esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of Rigidity Center
## 3 Results and Discussion
### Results of Group B Buildings  
Group B buildings had equally spaced columns and shear walls (Figure 17). Symmetrically planned B1 and B6 had torsion due to the reasons described above. A difference in the _modulus of elasticity_ resulted in unintentional torsion. However, clear torsion was visible in the buildings with asymmetrically placed shear walls. In B2, B4 and B5, the torsion was significant, and the buildings rotated on the opposite side of the shear walls. In B3, as the building had a symmetrically placed shear wall in the N-S direction, no considerable torsion was visible when the building was loaded in the S-N direction. It was again seen that shear walls influenced the direction of the rotation and resulted in torsion in the buildings.  
Figure 18 shows the rigidity center locations of Group B buildings comparatively. The rigidity center locations for the top story are summarized in Table 8. As seen from Figure 18, in the symmetrical B1 and B6 buildings, the rigidity centers from the tests (RCs) were almost identical to the ones found from ETABS (E). The difference in these buildings was at a maximum of 4%. As for B2, because of the asymmetrically placed shear wall, the RC was found far from the mass center, and it was close to the shear wall corner. In B3, as the shear walls had symmetry in the N-S direction, the RC was on the symmetry line but close to the shear walls. The difference between the RC and E were at a maximum of 4.87%. In B4 and B5, the RC and E differed too much compared to the others. It was at a maximum of 13.84% in B4 and 12.61 in B5. In B4, although the RC was found to be close to  
Figure 17: Test results of Group B.  
the corner of the shaped shear wall, E calculated it close to the mass center. However, in B5, although the rigidity center found from E was at the mass center, the test revealed that it was on the N-S symmetry axis but close to the north side. Therefore, it can be said that as the shear walls were placed symmetrically in either axis, E calculated the rigidity center on the symmetry line, considering the shear walls mainly in the strong axis without taking into account the global shear wall locations. However, in the tests, it was found that the global shear wall locations may also be important because a significant difference was seen in those buildings.  
Figure 18: Rigidity centers of Group B.
## 3 Results and Discussion
### Elementary Composition Using XRF  
According to the characterization by X-ray fluorescence (XRF) of the residues, it was found that the residues of the CRSI003 material are mainly composed of Fe and Cr at very similar percentages (\(\approx\)28%) with small amounts of Si, Al, Ca, Mg, Ti, and Mn. The main component in the residues of FEMA004 is Fe (\(\approx\)42%), followed by Ti (\(\approx\)14%) and Cr (\(\approx\)6%), as well as Mn, Si, Al, Ca, and Mg in small amounts. On the other hand, in the residues of the material FEMA011, the main element is Fe (\(\approx\)66%) and small quantities of Si, Al, Ca, and Mg can be found in its composition. The ILME007 residues are composed of Fe (\(\approx\)32%) and Ti (\(\approx\)25%) mainly, with small percentages of Si, Al, Ca, Mg, Na, and Mn. Mn (\(\approx\)53%) is the main element present in the residues of OXMN009, followed by percentages lower than 3% of Si and Fe, with very small amounts of Al, Ca, Mg, and P. Finally, in the residues of OXMN010A, Mn (\(\approx\)37%) is also present as the main component, but, unlike OXMN009, silicon is present in larger quantities--close to 20%--as well as small amounts of P, Al, Ca, Mg, Na, and K, along with Cu, Ti, and Zn. It was observed that, in general terms, the residues present a composition very similar to that reported in the fresh samples (Table 1), except for OXMN009 and OXMN010A, which present higher percentages. This can be explained by their high loss on ignition (LOI) content, which is burned in the first cycle.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**OC** & **Fresh** & **Reduced** & **Oudined** & **Reduced** & **Oudilized** \\ \hline
**Gas** & & **H\({}_{2}\)** & **H\({}_{2}\)** & **CH\({}_{4}\)** & **CH\({}_{4}\)** \\ \hline \end{tabular}
\end{table}
Table 5: SEM results of oxygen carriers.
