esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion  
### Headspace Gas Analysis  
Oxygen decrease occurred with concomitant carbon dioxide evolution during storage at all temperatures except for 0 \({}^{\circ}\)C (Figure 2a,b). Gases variations took place faster as storage temperature was increased from 4 to 12 \({}^{\circ}\)C, regardless of treatment. At 0 \({}^{\circ}\)C, gases retained close to the initial MAP composition both on ground beef treated with \"_tsipouro_\" and controls for almost 20 days in accordance with the recorded low TVC growth (Figure 2a,b and Figure 3.e). Controls stored at temperatures \(>4\)\({}^{\circ}\)C displayed a continuous change in headspace gas composition from the beginning of storage. On the contrary, in samples treated with \"_tsipouro_\", O\({}_{2}\)% and CO\({}_{2}\)% remained stable for 8, 3, and 1.3 days during storage at 4, 8, and 12 \({}^{\circ}\)C, respectively, followed by a linear change on both gases. At each of the aforementioned critical time points per temperature, TVC have reached almost 7.0 log CFU/g, suggesting that headspace gas composition could be used as a spoilage indicator (Figure 3.e-IVe). With regards to controls, similar trends were found with previous studies on beef steaks packaged in high oxygen MAP and the storage of 21 days at 4 \({}^{\circ}\)C resulted in a modified atmosphere of ca. 50% O\({}_{2}\) and 50% CO\({}_{2}\)[34]. Notably, on day 0, the concentration (%) of O\({}_{2}\) in packages of ground beef packaged with \"_tsipouro_\" ranged from 63.7 to 71.8% instead of the target MAP composition of ca. 80%, achieved in controls (Figure 2a). The latter observation may be related to the rapid release of \"_tsipouro_\" vapors right after its addition in the absorbent pads, which possibly increased vapor pressure in the package headspace and thus, partly limited the filling of packaging with the targeted MAP composition. However, even in this case, \"_tsipouro_\" showed higher stability on O\({}_{2}\) levels compared to controls, regardless of storage temperature, suggesting a strong preservation potential.  
Figure 2: Evolution of (**a**) O\({}_{2}\)% and (**b**) CO\({}_{2}\)% on ground beef (500 g) packaged with absorbent pads supplemented with a total volume of 50 mL “_tsipouro_” (40 mL under the ground beef piece and 10 mL under the labelling, facing the headspace) and stored under 80% O\({}_{2}\): 20% CO\({}_{2}\) at 0, 4, 8, and 12 \({}^{\circ}\)C. Untreated samples served as controls.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 4 Proposed VLC Network for High Mobility  
In this section, we introduce a comprehensive solution to ensure a seamless connection to vehicular VLC users. The system aims to operate as support or backup for DSRC based technologies. This solution modifies the typical VLC network star topology to increase uplink reliability for a vehicular tunnel scenario. Further to this, it includes a novel handover strategy that covers the signal detection and execution phases. The subsections below describe the proposed VLC network design for high mobility.
## 2 Experimental
### Hydrolysis Kinetics Analysis  
The proposed kinetic model of inulin hydrolysis catalyzed by homogeneous ILs was established and it successfully predicted the inulin hydrolysis in wider ranges of experimental conditions [13]. In this work, the hydrolysis kinetics of PEK-C-ILs powder catalyst was also studied in inulin hydrolysis system. Due to the catalytic mechanism of inulin hydrolysis by PEK-C-ILs is consistent with that by the homogeneous ILs, the proposed kinetic model may also be employed to describe the inulin hydrolysis by the former. The hydrolysis kinetics followed the typical first order model with hydrolysis reaction [23]. Hydrolysis ratio (\(R\)) can be obtained in the following equation,  
\[R=\frac{C_{\text{R}}}{C_{\text{max}}}=1-\exp(-kt) \tag{4}\]where \(C_{\rm R}\) is the reducing sugars concentration, and \(C_{\rm max}\) is the final reducing sugars concentration when inulin is hydrolyzed completely (mol/L).  
