esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WiFi 6/6E: IEEE 802.11 ax
### Spatial Reuse (SR)  
Access point (AP) densification is another challenge faced by WiFi networks. The number of WiFi access points in a given space is increasing at an exponential rate, causing extensive interference, which in turn results in performance degradation in the entire network. As such, improving the efficiency of dense WiFi networks was identified as a matter of paramount importance by the IEEE 802.11 ax task group.  
To overcome this problem, the concept of spatial reuse (SR) was introduced in dense deployments. The idea of SR is to allow access points with overlapping coverage, overlapping basic service sets (OBSS), to transmit data in parallel, thereby increasing the spectral efficiency in the networks. SR uses a combination of techniques, such as the basic service Set (BSS) colouring mechanism. The BSS colouring mechanism assigns a random ID to each AP, which is different from its neighbouring APs. Therefore, a user detecting a preamble of a packet can instantly identify whether it is coming from an interfering AP before decoding the packet [38].  
IEEE 802.11 ax also introduced two network allocation vectors (NAVs) to support SR. NAVs are countdown timers that are set at the beginning of the transmission. When the NAV is zero, the transmission is considered to be over and the channel is available. Although a NAV typically keeps track of its own AP's transmissions, there can be neighbouring APs that influence the NAV. Hence, in IEEE 802.11 ax, another NAV is introduced to keep track of neighbouring AP transmissions, such that the user can distinguish the status of its own channel and the neighbouring channels [38].  
Another SR mechanism that is being deployed by 802.11 ax is the overlapping basic service set-packet detection (OBSS-PD) mechanism. It manipulates the transmission power level to sustain a flexible trade-off between interference and spatial reuse capability [38].
## 4 Numerical Simulation of the Formation Effect of Grout Curtain in Ore-Rock Contact Zone
### Single-Hole Grouting Curtain Body Formation Effect
#### 4.2.3 Curtain Body Morphology under Different Water Pressures  
The shape of the curtain body was simulated under water pressure conditions of 0 MPa, 1.5 MPa, and 3 MPa. The corresponding results are displayed in Figure 9. At a water pressure of 0 MPa, the slurry is able to spread into a larger area without being dispersed or transported by groundwater. In the zone where the slurry contacts the ore-rock, it has already spread to the lower edge of the model. As the grouting time increases, the slurry will completely fill the entire contact zone. At water pressures of 1.5 MPa and 3 MPa, the groundwater disperses and carries the slurry diffusion. As the water pressure increases, the shape of the curtain body decreases to varying extents, and the range of slurry diffusion at the contact zone is significantly reduced.  
Figure 8: Single-hole curtain body morphology at different grouting rates. (**a**) Vg = 0.1 m/s; (**b**) Vg = 0.2 m/s; (**c**) Vg = 0.3 m/s.
# A Comprehensive Review of Micro UAV Charging Techniques
## 3 Battery Charging Techniques for UAVs  
Drones are currently used in a variety of applications, including military, power line monitoring, forest monitoring, disaster prevention and smart agriculture [25; 26]. Drones carry different types of payloads such as GPS, infrared cameras, batteries and sensors as delivery vehicles. These drones usually carry high energy batteries, e.g., lithium batteries, which support flight times of 20-40 min [27]. However, range and endurance are critical challenges in UAVs due to the limited battery capacity. It is not feasible to increase the battery size of UAVs because it will increase weight, which is another critical concern. Several research studies have addressed battery charging of UAVs, but it requires an intensive investigation by the research fraternity. [PERSON] et al. [25] suggested three ways to enhance flying time: (i) Drones can be equipped with high battery capacity but it can increase the drone weight. (ii) Battery swapping can be achieved after landing the drone. However, this also causes complexity and high cost when swapping the system. (iii) Recharging can be done at the base station of the drone. Charging can be achieved through a wired or wireless power transfer (WPT) system [28].
## 3 Experimental Results
### Gap Breakdown  
The typical experimental results of gap breakdown are shown in Figure 4. In this discharge, the initial corona started at t = 20.29 \(\upmu\)s. After developing for a certain period of time, it stopped developing when the first restrike occurred at t = 140.67 \(\upmu\)s. After six restrikes with larger current pulse amplitudes, the leader channel continued to steadily develop until the gap broke down. Among the seven restrikes, the current amplitudes of the fourth and seventh restrikes were significantly larger than those of the five other restrikes, with peak values reaching 6.05 A and 12.36 A, respectively.  
