esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation of Drop Process of Single DOF System
### Drop Impact-Rubbings Behavior of Power Amplifier Failure Model  
#### 3.4.1 Influence Law of Structural Parameters on Rotor Impact-Rubbing Behavior  
In failure model of power amplifier, the maximum radial impact force of rotor/stator collision changes with the structural parameters in the case as shown in Figure 13.  
According to Figure 13, maximum impact force \(F_{n\mathrm{max}}\) decreases with increase of plating thickness and increases with increase of liquid film thickness. Its maximum value occurs in the combination of \(h_{0}\) = 80 \(\upmu\)m and \(l\) = 50 \(\upmu\)m. Therefore, take it as a typical example in order to analyzing operation rules under this model as shown in Figure 14.  
According to Figure 14, in initial equilibrium Stage, rotor is dropped from zero position and stays at \(-\)39.3 \(\upmu\)m after a small vibration. The load is borne by electromagnetic force and hydrostatic force as shown in Stage 1.  
In dropping Stage, rotor drops rapidly. The current \(i_{My,1}\) and \(i_{My,2}\) become 1 A, and electromagnetic force decreases. The liquid resistance \(R_{h1}\) has little change, while the liquid resistance \(R_{h2}\) rises rapidly due to the impact-rubbing damping effect of oil. So that hydrostatic force rapidly increase as shown in Stage 2.  
In collision Stage, rotor reaches a new equilibrium state. As the contact depth between rotor and stator gradually increases, the impact force \(F_{n}\) increases, and the load of the rotor is borne by electromagnetic force \(F_{My}\) and hydrostatic force \(F_{Ly}\). The power amplifier failure, residual electromagnetic force \(F_{My}\) reduced to 116.4 N. The liquid resistance \(R_{h1}\) and \(R_{h2}\) are stable at 5.0 \(\times\) 10\({}^{8}\) N\(\cdot\)s/m and 1.1 \(\times\) 10\({}^{13}\) N\(\cdot\)s/m respectively. The latter is about 2.2 \(\times\) 10\({}^{4}\) times of the former, resulting in a tiny tremor after collision between rotor and stator. In addition, hydrostatic force \(F_{My}\) decreases rapidly and finally stabilizes to about 67.5 N as shown in Stage 3.  
Compared with former three failure models, the liquid film thickness and plating thickness make the greater impact on the degree of collision. Moreover, the dropping time is the shortest, the rotor reaches the new equilibrium state more quickly after collision, and the mean value of maximum collision force is 122.1 N. In addition, the phenomenon of \"dropping-collision\" under this model have the greatest influence on operation of the bearing.  
Figure 13: Maximum radial collision force.
# Noninvasive Magnetic-Marking-Based Flow Metering with Optically Pumped Magnetometers
## 2 Materials and Methods
### Procedure  
After starting the pump, the pre-polarized water flows through the apparatus. The water polarization interacts with the RF pulses of the RF coil in the interaction section (cf. Figure 3). This coil is operated with amplitude modulation (AM). The 8 kHz RF signal has a sinusoidal envelope of 1 V at 100 mHz. The RF pulses manipulate the water polarization with an intensity proportional to the magnetic field generated by the AM RF coil. Thus, the RF coil periodically changes the polarization amplitude of the water passing the interaction section. Finally, the OPM detects an alternating magnetic amplitude of the water polarization analogue to the RF envelope. The time difference \(\Delta t\) between pulse application and detecting its effect on the water polarization yields the time information to calculate the flow velocity \(v\) using the distance \(\Delta L\):  
\[v=\frac{\Delta L}{\Delta t} \tag{1}\]  
Figure 2: Image of the experimental setup with the syringe pump, the Halbach magnet with the syringe inside, the interaction section with the coil system and the magnetic shield containing the OPM sensors. The spatial directions are defined as indicated by the white arrows in the upper right corner. The interior of the magnetic shield is shown below. The white plastic pipe contains the water to be measured. The water crosses the image from left to right. The white OPM sensors are installed on a nonmagnetic mount in the center of the image. The mount enables accurate position referencing of the sensors. The sensitive axis of the sensors is in z direction. The red double arrow indicates the direction of the pumping mechanism.  