The kinetic constant \(k\) is a function of absolute temperature according to the Arrhenius expression as follows  
\[k=A\exp(-\frac{E_{\rm a}}{RT}),\ \ A=A_{0}C_{\rm IL}^{\rm m} \tag{5}\]  
where \(E_{\rm a}\) is the activation energy (kJ/mol), \(R\) is the molar gas constant (8.314 \(\times\) 10\({}^{-3}\) kJ/mol K), \(T\) is the temperature (K), \(A\) is the pre-exponential factor (min\({}^{-1}\)) and was expressed by the corresponding literature [24]. \(A_{0}\) is the pre-exponential parameter for inulin hydrolysis (min\({}^{-1}\)), m is the ILs concentration exponent for the rate constant \(k\). \(C_{\rm IL}\) is the concentration of ILs (\(w/w\) %, the weight ratio of ILs to water and PEK-C-ILs). \(C_{\rm IL}\) = \(x\)% \(\times\)\(y\)\(\times\)\(M_{\rm IL}\). \(x\)% is the concentration of PEK-C-ILs powder catalyst (\(w/w\) %, the weight ratio of PEK-C-ILs to water and PEK-C-ILs). \(y\) is loading amount of ILs (1.541 \(\times\) 10\({}^{-3}\) mol/g PEK-C-ILs powder). \(M_{\rm IL}\) is the relative molecular mass of the ILs (316 g/mol). In addition, activated energy and pre-exponential factor are related to the concentration of catalyst (\(C_{\rm IL}\)). Thus, \(E_{\rm a}\) could be expressed as follows  
\[E_{\rm a}=a\exp(-\frac{C_{\rm IL}}{b})+c \tag{6}\]  
where \(a\), \(b\) and \(c\) are constants.
## 3 Results and Discussion
### Engineering Durability of Foamed MOC Materials  
Engineering durability is a key issue in the application of materials, thus the long-term stability of foamed hydrophobic materials in complex environments is very important. Therefore, we studied the influence of mechanical polishing and chemical corrosion on the hydrophobic properties of the samples. The result of the mechanical grinding experiment is shown in Figure 11. With the increase of the thickness of mechanical grinding, the water contact angle of the sample did not decrease significantly, indicating that the fluorine-containing alkyl chains were evenly distributed in the sample,  
Figure 10: F-MOC hydrophobic effect: (**a**) is the water contact angle test graph, (**b**) is the overall hydrophobic effect graph of F-MOC, and (**c**) is the comparison graph of F-MOC and N-MOC.  
Figure 9: The water contact angle of different ratios of F-MOC ((**A1**)-(**A5**) are FAS content of 1%, n (FAS): n (TEOS) = 1:8, the addition of H\({}_{2}\)O\({}_{2}\) increased from 4% to 12%, (**B1**)-(**B5**) were when the additional amount of H\({}_{2}\)O\({}_{2}\) was fixed at 10%, and the TEOS content increased from n (FAS): n (TEOS) = 1:6 to n (FAS): n (TEOS) = 1:14, (**C1**)–(**C5**) were the additional amounts of H\({}_{2}\)O\({}_{2}\) fixed at 10%, n (FAS):n (TEOS) = 1:8, the FAS content increased from 0.5% to 2.5%).  
giving the material overall hydrophobicity. The result of immersion in NaCl, NaOH, and HCl solutions in Figure 12 showed that the material still maintained a high water contact angle, indicating the chemical stability of the foamed hydrophobic MOC material in harsh environments such as acid-base and salt solutions, proving that composite materials have potential value in engineering applications.
## 4 Numerical Method of Support Design
### Basic Assumptions and Methodology for Numerical Modeling
To accurately represent the true three-dimensional behavior of the rock mass, the 2D model must incorporate pre-face conditions, the state of displacement at the excavation face, and the progressive development of deformation and the plastic zone behind the face [47]. To address these challenges, a longitudinal displacement profile (LDP) can be employed to establish a quantitative relationship between the wall displacement perpendicular to the tunnel axis and the distance from the excavation face. In the present study, given the use of yielding support systems, an unsupported version of LDP based on the following relationships proposed by [PERSON] and [PERSON] [48], was applied, as follows:  
\[u_{0}^{*}=\frac{u(x)}{u_{max}}=\frac{1}{3}\cdot e^{-0.15\cdot\frac{\eta_{1}}{ \
u_{eq}}}, \tag{26}\]  
\[u_{-}^{*}=\frac{u(x)}{u_{max}}=u_{0}^{\prime}\cdot e^{\frac{\gamma}{\
u_{eq}}}, \tag{27}\]  
\[u_{+}^{*}=\frac{u(x)}{u_{max}}=1-\left(1-u_{0}^{\prime}\right)\cdot e^{-\frac{ 3\cdot x}{2-\gamma_{1}}}, \tag{28}\]  
where \(x\) denotes the distance from the tunnel face, \(u_{0}^{*}\), \(u_{-}^{*}\) and \(u_{0}^{*}\) represent the normalized radial wall displacements at the excavation face (\(x=0\)), ahead of the face (\(x<0\)), and behind the face (\(x>0\)), \(u_{max}\) is the maximum predicted radial wall displacement, while \(u(x)\) describes the radial wall displacement as function of the distance from the excavation face. The LDP curve, calculated using Equations (26) and (27), was incorporated into the numerical models by applying the average pressure reduction technique, following the guidelines provided by [PERSON] and [PERSON] [47]. An exemplary LDP curve, obtained for the hoisting machine chamber, is depicted in Figure 3.  
