esa-sceva/satcom-chunk-collection huggingface dataset:
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 5 Paths of a UAV in the Intersection  
We define the allowed movements in the intersection to reduce the avoidable complexity in the trajectories of the UAVs. Figure 3 shows the UAVs' allowed paths in the intersection in two-dimensional (2D) view based on their entrance lanes. If a UAV is to turn left, it should enter the intersection only through the two left-most entrance lanes. If it is to move straight, it will take the middle or second to the right-most lane. If it is to turn right, it will only take the right-most lane. UAVs can only enter and exit the intersection through the middle layer. This rule is applied to all the entrance ways.  
The allowed movements in the 3D intersection are shown in Figure 4. The allowed movements are left turn, move forward, right turn, move downward then turn left, move downward then forward, move upward then turn left, and move upward then forward. The movements in Figure 4 are for UAVs entering the south way. Similar movements are to be followed by UAVs entering from other ways.  
From the allowed paths shown in Figure 3, some movements will not be allowed for a UAV based on its entrance lane. For example, if a UAV is at the left most lane, then it cannot turn right in the intersection. Also, note that a UAV that will turn right can only enter the intersection from the right most lane, thus it will exit the intersection immediately and it can only move as shown in Figure 4c.  
To define the positions of the UAVs while moving in the intersection, the intersection is divided into blocks, and we define one waypoint at the center of each side of every block. The waypoints of the blocks are the black circles in the Figure 4. A block's length and width are equal to the lane's width while its height is equal to the layer's height. For simplicity, we assume a block has equal dimensions. When a UAV moves in the intersection, a movement should start with the UAV's center at the waypoint of a block and end with the UAV's center at the waypoint of the next block in the path. However, there is an exception that is, when the block is connected to the exit lane, then the UAV will not enter another block, but instead it will exit the intersection. For example, if a UAV is to turn left, as shown in Figure 4a, it will enter the waypoint at the back side of the current block. Then, it will turn to the left while maintaining its altitude until it reaches the waypoint at the right side of the next block in the path.  
Figure 3: Allowed paths of UAVs based on entrance lane.  
The turning movements in Figure 4a,c follow the arc of a circle with radius equal to the half of the block's width and angle of 90 degrees. The turning movements in Figure 4d-g follow the arc of a circle with radius equal to the half of the height of the block and angle of 90 degrees.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 5 Fuzzy-Based Dynamic Time Slot Allocation
### Fuzzification  
In the first step of fuzzification, the crisp inputs are converted into their corresponding linguistic values, which are represented through the use of fuzzy sets [42]. Each fuzzy set is related to a membership function that describes the way in which each crisp input is associated with the fuzzy set. The fuzzy model is shown in Figure 3. For the slot allocation to each node, the three input variables of fuzzy are energy ratio (ER), packet arrival rate (PAR), and buffer memory ratio. The fuzzy model uses three linguistic terms (low, medium, and high) in order to partition the input variable. To define each term, different membership functions such as Gaussian, S, and Z functions are used.
## 5 Evaluation and Discussion of the Kinematic Calibration Method Using Optimal Measurement Pose
When using the proposed calibration method with \(O_{1}\), there is no significant change in the integrated error by increasing the number of measurement poses. In addition, the proposed calibration using optimal measurement pose based on \(O_{1}\) requires only 20 measurement poses, which achieves a calibration accuracy approximately equal to the accuracy of traditional calibration methods using 50 measurement poses. The above results show that the proposed calibration method is effective, not only with high accuracy, but also with less measurement poses to achieve high accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Position Error/mm**} & \multicolumn{2}{c}{**Attitude Error/\({}^{\circ}\)**} \\ \cline{2-5}  & **Maximum** & **Mean** & **Maximum** & **Mean** \\ \hline Calibration with \(O_{1}\) & 0.001498 & 0.000771 & 0.000492 & 0.000262 \\ Traditional calibration & 0.006933 & 0.001424 & 0.001675 & 0.000839 \\ \hline \end{tabular}
\end{table} 
Table 8: Position error and attitude error after calibration using two methods. Figure 9: Position and attitude errors of 150 primary mirror poses. (**a**) Position errors; (**b**) Attitude errors.
