esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Generation of Solutions for Nominal CPS
In \(STN_{j}(V_{j},A_{j})\), the flows of parts in horizontal arcs denote parts in waiting state and flows of parts in arcs connecting to the end node represent the finished parts. Flows of parts in all the other arcs denote the parts in busy state (being processed by machines/resources). Figure 4 shows the structure of \(STN_{j}(V_{j},A_{j})\) for type \(1\) tasks (\(j=1\)) obtained by applying the above algorithm to \(GJ_{1}\) in Figure 2a. Let \(A_{jrt}\) denote the set of arcs involved in the use of type-\(r\) resource in period \(t\) in \(STN_{j}(V_{j},A_{j})\). The capacity constraints (1), the flow balance Equation (2), supply constraints (3) and demand constraints (4) must hold. Based on the spatial-temporal network \(STN_{j}(V_{j},A_{j})\), the following problem is formulated to check the feasibility to meet product demands, \(Q_{j}\), by the deadline \(\theta\). Feasibility Problem of Spatial-Temporal Network (FPSTN) \(STN_{j}(V_{j},A_{j})\)  
\[\min_{f_{j}}\sum_{a=(u,v)\in A_{jrt}}w_{a}f_{j}(u,v)\]  
\[\sum_{a=(u,w)\in A_{jrt}}f_{j}(u,w)\leq\overline{C}_{rt}\forall r\in\mathbf{R} \ \forall t\in\{1,2,\ldots,\Pi\} \tag{1}\]  
\[\sum_{w\in V}f_{j}(u,w)=0\ \forall u\
otin\{s,e\} \tag{2}\]  
\[\sum_{w\in V}f_{j}(s,w)=Q_{j} \tag{3}\]  
\[\sum_{w\in V}f_{j}(w,e)=Q_{j} \tag{4}\]  
\[f_{j}(u,v)\in Z\ \forall(u,v)\in A_{j},\ \text{where}\ Z\text{ is the set of nonnegative integers} \tag{5}\]  
To check whether it is feasible to meet an order with product demands, \(Q_{j}\), for \(j\in\mathbf{J}\)\(=\{1,2,3,\ldots,J\}\) by the deadline \(\theta\), the following algorithm can be applied by solving the feasibility problem of spatial-temporal network \(STN_{j}(V_{j},A_{j})\) for \(j\in\mathbf{J}=\{1,2,3,\ldots,J\}\) iteratively. By solving the above problem iteratively for \(j\in\mathbf{J}=\{1,2,3,\ldots,J\}\), a solution \(f=\sum\limits_{j\in\mathbf{f}}f_{j}\) will be found, where \(f_{j}\) is the component of \(f\) in spatial-temporal network \(STN_{j}(V,A)\) for type \(j\) task, where \(j\in\mathbf{J}\). For each \(j\in\mathbf{J}=\{1,2,3,\ldots,J\}\), we compute the residual capacity  
Figure 4: An example of \(STN_{j}(V_{j},A_{j})\) for \(GJ_{1}\) (\(j=1\)) in Figure 1a. \(\overline{C}_{rt}=C_{rt}-\sum\limits_{a=(u,w)\in A_{jrt}}f_{j}(u,w)\forall r\in \boldsymbol{R}\ \forall t\in\{1,2,\ldots,\Pi\}\) based on the solution found in \(STN_{j}(V_{j},A_{j})\). Obviously, the following property holds. **Property 1. **_If there exists a solution \(f=\sum\limits_{j\in\mathcal{I}}f_{j}\) such that the flows in all the arcs from node \(N_{j}\Pi+t\) to node \(e\) are all zero for all \(t\in\{\theta+1,\theta+2,\ldots,\Pi\}\) for \(j\in\boldsymbol{J}=\{1,2,3,\ldots,\}\), \(f\) can meet the deadline \(\theta\)._  
Based on Property 1 and the reasoning above, the algorithm below can be applied to generate a solution and check the feasibility to meet the deadline.
