esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of GPS Auto-Surveying Techniques
## 3 Results
### Convergence over Time of the Position Solution  
Another important metric is the time necessary for the position solution to converge to below a given error threshold. As such, the mean error over time was computed and is presented in Figures 3 and 4.  
From here, it is clear that the Hatch Filter does not produce significant changes to either of the mean error dynamics. Furthermore, while the EKF and UKF show a higher initial error, that error falls to values below those of the LS and WLS in only a few hours. From here, a clear trend of more accurate results for the EKF and UKF can be seen, with a lead for the UKF. It should be noted that there is an increase in the mean error after approximately 13 h of the survey; most likely, that is the result of constellation changes, as can be seen from Figure 5, where there is a jump in the GDOP parameter to much higher values.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**LS**} & \multicolumn{3}{c}{**WLS**} & \multicolumn{3}{c}{**EKF**} & \multicolumn{3}{c}{**UKF**} \\ \hline \(\gamma\) & **0.005** & **0.010** & **0.015** & **0.020** & **0.005** & **0.010** & **0.015** & **0.020** & **0.005** & **0.010** & **0.015** & **0.020** & **0.005** & **0.010** & **0.015** & **0.020** \\ \hline Mean (m) & 1.342 & 1.333 & 1.331 & 1
## 1 Introduction
Selecting the right evaluation method is key to accurately and scientifically assessing the suitability of UUS development [30; 50; 51]. When it comes to developing UUS, one must prioritize evaluating the geological suitability of the area based on the specific conditions of the city [52]. This involves choosing evaluation indexes that are tailored to the development and utilization conditions of the space, with a focus on addressing environmental geological issues that may pose obstacles to UUS development [17]. To ensure a comprehensive and accurate evaluation, key indicators, such as topography, landform, engineering geological properties of the rock and soil mass, hydrological conditions, adverse geological impacts, and the 3D structure, must all be taken into consideration [53]. Furthermore, it is crucial to also consider the presence and distribution of underground structures, cultural relics and remains during the evaluation process. secondary indexes are formulated depending on the municipality's specific attributes and the existing conditions of UUS [52]. Qualitative evaluation relies on geological data and urban geological survey standards to assess geological conditions and draw conclusions on suitability [52; 54]. Appropriate evaluation methods can assess geological data and relevant standards to determine the feasibility of UUS development. On the other hand, quantitative evaluation involves analyzing the impact of factors using mathematics, engineering geology theory, and local conditions [4; 55; 51]. Establishing a more complex evaluation system would necessitate the incorporation of additional methods for processing diverse data, making the use of computer assistance essential. This allows decision makers to draw conclusions on suitability to support urban planning and construction. Master planning of UUS is essential for guiding the orderly utilization of such spaces. Qualitative evaluation focuses on analyzing geological data and norms to determine suitability, while quantitative evaluation involves analyzing factors using mathematical and engineering geology theories. Master planning plays a significant role in guiding the orderly utilization of UUS [2; 56; 57; 58]. This study explores the significance of GSE for the utilization and development of UUS, drawing from experiences on global scale. Beginning with the evaluation objectives, elements, and steps, the theoretical framework of Geological Evaluation for GSEUUS is systematically presented. The indicators used in the evaluation are elucidated from the perspectives of geological bodies, geological resources, geological environments, geological disasters, and underground structures. It delves into the importance, function, and essential nature of these assessments, along with examining the content, methodologies, criteria, factors influencing, and suitable conditions associated with this practice. Additionally, it reviews and scrutinizes the present obstacles and future directions of GSE techniques. These results provide a basis of scientific knowledge for the sustainable advancement and use of UUS.
## 3 Acoustic Emission Experimental Platform for Detecting Leakage in a Valve  
A self-developed acoustic emission detection instrument was used for the acoustic emission detection experiment of the valve. The instrument adopts an integrated design. It is divided into two modules. One is the signal acquisition module, including data acquisition card, sensor, laptop, signal preamplifier, signal separator, instrument cabinet and other components. The data acquisition card has a sampling rate of 1 MHz. The AE sensor is a resonant transducer with a central frequency of 150 kHz and a frequency range of 20-220 kHz. The sampling frequency of the sensor is set to 200 kHz. The gain of the preamplifier is 40 dB. The other is the signal processing module developed based on LabVIEW (by National Instruments, Austin, TX, USA) and MATLAB (by MathWorks, Natick, MA, USA).  
The software of the detection system includes four functional modules: system setting, parameter setting, data processing and internal leakage diagnosis. The internal leakage diagnosis module integrates the valve internal leakage rate prediction model based on factor analysis and wavelet-BP neural network proposed in this paper.  