# Measurement and Analysis of Radar Signals Modulated by the Respiration Movement of Birds
## 5 Conclusions  
Current research provides few measurements of radar signals modulated by the respiration movement of birds in a microwave anechoic chamber. First, we investigated the periodic signals of birds produced by respiration with the water sphere model. Moreover, the theoretical simulation of the signal fluctuation is approximately 1.2 dB for a 1 kg duck as a two SATs bird. Then, we experimentally captured the weak signals of a living duck and a dead duck in an anechoic chamber using a dynamic measurement system composed of a vector network analyzer and a high-speed camera. The results for both LRRPs and HRRPs within the S-band, X-band, and Ku-band indicate that the respiration movement of the bird causes periodic amplitude fluctuation with a respiration rate of 0.7 Hz. Due to the scattering region theory, the higher the frequency, the stronger the modulation of the birds' echoes; thus, the signal fluctuations modulated by respiration within the S-band were \(\sim\)1 dB and stronger within the X-band and Ku-band (\(>\)2 dB).The fluctuation is approximately 1 dB level, similar to the simulated result of 1 dB level, which is much less than the 10 dB level reported in the references (e.g., [2; 3; 4; 5]). In addition, the 10 dB level of signal fluctuations in radar echoes of birds could mainly come from the wing-beating motion. Future work will quantify the contribution from flapping wings and explain the modulation mechanism.  
Conceptualization, J.G. and J.Y.; methodology, D.K. and J.Y.; software, D.K.; validation, J.G. and J.Y.; formal analysis, J.G.; investigation, J.Y., W.B. and S.W.; resources, D.L.; data curation, J.Y.; writing--original draft preparation, J.G. and D.K.; writing--review and editing, H.H.; visualization, H.H.; supervision, J.Y.; project administration, D.L.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Shanghai Aerospace Science and Technology Innovation Fund under Grant SAST2018-007.  
**Institutional Review Board Statement:** The authors declare that they obtained experimental approval for the Laboratory Animal Use Protocol (AUP) from the IACUC of Wuhan University Center for Animal Experiment, with the IACUC Pre-Review NO. WP20220122. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Some of the data presented in this study may be available on request from the corresponding author. The data are not publicly available due to the internal restriction of the research group.  
**Acknowledgments:** We appreciate both the testers during the collection of the data, and we want to thank the authors whose photographs are reproduced in this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System
## 5 Conclusions  
In this study, we analyzed the detection probability of the CSM-based FH-OFDMA system and compared it with the cat map and SBS-based FH-OFDMA systems. We theoretically derived the LPD capability of the CSM-based FH-OFDMA system using auto-correlation-based spectrum sensing technique with hypothesis testing, and we showed the CSM-based system has greater LPD capability than the cat map and SBS-based systems. Simulation results also show the CSM-based system has similar BER performance as the two systems, but the CSM-based system decreases the detection probability dramatically in various simulation environments. As the redundant number of detecting frames or SNR increases, and the number of simultaneous users decreases, an intentional eavesdropper has a higher detection probability to find FH patterns, and the CSM stands out among the three FH patterns. Additionally, we found that the gap among the three FH patterns is sustained as the redundant number of frames or SNR increases, since the detection probability of the FH-OFDMA system is the product of the symbol detection probability and the intercept probability. The CSM-based FH pattern can be applied to time-frequency hopping domain, as well as the FH-OFDMA system. The CSM-based FH pattern has a limit to having symbol buffers of cluster size. Even considering the limitation, the CSM-based FH pattern has powerful LPD capability because the time and frequency transmitting sequences are mixed.  
Conceptualization, J.J. and J.L.; methodology, J.J. and J.L.; software, J.J. and S.P.; validation, J.L., H.K., and S.K.; investigation, J.J.; writing--original-draft preparation, J.J. and S.P.;writing--review and editing, J.J and S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# RF Energy Harvesting Wireless Communications:
## 3 RF-EHWC Hardware
### Design Principles of RF Energy Harvester  
The energy harvester of an RF-EHWC system consists of three key components: an impedance matching circuit, a rectifier, and a voltage multiplier, as shown in Figure 8. Next, we discuss the design principle of each component of energy harvester.  
