esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Steady-State Model Tuning
In the experiment, the temperature increases above the secondary air injection at 6.0 m to the last measurement at 8.2 m. In the model, a temperature peak at 7.0 m is observed, before the temperature decreases again due to the impact of the cooling lances. At 8.2 m, experiment and simulation correspond. In addition to the riser temperatures, the temperature measurement after the cyclone is shown in Figure 5. In the experiment, the temperature increases between riser and cyclone outlet. It is reasonable, that the mixing of secondary air, volatiles, and char is considerably smaller in the experiment, as the penetration depth of the secondary air is not optimized in the pilot. Thereby, the combustion reactions are shifted to the top of the reactor and the cyclone. In the cyclone, the high velocity and the turbulent flow ensure  
Figure 5: Pressure and temperature along with the reactor height (steady-state test). a strong mixing leading to the post-combustion of char. In the model, the mixing is very efficient, due to its 1-dimensional nature. Therefore, the combustion reactions are taking place only in the riser and the post-combustion is not reproduced sufficiently. The flue gas composition is presented in Figure 6. On the left-hand side, the main gas components O\({}_{2}\), CO\({}_{2}\), and H\({}_{2}\)O are shown. The standard errors are presented, taking into account the measurement device uncertainty and the standard deviation of the fluctuating measurement signal. The simulation agrees very well with the experiment. Simulation results are always within the standard error of the experiment and the relative deviations are very small. Considering that the composition of the fuel is subject to fluctuations during the experiment, the agreement is considered very high. On the right-hand side of Figure 6, the content of the trace gases CO and SO\({}_{2}\) is presented. There is no CO in the flue gas in the simulation. This supports the findings from the temperature profile of the simulation, where all combustion reactions are completed in the riser. In the experiment, the CO content is around 200 ppm\({}_{\text{wet}}\), so the combustion reactions are not completed even after the post-combustion in the cyclone. The SO\({}_{2}\) content depends on the varying sulfur content in the fuel and on the efficiency of the sulfur capture with calcium oxide from the fuel ash. The sulfur capture in a CFB combustor is a complex process, which depends on many parameters, such as the flue gas composition (locally reducing conditions, water vapour content), the temperature, the residence times and the properties of the CaO-particles (porosity, size, shape) [40; 41; 42; 43]. Taking into account all these uncertainties and the fact, that especially the particle properties cannot be defined to this extent in the APROS model, the relative deviation of 41% between model and experiment is in a comprehensible range. It should be mentioned at this point that no tuning was carried out concerning the desulphurization reactions in the model, besides the definition of the S-content in the fuel and the CaO content in the fuel ash.
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 3 Modified 1D Map
### Sensitivity to Control Parameters and Initial Condition  
The sensitivity of this system to small changes in the initial conditions or the control parameter \(c\) is a key aspect to consider when studying its dynamics. Small changes in the initial conditions can lead to vastly different behaviors in the long term, resulting in a highly sensitive system. Similarly, small changes in the value of the control parameter \(c\) can lead to significant changes in the system dynamics. This is clearly depicted in Figure 3 where perturbations of \(10^{-6}\) in any of the initial conditions or control parameters produced two completely different sequences.  
This property is crucial for ensuring the security and robustness of encryption systems that rely on the chaotic nature of the resulting PRNGs. This will make it extremely difficult for an attacker to predict or reproduce the sequence without having knowledge of the initial conditions.
## 8 Use Case Output
### Use Case Layer 1: Performance Index per Manufacturer  
Situated in position \(V_{1}H_{1}\) of the analytics model, where \(V_{1}\) is Vertical Axis Element 1, and \(H_{1}\) is Horizontal Axis Element 1, Use Case 1 provides the key quality index per manufacturer in the network. Figure 5 outputs the results of the analysis. Only one service is considered in this case: video-streaming applications.  
