esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Test Procedure and Evaluation Methods
### Mathematical Evaluation  
The goal, as mentioned previously, is to find out and establish a relation between the kinetic energy of the rotor fragments and the energy absorption ability of the containment. In this context, it is essential to determine the shares of rotational and translational energy of each fragment as shown in Figure 15, since the translational share is predominantly responsible for safety containment deformation (in radial direction). The fewer parts, the higher the rotational share is.  
Surface enlargement through plastic deformation can be determined in 2 ways:  
1. It can be approximated analytically by measuring the circumference of the housing at several heights and summing up the surface area of several conical shells.
2. It can be determined numerically by comparing the 3D-scanned (as shown in Figure 14).
3. surface with the initial, undeformed cylinder. The surface area ca be determined using automated functions of commercial CAD software such as Dassault Systems CATIA, Siemens NX, or Autodesk Inventor.  
The strain \(\varepsilon\) of the containment is identified by comparison of the initial cylindrical surface area with the deformed surface area after burst. With a stress-strain diagram of the containment material at hand, the plastic deformation work can then be calculated as the area under the \"\(\sigma\varepsilon\)-curve\" as shown in Figure 16 on the right.  
However, it must be mentioned that these stress-strain diagrams are usually determined using a quasi-static tensile test and dynamic effects of the ballistic impact, which usually only last a few milliseconds, are neglected (A test flywheel with a diameter of 140 mm has a rim speed of approx. 220 m/s at 30,000 rpm. Rotor fragments traveling at this speed are decelerated to zero within only a OK few millimeters or centimeters at the most.). The strain-rate-dependent inelastic behavior of materials is called viscoplasticity [45]. Neglecting effects such as strain hardening [46] may result in  
Figure 14: 3D-scanning of burst containments (a) including results before (b) and after (c) burst.  
Figure 15: Step 3a—Determination of rotor fragment geometry and shares of energy.  
inaccuracies in the overall energy balance of the burst housing. This error can be approximated using numerical simulations based on a Johnson-Cook material model [47], but actual measured material data at these high deformation speeds is not available for any desired material. Since the scope of this paper is to address spin pit design and operation and not FEM simulations, more detailed information on data analysis and actual burst containment design will be published separately or can be acquired by contacting the authors. However, the basic principle of the evaluation of plastic deformation work is shown in Figure 16.
## 2 Noise Reduction Mechanism and Frequency Estimation Method Based on the Coupled Lorenz System
### Noise Reduction Model Based on the Coupled Lorenz System  
Referring to the principle block diagram in Figure 1, we used the drive-response method to build the noise reduction system based on the Lorenz system [21; 22]. The state equation of chaotic system I is  
\[\left\{\begin{aligned} \frac{dx_{d}}{dt}&=\sigma(y_{d}-x_{d}) \\ \frac{dy_{d}}{dt}&=x_{d}(\rho-z_{d})-y_{d}+r(t).\\ \frac{dz_{d}}{dt}&=x_{d}y_{d}-\beta z_{d}\end{aligned}\right. \tag{3}\]  
The state equation of the chaotic system II is  
\[\left\{\begin{aligned} \frac{dx_{r}}{dt}&=\sigma(y_{d}-x_{r}) \\ \frac{dy_{r}}{dt}&=x_{d}(\rho-z_{d})-y_{r}.\\ \frac{dz_{r}}{dt}&=x_{d}y_{d}-\beta z_{r}\end{aligned}\right. \tag{4}\]  
It can be proved that the chaotic system I and II can be synchronized when \(\sigma>0\), \(\beta>0\) and the input signal \(r(t)=0\).  
