esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Verification of Large-Scaled Flapping Wings for High Altitude Environment
## 2 Materials and Methods
### Numerical Simulation
#### 2.2.3. Mesh Quantity and Time Step Independence Validation  
The mesh quantity and time step size were validated for the bionic wing model before CFD analysis. To eliminate the influence of mesh resolutions on the calculation results, the mesh dependence study was performed using three different mesh resolutions, i.e., around 1.60, 2.88, and 5.86 million, respectively. Table 4 shows the size of each component of three meshes. All three mesh systems were in similar topology, as shown in Figure 5 and were calculated for five cycles with 500 time steps per period. The form of component cells is a tetrahedron, the form of background cells is a hexahedron, and the form of the boundary layer is a prism. The height of the first grid of the boundary layer is maintained at 0.035 mm  
Figure 5: Mesh strategy: (**a**) background mesh, (**b**) component mesh, and (**c**) mesh details.  
Figure 6: Comparison of thrust coefficient loops between the experiment and CFD simulations.  
in both the validation and the subsequent calculation, which meets the requirement of Y+ = 1. The grid height ratio is 1:1 to meet the requirement of arranging enough layers of grids (layers > 10) in the boundary layer so that sufficient grid density is ensured. The results of the last cycle are presented in Figure 7, Figure 7a shows the time history of comparison of transient lift force and Figure 7b presents the time history of transient drag force. As can be seen, the results of forces under the three quantities do not differ much.  
Time steps of T/250, T/500, and T/1000 were employed in the simulations. As shown in Figure 8, the transient lift and the transient drag for the three time-step sizes exhibit no obvious differences.  
The results of these two validation cases indicated that a medium mesh quantity of 2.88 million and a time step size of T/500 should be employed for subsequent simulations  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Component Cells** & **Background Cells** & **Wing Surface Cells** & **Total Cells** \\ \hline Coarse mesh & \(5.90\times 10^{5}\) & \(1.01\times 10^{6}\) & 7376 & \(1.60\times 10^{6}\) \\ Medium mesh & \(8.60\times 10^{5}\) & \(2.02\times 10^{6}\) & 19490 & \(2.88\times 10^{6}\) \\ Fine mesh & \(1.82\times 10^{6}\) & \(4.04\times 10^{6}\) & 40521 & \(5.86\times 10^{6}\) \\ \hline \end{tabular}
\end{table}
Table 4: Mesh magnitudes of three meshes.  
Figure 8: Validation of time step size: (**a**) comparison of transient lift and (**b**) comparison of transient drag.  
Figure 7: Validation of mesh quantity: (**a**) comparison of transient lift and (**b**) comparison of transient drag.  
in order to reduce the computation time while maintaining sufficient accuracy. The calculation was carried out for five periods, and the data from the final period were used for comparison.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 3 Results and Discussion
### Method Validation
#### 3.1.1 Jinsha River  
In the Jinsha River basin, a total of four major reservoirs were constructed between 2003 and 2014, with a combined adjustable capacity of 15.7 billion m\({}^{3}\). Analysis of the historical recorded data from 2014 to 2020 revealed that the runoff increment mainly occurred during the period from November to May of the following year. Specifically, the majority of the increments took place from January to May, accounting for approximately 90% of the total increments.  
The runoff increment in the driest month, i.e., February, was 817 m\({}^{3}\)/s, which was 16% among the total increments. Thus, based on Equation (2), the theoretical increment of the driest month could be calculated.  
The runoff increment in the driest month, specifically February, was determined to be 817 m\({}^{3}\)/s, accounting for approximately 16% of the total increments. Therefore, based on Equation (2), the theoretical runoff increment for the driest month can be calculated as 1028 m\({}^{3}\)/s.  