To compare the measured flow rate to the pump rates indicated by the pump, \(v\) was converted to the average volumetric flow \(Q\) inside the tube using the tube cross-section \(A\) and its diameter \(d\).  
\[Q=vA=v\left(\frac{d}{2}\right)^{2}\pi \tag{2}\]
## 3 Research Design of the Fieldwork and Results: Comparing Three Paradigms
### Research Results: Comparative Analysis of Three Paradigms
#### 3.2.2 Network Sovereignties [30]
This mutualistic model emphasizes cooperation and reciprocity over competition, fostering sustainable and equitable economic practices that strengthen social ties and resilience. * Storage and Exchange of Value (Alternative, Possibly Non-Monetary Systems): Unlike the cryptocurrency-driven economies of Network States, Network Sovereignties explore alternative, potentially non-monetary value exchange systems. These focus on reciprocal exchanges based on mutual benefit and shared values rather than financial transactions. This approach challenges the financialization of relationships, aiming to create economic systems rooted in trust, cooperation, and sustainability, where value is generated through collaboration rather than financial incentives. Technological Layer: Relationship with Technology  
The Network Sovereignties' relationship with technology is integral to its governance model:  
* Techno-Augmented Governance: Network Sovereignties leverage technology to enhance and support decentralized governance, prioritizing the use of digital tools to empower communities and manage commons effectively. * Responsible Innovation and Techno-Pragmatism: Unlike the more utopian or solutionist approaches, Network Sovereignties embrace responsible innovation, focusing on the practical and ethical use of technology to address real-world challenges. This approach is characterized by a balanced and pragmatic application of technology, ensuring that it serves the public good and fosters equitable outcomes. Territoriality and Legal Layer: Hybrid Boundaries and Strategic Compliance  
The Network Sovereignties' approach to territory and its relationship with existing nation-states are as follows:  
* Relationship to Territory: Network Sovereignties are not dependent on physical land for their existence. Instead, they emphasize land stewardship through meaningful engagement with local communities and ecosystems. This approach allows for a fluid and transnational presence, prioritizing the sustainability and well-being of the environments and societies they interact with. 2. Relationship to Nation-State Laws and Policies: Network Sovereignties adopt a creative approach in their interaction with traditional nation-states. They strategically leverage existing laws and regulations to establish autonomous spaces that align with their commons-centric ideals. This enables Network Sovereignties to operate within the legal frameworks of nation-states while fostering decentralized governance and autonomy beyond traditional state boundaries.
# Research on the Obstacle-Avoidance Steering Control Strategy of Tracked Inspection Robots
## 3 Kinetics of Tracked Robot during Steady-State Turning
### Dynamics Analysis for Skid-Steering  
According to the analysis of robot steering dynamics, it is known that the robot must meet the following conditions in the process of low-speed and uniform steering [12]:  
\[\left\{\begin{array}{l}F_{Ry}+F_{Ly}-F_{RR}-F_{RL}=0\\ (F_{RL}-F_{RR})B/2+M_{R}=0\end{array}\right. \tag{10}\]  
To ensure that the tracked robot turns at a constant speed, the resultant force on the longitudinal track must be zero. The longitudinal traction force \(F_{Ry}\) and \(F_{Ly}\) of the robot and the lateral torque m of the robot track are functions of the lateral relative offsets \(a_{R}\) and \(a_{L}\), respectively. In this study, ground deformation resistance in the steering process of the robot was replaced by ground deformation resistance when the robot was driving in a straight line, and the deformation resistances of the two tracks were equal, so \(F_{RL}-F_{RH}=0\). By substituting force and moment into the above equation, the following formula can be obtained:  
\[\left\{\begin{array}{l}\frac{a_{R}}{2}In\Big{|}^{1/a_{R}}+\sqrt{1/a_{R}^{2}+ 1}\Big{|}-\frac{a_{L}}{2}In\Big{|}^{1/a_{L}}+\sqrt{1/a_{L}^{2}+1}\Big{|}=\frac{ f}{\mu}\\ (a_{R}+\frac{\lambda}{2}a_{R}^{2})In\Big{|}^{1/a_{R}}+\sqrt{1/a_{R}^{2}+1} \Big{|}+(a_{L}+\frac{\lambda}{2}a_{L}^{2})ln\Big{|}^{1/a_{L}}+\sqrt{1/a_{L}^{ 2}+1}\Big{|}=\frac{\lambda}{2}\Big{(}\sqrt{a_{R}^{2}+1}+\sqrt{a_{L}^{2}+1} \Big{)}\end{array}\right. \tag{11}\]  
where \(\lambda=L/B\) is the structural parameter of the robot.  