The current pulses around the moments when the fourth (t = 290.37 \(\upmu\)s) and the seventh (t = 519.89 \(\upmu\)s) restrike events occurred were selected, in addition to the corresponding seven frames of high-speed optical photographs. Each photograph frame is labeled F1\({}^{\prime}\)-F7\({}^{\prime}\) and F8\({}^{\prime}\)-F14\({}^{\prime}\), respectively, as shown in Figure 5a,b. During the relaxation stage before the restrike pulse, as shown in Figure 5a, the luminosity at the head of the leader channel was relatively weak. It gradually became clear, starting from photograph F3\({}^{\prime}\) (the unclear optical morphology in F5\({}^{\prime}\) is due to the occurrence of restrike at the upper end of the electrode, and the intense luminosity caused the camera to automatically reduce the ISO sensitivity). In addition, the current formed by the discharge of the shielding cylinder on the upper part of the electrode did not enter the internal current sensor; there was therefore no change in the current on the current waveform.  
However, during the discharge relaxation stage shown in Figure 5b, the luminosity at the head of the leader channel was abundantly evident. Examining the currents corresponding to the photographs, excluding a very small discharge current (I \(<\) 0.1 A) near the end of the exposure time of photograph F12\({}^{\prime}\), the discharge currents corresponding to the first four frames of the photographs were all zero. The above phenomena are ultimately consistent with those shown in Figure 3a,b.  
Figure 4: The typical experimental results when the gap was broken down. (**a**) Waveforms of applied voltage and discharge current and (**b**) optical morphology during leader development.
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 3 L\({}^{p}\)-Order Time-Frequency Spectra (LTFS)
### Analysis of LTFS  
According to Equations (13) and (14), the output of LTFS depends on the time-frequency spectra, the sign function, the order parameter, and the two frequency integration parameters. A comparison of SGHT and LTFS shows that LTFS is a general representation of SGHT, and SGHT is the special case of LTFS. Thus, we can flexibly choose the order parameter and the two frequency integration parameters, to optimize the output resolution and computation efficiency of LTFS, respectively. Finally, high-stability and high-resolution results can be obtained with high efficiency.  
If we compare LTFS results with \(R(t,f)\), GHT, and SGHT results, then  
\[\left\{\begin{array}{l}h_{f_{1},f_{2}}(t)=R(t,f),\ \ \text{if}\ \ f_{1}=f_{2}=f\\ h_{f_{1},f_{2}}(t)=g(t),\ \ \text{if}\ \ \ p=(2k_{1}-1)\cdot(2k_{2}-1)^{-1},f_{1}=0,f_{2}=f_ {N}\\ h_{f_{1},f_{2}}(t)=s(t),\ \ \text{if}\ \ \ f_{1}=0,f_{2}=f_{N}\end{array}\right. \tag{16}\]  
and if we consider we consider \(T_{1}\), \(T_{2}\), and \(T_{3}\) as the time costs of GHT, SGHT, and LTFS, respectively; then, according to Equations (4), (9), and (14), we have  
\[\left\{\begin{array}{l}T_{2}=T_{1}\\ T_{1}\cdot T_{3}{}^{-1}=f_{N}\cdot(f_{2}-f_{1})^{-1}\end{array}\right. \tag{17}\]  
Above all, SGHT has higher stability than GHT without computation efficiency decreasing, while LTFS has higher computation efficiency than SGHT without stability decreasing. Moreover, LTFS have higher stability and computation efficiency than GHT, and finally obtain high-stability and high-resolution results with high efficiency.
###### Abstract  
The objective of the study was to investigate the effect of the partial replacement of carbon black (CB) by nanodiamonds (NDs) on the vulcanization, mechanical and dynamic properties of a natural rubber--butadiene rubber compound, a typical elastomer compound found in several applications (the tire and mining industry, for example). A studied hybrid filler system resulted in a 28% increase in tensile strength and 29% increase in 300% modulus at low ND loadings even though the total weight fraction of the filler system was kept constant at 25 parts per hundred rubber. The hybrid filler system improved dispersion of both fillers as was proven by scanning electron microscopy and the Payne effect study. In addition, the replacement of 2.5 and 5 phr CB by NDs resulted in 62% improvement in wear resistance. The DMA study showed that a certain ND-CB filler combination has a positive effect on tire properties such as wet grip and rolling resistance.  