Figure 8: An RF-EHWC system, where the red lines indicate the energy flow of the energy harvester charging the components of the communication module.  
Figure 7: Propagation attenuation of electromagnetic waves at the sea level in different weathers (recreated from Figure 2 in [45]).
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 1 Introduction
* We propose using the data rate constraint in our formulated one-hop and multi-hop problems, which departs from the traditional approach of limiting the hop length by a fixed number of hops. And a link budget analysis at 60 GHz is integrated to ensure the feasibility of achieving the specified data rate at the given frequency. In essence, the developed methodologies and formulations are not exclusively confined to the realms of IAB but extend their applicability to a broader spectrum of network planning scenarios involving mmWave and higher-frequency bands for wireless backhaul applications, especially under the stringent confines of limited potential node locations. The remainder of this paper is organized as follows: In Section 2, we present a system model for IAB network planning. Section 3 presents the problem formulation for one-hop and multi-hop IAB network deployments with the introduction of the data rate constraint, and a greedy approach to solve the multi-hop problem. Simulation results are presented in Section 4, providing insight into minimizing IAB quantities while ensuring the access and backhaul services requirement during planning. Finally, Section 5 provides a brief conclusion.
# Predicting the Life of Varistors via a Nonlinear Coefficient Based on a Small-Scale Data Model
## 3 Experiment and Analysis
### Performance Comparison of Different Structures  
In addition to the LSTM structure and BiLSTM structure, this paper also considers a lightweight LSTM structure called GRU for the characteristics of small-scale data.  
The Gate Recurrent Unit (GRU) is a simplified LSTM model. Compared with LSTM, GRU uses an update gate instead of a forgetting gate and an input gate, thus reducing the number of parameters by about one third. In addition, GRU omits the intermediate parameter \(\widetilde{\text{C}}\) in LSTM, which ignores the long-term memory and focuses more on the association between the previous moment and this moment. And the GRU structure cancels the second-order nonlinear function during output. Therefore, GRU tends to have a higher computational efficiency and a stronger generalization in the face of data with small samples and simple structures.  
In this paper, three different structures are tested on the original data and enhanced data, and the test results are shown in Table 1. In order to more clearly compare the computational efficiency of different algorithms, the training was set up with 100 epochs. In practice, five to ten epochs can already achieve very good results which require less than two seconds.  
Figure 16: Compare the predicted life curve with the observed life curve. (**a**) On test set 1. (**b**) On test set 2.  
Figure 15: Model training.  
The experimental results show that the training time required by the three structures is close to each other. However, compared with other structures, the BiLSTM structure has a higher prediction accuracy on both test set 1 and test set 2.
# Investigation of Quadrate Fresnel Zone Plates Fabricated by Femtosecond Laser Direct Writing
## 3 Results and Discussion
### Simulation Study on Diffraction Process of S-FZP
When the number is too small, a clear cruciform diffraction pattern cannot be produced. At the same time, too many zones make the energy more concentrated at the focus. The diffraction energy of the crossline decreases, which declines the clarity of the crossline. Figure 7 shows the comparison of focal point morphology. From Figure 7 and Table 1, the focal point radius decreases gradually from 18.2 \(\upmu\)m to 8.8 \(\upmu\)m with the increase in the number. The radius of the focal point has an essential effect on the resolution. The smaller the radius, the higher the resolution. The resolution of S-FZP increases with the increase of the number, which is like that of circular FZP. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline Number of zones & 5 & 9 & 13 & 17 & 21 & 25 & 29 & 33 & 37 & 41 & 45 & 49 \\ \hline Focal depth (mm) & 3 & 3 & 2.8 & 2.4 & 1.6 & 1.4 & 1.1 & 1.1 & 0.9 & 0.7 & 0.7 & 0.7 \\ Peak power (W/mm\({}^{2}\)) & 3.8 & 7.7 & 11.7 & 19.7 & 20.0 & 25.9 & 42.5 & 37.2 & 45.2 & 45.2 & 49.5 & 56.0 \\ Focus radius (\(\upmu\)m) & 18.2 & 14.1 & 14.1 & 14.8 & 13.6 & 12.5 & 10.9 & 10.8 & 9.5 & 10.0 & 9.4 & 8.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of diffraction properties of S-FZPs with different zone numbers. Figure 6: Diffraction pattern contrast of S-FZPs with different numbers of zones.