Compared with the practical increment of 817 m\({}^{3}\)/s, the bias, \(Dev_{0}\), was 26%, as seen in Table 2. The reason for this deviation was probably that the theoretical increment was the ideal status and the entire utilization of the adjustive capacity of upstream cascade reservoirs. However, the operation mode of reservoirs was so explicated that it could not exactly be considered in the estimation. Moreover, the studied reservoir was located widely; the farthest one was about 900 km upstream from Xiangjiaba station, so there were water inflow and evaporation along the river in all probability.  
When comparing the practical increment of 817 m\({}^{3}\)/s with the theoretical increment, a bias (\(Dev_{0}\)) of 26% was observed. This discrepancy can be attributed to the fact that the theoretical increment represents an ideal scenario, assuming the full utilization of the adjustable capacity of upstream cascade reservoirs. However, the actual operation mode of the reservoirs is more complex and may not align precisely with the ideal conditions considered in the estimation. Additionally, the studied reservoirs are located over a wide area, with the furthest one positioned approximately 900 km upstream of the Xiangjiaba station. Along such a long stretch of the river, there are other factors to consider, including water intake and evaporation, which can further contribute to the deviation between the theoretical and practical increments.
## 7 Results and Discussion  
The evaluation of network performance in this study focused on key metrics such as network latency, network jitter, packet loss, and convergence time. The findings indicate that both EIGRP and OSPF have distinct strengths and weaknesses in terms of load sharing and link failover. Specifically, EIGRP demonstrated effective load-sharing capabilities, which helped reduce network latency and packet loss. On the other hand, OSPF excelled in convergence time, especially under link-failure conditions. The integration of BGP further enhanced the system's resilience, making it more robust.  
The tools used, such as Wireshark and IPerf3, provided deep insights into packet-level dynamics, offering a thorough understanding of protocol behavior. This research serves as a valuable resource for network managers and engineers seeking to optimize IPv6 performance and resilience by making informed decisions about protocol selection and parameter adjustments.
## 1 Introduction
* It is demonstrated that NBR-Clust in many cases attains complete clustering in one step. * The performance of NBR clustering to detect and remove noise or outliers is evaluated. * The utility of using different resilience measures within the framework for achieving different purposes is examined. * The application of NBR clustering to datasets with overlap is investigated, including identifying overlap nodes and assigning them to multiple clusters. The practical utility of the NBR-Clust clustering method described in this paper is further demonstrated in [16; 17; 18], where it is applied to new medical and biological datasets. In [16], the method is applied to a database of Autism Spectrum Disorder phenotypes. Results for that dataset showed that the resilience measure _tenacity_ gave the best clustering results, and the minimum connectivity k-Nearest Neighbor (kNN) graph (as defined in Section 4.1.1) was the optimal representation for that data, in accordance with evidence presented in [15; 19]. In [17], DNA genomic data was clustered in order to determine biological origins and genetic similarity of worldwide individuals. _Integrity_ was the node-based resilience measure used due to its robustness when the number of ground truth clusters is unknown. NBR-Clust's ability to detect noise and overlaps was a useful advantage with the genomic data because individuals detected as overlaps were presumed to have a mixed genetic origin. In [18], a study of sources of resistance of grapevines to powdery mildew disease, genetic data was converted to geometric graphs and clustered. This article complements the work of [16; 17; 18] in several ways. First, this paper provides a theoretical justification for the NBR-Clust framework and demonstrates a class of graphs under which it performs better than spectral clustering and modularity. Second, we increase the scope of uses for the NBR-Clust framework by applying it to the overlapping community detection problem. Third, while the previous papers only consider point-set data transformed into graphs, here we consider a wide variety of synthetic network models in addition to well-known real network datasets from the community detection literature. Finally, in this paper, the algorithms have been generalized to include parallel approximations that allow the application of the framework to much larger network datasets.
# Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks
## 2 Theoretical Background
### Machine Learning and Neural Networks
#### 2.1.2 Overview of Neural Networks  
A neural network is a model that simulates the function of the human brain nervous system by simulating and connecting neurons [40], the fundamental units of the human brain, and creating an artificial system with intelligent information processing functions such as pattern recognition, memory, association, and learning [41]. An essential characteristic of a neural network is its ability to learn from its surroundings and store the results of its learning in its synaptic connections. A neural network's learning is a process. Under the influence of its environment, a sequence of sample patterns is fed into the network, and the weight matrix of each network layer is adjusted according to a set of rules. The learning process concludes when the weight of each stratum of the network converges to a certain value. A neural network is an acyclic graph comprising interconnected neurons. The output of the previous layer of neurons functions as the input for the next layer of neurons, which are typically arranged regularly and constructed with multiple neurons in layers of connections. A typical neural network structure is referred to as a full connection layer [42]. Neurons in the same stratum are not connected.  
Social media networks have become increasingly dependent on neural networks. They enable personalized content recommendations based on user preferences, sentiment analysis, image and video recognition, natural language processing (NLP), fraud detection and security, and NLP of images and videos. Social media platforms can utilize neural networks to improve user engagement [43], content curation [44], and platform security [45]. However, there are repercussions associated with the use of neural networks in social media networks. Algorithmic bias can contribute to discriminatory content recommendations and practices. As social media platforms need to manage and safeguard sensitive user information, privacy and data security are concerns. Personalization can result in filter bubbles and polarization, and misinformation and false news can undermine the credibility of the information shared on these platforms.  
To address and mitigate algorithmic bias, social media platforms need to implement robust data acquisition processes, diverse training sets, and regular audits. To address privacy and data security concerns, appropriate data anonymization, encryption, and consent mechanisms are required. Platforms need to establish a balance between personalization and exposure to diverse viewpoints. Continuous monitoring, fact-checking mechanisms, and user-reporting systems are required in order to reduce the dissemination of misinformation. In this manner, social media platforms can maximize the benefits of neural networks while minimizing their potential consequences. Figure 1 illustrates the significance of neural networks in social media by spotlighting their diverse applications and repercussions.
## 3 Results
### Flavor Compounds and Flavor Profile of Formulated Aggregates
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Flavor Compounds** & **TC** & **Cas** & **Cas\_TC** & **RT** & **RI** & **MW** & **Log P** & **VP** & **OD** \\ \hline Hexanal & + & + & + & 5.03 & 800 & 100.16 & 1.78 & 10.88 & green \\
