esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation of Thermal Processes in the Food Industry Using CFD
### Examples of Thermal Processes Modeled Using CFD
#### 3.2.3 Pasteurization and Sterilization Processes  
Pasteurization is a thermal process where food is subjected to controlled heating to a specified temperature for a defined time, followed by rapid cooling. The effectiveness of pasteurization hinges on the precise regulation of temperature and exposure time, parameters that can be rigorously optimized using CFD simulations. In these simulations, both heat conduction within the material and convective heat transfer at the fluid boundaries are critical for accurately capturing the thermal profile of the product [84].  
CFD simulations of pasteurization leverage the principles of fluid mechanics and thermodynamics, beginning with the fundamental energy and mass conservation equations. For these systems, the energy equation is typically expressed as Equation (2). This equation is often coupled with the Navier-Stokes equations governing fluid motion (Equation (1)).  
To simulate pasteurization accurately, CFD models often incorporate the Boussinesq approximation for natural convection, which accounts for buoyancy effects due to temperature-induced density variations. This approach enhances the realism of the model by simulating thermal gradients that cause fluid movement, thereby improving the accuracy of predictions for temperature distribution within the food matrix [14; 85]. In CFD applications in food pasteurization, the FVM is commonly used to discretize the governing equations, transforming partial differential equations into a set of algebraic equations that can be solved iteratively. The FVM's control volume approach conserves fluxes across each cell interface, making it particularly suitable for modeling complex fluid flows and heat transfer in food products with irregular geometries. Advanced meshing techniques are applied to create fine grids around regions of high thermal gradients, which ensures numerical accuracy in capturing rapid temperature changes at fluid-solid interfaces, such as the container wall [42]. These simulations generate detailed temperature profiles throughout the product, allowing engineers to evaluate thermal lethality by calculating the cumulative microbial reduction using the concept of FF-values (F-value equivalents), which measure the time-temperature effectiveness against microbial populations. The FF-value is defined as the equivalent time (in minutes) at a reference temperature, typically 60 \({}^{\circ}\)C, that would result in the same level of microbial inactivation as a given thermal treatment at varying temperatures. By adjusting parameters such as the inlet flow rate, heating time, and target temperature, engineers can optimize pasteurization processes to achieve microbial inactivation while preserving the sensory and nutritional qualities of the food.
## 1 Introduction
However, most of the current research on the combination of GCN and time-series models has been applied to terrestrial networks such as traffic flow or terrestrial mobile traffic. Although there are topological relationships in terrestrial networks, they are different from those between satellites. The topological structure in LEO constellations changes over time. Currently, there is no modification of the model structure specifically for the task of predicting on-board traffic in LEO satellites. If the method of combining a GCN with a time-series model is directly used to train the model, it will be unreasonable and lack interpretability. The model proposed by the authors also employs data fusion and separation techniques to independently process the collected traffic volume and link the connectivity data. By separately analyzing and then integrating the traffic data and link status data, the model can more accurately capture the spatio-temporal characteristics of network traffic. Additionally, by incorporating a spatio-temporal attention mechanism, the model dynamically adjusts the importance weights of different time steps and spatial nodes, thereby better understanding the dynamically changing topology and traffic distribution patterns in low Earth orbit (LEO) constellations. This design not only improves the model's accuracy in predicting traffic changes, but also enhances its adaptability to bursty traffic and complex network environments. In this paper, in light of the characteristics of the dynamic topology and high-frequency data streams of LEO constellation network traffic, the authors proposed a DST-LEO (dynamic spatio-temporal low Earth orbit) satellite traffic prediction model to accomplish the task of network traffic prediction in LEO constellations. Specifically, on the basis of the original GCN-time-series prediction model, the model adds multiple attention mechanisms including the traditional attention mechanism and the spatio-temporal attention mechanism. These mechanisms were used to capture the more subtle changes in the satellite positional relationships caused by time variations that were previously ignored. These details directly affect the presence or absence of traffic (for example, the disconnection of cross-orbital satellites). Afterward, a multi-source data separation and fusion strategy [14] was adopted to split the data of traffic volume and traffic connection/disconnection into two parts for processing. This enabled the model to fully learn the characteristics of the data with different physical meanings. Subsequently, the data were spliced and fused in specific dimensions. Finally, the ultimate prediction of the LEO constellation network traffic was achieved through the attention mechanism and the fully-connected layer. Figure 2: Topological relationship of LEO constellations.
