esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Imaging Position Step Determination  
Using the concept of calibration resolution \(r\), it was possible to further refine the convertible range, according to the desired scale of detection accuracy. For simplicity, provided that the image resolution acquired from the experiment was 8-bit (\(\Delta C\approx 0.0019\)), the distribution of \(r\) with respect to the entire convertible area was as shown in Figure 5A. As expected from the shape of the calibration curve shown in in Figure 4D, the resolution \(r\) had a smaller value near \(0\), and it increased with increasing absolute value of \(I_{det}\). Therefore, according to the scale of the desirable resolution, users could adjust the actual convertible range of the calibration curve. For instance, if the detection error size should be smaller than 2 nm, the convertible range of the lookup table must be reduced to the \(I_{det}\) value range of approximately \(-0.4\) and \(0.3\), as illustrated by the violet box in Figure 5B. Therefore, only within the final convertible range of \(I_{det}\) reflecting the desirable scale of calibration resolution determined the accurate axial position of the target point source, which led to the definition of practically detectable range of \(d\).  
Since the distance \(d\) that spans the detectable region with desirable accuracy could now be determined, it was possible to extend the depth of the imaging range by oscillating the objective lens. For example, as shown in Figure 5C, the lookup table for dual-focus optics could be drawn to span the axial range of \(3d\), where 2 nm of detection resolution was guaranteed, by concatenating three calibration curves. In this case, the objective lens, the initial axial position of which was located at \(P_{0}\), oscillated as following a two-stage step function with the amplitude of \(d\), as shown in Figure 5D.
## 5 Conclusions and Prospect  
### Conclusions  
Based on unit life and death technology, this paper considers the dynamic evolution process of soil loss during the collapse process, constructs a pipe-soil nonlinear coupling model for buried pipelines passing through the collapse area, and uses the PSO algorithm to calculate the number of neurons, Dropout, and Batch- size to optimize parameters and establish a settlement prediction model for the dynamic evolution process of buried pipeline collapse based on PSO-LSTM. The following conclusions were drawn:  
1. Based on the unit life and death technology, soil loss is simulated through the gradual failure of the mechanical effects of the collapse unit, which can accurately reflect the dynamic evolution process and mechanical characteristics of the collapse of the buried pipeline.
2. The axial stress is the control stress, the bottom of the mid-span pipe is the control point, and the mid-span section is the dangerous section. Excessive tensile stress is the main cause of the failure of buried pipelines.
3. Confirm the existence of the \"pipe-soil separation\" phenomenon, which occurs in the third layer of soil loss, and then supplement the assumption of the applicable prerequisite of \"pipe-soil is always in contact\" in the elastic foundation beam theory: settlement deformation of buried pipelines The calculation needs to be divided into two stages: cooperative deformation and non-cooperative deformation.
4. The collapse evolution process of buried pipeline deformation: first, the deformation first spreads from the middle to the surroundings, and the deformation in the middle takes on a \"rhombus\" shape; secondly, the maximum deformation continues to expand from the top downward until it \"penetrates\" the pipeline; the early stage of \"penetration\" is The deformation at the top is greater than the deformation at the bottom and continues to develop after \"penetration\". The tensile deformation at the bottom is gradually greater than the compression deformation at the top.
5. Through the comparison of three settlement prediction models, the model in this paper shows good trend prediction and has the highest accuracy throughout the collapse cycle. This shows that the model can unearth the length of the pipeline, load sub-steps, top position of the pipe, position of the bottom of the pipe, positions of both sides of the pipe, material parameters, axial stress, pipe-soil contact status, and buried ground during the iterative training process. The internal laws of pipeline settlement values output a more reasonable settlement prediction value. The use of this model can provide a reference for safety risk management, disaster early warning, and intelligent monitoring when buried pipelines suffer from soil collapse disasters.
