esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Establishment of Contact Mechanics Model
### Solution of Porosity and Real Contact Area
#### 4.2.1 Porosity  
(1) Influence of contact pressure \(p\) on porosity  
The change of porosity with contact pressure \(p\) is shown in Figure 12. Since asperities will be squeezed and filled into the pores of the sealing interface under the action of contact pressure, porosity decreases with the increase of \(p\). Larger \(D\) and smaller \(G\) corresponds to larger initial porosity, but the corresponding decrease of porosity is faster and larger in amplitude with the increase of \(p\). When the rough surface has large fractal dimension and small fractal roughness, the surface is smooth and the roughness is small, so the ratio of the initial pore volume to the total volume of the sealing surface is high. However, with the increase of the contact pressure and the deformation of the asperities, the decrease rate of the pore volume is greater than that of the total volume of the sealing surface, so the porosity continues to decline.  
(2) Influence of topography parameters on porosity  
Figure 12: Change of porosity with contact pressure \(p\).  
Seen in Figure 13a1-a3, with the increase of fractal dimension \(D\), the overall variation trend of porosity increased first and then reduced (when \(p\) = 0.1 MPa, all the porosity increases; the larger the \(p\) is, the earlier and faster the porosity decreases), and the smaller the \(G\) is, the larger the porosity decreases. At the beginning, with the increase of \(D\), many small pores within the rough sealing surfaces are shown, so the porosity increases. With the further increase of \(D\) and \(p\), it is easier for the asperities to deform and fill into the pores, so the porosity decreases. Seen in Figure 13b1-b3, with the increment of \(G\), all the porosity increases, larger \(p\) corresponds to lower porosity and larger \(D\) corresponds to larger difference and amplitude of porosity change. Larger \(G\) means rougher surface and coarser asperity, and it is much more difficult for the asperity to deform and fill into the pores, so the porosity is bigger.  
Figure 13: Change of porosity with (**a1–a3**) fractal dimension \(D\); (**b1–b3**) fractal roughness \(G\).
## 1 Introduction
More specifically, the algorithm proposed in this paper adopts a hierarchical strategy for the 4D cooperative path planning of multiple UAVs and the local cooperative collision avoidance planning for dynamic environments. The 4D cooperative planning algorithm based on DPRRT* consists of a heuristic dynamic prioritization strategy, priority planning order coordination, and an improved RRT* algorithm based on 4D cooperative constraints. The local threat avoidance planner based on HAPF effectively overcomes the problems of unreachable targets, the risks of falling into local minima, and the path oscillation which exists in traditional APF by improving potential field functions, heuristic optimal sub-target node selection strategies and memory resultant force strategies, respectively. In fact, the proposed algorithm based on a hierarchical strategy combines the advantages of the decoupling method and the collision avoidance method and overcomes their shortcomings. In addition, the algorithms based on the two methods are improved and upgraded to be used for more extensive practical applications in the future. Overall, the main contributions of this paper are as follows:  
1. A 4D dynamic cooperative path planning algorithm based on a hierarchical strategy is proposed. The proposed upper layer based on DPRRT* is able to plan a 4D cooperative path for UAVs that simultaneously meets the requirements of conflict-free space and consistent flight time. The proposed lower layer based on HAPF is able to deal with online dynamic local collision avoidance planning in frequently changing environments. 2. In the proposed DPRRT* algorithm, a heuristic dynamic prioritization strategy was designed. Within a planned environment, the heuristic prioritization algorithm was used to reasonably and dynamically adjust the priorities of UAVs to efficiently and robustly plan their path generation sequences. 3. In the single-UAV planning algorithm of DPRRT*, a 4D cost function based on time cooperative constraints was proposed, which guides the expansion of new nodes and the optimization process of neighborhood nodes to quickly plan a path that satisfies the 4D cooperative constraints of UAVs. 4. In HAPF, a heuristic sub-target node selection strategy was designed to select the optimal sub-target node for the UAVs in local obstacle avoidance planning in order to get rid of the local minimum node. In addition, a memory resultant force strategy was designed to improve path oscillation, so that the obstacle avoidance path is smoother and is able to meet the performance constraints of the UAVs. This paper is organized as follows. Section 2 describes the 4D dynamic cooperative path planning problem. In Section 3, the 4D dynamic cooperative path planning algorithm proposed in this paper is explained in detail. Section 4 outlines the simulation and comparison experiments of the proposed algorithm and discusses the simulation results. Section 5 describes some of the conclusions reached, as well as future works.