The above figure includes all the devices grouped by manufacturer and all the technologies included. The graph is influenced by the technology's coverage in the area where the network data is provided. The analytics shows that ZTE-based devices have the highest throughput when it comes to video streaming, representing 19.2% of all high-throughput devices. Popular manufacturers, including Samsung, Apple, Huawei, and Nokia appear in the report.  
In Figure 6, a similar performance is shown per manufacturer, now grouped by technology. In this case, the analyst or network operator can see the main contributor by network technology. LTE is currently the most recent deployed cellular technology in the market. For each manufacturer, the traffic distribution per technology is highlighted. The analysis reveals that most of the ZTE M2M traffic appears on 3G technology only. Samsung and Nokia have a percentage of traffic on 2G technology. Huawei and Apple devices benefit more from 4G/LTE with over 50% of video-related activities performed on 4G.  
Figure 5: Use Case 1: Top 10 device manufacturers with high video throughput.
## 3 Results and Analysis
### Consistency of Vegetation Phenology Trends  
By analysing the SOS and EOS trends under the different methods above 30\({}^{\circ}\)N from 1982 to 2015, we found that the SOS trend was almost consistent under the five selected methods. There was a significant advancing trend (\(p<0.05\)) for Methods 1-5 (Figure 4A-E). The number of pixels shows a significant advancing trend accounting for 73.12%, 68.35%, 75.10%, 72.62% and 73.88% of the total number of pixels, respectively. However, in some regions, the advancing trend under the five methods is also greatly different. For instance, in the Asian region between 30\({}^{\circ}\)N and 45\({}^{\circ}\)N, the SOS advancing trend for Methods 2 and 4 is significantly higher than that of the other three methods (Figure 4A-E and Table 1). For Asian and European regions between 45\({}^{\circ}\)N and 75\({}^{\circ}\)N, the SOS advancing trend for Methods 1,3 and 5 is significantly higher than that of Methods 2 and 4 (Figure 4A-E and Table 1). This may be because the SOS under the five selected methods is greatly influenced by vegetation type.  
Figure 4: _Cont._The variation law of the EOS under the five selected methods is basically consistent above 30\({}^{\circ}\)N. The EOS under the five methods mainly lags between 30\({}^{\circ}\)N and 60\({}^{\circ}\)N and advances between 60\({}^{\circ}\)N and 75\({}^{\circ}\)N (Figure 4F-J). However, there are also great differences in some regions. For Asia between 45\({}^{\circ}\)N and 60\({}^{\circ}\)N, the lagging trend and the number of significant pixels of the EOS in Method 1 are significantly higher than those of the other methods (Figure 4F-J and Table 2), and in Europe between 45\({}^{\circ}\)N and 60\({}^{\circ}\)N, the lagging trends in Methods 1 and 2 are significantly higher than in Methods 3, 4, and 5. However, in Asia and North America, there is a significant advancing trend from 60\({}^{\circ}\)N to 75\({}^{\circ}\)N. The advancing trend under Method 2 in Asia is especially obvious at 0.58 days/year (Table 2). Compared with other extraction methods, the advancing trend of the EOS under Method 5 is significantly lower than that under other methods, especially in North America.  
Figure 4: Phenological trend changes under different methods at mid and high latitudes. The trend changes under different extraction methods in the figure are all using the Theil-Sen method, and the Mann–Kendall method is used to test the trend. (**A–E**) represent the SOS trend under Method 1, Method 2, Method 3, Method 4 and Method 5 at mid and high latitudes in the Northern Hemisphere, respectively. (**F–J**) represent the EOS trend under Methods 1–5 at mid and high latitudes in the Northern Hemisphere, respectively. The histogram in the lower left represents the percentage of positive trend and negative trend pixels in the total pixels under MK test, and different colours represent different trend range, all corresponding to the colour bands on the right.