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 3 Proposed Model
### Random Destination with Pheromone Zone Mobility Model
#### 3.2.3 Exchanging Information  
We define the zone waypoint information of a zone \(z\) as the set of waypoints selected by UAVs in the zone \(z\), and denote it as \(W_{z}\). When a UAV arrives at the waypoint \(p\) in the next zone \(z\), it updates \(W_{z}\) by adding the point \(p\). The zone waypoint information should also v updated to other UAVs. Thus, each UAV broadcasts the zone waypoint information periodically, which is called as the pheromone information in [9]. In this work, we define the pheromone information as the list of all zone waypoint information managed by a UAV.  
Let us denote the zone waypoint information of \(z\) managed by a UAV \(u\) as \(W_{z}^{u}\). When a UAV \(u\) receives \(W_{z}^{\gamma}\) from a UAV \(v\), \(W_{z}^{u}\) is updated as \(W_{z}^{u}\cup W_{z}^{u}\). That is, the UAV adds the new waypoints in the zone \(z\), which are not in its own waypoint information.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**The Probability Table**} \\ \hline  & \multicolumn{4}{c}{**Step 1**} & \multicolumn{4}{c}{**Step 2**} & \multicolumn{4}{c}{**Step 3**} \\ \hline
**Zone** & \(z_{2}\) & \(z_{5}\) & \(z_{6}\) & \(z_{7}\) & \(z_{10}\) & \(z_{11}\) & \(z_{11}\) & \(z_{14}\) & \(z_{15}\) \\
**Probability** & \(\dfrac{5}{18}\) & \(\dfrac{6}{18}\) & \(\dfrac{8}{18}\) & \(\dfrac{5}{12}\) & \(\dfrac{4}{12}\) & \(\dfrac{3}{12}\) & \(\dfrac{4}{18}\) & \(\dfrac{7}{18}\) & \(\dfrac{7}{18}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The probabilities of selecting next zones in Figure 6.  
Figure 6: The examples of selecting next zone: (**a**) Step 1 (\(\text{C}=\{z_{1},z_{5},z_{6}\}\)); (**b**) Step 2 (\(\text{C}=\{z_{7},z_{10},z_{11}\}\)); (**c**) Step 3 (\(\text{C}=\{z_{11},z_{14},z_{15}\}\)); and (**d**) a path moved to the destination zone.
## 4 Conclusions  
In this study, a new horizontal skid damage test rig was developed. The running mode of the inner ring driving the roller was adopted to simulate the rolling/sliding contact between the roller and inner ring under the real slip. A test system was developed to measure the speed of the roller and the inner ring in real time so as to obtain the time-varying trend of slips under given conditions.  
The influence of radial load, inner ring speed, and lubricating oil quantity on the skidding damage of rolling bearings were studied. The results show that the radial load has the most significant effect on the time-varying slip rate in the contact area, and the slip rate decreases as the load increases. The effect  
Figure 11: Surface morphology of the roller and inner ring. (**a**) Roller morphology before the test; (**b**) Inner ring morphology before the test; (**c**) Roller morphology after the test; (**d**) Inner ring morphology after the test.  
of lubricating oil quantity on skidding damage is not significant. Furthermore, the influence of the lubricating oil cleanliness on the skidding damage of rolling bearings was obtained. Combined with the time-varying temperature and slip curve, it is concluded that the temperature rises and slip rate of the bearing are smaller under the clean oil conditions. In addition, the dynamic lubrication failure process of the contact pair in rolling bearings with the time-varying slip and temperature distribution was revealed.  
The surface roughness data demonstrated that the skidding damage has a certain effect. As for the cases of skid damage, the surface roughness of the contact area generally increased after the experiment. It also can be inferred that the larger slip rate can lead to more obvious surface roughness changes before and after the test. In various skidding damage cases, it can be inferred that the skidding damage always occurs when the slip ratio is bigger than 0.9. The results show that the skid damage under extremely light load is the comprehensive effect of adhesive wear and thermal failure. This study can provide an effective reference for improving the reliability and service life of aviation bearing.  