nanodiamond; carbon black; natural rubber; butadiene rubber; tire properties +
Footnote †: journal: Applied Sciences
# Investigation of Steep Waste Dump Slope Stability of Iron Ore Mine--A Case Study
## 3 Results and Discussion
### Slope Rectification
#### 3.5.2 PFC Numerical Simulation of the Rectified Slope Displacement  
Figure 14 illustrates the waste dump slope morphology and particle displacement distribution in the \(x\) and \(y\) directions after rectification. In the modified scenario, particle displacement occurs throughout the model. Approximately 30% of the particles exhibit displacement in the \(x\) direction with a maximum displacement of 0.25 m. In the \(y\) direction, the maximum displacement of 15% of the particles reaches 0.2 m. The intercepted profiles were used to analyze the movement of particles in Waste Dump#1.  
Figure 15 shows the top view of the rectified waste dump slope showing the location of the selected cross-section and the particle displacement distribution in the \(x\) and \(y\) directions in the profile. The results show that after correction, the maximum particle displacement distance is 0.25 m in the \(x\) direction and 0.15 m in the \(y\) direction. The location of slope  
Figure 14: Particle displacement distributions in the \(x\) and \(y\) directions after rectification: (**a**) slope model after rectification, (**b**) slope displacement in the \(x\) direction, and (**c**) slope displacement in the \(y\) direction.  
Figure 13: Stability analysis after rectification of the slope (note: the domains with dark-green, yellow, and light-green colors in order represent the quartz schist, discharged material, and slope prone to failure).  
failure is indicated by a light blue area in the graph, which accounts for 15% of the total particle distribution.  
By comparing the slope morphology and particle displacement before and after the rectification, the area where the slope slides after the rectification is substantially smaller than the area where the slope slides before the rectification, representing a 25% reduction. The distance of the particles' displacements is noticeably reduced, and the reduction of the amount of sliding accounts for 41.8% of the total amount of sliding, and the rectification of the slopes has a substantial effect.
# PiPatient Privacy Copyright Cloud Management
## 4 Security Analysis
### Known Key Secrecy  
Known key secrecy is often mistaken for perfect forward secrecy because malicious users may obtain the session key or any secret information through analysis and predict the next session key or member privacy. In our proposed protocol, the session key used by patients, the EPRS, and doctors in the communication is generated by a one-time random number \(SK=\text{h}\) (\(R_{Pi}{}^{new}\oplus R_{EPRS}\oplus R_{D}\)), where the random number \(R_{Pi}{}^{new}\) is generated by the patient randomly. The number \(R_{EPRS}\) is generated by the electronic medical record server, and the random number \(R_{D}\) is generated by the remote doctor. These one-time random numbers are irregular. Each parameter is randomly regenerated. Consequently, even if the attacker obtains any such confidential information in this session, the attacker cannot calculate the next session key, so the proposed protocol can provide known key secrecy.
# IoT Based Smart City Bus Stops
## 6 Conclusions  
This paper introduced a novel IoT-based bus stop that provided smart monitoring and maintenance solutions to reduce energy consumption and increase the satisfaction of commuters. The system consisted of layers corresponding to data acquisition, processing, storage, and presentation. The system monitored the bus stop's occupancy and sensors so that based on the sensor readings, the lights and air-conditioning could operate more efficiently. The air pollution in and around the bus stop vicinity was also monitored. The bus stop operation status and air pollution levels were then sent to a cloud-based server. A mobile app was developed to enable operation engineers to monitor the air conditioning and lights remotely. Utilizing Google Maps, a bus stop operation status was displayed using different colored attributes. In order to meet the system objectives, test cases were conducted to ensure that the system performance was aligned with the goals. The results were conclusive to determine that this project can be scaled to multiple bus stops. The proposed system cuts down on power consumption by controlling the bus stop's utilities based on its occupancy. By keeping constant track of the bus stop's occupancy, the system can control the lights and the air conditioning. This helps save energy, as compared to traditional bus stops where the utilities remain running even during hours of no occupancy, leading to a waste of resources. The exact amount of saved energy will be reported in future work as we plan to add a solar energy system to supply power to the bus stop. Another aspect than can be addressed in the future is the communication between the vehicle and the infrastructure (Bus Stop). Additionally, the versatility of the system can also expand its application to various settings such as schools, weather stations, hospitals, and the like.  