## 4 Results and Discussions
### Hub Identification
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Macro Region**} & \multirow{2}{*}{**ROI**} & \multicolumn{4}{c}{**SYNC**} & \multicolumn{4}{c}{**Pearson**} & \multicolumn{4}{c}{**Coherence**} \\ \cline{3-10}  & & **PH** & **CH** & **KH** & **PH** & **CH** & **KH** & **PH** & **CH** & **KH** \\ \hline \multirow{10}{*}{Frontal Lobe} & SFGdor.L (3) & & & & & ✓ & & & \\  & ORBinf.L (15) & ✓ & & & & & ✓ & & \\  & ORBinf.R (16) & ✓ & & & & & ✓ & & \\  & ROL.L (17) & ✓ & & & & ✓ & & ✓ & \\  & ROL.R (18) & ✓ & & & & ✓ & & ✓ & \\  & SFGmed.L (23) & & & ✓ & & & ✓ & \\  & SFGmed.R (24) & & & ✓ & & & ✓ & \\ \hline \multirow{10}{*}{Insula and Cingulate Gyri} & INS.L (29) & & & ✓ & & ✓ & & \\  & INS.R (30) & & & ✓ & & ✓ & & \\  & DCG.L (33) & & ✓ & & & & & \\  & DCG.R (34) & & ✓ & & & & & \\ \hline \multirow{10}{*}{Occipital Lobe} & CAL.L (43) & ✓ & & & & & & ✓ \\  & CAL.R (44) & ✓ & & & & & & ✓ \\  & LING.L (47) & ✓ & & & ✓ & ✓ & & \\  & LING.R (48) & ✓ & & & ✓ & ✓ & & \\ \hline \multirow{10}{*}{Farietal Lobe} & PoCG.L (57) & & ✓ & & & & ✓ & \\  & PoCG.R (58) & & ✓ & & & & ✓ & \\  & PCUN.L (67) & & & ✓ & & & & \\  & PCUN.R (68) & & ✓ & & & & & \\ \hline \multirow{10}{*}{Temporal Lobe} & FFG.L (55) & & & & ✓ & & & \\  & FFG.R (56) & & & & ✓ & & & \\  & HES.L (79) & & & & ✓ & & & \\  & HES.R (80) & & & & ✓ & & & \\  & STG.L (81) & & & & & ✓ & ✓ & \\  & STG.R (82) & & & & & ✓ & ✓ & \\  & MTG.L (85) & ✓ & & ✓ & & & ✓ & \\  & MTG.R (86) & ✓ & ✓ & & & & ✓ & \\  & ITG.L (89) & & & & ✓ & & & \\  & ITG.R (90) & & & & ✓ & & & \\ \hline \multirow{10}{*}{Posterior Fossa} & CRBLCrus1.L (91) & & ✓ & ✓ & & & & \\  & CRBLCrus1.R (92) & & ✓ & ✓ & & & & \\ \cline{1-1}  & CRBLCrus2.L (93) & ✓ & & & & & & \\ \cline{1-1}  & CRBLCrus2.R (94) & ✓ & & & & & ✓ & \\ \cline{1-1}  & CRBL\&L (99) & ✓ & & & ✓ & & \\ \cline{1-1}  & CRBL\&L (100) & ✓ & & & ✓ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Abbreviation of hub regions with the macro regions in which they are located and their roles for the three functional connectivity networks. Figure 8: Upper percentile (75 th) of the distribution of participation coefficient P for the three networks and all the fMRI sections. In summary the three networks showed significant differences among them. First, the modularity analysis showed higher values of modularity index Q in SYNC networks than the other two networks thus indicating a clearer division into communities. Second, the analysis of NMI values highlighted that the three networks showed modular partitions with a low overlap between them (below 50%). The different topological configurations in the three networks were confirmed by the analysis of the hubs, which outlined the presence of different central roles of functional areas. The results suggest that statistical correlation and coherence networks show more evenly distributed synchronization patterns of comparable size in the brain, while SYNC networks exhibit more granular partitions, highlighting more varied synchronization patterns.
