esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
## 6 Conclusions  
In summary, a multi-body dynamic model of a high-torque hub drive system, which considered the influence of contact stiffness and friction, was carried out in this paper. An experiment was conducted, and the results reveal that the accuracy of the proposed multi-body dynamic model could satisfy the analysis of the high-torque hub drive system's vibration characteristics with added load.  
During the investigation, it was found that the fluctuations in the angular velocity of the planetary carriers are primarily influenced by the excitation from the hub motor input and the meshing forces between the gears of the reducers. The fluctuations in the meshing forces are mainly determined by the contact stiffness, damping, and tooth clearance during gear contact. The variations in the system's output angular velocity and the meshing forces reflect the operational smoothness of the wheel hub transmission reduction system. Therefore, the key factors affecting the smoothness of the system's operation are the contact stiffness, damping, and tooth clearance of the reducer gears. Additionally, the results of this paper could be used to guide the fatigue life analysis of each component of the system and improve the reliability of the system.  
F.L.: conceptualization, methodology, software, investigation, formal analysis, writing original draft. H.J.: data curation, writing--original draft, visualization, investigation. J.Z.: funding acquisition, writing--review and editing. Q.F.: conceptualization, resources, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
The research was sponsored by the National Key Research and Development Program of China, China under Grant No. 2019 YFB2006402. The financial support is gratefully acknowledged.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors do not have any conflicts of interest with other entities or researchers.
# The Effect of Railway Projects Increasing Safety on the Frequency of Occurrences
## 3 Results
### The Impact of Evaluated Measures on Occurrences
#### 3.2.3 Evaluation of the Impact of Building Construction on the Incidence of Occurrences  
Within the monitored 33 projects, there were an average of 17.84 occurrences per year before the implementation of the project and 17.01 occurrences per year after the implementation of the project. In both cases, these are only occurrences of selected categories and caused by the human factor. It is therefore possible to state that due to the implementation of measures to increase the safety and reliability of the railway transport route (see Section 3.1), the number of occurrences decreased in the period after the implementation of the project by 4.63% on average.  
The annual societal costs associated with these occurrences amounted to an average of 9.11 million CZK/year before the implementation of the constructions. After construction, this value dropped to 8.68 million CZK/year.  
After the implementation of most constructions, there is an increase in track speed and, in some cases, an increase in the traffic load. Transport performance within the entire network increased between 2011 and 2018 by 9.08% for passenger transport and by 3.33% for freight transport (Figure 3). The relationship between traffic performance and the frequency of occurrence of emergencies is analyzed in the following part of the article.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Number** & **Consequences on the** & **Train Delays** & **Financial Impact** & \multirow{2}{*}{**Total Costs (CZK)**} \\  & **of Occurrences** & **Health of People (CZK)** & **(CZK)** & **(CZK)** & **(CZK)** \\ \hline A1 & 11 & 87,071,273 & 3,893,642 & 116,541,074 & 207,505,989 \\ A2 & 2 & 1,517,776 & 0 & 45,666,786 & 47,184,563 \\ A3 & 2 & 32,054,795 & 8740 & 125,833,889 & 157,897,424 \\ B1 & 42 & 44,570,962 & 1,910,633 & 80,788,498 & 127,270,093 \\ B2 & 70 & 3,794,441 & 6,970,892 & 138,979,460 & 149,744,794 \\ B3 & 18 & 25,224,801 & 1,762,251 & 12,389,192 & 39,376,244 \\ C1 & 92 & 0 & 0 & 692,969 & 692,969 \\ C2 & 450 & 0 & 8,164,829 & 46,648,040 & 54,812,868 \\ C3 & 282 & 0 & 15,694,683 & 22,545,910 & 38,240,593 \\ C6 & 572 & 0 & 35,283,240 & 12,585,865 & 47,869,105 \\ C12 & 105 & 0 & 9,091,843 & 2,554,216 & 11,646,058 \\ C19 & 117 & 0 & 7,524,256 & 10,188,512 & 17,712,768 \\ \hline
**Total** & **1763** & **194,234,048** & **90,305,009** & **615,414,411** & **899,953,468** \\ \hline
**Average occurrence** & **110,172** & **51,222** & **349,072** & **510,467** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Society-wide costs of extraordinary events in CZK, price level 2021.