The numerical calculations in the present study were conducted using a graded mesh with three-noded triangular elements, ensuring an optimal balance between computational efficiency and accuracy. While the element size varied across the model, a higher mesh density was applied in the vicinity of the excavations to improve result precision. This was achieved by locally refining the mesh using an automatic density adjustment function, which reduces element size in areas of interest while maintaining larger elements in less critical regions. As a result, the element size near the excavations was approximately 0.2 m, allowing for a more accurate representation of stress distribution and deformation. Based on the assumptions presented above, as well as the interpreted geological profile and the planned excavation sequence, two numerical models were developed for the hoisting machine chamber and the technological shaft inset (Figures 4 and 5). Figure 4: Numerical model for the hoisting machine chamber. Figure 3: LDP curve for the hoisting machine chamber.
# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Proactive (Link-State-Based) Routing Protocols
Proper energy-efficient policies in MQ-Routing can also increase the minimum lifetime of a node and may lead to a fairer balance of energy. The main disadvantage of MQ-Routing is that it exploits only a single path at a time for data transmission and rapidly switches to the best path along with the Q-values in the routing table. Finally, [PERSON] et al. [161] proposed QG-OLSR, a kind of new quantum-genetic-based OLSR protocol for MANETs. QG-OLSR adopts the MPR technology in OLSR. QG-OLSR can effectively reduce the consumption of network topology control, improve the delivery rate of data packets, and reduce the end-to-end delay of packet transmission between nodes. This is because it embeds a new augmented Q-Learning algorithm (i.e., a model-free reinforcement learning algorithm that learns a routing policy) and combines the OLSR algorithm to optimize the selection of MPR sets. Simulation results showed that the QG-OLSR protocol is reliable and highly efficient. In high mobility MANETs, proactive routing protocols exhibit the highest packet delivery ratio and the shortest end-to-end delay, while costing the most in protocol overhead. In such MANETs, we can measure the variation in links set by using the topology instability metric (TIM). TIM is defined by the number of links established and failed in a certain statistical period. In high mobility MANETs, a glitch in the TIM causes unnecessary protocol overhead. To address this problem, [PERSON] et al. [162] proposed a scheme using the exponential weight-moving average (EWMA) of the topology instability metric (TIM) to dynamically adapt the topology update intervals in proactive routing protocols. Simulation results showed that EWMA of TIM exhibits better glitch suppression than the instantaneous value of TIM and decreases the unnecessary protocol overhead effectively in relatively stable scenarios.
## 5 Results and Discussions
### Compact-Tension Specim  
In this section, the model's ability to predict the global response of a compact tension CT specimen, defined in [6], will be checked. After treating both the classical and the optimized G codes of the corresponding geometries, two finite element models were created. The numerical model definition with the boundary conditions is described in [24]. The part is sliced along the Z direction to match the layers from the G code. The local material's references are assigned for both kinds of samples. Figure 8 shows the vectors that correspond to the deposition trajectory in each element. For the optimized specimen, since two principal directions were reproduced, only two material's references definition per layer can be observed. The model's responses will be compared with the experimental data collected from the work of [PERSON] et al. [6]. The boundaries conditions defined in [24] are maintained the same for this study. Two rigid bodies are used. One to model the fixed pin and the other represents the mobile one.  
Figure 7: _Cont._  
Figure 7: Model response of the tensile test specimens compared to a representative experimental curve. (**a**): 0\({}^{\circ}\) raster orientation and (**b**): 45\({}^{\circ}\) raster orientation and (**c**): 90\({}^{\circ}\) raster orientation.  