To prove the above conclusion, we constructed the Lyapunov function, which is  
\[V\left(e_{x},e_{y},e_{z}\right)=\frac{1}{2}\left(e_{x}^{2}+e_{y}^{2}+e_{z}^{2} \right). \tag{5}\]  
In this function, the terms \(e_{x}=x_{d}-x_{r},e_{y}=y_{d}-y_{r}\), and \(e_{z}=z_{d}-z_{r}\). For its derivation, there is  
\[\frac{dV\left(e_{x},e_{y},e_{z}\right)}{dt}=e_{x}e_{x}^{\prime}+e_{y}e_{y}^{ \prime}+e_{z}e_{z}^{\prime}. \tag{6}\]  
Then, combined with Equations (3) and (4) and the condition \(r(t)=0\), the synchronization error system is obtained. The equation is  
\[\left\{\begin{aligned} \frac{de_{x}}{dt}&=-\sigma e_{x} \\ \frac{de_{y}}{dt}&=-e_{y}.\\ \frac{de_{z}}{dt}&=-\beta e_{z}\end{aligned}\right. \tag{7}\]  
Furthermore, this equation is insert into Equation (6), such that there is  
\[\frac{dV\left(e_{x},e_{y},e_{z}\right)}{dt}=-\sigma e_{x}^{2}-e_{y}^{2}-\beta e _{z}^{2}. \tag{8}\]When \(\sigma>0\) and \(\beta>0\), there is \(\frac{dV\left(e_{x},e_{y},e_{z}\right)}{dt}<0\). Thus, referring to the Lyapunov stability theorem [23], the synchronization error system of the above two chaotic systems is uniformly asymptotically stable at the origin. It means that the chaotic system I and II can be synchronized when \(\sigma>0\), \(\beta>0\) and the input signal \(r(t)=0\).
## 3 Analysis of Results
### Full Load Bending Working Condition
(**a**) Axle displacement curve, (**b**) crossbeam displacement curve, and (**c**) longitudinal beam displacement curve. and the axle, and the maximum stress is 71.76 MPa. According to the yield strength of the frame material, the safety factor of each part of the frame is 10.94. It can be seen that the strength of the frame meets the design requirements, and there is a certain design margin. As can be seen from Figure 8a, the stresses in the front and rear axles change from the ends of the axles to the middle with a trend of increasing first and then decreasing, and they increase rapidly when approaching the outer axle bracket. The maximum stresses at the midline of the upper surface of the front and rear axles are at \(l\) = 297 mm and \(l\) = 971 mm, with maximum stresses of 51.50 MPa and 52.80 MPa, respectively. During the stress increase from both ends of the axle to the outer axle bracket, a small amplitude of stress fluctuation occurs at \(l\) = 225 mm and \(l\) = 1043 mm. At the same time, under the action of the inner and outer axle brackets, there are two stress fluctuations in the process of stress drop from the outer axle bracket to the middle of the axle. Figure 8: Stress curves of main components under bending condition. (**a**) Axle stress curve, (**b**) crossbeam stress curve, and (**c**) longitudinal beam stress curve. Figure 7: Stress nephogram under bending condition. As can be seen from Figure 8b, the maximum stress position of the front and rear crossbeams occurs at the connection location between the longitudinal beams and the crossbeams. The maximum stress of the front crossbeam is 3.90 MPa, and the maximum stress of the rear crossbeam is 4.11 MPa. The maximum stress in the rear crossbeam is slightly larger than that of the front crossbeam, while the stress in the rear crossbeam increases suddenly at \(l\) = 599 mm and \(l\) = 1080 mm, mainly because the rear crossbeam is close to the installation position of the self-priming pump, and the concentrated load received is also relatively large, resulting in stress fluctuations at the inner contact between the rear crossbeam and the left and right longitudinal beam connections. Figure 8c represents the stress trend of the midline on the upper surface of the left and right longitudinal beams. From the front end of the longitudinal beam to the rear end of the longitudinal beam, the stress changes show an overall trend of first increasing and then decreasing. In the three intervals of \(l\) = 633 mm to \(l\) = 799 mm, \(l\) = 1357 mm to \(l\) = 1536 mm, and \(l\) = 2237 mm to \(l\) = 2650 mm, the stress of the longitudinal beam appears to decrease significantly and then rebound. This is mainly because the diesel engine and self-priming pump are fixed to the longitudinal beam via the connector, which reduces the stress at the contact area between the longitudinal beam and the bottom of the connector. The stress value at the contact area between the front and rear ends of the connector and the longitudinal beam is larger, which is consistent with the actual situation under bending conditions.
# Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
## 3 Evaluation and Discussion  
### Evaluation Condition  
We performed two evaluations to confirm the performance of the proposed method. Firstly, we evaluated whether the state, action, and reward are appropriate as definitions for the RL server to achieve the goal, which is to eliminate the temporal locality, in Section 3.2. We then evaluated the cellular infrastructure utilization efficiency of the proposed method in comparison with the no-control and the time-wise offloading of MDOP in Section 3.3. In this evaluation, it is necessary to determine the network structure and hyper-parameter of the proposed method. Figure 3 shows the network architecture of the proposed method. This network architecture consists of an input layer, four-hidden layers, and an output layer. We set the input to states in Table 1 and the output to QoS level. Furthermore, the network is divided such that we can output the state value function and state-dependent action advantage function separately because we apply the dueling-network architecture. The output of the state value function is 1 because the state value function represents the worth of a particular state \(s\). Also, the output of the state-dependent action advantage function is 4 because this function represents a relative measure of the importance of 4 QoS levels. In addition, we use batch normalization [23] for obtaining an accelerated learning. The state value function network has unit numbers of 12-400-200-100-1 in order from the input layer. Similarly, the state-dependent action advantage function network has unit numbers of 12-400-200-100-4. This network outputs action after layers the state value function and state-dependent action advantage function are concatenated to represent \(Q(s,a)\) following Equation (3).  
The output is 4 because we defined there are 4 QoS levels for MDOP UE. The activation function is a hyperbolic tangent (tanh). The discount factor \(\gamma\) and learning rate \(\alpha\), which are the parameters of the Q-learning, are 0.96 and 0.01, respectively. In addition, we set the normalization variable \(\beta\) as \(10^{3}\). The Q-learning selects actions such that \(Q(s,a)\) becomes the maximum value, which can imply overfitting. In order to avoid overfitting, we use a linear decay \(\epsilon\)-greedy policy that selects a random action with probability \(\epsilon\) and follows an action of the maximum value with probability \(1-\epsilon\). The linear decay \(\epsilon\)-greedy policy decreases \(\epsilon\) linearly. We decreased \(\epsilon\) linearly from 1 to 0.01.  
In this evaluation, we used CPU i7-6700k for learning. Besides, we used Scenario, which is a network simulator, to accurately reproduce the long-term evolution (LTE) environment [24]. Furthermore, we determined the cellular network parameters to make scenarios according to 3 rd generation partnership project (3 GPP) and next generation mobile networks (NGMN) [25; 26]. Table 2 shows the network environment model. We later present the details of the scenario and topology used for each evaluation.
## 4 The WADGPS Master Station Processes
### Dual-Frequency Carrier Smoothing of Pseudorange and Ionospheric Delays  
To mitigate the measurements noise and multipath effects, a dual-frequency carrier smoothing filter is used after raw GPS observations collecting from each RS [11]. Because the measurement noise of the carrier phase observations are much smaller than that of the pseudorange measurements, the pseudorange and carrier phase are combined to reduce the measurement noise [1]. The smoothing filter is implemented using three ionospheric measurements from the dual-frequency observables, and the filter design is detailed in [11]. The ionospheric delay measurements could be derived by the linear combination of GPS L1 and L2 pseudorange and carrier phase observables [11]:  
\[I_{L1,PR}\equiv\frac{PR_{L2}-PR_{L1}}{\gamma-1}=I_{L1}+\
u_{PR} \tag{11}\]  
\[I_{L1,\theta}\equiv\frac{\phi_{L1}-\phi_{L2}}{\gamma-1}=I_{L1}+Amb+\
u_{\phi} \tag{12}\]  
\[I_{L1,L1}\equiv\frac{PR_{L1}-\phi_{L1}}{2}=I_{L1}-\frac{N_{L1}\lambda_{L1}}{2 }+\
u_{L1} \tag{13}\]  
where:  
\(I_{L1}\) is ionospheric delay at the L1 frequency, the extra subscripts present the observations used in the combination,  
\(Amb\) is the combination of ambiguities from the L1 and L2 carrier phases, and  
the magnitude of noises are \(\
u_{PR}>\
u_{L1}>\
u_{\phi}\)[1].  