## 3 Proposed Method
### Adaptive Bin Estimator  
After performing multilevel shared decoding, the multilevel decoding features, \(\mathbf{D}_{0}\), \(\mathbf{D}_{1}\), \(\mathbf{D}_{2}\), \(\mathbf{D}_{3}\) and \(\mathbf{D}_{4}\) are sent to the proposed adaptive dynamic-range bin (AdaDRBins) estimator. The AdaDRBins estimator employs these multilevel decoding features and the original LRLQ depth map, \(d_{\text{rxp}}\), to produce the bin division patterns, i.e., the set of bin-division vectors \(\{\mathbf{b}_{0}\), \(\mathbf{b}_{1}\), \(\mathbf{b}_{2}\), \(\mathbf{b}_{3}\}\) and the finally predicted depth map \(\mathbf{\hat{d}}\), as Figure 6 shows. The \(i\)th component (i.e., the \(i\)th bin) of \(n\)th bin-division vector, \(\mathbf{b}_{n}\), is hereafter denoted by \(b_{n}^{i}\).  
The AdaDRBins estimator consists of four types of multiple layer perceptron (MLP): feature-embedding MLP (\(E_{k}\)-symbolized block), bin-initialization MLP (INIT-symbolized block), bin-splitter MLP (\(S_{k}\)-symbolized block) and bias-prediction MLP (\(B_{k}\)-symbolized block). Those MLPs exploit the individual fully connected (FC) structures to project the data into the different specified dimensions. The combination of those cascaded MLPs can make the dividing bins gradually be of a satisfactory fine granularity. The complete structures and signal flows of all MLPs are exhibited in Figure 7.  
Figure 6: The architecture of AdaDRBins estimator.  
Figure 7: _Cont._
## 4 Transfer Hydrogenation of Nitroarenes in Glycerol  
Selective reduction of nitroarenes is an important synthetic tool for the preparation of aromatic amines in organic chemistry. The reduction of nitro aromatic compounds is also of considerable interest because these chemicals are common groundwater contaminants and reduction reactions can play a central role in their environmental fate or cleanup. Many methods have been reported in the literature for the reduction of nitroaromatics, including the use of Raney nickel under TH conditions. In this context, [PERSON] and co-workers have demonstrated that the reduction of various nitrobenzene derivatives by means of Raney Ni can be performed using glycerol as the solvent and hydrogen donor [48,58]. Reactions, which were typically performed with a stoichiometric amount of Raney Ni and excess of NaOH (2.5 equivalents), delivered the desired anilines in moderate to good yields (44%-81%) after 24 h of heating at 100 \({}^{\circ}\)C, with those substrates containing electron-withdrawing substituents being reduced more easily than those with electron-donating groups.  
**Scheme 10.** Transfer hydrogenation of nitroarenes in glycerol using magnetic Fe\({}_{3}\)O\({}_{4}\)-Ni nanoparticles.  
Much faster transformations were subsequently described, under milder reaction conditions, using Fe\({}_{3}\)O\({}_{4}\)-Ni MNPs as catalyst in combination with KOH (Scheme 10) [52]. Contrary to the preceding case, the electronic nature of the substituents on the aromatic ring did not have a significant influence on the effectiveness of the process. This heterogeneous system also showed an exquisite chemoselectivity, reducing only the nitro group even in the presence of carbonyl functionalities. A stronger interaction of the nitro group to the catalyst surface compared to the carbonyl one was evoked to explain the chemoselectivity observed. It is also worthy of note that, starting from 1,3-dinitrobenzene, selective reduction of only one NO\({}_{2}\) functionality took place under the standard reaction conditions. In addition, after separation using a simple magnet, the Fe\({}_{3}\)O\({}_{4}\)-Ni MNPs could be reused eight times without any significant loss in catalytic activity and selectivity. Overall, this new methodology represents a real improvement with respect to classical methods based on the use of more expensive and hardly recyclable Raney nickel or Pd and Pt catalysts.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Description of the Algorithm ICCDRL  
The paper applies the deep reinforcement learning algorithm PPO2 to vehicle communications congestion control by observing the current network state information, learning from historical experience, optimizing decisions based on evaluative feedback from the network environment, using a large amount of historical QoS data as a training set to optimize the model with the help of congestion-sensitive factors, and enhancing the feedback signal when the action performed by a node is positively rewarded, and thereafter, the probability of performing the action becomes larger. On the contrary, the probability of a node performing the action becomes smaller and the congestion window size of the next moment is selected, thus forming an intelligent congestion control policy. The detailed steps of the ICCDRL algorithm proposed in the paper are described as follows.  
