esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Discussion  
### Analysis of the Flexural Behavior Characteristics  
In this study, the purpose of adding fibers to concrete was to increase the flexural toughness due to the fiber's bridging role, pullout, and fracture behavior, and to delay the rapid failure caused by the deterioration of the adhesion strength between the plastic bar and the concrete matrix. In addition, the addition of fibers can ensure the structural safety by converting brittle fracture to ductile fracture through the crack control process while transferring the load to the fiber after matrix failure in the concrete [24; 25; 26]. As shown in Figure 17, as the load transfer becomes smooth in the load transfer section of the fiber-reinforced concrete, the distribution of the overall load displacement graph increases, and the toughness also increases as the position of the starting point connected to the nonlinear section becomes high. In addition, as the mixing ratio of the fibers increases, the energy dissipation after the first peak load is small, and the load increase up to the second peak load is large, thus improving the ability to transfer energy to the fiber without dissipating it. This can also affect the overall toughness [27; 28; 29; 30]. Based on the results of the previous research, the load transfer factor obtained by using the area of the load reduction section after the first peak load and the load increase section up to the second peak load is represented by Equation (1). With respect to all the specimens (F1, F2, and F3), the areas of the load reduction section after the first peak load were compared, and their effect on the overall toughness was analyzed to evaluate the flexural performance of light transparent concrete due to the addition of fibers. For the specimens containing fibers, the load transfer capacities of the specimens with plastic bars with different diameters and that of the general specimens without plastic bars were compared, and the effects of the fiber mixing ratio and the insertion of plastic bars on the toughness and ductility of the specimens were analyzed by referring to the load transfer factor Equation (1) of the previous research literature (Table 5).  
\[\text{L.T.F.}=\sqrt{\frac{\text{S}_{3}}{\text{S}_{2}}} \tag{1}\]  
L.T.F.: Load transfer factor (LTF); S\({}_{3}\): Area of the third section (kN\(\cdot\)mm); S\({}_{2}\): Area of the second section (kN\(\cdot\)mm).  
Figure 17: Load reduction sections.
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Sensitivity Analysis of Learning Rate and RNN Sequence Length  
Furthermore, the effect of the learning rate and RNN sequence length on the generalization ability of the proposed network in predicting the severity of traffic accidents was investigated. The learning rates varied by 0.5, 0.1, 0.05, 0.01, and 0.001. The sequence length varied by 2, 5, and 10. The choices of learning rate and sequence length were purely on an arbitrary basis. Figure 5 shows the results of these experiments. The highest validation accuracy was achieved when a learning rate of 0.01 was used. Reducing the learning rate up to 0.001 did not improve the validation accuracy, but reduced it to 70.8%. Similarly, large learning rates (e.g., 0.5) significantly reduced the performance of the network  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimizer** & **Parameters** & **Training** & **Validation** \\  & & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline SGD & lr = 0.01, momentum = 0.0, decay = 0.0, nesterov = False & 71.79 & 71.77 \\ RMSprop & lr = 0.001, rho = 0.9, epsilon = \(1\times 10^{-8}\), decay = 0.0 & 71.90 & 70.80 \\ Adagrad & lr = 0.01, epsilon = \(1\times 10^{-8}\), decay = 0.0 & 71.35 & 71.24 \\ Adadelta & lr = 1.0, rho = 0.95, epsilon = \(1\times 10^{-8}\), decay = 0.0 & 70.24 & 71.24 \\ Adam & lr = 0.001, beta\_1 = 0.9, beta\_2 = 0.999, epsilon = \(1\times 10^{-8}\), decay = 0.0 & 73.12 & 71.68 \\ Adamax & lr = 0.002, beta\_1 = 0.9, beta\_2 = 0.999, epsilon = \(1\times 10^{-8}\), decay = 0.0 & 70.46 & 71.24 \\ Nadam & lr = 0.002, beta\_1 = 0.9, beta\_2 = 0.999, epsilon = \(1\times 10^{-8}\), schedule\_decay = 0.004 & 74.67 & 71.68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance of different optimization methods evaluated in this study.  