The dual-frequency carrier smoothing filter is depicted in Figure 4. The filter estimated the smoothed ionospheric delay, \(\hat{I}_{smth}\), and smoothed ionosphere-free pseudorange, \(PR_{L1}\). The first step in the filter is to generate \(\hat{I}_{smth}\) and its confidence by smoothing the \(I_{L1,PR}\) with the low noise \(I_{L1,\phi}\) Then combining the \(\hat{I}_{smth}\) and Equation (14) to estimate the constant \(N_{L1}\lambda_{L1}\) by moving average. If the cycle slip is not present, the N\({}_{L1}\lambda_{L1}\) is constant. Finally, substituting \(\hat{I}_{smth}\) and N\({}_{L1}\lambda_{L1}\) into the L1 carrier phase, \(\phi_{L1}\) (_i.e._, Equation (8)), the ionosphere-free pseudorange, \(PR_{L1}\), is obtained [11].
# Research on an Evaluation Method for the Adaptability of TBM Tunnelling
## 3 Selection of Evaluation Index and Establishment of Fuzzy Function
### Establishment of the Fuzzy Function
#### 3.2.1 Membership Function of the Tunnelling Parameters
\tag{20}\]  
(3) Membership function of the thrust  
The thrust of the TBM is the main performance index of the equipment, which is closely related to other tunnelling parameters. Based on the known parameters, calculating the thrust plays a key role in TBM tunnelling. [PERSON] believes that, when tunnelling in hard rock, the required thrust force comes from the propulsion force of the cutter head and the frictional resistance of the shield bottom [15]. Most of the tunnelling interval of this project is hard rock; therefore, it should be paid attention to when determining the membership function [11,12]. The thrust, \(F_{1}\), is determined by Equation (21):  
\[F_{1}=nP \tag{21}\]  
where \(n\) is the number of cutters and \(P\) is the rated bearing capacity of the cutters. The friction, \(F_{U1}\), between the shield and tunnel wall is determined by Equation (22):  
\[F_{U1}=\mu W \tag{22}\]  
where \(\mu\) is the friction coefficient and \(W\) is the shield gravity. The thrust of the TBM is between \(F_{1}\) and \(F_{1}+F_{UI1}\), as shown in Equation (23):  
\[\left. \begin{array}{l}F_{low}=F_{UI1}\\ F_{High}=F_{1}+F_{UI1}\end{array} \right\} \tag{23}\]  
Therefore, the membership function of the thrust is constructed as follows in Equation (24):  
\[U_{3}(x)=\left\{ \begin{array}{l}0(x<F_{low})\\ \frac{x-F_{low}}{F_{High}-F_{low}}(F_{low}\leq x<F_{High})\\ 1(x\geq F_{High})\end{array} \right. \tag{24}\]
## 2 Problem Description and Formulation
### Mathematical Model  
To solve the entropy generation problem due to natural convection under the conjectures stated above, the governing equations, in the transient state, are the following:  