2-heptanone & + & \(-\) & \(-\) & 9.85 & 885 & 114.19 & 1.98 & 4.73 & fruity \\ Heptanal & + & \(-\) & \(-\) & 10.80 & 897 & 114.19 & 2.442 & 3.85 & green \\ Benzaldehyde & + & + & + & 14.61 & 956 & 106.1 & 1.48 & 1.27 & fruity \\
1-octen-3-one & + & \(-\) & \(-\) & 16.45 & 979.5 & 128.2 & 2.52 & 0.531 & fruity \\ Octen-3-ol & \(-\) & + & \(-\) & 16.50 & 979 & 128.21 & 2.519 & 0.531 & earthy \\
6-methyl-5-hepten-2-one & + & \(-\) & \(-\) & 16.90 & 984.98 & 126.2 & 1.90 & 1.277 & citrus \\ Octanal & + & + & 18.00 & 998 & 128.22 & 2.951 & 2.068 & green \\ D-limonene & + & \(-\) & + & 19.17 & 1017.7 & 136.2 & 4.57 & 0.198 & citrus \\
2-ethyl-1-hexanol & + & + & + & 19.66 & 1026.5 & 139 & 3.10 & 0.207 & citrus \\ Benzyl alcohol & + & \(-\) & + & 19.81 & 1029 & 192.3 & 3.20 & 0.008 & fruity \\
1-octanol & \(-\) & + & + & 22.22 & 1069.1 & 130.2 & 3.00 & 0.079 & green \\ \(\alpha\)-terpinolene & + & \(-\) & \(-\) & 22.82 & 1078.4 & 136.24 & 4.470 & 1.126 & woody \\ Linalool & + & \(-\) & + & 23.74 & 1092.1 & 154.3 & 2.970 & 0.016 & citrus \\ Nonanal & + & + & + & 23.98 & 1095.5 & 142.24 & 3.461 & 0.532 & floral \\ Phenylethyl alcohol & + & \(-\) & \(-\) & 24.35 & 1101.4 & 122.2 & 1.360 & 0.087 & floral \\ \(\alpha\)-campholenal & + & \(-\) & \(-\) & 24.92 & 1113 & 152.24 & 2.587 & 0.415 & green \\
2-ethylhexyl acetate & + & \(-\) & \(-\) & 26.32 & 1146 & 172.27 & 3.686 & 0.413 & earthy \\ Ethyl benzoate & + & \(-\) & \(-\) & 27.45 & 1391 & 150.18 & 2.640 & 0.267 & fruity \\ Decanal & + & + & \(-\) & 29.38 & 1196.1 & 156.27 & 3.970 & 0.207 & floral \\ Nerol & + & \(-\) & \(-\) & 30.45 & 1218.6 & 154.25 & 3.47 & 0.013 & floral \\ Geraniol & + & + & + & 31.78 & 1246.9 & 154.25 & 3.56 & 0.021 & floral \\
4-propylbenzamidedehyde & + & \(-\) & \(-\) & 32.34 & 1258.5 & 148.20 & 2.918 & 0.039 & - \\ Decanol & + & \(-\) & + & 32.65 & 1265 & 158.28 & 4.570 & 0.008 & fatty \\ Virginire & + & \(-\) & \(-\) & 32.83 & 1277 & 192.30 & 6.62 & 0.022 & floral \\ Perillyl alcohol & + & \(-\) & + & 33.72 & 1286 & 152.24 & 3.170 & 0.006 & green \\ Eugenol & + & \(-\) & + & 36.43 & 1350.7 & 164.20 & 2.27 & 0.010 & spicy \\ Methyl acetate & \(-\) & \(-\) & + & 35.03 & 1310 & 74.08 & 0.180 & 368.3 & fruity \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Volatile compounds identified in tart cherry juice (TC), dairy protein powder (Cas) and formulated aggregates (Cas\_TC).
## 3 Results and Discussions
### Airflow Prediction
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Day**} & **Measuring** & \multicolumn{3}{c}{**Air Velocity, v**} & \multicolumn{2}{c}{**v\({}_{\text{med}}\)**} & \multicolumn{2}{c}{**Q\({}_{\text{v}}\)**} & \multicolumn{2}{c}{**V\({}_{\text{wind}}\)**} & \multicolumn{2}{c}{**T\({}_{\text{e}}\)**} & \multicolumn{2}{c}{**T\({}_{\text{cav}}\)**} \\ \cline{3-10}  & **Point** & & **(m/s)** & & **(m/s)** & **(m3/h)** & **(m/s)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** \\ \hline \multirow{2}{*}{16 July 13:00} & Inlet & 0.68 & 0.79 & 0.72 & 0.73 & 526 & & & \\  & h = 1.4 m & 0.39 & 0.42 & 0.46 & 0.42 & - & 1.8 & 25.7 & 32.3 \\  & Outlet & 0.69 & 0.69 & 0.71 & 0.70 & 504 & & & \\ \hline \multirow{2}{*}{17 July 13:00} & Inlet & 0.57 & 0.63 & 0.68 & 0.63 & 454 & & & \\  & h = 1.4 m & 0.31 & 0.35 & 0.21 & 0.29 & - & 0.9 & 24.6 & 39.3 \\  & Outlet & 0.42 & 0.58 & 0.53 & 0.51 & 367 & & & \\ \hline \multirow{2}{*}{18 July 10:00} & Inlet & 0.66 & 0.64 & 0.68 & 0.66 & 475 & & & \\  & Outlet & 0.23 & 0.25 & 0.27 & 0.25 & - & 0.4 & 23.4 & 38.4 \\  & Outlet & 0.45 & 0.49 & 0.4 & 0.45 & 324 & & & \\ \hline \multirow{2}{*}{19 July 16:00} & Inlet & 0.57 & 0.58 & 0.61 & 0.59 & 425 & & & \\  & h = 1.4 m & 0.31 & 0.35 & 0.42 & 0.36 & - & 0.4 & 31.7 & 34.7 \\  & Outlet & 0.52 & 0.49 & 0.56 & 0.52 & 374 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Air velocities at different heights in the DSF channel for SET 2: 14 th–20 th July. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Day**} & **Measuring** & \multicolumn{3}{c}{**Air Velocity, v**} & \multicolumn{2}{c}{**v\({}_{\text{med}}\)**} & \multicolumn{2}{c}{**Q\({}_{\text{v}}\)**} & \multicolumn{2}{c}{**V\({}_{\text{wind}}\)**} & \multicolumn{2}{c}{**T\({}_{\text{e}}\)**} & \multicolumn{2}{c}{**T\({}_{\text{cav}}\)**} \\ \cline{3-10}  & **Point** & & **(m/s)** & & **(m/s)** & **(m3/h)** & **(m/s)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** \\ \hline \multirow{2}{*}{23 July 10:00} & Inlet & 0.61 & 0.58 & 0.61 & 0.60 & 432 & & & \\  & h = 1.4 m & 0.22 & 0.29 & 0.24 & 0.25 & - & 2.2 & 19.8 & 22.4 \\  & Outlet & 0.26 & 0.28 & 0.31 & 0.28 & 2268 & & & \\ \hline \multirow{2}{*}{23 July 15:00} & Inlet & 0.71 & 0.74 & 0.68 & 0.71 & 511 & & & \\  & h = 1.4 m & 0.31 & 0.28 & 0.35 & 0.31 & - & 3.1 & 27.7 & 34.7 \\  & Outlet & 0.37 & 0.36 & 0.28 & 0.34 & 1652 & & & \\ \hline \multirow{2}{*}{24 July 12:00} & Inlet & 0.64 & 0.56 & 0.68 & 0.63 & 454 & & & \\  & h = 1.4 m & 0.3 & 0.42 & 0.29 & 0.34 & - & 2.2 & 28 & 38.9 \\  & Outlet & 0.4 & 0.32 & 0.34 & 0.35 & 2138 & & & \\ \hline \multirow{2}{*}{25 July 16:00} & Inlet & 0.56 & 0.61 & 0.49 & 0.55 & 396 & & & \\  & h = 1.4 m & 0.29 & 0.35 & 0.27 & 0.30 & - & 1.3 & 35.3 & 37.2 \\ \cline{1-1}  & Outlet & 0.31 & 0.35 & 0.29 & 0.32 & 1912 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Air velocities at different heights in the DSF channel for SET 3: 21 th–27 th July.
## 2 Materials and Methods
### WRPM: Weather-Robust Preprocessing Module  
In urban street scene analysis, severe weather conditions such as haze often lead to significant degradation in image quality, thereby affecting the accuracy of subsequent critical tasks like vegetation segmentation [37]. To address this, we propose the WRPM, which aims to maximize the restoration of image details through efficient dehazing and provide clear and reliable input for subsequent segmentation tasks.  
The WRPM module employs a parallel dual-branch structure, designed with specialized processing paths for regions with different haze concentrations. It then achieves adaptive integration of information through feature fusion. The overall architecture mainly consists of three parts. The WRPM module utilizes a multilevel convolutional neural  
Figure 2: The framework of UGSAM. (**a**) WRPM: Weather-Robust Preprocessing Module. The target image is defogged by a dense haze region processing network (i.e., FFA-Net) and a light haze region processing network (i.e., LS-UNet), and the feature fusion of the two modules is realized by using a feature fusion module (i.e., FIM). (**b**) SAM. The processed image and prompt information are input to the encoder, and the vegetation area mask is obtained by the decoder. (**c**) SCP-Net: Streetscape-Correction Network. The module corrects the SAM output result.  
network [38], combining feature fusion attention mechanisms, multiscale feature extraction, and feature fusion strategies, enabling the effective restoration of images in complex haze scenarios. Specifically, it is composed of a dense haze region processing network FFA-Net, a light haze region processing network LS-UNet, and a feature fusion module FIM.