In the experiment, a set of gas pipeline simulation internal leakage detection platforms was established, as shown in Figure 3. The platform uses nitrogen to simulate the gas medium in the gas pipeline in actual working conditions, and the valve used is a DN80 ball valve (5). During the experiment, the airtightness of the platform was tested firstly. Secondly, the data collection point on the upper and lower flanges of the valve were selected. The coupling agent was used and two AE sensors (4) were fixed at the data collection point using a magnetic fixture. The self-developed AE system (8) was used to collect the signal. Then, the atmospheric valve (8) was closed and the back-pressure valve (7) was opened. The regulating valve (2) of the nitrogen cylinder (4) was opened to a certain opening to make the gas in the pipeline of the experimental platform reach a certain pressure. Then, the ball valve was adjusted to a certain opening to free the pressure of the platform. When the upstream pipeline pressure gauge (3) reached the predetermined value, data was collected every 0.1 MPa. Data collection stopped until the pressure dropped to a certain value.  
Figure 3: Experimental platform for detection valve internal leakage in the gas pipeline.
## 3 Optimization Problem Formulation
### Objective Function
#### 3.1.2 Voltage Deviation  
The second objective of the proposed approach is to minimize the voltage deviation between the actual and desired voltage values. This can also be controlled by charging/discharging of the active and reactive power from EVs.  
\[F_{2}=Minimize\sum_{t=1}^{24}\sum_{n=1}^{N}\left|V_{Meas(n,t)}-V_{\text{Ref}}\right| \tag{2}\]  
The minimization of \(F_{2}\) will help to keep the bus voltages closer to the desired values.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 1 Introduction  
In spaceflight, each space mission requires countries to invest a large amount of their workforce, material, and financial resources. At the same time, the internal structure of spacecraft is complicated. In order to ensure the safety of the lives of astronauts and the successful completion of space missions, scientists have carefully designed the missions and the vehicles. However, vehicles in mission implementations are often in an extremely harsh environment, which massively tests the reliability of their devices once an accident occurs, which can lead to highly significant losses and even jeopardize the safety of personnel. Equipment failures in the chemical and military industries also lead to production stoppages, environmental pollution, and even personal injuries. Therefore, to detect potential failures in advance and take preventive measures, continuous monitoring and prediction of the health status of equipment are essential to ensure production safety and improve productivity. Failures and damages are usually defined as material, geometry, or equipment operation changes. Many methods for identifying failures and damages rely only on periodic inspections or non-destructive testing, so much of our experience of failure can only be acquired after an accident occurs with testing or analysis, which undoubtedlybrings substantial economic losses. Prognostics and health management (PHM) provides early fault diagnosis and repair ideas.  
PHM is a comprehensive technology that integrates the physical knowledge, information, and data on structures, systems, and components during operation and maintenance in detail [1]. Through this integrated information, PHM enables several vital functions: detecting equipment and process anomalies, diagnosing degraded states and failures, predicting the progression of degradation to failure, and estimating remaining useful life. These functions shift the maintenance strategy from traditional reactive maintenance, which reacts to equipment failures and breakdowns, to a more proactive and preventive maintenance strategy, including scheduled overhaul, proactive prevention, and predictive and integrated planning and management. Nowadays, in the context of Industry 4.0, various technologies, including digital twins, AR, VR, etc., have emerged, and they are also heavily used in various aspects of PHM, dramatically improving the efficiency of fault diagnosis. This paper summarizes the development of PHM in engineering, from the initial PHM used in aviation to the current PHM systems supported by various advanced technologies. Secondly, we analyze the digital twin (DT) and augmented reality (AR) technology for PHM and innovative point-of-service applications. Finally, we analyze the current PHM processes and present our expectations for the future direction of PHM based on the existing technologies. The specific research framework of this paper is shown in Figure 1.
# Quadrotor Formation Strategies Based on Distributed Consensus and Model Predictive Controls
## 3 Dynamic Model of a Quadrotor
### Model Predictive Control
#### 4.2.2 Unconstrained Quadratic Programming Problem
\right]\widetilde{\mathbf{u}}+ \underbrace{\frac{1}{2}\mathbf{x}_{0}^{T}\widetilde{A}^{T}\widetilde{Q}_{1} \widetilde{A}\mathbf{x}_{0}-\mathbf{x}_{0}^{T}\widetilde{A}^{T}\widetilde{Q}_{2} \widetilde{\mathbf{r}}}_{Constant\text{ contribution to cost function}}\]  
and ignoring the constant contribution part in cost function \(\widetilde{J}\), the cost function becomes  
\[\widetilde{J} =\frac{1}{2}\widetilde{\mathbf{u}}^{T}\left(\widetilde{B}^{T} \widetilde{Q}_{1}\widetilde{B}+\widetilde{R}\right)\widetilde{\mathbf{u}}+\left[ \begin{array}{cc}\mathbf{x}_{0}^{T}&\widetilde{\mathbf{r}}^{T}\end{array} \right] \left[ \begin{array}{c}\widetilde{A}^{T}\widetilde{Q}_{1}\widetilde{B}\\ -\widetilde{Q}_{2}^{T}\widetilde{B}\end{array} \right]\widetilde{\mathbf{u}} \tag{27}\] \[=\frac{1}{2}\widetilde{\mathbf{u}}^{T}H\widetilde{\mathbf{u}}+\overline {x}F\widetilde{\mathbf{u}},\]  
where \(H\in\mathbb{R}^{mN_{p}\times mN_{p}}\), \(\overline{x}\in\mathbb{R}^{(n+qN_{p})}\) is a row vector consists of initial condition \(x_{0}\) and the reference trajectory at each time step and \(F\in\mathbb{R}^{(n+qN_{p})\times mN_{p}}\). The optimal control sequence can be obtained by taking the partial derivative of \(\overline{J}\) with respect to \(\widetilde{u}\) and setting it to zero:  
\[ \begin{array}{rcl}\frac{\partial\overline{J}}{\partial\widetilde{u}}&=& \frac{1}{2}\left(H\widetilde{u}+H^{T}\widetilde{u}\right)+\left(\overline{x} F\right)^{T}=0\\ &=&H\widetilde{u}+\left(\overline{x}F\right)^{T}=0.\end{array}  \tag{28}\]  
The optimal control sequence is then given by  
\[\widetilde{u}=-H^{-1}F^{T}\overline{x}^{T}. \tag{29}\]  
Equation (29) will be used in the MPC in leader's and follower's horizontal formation strategies to compute the optimal tracking control sequence.
## 3 Aloha Protocols
### Framed Slotted Aloha and Dynamic Framed Slotted Aloha
A similar protocol solution provides different updating steps for no-reply and collided reply by calculating the probabilities of idle slots and collided slots, without considering the parameters in Gen-2 [28; 35]. Table 1 summarizes the observations regarding the Aloha protocols, where the efficiency measures the exploitation of the tags' responses and the influence of a collision on a tag response. It is calculated using the expression \(\frac{n}{sl_{t}}\times 100\), where \(n\) is the number of tags, and \(sl_{t}\) denotes the total number of consumed slots. Figure 11: Flow chart of the Q protocol. \begin{table}
\begin{tabular}{c l l l l l l} \hline
**Protocol Name** & **PA** & **SA** & **FSA** & **DFSA** & **Q** \\ \hline \multirow{4}{*}{Protocol feature} & Tags transmit after & Tags transmit their ID in & \multirow{4}{*}{One per frame.} & Tags transmit once per frame. & \multirow{4}{*}{Tags transmit once per frame.} & The reader dynamically adjust critical & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } \\  & random time to the reader. & & synchronous time slots. & & & \\  & In the case of a collision, tags & & & & \\  & tags will retransmit after a & retransmit after a random & & & \\  & random delay. & & delay. & & \\ \hline \multirow{4}{*}{Disadvantages} & In a dense tag & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & In a dense tag & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\  & environment the the & & & & \\  & number of collision & & & & \\  & number of collision & & & & \\  & increases significantly. & & & & \\  & increases significantly. & & & & \\  & increases significantly. & & & & \\  & synchronization with tags. & & & & \\ \hline RTF / TTF & TTF & RTF & RTF & RTF \\ \hline Efficiency & 18.4\% & 36.8\% & 36.8\% & 42.6\% & 36.8\% \\ \hline System cost & Very low & Low & Expensive & Expensive & Expensive \\ \hline Complexity & Very simple & Simple & Medium & High & Medium \\ \hline \end{tabular}
\end{table} 
Table 1: A comparison of Aloha protocols.
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 4 Results and Discussion
### Moderate Relative Velocity Scenario  
To test our concept more deeply, we increased the delta velocity between the vehicles to 50 kph. In Figure 7, the simple CACC was evaluated in multiple plots. From the relative distances and HV velocity plots, it can be seen that almost all of the modulations were able to adapt to the remote vehicle velocity and keep a safe distance between them. In the case of 64 QAM modulations, the PDR dropped to very low values, and the 3/4 coding rate was unable to react in time. At approximately the 18 th second of the simulation, a collision occurred between the two vehicles.  
