3 shows the number of nodes and elements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Shapes**} & \multicolumn{2}{c}{**First Mesh**} & \multicolumn{2}{c}{**Adapted Mesh**} \\ \cline{2-5}  & **Nodes** & **Elements** & **Nodes** & **Elements** \\ \hline
1 module & 168,489 & 158,948 & 371,043 & 330,158 \\
2 modules & 265,665 & 248,554 & 1,083,181 & 982,672 \\
3 modules & 272,987 & 252,044 & 1,395,533 & 1,202,846 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of nodes and elements before and after adaptive meshing.  
Figure 4: 3D models cut sections.
## 3 Results and Discussion
### Synthesis of Methyl and Decyl Esters
However, as can be clearly observed, MEBE was the ester showing higher melting temperature and not DEBE, as would have been expected. Upon reversing the order of the esters shown above, a trend is revealed between molecular structure and melting points (Table 2). The melting points are seen increasing based on both the fatty acid carbon length and the alcohol length, but those from decyl esters follow a parallel trend to methyl ester, and do not show the same behavior (Figure 11). What reported above is found to be true for both T\({}_{\text{m}}\) and T\({}_{\text{c}}\) for all samples. This could be due to the odd-even numbered effect explained by [PERSON] et al. [17] and [PERSON] et al. [18], according to which different but parallel trends can be observed between odd-numbered and even-numbered carbon chain series. In addition, the length of the fatty acid carbon chain seems to have a higher impact on the resulting phase transition temperature of the ester, compared to the alcohol. The molar enthalpies of fusion (\(\Delta\)H in KJ/moL) could be seen increasing with increasing chain length and molecular weight; however, no clear trends could be observed for even and odd-numbered \begin{table}
\begin{tabular}{c c c c} \hline  & **Carbon Number** & **T\({}_{\text{c}}\) (Onset, \({}^{\circ}\)C)** & **T\({}_{\text{m}}\) (Onset, \({}^{\circ}\)C)** \\ \hline MEMY (C1-C14) & 15 & 13.5 \(\pm\) 1.2 & 14.7 \(\pm\) 1.9 \\ DEMY (C10-C14) & 24 & 22.9 \(\pm\) 0.2 & 25.2 \(\pm\) 0.4 \\ MEPA (C1-C16) & 17 & 24.2 \(\pm\) 0.2 & 26.3 \(\pm\) 0.0 \\ DEPA (C10-C16) & 26 & 28.9 \(\pm\) 1.2 & 29.0 \(\pm\) 1.9 \\ MESA (C1-C18) & 19 & 31.9 \(\pm\) 1.1 & 35.6 \(\pm\) 1.0 \\ DESA (C10-C18) & 28 & 33.8 \(\pm\) 1.3 & 36.2 \(\pm\) 0.8 \\ MEBE (C1-C22) & 23 & 41.5 \(\pm\) 1.0 & 47.9 \(\pm\) 0.7 \\ DEBE (C10-C22) & 32 & 44.7 \(\pm\) 0.6 & 44.8 \(\pm\) 0.7 \\ \hline \end{tabular}
\end{table} 
Table 2: Rearrangement of the previous table to show the dependency of phase transition temperature on fatty ester type and carbon chain length. As for Table 1, the colors indicate the temperature gradient from the lowest temperature values (blue) to the highest (red). Figure 11: Methyl esters and decyl esters both show a correlation between T\({}_{\text{m}}\) and T\({}_{\text{c}}\) and carbon chain length, but following different behaviors; this is believed to be due to the odd-even effect [17; 18; 19; 20; 22; 23]. The acid carbon chain length seems to have higher impact than the alcohol. esters as for T\({}_{\rm m}\). Nevertheless, all values measured were above 190 J/g with uncertainties around 1.5%, confirming the suitability of fatty esters as PCM. All of the samples proved to be stable to short cycles (three times), with variations lower than 0.5 \({}^{\circ}\)C. Additionally, supercooling lower than 7 \({}^{\circ}\)C were observed in all cases at a rate of 10 \({}^{\circ}\)C/min.
# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
###### Abstract  
Biometric identification and verification are essential mechanisms in modern society. Palm vein recognition is an emerging biometric technique, which has several advantages, especially in terms of security against forgery. Contactless palm vein systems are more suitable for real-world applications, but two of the major challenges of the state-of-the-art contributions are image deformations and time efficiency. In the present work, we propose a new method for palm vein recognition by combining DAISY descriptor and the Coarse-to-fine PatchMatch (CPM) algorithm in a parallel matching process. Our proposal aims at providing an effective and efficient technique to obtain similarity of palm vein images considering their displacements as discriminatory information. Extensive evaluation on three publicly available databases demonstrates that the discriminability of the proposed approach reaches the state-of-the-art results while it is considerably superior in time efficiency.  
Keywords:individuals identification; palm vein recognition; biometric; sparse matching; multi-core algorithm +
Footnote †: journal: Applied
## 3 Proposed Method
### General Architecture of the Model
This ensures that it is not necessary for the decoder to decode the watermark without error, as it is possible to average out the interference that occurs in order for the mapper to decode the watermark correctly, as shown in the diagram in Figure 13.  
A reverse mapping is then carried out. The mapper transforms the binary symbol into the pixel value in the given mosaic square, while the demapper transforms the decoded pixel value in the mosaic square into the binary symbol according to the adopted key. The sum of the symbols from all squares of the mosaic forms the final decoded binary signature. The proposed information mapper and demapper are based on entropy. According to information theory, entropy (also called Shannon entropy) is a measure of the uncertainty associated with a random variable. The Shannon entropy equation estimates the average minimum number of bits needed to encode a sequence of symbols based on the frequency of the symbols:  
\[H(x)=-K\sum_{i=0}^{N-1}p_{i}log_{2}p_{i} \tag{6}\]  
where \(K\) is a positive constant. It follows from the equation that any operation to increase the number \(N\) and equalize the values of the probabilities \(p\) results in an increase in entropy [42]. In the implemented mapper, regardless of the number of bits per symbol \(n\), the probability of the occurrence of each symbol is always equal for a given \(n\). For example, if \(n=1\), \(p=0.5\) for symbol 0 and \(p=0.5\) for symbol 1, and if \(n=2\), \(p=0.25\) for each of the symbols: 00, 01, 10, and 11. This means that as the number of bits in the binary signature \(N\) and the number of bits per symbol \(n\) increase, the entropy of the watermark increases. As shown in [43], the background of images influences human visual perception. A single texture feature can be easily noticed by the viewer, but when the texture concerns a more complex image, it can be difficult to detect. The complexity and uncertainty of the original image alter the visual perception threshold of the target image, a phenomenon described in 1997 by [PERSON] et al. [44] and termed entropy masking. Entropy is higher where the complexity and uncertainty of the image are greater. This leads to a reduction in the sensitivity of these areas, so the threshold for their perception increases accordingly, facilitating the transparent embedding of the watermark. On this basis, the following paper assumes that with the increase in the entropy of a watermark signature, its transparent embedding will be easier  
\[c=2^{n} \tag{5}\]0 Three different examples of watermark signatures after decoding with various artifacts visible, and the results of averaging the pixel values during the decoding process to remove artifacts and error-free decoding. to obtain than in the case of watermarks with much lower entropy, which is verified in Chapter 4. Below are examples of watermarks with increasing entropy obtained using the implemented mapper for different values of the parameters \(N\) and \(n\) (Figure 14).
## 3 Case Study
### Structural Modal Identification of Low Frequency Flexible Suspension Beam
The ERA is used to analyze the test data, and the first five order peaks are selected for analysis and calculation, and the first five order modal frequencies of the structure are obtained. Figure 12 shows the displacement response diagram obtained by the DIC. Figure 12: Displacement response diagram. (**a**) Displacement response curve of point 1. (**b**) Displacement response curve of point 2. (**c**) Displacement response curve of point 3. (**d**) Displacement response curve of point 4. (**e**) Displacement response curve of point 5. (**f**) Displacement response curve of point 6. Figure 13 is the test versus theoretical frequency response diagram. Table 6 is the frequency error table based on the ground test system identification based on the dynamic characteristics of the flexible structure and the theory modal frequency. The analysis shows that the error between the first five-order modal parameters identified and the finite element theory result is small, the error of the first order frequency  
thus verifying the feasibility and accuracy requirements of the designed flexible structure dynamic characteristics ground test system. The comparison results of the identified mode shape and the simulated mode shape are shown in Table 7:  
According to the modal confidence theory, the MAC value of the result obtained by the ERA and the mode shape result obtained by Patran is compared, as shown in Figure 14. The first five-order experimental modal vibration obtained by the ERA after the random initial displacement is applied to the structure. The model has a high degree of matching with the modal shape obtained by theory, and the modal confidence can reach more than 0.9, which verifies the feasibility and accuracy requirements of the ground modal test system for the dynamic characteristics of the flexible structure designed in this paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modal Order** & \begin{tabular}{c} **Identification** \\ **Frequency/Hz** \\ \end{tabular} & \
\begin{tabular}{c} **Theory** \\ **Frequency/Hz** \\ \end{tabular} &
\begin{tabular}{c} **Error/\%** \\ \end{tabular} \\ \hline
1 & 0.183 & 0.185 & \(-1.081\) \\
2 & 0.463 & 0.451 & 2.661 \\
3 & 1.081 & 1.119 & \(-3.396\) \\
4 & 2.488 & 2.458 & 1.221 \\
5 & 3.975 & 3.802 & 4.550 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of identification frequency. Figure 13: Test versus theoretical frequency response diagram.