Conceptualization, J.L.; Data curation, W.C. and J.X.; Formal analysis, K.H. and Q.W.; Funding acquisition, J.L.; Investigation, J.X.; Methodology, J.X. and J.L.; Software, Q.W. and K.H.; Writing--original draft, J.X. and J.L.; Writing--Review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Program of State Key Laboratory of Mechanical Transmissions (No. SKLMT-KFKT-201808), The National Natural Science Foundation of China (No. 51505361), and The Innovative Talents Promotion Plan in Shaanxi Province (No. 2017 KJXX-58).  
The authors would like to thank [PERSON] for their valuable discussions on this article.  
The authors declare no conflict of interest.
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 5 Optimization of Partially Information-Coupled Polar Code Based on EXIT Idea
### Optimization of Coupling Ratio \(\delta\) for PIC PCCA-Polar Code  
In Section 3, we introduced a coupling ratio \(\delta\), but did not explain what is the optimal value of the factor. In this subsection, we use the TC chart to analyze the impact of the factor \(\delta\) and guide our search for the optimal \(\delta\)-value. Factor \(\delta\) is taken from 0 to 1, the information bits length is set to \(K=64\), 100 and the code rate is \(R=\) 1/3, 1/2.  
As shown in Figure 11, the curves with coupling ratio \(\delta=0.2\) and 0.3 are the rightmost, and have similar performance. For the coupled code, high coupling ratio corresponds to high decoding complexity (the higher base code rate). Thus, we can just choose \(\delta=0.2\) as the optimal value for PIC polar when \(K=100\) and \(R=\) 1/2. Similarly, when \(K=64\) and \(R=\) 1/3, we choose \(\delta=0.3\) as the optimal value for PIC polar based on Figure 11.  
Based on the previous optimization results, Figure 12 shows the transfer characteristics of PIC polar codes and uncoupled polar codes. The performance of uncoupled polar code with SCAN decoder is worst among other codes. At the same time, the performance of PIC PCCA-polar code is the best. Additionally, when the input mutual information is small, code blocks that are coupled to each other cannot exchange correct extrinsic messages, the output mutual information of PIC polar code (whether it's PCCA-based or CA-based) is smaller than that of the uncoupled polar code.  
Figure 10: Transfer characteristics of systematic polar code with successive cancellation list (SCL) decoder.  
Figure 9: Performance of systematic polar code with SCAN decoder. (**a**) Transfer characteristics; (**b**) code block error rate (CBER) of PC-SCMA over additive white Gaussian noise (AWGN) channel.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 2 Cooperative HSTN
### HSTN Relay Network
#### 2.2.3 Aerial Relays  
Aerial relays can provide fast and reliable coverage in cases of natural disasters for which the terrestrial infrastructure is not available or for poor communication quality due to deep shadowing and line-of-sight (LOS) signal loss [34; 92]. The utility of aerial segment of HSTNs is not limited to relay networks as it can also be used as a base station or user equipment, especially in emergency situations [34]. Assuming perfect CSI for satellite-relay and relay-destination links and considering the impact of satellite antenna patterns and path loss in the channel models, an analytical framework for a multi-antenna unmanned aerial vehicle (UAV) relay-based HSTN was defined in [93]. Asymptotic OP expressions at high SNRs were derived to gain full insights into system performance when the satellite link was subject to shadowed Rician fading and terrestrial links were subject to Nakagami-m fading. Guidelines for future research were also provided. Considering an AF-based dual-hop satellite relay system operating over 10 GHz, the authors of [94] obtained OP expressions by considering path loss and satellite patterns and evaluated the performance of the system, as rain attenuation becomes a prime factor affecting the satellite links. Similarly, ref. [95] investigated the OP of an HSTN comprised of a multi-antenna satellite communicating with a ground receiver via multiple AF 3D mobile UAV relays under opportunistic relay selection, and the performances of the proposed 3D mobile UAV relaying versus fixed-altitude mobile UAV relaying and fixed-distance static relaying schemes were compared. The authors of [96] considered network traffic as an important reference point for the true reflection of the current load on a network segment. An adaboost-based link planning (ALP) algorithm was proposed for faster relay selection and improved load balancing. For the node performance evaluation, an auto-regressive moving average (ARMA)-based network traffic prediction scheme was also utilized. In [97], the superiority of UAV 3D mobile relays was established over static relays by comparing the OP performances of both systems. In this section, a detailed overview of cooperative HSTNs was provided. State-of-the-art coordinated and relay-based HSTNs were discussed. Different relay scenarios, such as single and multiple relays and aerial relays, were presented. A list of major contributions to HSTN relay networks is provided in Table 4.