1. Input the initial state \(s_{t}=\{cwnd,\,ACK,\,RTT,\,throughput,\,P_{x}\}\) of the network and initialize the parameter \(\theta\) of the policy function \(D(\theta)\);
2. Collect the actions \(a_{t}\) corresponding to the state \(s_{t}\) needed to run the policy function \(\pi(a_{t}|s_{t})\);
3. At state \(s_{t}\), perform action \(a_{t}\) to obtain reward \(r_{t}=\alpha_{o}(o/o_{\max})-\alpha_{d}\big{(}d_{\min}/\overline{d}\big{)}- \alpha_{l}\big{(}\overline{l}/l_{\max}\big{)}\);
4. Obtain the expectations of the reward according to \(\hat{R}\)= \(r_{0}+\gamma r_{1}+\cdots+\gamma^{t}r_{t}=\sum\limits_{t=0}^{T}\gamma^{t}r_{t}\);
5. Obtain the optimization objective function according to \(D(\theta)=E_{n_{\theta}}\big{(}\hat{R}_{t}-Q(s_{t})\big{)}^{2}\);  
Figure 4: Diagram of Clip Function with different region (**a**) \(D>0\) and (**b**) \(D<0\).  
* Obtain the optimal value of the optimized objective function \(D(\theta)\) with the help of the gradient ascent method;
* Execute action \(a_{t}\) and update \(cwnd\) according to \(cwnd=cwnd+ns^{\prime}\);
* Repeat step 2 through step 7.
## 3 Results
### Comparison of ERA5 with Observed Meteorological Profile  
Figure 9a shows that, for January, February, and March, ERA5 horizontal wind speeds are slightly lower than observed values below 500 m, but higher above 500 m. For July and August, ERA5 overestimates horizontal wind speeds at all heights, with a 4 m/s overestimation at 2000 m.  
Figure 9b indicates that, for both January, February, and March, and July and August, ERA5 vertical wind speeds show large deviations above 1000 m, while deviations below 1000 m are smaller. However, Figure 3 reveals that ERA5 vertical wind speed accuracy is low at all heights, with average vertical wind speeds near 0 m/s, not closely matching observations.  
Figure 9c demonstrates that, for January, February, and March, ERA5 wind direction shows small deviations below 500 m, with good alignment between 500 and 1000 m. Above 1000 m, deviations increase with height, with average deviations between \(-15^{\circ}\) and \(30^{\circ}\). For July and August, ERA5 wind direction shows large deviations below 100 m, good alignment between 100 and 1000 m, and slightly larger deviations between 1000 and 2000 m, with average deviations between \(-10^{\circ}\) and \(40^{\circ}\).  
Figure 9d shows that, during January, February, and March, ERA5 underestimates temperatures in the lower layers compared to observations, while it overestimates temperatures in the upper layers, with deviations increasing above 1500 m. For July and August, ERA5 overestimates temperatures below 500 m, underestimates them between 500 and 1200 m, and again overestimates temperatures above 1200 m, with deviations increasing with height.  
Figure 9e shows that ERA5 relative humidity data exhibit large deviations for both January, February, and March, and July and August. Although ERA5 humidity data follow a similar vertical trend to observed data, significant differences exist between ERA5 and observed humidity values. One reason for ERA5's humidity inaccuracies is the Foehn effect, where humidity decreases after wind passes over mountains east, west, and north of  
Figure 9: A comparison of observed (JFM OBS) and ERA5 (JFM ERA5) values for January, February, and March, and observed (JA OBS) and ERA5 (JA ERA5) values for July and August, at various heights for horizontal wind speed (**a**), vertical wind speed (**b**), wind direction (**c**), temperature (**d**), and humidity (**e**).  
the observation site [44]. ERA5's low resolution, especially in complex terrain like Baotou, limits its data accuracy.
