esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
We have introduced the energy efficient multipath opportunistic routing protocol for SGs NAN environment. The previous opportunistic routing schemes were designed without consideration of service differentiation and multiple decision variables for calculation of optimal path connectivity. In EMOR, we utilized the concepts of multiple decision variables in optimal path connectivity and service differentiation in optimal path selection for real-time and non-real-time traffic. The proposed design of EMOR could be applicable to the following different approaches in future:  
1. Incorporation of relay nodes and finding the optimum number of relay nodes to support network lifetime.
2. Design an energy efficient multipath opportunistic routing protocol for wireless cognitive sensor networks.
3. Keeping in view the variable design of sensors and network architectures, heterogeneous clustering could be used with EMOR for wide-range connectivity.
4. How to increase the network lifetime of WSNs having sensor nodes which follow asynchronous working-sleeping cycle strategy by achieving a tradeoff between EMOR and energy balancing routing protocols.
5. How to reduce the network end-end delay of WSNs having sensor nodes which follow asynchronous working-sleeping cycle strategy by achieving a tradeoff between POFA and real-time EMOR.
6. Unmanned Aerial Vehicle (UAV) sensor networks, Internet-of-Things (IOT) enabled home energy systems, cyber-physical systems, etc.
7. Energy harvesting wireless sensor networks [60].
## 4 Research on Platform Performance Testing
### Platform Motion Accuracy Testing  
#### 4.4.1 Composition and Principle of the Accuracy Testing System  
In addition to platform stiffness testing, platform motion performance tests were also performed, including motion resolution and repeated positioning accuracy tests. The parallel platform test system was mainly composed of the test tooling, the simulation load, the parallel platform, the grating length gauges, and the display instruments, as pictured in Figure 21. The accuracy of each of the six degrees of freedom of the parallel platform was tested using the six grating length gauges in the test system.  
To improve the test accuracy, six standard blocks were processed using surface grinding treatments, and these standard blocks were then mounted on the simulated load to provide a contact surface for the test head of the grating length gauges, as shown in Figure 22. The defined coordinate system for the parallel platform and the grating length gauges layout are also shown in Figure 22. Grating length gauges no. 1 and no. 2 were used to test the translational displacements in the X direction and the Y direction, respectively. Grating length gauge no. 3 was used to test the Rz angular displacement. Grating length gauges no. 4, no. 5, and no. 6 were used to test the translational displacement in the Z direction and the Rx and Ry angular displacements, respectively.  
Figure 21: Test system configuration.  
Figure 20: Calibration of the initial parallel platform position: (**a**) Overview; (**b**) Details.  
When the platform target pose was given, the displacement of the inclined block could be calculated using the inverse kinematics algorithm. Then, using the ball screw lead, the reduction ratio of the harmonic reducer, the step length of the stepping motor, and the motor driving steps corresponding to each slide displacement could be calculated. To improve the position control accuracy and stability, the electronic subdivision method was used to drive the motor. Open-loop control was adopted for each motion cycle. After the motion was complete, the controller evaluated the position error of the inclined block based on the value of the feedback from the grating ruler reading head that is installed on the inclined block. If a slider position error occurred because of the transmission clearance or a screw lead error, then the motor driving steps to be compensated were calculated, and the motor must then be driven to move into the next movement cycle. The previous step was repeated until the position error was less than a preset tolerance, which caused the motor to stop moving.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 2 Single-Element Antenna
### Parametric Analysis of the Single-Element Antenna  
In this section, the effect of the reflection coefficient and drift in the resonance frequency of the single-element antenna (Ant1) are discussed due to the variations in the major (\(El1\)) and minor (\(Ev1\)) axes of the elliptical patch and the major (\(El2\)) and minor (\(Ev2\)) axes of the elliptical slot. Figure 8a,b illustrates that the changes in the length and width of the elliptical patch significantly impact the impedance matching. In fact, the increase in the width of the patch increases the area and the length of the electrical current, thus drifting the resonance to a lower frequency, though with poor impedance matching.  