\[\
abla\overline{U}=0 \tag{1}\]  
\[\frac{\mathrm{D}\overline{U}}{\mathrm{D}t}=-\
abla\bigg{(}\frac{p}{\rho} \bigg{)}+\
u\
abla^{2}\overline{U}-\beta\big{(}T-T_{0}\big{)}\overset{\rightarrow} {g} \tag{2}\]  
\[\frac{\mathrm{D}T}{\mathrm{D}t}=\alpha\
abla^{2}T+S_{h} \tag{3}\]  
where \(\
abla\) is the nabla operator, \(D/DT\) is the material derivative, \(\overline{U}\big{(}U,V\big{)}\) is the velocity vector, \(p\) is the pressure, \(T\) is the temperature, \(T_{0}\) is the reference temperature (= \(\big{(}T_{h}+T_{c}\big{)}/2\big{)}\), \(\rho\) is the fluid density, \(\
u\) is the kinematic viscosity, \(\beta\) is the thermal expansion coefficient, \(\alpha\) is the thermal diffusivity, \(\overset{\rightarrow}{g}\) is the gravity downward and \(S_{h}\) (= \(\frac{L_{f}}{c_{p}}\frac{\partial\varepsilon}{\partial t}\)) is the latent heat term (source or sink), \(\varepsilon\) being the liquid fraction. Note that this quantity is negative for melting, positive for solidification and zero in the absence of a phase change.  
To cast the above equations in a dimensional form, we employ the following dimensionless variables:  
\[\tilde{t}=\frac{\alpha t}{E},\ \ \ \big{(}X,Y\big{)}=\bigg{(}\frac{x}{L},\frac{y }{L}\bigg{)},\ \ \ \big{(}\tilde{U},\tilde{V}\big{)}=\bigg{(}\frac{uL}{\alpha},\frac{\
u L}{ \alpha}\bigg{)},\ \ \ \tilde{p}=\bigg{(}\frac{L}{\alpha}\bigg{)}^{2}\frac{p}{\rho},\ \ \ \tilde{T}=\frac{T-T_{0}}{T_{h}-T_{c}} \tag{4}\]  
Equations (1)-(3), which govern the convective melting problem, then take the following dimensionless form:  
\[\
abla\overline{\tilde{U}}=0 \tag{5}\]  
\[\frac{\mathrm{D}\overline{\tilde{U}}}{\mathrm{D}\tilde{t}}=-\
abla\big{(} \tilde{p}\big{)}+\Pr\
abla^{2}\overline{\tilde{U}}-\Pr\mathrm{R}\beta\tilde{T }\overline{\tilde{Y}} \tag{6}\]  
\[\frac{\mathrm{D}\tilde{T}}{\mathrm{D}\tilde{t}}=\
abla^{2}\tilde{T}-Ste^{-1} \partial\varepsilon  
These equations are subjected to the following boundary (BCs) and initial conditions (ICs): The non-slip boundary condition for the hydrodynamic problem is applied at all cavity walls. For the thermal problem, the hot wall was kept with \(\tilde{T}=0.5\) and the cold wall with \(\tilde{T}=-0.5\). As for horizontal sides, the Neumann boundary condition (zero heat flux) is applied, _i.e._, \(\partial\tilde{T}\,/\,\partial Y=0.0\). In the cavity, the initial conditions (ICs) are as follows: zero for the velocity field and \(\tilde{T}=-0.5\) for the temperature field.