## 2 Materials and Methods
The inexpensive TowardPi DAQ card operates at 6 GB/s, 12 bits, and a SNR of 56 dB, which is suitable for the 400 KHz laser source. Theoretically, the acquisition time for a single 120\({}^{\circ}\) OCTA image is approximately 11 s. Given the reality of eye motion and blinks, the average acquisition time in daily practice is approximately 15 s. As for the choroid vessels, traditional OCTA is sensitive to retinal blood flow as well as the choriocapillaris, but the imaging of flow from the choroidal Satter's layer and Haller's layer, which are deeper and thicker than in the retina, is a challenge for OCTA. The lower signal strength and saturation weaken the capability of visualization with the spectral domain of swept-source OCT. [PERSON] has developed a three-dimensional spatial identification algorithm to visualize choroidal vessels from Sattler's layer and Haller's layer. The morphological reconstruction of large choroidal vessels was achieved by using Threshold Segmentation Algorithm to identify the OCT signal differences between the large vessels and the stroma, supplemented by various signal enhancement and pseudo-signal elimination techniques. As a result, medium- and large-sized choroid vessels are distinctive and vivid, and there is almost no mask from false retinal vessel shadows. In addition, the dataset is in three dimensions, which enables quantification analysis for choroid vessels in various 2D and 3D methods. For the SS-OCTA imaging of this study, automatic segmentation was first used to identify the avascular retina and choroidal vessel layers. The outer avascular retina slab (from the outer plexiform layer to Bruch's membrane) was considered to identify the shape of neovascularization. The upper boundary of the choroidal vessels slab followed the surface of the tumor, and the lower boundary followed the interface between the tumor and the sclera. Manual adjustment on the segmentation was then conducted in all cases with segmentation errors to optimally visualize the morphology of vasculature within the CO, as well as the boundary of the tumor. All OCTA images were carefully reviewed and analyzed by two of the authors ([PERSON] and [PERSON]).
# Effect of Controlled Expiratory Pressures on Cerebrovascular Changes During Valsalva Maneuver
## 3 Results
### NIRS Measurements  
The brain hemodynamic changes were different at 30 and 40 mmHg VM (Table 4). At 30 mmHg VM, the prefrontal HbO on the right side significantly decreased, while the left HbO showed a non-significant tendency to decrease. Furthermore, right HbO decreased on average during VM, POST1, and POST2 compared to PRE; in particular, POST1 showed a significant decrease compared to PRE (T = 3.40, \(p\) = 0.023). However, rSO2 showed no significant changes after the VM.  
At 40 mmHg VM, the right rSO2 significantly increased after VM (F = 3.86, \(p\) = 0.035). The right rSO2 tended to increase on average during VM, POST1, and POST2 compared to PRE and showed a significant increase in POST1 compared to VM (T = \(-\)3.17, \(p\) = 0.036). However, there was no significant change in the HbO levels before and after VM.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{8}{c}{**Repeated Measure Comparisons**} & \multicolumn{8}{c}{**Post Hoc Comparisons (Scheffe)**} \\ \hline \hline \multirow{4}{*}{30 mmHg} & **Outcome** & \multirow{2}{*}{**Testing**} & \multirow{2}{*}{**Mean \(\pm\) SD**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**\(p\)**} & \multirow{2}{*}{**Testing**} & \multirow{2}{*}{**T**} & \multirow{2}{*}{**\(p\)**} \\  & **Variable** & & & & & & & & \\ \hline \multirow{8}{*}{30 mmHg} & \multirow{2}{*}{PSV (cm/s)} & PRE & 96.69 \(\pm\) 15.79 & \multirow{8}{*}{6.09} & \multirow{8}{*}{0.014 *} & \multirow{2}{*}{PRE} & POST1 & \(-\)2.23 & 0.102 \\  & & POST1 & 101.09 \(\pm\) 16.81 & & & & & POST2 & \(-\)2.82 & 0.031 * \\  & & POST2 & 102.05 \(\pm\) 16.50 & & & & POST1 & POST2 & \(-\)1.30 & 0.442 \\  & & PRE & 0.73 \(\pm\) 0.06 & & & & POST1 & \(-\)1.14 & 0.529 \\  & & POST1 & 0.74 \(\pm\) 0.05 & & 3.09 & 0.069 & & & POST2 & \(-\)2.30 & 0.090 \\  & & POST2 & 0.74 \(\pm\) 0.06 & & & & POST1 & POST2 & \(-\)1.68 & 0.263 \\  & & PRE & 67.46 \(\pm\) 8.09 & & & & & POST1 & 1.66 & 0.270 \\  & & PRST1 & 65.60 \(\pm\) 8.84 & & & & & POST1 & 1.53 & 0.324 \\  & & POST2 & 66.18 \(\pm\) 8.96 & & & & POST1 & POST2 & \(-\)0.89 & 0.678 \\ \hline \multirow{8}{*}{40 mmHg} & \multirow{2}{*}{PSV (cm/s)} & PRE & 94.11 \(\pm\) 13.42 & \multirow{8}{*}{5.83} & \multirow{2}{*}{0.012 *} & \multirow{2}{*}{PRE} & POST1 & \(-\)2.49 & 0.064 \\  & & POST1 & 98.83 \(\pm\) 17.47 & & & & POST2 & \(-\)2.72 & 0.040 * \\  & & POST2 & 98.88 \(\pm\) 15.88 & & & & POST1 & POST2 & 0.05 & 0.999 \\  & & PRE & 0.72 \(\pm\) 0.05 & & & & POST1 & \(-\)3.11 & 0.017 * \\  & & POST1 & 0.74 \(\pm\) 0.05 & 4.92 & 0.016 * & & & POST2 & \(-\)1.85 & 0.201 \\  & & POST2 & 0.73 \(\pm\) 0.06 & & & & POST1 & POST2 & 1.07 & 0.573 \\  & & PRE & 66.78 \(\pm\) 9.11 & & & & POST1 & 3.84 & 0.003 * \\  & & POST1 & 62.78 \(\pm\) 8.94 & 9.45 & 0.001 * & & & POST2 & 2.08 & 0.137 \\  & & POST2 & 64.73 \(\pm\) 9.96 & & & & POST1 & POST2 & \(-\)2.93 & 0.025 * \\ \hline \hline \end{tabular}
\end{table}
Table 3: CCA hemodynamic changes during 30 and 40 mmHg VM.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 4 Parameter Settings
### The Selection of Embedding Radius  
The magnitudes at different embedding radii \(R\) have different variation rules which affect the robustness of the algorithm. Considering the imperceptibility of the algorithm, the embedding strength \(\beta\) can vary according to different embedding radii.  
Figure 10: Magnitude coefficients before (**a**) and after (**b**) screen-cam.  
To select the most suitable embedding radius for the algorithm, we designed an experiment. The eight host images are resized to \(241\times 241\), which can be treated as an LSFR. We generate the watermark information with the key \(K_{1}\), where totality of 32 watermark bits is \"\(1\)\".  
Based on the discussion in Section 3.3.3, the embedding radius should be no less than 55 to avoid the watermark bits being too close affecting each other. According to the method in Section 3.2.2, the DFT magnitudes of the experiment images are preprocessed first. Then, watermark information is embedded at different radii for all images based on Equation 15. The PSNR value of the watermarked images is controlled to be around 42 dB by adjusting the embedding strength. The relationship between embedding radius \(R\) and the average embedding strength \(\beta\) is shown in Figure (a)a. With the increase in the embedding radius, the embedding strength can be increased.  
In order to compare the variation of the watermarked magnitudes in different radii and at different shooting distances, we designed an index \(K_{r,d}\) as an evaluation indicator to describe the significance of watermark information. Because only the magnitudes of the positions where watermark bit is \"\(1\)\" are modified, \(K_{r,d}\) only need to consider the modified magnitudes. According to Equation (17), it is defined as:  
\[K_{r,d}=\frac{\sum_{1}^{32}\left(m_{c(r,i)}-\overline{M}_{w}\right)}{32\cdot \sigma_{w}} \tag{18}\]  
where \(K_{r,d}\) defines the index of the image captured at the distance of \(d\) with embedding radius \(r\). \(m_{c(r,i)}\) defines the magnitude in i-th position where watermark bit is \"\(1\)\" in the captured image with embedding radius \(r\).  