## 3 Design of Control System
### Mpc
#### 3.1.2 Design of Objective Function
\begin{array}{cccc}\hat{\bar{C}}_{k}\hat{\bar{B}}_{k}&0& \cdots&0\\ \hat{\bar{C}}_{k}\hat{\bar{A}}_{k}\hat{\bar{B}}_{k}&\hat{\bar{C}}_{k}\hat{\bar{ B}}_{k}&0&0\\ \cdots&\cdots&\ddots&0\\ \hat{\bar{C}}_{k}\hat{\bar{B}}_{k}\hat{\bar{I}}_{i=1}^{N_{p}-1}\hat{\bar{A}}_ {k+i}&\hat{\bar{C}}_{k}\hat{\bar{B}}_{k+1}\prod_{i=2}^{N_{p}-2}\hat{\bar{A}}_{k +i}&\cdots&\hat{\bar{C}}_{k}\hat{\bar{B}}_{k+N_{c}-1}\\ \hat{\bar{C}}_{k}\hat{\bar{B}}_{k+1}\hat{\bar{A}}_{k+i}&\hat{\bar{C}}_{k}\hat{ \bar{B}}_{k+1}\prod_{i=2}^{N_{p}}\hat{\bar{A}}_{k+i}&\cdots&\hat{\bar{C}}_{k} \hat{\bar{A}}_{k}\hat{\bar{B}}_{k+N_{c}-1}\\ \vdots&\vdots&\ddots&\vdots\\ \hat{\bar{C}}_{k}\hat{\bar{B}}_{k}\prod_{i=1}^{N_{p}-1}\hat{\bar{A}}_{k+i}& \hat{\bar{C}}_{k}\hat{\bar{B}}_{k+1}\prod_{i=2}^{N_{p}-1}\hat{\bar{A}}_{k+i} &\hat{\bar{C}}_{k}\hat{\bar{B}}_{k+N_{c}-1}\prod_{i=N_{c}}^{N_{p}-1}\hat{\bar{ A}}_{k+i}\end{array} \right]_{N_{p}\times N_{c}},\]  
\[\Delta U_{k}=\left[ \begin{array}{c}\Delta u(k)\\ \Delta u(k+1)\\ \cdots\\ \Delta u(k+N_{c}-1)\end{array} \right]_{N_{p}\times 1},\;N_{p}\text{ is the prediction horizon, }N_{c}\text{ is the control horizon,}\]  
and \(N_{p}\)\(>\)\(N_{c}\). The MPC in this paper was a relatively simplistic but standard non-linear MPC algorithm, in which all the \(\hat{\bar{A}}_{k+i}\) were the same in the processing of the prediction model and all the \(\hat{\bar{B}}_{k+i}\) were the also same [23]. According to the MPC strategy, the optimization problem should be converted into a quadratic programming (QP) problem [7]. The objective function was the quadratic function of the spatial state of the omni-directional wheel chassis and the control inputs. Additionally, the relaxation factor was introduced, which could not only directly limit the control increment, but also avoid the emergence of non-feasible solutions in the execution process [25]. The objective function is:  
\[J(k)=(Y(k)-Y_{ref}(k))^{\text{T}}Q(Y(k)-Y_{ref}(k))+\Delta U(k)^{\text{T}}R \Delta U(k)+\rho\epsilon^{2} \tag{17}\]  
where \(Q\) is the weight matrix of \(Y\), \(Q=diag(Q_{1},Q_{2},\cdots,Q_{N_{c}},\cdots,Q_{N_{p}})\), \(Q_{i}=diag(q_{x},q_{y},q_{\varphi})\), \((i=1,2,\cdots,N_{c},\cdots,N_{p})\), \(R\) is the weight matrix of \(\Delta U\), \(R=diag(R_{0},R_{1},\cdots,R_{N_{c}-1})\), \(R_{i}=diag(r_{\Delta 1},r_{\Delta 2},r_{\Delta 3},r_{\Delta 4})\), \((i=0,1,2,\cdots,N_{c-1})\), \(\rho\) is weight coefficient, and \(\varepsilon\) is the relaxation factor.