## 3 Results and Discussion
Potential sweep ranging from 0.4 V to \(-\)0.3 V vs. RHE under the dark condition, as shown in Figure 5b, and the flat band potential was calculated using the Mott-Schottky equation [27]:  
\[1/C_{ss}{}^{2}=2(E-E_{fb}-kT/e)/(e\epsilon\epsilon_{0}N) \tag{7}\]  
where \(C_{sc}\) is the capacitance in space charge region, \(E\) is the applied potential, \(E_{fb}\) is the flat band potential that can be determined from the extrapolation to zero capacitance (Figure 5b), \(k\) is the Planck constant, \(T\) is the temperature, \(e\) is the electron charge, \(\epsilon\) is the dielectric constant of the Si semiconductor (photoelectrode), \(\epsilon_{0}\) is the permittivity of the free space, \(N\) is the hole acceptor concentration for the p-type Si semiconductor, calculated from the slope in the figure plot shown above. On the basis of these relations, rGO-SiNW exhibits an \(E_{fb}\) of 0.128 V vs. RHE, which is 88 mV higher than the \(E_{fb}\) for the bare SiNW. The higher \(E_{fb}\) indicates that the band bending near the depletion of the semiconductor at the electrode/electrolyte interface is higher. Based on this equation, the magnitude of the band bending (\(E_{b}\)) is equals to \(E-E_{fb}\)[28]. The band bending at the boundary between the electrode surface and electrolyte accelerates for rapid separation of charge carriers and increase in recombination of protons to generate hydrogen [29]. To sum up, the results of the capacitance and transient photoresponse measurements substantiate that the rGO-SiNW system is highly active in terms of decrease in recombination of charge carriers, and reduces the energy barrier for HER along the electrode/electrolyte interface. The long-term stability test of the 0.8 mg rGO-SiNW was implemented for 2 h at 0.25 V vs. RHE, showing a stable current density, as shown in Figure 6. The photocathode maintains a current density of approximately \(-\)2 A/g and more than 92% of the current, even after 2 h. Our stability testing result implies that the rGO co-catalyst efficiently suppresses the degradation of photoelectrochemical performance. Figure 4.0 **Electrochemical activity of SiNW and rGO-SiNW. ** (**a**) The transient curve of the photocurrent from SiNW and rGO-SiNW when the light was turned on and off at the open circuit potential region (OCPT). (**b**) Mott-Schottky plot from capacitance measurement.
## 3 Model Architecture
### ASTGC-LSTM Network
#### 3.3.3 Learning of the ASTGC-LSTM  
Finally, the global feature \(F_{t}^{S}\) and local features \(F_{t}^{l}\) of each time step were converted into scores \(o_{i}^{g}\) and \(o_{i}^{l}\) of each class. According to Equation (3), the predicted probability of the \(i^{th}\) class can be obtained.  