## 3 Analysis and Design of Key Components
### High-Temperature and Ultrahigh-Pressure Simulation Test Cabin
#### 3.1.1 Overall Structure of the Simulation Test Cabin  
The high-temperature and ultrahigh-pressure simulation test chamber is used to create the downhole environment and accommodate large rock samples, and it must also meet operating space requirements for the coring tester. The overall structure is shown in Figure 4. The chamber consists of two main parts. The first part is the rock sample chamber, which contains a \(\mathcal{O}\)480 \(\times\) 1500 mm rock sample to be drilled. Based on the application space needed for control of the four pressures and the temperature acting on the rock sample, the inner diameter of the rock sample chamber is set at 500 mm, and the length is 2400 mm. The second part is the drill pipe cabin located in the upper part of the core cabin. Based on the operational size requirements of the core, the drill pipe cabin is divided into two sections. The length of the first drill pipe cabin is 2 m, the length of the second drill pipe  
Figure 3: Schematic diagram of temperature-pressure application.  
cabin is 3 m, and both are 150 mm in diameter. During simulation tests, the coring device is put into the drill pipe cabin to simulate the coring of rock samples in the rock cabin. In addition, an intermediate transition cabin and the bottom oil cylinder are included.
# Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
## 1 Introduction
### Proposed Method  
Firstly, the green channel extraction was performed on the retinal image in order to effectively detect OD location in the proposed method. The reason of using the green channel for extraction is that the channel contains the best contrast value between the background and the anatomical structures and gives more information about vessels than other channels [24].  
Then, contrast-limited adaptive histogram equalization (CLAHE) was used to increase the clarity of the images. Based on the rule that the OD region is the brightest region of the retinal image, the average brightness level of each image was calculated and the pixels below the average value were eliminated from the image. As is known, there are differences between the brightness of the images due to the settings of the fundus cameras. Due to the different brightness level of each image, the threshold value was adjusted dynamically for each image rather than defining a constant threshold value.  
Next, a modified robust rank order-based edge-detection method was applied [25], which has never been implemented in the OD localization problem and shows better performance than conventional edge-detection methods in noisy images. After obtaining the edges of the image, the circular Hough transform was performed due to the fact that the OD has a circular structure.  
Figure 2 illustrates the steps of the proposed method.  
The main issues addressed in the proposed procedure are:  
(a) The average brightness level of each image was calculated, and the pixels below the average value were eliminated from the image. Due to the different brightness levels of each image, the threshold value was adjusted dynamically.  
(b) Dust accumulates in the lens of the fundus camera due to lack of maintenance, and this causes noise in the retinal images and reduces the performance of OD localization methods. Therefore, a statistical edge-detection framework was applied to retinal images, and successful results were obtained in this work. The edge detection performance of the applied method has been proven to be higher in noisy images than other conventional edge-detection methods [25; 26].  
This paper is organized as follows: In Section 2, the methods and algorithms for OD detection are presented in detail. Experimental results are explained in Section 3. Section 3 also gives a discussion about the experimental results. Finally, a conclusion is given in Section 4.
## 3 Circuit Design
### Single-Ended-Differential Amplifier  
The current signal from the photodiode is a unipolar non-return-to-zero (NRZ) single-ended signal. Compared with a single-ended signal, a differential signal offers superiority [26] that makes it the optimal choice for signal processing. The STD stage following the input TIA stage transforms the single-end signal to a differential signal. The STD stage block diagram is shown in Figure 7.  
A common differential amplifier with cascode structure and a variable current source comprises the STD stage. The variable current source controlled by the power mode equips the STD stage with variable gain. The differential inputs of the STD stage are the output of the  
Figure 5: The simulated relationship between the damping factor and \(R_{E}\).  
Figure 6: The simulated frequency response of the input TIA.  
Figure 7: The single-ended–differential amplifier (STD) stage block diagram.  
bias generated by a dummy TIA. Meanwhile, the bias is set equal to the output DC component of the TIA stage. Then, the phase and amplitude discrepancy in differential signals can be minimized with this structure. As shown in Figure 8a,b, the simulated gain and phase discrepancy of the optical receiver are smaller than 0.05 dBc and 0.4\({}^{\circ}\) within the bandwidth of interest, respectively, which provides good matching performance.
# Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts
###### Abstract  
Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies.  
CAV platoon; bidirectional sensing and communication; phase shift; safety; stability Article Exploring Safety-Stability Tradeoffs in Courville  
20242461414[[https://doi.org/10.3390/s24051614](https://doi.org/10.3390/s24051614)]([https://doi.org/10.3390/s24051614](https://doi.org/10.3390/s24051614))
# Experimental Validation of a Gyroscope Wave Energy Converter for Autonomous Underwater Vehicles
## 2 Mathematical Model
### Power Extraction  
In this paper, the torque extracted from the precession shaft damper is used to generate electricity.  