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
###### Abstract  
The fusion of low earth orbit (LEO) constellation and Global Navigation Satellite Systems (GNSS) can increase the number of visible satellites and optimize spatial geometry, which is expected to improve the performance of precise point positioning (PPP) ambiguity resolution (AR). In addition, the multi-frequency signals of LEO satellites can bring a variety of observation combinations, which is potential to further improve the efficiency of PPP AR. In this contribution, multi-frequency PPP AR was achieved with the augmentation of different LEO constellations. Three types of LEO constellations were designed with 60, 192, and 288 satellites. Moreover, the corresponding observation data were simulated with the GNSS observations over the ground stations. The LEO constellations were designed to transmit navigation signals on three frequencies: L1, L2, and L5 at 1575.42, 1227.6, and 1176.45 MHz, respectively, which are consistent with the GPS signals. For PPP AR, the uncalibrated phase delay (UPD) products of GNSS and LEO were estimated first. Furthermore, the quality of UPD products was also analyzed. The research findings show that the performance of estimated LEO UPD is comparable to that of GNSS UPD. Based on the UPD products, LEO-augmented multi-GNSS PPP AR can be achieved. Numerous results show that the performance of single-system and multi-GNSS PPP AR can be significantly improved by introducing the LEO constellations. The augmentation performance is more remarkable in the case of increasing LEO satellites. The time to first fix (TTFF) of the GREC fixed solution can be shortened from 7.1 to 4.8, 1.1, and 0.7 min, by introducing observations of 60-, 192-, and 288-LEO constellations, respectively. The positioning accuracy of multi-GNSS fixed solutions is also improved by about 60%, 80%, and 90% with the augmentation of 60-, 192-, and 288-LEO constellations, respectively. Compared to the dual-frequency solutions, the triple-frequency LEO-augmented PPP fixed solution presents a better performance. The TTFF of GREC fixed solutions is shortened to 33 s with the augmentation of 288-LEO constellation under the triple-frequency environment. It is worth indicating that the 288-satellite LEO-only PPP AR was conducted in dual-frequency and triple-frequency modes, respectively. The averaged TTFFs of both modes are 71.8 s and 55.2 s, respectively. It indicates that LEO constellation with 288 satellites is capable of achieving high-precision positioning independently and shows an even better performance than GNSS-only solutions.  
LEO constellation; precise point positioning (PPP); ambiguity resolution; multi-frequency; time to first fix (TTFF); positioning accuracy
## 2 Materials and Methods
### Pre-Processing  
At the initial stage, normalization was carried out on the ECG Physiobank (PTB) open-access dataset. ECG signals are commonly used in MI classification [16]. The ECG signal can be damaged due to interference from various types of artifacts and electrical currents [17]. After normalization, the raw ECG signal can be enhanced by removing various types of noise and artifacts. In our research, we reconstruct the ECG signal from the noise signal by using Discrete Wavelet Transform (DWT). The baseline of the signal is corrected, and the ECG signal is close to zero. From several experiments conducted, Sym5 was chosen as the mother wavelet because it offers better ECG signal denoise [8].  
The number of beats for the normal class was 18.78% (126,347 beats) out of a total of 672,907 beats. Among MI classes, the highest and lowest number of beats were in the inferior (140.813 beats, 20.93%) and inferior posterior (547 beats, 0.09%). For all leads, the highest and lowest total beats are in lead v2 and lead avf which are 50,333 and 33,674, respectively. Figure 2 shows a segment of the 15 lead ECG signals from the Normal and MI classes. Each ECG signal from each lead exhibits different characteristics as it represents vector information from position sensors placed in different geographic locations of the chest. An example of reading the lead v2 signal waveform for various MI classes and healthy classes is depicted in Figure 3.  
Figure 2: 15 lead ECG signal for MI and Normal/Healthy class.
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 2 Theory Background  
### BP Neural Network  
The BP neural network in Figure 1 is a multilayer feedforward neural network trained according to error backward propagation algorithm [28]. It is good at capturing the potential relationship between input and output. With its arbitrarily complex network structure and powerful nonlinear mapping ability, it is one of the most widely used neural networks. However, the BP neural network can only accept the input of a fixed dimension, thus generating the output of a fixed dimension, and the completion process is point-to-point mapping, unable to consider the temporal properties of BHP and unable to analyze the relationship in the contextual information of the time series data.
# Frequency Modulation and Erosion Performance of a Self-Resonating Jet
## 3 Results and Discussion
### Frequency Modulation  
The pressure signals and marginal spectrum with various inspiratory methods are presented in Figures 15 and 16, respectively. By connecting with the atmosphere through the suction hole, the negative pressures of the self-inspiratory jet decreased to some degree, while the absolute peak values of negative pressure decreased drastically with an increasing numbers of suction holes. Meanwhile, a wider frequency range occurred due to the intake air, which means the fluid-field in the chamber became more complicated. Compared with the Figure 11, it transpired that the distribution of suction holes also affected the peak value and the oscillating frequency of the chamber pressure.  
Figure 14: Time evolution of cavity volume during one typical cycle.  
