esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Solutions and Policy Recommendations
### Recommendations on Space Traffic Management
#### 5.2.3 Strengthening ITU's Role in Space Traffic Management  
Firstly, to address the issue of harmful interference and enhance the monitoring capabilities, a new resolution was proposed on the strengthening of the role of the ITU with regard to transparency and confidence-building measures in outer space activities [108]. The European Common Position provides the ITU with the ability to monitor and measure detrimental interferences affecting satellite systems using an independent network of monitoring control stations, irrespective of any data supplied by a complaining state. This could prove useful, especially with the increased number of satellites being launched, in terms of the accountability of all space actors, as well as granting autonomy to the ITU to record, regulate, and enforce international laws.  
Secondly, the ITU could amend its trinity laws to make the license requirements more limited and stringent as a deterrent regarding the rise of unprepared space actors to ensure that, in the event of a failure to fulfill these requirements, satellites are not launched, as well as a follow-up process when the constellations are deployed.  
Thirdly, to address the problem of congestion, the International Telecommunication Union (ITU) should manage the utilization of both orbital allocations and slots at the Earth's geostationary orbit (GEO), which is located 35,786 km above the equator, and exploitable sections of the electromagnetic spectrum. The goal of ITU coordination is to promote the economical, equitable, efficient, and sensible use of these resources. Actors must coordinate to make use of both usable areas of the spectrum and orbital locations, which are considered restricted resources. This is carried out on a \"first-come, first-served\" basis. This, however, does not preclude the needs of potential future users. Thus, striking a balance between current and future users will be challenging, but it will also guarantee that no state or other space actor will be able to rush to claim orbital or spectrum resources.  
Finally, the ITU could also adopt the method of limiting the number of satellites launched per constellation in such a way that, if the constellation is launched for communication purposes and there are about three or more applications for the same purpose, the ITU could encourage collaborations between these parties, potentially assigning each party a number of satellites under the same constellation. To ensure success, the ITU could categorize the constellations into classes of relevance to avoid unnecessary applications and an upsurge in constellations. This could make way for the more productive cooperation and development of all collaborating parties.
## 5 LBMSA Algorithm
Where \(\Delta\) represents the difference in order of magnitude between the end temperature and the initial temperature. Then the number of external loops of the temperature control is \(\mathrm{M}=\frac{\ln\frac{\Lambda}{\Omega}}{\ln\beta}+1\). For the objective function of the SA algorithm, the time complexity of the whole algorithm is \(\mathrm{O}(\mathrm{M}\times\mathrm{N}\times\mathrm{n})\) when the number of internal cycles is N at each specific temperature. ```
1:Network topology\(G=(V,E)\), traffic request set \(T=\left\{t_{1},t_{2},\ldots,t_{\lceil T\rceil}\right\}\), \(t_{i}=(s_{i},d_{i},r_{i})\)
2:\(f(X_{best})\)
3:for\(\forall t\in T\)do
4: Classify different traffic requests
5:\(t_{i}\in\overline{T}_{s,d_{i}}\), \(t_{i}\in\overline{T}_{s,d_{i}}\leftarrow\left\{ \begin{matrix}s_{i}=s_{j}\\ d_{i}=d_{j}\end{matrix} \right.\) or \(\left\{ \begin{matrix}s_{i}=d_{j}\\ d_{i}=s_{j}\end{matrix} \right.\)
