esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Non-Terrestrial Networks
UAVs and HAPs supplement this backbone infrastructure with dynamic and mobile capabilities, allowing for targeted coverage, rapid deployment, and persistent communication in specific areas. This synergistic combination of global reach and agility ensures the network's ability to adapt to changing communication requirements and effectively serve remote and dynamic environments [64; 65]. In conclusion, the utilization of non-terrestrial networks presents significant potential for expanding communication capabilities, enhancing coverage, facilitating the distribution of computing resources, reducing data processing delays, and establishing local IoT networks that can adapt to diverse Quality of Service (QoS) demands [66]. These platforms are characterized by their rapid and flexible development, which further enhances their ability to adapt to remote environments. In this context, the implementation of NTNs can facilitate the realization of different scenarios and services related to the IoRT ecosystem [47]. This involves providing support for demanding applications, including remote infrastructure control, monitoring, data collection, and connectivity, specifically in environments characterized by their remoteness, such as the Arctic, maritime regions, and rural areas.
###### Abstract  
The subsea jumper has become an essential part of subsea production systems as a gas-liquid mixing pipeline connecting the pipeline end manifold (PLEM) to the Christmas tree. During oil and gas transportation, as a common flow pattern, the alternating flow characteristics of the slug flow easily cause pipeline vibration, resulting in pipeline instability or fatigue damage. The present study investigates experimentally and numerically the slug flow characteristics in the subsea M-shaped jumper and its induced vibrations of the jumper. The flow pattern evolution and slug characteristics of the inner slug flow under different gas-liquid velocities are obtained: the slug frequency and slug velocity, as well as the pressure fluctuation and vibration characteristics caused by the slug flow. The results show that the pressure fluctuations in the front and rear parts of the M-type jumper are obviously different. With the increase in the air-water mixing the two characteristics, the slug frequency, and the slug velocity also increase. The gas velocity has a greater influence on the slug frequency than the liquid velocity. The slug length decreases as the slug frequency increases. Furthermore, numerical simulations under various experimental conditions are carried out. The results show that the simulation results of the pressure data, the slug characteristics, and the induced vibration amplitude are in good agreement with the experimental data.  
slug characteristics; gas-liquid flow; flow-induced vibration; experiment; M-shaped jumper; CFD 2023
# A High-Thrust Screw-Type Piezoelectric Ultrasonic Motor with Three-Wavelength Exciting Mode
## 3 Finite Element Analysis  
### Modal Simulation of the Stator  
The structure of the stator includes the twelve rectangular piezoelectric plates and the hollow metal elastomer with internal thread. The vibration characteristics of the stator are analyzed using Finite Element Method (FEM). During the simulation, some important parameters are set as follows: The Young's modulus \(E\), Poisson ratio \(\sigma\), and mass density \(\varrho\) of the metal elastomer are 90 GPa, 0.33, 8900 kg/m\({}^{3}\), respectively. The PZT-4 typeis adopted for rectangular piezoelectric plates (Bao Ding Hong Sheng Acoustics Electron Apparatus Co., Ltd., Baoding, China). The density of the twelve rectangular piezoelectric plates is 7500 kg/m\({}^{3}\). The other mechanical and physical parameters of the rectangular piezoelectric plates are listed in Table 1.  
The element types SOLID5 and SOLID45 were used for twelve rectangular piezoelectric plates and the brass, respectively. A finite element model of the stator for the screw-type piezoelectric ultrasonic motor was established in ANSYS 14.5 (ANSYS Inc, Canonsburg, PA, USA). A modal analysis was carried out. It should be noted that the stator adopts the free-free beam. The \(d_{31}\) working mode is adopted for the twelve rectangular piezoelectric plates. Meanwhile, no voltage was applied on the piezoelectric plates. The glue layers between the metal elastomer and piezoelectric plates were neglected, which are too thin to be meshed and the deviations are acceptable. Free meshing and mapped meshing were adopted for the metal elastomer and the rectangular piezoelectric plates, respectively. The Block Lanczos Method was employed to extract the mode shapes and the resonant frequencies, as shown in Figure 3. Simulation result indicates the combined circumferential-axial mode of the stator can be excited and the resonant frequency of the stator is about 30.08 kHz. The simulation result provides an important reference for the selection of the resonant frequency in the three-wavelength exciting mode.  