The dominant frequency under various pressure declines via the air intake of the marginal spectrum is presented in Figure 17. Under the different pressure declines, the modulation range was different. While the dominant frequency increased linearly with the pressure decrease, it was observed that the frequency of each inspiratory method did not vary monotonically with the falling pressure. It transpired that the mechanism of frequency modulation was different with/without air intake. Besides, the range frequency modulation was different at various pressure declines. To be more specific, the modulation range was quite large for the case at 1.25 MPa and 1.50 MPa, while quite narrow at the pressure decrease of 1.75 MPa. The oscillating mechanism was rather complicated when air was entrained into the chamber, especially for the multi-hole condition. With a certain nozzle geometry, the oscillating frequency can be attributed to the characteristics of the mixture flow, such as density and bulk modulus of elasticity. Although the average density of the mixture flow was considered to be almost constant, the bulk modulus of elasticity of air is far less than that of water, which causes the bulk modulus of elasticity of the mixture flow to have an appreciable change. Therefore, the compressibility of the mixture flow increased. Besides, the instabilities of the bulk flow were also intensified with the  
Figure 16: Marginal spectrum.  
Figure 15: Chamber pressure signal under various suction series.  
entrained air. The complicated coupling between the bulk flow and air should be considered in further studies based on a large number of experiments and using advanced technology. This preliminary study can provide a guide for practical applications. The frequency modulation was achieved and its effects on the material removal is discussed in the next section.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range-Based Technique
#### 3.1.4 Received Signal Strength Indicator  
The received signal strength measurement technique uses the path loss log-normal surveillance framework to infer trilateration or uses the received signal strength fingerprints [25]. The primary method estimates the distance between the anchor node and the unknown node. Then it uses trilateration for estimating the position of the unknown node with the help of three anchor nodes. The latter method gathers the RSS patterns of the scenario, and the online dimensions are matched with the nearest probable position corresponding to the dimension database to determine the position of the unknown node, as depicted in Figure 6. RSS is a cheaper measurement technique that needs smaller hardware, but its location precision is sensitive to multipath broadcasting of radio signals [26].  
Generally, independent localization approaches face an issue of precision. Subsequently, blending AoA, ToA, TDoA and RSS has been proposed to enhance position estimate precision [27]. The hybrid approach developed in [28] combines TDoA with RSS and improves position estimate precision. RSS measurement based on a fuzzy logic algorithm enhances the localization precision. A novel multi-objective optimization (MOO) agent-based on particle swarm grey wolf optimization (PSGWO) and inverse fuzzy rank  
Figure 5: Time difference of arrival.  
Figure 6: Received signal strength indicator.  
ing is proposed in [29]. Initially, an enhanced PSGWO model is developed, and then it is utilized to develop population and multi-criteria based soft computing algorithms. This bio-inspired optimization technique determines the optimal path with minimum energy consumption and transmission cost in IoT networks.
## 6 Network Setup
### Simulation Results of 3D-Ocdma-Pon
Figure 18 shows the SMF length variation compared to the Q-factor. It is clear that if the length of the SMF increases, the Q-factor will be diminished, which signifies that the relationship between them is an inverse relationship. Furthermore, the optical fiber dispersion also influences the system performance. Due to the fact that each user exploits a high data rate, estimated to be 0.622, 1, 1.5, and 2 Gbps in the four different cases, it is remarkable that the SMF length of our proposed system, for acceptable BER, reaches 122, 69.2, 48.4, and 33.8 km, respectively. Similarly, it is very clear that our code satisfies the requirements of optical communication systems. Finally, Figure 19 shows the Q-factor variation as a function of the data rate for fixed SMF lengths of up to 20, 30, and 40 km. It is evident that the Q-factor diminishes when the data rate is increased. This indicates the direct relationship between them. The analysis is performed for eight users accessing the milieu simultaneously. It can be seen that the performance of our proposed network lessens when the SMF length is increased. However, better performance for the 3D-SWZCC setup with \(L_{1}\) = 2, \(L_{2}\) = 2, and \(L_{3}\) = 2 can be achieved at 10 km of distance with a provided data rate of 3.02 Gbps. Moreover, the BER increases at a distance of 30 km; in this case, the system does not perform as well until the data rate goes from 3.02 to 2.06 Gbps. Similarly, the system is impaired until a data rate of 1.45 Gbps is used at 50 km. Figure 19: Q-factor versus data rate for the SMF lengths 10, 30, and 50 km. Figure 18: Q-factor versus distance.