## 3 Experimental and Results
### Physical Properties and Strength  
The comprehensive study of the physical properties of unweathered (new) kurkar stones and weathered stones drilled off the Panorama Sea Walls was carried out to reveal kurkar deterioration trends. Considering the anisotropy of kurkar sandstone [1], the physical properties and strength of kurkar stone were tested in two directions: parallel to rift and perpendicular to it. (The rift is the plane along which the stone cleaves with the greatest ease [19]. The rift is due to parallel cracks in the quartz of the rocks. The rift plane's effect on the physical and mechanical properties of the natural stone was discussed in detail elsewhere [20; 21].) Saturation (Hirschwald's) coefficient was calculated as a ratio of capillary and total porosities [22]. [PERSON]'s coefficient has been widely used for estimating the stones' softening trends since 1911 [22]. The [PERSON]'s coefficient is the saturation ratio of the sample obtained from the 24 hours' porosity test. It quantifies the proportion of the porous network that is freely accessible to water under natural absorption conditions. This coefficient was found to be a beneficial one for assessing the trends of building stones' durability [3; 19; 20; 21; 22]. The saturation ratio (\(\leq\)1) is linked to the porous structure's connectivity: a high saturation coefficient often means high connectivity [24]. Stones with high saturation coefficient are substantially more susceptible to weathering, especially to frost weathering [3; 19]:  
Figure 12: Kurkar stone upon the petrographic investigation. The black areas are pores: (**a**) Plain light; (**b**) Polarized light. S > 0.85--susceptible. * 0.75 < S < 0.80--moderate resistant. * S < 0.75--frost resistant. Generally, the frost climate conditions are a sporadic event in Acre Bay. However, [PERSON]'s coefficient is a more straightforward, smarter, and faster way to estimate weathering trends of stones subjected to the salt crystallization. As the results published elsewhere have revealed, this approach is possible: the life cycle of sandstones subjected to the salt crystallization might be at least twice shorter than during their exposure to frost weathering [25]. One of the most critical issues affecting sandstones' resistance to salt weathering is the drying rate function, i.e., the sandstones characterized by a relatively slow drying rate show very intensive scaling damage [26]. The test of water evaporation capacity was carried out for both unweathered and weathered kurkar stones according to the procedure described in [24]. The ratio between amounts of evaporated water and water absorbed in 7 days was calculated. The results of the tests are summarized in Table 2.  
The results presented in Table 2 have revealed the severe deterioration of kurkar sandstone caused by stone weathering in exposure to the hazardous offshore conditions. In comparison to unweathered kurkar stone, the weathered samples were characterized by the following properties:
## 3 Results
### Conditions under Which PROS Can Be Trapped in A Local Optimum  
Given the formulation of the algorithm, it is obvious that a candidate solution can only move strictly orthogonally in the search space. No diagonal or other kind of movement is allowed, which can lead to the candidate solution being trapped in a local optimum under specific conditions. This behavior was not exhibited in any of the test examples examined. However, theoretically there are specific cases where it can happen if there is no improvement in the value of the objective function in any orthogonal direction. Below we  
Figure 9: Time needed for the various optimizers, for all 12 objective functions and \(D\) = 5, 10, 30, and 50. The y-axis is in logarithmic scale.  
examine such a special case in a two-dimensional problem, where the aim is to minimize the simple two-variable function \(f(x)\):  
\[f(x)=f(x_{1},x_{2})=x_{1}+x_{2}-3x_{1}x_{2}+1 \tag{2}\] \[x_{1},x_{2}\in[0,1]\]  
We assume that at a specific point of the search, the current candidate solution lies on the origin of axes, i.e., the point \(x_{1}=0\), \(x_{2}=0\). The values of the objective function at the four corners of the \([0,1]^{2}\) square are the following:  
* \(f(0,0)=1\) (current solution)
* \(f(1,0)=f(0,1)=2\)
* \(f(1,1)=0\) (global minimum)  
The function is plotted in Figure 10 in 3D (as a surface) and 2D (using contour lines). If the current point is (0, 0) then PROS will be permanently trapped at this position. Indeed, it is \(f(x_{1},0)>f(0,0)\) for every \(x_{1}\in[0,1]\), while \(f(0,x_{2})>f(0,0)\) for every \(x_{2}\in[0,1]\). The current point (0, 0) is a local minimum and, in addition, any change in only one of the \(x_{1}\) or \(x_{2}\) variables cannot lead to a better value of the objective function (\(f\)). There is no improvement in \(f\) in any of the orthogonal \(x_{1}\) or \(x_{2}\) directions considered on their own. To find a better value of the objective function, the optimizer would need to move diagonally, but such a movement is not allowed in PROS.