On the other hand, the elliptical slot plays a vital role in impedance matching and tuning the resonance frequency to the desired response. Figure 8c,d demonstrates that the electrical current taking the shortest path is the slot's circumference, where the conductive area exists. As a result, for a smaller major axis (\(El2\)), the antenna results in resonance at a higher frequency. Further, with an increased \(El2\), the resonance drifts to the lower frequency. A similar effect is observed for the change in \(Ev2\). Thus, the resonance can be varied with the slot length and width.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### GNSS-Based Technologies  
The RS technologies which operate based on signals received from satellites have various applications in civil engineering at global, regional, and local levels. Here we focus on the local applications of satellite systems in construction engineering and management, with emphasis on the global positioning system (GPS) as a real-time location tool. The most recent GPS-based systems with applications in outdoor positioning and tracking of resources are global navigation satellite system (GNSS), real-time kinematic (RTK), GNSS, and Russian GLONASS technology. These systems benefit from widespread access to the free global satellite system. However, since an unobstructed line-of-sight to the air is needed, the application of these systems for indoor tracking of resources is challenging in confined areas. Plus, relatively high cost of the accurate GPS receivers is another limitation of these systems for usage in a large-scale level. Below is a brief description of these tracking technologies along with their limitations and capabilities.
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 3 Principle of the Proposed DTDAS
### Simulation Verification  
As shown in Figure 7, this module includes two parts, visualization simulation and evaluation simulation. Visualization of the scheduling process is the basic function of this module. Firstly, we build the 3D model of the AGVs and import them into Flexsim (A 3D Simulation Modeling and Analysis Software), as shown in Figure 8. Then, the process flow and data interface of the AGV are established. The Emulation module in Flexsim is used to establish the Modbus TCP Connection, as shown in Figure 9. Finally, the acquired sensor data of the physical device is transmitted to Flexsim through the network port. Consequently, AGVs and other devices perform pre-defined actions based on these real-time data.  
Figure 6: A randomly generated chromosome and the gene segment.  
Evaluation simulation refers to the simulation of the entire process before the scheduling plan is executed on the physical workshop. The 3D models in Flexsim can also be driven by scheduling plan tables. When a schedule plan is generated, it will first be sent to Flexsim for evaluation. In this way, we can dynamically evaluate the utilization rate of each AGV, the delivery and completion time, the delay time of each workpiece, etc. At the same time, the path conflict problem of the AGV system can also be found in the simulation. The evaluation indicators have a strong guiding significance for the execution system of the physical workshop. Simulation based on digital twins can evaluate the performance of the workshop from different scales according to the needs of users. In addition, since the model in the virtual workshop can be continuously updated based on real-time data, the simulation results can be closer to the real situation.  
Figure 8: AGV 3D model imported into Flexsim.  
Figure 7: Simulation and Verification.  
Figure 9: Emulation module and process flow configuration in Flexsim.
## 3 Results  
### Appropriate VIs for FPAR Estimation  
To investigate the capability of VIs for interpreting the variations in the FPAR, the empirical regression models between different VIs and simulated FPAR based on two  
Figure 4: Flowchart for establishing and validating hybrid inversion models based on the PROSAIL model and UAV images.  