# Human Activity Classification Using the 3 DCNN Architecture
## 2 Materials and Methods
### 3 DCNN Architecture  
In recent years, the neural network architecture based on 3D convolution layers is a very frequently used approach in the classification of video data. Because the 3 DCNN architecture is able to analyze the positions of objects in time, it is often used for moving 3D images, especially medical images. The 3 DCNN creates a 3D activation map during the convolution step, which is needed not only for data analysis but also for time and volumetric context. A three-dimensional filter is used for the 3D convolution of the dataset to calculate the representation of elements at a low level. The kernel moves in three directions (\(x,y,z\)), as shown in Figure 1. The value at each position of the feature map in the layer is given by the following equation:  
\[v_{ij}^{xyz}=\tanh(b_{ij}+\sum_{m}\sum_{p=0}^{P_{i}-1}\sum_{q=0}^{Q_{i}-1}\sum _{r=0}^{R_{i}-1}w_{ijm}^{pqr}v_{(i-1)m}^{(x+p)\langle y+q\rangle\langle z+r \rangle}, \tag{1}\]  
where \(w_{ijm}^{pqr}\) is the value of the kernel connected to the feature map in the previous layer and \(R^{i}\) is the size of the 3D kernel [24].  
The output shape is a three-dimensional volume space. We achieve 3D convolution by winding around the center of a cube and stacking adjacent layers on top of each other. Functional maps are interconnected to capture motion information. However, the convolution kernel can extract only one type of element. The overall network is similar to a 2D convolutional neural network. In general, as with 2D convolution, we can achieve better results by combining several convolution layers. When creating the 3 DCNN, our results depend on the number of layers as well as the number of filters in each layer and the size of the filters. If pooling is used when creating the neural network, the pooling size must be formed by three values because we are working with 3D data [25].  
The three-dimensional MaxPooling3D layer is a form of nonlinear downsampling of an input tensor. This method partitions the input tensor data into 3D subtensors along three dimensions and selects the element of each subtensor with the maximal numeric value. Finally, it transforms the input tensor to the output tensor by replacing each subtensor with its maximum element. MaxPooling3D is often applied to color image and is shown in Figure 2[26].  
Figure 1: Convolution operation in the 3 DCNN architecture [26].  
Figure 2: MaxPooling3D operation in the 3 DCNN architecture [26].
## 6 Optimization Benefit Analysis
### Stability Control Measures  
Based on the results of the field test and numerical simulation analysis, starting from the control of tunnel deformation and stability, and considering the control effect of the construction scheme on the stability of the surrounding rock, the following stability control measures are formulated:  
* Strengthen the advance support. The tunnel section adopts a \(\Phi\)108 mm hot rolled steel pipe roof umbrella system with a thickness of 6 mm; the length of the pipe roof umbrella system is 30 m, the ring spacing is 40 cm, and the lap length is 3 m. After entering the tunnel, with the increase in the buried depth, the double-layer grouting pipe is adopted, with lengths of 3 m and 4.5 m, respectively, and the longitudinal spacing is 1.8 m and 3 m, respectively.  
* Improve the initial support stiffness. Add a feet-lock anchor pipe at the arch frame of each drift bench and temporary steel frame joint. The external insertion angle should be 20-30\({}^{\circ}\), and I20b I-steel temporary horizontal support can be installed if necessary.
* Apply bolt and surrounding rock grouting. Adopt a hollow grouting bolt, with a length of 5 m, and spacing of 1.0 m \(\times\) 0.6 m, and the grouting strength is not lower than M20.