Conceptualization, M.K., M.A., H.M., T.S., A.R.A.-A., and A.A.N.; Methodology, M.K., M.A., H.M., T.S., A.R.A.-A., and A.A.N.; Software, M.K., M.A., and H.M.; Validation, X M.K., M.A., and H.M.; Formal analysis, M.K., M.A., and H.M.; Investigation, M.K., M.A., and H.M.; Resources, M.K., M.A., H.M., and A.A.N.; Data curation, M.K., M.A., and H.M.; Writing--original draft preparation, M.K., M.A., and H.M.; Writing--review and editing, M.K., M.A., H.M., T.S., A.R.A.-A., and A.A.N.; Visualization, M.K., M.A., and H.M.; Supervision, T.S., A.R.A.-A., and A.A.N.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 7: Testing scalability: around 99 bus stops.
## 4 Systems Implementation and Evaluation
### Evaluation Metrics  
The performance of the developed spider Taylor-ChoA-based HAN and RMDL is computed with precision, recall, F1-score, and RMSE.  
_Precision:_ It is termed as nearness of more than two dimensions among each other, and is given by  
\[P=\frac{m_{p}}{m_{p}+n_{p}} \tag{61}\]  
where \(m_{p}\) symbolizes the true positive and \(n_{p}\) represents the false positive.  
_Recall:_ It computes the complete actual positives which the system captures using the label of true positive, and it is formulated as  
\[R=\frac{m_{p}}{m_{p}+n_{f}} \tag{62}\]  
where \(n_{f}\) symbolizes the false negative.  
_F1-score:_ It is weighted by the harmonic mean of recall and precision, and is modeled as  
\[F_{m}=2\times\left(\frac{P*R}{P+R}\right) \tag{63}\]  
_RMSE:_ It refers to the differences between values predicted by a technique and the observed values and is given by  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}(x_{i}-\hat{x}_{i})^{2}}{N}} \tag{64}\]  
where \(N\) refers to the number of non-missing data points, \(x_{i}\) represents the actual observation time series, and \(\hat{x}_{i}\) signifies the estimated time series.
## 4 Seismic Fragility Assessment Procedure  
### Modeling of Randomness  
The randomness considered in the SPRA in this study is related to ground motion in two ways. First, the variability of the spectral shape was considered when constructing the target spectra for the three CS and neglected for the UHRS, as described in Section 3.3. Second, the horizontal component-to-component directional variability, which represents the difference between the spectral acceleration in one of the two orthogonal directions and that of the target spectrum, defined as a geometric mean, was considered. To represent this variability for the ground motions matched to the UHRS, one horizontal component was multiplied by \(F_{H}\) whereas the other component orthogonal to the former was divided by the same \(F_{H}\). Consequently, the geometric mean of the two modified ground motion components did not change. \(F_{H}\) conformed to a lognormal distribution with a median of 1.0 and a standard deviation of 0.18 in accordance with EPRI 3002012994 [1]. The spectral distribution of the H1 component for 30 ground motions is shown in Figure 6b. Similarly, the target spectra defined by CS are also defined as a geometric mean. However, in contrast to the UHRS, ground motion records were modified by amplitude scaling only so that the response spectra already had unintentional variability. To introduce directional variability more strictly into the CS, the spectral acceleration at the control frequency was calculated for each ground motion pair by rotating the two orthogonal axes in the horizontal plane from \(1^{\circ}\) to \(180^{\circ}\) at \(1^{\circ}\). Thirty equally spaced percentiles, ranging from 0 to 100, were randomly assigned to 30 ground motion pairs. Then, each ground motion was rotated by an angle corresponding to the randomly chosen percentile of the spectral acceleration in the H1 axis and applied to a three-dimensional dynamic analysis. The spectral distributions of the H1- and H2-components of 30 scaled ground motion pairs with randomly rotated orientations are plotted in Figure 9.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 3 Methodology: Mapping Study
### Realization
\begin{table}
\begin{tabular}{c c} \hline