PROSAIL datasets are depicted in Table 4. The results indicate that the performances of different VIs for retrieving the FPAR varied across the two datasets, of which the parameter ranges were non-equivalent. Generally, the performance of the VIs could be summarized into four types of features. First, some VIs, like MCARVI, RDVI\({}_{\text{red-edge}}\) and MSAVI, presented good estimation performance for the general dataset but showed worse accuracies in the specific dataset, with a decline in R\({}^{2}\) and an increase in RMSE. Contrarily, certain VIs (e.g., NDVI, RVI, SR and mNDVI) displayed superior regression model performance only with a specific dataset. The third type of VIs demonstrated stable and consistent model capabilities for both general and specific datasets, including OSAVI, WDRVI, mSR and OSAIV\({}_{\text{red-edge}}\). Lastly, the remaining VIs exhibited poor model properties compared with the others, indicating that these limited the effectiveness of capturing the FPAR under common conditions. Therefore, considering the modeling signatures and accuracy ranking for both datasets, the top three VIs of OSAVI, WDRVI and mSR, which presented robustness and adaptableness among the 29 VIs, were identified as effective variables for further retrieving the crop FPAR.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 5 Verification of the NOMA Simulator
### Exemplary Simulated Results
#### 5.1.4 Ergodic Sum Rate  
NOMA is believed to improve the spectral efficiency of FRA by up to three folds [30]. Typically, the research related to NOMA is considers two users in a pair, but this article provides additional analysis for more than two users. However, if we consider more than two users in a pair, say three, the intermediate user will perform SIC for the high-power (far) user's signal while simply subtracting the near user signal as noise. Hence, the achievable data rate for the three users is given as [34; 39]  
\[R_{NEU}=B\log_{2}\left(1+\frac{P_{NEU}|h_{NEU}|^{2}}{\eta_{1}}\right) \tag{8}\]  
Figure 11: Throughput performance of un-coded and coded NOMA with perfect SIC in the presence of AWGN and Rayleigh fading channel.  
\[R_{IEU}=B\log_{2}\left(1+\frac{P_{IEU}|h_{IEU}|^{2}}{P_{NEU}|h_{IEU}|^{2}+\eta_{2}}\right) \tag{9}\]  
\[R_{FEU}=B\log_{2}\left(1+\frac{P_{FEU}|h_{FEU}|^{2}}{P_{NEU}|h_{FEU}|^{2}+P_{IEU }|h_{FEU}|^{2}+\eta_{3}}\right) \tag{10}\]  
where \(R_{NEU}\), \(R_{IEU}\) and \(R_{FEU}\) are the data rates of the NEU, IEU, and FEU, respectively. B is the total system bandwidth of the channel whereas \(\eta_{1}\), \(\eta_{2}\), and \(\eta_{3}\) represent the AWGN noise. The power coefficient in the denominator show the interference from users with a higher channel gain. As \(B\) is the same for all users because the entire bandwidth is available, \(B\) can be ignored in the equations. Therefore, the sum rate of the NOMA system with two users is given as  
\[R_{sum_{2}}=\log_{2}\left(1+\frac{P_{NEU}|h_{NEU}|^{2}}{\eta_{1}}\right)+\log _{2}\left(1+\frac{P_{FEU}|h_{FEU}|^{2}}{P_{NEU}|h_{FEU}|^{2}+\eta_{2}}\right) \tag{11}\]  
where, the sum rate of the NOMA system for the three users is given as  
\[R_{sum_{3}}=\log_{2}\left(1+\frac{P_{NEU}|h_{NEU}|^{2}}{\eta_{1}}\right)+\log _{2}\left(1+\frac{P_{IEU}|h_{IEU}|^{2}}{P_{NEU}|h_{IEU}|^{2}+\eta_{2}}\right)+ \log_{2}\left(1+\frac{P_{FEU}|h_{FEU}|^{2}}{P_{NEU}|h_{FEU}|^{2}+P_{IEU}|h_{ FEU}|^{2}+\eta_{2}}\right) \tag{12}\]  
The ergodic sum-rate capacity of the NOMA system is shown in Figures 12 and 13.  
Figure 12: Capacity performance of the NOMA system.
## 3 Results and Discussion
### Images Alignment and 3D Reconstruction
For instance, for the DC's set of images of the square beam, an average of 47% of the 43 input images were utilized to compute each target point of the four targets. As presented in the bar chart, the average projections percentage for the four targets is almost the same for both cameras in each data set, which is expected given that the same number of images were taken by both cameras from the same positions. Figure 9: Image residuals across the area of. (**a**) the digital camera sensor; (**b**) the smartphone camera sensor. The scale bar represents 1 pixel. The pixel size is 4.04 μm for the digital camera and 1.06 μm for the smartphone camera. The magnification factor is \(\times\)398 in both plots. The projection percentage for both cameras is highest with the cylinder data, followed by the sand pile and lowest with the beam data. This is due to their difference in size; the cylinder has the smallest size, so the targets are visible in most of the captured images which results in a higher image redundancy and vice versa in the square beam data. The image redundancy of a given target affects the RMSE associated with that target. This effect is evident in the other bar chart, Figure 10b, in which the average RMSE for the four targets of the cylinder data is relatively smaller for both cameras compared with the other two specimens. Furthermore, the bar chart shows the remarkable variation between both cameras in terms of the average RMSE of the four targets. The smartphone's data sets are associated with almost 3-4 times the error associated with those of the digital camera. This significant difference in RMSE associated with the scaling target points reflects on the overall geometrical measurements' accuracy.