\begin{table}
\begin{tabular}{c c c} \hline
**DielectricCoefficientMatrix (F/m)** & **PiezoelectricStressMatrix (C/m\({}^{2}\))** & **ElasticCoefficientMatrix (GPa)** \\ \hline \([\varepsilon]=\left[\begin{array}{cccc}7.30&0&0\\ 0&7.30&0\\ 0&0&6.35\end{array}\right]\times 10^{-9}\) & \([\varepsilon]=\left[\begin{array}{cccc}0&0&-5.2\\ 0&0&-5.2\\ 0&0&15.1\\ 0&0&0&0\\ 0&12.7&0\\ 12.7&0&0\end{array}\right]\) & \([\varepsilon]=\left[\begin{array}{cccc}139&77.8&74.3&0&0&0\\ 77.8&139&74.3&0&0&0\\ 74.3&74.3&115&0&0&0\\ 0&0&0&30.6&0&0\\ 0&0&0&0&25.6&0\\ 0&0&0&0&0&25.6\end{array}\right]\) \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical and physical parameters of the rectangular piezoelectric plates.  
Figure 3: Working modal shape of the stator by Finite Element Method (FEM).
## 3 Results  
### Distance Treatment Zone Diameter (DTZD)  
Figure 3 shows the DTZD for each lens (OK 1: 3.68 \(\pm\) 0.46 mm; OK 2: 3.06 \(\pm\) 0.68 mm; OK 3: 2.83 \(\pm\) 0.54 mm; OK 4: 3.20 \(\pm\) 0.57 mm). The distance treatment zone diameter was significantly different between the OK lens designs (F = 19.927, 3) \(p\) < 0.001, 95%CI = [3.136, 3.331]. Post hoc comparison indicated that OK 1 was significantly different from the 3 other designs used. (OK 1 vs. OK 2; \(p\) < 0.001, OK 1 vs. OK 3; \(p\) < 0.001, OK 1 vs. OK 4; \(p\) < 0.001). The differences in DTZD between OK 1 and OK 2; OK 1 and OK 3; OK 1 and OK 4 were 0.63 mm, 0.85 mm, and 0.48 mm, respectively.  
Figure 2: Schematic representation of the plus power ratio component. \(r_{1}\) represents radius of distance treatment zone diameter, \(r_{2}\) represents radius of treatment zone diameter and \(p\) is the radius of pupils (drawing not to scale).
## 5 Conclusions  
In this study, an IT2 FNN based on DGCPSO learning was proposed for identification, prediction, and control applications. The noise suppressing ability of the IT2 FNN was superior to the traditional T1 FNN. In addition, the proposed DGCPSO has a superior local search ability compared to that of the traditional PSO. Three types of problems, namely two supervised learning identification and prediction problems and one reinforcement learning wall-following control of a mobile robot problem, were used to verify the proposed model and related algorithms. The results revealed that the average fitness value of the proposed DGCPSO was 1.2% higher than that of traditional PSO in identification, prediction, and wall-following control.  
The advantages of the proposed IT2 FNN based on DGCPSO learning are presented as follows:  
1. The proposed DGCPSO uses the dynamic grouping and cooperative particle swarm optimization to improve search capabilities and reach near global optimum;
2. Interval type-2 fuzzy sets are used in IT2 FNN to reduce sensor-sensing noise and disturbance;
3. The effectiveness and robustness of the proposed method are improved in identification, prediction, and control problems.  
However, the proposed IT2 FNN based on DGCPSO learning has limitations. That is, determining the initial parameters of the DGCPSO, such as the number of fuzzy rules, depends on user experience or trial and error. In future research, a self-adapting number of fuzzy rules will be considered in the IT2 FNN model. Meanwhile, in order to achieve high-speed operation in real-time applications, the IT2 FNN model will be also implemented on a field programmable gate array in a future study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{3}{c}{**Fitness Value**} \\ \cline{2-4}  & **Training** & **Testing 1** & **Testing 2** \\ \hline DGCPSO & 0.942 & 0.905 & 0.922 \\ ABC [22] & 0.862 & 0.794 & 0.837 \\ WOA [23] & 0.933 & 0.826 & 0.872 \\ DE [24] & 0.848 & 0.824 & 0.853 \\ PSO [25] & 0.932 & 0.844 & 0.901 \\ QPSO [26] & 0.937 & 0.867 & 0.921 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Efficiency of difference algorithms in the testing setting.  