## 3 Results and Discussion
### Evaluation and Discussion of the Sensor and Algorithm  
Using the above-described, new sensor system in conjunction with this developed algorithm, 80 cutting interruptions in a series of 83 intentionally enforced incomplete cuts in mild steel and stainless steel have been successfully detected (alpha error of 3.6%). In these experiments, cutting interruptions were activated by increasing the velocity, reducing the laser power, defocusing the laser, reducing of the gas pressure, or increasing the workpiece thickness. On the other hand, the new sensor identified no cutting interruption in 110 faultless cuts using the described algorithm (beta error of 0%). Additionally, a variation of the nozzle-to-workpiece distance between 1 mm and 3 mm showed no influence on the detection rate. This impressive high detection rate highlights the qualification of this system and evaluation algorithm for cutting interruption detection using fiber laser cutting machines. Moreover, the signals recorded during cutting either stainless steel or mild steel are very similar in terms of their signature. As a consequence, no adjustment of the detection scheme, nor the algorithm, is required when changing between these materials. The calculation of a cutting interruption shown in this publication requires 52 sampling points of the interrogation system, which at 20 kHz causes a sample delay of 2.6 ms. The presented algorithm is based on simple mathematical calculations, which require low computing power and time. In combination with a well-optimized digital signal processor, it should be able to detect a cutting interruption within less than 3 ms, which highlights the short reaction time. A further advantage of this system is the circular collection of the radiation by a ring mirror, which does not influence the primary laser beam. In addition, the ring mirror allows a movement direction independent sensing, which is not possible in lateral offset mounted sensors (e.g., the system from [PERSON] _et al._[18]). In contrast to camera-based sensing, which allows to calculate more cutting characteristics, such as kerf width or striations [14], the here-demonstrated system requires less calculation effort and, therefore, shorter reaction times.
## 3 Numerical Simulation
### Boundary Conditions and Element Types  
This study focuses on the numerical modeling of tunnels under airport pavements. Because of the high excitation impact effect of aircraft loads, the problem of stress wave reflection in the numerical simulation is encountered; therefore, the boundary conditions applied to the soil must be considered. Many types of soil boundaries, such as viscous, transmissive, and viscoelastic boundaries (each with its own characteristics and application ranges), are used in finite element calculations [30]. Based on previous studies, the problem of stress wave reflection inside the soil can be solved by extending the model size. In addition, because of the large scale of the numerical model built in this study, the stress wave reflection of the soil can be handled well; hence, an artificial static boundary can be set in the soil model. Hence, in the numerical simulation, the normal displacements, U, in the X and Y directions are all set to 0 for the front, rear, left, and right of the model; the bottom is completely fixed (U1 = U2 = U3 = UR1 = UR2 = UR3 = 0).  
[PERSON] et al. [31] conducted an in-depth study on the establishment of a finite element model of an asphalt pavement structure. They compared and analyzed the effects of various contact conditions and mesh cell types on the calculation and accuracy of the model. Based on their conclusions, the C3D8R (8-node linear unit) cell was chosen to simulate each component of the numerical model to represent the system characteristics as accurately as possible and improve computational accuracy and efficiency.
# Analysis of Available Components and Performance Estimation of Optical Multi-Band Systems
## 3 Quantifying the Capacity and Transparent Reach of an OMB System
### Results
For example, the worst channel of the S\({}_{1}\)-band attains an OSNIR slightly above 10 dB after 2250 km. This OSNIR value allows supporting a PM-QPSK modulation format for up to 2250 km, which is the attainable reach for this band when PM-QPSK is employed. Next, in order to calculate the overall capacity, we consider line rates of 100 G, 200 G, and 300 G for PM-QPSK, PM-16 QAM, and PM-64 QAM, respectively. The attainable capacity in each band is the product of the number of channels that clear the OSNIR threshold for a BER of \(10^{-3}\) times the channel line rate. This analysis reveals that when a higher modulation format is enabled, the overall capacity increases; however, the bit rate (C) times transmission reach (L) product decreases. This is expected, since a migration from PM-QPSK to PM-16 QAM doubles the overall capacity; however, an about four times higher OSNIR is expected in order to attain the  
Figure 3: OSNIR for different transmission lengths. An inter-node distance of 150 km is considered. same BER. Finally, with the proposed amplification scheme, a capacity of 43.8 Tb/s can be attained for up to 15 nodes when PM-QPSK is employed, whilst between adjacent nodes, the overall capacity can reach up to 131.4 Tb/s. A direct benchmarking of these results with relevant experimental works [5, 8, 15, 28] is not possible, as we consider different system details, such as the amplification range, existence of WSSs, etc. However, our results are aligned with the general trends in terms of capacity, due to which more than 100 Tb/s can be attained only in short links, e.g., less than a few hundreds of km, while in longer links, a capacity of less than 100 Tb/s can be achieved. It is worth mentioning that in our study, we consider transmission only in the S, C, and L-bands, as there, the amplification technology is more mature compared with the O and E-bands. The overall capacity will be greatly enhanced as more amplifiers with the required features, e.g., flat gain over a spectral region, sufficient gain, low-noise figure, etc. for the O, the E, and even the U-band will become commercially available.