Figure 9 compares both experimental and numerical results. The drop of the stress observed in the experimental curves results from the crack initiation and growth from the notch tip. Therefore, the simulation cannot reproduce this behavior because the material damage and fracture are not taken into account by the model. Regarding the linear behavior of the samples, the model gives a good prediction of the stiffness for the optimized specimen and slightly overestimates it for the classical one. For the optimized specimen, where a ductile-like curve is obtained by the test, the model underestimates the load values (see Figure 9a). This is mainly related to a numerical plastic behavior along the transversal direction that is not omitted in the model.
## 5 Evaluation of Bubble Size Correlation with CFD Code
### Evaluation of the New Bubble Size Model
In V01 and V03, the void fraction in the nearby wall was overpredicted, and hence, bubble size was also overpredicted near the wall. The velocity of each phase was predicted to be slightly higher at the center. Nevertheless, the evaluation confirms that the proposed bubble size correlation predicts the overall value and tendencies of bubble size and phase velocity with reasonable accuracy. Figure 9 shows the performances of the existing bubble size correlations and the new bubble size correlation against the four experiments. The Akita and Yoshida correlation as well as the Kocamustafaogullari correlation significantly overpredicted the local bubble size and were unsuitable for the CFD code for a forced convective bubbly flow in a vertical pipe because the two correlations were developed under bubble column and horizontal tube conditions, respectively. Although the two Hibiki and Ishii correlations were developed using the average bubble size data in a flow channel, these correlations yielded acceptable predictions for the experimental data. The new bubble size correlation shows the best performance among all correlations. For quantification of the prediction accuracy of the existing and new correlations for the local bubble diameter, the average error and root-mean-square error (RMSE) are defined as follows:  
\[E_{prediction,D_{b}}=\frac{\left|D_{b,\textit{meas.}}-D_{b,\textit{calc.}} \right|}{D_{b,\textit{meas.}}}\times 100 \tag{33}\]  
\[E_{RMSE,D_{b}}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(D_{b,\textit{meas.}}-D_{b, \textit{calc.}}\right)^{2}}\times 100 \tag{34}\]  
where \(N\) is the number of experimental datasets, \(D_{b,\textit{meas. }}\) is the local bubble size obtained through experiments, and \(D_{b,\textit{calc. }}\) is the calculated local bubble size with the correlation. The average error and RMSE are summarized in Table 6. Through the assessment of the new correlation with other correlations, the new correlation has an excellent agreement within an RMSE of \(\pm\) 11% in the vertical adiabatic bubbly flow under the low pressure condition. Figure 9: Comparison of the predicted local bubble size and experimental data with (**a**) Akita and Yoshida, (**b**) Kocamustafaogullari, (**c**) Hibiki and Ishii (2001), (**d**) Hibiki and Ishii (2002), and (**e**) the present correlation. \begin{table}
\begin{tabular}{c c c} \hline
**Bubble Size Correlation** & **Average Error** & **RMSE** \\ \hline Akita and Yoshida & 125\% & 155\% \\ Kocamustafaogullari & 180\% & 188\% \\ [PERSON] and Ishii (2001) & 21.6\% & 24.4\% \\ [PERSON] and Ishii (2002) & 27.1\% & 36.2\% \\ Present & 7.9\% & 11\% \\ \hline \end{tabular}
\end{table} 
Table 6: Average error and RMSE of the bubble size correlation for each experiment.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 3 Methods
### Structure of the WFCNN Model
#### 3.1.1 Encoder  
The encoder consists of five serial connection feature extraction units that can extract five levels of semantic features for each pixel. Each unit consists of three convolutional layers, a batch normalization layer, an activation layer, and a pooling layer. The encoder contains a total of 15 convolutional layers with differing amounts of convolution kernels (Table 2). The advantage of this structural design is that the influence of the image's spatial resolution and geographical coverage is fully considered. Ensuring that sufficient semantic features can be extracted avoids the risk that the excessively deep convolution structure may cause noise in extracted feature values. This is beneficial to the classifier when performing pixel-by-pixel classification.  
The activation layer uses the widely used rectified linear unit function as the activation function. Commonly, the pooling operation can accelerate feature aggregation and eliminate feature values with poor discrimination, which is beneficial to the operation of the classifier. However, the pooling operation generally leads to a reduced feature map resolution, affecting segmentation accuracy. When pooling the edge area of two object types, the fact that two adjacent pixels often belong to different categories makes it is easy to mistakenly apply the feature values of one category to another category.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Number of Convolution Kernels** \\ \hline
1, 2, 3 & 64 \\
4, 5, 6, & 128 \\
7, 8, 9 & 256 \\
10, 11, 12, 13, 14, 15 & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of convolution kernels for each convolutional layer.  