Figure 4: Accuracy performance and loss of the RNN model calculated for 100 epochs; (**a**) model accuracy; and (**b**) model loss.  
with a validation accuracy of 66.37%. On the other hand, the best validation accuracy (71.77%) was achieved with a sequence length of 2. In addition, the model performed better with a sequence length of 10 (70.87%) than 5 (70.43%). The unseen structured pattern in the model performance with different learning rates and sequence lengths indicated that the optimization of these parameters is critical to design a network that can best predict the severity of traffic accidents.
## 4 Modeling a Solar-Hydrogen System in the University
### Simulation Result
Several important conclusions may be made from the analysis of the solar-hydrogen storage system variables:  
* Energy Management Flexibility: As seen by the changes in SOC, PStorage, and PElectrolyzer in response to variations in PSolar, the system demonstrates flexibility in controlling energy inputs and outputs. This adaptability enables effective use of renewable energy sources and conditions for energy generation to change. * Optimal Energy Utilization: The link between PElectrolyzer, PStorage, and PSolar shows how best to use energy in the system. In times of plentiful solar power generation, higher PSolar values ensure efficient storage of excess energy and hydrogen production through improved energy storage and electrolyzer operation. * Balanced Energy Storage and Production: Energy storage and hydrogen generation have a balanced relationship, as shown by the analysis of PStorage, PElectrolyzer, and mH2. PElectrolyzer and mH2 rise in tandem with higher PStorage values, suggesting effective conversion of stored energy into hydrogen. This equilibrium preserves sufficient energy reserves while guaranteeing steady hydrogen generation. * Dynamic Response to Demand: The system has the ability to adapt dynamically to changes in energy consumption. Variations in SOC, PStorage, and mH2 show how the system can adapt energy storage capacities and hydrogen production rates to changing energy needs, guaranteeing a consistent and sustainable energy supply. * Efficiency Optimization Opportunities: The analysis's findings offer perceptions into possible avenues for enhancing system effectiveness. The energy storage capacity, electrolyzer efficiency, control techniques, and other factors can be adjusted to optimize the system's performance and make the most use of renewable energy sources. The robustness, adaptability, and efficiency of the solar-hydrogen storage system in controlling the generation, storage, and use of renewable energy are highlighted by the analysis of the system's variables. These realizations can direct additional improvements in  
Figure 11: Performance results of SOC and mH2. operational tactics, control algorithms, and system architecture to produce energy solutions that are both affordable and sustainable.
## 1 Introduction
There are few studies on weathered clay layers, which cannot explain how oil and gas enter the reservoir and are completely preserved and do not understand the accumulation mode of unconformity reservoirs, resulting in omissions in predicting the location of oil and gas. A significant number of unconformities are developed in the Permian, Triassic, and Carboniferous strata of the Junggar Basin. Among these, numerous oil and gas reservoirs beneath unconformity structures have been confirmed in the central part of the basin. [PERSON] [19] identified eight types of reservoir control for glutenite reservoirs on the upper and lower surfaces of unconformities. With ongoing exploration and development, many geological experts have suggested shifting the focus of exploration from fault zones to slope areas. Due to the insufficient characterization of the internal structure of unconformities and the unclear relationship between unconformities and the reservoir and caprock of oil and gas reservoirs, a misconception has arisen that \"gravel in the depression zone is difficult to form a reservoir\". However, this is not the case, as the oil and gas reserves in the Mahu Depression are extremely abundant. Therefore, this study focuses on the Baikouquan Formation oil and gas reservoirs in the slope area of the Mahu Depression, located on the northwestern margin of the Junggar Basin. By utilizing relevant geological data, combining previous research findings, and using well logging response characteristics to distinguish the upper, middle, and lower structural layers of the uncomformity, this study identifies the layers involved. Through fracture pressure experiments, the actual bearing capacity of the weathered clay layer core is determined. Using large-scale formation pressure calculations in the study area with the relevant formulas, the roof and floor of the single-well reservoir are identified based on the differences in formation pressure. This allows for the construction of the reservoir well profile, the analysis of the integrity of the oil and gas trap, and the evaluation of the sealing properties of the uncomformity structure. This study also summarizes the control effect of the unconformity structure on oil and gas migration and accumulation, providing crucial technical support for the exploration of unconformity-related reservoirs in the Mahu Sag.