## 3 Results
### Clinical Measurements for CAL and GRD  
Apart from CAL (\(p\) = 0.029) and GRD (\(p\) = 0.044), the mean baseline values of the clinical parameters did not differ statistically significantly in the intergroup comparison (Table 2). The initial mean CAL0 value for the test group was 4.19 \(\pm\) 1.07 mm, while the corresponding mean value for the control group was 4.62 \(\pm\) 1.26 mm. No statistically significant differences in terms of CAL were observed between the groups at 6, 12 and 24 months after the procedure. The mean values after 24 months of observation were equal to 1.14 \(\pm\) 0.77 mm and 1.31 \(\pm\) 0.81 mm respectively. In both groups, a statistically significant improvement was noted in the position of CAL after 6, 12 and 24 months in comparison to baseline. The mean percentage of clinical attachment level gain (%CAL) for the test group, 6 months after the surgery was 71.96 \(\pm\) 15.89%, 12 months--73.48 \(\pm\) 16.72% and 2 years--73.56 \(\pm\) 16.77% and for the control group 72.93 \(\pm\) 15.38%, 73.12 \(\pm\) 14.56%, 71.98 \(\pm\) 13.96% respectively. The differences between both groups were insignificant for each stage of the observation period. The initial mean value for GRD in the test group was 2.72 \(\pm\) 1.00 mm, while the corresponding value for the control group was 3.08 \(\pm\) 1.14 mm. In both groups the mean GRD values at 6, 12 and 24 months post-treatment had declined significantly in relation to the pre-operative state. Two years after surgery, the test group mean value was equal to 0.27 \(\pm\) 0.44 mm and in the control group 0.37 \(\pm\) 0.55 mm. The differences between both groups were not statistically significant. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Test Group** & **Control Group** \\  & **(CAF + A – CTG)** & **(CAF + NA – CTG)** \\ \hline Number enrolled patients & 16 & 19 \\  & (12 females, 4 males) & (15 females, 4 males) \\ Number covered recessions & 77 & 71 \\ Number type of recession (Miller) & & \\ \(I\) & 34 & 36 \\ \(II\) & 30 & 30 \\ \(III\) & 13 & 5 \\ Number treated teeth per patient & & \\ \(Mean\ value\ \pm\ SD\) & 4.81 \(\pm\) 2.40 & 3.74 \(\pm\) 2.08 \\ \(Range\) & 2–10 & 2–9 \\ Number localisation of recessions & & \\ \(Incisor\) & 19 & 24 \\ \(Canine\) & 22 & 13 \\ \(Premolar\) & 30 & 28 \\ \(Molar\) & 6 & 6 \\ \(Maxilla\) & 66 & 54 \\ \(Mandible\) & 11 & 17 \\ Number surgical sessions per patient & & \\ \(Mean\ value\ \pm\ SD\) & 1.63 \(\pm\) 0.62 & 1.26 \(\pm\) 0.56 \\ \(Range\) & 1–3 & 1–3 \\ \hline \hline CAF, Coronally advanced flap; A – CTG, Augmented connective tissue graft; NA – CTG, nonaugmented connective tissue graft; SD—standard deviation. & \\ \end{tabular}
\end{table} 
Table 1: Patient related records.