Figure 5: Structure of the weight feature value convolutional neural network model.  
When the pooling operation is carried out on images with a high level of detail or in the shallow convolutional structure, the influence of this problem is not obvious, but if this operation is carried out in the deep convolutional structure, the problem has a serious influence on feature extraction.  
WFCNN uses a new pooling strategy to solve this problem. In the E1, E2, and E3 feature extraction units, a \(2\times 2\) pool core is used and the pooling step length is 2, encouraging the advantages of the pooling operation in accelerating feature aggregation. In the later stages, due to the large difference between the current resolution and that of original images, the step length of the pooling layer in E4 and E5 cells is adjusted to 1. Here, the pool kernel size is still \(2\times 2\), but when the size of the function block is smaller than that of the kernel participating in the pool operation, the size of the kernel pool is adjusted to match the function block. This ensures that a valid pool value is obtained, so the size of the feature graph output through E4 and E5 remains the same.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 3 Numerical Study of Blasting Seismic Energy Generation and Attenuation under In Situ Stress
### Seismic Energy Generation and Attenuation under Hydrostatic In Situ Stress
#### 3.3.2 Seismic Energy Attenuation with Distance
Figure 8: Regression analysis of blasting seismic energy attenuation with distance under varied hydrostatic in situ stress levels. By applying a harmonic wave on the free surface and calculating the seismic energy dissipation over a wavelength, the seismic \(Q\) is obtained, as shown in Table 2. The seismic \(Q\) first increases with increasing in situ stress. Then it falls off after the hydrostatic stress level exceeds 30 Mpa. This corresponds to the change of the seismic energy attenuation index with in situ stress levels. Actual rock masses are discontinuous and contain a lot of defects, such as microcracks, micro-cavities, and joints. With regard to the RHT material model, the initial material defects are considered and described by two constants, the initial porosity and the porosity exponent. Therefore, the RHT model can well reflect the mechanical behavior of defective rock mass under loads. These defects within rock masses tend to close due to compression as the in situ stress increases from zero to a certain level. The rock mass integrity and the seismic \(Q\) are thereby improved, and the energy dissipation during the seismic wave propagation is thus reduced. However, when a higher level of in situ stress is reached, these initial defects would be extended, and furthermore, new cracks would be generated due to the compression-shear action under in situ stress. As a result, the rock mass integrity and the seismic \(Q\) are deteriorated, and consequently, the seismic energy is more easily attenuated.
## 3 Numerical Simulations
### Stability against the SASE Fluctuation  
To analyze the stability of the proposed technique, multiple runs of the simulation are carried out with different shot noise initializations [39]. As we have mentioned above, the output properties of the SASE-based FEL can fluctuate a lot from shot-to-shot. According to Figure 3a, the different gain processes of ESASE also result in different output pulse durations. However, radiation in the afterburner, which is determined by \(I^{2}b^{2}\), only occurs in the region where the current and bunching factor are relatively large, making the pulse duration nearly immune to the slippage effect in the ESASE radiator. The statistics of the evolutions of peak power and pulse duration are shown in Figure 7, where one can find that both the power and pulse duration jitters in the afterburner are much smaller than those in the radiator. The average peak powers reach 1 GW at about 13.65 m of the radiator and about 0.72 m of the afterburner.  
Figure 8 shows the simulation results of the FEL peak power and pulse duration at 13.65 m in the radiator and 0.72 m in the afterburner. The Gaussian fits are given to depict the distribution of peak power and pulse duration of the whole 280 shots. The standard deviations of the peak power and the pulse duration are 0.765 GW and 163 as in the radiator while 0.445 GW and 62.5 as in the afterburner, respectively, as shown in Figure 8. These results demonstrate that the stabilities of the FEL pulses from afterburner can be well improved due to the utilizing of pre-bunched electron beams.  
Figure 6: Change of the bunching profile at: (**a**) the first kicker; (**c**) the second kicker. Performance of the FEL pulse in: (**b**) the first afterburner; (**d**) the second afterburner.  
Figure 7: For 280 shots, the average values and deviations of: (**a**) the peak power; (**b**) the FEL pulse duration.