# Transition Metal Coordination Compounds as Novel Materials for Dye-Sensitized Solar Cells
## 2 Characterization and Principle of Operation of Dye-Sensitized Solar Cells
### Application of Coordination Compounds in DSSCs  
The development of DSSCs is mainly focused on improving the performance of the utilised dyes. The most recent reports are dedicated to the use of coordination compounds of transition metals, focusing on dyes based on non-heavy metal elements, so as to serve as alternatives to dyes based on highly toxic heavy metals, such as Pt\({}^{2+}\), Ir\({}^{3+}\) and Ru\({}^{2+}\)[40]. Developments regarding such alternative dyes focus on compounds that are environmentally friendly and originate from transition metals that are most commonly present in the natural environment, such as from copper [14]. Due to advances in photophysical and photoelectrochemical research using copper compounds as catalysts or photosensizers, applications were found, for example, in energy conversion or storage.  
Copper in complex compounds contained in DSSCs can serve not only as a dye but also as a redox mediator. Copper mediators as redox couples have found applications in electronic devices despite the sparse presence of solar cells in the device [23]. This is due to their high ability to regenerate the dye in the cell using merely 100 mV of driving force [49], providing and supporting a large cell output voltage of up to 1 V. Compared to previously mentioned redox mediators, such as \(\mathrm{I}^{-}/\mathrm{I}^{3-}\), copper compounds do not pose a threat in terms of corrosivity [23]. Moreover, their longer diffusion length in systems with similar properties can provide higher photocurrents and higher fill factors [50]. Compared to iodine/iodide or cobalt-based complex compounds, the copper-containing compounds provide less energy loss arising from the transition from the reduced to the oxidized state [51].  
An application of Cu-based compounds as dyes in solar cells is possible by improving the lifetime or degradation of some cell components caused by photoinduced oxidation under UV light (UV absorption capacity) [52; 53]. These compounds exhibit luminescent properties [54]. The use of copper compounds in dye-sensitized solar cells, as recently reported in the literature, has achieved power conversion efficiencies up to 32%, under 1000 lux fluorescent illumination [23]. Copper compounds are also used in OLED devices, batteries or systems responsible for water oxidation using electrocatalysts [39].
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 3 Results
### The Proposed Single Element Monopole Antenna Results  
By investigating the performance of the proposed monopole antenna shown in Figure 5, it is evident from Figure 11 that a bandwidth of 2.38 GHz was achieved with a return loss of \(-\)24.12 dB at the resonance frequency. As previously mentioned in Tables 1 and 2, the bandwidth was enhanced from 1.15 GHz, which was obtained by using iteration 1 with ground 1, to 2.38 GHz obtained when using iteration 5 with ground 4, which presented the effect of introducing the DGS on the antenna performance, as well as applying different techniques on the patch; these techniques performed on the patch and the ground plane caused the proposed single antenna to have a wider bandwidth than that achieved in [25] by an increase of 5.24% in the same band. Figure 12 presents the radiation characteristics of the single patch antenna for 5G operation; the 2-D and 3-D radiation patterns were carried out at 60.5 GHz. From Figure 12, it could be observed that the antenna had a directional pattern in both planes and achieved a high gain of 9.9 dB in the broadside direction. This gain was higher when compared to the works proposed in [26; 27], which achieved a maximum gain of 6 and 7.8 dBi, respectively. The achieved gain of the proposed antenna, as well as the efficiency, that came over 90%, was considered a suitable result for a compact monopole patch antenna. For further explanation of the monopole antenna performance, the simulated surface current distribution for the proposed antenna was implemented at 60.5 GHz, as shown in Figure 13. The current distribution was mainly concentrated in the feed line and adjacent to rectangular parasitic strips beside the feed line. Besides,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline A1 & 0.3 & A5 & 0.5 \\ A2 & 0.2 & AL1 & 1.8 \\ A3 & 0.4 & AL2 & 3.9 \\ A4 & 0.8 & AL3 & 3.04 \\ SGL & 3.84 & SGW & 0.4 \\ Wa & 10.4 & - & - \\ \hline \end{tabular}
\end{table}
Table 4: Parameters of the antenna array.  