* Control the length of the benches. The excavation length of the drift benches on both sides should be controlled at 3-5 m, and the drifts on both sides should not be less than 15 m.
* Optimize the construction sequence. Super-large-span tunnels have a flat structure, leading to more complex structural deformation and forces compared to other tunnel types. In contrast to the research findings of [PERSON] et al. [28], the simulation results found that the excavation method of the shallow side first and then the deep side for super-large-span tunnels is more favorable for controlling structural deformations. The study suggests that when super-large-span tunnels are constructed using the double-side-wall drift method to traverse shallow buried unsymmetrical sections, the construction sequence of excavating the shallow side first, then excavating the deep side, and finally excavating the middle drift is recommended.
* Optimize the proportion of the drifts on both sides. The ratio of drifts on both sides of the super-large-span tunnel not only significantly affects the structural forces but also plays a crucial role in construction efficiency and economic benefits. Based on comprehensive analysis results, it is recommended to use a ratio of approximately 0.3 for on-site construction.
# Uncertainty Propagation through a Point Model for Steady-State Two-Phase Pipe Flow
## 2 Materials and Methods
### Uncertainty Quantification
#### 2.2.1 Measurement error  
Mass rateThe devices used to measure the mass rates have uncertainties relative to the measured value. The uncertainties are reported as 0.05% of the measured value for liquid and 0.4% of the measured value for gas. ViscosityThe gas viscosity is found from reference data, and the value for various gases are in the range \(1\cdot 10^{-5}\,\mathrm{Pa}\,\mathrm{s}\) to \(2\cdot 10^{-5}\,\mathrm{Pa}\,\mathrm{s}\), and the viscosity has only a slight dependence on pressure and temperature. The uncertainty in the reference data is quoted as 2%, and it is reasonable to use this value for the uncertainty in gas viscosity. Liquid viscosity is difficult to measure under relevant conditions and is significantly affected by temperature. If the viscosity of a hydrocarbon fluid is measured, a typical uncertainty will be 3% of the reading. The viscosity of a single-compound fluid such as water can be obtained from reference data. The uncertainty in water reference data in the relevant range is 0.5%. DensityFor well-known gas compositions, the gas density can be calculated accurately from reference data. Alternatively, the density can be measured by weighing. In both cases, the uncertainty will typically be \(0.2\,\mathrm{kg}/\mathrm{m}^{3}\). This value also includes the effect of various degrees of saturation of vapors from the liquid present in the loop. Liquid density can be measured using Coriolis meters and a reasonable uncertainty in such measurements is \(1\,\mathrm{kg}/\mathrm{m}^{3}\). Pipe diameterIf the pipe diameter is obtained from the nominal diameter, the production tolerance must be used to infer the uncertainty. Typically, such an analysis will yield an uncertainty in diameter of 1%. If the diameter is measured by filling experiments, an uncertainty in diameter of 0.2% can be obtained. Note that the pipe diameter enters into many calculated quantities, and usually to a high degree. This includes the superficial velocities and hydraulic roughness. In the evaluation of the uncertainty in these quantities, the contribution from the uncertainty in pipe diameter is not included. The contribution from error in pipe diameter is unique because it will be the same for all experiments carried out in one particular test section. Hydraulic roughnessThe hydraulic roughness is inferred from single phase liquid flow experiments, and the uncertainty in roughness in the current case is \(1\,\mathrm{\SIUnitSymbolMicro m}\). Surface tensionSurface tension is a parameter that can only be measured off-line. The actual value of the surface tension in situ is hardly known due to contamination and dynamic effects. The uncertainty in surface tension is set to 30%. Pipe inclinationThe uncertainty in pipe inclination is estimated based on how the pipes are mounted. By inspection of the setup, we believe that the pipe can deviate \(6\,\mathrm{mm}\) in the vertical direction over a section of \(30\,\mathrm{m}\). This corresponds to an uncertainty in the pipe inclination of \(2\cdot 10^{-4}\) rad.