The relationship between the average of calculated \(K_{r,d}\) and different shooting distances with different embedding radii is shown in Figure (b)b. When the shooting distance is close to the screen, the watermark information with a larger embedding radius is more significant due to the higher embedding strength. However, the captured details of the watermark will be less and less as the shooting distance increases, so the higher frequency band coefficients will be poorly preserved. When the embedding radius is 56 and 60, the watermark information can be better preserved at different shooting distances. Considering the real scene, in order to better capture the image displayed on the screen, we usually shoot at 40 to 60 cm. At these distances, results with an embedding radius of 60 are better. Therefore, \(R\) is set to 60 in our experiment.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 4 Secure IoT Communication and Attack Mitigation
### IPv6 Protocol and IoT Communications
#### 4.1.2 IPv6 Addressing for IoT Devices  
The 128-bit IPv6 address provides a huge addressing space for IoT deployment. IPv6 addresses have a format like 2001:0 db8:85a3:0000:0000:8a2e:0370:7334 consisting of eight groups of 16-bit hexadecimal values separated by colors. The 128 bits allow for 2128 = 340 undecillion unique addresses. Table 7 summarizes the advantages of the expanded addressing capability in IPv6.  
As shown in Table 6, the huge addressing space addresses the global IoT device explosion, allows simplified addressing without NAT, improves device mobility with end-to-end IP connectivity, enables efficient auto-configuration, and allows hierarchical allocation for efficient routing. There are various types of IPv6 addresses allocated for IoT devices, including global unicast addresses (GUA), unique local addresses (ULA), link-local addresses (LLA), multicast addresses, and anycast addresses. GUAs are most relevant, as IoT devices need global connectivity. GUAs have a global routing prefix and a 64-bit interface ID.
# Lymphedema Surgical Treatment Using BioBridgeTM: A Preliminary Experience
## 1 Introduction
Thanks to its peculiar multi-lumen structure, it allows unidirectional capillary flow, driven by hydrostatic and oncotic pressure, which aids the connection between two healthy tissues and the release of growth factors to stimulate lymphatic regeneration [14]. These grafts are biomimetic, characterized by high mechanical strength, completely absorbable [15], and designed to facilitate soft tissue strengthening. The scaffold-aligned collagen fibrils enable endothelial cells to adhere to the scaffold and migrate along it to finally produce functional capillaries within and around the scaffold. These are then integrated into the tissue [16] (Figure 2). According to preliminary research by [PERSON] et al. [17], BioBridgeTM guides cellular organization, controls endothelium inflammatory response that can actively participate in the phenotypic effects of an abnormal lymphangiogenesis related to fluid accumulation in the pathophysiology of LE [18] and enhances cell survival following implantation in healthy and ischemic tissues. Collagen scaffolds have been shown to encourage lymphangiogenesis, angiogenesis, and aortigenesis in animal models [14]. They have also shown a therapeutic and protective function in rat models [19]; however, the available data on their efficacy in treating lymphedema in human patients in the literature are limited [19]. We aimed to describe our preliminary experience with adult patients affected by primary LE, who were treated with autologous lymph node transplantation and venolymphatic anastomosis, combined with the positioning of BioBridgeTM, to assess potential lymphatic regeneration. Figure 1: The appearance of the BioBridgeTM packaging shows the collagen fibers contained in cylindrical structures. The scaffolds are moistened with saline before surgical implantation. Figure 2: Picture showing damaged lymphatic vessels, resulting in progressive accumulation of fluid and proteins in the subcutaneous soft tissues (**A**). Potential treatments: (**B**) creation of anastomoses between lymphatic and venous vessels, to allow the lymph to drain into the venous flow or harvesting of a flap including lymph nodes from a donor site (**C**) and transplant in a recipient site where lymph nodes are missing (**D**). The concomitant positioning of BioBridgeTM in the surgical site allows the progressive creation of new lymphatic vessels along the course of the collagen scaffold (**E**), with progressive increase over time (**F**).