\[P_{d}=\frac{c_{y}}{2}\omega^{2}\epsilon_{0}^{2}=\frac{c_{y}}{2}\omega^{2}\frac{ \left(I_{z}\dot{\varphi}\dot{\phi}\cos\epsilon\right)^{2}}{\left(I_{y}(\omega_{ n}^{2}-\omega^{2})\sin(\omega t)+c_{y}\omega\cos(\omega t)\right)^{2}} \tag{7}\]  
It is obvious from Equation (7) that when the system resonates, the power \(P_{d}\) reaches the maximum. Therefore, under that condition, Equation (7) can be simplified as:  
\[P_{d}=\frac{c_{y}}{2}\omega^{2}\frac{\left(I_{z}\dot{\varphi}\dot{\phi}\cos \epsilon\right)^{2}}{\left(c_{y}\omega\cos(\omega t)\right)^{2}}=\frac{\left(I _{z}\dot{\varphi}\dot{\phi}\cos\epsilon\right)^{2}}{2c_{y}\cos^{2}(\omega t)}= \frac{\left(I_{z}\dot{\varphi}\right)\omega\dot{\delta}\epsilon_{0}\cos \epsilon}{2\cos(\omega t)}=\frac{J_{z}\dot{\varphi}\dot{\phi}\dot{\xi}\cos \epsilon}{2\cos^{2}(\omega t)} \tag{8}\]  
It can be derived from Equation (8) that the maximum output power is proportional to flywheel rotational speed \(\dot{\varphi}\), angular velocity of precession \(\dot{\epsilon}\) and angular velocity of pitch \(\dot{\delta}\) with the resonance.
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
## 4 System Model
### Trust Evaluation Mechanism
#### 4.4.2 Forwarding Rate  
This is used to evaluate the integrity of the data packets to avoid data tampering by the malicious nodes. It is calculated as the ratio of packets received by the ANs to the data packets sent by the SNs [19].  
\[\mathit{FR}=\frac{\mathit{td}}{\mathit{sd}}, \tag{2}\]  
where \(\mathit{td}\) and \(\mathit{sd}\) are the packets received by the ANs and packets sent by the SNs, respectively.
## 4 Analysis of Test Results for Diversion Function Pipe Segment Models
### Analysis of Earth Pressure Distribution on Both Sides of the Pipe Segment under Different Pressures  
#### 4.2.1 Distribution and Variation in Earth Pressure on Both Sides of the Pipe Segment  
The distribution and variation in earth pressure around the pipe segment on both the sides change with the alteration in pressure difference, as depicted in Figure 15. On the left side, at distances of 0.3 m, 0.5 m, and 0.7 m from the bottom, the earth pressure around the pipe segment increases with the rise in pressure difference. The earth pressure increases by 1%, 2.2%, and 1.6%, respectively, as the pressure difference grows from 0.5 kPa to 2.5 kPa, indicating a minimal increase in left side earth pressure. Similarly, on the right side, at distances of 0.3 m, 0.5 m, and 0.7 m from the bottom, the earth pressure around the pipe segment also increases with the increase in pressure difference. The earth pressure increases significantly by 50.7%, 50.3%, and 50.3%, respectively, within the same pressure difference range. Moreover, under the same pressure difference, the earth pressure value at a distance of 0.3 m from the bottom on the right side is consistently higher than those at the other five positions.  
Figure 14: Change in strain difference in symmetric positions.  
This is because when the pressure difference between the two sides is adjusted, there is only a slight fluctuation in the water level on the left side, and there is minimal change in earth pressure. On the right side, the decrease in the water level is used to increase the pressure difference on both the sides. The effective stress on the right side increases, manifesting as an ongoing increase in earth pressure.