## 4 Results
### Simulations
These simulation results showed that the development of the CBL height was the best described in the simulation with the MYNN scheme. Figure 7: Hovmöller diagrams of potential temperature in simulations with (**a**) YSU, (**c**) MYJ, (**e**) MYNN, and (**g**) ACM2 PBL parameterization schemes; the water vapor mixing ratio in the simulations with (**b**) YSU, (**d**) MYJ, (**f**) MYNN, and (**h**) ACM2 PBL parameterization schemes. At 1900 LST on 17 January, the moisture advection was in the initial stage (Figures 4d and 8c). Except for the simulation with the MYJ scheme, the simulated moisture advections appeared earlier than in the observations, which increased the water vapor mixing ratio at \(\sim\)0.5 to \(\sim\)2.0 km. Below 2.0 km, the simulation with the MYNN scheme showed a larger water vapor mixing ratio in general compared with the other simulations. At 0000 LST on 18 January, the moisture advection increased the water vapor mixing ratio below 2.0 km in general, and those in the four simulations also generally increased. The water vapor mixing ratios below 1.3 km in the four simulations were similar to each other, but at 1.3-2.0 km, the water vapor mixing ratio in the simulation with the MYNN scheme was still larger than that in the other simulations. The observed average water vapor mixing ratio below 2.0 km (2.8 g kg\({}^{-1}\)) was larger than in the four simulations. The simulation with the MYNN scheme produced the largest average water vapor mixing ratio (2.3 g kg\({}^{-1}\)) among the simulations (2.1, 1.9, and 2.0 g kg\({}^{-1}\) for the simulations with the YSU, MYJ, and ACM2  
Figure 8: Vertical profiles of simulated and observed potential temperature at **(a)** 1000 and **(b)** 1500 LST on 17 January and water vapor mixing ratio at (c) 1900 LST on 17 January and (d) 0000 LST on 18 January. schemes, respectively). These simulation results showed that the moisture advection in the simulation with the MYNN scheme produced a larger water vapor mixing ratio below 2.0 km, which was the closest to that in the radiosonde observations.
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 5 Experimental Verification
### DSLMS Simulation Parameter Selection and Experimental Analysis
This result shows that the algorithm in this paper shows better results in reducing noise interference, improving convergence speed, and reducing steady-state error. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MSE (dB)** & **Traditional LMS** & \
\begin{tabular}{c} **Sigmoid** \\ **Variable-Step-Size LMS** \\ \end{tabular} &
\begin{tabular}{c} **The Algorithms in** \\ **This Paper** \\ \end{tabular} \\ \hline
15 & 400 & 210 & 85 \\
10 & 410 & 235 & 90 \\
20 & 425 & 300 & 105 \\
30 & 450 & 400 & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the number of iterations of the algorithm with different signal-to-noise ratios. Figure 16: Comparison of the filtered outputs of the three algorithms against the same SNR background.
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks
### Overall Delay Analysis  
#### 3.3.1 Local Processing  
For terrestrial IoT device \(j\), it can process part of the task on the local CPU. Assuming that the processing frequency of IoT \(j\) is \(f_{j}^{l}\), the local processing delay can be written as follows:  
\[t_{j}^{l}=\frac{x_{j}D_{j}c_{j}}{f_{j}^{l}}. \tag{5}\]
## 5 Experiments
### Robustness Under Disturbances  
To validate the robustness of PGDD under disturbances, we conducted additional experiments with external and internal disturbances.  
Figure 8: Averaged performance of all algorithms for UDVRP400, which shows a comparison of the average total cost of PGDD (called DD), attention model (AM), deep reinforcement Learning (DRL), deep reinforcement-learning algorithm for path-planning (DRLPP), deep q-learning (DQL) and pointer network (PN) in tow cases. Among them, each color corresponds to a type of method, and different wire styles represent different baselines or different improvement points.  
Figure 9: The graphic shows the example of node distribution and solution, which indicates that our method can clearly exhibit the path-planning outcomes and shows how many nodes each route comprises and the amount of the cost.  
External and internal disturbances include the following:  
* Introduce Gaussian noise (\(\sigma=0.1\)) in the measurement of vehicle position \(x,y\) and velocity \(v\).
* Simulate the disturbance of energy consumption coefficient \(e\) due to rainy/snowy weather by increasing it by 20%.
* Model the linear degradation of battery capacity over time, with a 10% reduction per year.  
Results, as shown in Table 12, demonstrate PGDD's robustness via dynamic point-graph joint embedding and dual-decoder co-optimization.