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 4 Results
### Ablation Studies
#### 4.5.3 Effectiveness of Decoder Channels  
To verify the effectiveness of decoder channels, we conducted three sets of experiments, all utilizing the complete decoding part with only differences in the decoding channel.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Acc** & **mAcc** & **mIoU** \\ \hline concatenation & 78.42 & 67.20 & 54.19 \\ LAM & 78.83 & 68.18 & 54.30 \\ GAM & 78.60 & 67.86 & 54.34 \\ SSFM & **78.55** & **68.39** & **54.45** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation studies on Spatial-Semantic Fusion Module.  
Figure 4: Simplified diagram of different component models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **ORGM** & **SFRRM** & **RFRRM** & **SSFM** & **Acc** & **mAcc** & **mIoU** \\ \hline No. 1 & & & & & 78.24 & 66.6 & 53.5 \\ No. 2 & ✓ & ✓ & & & 78.1 & 67.25 & 53.8 \\ No. 3 & ✓ & & ✓ & & 78.28 & 67.48 & 54.07 \\ No. 4 & ✓ & ✓ & ✓ & & 78.35 & 67.77 & 54.19 \\ No. 5 & ✓ & ✓ & ✓ & ✓ & 78.55 & **68.39** & **54.45** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation studies on our proposed modules.  
The decoding channels for the three experiments were set to 256, 512, and 1028, and the results are presented in Table 7.  
The relatively poor performance in the first row can be attributed to the relatively small number of channels, which leads to incomplete feature extraction and affects the final results. Considering the second row, the experiment with 512 channels yields the best results in terms of Acc, mAcc, and mIoU, indicating that using 512 as the channel number for the decoder is the most effective choice. Although the third row's experiment can extract the most features in the decoder, its performance in Acc, mAcc, and mIoU is lower than that of the second row, suggesting that 1024 channels are not suitable for the decoder.
# Optimization Method of Square Hole Measurement Based on Generalized Point Photogrammetry
## 2 Mathematical Model of Generalized Point Photogrammetry  
The collinearity equation is the core of photogrammetry, and the collinearity of points is the basis of photogrammetric solution. As shown in Figure 2, point \(m\) and point \(n\) represent the projection point of the space point on the image. The angle between the straight line segment where point \(m\) is located and the x-axis is greater than 45 degrees, and the angle between the straight line segment where point \(n\) is located and the x-axis is less than 45 degrees. Point \(m\) is closer to the y-axis, and point \(n\) is closer to the x-axis.  
As shown in Figure 3, \(S\) is the projection center, and the projection of spatial points \(A\) and \(B\) on the image are \(a\) and \(b\). The image coordinates of \(a\) and \(b\) are calculated through photogrammetry, and the line \(l\) is the projection of the line \(AB\) on the image. Due to errors,  
Figure 1: Segmental beam.  
Figure 2: Included angle of projection.  
When the angle between the straight line and the x-axis direction is less than 45 degrees, the observation equation in the \(y\)-direction is established. The value of parameter \(t\) can be obtained from the collinear equation in the \(x\) direction, and the expression is as follows.  
\[t=-\frac{f(a_{1}(X_{1}-X_{s})+b_{1}(Y_{1}-Y_{s})+c_{1}(Z_{1}-Z_{s}))}{f(a_{1}(X_ {2}-X_{1})+b_{1}(Y_{2}-Y_{1})+c_{1}(Z_{2}-Z_{1}))} \tag{13}\]  
On the premise of finding t, the error equation of y direction can be listed.  
\[v_{y}=y_{1}-y_{0}+f\frac{a_{2}((X_{2}-X_{1})t+X_{1}-X_{s})+b_{2}((Y_{2}-Y_{1})t +Y_{1}-Y_{s})}{a_{3}((X_{2}-X_{1})t+X_{1}-X_{s})+b_{3}((Y_{2}-Y_{1})t+Y_{1}-Y_{ s})} \tag{14}\]  
The following formula is used to linearize the error equation.  