Figure 10: Simulated S-parameters curves versus frequency of the proposed mmWave 5G antenna array.  
the current was primarily distributed underneath the feed line, which showcased the essential effect of the DGS and its constructive coupling effect on the overall performance of the antenna.  
Figure 11: Simulated S-parameters curves versus frequency of the proposed mmWave 5G antenna.  
Figure 12: Simulated radiation characteristics of a single patch antenna (**a**) 2-D radiation pattern; (**b**) 3-D radiation pattern.
# A New Calibration Method for Broadband Complex Resistivity Measurement System of Rocks and Ores
## 2 Measurement System
### System Error Analysis
Based on the principle of current division, we can derive:  
\[I_{3}\ =\ I_{\rm s}\frac{R_{\rm s}}{Z_{\rm c}},\ I_{2}\ =\ \frac{(I_{3}+I_{\rm s})Z_{4}+I_{ \rm s}R_{\rm s}}{Z_{\rm c}+Z_{3}} \tag{2}\]  
By combining Equation (2), we obtain:  
\[I_{\rm x}\ =\ I_{\rm s}\frac{(Z_{3}+Z_{\rm c}+Z_{4})(R_{\rm s}+Z_{\rm c})+R_{ \rm s}Z_{\rm c}}{Z_{\rm c}(Z_{3}+Z_{\rm c})} \tag{3}\]  
Similarly, based on the principle of voltage division in circuits:  
\[\frac{U_{\rm m}}{U_{\rm m}}^{\prime}\ =\ \frac{Z_{\rm c}}{Z_{\rm c}+Z_{2}},\ \frac{U_{\rm n}}{U_{\rm n}}^{ \prime}\ =\ \frac{Z_{\rm c}}{Z_{\rm c}+Z_{3}} \tag{4}\]  
Substituting \(\Delta U_{\rm mn}=U_{\rm m}-U_{\rm n}\) and \(\Delta U_{\rm mn}\)\(\,\)\(=U_{\rm m}\)\(\,\)\({}^{\prime}-U_{\rm n}\)\(\,\)into Equation (4):  
\[\Delta U_{\rm mn}\,\)\({}^{\prime}\ =\ \Delta U_{\rm mn}+\frac{Z_{2}U_{\rm m}-Z_{3}U_{ \rm n}}{Z_{\rm c}} \tag{5}\]  
In actual testing, \(Z_{2}\), and \(Z_{3}\) are often caused by the resistance of the electrodes themselves or the resistance of the electrolyte. Thus, \(Z_{2}\approx Z_{3}=Z_{2,3}\), and therefore Equation (5) can be simplified as:  
\[\Delta U_{\rm mn}\,\)\({}^{\prime}\approx\Delta U_{\rm mn}+\frac{Z_{2,3}\Delta U_{\rm mn}}{Z_{\rm c}} \tag{6}\]  
In the current and voltage calibration equations discussed above, we primarily considered the effects of parasitic capacitance in the acquisition channels and the device effects of the measurement system itself. These considerations are based on the assumption that the electronic components selected in the circuit design are ideal. However, even high-precision resistors are subject to parasitic capacitance and lead inductance effects [36]. For instance, a 10 k\(\Omega\) resistor with a 1 pF parasitic capacitance can produce a phase shift of 6 mrad at 100 kHz. These subtle parasitic capacitances similarly affect the final measurement results. To accurately measure the spectral response characteristics of high-impedance samples, this instrument uses sampling resistors up to 10 M\(\Omega\). Therefore, it is necessary to account for the parasitic capacitance of the sampling resistors in the calibration model.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 3 Proposed Methodology
### Avoiding Overfitting
#### 3.4.2 Data Variation for Temporal Stream  
Instead of clipping the prominent areas of the picture center, as performed in [32], we incorporated a method of data variation in our proposed work to enhance the data variety. All frames are cropped from four corners by randomly choosing the height and width for each set, which was to make use of multiple scale representations. After resizing the clipped areas to \(224\times 224\) and flipping them horizontally, they were presented to the proposed model as input for training the network. This kind of augmentation method significantly increases the variability in inputs during the training process, which helps to minimize the issue of overfitting.