## 5 Simulation Results
### Cluster Performance for Static Networks
#### 5.2.2 Performance Evaluation
It shows that our algorithm has fewer average cluster numbers for one group than the comparison algorithms and can assign UAVs performing the same task to one identical cluster regardless of the number of groups. The total inter-cluster communication link correlation under different group counts is shown in Figure 13. Our algorithm has the best inter-cluster communication link correlation. Moreover, with the increase of the group number, the total inter-cluster communication link correlation is gradually declining. The reason is that the distribution of UAVs is more dispersed when more groups are in the swarm. Figure 7.0 The load balance for each algorithm under different numbers of groups. Figure 7.1 The number of clusters in the clustering results under different numbers of groups. The above simulation results illustrate that our algorithm achieves the three goals proposed in the group-oriented structured clustering problem with a good load balance.
## 2 The Proposed Image Fusion Method
### Quality Evaluation of Fused Image  
The evaluation of a fused image is usually performed by subjective visual and objective indexes. Subjective vision is mainly used to judge the fused effect of image contrast, ambiguity, noise elimination, target edge and texture features. Furthermore, objective indexes is able to accurately reflect the overall effect of the fused image and the visual information content of important targets, which is a complementary to the subjective visual effect. Therefore, some objective evaluation indexes of a fused image and its mathematical formula are listed in Table 1. Combining the characteristics of underwater sonar images, average gradient (AG) [36], figure definition (FD), information entropy (E) [37], root mean square cross entropy (RCE) [38], mutual information (MI) [9], edge-based similarity measure (\(Q^{ABF}\)) [14], structural similarity index (SSIM) [39], and indexes of Piella [40] are applied to objectively evaluate the fused image.
## 4 Results and Discussion
Figure 7: **(left)** Correlation between the examined variables (level of SCM and BD usage and cost level) and (right) linear correlation between variables. The negative hypotheses examined were rejected (Table 7). This has been shown by research, so we can say that BD and SCM reduce the supply of material and shorten the construction time. This assumption was also confirmed for costs. Thus, it has been empirically proven that these technologies have a positive effect on the monitored indicators. Furthermore, a strong negative correlation was recorded in all cases, representing a real positive effect on these parameters. As already mentioned, the economic sustainability of building design and planning is based on the effort to reduce costs and construction time. Therefore, this dimension of sustainability needs to be planned and designed at an early stage. Studies confronted by us have hypothesized that these technologies may be helpful in the field. However, none of them confirmed their positive impact strictly. Sustainable building design is based on a large amount of data. Therefore, big data and SCM technology concepts are also very important in the sustainable construction industry. In contrast to previous studies, the results of this study quantified the level of correlation between the studied variables. There is a strong correlation, especially concerning the use of these technologies in shortening the supply chain for materials and reducing costs. Likewise, despite being carried out in a small market, this study has a significant contribution to this market, as conditions and factors in this market have been largely considered. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hypotheses** & **Correlation Coefficient** & **Degrees of Freedom** & **Results** \\ \hline
**Hypothesis 1** & & & Rejected \\ H\({}_{10}\): Exploitation of big data concept and supply chain management doesn’t shorten delivery time of materials and services in sustainable building project & \(-\)0.921711092 & 0.273 & \\ H\({}_{11}\): Exploitation of big data concept and supply chain management shortens delivery time of materials and services in sustainable building project & & & \\ \hline
**Hypothesis 2** & & & Rejected \\ H\({}_{20}\): Exploitation of big data concept and supply chain management doesn’t shorten construction time. & \(-\)0.788752732 & 0.273 & \\ H\({}_{21}\): Exploitation of big data concept and supply chain management shortens construction time. & & & \\ \hline
**Hypothesis 3** & & & \\ H\({}_{30}\): Exploitation of big data concept and supply chain management doesn’t reduce construction project cost. & & & Rejected \\ H\({}_{31}\): Exploitation of big data concept and supply chain management reduces construction project cost. & \(-\)0.936777467 & 0.273 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hypotheses-Results. Figure 8: Degrees of freedom for hypotheses.