## 6 Analysis of Influencing Factors of Rock Bolt Body Deformation
### Analysis of Simulation Results  
#### 6.2.1 The Deformation Characteristics of Rock Bolts  
Based on the roadway model with layered composite roof constructed in the above text, the deformation characteristics of the anchor bolt body under different factors were simulated. Figure 14 shows the simulation results of the deformation characteristics of the anchor bolt under different strengths of the surrounding rock and structural planes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Number** & **I** & **II** & **III** & **IV** \\ \hline
1\({}^{\#}\) Interface strength & High & Low & Low & High \\ Coal seam 1 & High & High & Low & Low \\
2\({}^{\#}\) Interface strength & Low & High & High & Low \\ Coal seam 2 & Low & Low & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical Performance Parameters of Surrounding Rock of Structural Planes.  
Figure 14: Deformation Characteristics of Rock Bolts with Different Surrounding Rock and Structural Plane Strengths.  
It can be concluded from Figure 14 that in Test I, no obvious deformation occurred at the \(1^{\#}\) interface of the bolt body, and the relative dislocation was not obvious, but obvious shear deformation occurred at the \(2^{\#}\) interface of the anchor bolt, and there was a large relative dislocation of the rock strata above and below the \(2^{\#}\) interface. In Test II, shear deformation occurred at both the \(1^{\#}\) and \(2^{\#}\) interfaces of the anchor bolt, and the relative dislocation of the upper and lower rock strata was obvious, but the deformation degree of the anchor bolt near the \(1^{\#}\) interface was smaller than that near the \(2^{\#}\) interface. In Test III, obvious shear deformation occurred at the \(1^{\#}\) interface, and the relative dislocation was obvious, while no obvious deformation occurred at the \(2^{\#}\) interface, and the relative dislocation was not obvious. In Test IV, shear deformation occurred at both the \(1^{\#}\) and \(2^{\#}\) interfaces of the anchor bolt, and the relative dislocation of the upper and lower rock strata was obvious. Comparing the deformation of the anchor bolt near the \(2^{\#}\) interface of Test I and the \(1^{\#}\) interface of Test II, it can also be shown that under the condition of the same structural plane strength, the higher the strength of the surrounding rock, the smaller the shear and bending deformation of the anchor bolt. Comparing the deformation of the anchor bolt near the \(2^{\#}\) interface of Test I and the \(2^{\#}\) interface of Test IV, the strength of both interfaces was small, and the difference in the strength of the rock strata above and below the interface was also large. The weak coal seam of the \(2^{\#}\) interface in Test I was below the interface, and the weak coal seam in Test IV was above the \(2^{\#}\) interface. This indicates that under the condition of the same structural plane strength, when the weak rock mass is below the structural plane, the structural plane is more prone to transverse dislocation.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 3 System Model Design  
The routing algorithm is a primary issue in the research field of opportunistic networks because of its particularity. The improvement of the transmission environment is reflected in increasing the delivery ratio and decreasing end-to-end delay in opportunistic networks. Recently, many routing algorithms have been proposed based on different application scenarios. However, most routing algorithms tackle the issue of limited network resources using a deterministic approach, which lacks an adaptive data routing mechanism [45]. Moreover, these methods show relatively low scalability because they are probably built up on the basis of some special scenarios rather than general ones. This will likely cause a low quality of service from networks to clients. On the other hand, when the cache of nodes is insufficient, they cannot store and process the data information obtained from message carriers [46]. Consequently, this paper comprehensively considers the significant two-factor including cache space and energy consumption through the fuzzy inference CMFI system.
# Wind Loads on a Solar Panel at High Tilt Angles
## 1 Introduction
[3] showed that the system is subject to salt water corrosion and to the dynamics of tides, wind, and waves. In extreme winds and waves, the wind loads on the PV panels in a harsh sea environment are not the same as the load on the PV panels that are on land. This study conducts a wind load analysis using wind tunnel experiment and numerical simulation for a stand-alone panel at high \(\alpha\). The effect of \(\beta\) is also determined. The data is useful for the detailed structural design of offshore PV panels.