Figure 6: The architecture of the proposed ASTGC-LSTM network.  
\[\widehat{y}_{ti}=\frac{e^{\alpha_{ti}}}{\sum_{j=1}^{C}e^{\alpha_{ij}}},i=1,\dots,C \tag{3}\]  
In the training process, considering that the hidden state of each time step on the top, the ASTGC-LSTM contains short-term dynamics, and the loss function in the form of Equation (4) was adopted into the training model.  
\[\begin{array}{ll}L&=-\sum\limits_{t=1}^{T_{3}}\sum\limits_{i=1}^{C}y_{i}\log \widehat{y}_{ti}^{g}-\sum\limits_{t=1}^{T_{3}}\sum\limits_{i=1}^{C}y_{i}\log \widehat{y}_{ti}^{l}\\ &+\lambda\sum\limits_{j=1}^{3}\sum\limits_{n=1}^{N}\left(1-\frac{\sum_{t=1}^{ T_{j}}\alpha_{in}}{l_{j}}\right)^{2}+\beta\sum\limits_{j=1}^{3}\frac{1}{T_{j}} \sum\limits_{t=1}^{T_{j}}\left(\sum_{n=1}^{N}\alpha_{inj}\right)^{2}\end{array} \tag{4}\]  
where \(y=(y_{1},\dots,y_{C})\) is the ground-truth label. \(T_{j}\) denotes the number of time steps on the \(j^{th}\) ASTGC-LSTM layer. The third term aims to pay equal attention to different joints. The last term is to limit the number of interested nodes. \(\lambda\) and \(\beta\) are weight decaying coefficients.
# Post-Buckling Behaviour of Steel Structures with Different Types of Imperfections
## 3 Nonlinear Model of the Structure with a Complex Geometrical Imperfection Pattern
### Model without Initial Geometrical Imperfections  
In order to determine geometrical relationships in the proposed model, a representative segment was analysed (Figure 2). Without initial geometrical imperfections, compression force \(P\) induces only elastic displacements, which are represented by superscript \"\(e\)\".  
Based on Figure 2, the elastic bar rotation can be expressed as follows:  
\[\phi_{n}^{e}=asin\frac{u_{n}^{e}-u_{n-1}^{e}}{l_{n}}\text{ and }\phi_{n+1}^{e}=asin \frac{u_{n+1}^{e}-u_{n}^{e}}{l_{n+1}} \tag{1}\]  
The nodal rotation can be formulated in the following form:  
\[\phi_{n}^{e}=\phi_{n}^{e}-\phi_{n+1}^{e} \tag{2}\]  
and introducing geometrical relations (1), the following can be obtained:  
\[\phi_{n}^{e}=asin\frac{u_{n}^{e}-u_{n-1}^{e}}{l_{n}}-asin\frac{u_{n+1}^{e}-u_ {n}^{e}}{l_{n+1}} \tag{3}\]  
where \"\(asin\)\" denotes arcsin.  
Figure 1: Proposed model of the structure.  
So, the set of equilibrium equations will take the form:  
\[k_{n}\cdot\phi_{n}^{e}=P\cdot u_{n}^{e} \tag{4}\]  
For \(n=1,2,3\) the close-form Formula (4) accounting for large displacements takes the form of a set of three nonlinear algebraic equations of equilibrium:  
\[\begin{cases}k_{1}\left[asin\frac{u_{1}^{e}}{L}-asin\frac{u_{3}^{e}-u_{4}^{e}}{ L}\right]=Pu_{1}^{e}\\ k_{2}\left[asin\frac{u_{2}^{e}-u_{1}^{e}}{L}-asin\frac{u_{3}^{e}-u_{4}^{e}}{L} \right]=Pu_{2}^{e}\\ k_{3}\left[asin\frac{u_{3}^{e}-u_{2}^{e}}{L}-asin\frac{-u_{3}^{e}}{L}\right]=Pu_ {3}^{e}\end{cases} \tag{5}\]  
where \(L=l_{n}\).  
The above equations represent a non-linear eigenvalue problem, in which the elastic displacements of nodes \(u_{n}^{e}\) are unknown.  