## 5 Simulation Examples
### Resolution Performance Simulation
#### 5.1.2 The Resolution Probability _versus_ SNR  
Two signal DOAs are (90\({}^{\circ}\), 81\({}^{\circ}\)) and (90\({}^{\circ}\), 85\({}^{\circ}\)), respectively, and both the signal frequencies are 6 GHz. Figure 4 displays the resolution probability of the eight-element R-MUSIC, 10-element R-MUSIC, five-element UCA MUSIC, and eight-element UCA MUSIC _versus_ the SNR from \(-2\) to 8 dB with snapshots number \(L=100\). While Figure 5 displays the resolution probability of the four methods _versus_ the snapshots number from 16 to 144 with \(\mathrm{SNR}=20\) dB. Every data is averaged over 200 Monte Carlo simulations. It can be seen from Figures 4 and 5 that the resolution performance of the proposed eight-element R-MUSIC is close to that of the five-element UCA-MUSIC, but the successful probability is lower than that of the eight-element UCA-MUSIC. However, if we select 10  elements to form a UCA, then the resolution performance will be better than that of the eight-element UCA-MUSIC.  
Figure 3: Spatial spectrum with multiple signals. **(a)** Two signal DOAs are (90\({}^{\circ}\), 70\({}^{\circ}\)) and (90\({}^{\circ}\), 80\({}^{\circ}\)); **(b)** Three DOAs are (60\({}^{\circ}\), 30\({}^{\circ}\)), ( 90\({}^{\circ}\), 60\({}^{\circ}\)) and ( 120\({}^{\circ}\), 80\({}^{\circ}\)); **(c)** Four DOAs are (60\({}^{\circ}\), 40\({}^{\circ}\)), (90\({}^{\circ}\), 80\({}^{\circ}\)), (120\({}^{\circ}\), 60\({}^{\circ}\)) and (140\({}^{\circ}\), 40\({}^{\circ}\)).
## 2 Literature Review
### Current Challenges in 5G Network Slice Orchestration
#### 2.1.2 Need for Intelligent Optimisation  
With the growing complexity and scale of 5G networks, manual or rule-based approaches are no longer effective, as they cannot adapt quickly to the continuously changing demands of the network.  
A more intelligent, automated approach is required and, by learning from historical data and real-time conditions, machine learning can predict demand, allocate resources dynamically, and ensure high network performance.
###### Abstract  
It is common to assume the relationship between temperature and temperature response is instantaneous in bridge health monitoring systems. However, a time-lag effect between temperature and thermal strain response has been documented by the analysis of monitored field data of concrete box-girder s. This effect is clearly reflected by the ring feature in the temperature-strain correlation curve. Inevitably, the time-lag effect has an adverse impact on the accuracy and reliability of state assessment and real-time warning for structural health monitoring (SHM) systems. To mitigate the influence of the time-lag effect, a phase-shifting method is proposed based on the Fourier series expansion fitting method. The time-domain signal is firstly converted into the frequency domain signal to compute the phase difference between temperature data and response strain data at each decomposed order. Subsequently, the total phase difference can be obtained by weighted summation. The signal processing effectively reduces the hysteresis loop area and enhances the correlation between the structural response data and the temperature data. When processing the daily data in different seasons, it is found that after subtraction by the proposed method, the linear feature becomes dominant in the relationship between temperature and the strain during long-term observation.  