## 3 BIM and DTs--Latest Development Trends and Challenges
### Perspective for New Solutions  
The DT and SRI can be used to facilitate more informed decision making given that both are dynamic and possess the capacity to learn. They have the potential to collaborate on various stages of a project. The DT enables the simulation of scenarios, while the SRI indicates the optimal actions to be taken, thereby contributing to the optimization process. The DT can be updated in real time as new data arrive, while the SRI can adjust its metrics based on changing needs and conditions. The common features of the DT and SRI provide a starting point for further research and development in the area of intelligent systems. The integration of these two novel systems opens up new possibilities and opportunities in areas such as modeling and simulation, as discussed in [115]. Together, they can contribute to the optimization of operational and building management. These tools are highly promising in the context of smarter buildings and cities [17].  
At this stage, the SRI focuses on the technical elements of the building. Nevertheless, it is recommended that it should be extended with additional factors relegated to energy efficiency, user comfort, and sustainability. Indicators such as energy consumption, CO\({}_{2}\) emissions, user satisfaction, and the share of renewable energy sources should be included in the assessment of buildings. This approach, which considers the mentioned parameters, should be relatively easy to use in BIM solutions, facilitating the organization of more integrated BACS and BMS with developed IoT modules. Furthermore, at the design stage, it will facilitate more accurate parameterization of BACS functions, thereby providing a foundation for verification methods with DT applications.  
In order to remain relevant in the context of the construction industry, both the SRI and EN ISO 52120 must keep pace with the latest technological developments and support the construction of energy-efficient and sustainable buildings in the digital age. The norm is currently lagging behind new developments in construction, such as BIM and AI. It is necessary to implement an update that considers the impact of these developments on data formatting and coding, which can be a long process. In essence, the objective is to stimulate and facilitate the utilization of digital tools for the assessment of the energy efficiency of buildings already at the design and construction stages. The standards should support open BACS and IoT standards and data formats. With this approach, different programs can communicate with each other freely and, at the same time, facilitate the creation of digital platforms where data on the comfort management, security, and energy efficiency of buildings can be shared.
# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
## 4 Discussion
Subsequently, we utilized environmental factors, communication equipment parameters, and other relevant variables at the given time point as features to predict the likelihood of poor communication quality in similar communication events. Such predictions are crucial as they directly impact the transmission quality of emergency signals in comparable environments. Leveraging these results, we can effectively guide Nangang Fishery No. 2 to make timely adjustments to the rescue time and direction, ultimately enhancing the efficiency of rescue operations and ensuring the safety of crew members. **Case 2. ** Compared to traditional relational databases, graph databases model based on entities and relationships in the real world, and their expression is more intuitive and concise. Graph databases are very suitable for querying and analyzing complex relationships with multiple levels and diversity; relational databases are complex and inefficient in handling complex relationship queries, especially when involving multi-table associations or recursive queries. Cypher is a property graph data query language implemented in the graph database Neo4j [23]. Cypher query language provides the basis for data correction, analysis, and expansion for the ship knowledge graph system. The following will introduce in detail the operation content and implementation method of querying ship knowledge based on Cypher language. Cypher query language relies on matching graph patterns. The MATCH keyword is used to specify the search pattern, the WHERE keyword is used in conjunction with the MATCH keyword to add predicate constraints to the matching pattern, and the RETURN keyword is used to return result variables. Below are two examples of querying ship communication knowledge. **Example 1. Query the sailing speed of Nangang Fishery No.1 at 17:10 on 7 June 2023. **  
MATCH (x1:Plat)-[:hasState]->(x2:State)-[:hasDevice]->(:Equipment) <-[]-(:Event)-[:hasInstant]->(x3:Instant) WHERE x1.name = *Nanggang Fishery No.1\" AND x3.time = *2023-06-07 17:10\" RETURN x2.speed as Speed  
**Query Result**  
Speed: 25 Kn/h  
**Example 2. Query the communication status of Nangang Fishery No.1 and Nangang Fishery No.1 at 17:10 on 7 June 2023. **  
MATCH(x1:Plat)-[*]-(x2:Event)-[*]-(x3:Plat) WHERE x1.name = *Nanggang Fishery No.1\" AND x2.time = *2023-06-07 17:10\" AND x3.name = *Nangang Fishery No.2\" RETURN x2.result as Result  
**Query Result: Successfully**