Figure 6: Results from SRI-based CACC for low relative velocity (8.33 m/s = 30 km/h).  
The QPSK modulations exhibited resilience to the noisy environment, likely due to the higher velocities.  
An analysis of the proposed control in Figure 8 reveals that a similar outcome occurs with the QPSK modulation. The network quality remains almost perfect throughout the simulation, even in a noisy environment. However, when we examine the 64 QAM modulation, we observe a decline in network performance when an additional interference occurs. As a result of the deteriorated network, SRI begins to rise, prompting the HV to decelerate in order to reduce the probability of a collision. From a relative distance, it can be inferred that our control is excessively cautious in this instance, yet there were no incidents in any of the aforementioned scenarios.  
Figure 7: Results from simple CACC for moderate relative velocity (13.89 m/s = 50 km/h).
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 3 Methodology
### Multi-Agent Modeling Approach
#### 3.1.3 Jammer Agent  
A jammer is a transmitter that disrupts electronic signals, such as radio and radar signals. In this paper, we focus on the impact of movable jammers on the reliability of WSNs. The jammer considered in this scenario is an active jammer that transmits jamming signals to disrupt communication between surrounding sensor nodes and base stations.  
One of the defining properties of jammer agents is their mobility. Therefore, we designed two mobility modes for jammer agents: fixed and random mobility. The interference signal sent by the jammer weakens with distance, so closer sensor nodes will have their communication transmission capabilities completely destroyed, making it impossible to transmit data to other nodes and base stations. However, nodes that are farther away from the jammer can still maintain their connection to other nodes and base stations. Hence, the range of influence is another critical attribute of jammers. We define two ranges of  
Figure 2: Depth broadcasting process.  
influence for jammers, one being slightly smaller, affecting only nodes within its coverage, and the other having a broader coverage.  
Additionally, the deployment location of the jammer, whether on a UAS (unattended aerial system) or a ground vehicle, also affects the coverage of its jamming signal. Therefore, we designed two groups of influence ranges for jammers corresponding to UAS and ground-based. However, the coverage radius of a jamming device with the same power is significantly different when mounted on a UAS and a ground vehicle. For instance [43], the coverage radius when mounted on a UAS is 75 km, while on a ground vehicle, it is only 7.1 km.  
Finally, the number of jammers has a significant impact. A single jammer can destroy the information relay function of a sensor node. However, our routing protocol allows the message transmission line to bypass the jammed area and re-establish contact with the base station. When multiple jammers work in tandem, they can cause the message transmission line to fail to bypass the jammed area, making it impossible to upload sensing data to the base station.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Modeling the Handover Latency  
#### 3.2.1 Handover Latency of MIPv6  
IETF developed MIPv6 at the network layer for handling mobility management. It keeps an MN accessible while it is on the move within the IPv6 network. A home address (HoA) identifies its MN regardless of its present point of connection to the network. The MN is linked with a care-of-address  
Figure 3: Network topology for performance evaluation. AR: access router, LMA: local mobility agent, MAG: mobile access gateway, MAP: mobility anchor point.  
(CoA), as well as when it is away from its home domain. The MN uses mobility signaling messages to register the CoA with the HA. The packets received by the HoA for the MN are then routed to the MN's CoA. The handover latency of MIPv6 in this case can be given as [26]:  
\[L_{HO}=T_{L2}+T_{MD}+T_{DAD}+T_{R} \tag{1}\]  
where \(L_{HO}\) represents the handover latency, \(T_{L2}\) is the link layer handover latency, \(T_{DAD}\) is the duplicate address detection latency, and \(T_{R}\) is for registration latency.