# Effect of Parameters on Vapor Generation in Ship-to-Ship Liquefied Natural Gas Bunkering
## 3 Simulation and Input Preparation
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Notes** \\ \hline Quantity of receiving tank & 1 & ea & Receiving ship has an independent IMO type C tank \\ Net capacity of receiving tank, V\({}_{\text{net, rt}}\) & 1000 & m\({}^{3}\) & \\ Overall length of receiving tank & 27.15 & m & 2:1 ellipsoidal head, D/2h = 2, head height 1.75 m \\ Diameter of receiving tank & 7 & m & Assumed \\ Surface area of receiving tank, A\({}_{\text{nt}}\) & 520.1 & m\({}^{2}\) & Calculated from length and diameter \\ Maximum allowable relief valve setting, P\({}_{\text{rt, MAXVS}}\) & 6 & bar & Assumed \\ Soil-off rate (Normal) & 0.35 & \% & Assumed \\ Thermal conductance of tank, (UA)\({}_{\text{rt}}\) & 46.25 & \% & Calculated from Equations (2) and (3) \\ Overall heat transfer coefficient of tank, U\({}_{\text{rt}}\) & 0.320 & kJ/h-m\({}^{2}\)-K & Calculated from Equations (2)–(4) \\ Ambient Temperature & 25 & \({}^{\circ}\)C & \\
**Base case conditions at bunkering** & & & \\ LNG Volume \% (Heel) & 20 & \% & \\ LNG temperature & \(-147\) & \({}^{\circ}\)C & \\ Pressure in receiving tank & 2.94 & bar & \\ Loading limit & 90 & \% & \\ It was assumed that a dual fuel engine generator was installed on the receiving ship, and that the receiving ship did not use the LNG as fuel during bunkering. \[\Delta m_{v,r}=m_{i,v,r}-m_{f,v,r} \tag{7}\]  
In the above, \(\Delta m_{v,b}\), \(m_{i,v,b}\), and \(m_{f,v,b}\) denote the vapor mass difference in the bunker tank, initial vapor mass in the bunker tank, and vapor mass in the bunker tank after completion of bunkering, respectively. Similarly, \(\Delta m_{v,r}\), \(m_{i,v,r}\), and \(m_{f,v,r}\) denote the vapor mass difference in the receiving tank, initial vapor mass in the receiving tank, and vapor mass in the receiving tank after completion of bunkering, respectively. By summing \(\Delta m_{v,b}\) and \(\Delta m_{v,r}\), the net amount of vapor generation in the overall bunkering system, \(\Delta m_{net,v,overall}\), can be calculated as follows:  
\[m_{net,v,overall}=\Delta m_{v,b}+\Delta m_{v,r} \tag{8}\]  
The net vapor generation in the receiving tank, \(m_{net,v,g,r}\), can be estimated as follows:  
\[m_{net,v,g,r}=\int_{0}^{t}\dot{m}_{vapor\ retrun}+\Delta m_{v,r} \tag{9}\]  
here, \(\dot{m}_{vapor\ retrun}\) represents the mass flow rate (kg/s) of the vapor return.
## 3 Model Analyses
### Analysis of Influencing Factors  
In order to show the relationship between the uneven settlement, the seismic acceleration, and the displacement angle between the structural layers, a graph of the relationship between the three parameters was drawn. Due to limited space, the 10 th ground motion record was taken as an example, as shown in Figure 12.  
Figure 12: _Cont._  
It can be seen from Figure 12 that no matter where the settlement zone is located in the structural plane, the ground motion intensity and the settlement quantity will affect the interstorey drift ratios. The interstorey drift ratios will increase with the increase of the acceleration peak. At the same time, the interstorey drift ratios will increase with the increase in settlement.  
It can also be seen from Figure 12 that when the seismic acceleration peak is less than 1.2 g, the ground motion intensity has a significant influence on the interstorey drift ratios. When the seismic acceleration peak is greater than 1.2 g, the interstorey drift ratios no longer increase with the increase in the seismic acceleration peak. This phenomenon is particularly evident in the Zone II and Zone III uneven settlement areas. This shows that when the peak acceleration of the earthquake reached about 1.2 g, some components in the structure yielded, and the structural response caused by the earthquake reached a relatively stable state.  
The influence of uneven settlement on the structural response was most significant in Zone I. It can be seen that the interstorey drift ratios of the structure increased with the increase in uneven settlement under the same seismic acceleration. In Zone II and Zone III, even if the settlement quantity was greatly increased, the interstorey drift ratios of the structure did not show a corresponding large increase. Moreover, the larger the peak value of seismic acceleration, the smaller the influence of uneven settlement on the seismic response of the structure.