# Multi-Label Emotion Recognition of Korean Speech Data Using Deep Fusion Models
###### Abstract
For example, the Mel spectrograms, which represent the frequency and amplitude of speech signals over time, and MFCCs, which represent the distinctive spectral features of speech over time, can be used together [21,22]. Therefore, studies on models that exploit a combination of diverse speech features are needed to improve SER performance. Since few studies have conducted multi-label emotion classification based on Korean speech data, this study proposes deep learning-based fusion models for multi-label classification using Korean speech data. Specifically, three types of speech features are utilized: Log-mel spectrogram, MFCCs, and Voice Quality Features (VQFs). Subsequently, the performances of the models constructed using one of these speech features, fusion models constructed using two of three speech features, and a fusion model using all three speech features are compared and analyzed. The model construction follows four steps: (1) preprocessing Korean speech data which are labeled with 'Sadness', 'Happiness', 'Neutral', 'Anger', and 'Disgust'; (2) applying data augmentation techniques to reduce the variation in the number of labeled data; (3) extracting speech features, including the Log-mel spectrogram, MFCCs, and VQFs for constructing multi-label classification models; (4) validating performance of the constructed models. The models proposed in this study are expected to have applications in fields such as mental health care, customer service, and entertainment. The remainder of this paper is structured as follows: Section 2 reviews the related literature. Section 3 introduces the data and labeling process used in this study. Section 4 describes the speech feature extraction process and model construction. Section 5 presents the experimental results and Section 6 presents the concluding remarks, including the study's contributions and future study directions.
## 4 Description of the Experiments  
### Concrete Properties and Production of Specimens  
The present research focused on the analysis of experimental results obtained by testing real 8 m long RC beams. One single-layer beam (SLB) and one TLB were tested. The TLB consisted of normal strength concrete (NSC) in the tensile zone and steel fiber high-strength concrete (SFHSC) in the compressed one. The tests were carried out according to the German Concrete Association requirements [23], which are similar to those of RILEM [24] and other publications [25]. According to [23], the beams were loaded orthogonal to the casting direction. The steel fibers had an ultimate tensile strength of 1100 MPa, a length of 50 mm and a diameter of 1 mm. The number of fibers per 1 kg was about 3150.  
The concrete component properties are presented in Table 1. Dried aggregates were used for all mixtures. The concrete mixture proportioning per 1 m\({}^{3}\) of concrete was as follows.  
Steel bars with an ultimate strain of 25% and a modulus of elasticity, yield strength and ultimate strength of 200 GPa, 500 MPa and 525 MPa, respectively, were used for the reinforcement [26]. The reinforcement schemes of the tested beams are shown in Figure 1. The initial pre-stress was 900 MPa for both specimens.  
To obtain the compressive and splitting tensile strengths of SFHSC, 6 15 \(\times\) 15 \(\times\) 15 cm cubes were tested. With this aim, a testing machine with a 5000 kN load capacity and a 100 cm maximum stroke was used. Three specimens were tested to obtain the compressive strength and three others were tested for the splitting tensile strength. The results of these tests are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Units** & **Quantity** \\ \hline Cement density & kg/m\({}^{3}\) & 3100 \\ Fly ash density & kg/m\({}^{3}\) & 2300 \\ Poly-carboxylic ether-based & \(\mathrm{kg}\)/m\({}^{3}\) & 1070 \\ super-plasticizer & \(\mathrm{kg}\)/m\({}^{3}\) & 1170 \\ Long-term retarder & \(\mathrm{kg}\)/m\({}^{3}\) & 1170 \\ Natural sand fraction size & mm & 0 to 2 \\ Sand density & \(\mathrm{kg}\)/m\({}^{3}\) & 2660 \\ Gravel fraction & mm & 2 to 8 \\ Gravel density & \(\mathrm{kg}\)/m\({}^{3}\) & 2640 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete component properties.  
Concrete class C 40/50 was used for the SLB and for the NSC layer of the TLB. Concrete class C 90/100 was selected for the SFHSC layer of the TLB. The cross-section of both beams was \(25\times 60\) cm\({}^{2}\). The depths of the TLB NSC and SFHSC layers were 45 cm and 15 cm, respectively.  
The pre-stress was transferred to both the SLB and TLB one day after casting in real industrial conditions. After that, the beams hardened in natural conditions. After casting the SFHSC layer of the TLB at the university laboratory (Figure 2), both specimens were kept in natural conditions and tested after 28 days.