## 5 Conclusions
\begin{tabular}{c l} \hline \hline
**\(a\), \(b\), \(c\)** & coefficients in corrections \\ \(A\) & surface area (m\({}^{2}\)) \\ \(B\) & baffle spacing (m) \\ \(C_{\rm I}\), \(C_{\rm 2}\) & coefficients in \(k\)-\(\varepsilon\) turbulence model \\ \(Eu\) & Euler number \\ \(f\) & friction factor \\ \(F_{\rm T}\) & correction factor \\ \(j\) & j-factor \\ \(k\) & turbulent fluctuation kinetic energy (m\({}^{2}\cdot\)s\({}^{-2}\)) \\ \(K\) & consistency index (Pa\(\cdot\)s\({}^{\rm n}\)) \\ \(l\) & length of tube (m) \\ \(M\) & mass flow rate (kg\(\cdot\)s\({}^{-1}\)) \\ \(n\) & power law index \\ \(Nu\) & Nusselt number \\ \(P\) & pressure (Pa) \\ \(Pr\) & Prandtl number \\ \(r\) & effect variable \\ \(R\) & thermal resistance (m\({}^{2}\cdot\)K\({}^{-1}\cdot\)W\({}^{-1}\)) \\ \(Re\) & Reynolds number \\ \(T\) & temperature (K) \\ \(TPF\) & thermal performance factor \\ \(u\), \(v\), \(w\) & velocity (m\(\cdot\)s\({}^{-1}\)) \\ \(U_{\rm o}\) & overall heat transfer coefficient based on \(A_{\rm o}\) \\  & (W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\)) \\ \(V\) & volume flow rate (m\({}^{3}\cdot\)s\({}^{-1}\)) \\ \(x_{\rm n}\) & effect variable \\ \(x\), \(y\), \(z\) & coordinate (mm) \\ \(y^{*}\) & dimensionless distance from the wall \\ \hline
**Greek Symbol** \\ \hline \(\rho\) & density (kg\(\cdot\)m\({}^{-3}\)) \\ \(\eta\) & dynamic viscosity (Pa\(\cdot\)s) \\ \(v\) & kinematic viscosity (m\({}^{2}\cdot\)s\({}^{-1}\)) \\ \(\lambda\) & thermal conductivity (W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\)) \\ \(\tau\) & turbulent kinematic viscosity (m\({}^{2}\cdot\)s\({}^{-1}\)) \\ \(\varepsilon\) & turbulent kinetic energy dissipation rate (m\({}^{2}\cdot\)s\({}^{-3}\)) \\ \(\delta\) & wall thickness (mm) \\ \(\sigma_{\rm k}\) & Prandtl number for \(k\) \\ \(\sigma_{\varepsilon}\) & Prandtl number for \(\varepsilon\) \\ \(\gamma\) & shear rate (s\({}^{-1}\)) \\ \(\zeta\) & heat balance deviation \\ \(\varphi\) & weight fraction of nanoparticle \\ \(\Delta\) & difference \\ \hline
**Subscripts** \\ \hline bf & base fluid \\ \(\rm f\) & non-Newtonian fluid \\ \(\rm i\) & inside \\ \(\rm in\) & inlet \\ \(\rm max\) & maximum \\ \(\rm nf\) & nanofluid \\ \(\rm o\) & outside \\ wall & tube wall \\ \hline \hline \end{tabular}
## 4 Results and Discussion
### Effect of Feed Rate of Sand on the Flow Pattern
#### 4.3.1 Pressure Distribution  
The cross-sectional distribution of pressure in the flow passage of the lower part of the primary impeller is shown in Figure 11. At \(n=850\) rpm, the pressure distribution varies insignificantly with an increasing feed rate of sand. In the volume chamber, the distribution of pressure in the circumferential direction is not uniform, as is common for the three cases. Meanwhile, the pattern of pressure distribution in the impeller passage is similar to that associated with the pump transporting pure water. A comparison between Figures 6a and 11b demonstrates that the decrease in the rotational speed causes an increase in the overall pressure. Especially in the impeller eye, those elements of high pressure are observed in Figure 11b.  
The cross-sectional distributions of pressure in the plane overlapping the mid-span cross-section of the upper part of the primary impeller are shown in Figure 12. It is observed that pressure increases progressively when the mixture travels toward the solute chamber. With an increasing feed rate of sand, the pressure in the secondary impeller decreases on the whole. Since the dimensions of the flow passage accommodating incoming particles remain invariant, increasing the number of solid particles per unit time results in an increase in particle velocity. Hence, the pressure decreases. Meanwhile, in the volute chamber, the overall pressure decreases with an increasing feed rate of sand as well. In this case, the pressure at the outlet of the upper part of the primary impeller decreases, while the pressure at the outlet of the lower part of the primary impeller remains certain. Consequently, in the volute chamber, the pressure gradients stimulate the mixing of water and sand.  
Figure 10: Pump head and efficiency obtained at different feed rates of sand and rotational speeds of the impeller.  
Figure 11: Cross-sectional distribution of pressure in the flow passage of the lower part of the primary impeller at different feed rates of sand.  
Figure 12: Cross-sectional distribution of pressure in the plane overlapping the mid-span plane of the upper part of the primary impeller at different feed rates of sand (\(n\) = 850 rpm).