It can be seen from the above formula that \(f\) and \(\mu\) are ground parameters and \(\lambda\) is the structural parameter of the robot. When the ground and structural parameters are determined, the equation contains only two unknowns, \(a_{R}\) and \(a_{L}\), with a given relative radius, indicating that the system of equations has a solution.
# Towards an Ergonomic Assessment Framework for Industrial Assembly Workstations--A Case Study
## 2 Materials and Methods
### Preassembly Workstation--Ergonomic Assessment
Relatively to the selection of assessment methods, the RULA and KIM-MHO are observational methods [9] available for assessing WMSD risk during MHO. In order to summarize the main characteristics of the assessment methods applied, Table 3 was constructed taking into account the following parameters: focus' method, risk factors included and not included, output, advantages, and limitations related to the method application. Since this assessment was developed across the different assembly tasks, the need for a comparison between the results obtained by both methods was identified. Based on this need, four global risk levels were defined, integrating/combining the different risk levels considered by each method. These global risk levels are presented in Table 4. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Questions’ Category** & **Parameters Assessed** & **Objectives** & **Tools Applied** \\ \hline A. & Workers’ characterization & Gender, age, work experience, preferred hand; WMSD. & To characterize the workers’ sample with demographic data. & Direct questions. \\ B. & Assessment of Musculoskeletal symptoms & Musculoskeletal self-reported for the entire body. & To characterize musculoskeletal symptoms of the pressembly workers. & NMQ with closed answers \"yes/no\" and VAS to assess pain intensity. \\ C. & Perceived exertion associated with the tasks & Perceived exertion for each pressembly task. & To assess physical exertion perceived by the workers; To identify the most demanding tasks. & CR-10 Borg scale. \\ D. & Global assessment of the workstation & Global opinion about the preassembly workstation. & To assess workers’ opinions about possible improvements to introduce in the pressembly workstation; & 5-Likert scale. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the questionnaire’s structure—preassembly. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Focus** & **Risk Factors Included** & **Output** & **Advantages** & **Limitations** \\ \hline \multirow{6}{*}{**RULA**} & Manufacturing and handling & \multirow{6}{*}{Posture of arm, forearm, posture, as well as work} & \multirow{6}{*}{Posture of arm, forearm, wrist, neck, and trunk; Repetition/frequency; Rep
## 2 Materials and Methods
### Geometry Optimization of Diodes  
For a given input power and frequency, the best frequency multiplication efficiency varied with the geometry size of the diode, as well as bias conditions of direct current (DC). As shown in Figure 1, the surface channel was approximately equivalent to a plate capacitor and the air bridge finger mainly introduced inductance. When the width increased, \(C_{pp}\) decreased and \(L_{f}\) increased. On the contrary, \(C_{pp}\) increased and \(L_{f}\) decreased. Excessive inductance or capacitance increased the difficulty of impedance matching and reduced the cut-off frequency of the diode, thereby degrading the performance of the diode. Moreover, \(R_{j}\) and \(C_{j}\) were intrinsic parameters for representing the nonlinear effect of anode junction and they were also contradictory. When one parameter changed with the area of the anode junction, the other parameter changed in the opposite direction. Therefore, the diode geometry had  
Figure 1: The cross-sectional view of the planar Schottky diode and a simplified equivalent circuit model.  
to be optimized to achieve optimal multiplication efficiency. The area of the anode junction and the width of the surface channel were the main optimization objects.  