## 3 Experiments  
### Experimental Setup  
We took spectroscopic scatterometry as an example to demonstrate the capability of the proposed method in identifying and eliminating the measurement configurations with redundant information. The experiments were carried out by a commercial Mueller matrix ellipsometer (ME-L ellipsometer, Wuhan Eoptics Technology Co., China). As schematically shown in Figure 1, the system layout of the dual rotating-compensator ellipsometer in order of light propagation is \(\text{PC}_{\text{r1}}(\omega_{1})\text{SC}_{\text{r2}}(\omega_{2})\text{A}\), where P and A stand for the fixed polarizer and analyzer, respectively, \(\text{C}_{\text{r1}}\) and \(\text{C}_{\text{r2}}\) refer to the 1 st and 2 nd frequency-coupled rotating compensators, respectively, and S stands for the sample [31; 32]. The 1 st and 2 nd compensators rotate synchronously at \(\omega_{1}=5\omega\) and \(\omega_{2}=3\omega\), respectively, where \(\omega\) is the fundamental mechanical frequency. With the light source used in this ellipsometer, the spectral range is from 200 to 1000 nm, covering the spectral range of 300-900 nm used in this work. The beam diameter can be changed from the nominal values of \(\sim\)3 mm to a value less than 200 \(\upmu\)m with the focusing lens. The two arms of the ellipsometer and the sample stage can be rotated to change the incidence and azimuthal angles in experiments. Except for the reflectance and ellipsometric angles of the sample under measurement, the 16 Mueller matrix elements also can be obtained with the dual-rotating compensator setting. An in-house developed MATLAB(r) (version R2017a, The MathWorks, Inc., Natick, MA, USA) program for analyzing the measured signatures ran on a workstation equipped with double 2.0 GHz Intel Xeon CPUs. The forward optical model in this program was developed based on rigorous coupled-wave analysis (RCWA) [33; 34; 35], and the inverse optical scattering problem was solved through the commonly used Levenberg-Marquardt (LM) algorithm [36].
# Dynamic Modeling and Characteristic Analysis of Articulated Steering Vehicles
## 2 Dynamic Model of ASVs
### Model of the Power Train  
The target ASV, an LHD vehicle, is driven with an electric motor, and the relationship between the main components in the power train is shown in Figure 4. The arrangements of the front and rear driving systems are the same, and two driven motors are used in the power system. The characteristics of the motor are shown in Figure 5, and the maximum speed and torque are 3000 r/min and 2500 Nm, respectively, with a maximum power of 200 kW. According to the curve in Figure 5, during 0-500 r/min, the maximum torque remains constant, and the power gradually increases during this period.  
An LHD vehicle always runs in a narrow area, and its turning radium is very short. During steering maneuvers, the speed difference between the inner and outer wheels is very large; therefore, the differential mechanism is necessary for the drive axle of this ASV. According to this principle, the model of the differential mechanism can be expressed as  
\[\left\{\begin{aligned} \dot{\omega}_{in}J_{in}+\omega_{in}c_{in}& =\eta_{f}T_{in}-T_{in,r}\\ \dot{\omega}_{I}I_{l}+\omega_{l}c_{l}&=T_{l}-T_{l,r }\\ \dot{\omega}_{r}I_{r}+\omega_{r}c_{r}&=T_{r}-T_{r,r }\end{aligned}\right. \tag{19}\]  
where \(J_{in}\), \(J_{l}\), and \(J_{r}\) are the rotational inertia of the input axle, left output axle, and right output axle, respectively; \(T_{in}\), \(T_{l}\), and \(T_{r}\) and \(\omega_{in}\), \(\omega_{l}\), and \(\omega_{r}\) are the corresponding torques and angular velocities. \(c\) denotes viscous damping. \(T_{in,r}\), \(T_{l,r}\), and \(T_{r,r}\) are the resistance torques of the different axles.  
The out torque of the differential mechanism is transmitted to the wheel with a half axis, and the model can be given by  
\[\left\{\begin{aligned} & T_{l}=(\eta T_{l,r}-T_{c})/2\\ & T_{r}=(\eta T_{r,r}+T_{c})/2\\ &\omega_{in}=\eta(\omega_{l}+\omega_{r})/2\end{aligned}\right. \tag{20}\]  
Figure 4: Main components of ASV power train.  
Figure 5: External characteristics of driven motor.  
where \(T_{c}\) is the clutch torque, and \(\eta\) is the gear ratio, which is 55 in this LHD power train.  
The final drive torque in the wheel overcomes the rolling resistance force and braking force to produce the longitudinal driving force, and the detailed model can be expressed as  
\[I_{w}\dot{\omega}_{t}=T_{ij}-f_{r}F_{tzij}r_{dij}-F_{txij}f_{dij} \tag{21}\]  
where \(I_{w}\) is the rotational inertia of the wheel, and \(T_{ij}\) (\(i=f,r;j=l,r\)) is the corresponding driving torque. \(f_{r}\) is the rolling resistance force, which can be expressed as  
\[\left\{\begin{array}{ll}f_{r}=0.00792+0.00025v_{x}&\quad v_{x}\
eq 0\\ f_{r}=0&\quad v_{x}=0\end{array}\right. \tag{22}\]
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 4 Results
### Assessment of the AoI before the Excavations  
In Figure 10, the MT-InSAR LOS cumulative displacements in mm from 7 March 2018 to 2 July 2020, prior the beginning of the first excavation, along the whole tunnel alignments are presented.  