structural health monitoring; temperature effects; time-lag effect; Fourier series expansion; box-girder bridges
## 2 Results and Discussion
### Protein Recovery Percentage  
The results of protein recovery in control and PLE extracts from side streams of gilthead sea bream are shown in Figure 2. The percentage of protein recovery in PLE extracts of salmon muscle, head, viscera, skin, and tailfins were 26.65 \(\pm\) 1.57, 27.50 \(\pm\) 3.83, 92.03 \(\pm\) 4.80, 29.39 \(\pm\) 0.05, and 28.29 \(\pm\) 3.66, respectively, whereas those of their corresponding control extracts were 23.51 \(\pm\) 0.31, 18.57 \(\pm\) 1.14, 56.76 \(\pm\) 1.87, 18.41 \(\pm\) 0.64, and 5.82 \(\pm\) 0.63. Therefore, PLE improved the protein recovery for all side streams. The improvement in protein recovery was close to 1.5-fold for heads, viscera, and skin extracts. The tailfin extracts experienced a 5-fold increase with the PLE technique, whereas salmon muscle results were similar for both conventional stirring and PLE extraction. The best protein recovery was observed in viscera, consistent with previously observed protein recoveries in extracts of sea bass and sea bream side streams after applying PLE-assisted extraction [18; 19]. Few food matrices or related side streams have been used for protein extraction by means of PLE. For instance, different seaweeds, as well as seeds from red pepper, showed protein recovery percentages about 5% and 50%, respectively [20; 27].
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 4 Results
### Indoor Measurements
#### 4.2.3 Ground Floor  
The ground floor measurements were performed in a living room with multiple large windows. The outer wall consists of the same materials as on the first floor. Satellite signals on this floor were still well received at lower elevation angles. When satellites reached their maximum elevation angle, the signal showed a large decline in signal strength similar to the pattern seen in the attic. Noteworthy is that a higher elevation angle did not imply a higher signal strength in this location, which can be observed in Figure 4.  
Figure 4: These figures illustrate the Orbcomm indoor signal strength in various environments. (**a**) Comparison. of Orbcomm satellite signal strength across various environments. Each boxplot depicts the signal strength of one satellite pass. (**b**) The \(C/N_{0}\) of FM116 in the attic with a maximum elevation angle of 41.8\({}^{\circ}\). A 5-second moving average filter was applied to the \(C/N_{0}\) data.
## 3 Results and Discussion
### Connector Selection and Integration
Even small deviations in the connector performance can significantly alter the antenna's operational characteristics, underscoring the need for precise engineering to optimize performance.
## 2 Experimental Work
### Sample Preparation
#### Solvent Selection for Wet Mixing  
The main problem in wet mixing is the selection of solvent that can fully evaporate leaving behind the dispersed CNTs. If solvent remains in bitumen, then it can affect its properties and the real effect of CNTs cannot be judged. Three solvents, i.e., methanol, acetone and toluene, were tested for time of evaporation. Each solvent was separately mixed in bitumen using high shear mixer for 5 min at 3000 rpm. To evaporate the solvent, these mixes were kept in an oven at 158 \(\pm\) 5 \({}^{\circ}\)C and were continually (manually) stirred until a constant mass was achieved. The time taken by each solvent tofully evaporate from the modified bitumen was also noted. Methanol was selected as the solvent due to the shorter time taken for its removal from the bitumen.  
**Technique (Dry/Wet) Selection**  
Samples were prepared using both wet and dry mixing techniques. Hot storage test was used to evaluate the high temperature storage stability of modified bitumen. An aluminum tube (25 mm in diameter and 140 mm in height) was filled with about 50 g of hot CNT-modified bitumen, and then stored vertically in an oven at 163 \({}^{\circ}\)C for 48 h. It was then taken out and cooled in a refrigerator at \(-7\)\({}^{\circ}\)C for 4 h. Finally, the tube was cut into three equal sections. The difference of softening point value between top and bottom section of tube is known as Separation Index (SI). If SI is less than 2.2 \({}^{\circ}\)C, the sample is regarded as storage stable according to ASTM D5892. With dry mixing, maximum SI was about 2.5 \({}^{\circ}\)C, while, in wet mixing, it was about 1.3 \({}^{\circ}\)C, thus the sample prepared using wet mixing was storage stable. Wet mixing technique was selected for this study. Thus, the samples were prepared using wet mixing technique with methanol as a solvent using high shear mixer.