Figure 21: Moving path of setting 2 of (**a**) DGCPSO, (**b**) ABC [22], (**c**) WOA [23], (**d**) DE [24], (**e**) PSO [25], and (**f**) QPSO [26].  
**Author Contributions:** Conceptualization, C.-J.L.; methodology, C.-J.L. and S.-Y.J.; software, C.-J.L., S.-Y.J., H.-Y.L. and C.-Y.Y.; data curation, H.-Y.L. and C.-Y.Y.; writing-original draft preparation, C.-J.L. and S.-Y.J.; writing-review and editing, C.-J.L. and S.-Y.J.; funding acquisition, C.-J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology of the Republic of China, grant number MOST 108-2221-E-167-026.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methods  
Remotely operated vision systems equipped with cameras, sensors, lighting, and natural or artificial features form an image through a process. This process starts from a light source with an intensity, polarization, and color spectrum that travels through a medium, then hits and is scattered on the surface of an object. The reflected rays on the object surface are captured by a camera sensor, and are then converted into electrons to form a two-dimensional pixel intensity map, i.e., an image [37]. Therefore, lighting directly affects the pixel intensity map and significantly simplifies the applied classical to advanced matching algorithms procedure, if the object is illuminated adequately [18,38,39,40,41,42].  
Matching principals from the classical correlation coefficient [1; 10; 43; 44; 45; 46], intensity interpolation [47; 48; 49; 50], Newton-Raphson method [51; 52; 53], gradient-based method [54; 55; 56; 57], and genetic algorithm [58; 59; 60] to the advanced artificial and convolutional neural network [61; 62; 63; 64; 65; 66] requires a high dynamic range of grayscale values, sharp edges, and high contrast images. These approaches can be challenging to implement because automatic and robust measurement identification and matching at either the pixel or the sub-pixel level is difficult for large image data captured under a low-light setting. Therefore, as shown in Figure 1, image enhancement is proposed in this study as an SHM framework specifically for monitoring in low-light and dark environments. It is implemented in close-range photogrammetry and SHM imaging that requires feature detection and computes displacement based on template matching.
## 4 Identity Management and Access Control Based on Blockchain under Edge Computing for IIoT
### System Architecture of IIoT  
The architecture of our system is shown in Figure 3. The system consists of the following components: (1) Intelligent machines/edge network devices, (2) industrial control edge networks/ intelligent control systems, (3) edge centers/management and control centers, (4) blockchain networks, and (5) cloud computing centers/manufacturing collaboration network.  
(1) Intelligent machines/edge network devices: Various types of industrial terminals are utilized to monitor and control the manufacturing processing.  
Figure 3: Identity management and access control model under edge computing.  
(2) Industrial control edge networks/intelligent control systems: A large number of monitoring and controlling terminals construct an edge network through the form of ad hoc networks. The edge networks can collect all kinds of manufacturing information and can process the information locally. In many cases, real-time intervention can be carried out. In addition, the industrial control edge networks can be linked to nearby edge center and receive management by the centers.  
(3) Edge centers/management and control centers: Edge centers/management and control centers are the entity responsible for managing access control rights for nearby manufacturing terminals/edge network devices. These manufacturing terminals form manufacturing edge networks. At the same time, manufacturing edge networks are connected to nearby management and control centers and managed by the centers.  
Edge centers/management and control centers will be assumed as the miners in the blockchain network: Storing blockchain information and validating blockchain transactions. Moreover, the manufacturing terminals with limited resources and capabilities are subject to the management of edge centers, and they are prevented from participating in the blockchain network due to limitations of their hardware. Every manufacturing terminal must be registered under the control of the edge center node.  