## 2 Literature Review
### Research Gap
[49] optimize ship scheduling under port congestion and environmental regulations, but their work focuses more on service reliability and emission reduction, not on explicitly balancing profitability withservice volume. Our research bridges this gap by explicitly modeling the bi-objective conflict between profitability and service scale in the context of regulatory surcharges, which significantly affect route-specific costs and margins. By incorporating regulatory costs into our multi-objective optimization model, we allow liner shipping companies to make more informed decisions on how to balance profitability and service commitments. To bridge these gaps, we propose a bi-objective MIP model designed for the HSR-DA problem, which integrates route operation, ship assignment, and transport demand acceptance. This MIP model uniquely incorporates real-world cost structures, especially surcharges such as those imposed on China-built ships. These surcharges are embedded in the objective function and play a critical role in shaping routing and deployment decisions across a heterogeneous fleet. The model formulation incorporates several practical constraints, including ship assignment limitations, fleet availability, demand-related transport volume constraints, and transport capacity requirements. This model provides a comprehensive decision-support tool for operational planning under real-world regulatory and market conditions. In particular, it provides guidance for ship routing and demand acceptance decisions under the influence of recent policy proposals to impose substantial surcharges on China-built ships calling at U.S. ports.
## 7 SMEP Algorithms
### AuxDataQueue Compression Algorithm
Actually, the _DataQueue_ size is much bigger than this size because the _DataQueue_ size can be the space size capable of accommodating \(2^{m}+1\) sensor data from available free memory space. The real sizes of _DataQueue_ in current technologies can generally range from dozens of kilobytes to hundreds of megabytes in available flash memory spaces. Despite this, the reason why we prepared such a small-sized _DataQueue_ is to validate the merits of the SMEP method in _Zones_ compressions, just with small _DataQueue_ size. We also prepared five _Zones_ patterns, i.e., 1-0-0-0, 2-0-0-0, 3-0-0-0, 3-2-2-1, and 3-2-2-2, by randomly choosing them because there are too many cases to investigate all cases in _Zones_ patterns. The sample sizes corresponding to these patterns are 25, 41, 73, 93, and 97 in order that the ground sensor data should be full in each 0-valued zone. We experimented with five samples with these sizes for each of the five compression methods, which are _winway_, _delta_, _CQP_, _2 MC_, and _SMEP_. Five samples are constructed for each of the four real-field sensor data sets, which are the relative humidity, air temperature, underwater pH, and underwater temperature sensor data sets. According to sample sizes corresponding to the five _Zones_ patterns, each of the five samples consisted of consecutive ground sensor data extracted randomly from each of these four sensor data sets. With these samples corresponding to five _Zones_ patterns, we estimated the individual AERs and compared them for the five methods. In Table 4, we present the results. Table 4 shows us that even in terms of the zones value patterns, the AER of the SMEP method is better than the AERs of the other methods, too. Figure 12 shows more explicitly the graph shape changes in accordance with _Zones_ patterns. The figure shows not only graph patterns of sensor data recovered from relative humidity sensor data compressed by the _winway_, _delta_, _CQP_, _2 MC_, and _SMEP_ methods, but also comparisons among between of these patterns and the original sensor data pattern. In the 1-0-0-0 _Zones_ pattern, sensor data from the 1 st to the 17 th covered by _Zones[0]_ are one-time compressed data, but sensor data from the 17 th sensor data to the 25 th sensor data covered by _Zones[1]_, _Zones[2]_, and _Zones[3]_, of which the values are 0, are ground sensor data. Figure 12a shows the 1-0-0-0 _Zones_ pattern. In this graph, we can see that the _CQP_, _2 MC_, and SMEP methods follow patterns similar to original sensor data.