6:for\(\forall\overline{T}_{s,d}\)do
7:\(\sum_{t\in\overline{T}_{s,d_{i}}}r\leftarrow\mathrm{sum}\{r_{i}\text{ of }\left(t_{i}\in\overline{T}_{s,d}\right)\}\)
8:\(T=\left\{\overline{T}_{s,d_{i}},\overline{T}_{s,d_{i}},\ldots,\overline{T}_{s,d_{i}}\right\}\leftarrow\text{sort }T\underline{y}\sum_{t\in\overline{T}_{s,d}}r\) from large to small
9:for\(q=1:M\)do
10:\(\overline{T}_{s,d}=\left\{t_{1},t_{2},\ldots,t_{\lceil T_{s,d}\rceil}\right\} \leftarrow\text{sort }\overline{T}_{s,d}\) randomly
11: Generate a solution \(X_{0}(\Lambda,\Gamma)\)
12: Calculate fitness values \(f(X_{0})\)\(\leftarrow\)Algorithm 2
13:\(X_{best}\gets X_{0}\), \(k\gets 0\), \(t_{k}\gets T\)
14:if\(t_{k+1}\)<thresholdthen
15:for\(i=1:L\)do
16:swap two randomly selected traffic requests
17:\(X_{new}(\Lambda_{new},\Omega_{new})\), \(f(X_{new})\)\(\leftarrow\)Algorithm 2
18:if\(f(X_{new})<f(X_{k})\)then
19:\(X_{k}=X_{new}\)
20:if\(f(X_{k})<f((X_{best})X_{best}=X_{k})\)then
21:continue
22:endif
23:\(P(t_{k})=e^{-[f(X_{new})-\frac{f(X_{k})}{t_{k}}]}\)\(\leftarrow\) calculate
24:\(X_{k}=X_{new}\)\(\leftarrow\)\(\text{random}(0,1)<P\)
25:endfor
26:\(t_{k+1}\)\(\leftarrow\)\(\text{drop}(t_{k})\)
27:\(k=k+1\)
28:endif
29:endfor
30:endfor
```  
**Algorithm 1** Traffic Request Order Optimization with SA
# Relative Pose Estimation between Image Object and ShapeNet CAD Model for Automatic 4-DoF Annotation
## 4 Experiments
### Pose Estimation and Similarity Measurement  
In the previous section, the details of the object category and segmentation are described. As the next step, the rotational pose of the object is estimated. In Figure 8, the bounding box images of four sample objects are shown, _Car, Bench, Bag_, and _Clock_. At the right side of the image, ten CAD models of the detected categories are shown. In this figure, the rotational pose of the CAD models is determined with the results from PoseContrast. The images of the CAD models are rendered by Blender Python API. As shown in this figure, the pose of the CAD models is very close to that of input real object. Appearance of the CAD models look different so that image similarity measurement can recognize the instance of the models.  
Figure 9 shows DELF feature matching results of sample four object categories between the RGB input and CAD candidates. In each result, the left is RGB query and right is rendered CAD image. The DELF features are detected in both RGB input images and the rendered images. The blue-colored lines show the inlier matching features between the two images. Because the pose of the real objects and CAD models are very similar, the feature matching results implies that there is 2D affinity relationship between the real and CAD images. Based on the number of inliers, only top five matching results are shown and  
Figure 7: Example images of ten test objects. Clockwise direction from the top-left: Clock, Chair, Bench, Car, Bottle, Bag, Bed, Desk, Motobike, and Oven. (**a**) RGB input images (**b**) Detection and semantic segmentation results.  
the left one is the best match. Among them, the red-colored box in each category means ground truth CAD model.  
Figure 8: Rendering images of CAD candidates using the pose estimation results. From the top to the bottom, results of four test objects are shown, _Car, Bench, Bag_, and _Clock_. In each RGB image at the left, the rendered images of the ten CAD candidates are shown in the right. Comparing the pose of the objects in both RGB and rendered images, it is found that the rotational pose of the objects is successfully estimated (Due to the space limit, results of only four objects are shown).  
Figure 9: From the top, DELF feature matching results of CAR, Bench, Bag, and Clock. In each row, the leftmost CAD model is determined as the best similar object with the RGB object.
# An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud
## 3 Problem Description  
Whenever a cloud broker (CB) receives tasks from a user that must be scheduled, a cloud information service (CIS) issues a query to identify the services required by the user in order to execute the tasks; then, the tasks are scheduled for the services that were discovered. For example, tasks \(\{T_{1},T_{2},T_{3},\ldots,T_{n}\}\) might be submitted to the CB in an established interval of time. The fact that processing elements (VMs) are heterogeneous implies that there are uneven processing speeds and memory, which suggests that a task executed on different VMs results in variable costs of execution. Assume that the available VMs are \(\{V_{1},V_{2},V_{3},\ldots,V_{m}\}\) when the CB receives the tasks. As such, the tasks are scheduled on the available VMs, and the task execution is performed on a \"first come, first serve\" basis.  
Our main goal is to accomplish greater utilisation of the VMs with a minimum makespan through the scheduling of the tasks on the VMs. Accordingly, the expected time to compute (ETC) of the tasks to be scheduled on individual VMs is used in the proposed strategy in order to settle on a scheduling choice. Moreover, the values of the ETC are obtained by using the ratio of the value of million instructions per second (MIPS) in a VM to the length of the task [86; 87], as exemplified in Table 3.  
The values of the ETC are normally presented in matrix form [86; 87], where the number of tasks to be scheduled is presented row-wise in the matrix, while the number of available VMs is presented column-wise in the matrix. Hence, each row of the ETC matrix indicates the execution times of a given task for each VM, while each column indicates the execution times of each task on a given VM.  
Furthermore, the aim of this paper is to minimise the makespan and response time by obtaining the best combination of VMs with which tasks are to be executed. Let \(C_{ij}(i\in\{1,2,3,\ldots,m\},j)\in\{1,2,3,\ldots,n\})\) be the time needed to execute the \(j\)th task on the \(i\)th VM, where \(m\) and \(n\) are the numbers of VMs and tasks, respectively. Therefore, the fitness value of each organism is decided by applying Equation (9), which decides the extent of adaptation of the organism to the ecosystem.  
\[fitness=min\{C_{ij},\forall\;tasksj\;mapped\;to\;VM\;i\}\;i\in 1,2,3, \ldots,m \tag{9}\]
## 2 Trajectory Fitting Based on the NURBS Curve
Owing to the matrix \(\mathbf{D}\) being invertible, Equation (5) can be modified to:  
\[\mathbf{d}=\mathbf{D}^{-1}(\mathbf{L}+\mathbf{U})\mathbf{d}+\mathbf{D}^{-1}\mathbf{C} \tag{7}\]  
In this case, corner marks \((k)\) and \((k+1)\) are added in Equation (7) to identify the number of iterations. Then, the elementary iterative scheme of \(\mathbf{d}\) can be written as:  
\[\mathbf{d}^{(k+1)}=\mathbf{M}_{1}\mathbf{d}^{(k)}+\mathbf{D}^{-1}\mathbf{C} \tag{8}\]  
where \(\mathbf{M}_{1}=\mathbf{D}^{-1}(\mathbf{L}+\mathbf{U})\). The component form of Equation (8) can be expressed as:  
\[\mathbf{d}_{i}^{(k+1)}=\frac{1}{a_{ii}}\left(-\sum_{ \begin{array}{c}j=1\\ j\
eq i\end{array} }^{n+1}a_{ij}\mathbf{d}_{j}^{(k)}+\mathbf{C}_{i}\right),\;i=1,2, \ldots,n+1 \tag{9}\]  
which is also known as the Jacobi iteration scheme, noting that, before calculating \(\mathbf{d}_{i}^{(k+1)}\), the iterative values of the first \(i-1\) components in \(\mathbf{d}^{(k+1)}\) have been generated, which are more approximate to the true value than the results obtained by the previous iteration. Therefore, \(\mathbf{d}_{1}^{(k)},\mathbf{d}_{2}^{(k)},\ldots,\mathbf{d}_{i-1}^{(k)}\) can be replaced by \(\mathbf{d}_{1}^{(k+1)},\mathbf{d}_{2}^{(k+1)},\ldots,\mathbf{d}_{i-1}^{(k+1)}\) to make \(\mathbf{d}_{i}^{(k+1)}\) closer to the true value. To improve the convergence rate of the iteration further, a proper parameter \(\mu\) is selected to conduct the weighted averaging on the aforementioned iterative scheme:  
\[\mathbf{d}_{i}^{(k+1)}=\mu\widetilde{\mathbf{d}}_{i}^{(k+1)}+(1-\mu)\mathbf{d}_{i}^{(k)},i =1,2,\ldots n+1 \tag{10}\]  
with:  
\[\widetilde{\mathbf{d}}_{i}^{(k+1)}=\frac{1}{a_{ii}}\left(-\sum_{j=1}^{i-1}a_{ij} \mathbf{d}_{j}^{(k+1)}-\sum_{j=i+1}^{n+1}a_{ij}\mathbf{d}_{j}^{(k)}+\mathbf{C}_{i}\right) \tag{11}\]  
Substituting Equation (11) into Equation (10) leads to the SOR iteration scheme:  
\[a_{ii}\mathbf{d}_{i}^{(k+1)}+\mu\sum_{j=1}^{i-1}a_{ij}\mathbf{d}_{j}^{(k+1)}=a_{ii} \mathbf{d}_{i}^{(k)}-\mu\sum_{j=i}^{n+1}a_{ij}\mathbf{d}_{j}^{(k)}+\mu\mathbf{C}_{i}\,,\;i= 1,2,\ldots,n+1 \tag{12}\]  
Note that \(\mathbf{A}\) is a tridiagonal positive definite matrix, so the optimal value \(\mu_{opt}\) of the relaxation factor can be expressed as [29]:  
\[\mu_{opt}=\frac{2}{1+\sqrt{1-\left[\rho(\mathbf{M}_{1})\right]^{2}}} \tag{13}\]  
where \(\rho(\mathbf{M}_{1})\) denotes the spectral radius of \(\mathbf{M}_{1}\). It is worth noting that, for a flat element, the aforementioned method can be directly applied to calculate the NURBS trajectory, but for a curved element, the polishing shaft should always lie along the normal direction of the element. In this case, it is necessary to solve two trajectories of end-point and reference-point on the polishing shaft to determine the polishing attitude (for more details, please see [21]).
## 4 Results and Discussion
### Relationship between Participation and Spectatorship  
Pearson correlation coefficient analysis revealed a noticeable strengthening of the link between participation and spectatorship. Initially, this relationship was moderate in the prior survey (0.369, \(p<0.001\)), escalating to a high correlation in the post-survey  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Pre-Test**} & \multicolumn{4}{c}{**Post-Test**} \\ \hline  & \begin{tabular}{c} **Construct** \\ **Validity** \\ \end{tabular} & \begin{tabular}{c} **Sports** \\ **Identification** \\ \end{tabular} & \begin{tabular}{c} **Playing** \\ **Intention** \\ \end{tabular} & \begin{tabular}{c} **Watching** \\ **Intention** \\ \end{tabular} & \begin{tabular}{c} **Construct** \\ **Validity** \\ \end{tabular} & \begin{tabular}{c} **Sports** \\ **Identification** \\ \end{tabular} & \begin{tabular}{c} **Playing** \\ **Intention** \\ \end{tabular} & \begin{tabular}{c} **Watching** \\ **Intention** \\ \end{tabular} \\ \hline \begin{tabular}{c} Sports \\ identification \\ \end{tabular} & 0.90 & **0.53** & 0.61 & 0.21 & 0.88 & **0.49** & 0.55 & 0.36 \\ \
\begin{tabular}{c} Playing intention \\ \end{tabular} & 0.89 & 0.78 & **0.66** & 0.15 & 0.87 & 0.74 & **0.63** & 0.46 \\
\begin{tabular}{c} Watching intention \\ \end{tabular} & 0.89 & 0.46 & 0.39 & **0.68** & 0.89 & 0.60 & 0.68 & **0.66** \\ \hline \hline \end{tabular} Note: In the grey area, the average variance extracted (AVE) scores are in bold and positioned along the diagonal. The lower-left area contains the correlation coefficients, while the upper-right area displays the corresponding squared correlation coefficients, representing the shared variance.  