# Soft Sensor Technology for the Determination of Mechanical
## 4 Conclusions  
In this paper, a soft sensor algorithm based on temperature measurements is presented that can be used to determine the frictional power performance of a mechanical seal. The algorithm is based on a heat flow model. The model assumes stationary temperature fields in the rings of the mechanical seal. Transmission efficiencies are used to describe the temperature fields. This allows the stationary temperature fields in the mechanical seal rings to be described by a few temperature measurements to determine the heat flows. To apply the model, the heat flow model can be reduced to an equivalent dimensionless form coefficient and compared with experimentally determined values. The following conclusions can be drawn from these results:  
* The calculation of the equivalent dimensionless form coefficient is highly dependent on the model used to determine the averaged Nusselt number. A variety of modelling approaches exist. The heat transfer coefficients determined by the models exhibit considerable variability, which results in discrepancies in the calculation of the dimensionless equivalent mould coefficient.
* By varying the differential pressure across the mechanical seal and the preload on the mechanical seal primary ring, a small deviation between the algorithm and the measurement could be demonstrated.
* Varying the geometry around the mechanical seal affects the heat transfer between the mechanical seal and the fluid. The arrangement of fins around the mechanical seal improves heat transfer and therefore increases the equivalent dimensionless form coefficient.
* The soft sensor heat flow model is based on stationary assumptions. The algorithm reacts slowly to changes in the stationary operating conditions. Rapid changes, e.g., sudden increases in pressure, will not be considered by the algorithm. This significantly increases the errors. The algorithm is not suitable for the representation of dynamic changes in the operating conditions.
* The described algorithm is easy to implement compared with data-based or machine-learning concepts of soft sensors. For this, only the equivalent dimensionless form coefficient characteristics need to be known. It is not necessary to have large amounts of data in order to train a model. There is no need for mechanical machining of the mating ring, as is required when integrating a torque sensor.
## 2 Related Work  
### Network Slicing  
For the research of network slicing, DRL has played an important role in solving slicing resource allocation. The authors in [11] used the DRL-based algorithm, supported by the generative adversarial network (GAN), to solve the problem of resource allocation in three service scenarios, and made the training of the algorithm more stable through a reward clipping mechanism. [PERSON] et al. [12] established a network slicing scenario of user mobility and proposed an advantage actor-critic (A2C) learning algorithm based on LSTM to solve the environment perception problem caused by user mobility. A dynamic running slice framework was designed in [13] to address the different QoE requirements of IoV, and a two-layer constrained reinforcement learning (RL) algorithm was proposed to solve the complex coupling constraints of the problem. Due to the mobility in the vehicle network and the complexity of the network, [PERSON] et al. [14] proposed a deep deterministic policy gradient algorithm based on long short-term memory (LSTM-DDPG) to ensure the stable performance of slices. To jointly manage network slicing and routing, reference [15] proposed a multi-task DRL-based graph convolutional network (GCN). In the field of network resource allocation, game theory is also a very effective method [16; 17]. In [18], the problem of resource allocation and pricing for network slicing was studied, and interactions between access or backhaul service providers and their UE were captured by a multi-leader multi-follower Stackelberg game approach. For the problem of multi-tenancy in network slicing, reference [19] used game theory to solve the problem of resource allocation of network slices under the shared constraint proportional allocation mechanism, and analyzed the efficiency and fairness of the allocation. Reference [20] described the strategic decision-making process made by tenants as a game, and proposed a heuristic algorithm so that the game could reach a unique Nash equilibrium (NE). To avoid the negative impact of network congestion on slicing, a distributed slicing strategy based on alliance games and matching theory is proposed in [21], which improves QoS and throughput and reduces energy consumption and latency.
## 4 Circuit Design
### Detection Circuit  
In the solar and SWT hybrid charging circuit, it is necessary to collect multiple sets of voltage and current data, such as the voltage of the wind turbine, the voltage of the solar cell, and the voltage of the battery. The collected current includes the charging current of the SWT and the charging current of the battery. The MAX471 ESA (Maxim, San Jose, CA, USA) is selected as the core of the circuit during the design process. The MAX471 is a complete, bidirectional, high-side current-sense amplifier for portable battery monitoring, and operates at a temperature range between \(-40\)\({}^{\circ}\)C and \(85\)\({}^{\circ}\)C.