The schematic diagram of the wave port configuration of the diode anode junction and the setup for high-frequency 3D EM simulations is shown in Figure 2. Each diode's wave port was defined as a miniature coaxial hollow ring piece surrounding the anode, as illustrated in Figure 2a. The electromagnetic field around the anode junction was calculated with HFSS. The gap between the edge of the anode and the coaxial hollow ring piece must be very narrow to avoid underestimating the parasitic capacitance [34]. The 4-die diode chip was embedded in the suspended microstrip line with both ends of the diode extended toward the two wave ports, numbered as 5 and 6, as shown in Figure 2b. The effect of the transmission line was removed by de-embedding the wave port to the diode chip. Thus, the 3D EM simulation model was set up and measured by the six wave ports. Using the obtained linear S-parameter results already considered the parasitic effects caused by the diode geometry, which is represented by the several parasitic elements in Figure 1. Harmonic balance simulation was then performed in Keysight Advanced Design System (ADS) to calculate the conversion efficiency. Finally, parametric sweep simulations were conducted for the circuit's optimum performance under a specific channel width and anode junction area. The optimization results are shown in Figure 3a,b, from which we can see the best surface channel width of 8.5 \(\mu\)m and anode junction area of 10 \(\mu\)m\({}^{2}\).
## 2 Theoretical Background
### Fuzzy C-Means
\[\hat{o}_{i}^{(t)}=\frac{\sum\limits_{j=1}^{N}{(\hat{u}_{ij}^{(t)})^{m}x_{j}}}{ \sum\limits_{j=1}^{N}{(\hat{u}_{ij}^{(t)})^{m}}}, \tag{9}\]  
\[\hat{u}_{ik}^{(t)}=\Bigg{(}\sum\limits_{j=1}^{C}{\left(\frac{|x_{k}-\hat{o}_{ i}^{(t)}|}{|x_{k}-\hat{o}_{j}^{(t)}|}\right)^{\frac{2}{m-1}}},\Bigg{)}^{-1}, \tag{10}\]  
with \(m>1\) and \(x_{k}\
eq\hat{o}_{j}^{(t)}\), \(\forall j,k\). At each iteration, each object \(x_{k}\) is compared with the elements of the centroid vector and is assigned to the nearest cluster. The process stops when the convergence condition (i.e., the matrix norm distance between \(\hat{U}^{(t+1)}\) and \(\hat{U}^{(t)}\) is less than a fixed value (i.e., minimum improvement in the objective function \(\mathcal{J}\) between two consecutive iterations \(\epsilon\)) or the maximum number of iterations \(T_{\text{max}}\) is reached. After the convergence, a defuzzification is applied to assign each pixel to the cluster with the highest membership degree, thus achieving a binary classification.
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
## 2 MFC Power Supply for Sensors and Biosensors
The devices were  
Figure 4: Schematic diagram of a remote sensor powered by an MFC with a DC/DC converter. endowed with a power management system, based on a DC/DC converter. The tested f-MFC, equipped with a DC/DC converter, successfully allowed the supply of simple electronic devices (a LED-light and a buzzer), and the transmission of remote data. Interestingly, using dense rice plants (Oryza Sativa) to ensure the long-term performance, several artistically-defined \"floating garden\" MFCs were set in the context of demonstrative events at the 2015 Milan World Exposition EXPO2015. As a proof of the possible applicability of those cells, some of the \"floating garden\" MFCs were operative for more than one year [57]. Ultimately, several environmental factors, such as temperature, pH, organic loading rate (OLR), as well as the presence of toxicants and inhibitors, should be strictly considered in the design of MFCs as they could affect the power and electric potential outputs [30; 58]. The influence of those key parameters, among others, must be understood in order to project the components and the configuration of an MFC-driven sensor to scale-up for practical applications [16; 59].