\[y_{1}=(y_{1})+\frac{\partial y_{1}}{\partial X_{1}}dX_{1}+\frac{\partial y_{1 }}{\partial Y_{1}}Y_{1}+\frac{\partial y_{1}}{\partial Z_{1}}dZ_{1}+\frac{ \partial y_{1}}{\partial X_{2}}dX_{2}+\frac{\partial y_{1}}{\partial Y_{2}}dY _{2}+\frac{\partial y_{1}}{\partial Z_{2}}dZ_{2} \tag{15}\]  
where, (y1) is the approximate value of the result of the previous iteration. The calculation formulas of each coefficient are as follows.  
\[\begin{array}{c}\frac{\partial y_{1}}{\partial X_{1}}=-(1-t)a_{21},\frac{ \partial y_{1}}{\partial Y_{1}}=-(1-t)a_{22},\frac{\partial y_{1}}{\partial Z _{1}}=-(1-t)a_{23}\\ \frac{\partial y_{1}}{\partial X_{2}}=-ta_{21},\frac{\partial y_{1}}{\partial Y _{2}}=-ta_{22},\frac{\partial y_{1}}{\partial Z_{2}}=-ta_{23}\end{array} \tag{16}\]  
\[\begin{array}{c}a_{21}=\frac{1}{2}[a_{2}f+a_{3}(y-y_{0})]\\ a_{22}=\frac{1}{2}[b_{2}f+b_{3}(y-y_{0})]\\ a_{23}=\frac{1}{2}[c_{2}f+c_{3}(y-y_{0})]\end{array} \tag{17}\]  
The accurate values of the coordinates of two points (\(X_{1}\), \(Y_{1}\), \(Z_{1}\)), (\(X_{2}\), \(Y_{2}\), \(Z_{2}\)) on the straight line can be obtained by solving the error equation iteratively.
## 4 Methodology
### Analytical Method  
This study used a two-stage approach to test hypotheses and construct predictive models. First, SPSS 30.0 and AMOS 24.0 were used to conduct the measurement analysis, and structural equation modeling (SEM) was employed to identify the linear relationships between the exogenous and endogenous variables. SEM is a validation technique used to test conceptual models from previous studies and assess the consistency of these theoretical frameworks with the collected data [79]. Compared with multiple regression and other methods, SEM can estimate the measurement errors of independent and dependent variables while simultaneously analyzing all path relationships in the proposed model.  
However, SEM has limitations in capturing non-linear and non-compensatory relationships, which are crucial for understanding the complex dynamics in technology diffusion and e-commerce contexts. Therefore, researchers recommend combining SEM and ANN studies to balance and generate more comprehensive data analysis by utilizing the unique advantages of both [80]. To address the limitations of SEM, the second phase of this study integrated an artificial neural network (ANN).  
ANNs can capture both linear and non-linear relationships that do not follow a normal distribution [81], thereby improving predictive accuracy. The latest research by [PERSON] et al. [82] emphasizes the robustness of ANNs in handling complex data patterns and their applicability in predictive tasks. Moreover, ANNs demonstrate robustness to outliers and small sample sizes and are well-suited for non-compensatory models. The ANN analysis was performed with the Neural Network module in SPSS. This algorithm learns through iterative training and employs the feed-forward back-propagation (FFBP) approach for predictive analysis [83]. Both the input and hidden layers employed a multi-layer perceptron architecture with a sigmoid activation function [84]. Through multiple learning iterations, the prediction error was minimized, thereby enhancing predictive accuracy [85].  
Furthermore, the specific measurement process is presented in Figure 3. First, the model was measured. Then, SEM and the ANN were examined separately. Finally, the results of the two methods were compared. By combining SEM and an ANN, this study enhanced the depth and accuracy of data analysis, thus comprehensively understanding the predictive factors that influence university students' behavior in using LLMs to assist with purchasing sustainable products online.
# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
###### Abstract  
Machine learning is taking on a significant role in materializing a new vision of 6G. 6G aspires to provide more use cases, handle high-complexity tasks, and improvise the current 5G and beyond 5G infrastructure. Artificial Intelligence (AI) and machine learning (ML) are the optimal candidates to support and deliver these aspirations. Traffic steering functions encompass many opportunities to help enable new use cases and improve overall performance. The emergence and advancement of the non-terrestrial network is another driving factor for creating an intelligence selection scheme to have a dynamic traffic steering function. With service-based architecture, 5G and 6G are data-driven architectures that use massive transactional data to emerge a new approach to handling highly complex processes. A highly complex process, a massive volume of data, and a short timeframe require a scheme using machine learning techniques to resolve the challenges. In this paper, the study creates a scheme to use the massive historical data and provide a decision scheme that enables dynamic traffic steering functions addressing the future emergence of the heterogeneous transport network and aligns with the Open Radio Access Network (O-RAN). The proposed scheme in this paper gives an inference to be programmed in the telecommunication nodes. It provides a novel scheme to enable dynamic traffic steering functions for the 6G transport network. The study shows an appropriate data size to create a high-performance multi-output classification model that produces more than 90% accuracy for traffic steering functions.  
machine learning; Open Radio Access Network; non-terrestrial network; QoS flow identifier; mixed integer linear programming; optimization solver service function chaining; Virtual Network Function; multi-output classification; random forest algorithm +
Footnote †: journal: _Big Data Cogn. Comput._**2024**, 8, 172. [[https://doi.org/10.3390/bdc8120172](https://doi.org/10.3390/bdc8120172)]([https://doi.org/10.3390/bdc8120172](https://doi.org/10.3390/bdc8120172))
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
###### Abstract  
The effect of ionizing radiation (\(\gamma\)-rays and electron beam) on anticancer drug delivery systems (DDSs) properties was evaluated concerning potential sterilization. For this purpose, paclitaxel (PTX)-loaded nanoparticles were obtained using a biodegradable, self-developed copolymer of L-lactide and glycolide (PLGA), synthesized in the presence of bismuth 2-ethylhexaoate catalyst. The nanoparticles were obtained with a high encapsulation efficiency of PTX (\(EE=94.2\%\)). The average size of the nanoparticles was 253.5 nm. The influence of irradiation (sterilization dose, 25 kGy) on the microstructure and the physicochemical and thermal properties of the polymer matrix was investigated, as well as the effect of irradiation on the morphology and physicochemical properties of the pharmaceutical formulations of the nanoparticles. Additionally, an in vitro drug release study was conducted regarding any alterations in the kinetic profiles of drug release. It was confirmed that the irradiation with both types of ionizing radiation, i.e., \(\gamma\)-rays and electron-beam (EB), slightly decreased the average molecular weight of the polymer matrix. While only negligible changes in the microstructure and thermal properties of PLGA were observed after irradiation with EB, the average length of lactidyl blocks (\(l_{\text{LL}}\)) in the copolymer chains irradiated with \(\gamma\)-rays decreased from 4.33 to 3.35. Moreover, the contribution of crystalline phase (\(X_{c}\)) in \(\gamma\)-irradiated samples decreased significantly from 35.1% to 22.7%, suggesting a dominant mechanism of chain scission over cross-linking in PLGA samples irradiated with \(\gamma\)-rays. In vitro drug release results demonstrate a sustained and controlled release of PTX from the nanoparticles based on PLGA. The kinetics of drug release was defined as first order with non-Fickian diffusion. Only negligible differences in the kinetic profiles of PTX release from PLGA drug carriers were observed after irradiation. The overall results suggest good resistance of PLGA nanoparticles to irradiation within the conditions used and the great potential of EB in the sterilization process of the polymeric DDSs.  
drug delivery systems; radiation sterilization; electron beam; gamma irradiation; paclitaxel; biodegradable polymers; L-lactide and glycolide copolymers; nanoparticles +
Footnote †: journal: