esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 2 System Model
### Core Network Domain
 \tag{7}\]  
The following constraint ensures that each VNF must be successfully deployed and be deployed in only one server node. \[\sum_{v\in\mathcal{V}}\eta^{s}_{j,\
u}=1 \tag{8}\]  
To simplify the complexity of the model, we assume the computing resources required by the VNFs to be linear with the data rate passing through the VNFs [12]. Therefore, the relationship between the VNF computing resources required and the data processing rate is expressed as follows:  
\[c^{s}_{j}=\alpha R^{s}_{j} \tag{9}\]  
where \(\alpha\) represents the correlation coefficient between the computing resources and the data rate. The CN propagation delay of Slice \(s\) is defined as the sum of the delays of each link and it can be expressed as  
\[d^{s}_{\text{prop, CN}}=\sum_{j=1}^{t_{s}-1}\sum_{(v,v^{\prime})\in\mathcal{E} }\eta^{s}_{j,v}\eta^{s}_{j+1,v^{\prime}}d_{v,v^{\prime}} \tag{10}\]  
The CN processing delay for Slice \(s\) is defined as the sum of the processing delays of each VNF as follows:  
\[d^{s}_{\text{proc, CN}}=\sum_{j=1}^{t_{s}}d_{f^{s}_{j}} \tag{11}\]  
Hence, the total CN delay of User \(u\) in Slice \(s\) is given as  
\[d^{s,u}_{\text{CN}}=d^{s}_{\text{prop, CN}}+d^{s}_{\text{proc, CN}} \tag{12}\]  
which is only a function of \(s\) and not \(u\), i.e., for users belonging to the same slice, the CN delay is the same. Thus, the total delay of user \(u\) in Slice \(s\) is  
\[d^{\text{total}}_{s,u}=d^{s,u}_{\text{RAN}}+d^{s,u}_{\text{CN}} \tag{13}\]  
where \(d^{s,u}_{\text{RAN}}\) is given by (5). In order to reflect the higher security and privacy of NPN slices compared to public network slices, we require VNFs of the NPN slices to have dedicated server nodes and links. More specifically, to ensure the security and privacy of the NPN slices, we require that if a server node \(v\) is occupied by a VNF of an NPN slice \(s^{\prime}\), then VNFs of other slices cannot be deployed on this server node, i.e.,  
\[\sum_{j=1}^{t_{s^{\prime}}}\eta^{s^{\prime}}_{j,v}\sum_{s\in\mathcal{S},s\
eq s ^{\prime}}\sum_{j=1}^{t_{s}}\eta^{s}_{j,v}=0\quad\forall v\in\mathcal{V}, \forall s^{\prime}\in\mathcal{S}_{np} \tag{14}\]  
Also, for security reasons, if a link is used by an NPN slice \(s^{\prime}\), then other slices cannot use this link, i.e.,  
\[\sum_{j=1}^{t_{s^{\prime}}-1}\eta^{s^{\prime}}_{j,v}\eta^{s^{\prime}}_{j+1,v^ {\prime}}\sum_{s\in\mathcal{S},s\
eq s^{\prime}}\sum_{j=1}^{t_{s}-1}\eta^{s} _{j,v}\eta^{s}_{j+1,v^{\prime}}=0\quad\forall l_{v,v^{\prime}}\in\mathcal{L}, v\
eq v^{\prime},\forall s^{\prime}\in\mathcal{S}_{np} \tag{15}\]
## 3 Results and Discussion
### Experimental Results
Regarding its applicability in real-world scenarios, several additional factors should be considered when deploying the proposed MIMO antenna system. These include environmental conditions, integration with existing communication infrastructure, potential interference from other devices, and the impact of physical obstructions. Furthermore, practical aspects such as manufacturing tolerances, cost-effectiveness, and scalability for mass production must also be addressed to ensure seamless its implementation in real-world applications.
# Electrical and Optical Properties of Laser-Induced Structural Modifications in PbSe Films
## 3 Results and Discussion
### Modification Mechanism  
Based on the above results, an assumption can be made about the mechanism of film structure laser modification (Figure 9).  
The structure of the film in the track region was studied using scanning electron microscopy (SEM) (Figure 9). Within the track, local modification regions are formed during photodarkening. These regions are at the same distance from each other, which is associated with the formation of a film melt pool in the track area and the redistribution of the material in the mode of sharp heating-cooling with a moving local heat source (Figure 9b). The mechanism of the formation of periodic microdomains is related to the Marangoni effect [20,21] when a part of the material from the melt pool is periodically drawn into the center of a heat source bounded by a laser spot. As a result of the redistribution of the film material, local hills with a diameter of 1 \(\upmu\)m at the top are formed in the center of the track, and depressions are formed on its periphery. The structure's morphology and periodicity depended on the scanning rate, the size of the laser spot, and the power density of the incident radiation, which determined the size of the heat source, its maximum temperature, and the heating-cooling rate. In some places of the track, cavities are formed with the removal of material (dark holes), under which there is a film substrate with fragments in the form of nanoparticles remaining from the film.  
Cracks (Figure 9) are the result of an overheating of the film in the area of laser exposure due to the high temperature gradient dT/dr, which contributed to uneven thermal expansion and the occurrence of excessive stresses in the film. During the motion of the laser spot (Figure 9), the formed crack (Figure 9a) began to move, which led to its scribing. It is likely that the processing conditions were normal during the scan, however, a crack was initiated at the beginning of each track when the laser spot accelerated.  
Figure 9: Mechanism of defective centers occurrence in the film modification area: (**a**) crack formation, (**b**) Marangoni effect.  
As a result of laser action on the film material, the concentration of defect centers increased, which became secondary sources of free charge carriers in the film. For this reason, after sample irradiation, the current increased and the resistance decreased. In addition, a decrease in the band gap indicated an increase in the probability of electron-hole pair transfer from defect states.
# Uniform Circle Formation for Fully, Semi-, and Asynchronous Opaque Robots with Lights
## 5 The Algorithm for the Fully Synchronous Mode
### Pseudo-Code  
The pseudo-code of the fsynch algorithm is presented in Algorithm 1.  
Figure 14: Collision-free traveling towards projections onto the safe diameter (Cycle 2), and back to final destinations on the SEC (Cycle 3).  
```
1:A valid configuration \(C_{0}\) of the swarm \(\mathcal{R}\) of \(n\) robots
2:Preliminary Steps:
3:\(C_{1}\leftarrow\) Complete Visibility on \(C_{0}\) by [39];
4:\(C_{H}\leftarrow\)\(\mathcal{R}\) on the SEC of \(C_{1}\);
5:if\(C_{H}\) is a perfect convex hull then
6:\(C_{R}\leftarrow\) Regular Polygon by [42];
7:return\(C_{R}\);
8:endif
9:Cycle 1:
10:switch\(C_{H}\)do
11:case asymmetry
12: unambiguously fix the main diameter splitting \(\mathcal{R}\) in two halves;
13:if Even \(n\)then
14:set pivot and angles according to Figure 4;
15:else
16:set pivot and angles according to Figure 5;
17:endif
18:case symmetry with exactly one axis
19:unambiguously fix the main diameter;
20:switch\(C_{H}\)do
21:case odd \(n\)
22:set pivot and angles according to Figure 6;
23:case even \(n\)\(\wedge\) two robots on the axis
23:set pivots and angles according to Figure 7;
24:case even \(n\)\(\wedge\) no robots on the axis
25:set pivots and angles according to Figure 8;
26:case rotational symmetry
27:if\(2!\) main class of symmetry then
28:\(P\leftarrow\) the unique main class of symmetry;
29:make robots in \(P\) set their light as _pivot_;
30:set the regular \(3\)-tuples as in Figure 9, or Figure 10;
31:else
32:\(P_{u},P_{v}\leftarrow\) the \(2\) main classes of symmetry;
33:make robots in \(P_{u}\cup P_{v}\) set their light as _placeholder_;
34:set regular \(4\)-tuples as in Figure 12 or Figure 13;
35:endif
36:\(C_{2}\leftarrow\) Output of Cycle 1;
37:Cycle 2:
38:\(I\leftarrow\) robots not forming the regular tuples;
39:if\(C_{2}\) has \(3\) or more regular tuples then
40:spot the safe (symmetry) chords;
41:make robots in \(I\) move onto safe (symmetry) chords, setting their light as _internal_;
42:else
43:spot the safe diameters;
44:make robots in \(I\) move onto safe diameters, setting their light as _internal_;
45:endif
46:\(C_{3}\leftarrow\) Output of Cycle 2;
47:Cycle 3:
48:\(I\leftarrow\) internal robots;
49:make robots in \(I\) reach their destination on the SEC, setting their light as sec;
50:\(C_{R}\leftarrow\) Output of Cycle 3;
51:return\(C_{R}\);
52:\(C_{R}\) forming a regular \(n\)-gon.
```  
**Algorithm 1**fsynch algorithm.
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## Appendix A RiB First-Order Cumulative Distribution Functions
### Rayleigh Approximation  
Whenever \(\varepsilon\to 0\), this implies that \(\rho\to 0\) and \(I_{0}\left(\frac{x\varepsilon}{\sigma^{2}}\right)\to 1\) for any possible value of \(x\). In this case, no coherent contributors arise in (5).  
In this particular case, it happens that \(G_{X}\left(x|\varepsilon,\sigma\right)=-\exp\left(-\frac{x^{2}}{2\sigma^{2}} \right)=g\), and consequently \(G_{X}^{-1}\left(x|\varepsilon,\sigma\right)=\sqrt{2\sigma^{2}\log\left(-g \right)}\). From (11) we have \(\mathrm{RiB}(\kappa)=F_{X}^{-1}\left(\kappa\cdot F_{X}\left(x|\varepsilon, \sigma\right)|\varepsilon,\sigma\right)\), which here may be written as  
\[\mathrm{RiB}(\kappa)=G_{X}^{-1}\left(\kappa\cdot G_{X}\left(x|\varepsilon, \sigma\right)|\varepsilon,\sigma\right) \tag{13}\]  
Thus,  
\[\mathrm{RiB}(\kappa)_{\varepsilon\to 0}=\sqrt{-2\sigma^{2}\log\left(\left(1- \kappa\right)+\kappa\exp\left(-\frac{x^{2}}{2\sigma^{2}}\right)\right)} \tag{14}\]  
Figure 12 depicts penalties corresponding to RiB. All possible values for \(\kappa\) introduce a penalty on RiB input value, and whenever these lay below \(\sigma\) (noise level), RiB behaves as a conventional expander.
# Characterization and Optimization of a Conical Corona Reactor for Seed Treatment of Rapeseed
## 2 Materials and Methods
### Seeds and Plasma Treatment  
Rapeseed (_Brassica napus_ L., Cv. Atora) seeds were obtained from NPZ Innovation GmbH (Holtsee, Germany). They are small (around 2 mm in diameter), round, and present a smooth surface morphology. To investigate the influence of the number of seeds on the produced discharge, the number of seeds was gradually increased from zero to around 1015 \(\pm\) 2 (4.87 g) while the power density was measured. For the other experiments, 55 \(\pm\) 2 seeds (around 0.26 g) were evenly distributed in the glass Petri dish prior to plasma treatment. The seeds were treated for 3 min using different voltage duty cycle values varying from 10% to 90%. Two control groups were considered: control \"Ct\" with untreated seeds and gas control \"Ct\({}_{\text{g}}\)\", where seeds were subjected to argon gas flow for 3 min with plasma off. Every treatment parameter was repeated 4 times (\(n\) = 4).
# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 3 Methodology
### Domain--Expertise
The cycle ends with the disposal phase, encompassing disassembly, waste treatment, and recycling. This phase aims to recover value from the product, minimize waste, and reduce the environmental burden associated with its end of life. The inclusion of recycling and disassembly processes highlights the potential for circularity in the system, where materials are reintegrated into new production cycles. Throughout the life cycle, the transport process plays a vital integrative role, connecting each phase by enabling the movement of raw materials, intermediate products, energy, and finished goods. The flows in the system are depicted through product flows (represented by solid black arrows) and elementary flows (illustrated by gray arrows). Product flows show how tangible materials and products move through each stage, linking raw materials extracted from natural sources with manufacturing processes,  
Figure 3: Stages of the product life cycle that are included in the methodology: from the engineering phase to the disposal phase. guiding intermediate products along supply chains toward assembly, and ultimately transporting finished goods to markets. Elementary flows illustrate direct interactions with the environment, encompassing resource inputs such as water or minerals drawn from nature, as well as outputs in the form of emissions, effluents, or wastes released back into the environment.
# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
## 3 Algorithm Improvement
### Path Planning of Dual-Arm Based on GA_RRT
#### Overall Process of GA_RRT  
The GA_RRT algorithm combines the probabilistic bias method and A* algorithm with RRT. The process of the algorithm is shown in Algorithm 2.  
```
1:\(T=\) init Tree(); // Initialize the random tree
2:\(R=\) init R(0); // Initialize the step size
3:\(T[0]=\) Node(\(X_{init}\));
4:for \(i=1\) to N:
5:for \(j=1\) to M: // M random states are generated, recommended value 4
6:if \(P_{rand}<P_{a}\):
7:\(X_{direction}=X_{goal}\); // Select random points as target points
8:else:
9:\(X_{direction}=\) Random sampling(); // Random sampling
10:\(X_{nearest}=\) min the distance \(X_{direction},Node_{true}\));
11:\(X_{select}=\) extend (\(X_{nearest},X_{direction},R\)); // Expand
12:\(X_{path}()=\) add \(X_{select}\) // Add random states to cost function group
13:endfor
14:\(F()=\) the distance(\(X_{path}()\), \(X_{init}\)) + the distance(\(X_{path}()\), \(X_{goal}\)); // cost function
15:\(X_{new}=\) min \(F()\); // The minimum cost node is taken in tree
16:if not obstacle(\(X_{nearest},X_{new}\)):
17:\(T[i]=\) add Node(\(X_{new}\)); // Add new nodes
18:endif
19:endfor
20:return\(T\)
```  
Figure 4: Bounding sphere model of the dual-arm robot.  
The overall flow of GA_RRT algorithm is shown in Figure 5. First, the main flow of the algorithm is used to perform random sampling. Then, the target point is expanded as a direction point with a probability of \(P_{a}\), and a random point is generated with a probability of \(1-~{}P_{a}\) for expansion. After randomly generating multiple random points for expansion, the algorithm calculates the A* cost function and selects the node with the smallest cost function for the next collision detection. If it passes the detection, the node is added to the random tree. Loop this process until you are near the target point.
## 2 Adaptive Time-Delayed Control Schemes
### The Proposed Adaptive Time-Delayed Control Scheme
Owing to the proper control gain \(\hat{M}_{t}\) with fast adaptation speed of the update parameter \(\omega_{t}\), the proposed ATDC-SM provides good tracking performance without the undesired side effects, e.g., chattering or fluctuation. It is shown in Appendix A that the proposed ATDC-SM has uniformly ultimated boundedness (UUB). **Theorem 1**. : _For a robot manipulator (Equation (1)) with the proposed ATDC-SM in Equation (9), the sliding variables stay within the close vicinity of the sliding manifold within a finite time \(t_{f}>0\), which is guaranteed to be uniformly ultimately bounded for \(t>t_{f}\) as shown below:_  
\[|s_{t}|\leq\sqrt{\left(\frac{\omega^{*}}{\beta}+\bar{\epsilon}\right)^{2}+ \frac{1}{\bar{a}_{B}}\omega^{*}}\]  
_where \(\bar{\epsilon}\) is any positive value that is introduced in the Appendix A._
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 5 Conclusions  
We used spaceborne GNSS-R data (CYGNSS Level 1 Science Data products) as observations to detect and map the Henan flood. We obtained the reflectivity by calculating the bistatic radar equation. Afterwards, taking into account that reflectivity was sensitive to inland surface water, the CYGNSS reflectivity was corrected and used to monitor the flood. By calculating the amount of change in the CYGNSS reflectivity before and after the flood, and verifying it with the MODIS optical image and SMAP, we monitored the main affected areas. The results show that the monitoring of flood changes based on CYGNSS observations is consistent with the SMAP results. Notably, spaceborne GNSS-R data with high temporal and spatial resolution can achieve rapid flood monitoring and inundated area mapping, which can help relevant departments in emergency management (deter  
Figure 17: The amount of change in CYGNSS reflectivity before (on 18 July 2021) and after (on 26 July 2021) the flood.  
mine risk areas, exercise safe evacuation options, and update response plans). Although CYGNSS is designed to observe ocean surface wind speed during hurricanes, this study further confirms that CYGNSS data can also be applied to land surface remote sensing and has high application value in hydrology and agriculture. Future GNSS-R missions will be capable of collecting reflected signals from GNSS constellations and will provide more detailed information to help flood studies.  
Conceptualization, W.Y. and F.G.; methodology, W.Y., F.G. and T.X.; software, W.Y.; validation, W.Y., L.J., N.W., J.T. and Y.K.; writing--original draft preparation, W.Y. and F.G.; writing--review and editing, F.G. and T.X.; supervision, T.X.; project administration, F.G.; funding acquisition, T.X. and F.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant numbers 41604003, 41704017, 41704018, 41874032 and 41931076 and National Key Research and Development Program of China, grant number 2020 YFB0505800.  
Not applicable.  
The authors are grateful to relevant authorities for providing free data from CYGNSS and SMAP.  
The authors declare no conflict of interests.
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 3 Dataset and Methods
### Improvements to SlowFast Network Structure
#### 3.2.1 CA Attention Modules
To more effectively extract spatial information related to behavior in the video, this paper introduces a CA module, called CA residual block (Figure 8b), which is introduced in each original residual block (Figure 8a) of the slow branch.
## 3 Results and Analysis
### Performance Metric Definition
Because VR systems are real-time applications, a large number of retransmissions is not suitable. In this study, we simulated cases involving no retransmissions up to two retransmissions. In order to test the robustness of the proposed system, we considered the presence of WLAN interference devices. \(N_{\textit{OBSS}}\) is a parameter that shows the number of WLAN interference devices that exist. In the no-interference cases, \(N_{\textit{OBSS}}\) is set to 0; otherwise, nine interferences devices are assumed. In order to measure the actual effect caused by interference devices, the reasonable rate of WLAN frames should be set to WLAN interference devices. In this paper, \(\lambda_{\textit{OBSS}}\), which refers to the frame arrival rate of each WLAN interference device, is set to 1 Hz. This means that every 1 s, WLAN interference devices generate a frame to transmit. The frame generated by WLAN interference devices has an air-time duration of \(T_{\textit{OBSS}}\), and in this study, we set it to 10 ms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \(N_{\textit{VR}}\) & Number of VR consumer devices & 9 (3 headsets, 6 controllers) \\ \hline \(T_{\textit{UL-SU}}\) & Transmission opportunity time of a control frame in single-user transmission case & 90 \(\upmu\)s \\ \hline \(T_{\textit{UL-MU}}\) & Overall transmission opportunity time for UL multiuser transmission case & 540 \(\upmu\)s \\ \hline \(T_{\textit{AIFS}}\) & Channel sensing duration before initiating channel access procedure & 36 \(\upmu\)s \\ \hline \(M_{\textit{Queue}}\) & Size of frame queue in number of frames & 1000 frames \\ \hline \(\lambda_{\textit{Trigger}}\) & Trigger Frame delivery rate (Configurable) & 100 Hz to 1000 Hz, 1000 Hz is default \\ \hline \(\lambda_{\textit{Motion}}\) & Motion sensing rate & 1000 Hz \\ \hline \(N_{\textit{retx}}\) & Maximum number of retransmission (Configurable) & 0 or 2 \\ \hline \(N_{\textit{ObsS}}\) & Number of interference devices (Configurable) & 0 or 9 \\ \hline \(\lambda_{\textit{ObsS}}\) & Frame arrival rate of each interference devices. & 1 Hz \\ \hline \(T_{\textit{ObsS}}\) & Transmission opportunity time of an interference frame & 10 ms = 10,000 \(\upmu\)s \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters.
###### Abstract  
The Shuangjiangkou hydropower station in China has complex geological conditions with high in situ stress. During the tunnel excavation, rockbursts occurred frequently, which seriously affected construction progress. Microseismic (MS) monitoring technology was used to explore rock MS activities to predict rockbursts. The MS monitoring system can capture a large number of MS signals. Based on Hilbert-Huang transform (HHT) instantaneous frequency analysis technology, using MATLAB software (R2022a) to write a program to convert the MS waveform, the frequency and energy characteristics of MS signals at a certain time can be obtained. By analyzing the frequency and energy characteristics of every event, the microseism active areas can be determined, and then rockbursts can be predicted scientifically. This paper selected two different construction sites, which were the main powerhouse and the access tunnel in the main powerhouse, as the research background. Introducing HHT instantaneous time-frequency analysis technology conducted MS event dynamic analysis and predicted rockbursts. The HHT spectrum scientifically and comprehensively displayed MS signal frequency characteristics at a certain time and reflected the change laws of signal instantaneous energy and local abrupt change information. The results indicated that some parameter anomalies in the event spectrum can predict rockbursts. For complex tunnel construction conditions, the HHT time-frequency analysis technology can realize a new idea of using a single-channel signal to predict rockbursts, which was very meaningful.  
high in situ stress; rockburst; MS monitoring; Hilbert-Huang transform; early warning 2023  
13 May 2023  
8 June 2023