\end{table}
Table 3: Validity analysis.  
(0.682, \(p<\) 0.001). The primary drivers of this enhancement were the experimental groups, who, after 16 weeks of participation engagement in the camps, saw their correlation rise significantly from an initially non-significant 0.206 to 0.624 (\(p<\) 0.001).  
Notably, baseball participants reported greater strides in comparison with badminton players. Moreover, within these experimental cohorts, those who received spectatorship intervention exhibited more pronounced improvements than those without such intervention, with baseball again outperforming badminton potentially because of the greater availability of baseball viewing opportunities (that is, high spectatorship level) in real life in Taiwan. These findings suggest that both the participation program and the spectatorship intervention positively influenced the bond between participation and spectatorship. Additionally, these outcomes could suggest that baseball likely provides a more enjoyable experience than badminton in the camp, which led to an increased identification with the sport after the camp with the intervention.
## 2 Materials and Methods
### WSN Nodes  
The WSN consists of the sensor nodes and the network coordinator. The network coordinator, which creates and controls the entire network and aggregates the sensor data from the sensor nodes, is the key device in the WSN. The sensor nodes, which are responsible for the temperature and relative humidity data acquisition and sending, include a microcontroller, a temperature-relative humidity sensor and a battery power supply. The network coordinator consists of a microcontroller and a general packet radio service (GPRS) remote module (Comway Electronic Corp., Beijing, China). The CC2538 wireless sensor system-on-chip (SoC) (Texas Instruments Inc., Dallas, TX, USA), which integrates a radio frequency transceiver with a powerful Advanced RISC Machine (ARM) Cortex-M3-based microcontroller system with up to 32 KB on-chip RAM and up to 512 KB on-chip flash with a robust 2.4-GHz IEEE 802.15.4 radio, is adopted as the microcontroller in the sensor nodes and the network coordinator. The microcontroller enables the nodes to handle complex network stacks with security, demanding applications and over-the-air downloads to improve the integration and optimization of the hardware design and transmission distance.  
The temperature and relative humidity parameters are monitored in real time and controlled by the sensor nodes in the refrigeration truck (China International Marine Containers (GROUP) Co., Ltd, Shenzhen, China) to satisfy the best storage requirement for the table grapes. The ranges of the temperature and relative humidity in table grapes' cold chain logistics are from \(-1\)\({}^{\circ}\)C to \(0\)\({}^{\circ}\)C and 90% to 95%, respectively [6; 9]. The temperature and relative humidity data in the cold chain logistics are acquired and monitored by adopting the digital temperature and relative humidity sensor SHT10, whose range of temperature and relative humidity are from \(-40\)\({}^{\circ}\)C to \(+123.8\)\({}^{\circ}\)C and 0% to 100%, respectively,and the accuracy is \(\pm 0.5\)\({}^{\circ}\)C and \(\pm 3.0\%\) respectively. The supply voltage of the sensor node is supplied by a lithium battery (Anwin Electronics Co. Ltd, Shenzhen, China), whose nominal voltage and capacity is 3.7 V and 3600 mAh, respectively, while the network coordinator is equipped with a 5-V, 2-A power adapter to provide a continuous supply. Figure 1 illustrates the diagram of the sensor nodes and network coordinator hardware.
# Application Research of Negative Pressure Wave Signal Denoising Method Based on VMD
## 4 Conclusions  
Due to the influence of noise contained in the pressure signal, it is difficult to locate the leakage point when the leakage occurs. In order to solve the problem of noise interference, this paper proposed an adaptive denoising method for the leakage signal based on VMD, which effectively improves the accuracy of pipeline leakage location. To verify the effectiveness of the method, laboratory and field tests were carried out. Laboratory experiments show that the noise reduction effect of the proposed method is better than that of the low-pass filter method, the wavelet method, and the EMD. The field experiment results show that the size of leakage will affect the effect of the leakage location. For a leakage with a small area in particular, the pressure change after leakage is not obvious, the time of the leakage is difficult to identify, and the location of the leakage is difficult to carry out. The test results of the real pipeline provide strong support for the application of our method in practical engineering. Obviously, shape changes such as changing the diameter of the pipe introduce additional reflected noise and may reduce positioning accuracy. An increase in the pipeline length will increase the attenuation of negative pressure waves, and may cause more noise along the way, which may also reduce the accuracy of results. These important questions will be addressed in in our future study.  