In the case of small displacements, it can be assumed that \(arcsin(\alpha)=\alpha\), that is, the operator \(sin=1=>arcsin=1\), then the Equation (5) becomes a linear eigenvalue problem. After the introduction of the dimensionless nodal displacement \(\overline{u}_{n}^{e}=u_{n}^{e}/L\) it will be received:  
\[\begin{cases}k_{1}[2\;\overline{u}_{1}^{e}-\overline{u}_{2}^{e}]=PL\overline{ u}_{1}^{e}\\ k_{2}[-\overline{u}_{1}^{e}+2\;\overline{u}_{2}^{e}-\overline{u}_{3}^{e}]=PL \overline{u}_{2}^{e}\\ k_{3}[-\overline{u}_{2}^{e}+2\;\overline{u}_{3}^{e}]=PL\overline{u}_{3}^{e} \end{cases} \tag{6}\]  
Equation (6) can be written in the matrix form:  
\[(\mathbf{K}-PL\mathbf{I})\overline{u}^{e}=0 \tag{7}\]  
where \(\mathbf{I}\) is a unit matrix.  
The solution of Equation (7) are the three eigenvalues (\(\lambda_{1}^{cr}=2-\sqrt{2}\), \(\lambda_{2}^{cr}=2\), \(\lambda_{3}^{cr}=2+\sqrt{2}\)) and the associated normalised eigenvectors \(u_{n}^{cr}\).  
\[u_{1}^{cr}=\left[\frac{1}{2},\frac{\sqrt{2}}{2},\;\frac{1}{2}\right]^{T},\;u _{2}^{cr}=\left[\frac{1}{2},0,-\frac{\sqrt{2}}{2}\right]^{T},\;u_{3}^{cr}= \left[\frac{1}{2},-\frac{\sqrt{2}}{2},\;\frac{1}{2}\right]^{T} \tag{8}\]  
The three first buckling modes obtained from the linear eigenvalue problem are presented in Figure 3.  
Figure 3: The buckling modes of the considered structural model.
## 2 Literature Review  
### Overall Equipment Effectiveness Overview  
A significant amount of literature has been published in recent years concerning the definition of OEE and its application [18; 19; 20; 21]. Nowadays, OEE is widely used to analyze not only the effectiveness of individual equipment but also for integrated equipment systems [22]. OEE helps management recover hidden capacity, reduce production losses, and identify additional capital investment needs [23]. OEE measurement can be applied at several different levels, and the initial OEE measurement can be compared with future OEE values, thereby quantifying the level of improvement achieved [15]. The main advantage of OEE over other performance metrics is that it combines the three individual components into a single numeric value and can be viewed as a measure of overall equipment performance [24]. This metric measures the performance of operations by the availability, performance, and quality of individual equipment. OEE is the result of the multiplication of these three parameters in a percentage [2; 25; 26; 19]. The availability component measures the downtime losses due to breakdown, setup, and adjustment, indicating the ratio of actual operating time to planned available time. The performance component measures speed losses due to minor stoppages and reduced speed as metrics of production speed,and is the ratio of the production speed of equipment to the planned speed. Finally, the quality component measures quality losses due to process defects and represents the ratio of quality-related production losses to actual production [27]. The OEE value range has been discussed by several authors who concluded that it would be difficult to establish an optimum OEE figure for reference, since every industry has its norms depending on the application and situation [23]. However, some studies have found that the OEE benchmark value of 85% is considered world-class performance [26; 28; 29].
## 3 Proposed Cumulative QoE Model
### Memory Effects
#### 3.2.1 Primacy Effect  
The primacy effect [16; 41] describes the human behavior to recall (bitrate or rebuffering) initial events occurred at the beginning of the streaming session when providing the overall evaluation [42]. In fact, the primacy effect always exponentially decreases by time [41]. Therefore, its characteristics can be expressed by an exponential curve as follows:  
\[f_{P}(t)=exp(-\alpha_{P}*t),\quad 0\leq t\leq L \tag{3}\]  
where \(\alpha_{P}\) determines the _intensity_ of primacy effect (how fast the primacy effect diminishes over time) and \(t\) denotes a time instant within a session of \(L\) seconds.