(4) Blockchain network: A blockchain network can be constructed by multiple edge centers. The manufacturing terminals are limited to their computing power. Thus, they are not parts of a blockchain network. The blockchain can be used by the authorized entities to read but can only write by the edge centers. The edge center can use the blockchain to get a specific access control policy that suits itself. The access control information is completely decentralized and can prevent from tampering.  
(5) Cloud computing center: Used to support the cyber foraging service under edge computing. The cyber foraging service is an important means to solve the shortage of computing resources of the manufacturing terminals and edge manufacturing networks. Through the cyber foraging service, the heavy computing task can be transferred to the cloud computing center.
# A MAP Overhead Aware Two-Dimensional OFDMA Burst Construction Algorithm
## 6 Conclusions  
This study proposed an EHA algorithm to divide a burst into several bursts and place these bursts to the subchannel with better channel quality, under the consideration of MAP overhead. This strategy maximizes the overall network throughput and optimizes burst indexing issues. That is, EHA not only allocates the subchannels with best channel quality for each burst, but also groups the bursts to alleviate the MAP overhead. The two contributions of EHA are: (1) the overhead of burst indexing decreases because massive numbers of connections can be accommodated by one burst; and (2) the overall throughput increases due to that one connection with large data transferring requirements that can be split and distributed into several bursts and placed on the subchannels with good channel quality to adopt better MCS, if the saved bandwidth in this burst construction is more than the increased overhead of burst indexing. It should be noted that EHA might not provide superior performance when the number of connections is large, because there is no sufficient bandwidth to divide bursts.  
As described above, EHA maximizes the throughput during OFDMA burst construction for each connection. The EHA considers the issues of subchannel diversity and burst indexing overhead to obtain outstanding throughput. The simulation results confirm that EHA provides higher throughputs compared with eOCSA and WLFF. In addition, the improvement ratios achieved by EHA increased in conjunction with the requested bandwidth, as the number of connections decreased, and as the channel quality varied. In addition, the performance of EHA slightly increases when the channel quality between subchannels became more diverse, whereas that of HA, WLFF, and eOCSA decreased considerably, thereby causing an increase in the EHA improvement ratio.  
Conceptualization, Y.-H.C.; Data curation, P.-T.J.; Formal analysis, Y.-H.C.; Investigation, Y.-H.C.; Methodology, Y.-H.C.; Project administration, Y.-H.C.; Software, Y.-H.C. and R.-Z.H.; Supervision, Y.-H.C.; Writing--original draft, Y.-H.C., L.-K.C. and Y.-J.L.; Writing--review and editing, Y.-H.C., L.-K.C. and P.-T.J.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 10: The effect of variance of signal-to-noise ratios (SNRs).
# Prediction of Tunnel Blasting Vibration Velocity Considering the Influence of Free Surface
## 4 Blast Velocity Formula Considering the Effect of Free Surface
### Verification of Blast Vibration Velocity Correction Formula  
The data in Table 2 were substituted into the Sadovsky formula and the modified formula, respectively, and the values of the coefficients \(k\), \(\alpha\), and \(\eta\) were solved using linear regression. Additionally, to measure the accuracy of the prediction coefficients, the correlation coefficient \(R^{2}\) of each formula was solved, as shown in Equation (5):  
\[R^{2}=1-\frac{\sum\left(y-\widetilde{y}\right)^{2}}{\sum\left(y-\overline{y} \right)^{2}} \tag{5}\]  
where, \(y\) is the measured vibration velocity value; \(\widetilde{y}\) is the fitted regression value; and \(\overline{y}\) is the mean value of the measured data.  
Additionally, the obtained formulas are shown in Equations (6) and (7) and plotted in Figure 6. The regression coefficient \(R^{2}\) of the correction formula obtained by fitting is 0.83, which is more accurate than that of the Sadovsky formula, which is 0.74. The parameters \(\alpha\) and \(\eta\) in Equation (4) reflect the contribution of the proportional distance factor and the free surface factor to the vibration velocity attenuation, and the calculated \(\alpha\):\(\eta\) = 0.73:0.21, indicating that the vibration speed attenuation caused by the free surface factor accounts for about 21% of the total attenuation factor, which also indicates that the blast free surface factor has a greater impact on the vibration. Therefore, the proportion of the distance factor (distance, explosive charge) and free surface factors (free surface number, burden distance) will have a greater degree of impact on the attenuation of the blasting vibration. Vibration prediction should be considered with the impact of the above factors.  