Conceptualization, Z.J.; methodology, G.G. and B.L.; software, G.G. and B.L.; validation, Z.J., G.G. and B.L.; formal analysis, Z.J.; investigation, G.G. and B.L.; resources, Z.J.; data curation, G.G. and B.L.; writing--original draft preparation, Z.J., G.G. and B.L.; writing--review and editing, Z.J.; visualization, Z.J., G.G. and B.L.; supervision, Z.J.; project administration, Z.J.; funding acquisition, Z.J. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# The Effect of Inflatable Pressure on the Strain Deformation of Flexible Wing Skin Film
## 3 Inflation Deformation Modeling and Bearing Deformation Simulation
### Finite Element Simulation  
Considering the inconsistency of the deformation and curvature distribution of the matrix and the fiber bundle in the flexible inflatable wing membrane material, the influence of different internal inflation pressure changes on the strain deformation distribution of the flexible inflatable wing membrane material was studied by finite element software ABAQUS 2025. The strain variation characteristics were simulated and analyzed. The established finite element analysis model and load simulation loading are shown in Figure 3.  
According to the inflation molding principle of flexible inflatable wings, the internal inflation pressure load could be directly applied to the inner surfaces of the upper and lower wing surfaces, the environmental pressure load could be directly applied to the outer surfaces of the upper and lower wing surfaces, and the internal gas pressure is always uniform. Consistently, the brace inside the flexible wing is only subjected to tensile forces and does not require the application of an internal inflation pressure load. Based on Reference [6], the simulated flight aerodynamic load of the upper and lower airfoil of the flexible wing was set to 150 Pa. The different inflation pressure loads and the internal and external environmental pressure loads acting on the flexible wing skin film are all listed in Table 2.  
According to the simulation parameters in Table 2, the different internal and external differential pressure loads were set combined with the finite element simulation model, and then the corresponding strain-deformation characteristics of the skin film could be simulated and calculated out.  
Figure 3: Flexible inflatable wing inflating pressure loading simulation.  
Considering that the flexible skin film of the flexible inflatable wing would produce large nonlinear biaxial deformation under the action of internal and external inflation pressure, in the finite element simulation process, the upper and lower wing surfaces use the quadrilateral membrane mesh elements M3D4R, and the grid size is 12. The triangular membrane grid unit M3D3 is selected for the flanking surface, and the grid size is 7. Moreover, the analysis step is performed with the dynamic explicit nonlinear analysis method, and the job type is the full analysis; meanwhile, the stable time increment is about \(4.26277\times 10^{-7}\).
## 5 Discussion
### Effectiveness of the Proposed Circuit  
With application of the proposed EFCLC in fault loop, the requirement of DCCB's interruption ability is reduced, and fault current interruption speed is accelerated. A comparison is performed in Table 4 between a fault situation with EFCLC and that without EFCLC, in terms of maximum fault current and fault interruption time. As mentioned in Section 4.3, \(L_{FCL\_min}\) and \(R_{FCL}\) are set as 250 mH and 20 \(\Omega\) in the test circuit. The test results shown in Table 3 reflect that the peak fault current needed to interrupt at DCCB operation is reduced from 12.4 kA to 5.1 kA with the help of EFCLC. Meanwhile, the time taken for the whole fault interruption process decreases from 34.8 ms to 13.6 ms. In summary, the effectiveness of the proposed circuit is verified, and reduced peak fault current and accelerated fault interruption would contribute to lower cost in relay and fast fault clearance.