Figure 10: Cumulative displacements in mm along the LOS occurring from 7 March 2018 to 2 July 2020, prior the beginning of the first tunnel excavation (image modified after Maxar for Microsoft). Sections A to I are the ones that will be investigated.  
It can be noted that the area does not show significant deformations in this timeframe. Indeed, the maximum and minimum cumulative displacements detected along the LOS are roughly \(5\) mm and \(-8\) mm, which lead to a velocity of \(2\) mm/year and \(-3\) mm/year, respectively. The area was not affected by any phenomenon of subsidence or uplift, and the forthcoming results of Figure 11a,b can be seen as results of the tunnels' excavation processes.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HS & heat switch \\ GGHS & gas-gap heat switch \\ LHP & loop heat pipe \\ Evap & evaporator \\ CC & compensation chamber \\ LPM & lumped parameter model \\ ODE & ordinary differential equations \\ \(Q_{evap}\) & input heat power on the evaporator, W \\ \(Q_{CC}\) & input heat/cool power on the evaporator, W \\ \(T\) & temperature of node(s), \({}^{\circ}\)C \\ \(M\) & mass of node(s), kg \\ \(c_{p}\) & specific heat at constant pressure, J/(kg\({}^{\circ}\)C) \\ \(C_{th}\) & thermal capacitance of node(s), J/\({}^{\circ}\)C \\ \(G\) & thermal conductance, W/\({}^{\circ}\)C \\ \(C_{fl}\) & fluid storage capacitance, s\({}^{2}\cdot\)m \\ \(P\) & absolute pressure, Pa \\ \(\dot{V}\) & volumetric flow rate, m\({}^{3}\)/s \\ \(\rho_{v}\) & volumetric mass density of vapour, kg/m\({}^{3}\) \\ \(t\) & time, s \\ \(V_{v}\) & volume of stored vapour, m\({}^{3}\) \\ \(\gamma\) & adiabatic index \\ \(R_{gas}\) & specific gas constant, J/(kg\(\cdot\)K) \\ \(R_{0}\) & universal gas constant, J/(K\(\cdot\)mol) \\ \(T_{v}\) & vapour temperature, K \\ \(L_{fl}\) & fluid inductance, 1/m \\ \(L_{eff}\) & fluid tube effective length, m \\ \(A_{p}\) & vapour hydraulic area, m\({}^{2}\) \\ \(A_{h}\) & convective surface area, W/(m\({}^{2}\cdot\)\({}^{\circ}\)C) \\ \(R_{fl}\) & fluid flow resistance, s\({}^{2}\cdot\)m \\ \(R_{th}\) & thermal resistance, \({}^{\circ}\)C/W \\ \(L\) & length, m \\ \(k\) & thermal conductivity, W/(m\({}^{\circ}\)C) \\ \(d_{out}\) & outside diameter, m \\ \(d_{in}\) & internal diameter, m \\ \(h\) & convective heat transfer, W/(m\({}^{2}\cdot\)\({}^{\circ}\)C) \\ \(k_{w}\) & equivalent thermal conductivity of Wick, W/(m\({}^{\circ}\)C) \\ \(k_{s}\) & thermal conductivity of solid, W/(m\({}^{\circ}\)C) \\ \(k_{l}\) & thermal conductivity of liquid, W/(m\({}^{\circ}\)C) \\ \(\varepsilon\) & Wick porosity, \% \\ \(V\) & volume, m\({}^{3}\) \\ \(h_{fg}\) & fluid latent heat, J/kg \\ \(Q_{h}\) & input heat power, W \\ \(m\) & mass flow rate, kg/s \\ \(M_{w}\) & fluid atomic weight, g/mol \\ \(\left(\frac{\partial T}{\partial P}\right)_{Sat}\) & Clausius-Clapeyron relation at saturation curve, g/mol \\ \(\mu\) & fluid dynamic viscosity, Pa\(\cdot\)s \\ \(\kappa\) & Wick permeability, m\({}^{2}\) \\ \(\Delta P_{cap}\) & capillary pressure generated between liquid/vapour, Pa \\ \(r_{c}\) & capillary meniscus radius, Pa \\ \(\sigma\) & fluid surface tension, N/m \\ \(f_{\epsilon}\) & correction coefficient to account the Wick dry-out \\ \(f_{m_{1}}\) & correction coefficient on \(\dot{m}_{1}\) \\ \(f_{G_{1-(1-2)}}\) & correction coefficient on \(G_{1-(1-2)}\) \\ \end{tabular}