Sadovsky formula (\(R^{2}\) = 0.74):  
\[\ln v=3.267+1.175\ln\left(\frac{\sqrt[3]{Q}}{R}\right) \tag{6}\]  
Correction formula (\(R^{2}\) = 0.83):  
\[\ln v=3.769+0.858\ln\left(\frac{\sqrt[3]{Q}}{R}\right)+0.317\ln\left(\frac{W_ {\min}}{mR}\right) \tag{7}\]Comparing the vibration velocity prediction values obtained from Equation (3) and Equation (4) with the measured peak vibration velocity in the literature (Figure 6), it is clear that the modified formula improves the prediction effect. When the amount of single-hole charge and propagation distance is the same, it is obvious that considering the effects of the number of free surfaces and the burden distance will improve the accuracy of vibration velocity prediction.
## 6 Conclusions  
Current rubber expansion joints are subject to periodic replacement due to natural aging, vehicle loads, and sedimentation, leading to ongoing maintenance costs. Conversely, superelastic shape memory alloys exhibit the same durability as regular steel; therefore, they can be used semi-permanently, without the need for separate maintenance. The analysis of the IoT sensor data shows that bridge girders exhibit repetitive expansion behavior over extended periods, suggesting that superelastic shape memory alloy expansion joints could offer a more flexible performance. Furthermore, considering that most bridges in South Korea are small-to-medium in size, with relatively modest expansion amounts, as evidenced in this study, they are less likely to be subjected to extreme loads. The manufacturing of these superelastic shape memory alloys is more expensive than that of rubber. However, as the service life of the bridge increases, if the rubber stopper material sustains damage, not only does the cost of material replacement rise, but also the expense of labor. These costs can be continuously reduced over time, leading to potential economic benefits in the future. Therefore, optimizing the cross-section for enhanced efficiency could render these superelastic shape memory alloy expansion joints more effective. In conclusion, superelastic shape memory alloy expansion joints are an innovative alternative to traditional rubber expansion joints, with the potential for widespread application in bridge maintenance.  
Conceptualization, H.-M.C.; methodology, H.-M.C. and H.-W.L.; software, H.-M.C. and H.-W.L.; validation, K.-S.K.; formal analysis, H.-M.C. and K.-S.K.; investigation, H.-M.C. and H.-W.L.; resources, H.-M.C. and H.-W.L.; data curation, H.-M.C. and K.-S.K.; writing--original draft preparation, H.-M.C. and H.-W.L.; writing--review and editing, K.-T.P.; visualization, H.-M.C. and H.-W.L.; supervision, H.-M.C. and [PERSON]-T.P.; project administration, H.-M.C.; funding acquisition, H.-M.C., K.-S.K. and K.-T.P. All authors have read and agreed to the published version of the manuscript.  
This research was conducted as part of a project funded by the Ministry of Science and ICT and the Korea Institute of Construction Technology (Project No. 20240142-001, Development of a DNA-based smart maintenance platform and application technologies for aging bridges).  
Not applicable.  
The data presented in this article are not publicly available because generated within the project. Requests to access the data should be directed to the authors.  
The authors declare no conflicts of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.
## 4 UVSQ-SAT First Observations
### UVSQ-SAT Maps Reconstruction
The model mesh size is about 100-200 km. The Baseline Surface Radiation Network (BSRN) and Atmospheric Radiation Measurements Program network (ARM) can be used on local and regional scale models. They are mainly used in the calibration and use of spatial data. These data are of primary importance to help validate spatial data and numerical model simulations. UVSQ-SAT and its future constellation (Figure 16) address the same scientific theme as Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM), but from a completely different angle. FORUM will be put into orbit in 2026. It will measure the Earth's infrared emission spectrum at high spectral resolution. Libera (named for the daughter of agricultural goddess [PERSON] in ancient Roman mythology) represents another important mission to measure the Earth's outgoing radiative energy. The Libera instrument will fly on National Oceanic and Atmospheric Administration (NOAA) operational Joint Polar Satellite System-3 (JPSS-3) satellite, which is scheduled to launch by December 2027.