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
###### Abstract
The latest research shows that fusion of the diverse sensor systems to acquire crop and canopy structure, texture, and spectral and thermal information significantly improves the data related to plant trait determination in a diverse agricultural system [48]. These systems provide efficient data, with their integration at the ground-based platform, such as tractors. Studies have been performed on autonomous tractor systems for video surveillance and mapping. [PERSON] et al. [49] studied the routing protocols for semi-autonomous tractors in real-time conditions. An autonomous multi-tractor system was analyzed by [PERSON] et al. [50], [PERSON] et al. [51], and [PERSON] et al. [52]. They allowed for the installation of multiple systems, such as GPS, sensors, and computer vision systems. However, studies on autonomous and semi-autonomous tractors for crop monitoring are limited. Robots, however, are designed for guidance, detection, action, and mapping [53] and for developing field resource efficiency. However, the robot GPS has low accuracy due to signal blockage or occasional multireflection. Space-borne platforms such as satellites (Sentinel-2/Sentinel-1 10-60 m, MODIS 30 m and Landsat 250 m) provide real-time crop information and agrometeorological data for global food production estimates [54]. They provide early warnings for crop monitoring and food supply by assessing crops, farming activities, and rural developments. However, their coarse spatial resolution does not optimally classify the crops within mixed pixels [55] and thus presents errors. Unmanned aerial vehicles (UAV) provide high spatiotemporal resolution, cost-efficiency, and flexibility for crop monitoring at fine scales [2; 3]. However, UAV-collected imagery may contain a degree of geometric distortion, inaccurate radiometric estimation, and environmental condition effectiveness to the quality of the data, such as vegetation indices [56]. Technology and data fusion provide a more accurate and robust analysis. Through data fusion, several data sources are joined together to create a more useful and accurate analysis. There are several methods to perform data fusion, such as via installation of a field sensor for crop parameters and moisture estimation. The objective of this study is to provide the latest data fusion methods for the efficient fusion of model data with different technologies. Considering this, the study highlights digital farming technologies and establishes a connection to show how a developed system (with technology and data fusion) in turn influences crop monitoring. This article is structured as follows: Section 2 highlights the best sensors for crop monitoring. Section 3 presents platforms with new fusion methods for ground-based, space-borne and aerial platforms. Section 4 shows data fusion methods for site-specific crop monitoring for N, chlorophyll, LAI, and AGB. Finally, Section 5 concludes the study and points out the current challenges, opportunities, and future prospects of the technology and data fusion methods.
## 2 Materials and Methods
### Applied Methodology
#### 2.2.3 Additive Manufacturing and 3D Scanning Procedures  
The main objective of the applied AM process is the construction of 3D-printed heart models that possess similar properties to the actual heart tissue, hence the material jetting AM technology was employed utilizing the Projet MJP 5600 3D Systems(tm) 3D printer. The material jetting (MJ) process uses inkjet printing techniques to selectively deposit droplets (voxels) of photopolymer material through the jets of the printhead and solidify them with the aid of UV lights, resulting in the creation of the desired part [37]. The main advantage of this process is the ability of multi-material 3D printing, i.e., the fabrication of components consisting of two or more composite materials transfusing different properties in different regions. Therefore, in order to imitate the basic properties of heart tissue, such as elasticity and strength, the MJ technique was employed in the present work. According to the manufacturer's datasheet, the composite material with the closest properties to heart tissue [11; 38] is RWT-ENT A50(tm). RWT-ENT A50(tm) is a composite material consisting mainly of Visijet CE-NTT(tm) (elastomer material) and small portions of Visijet CR-CL 200(tm) (rigid material). Table 1 lists the main properties of heart tissue measured in a passive state and the employed materials for the 3D printing process. Finally, the maximum resolution of 750 \(\times\) 750 \(\times\) 2000 DPI and the minimum layer height of 13 \(\upmu\)m were applied, leading to a sufficiently fast 3D printing process of around 29 h per heart model and 12 h per aorta, achieving the maximum dimensional accuracy of the employed 3D printer.  
In the context of this study, the 3D models of the heart and aorta were evaluated in terms of dimensional accuracy, utilizing the HP PRO S3 3D Structured Light Scanner (SLS). The HP PRO S3 3D SLS is a desktop 3D scanner that uses the structured light technique to capture the desired 3D geometry, and it is suitable for the scanning of relatively small objects (30-500 mm). Furthermore, this scanner is able to capture high-definition data, up to 2,300,000 vertices per scan, performing with a scan accuracy of 0.1% of the scan size. In detail, three different 360\({}^{\circ}\) scans were conducted with three different object orientations to ensure the total scanning of the objects. The dimensional accuracy inspection was performed with CloudCompare software, comparing the digital 3D models that were reconstructed from CT scans with the scanned 3D-printed hearts. Finally, the evaluation process was conducted via the employment of the surface distance map (SDM) methodology as well as the calculation of the mean absolute error (MAE) and root mean square error (RMSE) parameters.