# Road Obstacle Detection Method Based on Improved YOLOv5
## 2 Related Work  
In 2020, the YOLOv5 algorithm was introduced, offering various versions (n, s, m, l, and x) to cater to different requirements for accuracy and speed. YOLOv5 has gained widespread adoption over the years, and after considering the balance between speed and algorithm size, this paper selects YOLOv5s as the foundational algorithm. The architecture of YOLOv5, illustrated in Figure 1, comprises four primary modules: Input, Backbone, Neck, and Head.  
1. Input: In this initial stage, the input data undergo a series of enhancement and preprocessing techniques aimed at augmenting the robustness of the algorithm throughout the training phase. These processes are crucial for ensuring that the data are optimally prepared, thereby facilitating improved performance and reliability of the model.
2. Backbone: The Backbone is responsible for extracting features from the input image. It utilizes the CSP-Net structure [36], which reduces the number of parameters while enhancing the algorithm's generalization ability. CSP-Net achieves this by dividing the feature map into two parts and introducing jump connections, maintaining efficient computation. The Backbone contains three critical modules: the ConBnSiLU module,  
Figure 1: YOLOv5 algorithm structure.  
the C3 module, and the SPPF module. The design of CSP-Net not only optimizes information flow but also strengthens feature expression, enabling the network to extract richer image features with lower computational effort.
3. Neck: The Neck integrates the Feature Pyramid Network (FPN) [37] and the Path Aggregation Network (PAN) [38]. In traditional CNNs, deeper features typically contain rich semantic information but exhibit poor spatial localization, whereas shallower features offer strong localization but lack semantic depth. By combining FPN and PAN, YOLOv5 enhances semantic information in the shallow layers while improving localization in the deeper layers. This fusion of multi-scale features significantly elevates the overall performance of the network.
4. Head: The Detection Head is responsible for making the final predictions regarding an object's category and location. YOLOv5 utilizes CIOU as its loss function, which not only assesses the quality of the predicted bounding box but also takes into account elements such as position and shape. This approach facilitates better optimization of the learning and prediction processes, resulting in enhanced detection accuracy.  
The architectural design of the YOLOv5 significantly enhances detection accuracy while concurrently minimizing computational overhead. This dual capability renders YOLOv5 robust and suitable for a diverse array of practical applications, demonstrating its efficacy within the field of computer vision.
# Characterization and Prediction of Air Transport Delays in China
## 2 Literature Review  
Many analytical models have been proposed to study flight delays. Reference [24] developed a delay tree to quantify the propagation of delays; this is based on the concept of delay multiplier, i.e., the ratio between the initial delay over the sum of all potential downstream delays. Reference [25] developed two models that measure the level of flight delays [18] to examine the delay propagation in different spatial and temporal terms. Reference [26] analyzed the data of departure and arrival for ten major airports in order to improve the accuracy of delay prediction. The distribution associated to delay time probability was modeled though different functions, among which the Poisson one showed a better performance than the normal distribution in modeling the departure delay. Reference [27]proposed a model for predicting the distributions of departure delays by studying the related factors. Inspired by the ideas of genetic algorithm, an improved expectation-maximization algorithm was developed. The experiments showed the good performance of the model on predictive capabilities and the robustness to the parameter selection. By considering both temporal (e.g., the hour of the day) and spatial (e.g., the status of the system at that time) variables, Reference [13] proposed a new group of models to predict flight delays. In addition to delay states of main airports and links (i.e., local variables), the global delay state was also characterized by new variables. Reference [28] proposed an approach to predict the flight delays using deep learning. Moreover, simulation-based models have also been proposed to study the delays [29]. Based on the simulation of service queue at airport and the itineraries of aircraft, Reference [30] enhanced the Approximate Network Delays (AND) model to study the local delay that occurs at airports (by a queuing engine) and the delay propagation through the airport network (by a delay propagation algorithm). Reference [31] proposed two multi-factor models to predict flight delays in fifteen-minute epochs for 34 airports in the US. In order to predict generated delays and absorbed delays, the piece-wise linear regressions and multi-adaptive regression splines were used. Finally, many studies estimate the impact of delays on social welfare and the environment. Reference [2] highlight that flight cancellations and missed connections can lead to substantial passenger delays, which are usually not captured in traditional flight delay statistics.