# How Human Mobility Models Can Help to Deal with COVID-19
## 3 Mobility Models
### Mobility Simulators  
Due to the limitations of mobility traces and synthetic models, some authors often make use of pedestrian simulators to evaluate mobility. A mobility simulator is a tool where we can create our own scenarios (mainly buildings or city areas) and define the number of pedestrians or vehicles, their type of movement, and destination. Then, when running the simulation we can see the mobility of the pedestrians on the defined spaces. These types of tools are mainly used by architects and civil engineers for urban and traffic planning. Nevertheless, the use of these mobility simulators has been proposed in various research works for evaluating OppNets In [8; 11; 61].  
Properly speaking, mobility simulators cannot be considered a model, since they are a combination of several models, used together in order to generate a realistic mobility trace.  
There are several mobility simulators. For example, LEGION [62] is a commercial tool aimed to simulate and analyse the foot traffic on urban scenarios including rail and metro stations, stadiums, shopping malls, and airports. Simulation of Urban MObility (SUMO) is also a mobility traffic generator written by the German Aerospace Center (DLR) [63]. The main focus is on the simulation of public transport, pedestrians and vehicles, including speed limits, traffic lights, etc.  
Finally, PedSim [64], the tool used in this paper, is an open-source pedestrian simulator. Similar to the previous simulators, we can create our own urban scenarios, defining buildings, train stations, etc. as well as other open areas of the city such as squares, streets, parking lots, among others. Pedestrian mobility can be defined in several ways, considering the number of pedestrians in a particular area, the speed at which they travel, the time a group of pedestrians enters or leaves a place, etc. Concretely, the scenario is defined in a script that declares the different elements of the scenario to simulate as well as the behaviour of pedestrians.  
When the scenario is defined, PedSim simulates the pedestrian movement based on a coupled generic model using a differential equation, known as the social force model, developed by [PERSON] and [PERSON] [65]. This model of social force is generally used in this type of pedestrian simulators such as the previously mentioned Legion [62] and SUMO [63]. The simulation generates the movement of pedestrians in the defined walkable areas, avoiding obstacles. Finally, we obtain a mobility trace with the pedestrian locations throughout the simulated time in the designed scenario. In OppNet evaluation, this trace is used to study the diffusion of messages using Opportunistic Networks simulators. In this paper, we will use this trace to study the probability of infection in the studied scenarios.
# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 2 Food Spectral Print Approaches
### UV-Vis Spectroscopy
[37] studied 112 samples, collecting UV-Vis absorption spectra from 190 to 800 nm. They employed principal component analysis (PCA) to differentiate the samples and developed classification models, including SIMCA, linear discriminant analysis (LDA), discriminant partial least squares (DPLS), and support vector machines (SVM). Standard normal transformation (SNV) was the most effective among seven mathematical pre-treatments. The SNV-treated LDA models showed a high average classification rate of 98.96% in the training set and 100% in the testing set, demonstrating the effectiveness of UV spectroscopy combined with chemometric analysis for classifying rice wines by region. Table 2 presents the examples discussed above and others that summarize the use of UV-Vis spectroscopy in analyzing wines and wine byproducts. UV-visible and infrared (IR) spectroscopy are both valuable analytical techniques, but they differ in their principles, applications, and the types of information they provide. UV-Visible spectroscopy focuses on electronic transitions and is particularly useful for quantitative analysis. In contrast, IR spectroscopy centers on vibrational transitions and is ideal for qualitative analysis of molecular structures. In the following section, we will discuss some applications of IR spectroscopy techniques in wine analysis, specifically mid-infrared (MIR) and near-infrared (NIR) spectroscopy.