**Item ID** & **Bibliographic Reference** \\ \hline A-01 & [27] [PERSON], [PERSON], [PERSON] y [PERSON]. *Improved Chimp Optimization Algorithm for Three-Dimensional Path Planning Problem*. Multimedia Tools and Applications 81, n.o 19 (2022): 27397-422. \\  & [[https://doi.org/10.1007/s11042-022-12882-4](https://doi.org/10.1007/s11042-022-12882-4)]([https://doi.org/10.1007/s11042-022-12882-4](https://doi.org/10.1007/s11042-022-12882-4)). \\ \hline A-02 & [22] [PERSON], [PERSON], y [PERSON]. *Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments. Journal of Intelligent and Robotic Systems: Theory and Applications 105, n.o 3 (2022). [[https://doi.org/10.1007/s10846-022-01608-1](https://doi.org/10.1007/s10846-022-01608-1)]([https://doi.org/10.1007/s10846-022-01608-1](https://doi.org/10.1007/s10846-022-01608-1)). \\ \hline A-03 & [28] [PERSON], [PERSON], [PERSON]-[PERSON], [PERSON][PERSON], [PERSON][PERSON], [PERSON][PERSON], [PERSON][PERSON], y [PERSON]. *A New Hybrid Algorithm Based on Golden Eagle Optimizer and Grey Wolf Optimizer for 3D Path Planning of Multiple UAVs in Power Inspections. Neural Computing and Applications 34, no 14 (2022): 11911-36. [[https://doi.org/10.1007/s00521-022-07080-0](https://doi.org/10.1007/s00521-022-07080-0)]([https://doi.org/10.1007/s00521-022-07080-0](https://doi.org/10.1007/s00521-022-07080-0)). \\ \hline A-04 & [29] [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], y [PERSON]. *Modified A-Star (A*) Approach to Plan the Motion of a Quadrotor UAV in Three-Dimensional Obstacle-Cluttered Environment*. Applied Sciences (Switzerland) 12, n.o 12 (2022). [[https://doi.org/10.3390/app12125791](https://doi.org/10.3390/app12125791)]([https://doi.org/10.3390/app12125791](https://doi.org/10.3390/app12125791)). \\ \hline A-05 & [30] [PERSON], [PERSON], [PERSON], [PERSON], y [PERSON]. *CROSSOVER RECOMBINATION-BASED GLOBAL-BEST BRAIN STORM OPTIMIZATION ALGORITHM FOR UAV PATH PLANNING*. Proceedings of the Romanian Academy Series A - Mathematics Physics Technical Sciences Information Science 23, n.o 2 (2022): 207-16. \\ \hline A-06 & [3] [PERSON], [PERSON], [PERSON], y [PERSON]. *Energy-Efficient Trajectory Optimization for UAV-Enabled Cellular Communications Based on Physical-Layer Security*. Aerospace 9, n.o 2 (2022). [[https://doi.org/10.3390/aerospace9020050](https://doi.org/10.3390/aerospace9020050)]([https://doi.org/10.3390/aerospace9020050](https://doi.org/10.3390/aerospace9020050)). \\ \hline A-07 & [31] [PERSON], [PERSON], [PERSON], [PERSON], y [PERSON]. *UAV Path Planning and Collision Avoidance in 3D Environments Based on POMPD and Improved Grey Wolf Optimizer-. Aerospace Science and Technology 121 (2022). [[https://doi.org/10.1016/j.ast.2021.107314](https://doi.org/10.1016/j.ast.2021.107314)]([https://doi.org/10.1016/j.ast.2021.107314](https://doi.org/10.1016/j.ast.2021.107314)).  
\end{table} 
Table 4: List of analyzed papers. Figure 10: Scope of the study, based on [24].
# Experimental Analysis on Pre-Stress Friction Loss of Crushed Limestone Sand Concrete Beams
## 1 Introduction
Thus, the suggested values are not suitable for evaluating the pre-stress friction loss of CLS concrete. With the application of manufactured sand concrete in pre-stressed structures, a systematic study of the pre-stress friction loss is highly necessary. In this paper, to accurately calculate the pre-stress friction loss in CLS concrete members with post-tensioning, 26 full-scale pre-stressed concrete beams were constructed based on two bridge projects. The tensile forces were applied and recorded through a loading and measuring system, then the pre-stress friction loss at each stress degree was calculated. Investigations have been carefully carried out to measure the effect of a variety of parameters, such as the duct-forming material (rubber hose, metal bellow and plastic bellow), wires of tendons (single-wire and multi-wire) and arrangement of ducts (straight and curve duct). The results show that the friction is influenced by multiple variables, with the pre-stress friction loss in each case having been compared. The deviation coefficient \(\kappa\) and friction coefficient \(\mu\) are modified and proposed for the CLS concrete member with post-tensioning, which can provide theoretical support and suggestions for actual projects.