esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analysis
### Results and Discussion: Torque-Speed Characteristic and Efficiency Performance  
To examine the speed range of the constant torque output operation and output torque capability in a high-speed situation, the output torque and power performance of the two structures with different speeds are simulated with the field-weakening control method. Under the maximum driving current (250 A) and DC bus voltage equal to 320 V, the torque-speed and power-speed characteristic curves are drawn as in Figure 12. The original constant output speed turning point is around 3400 rpm, while the optimized one is around 3700 rpm. The maximum output torque and power value of the optimized scheme can satisfy the original requirements, and the constant torque speed range of the optimized scheme is marginally wider. However, in the high-speed range, there is a significant improvement in the output torque/power than the original structure, which means the optimized structure has more advantages in high-speed and high-torque-density applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Parameter** & **The Original** & **The Optimized** \\ \hline \multirow{2}{*}{Without skewed slot} & Average torque & 98.3 N\(\cdot\)m & 98.2 N\(\cdot\)m \\  & Torque ripple & 14.9\% & 7.1\% \\ \hline \multirow{2}{*}{With skewed slot} & Average torque & 96.4 N\(\cdot\)m & 96.6 N\(\cdot\)m \\  & Torque ripple & 7.9\% & 3.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The output average torque and torque ripple of both structures.  
Figure 11: Simulation results of the torque waveforms and harmonic distribution under the rated operating condition: (**a**,**b**) skewed-slot angle is 0\({}^{\circ}\); and (**c**,**d**) skewed-slot angle is 7.5\({}^{\circ}\).  
Moreover, in the operating speed range, the efficiency distribution of the motor within the operating range acquired through the simulation is shown in Figure 13. The difference in the highest efficiency area between the two structures is not noticeable, and the two structures can both reach around 97% in a wide speed range. The efficiency of the two structures meets the requirement of the highest efficiency of 96.7%, while the optimized structure has higher efficiency during high-speed operation conditions compared with the original one. Because wind friction losses are neglected, the efficiency simulation results are slightly higher than the actual value.  
In summary, focusing on the requirements of the output power and torque, the optimized structure can perfectly replace the original structure in practical application. And the superior high-speed output torque capability makes it more advantageous in high-speed situations.
## 3 Results and Discussions
### Effect of the Water Content of Molasses on MMBs
#### 3.1.2 Softening Point  
The softening point (SP) test results shown in Table 3 indicate that the softening temperature of the binders increased with the reduction in the water content of molasses. Unlike the penetration resistance, the softening temperature was not affected by the molasses content of the binders. The softening temperature was increased by 17.7% for MMB5, 10.6% for MMB10, 14.2% for MMB15, and 18.8% for MMB20 when the water content of molasses was reduced by about 80%. This could be related to the effect of water on viscosity and surface tension. The reduction of water in molasses increases the heat resistance and surface tension between molecules, which improves the viscosity of the binders.
## 2 Materials and Methods
### Methods
#### 2.3.3 Evaluation Methods  
To effectively evaluate the fire severity classification capabilities of different bands and indices, they were all pre-processed using normalization. The workflow for determining fire severity classification thresholds is outlined in detail below.  
1. Search method: The fixed step search method.
2. Operational parameters: Step size (fixed at 0.01 prior to the search) and search direction (determined during the search as increasing (+) or decreasing (\(-\))).
3. Evaluation Metric: F-Score (the F1_Score for the target class in the validation data (Single F1_Score, SF1) was used during the search, and the Weighted F1_Score (WF1), representing the weighted average of the F1_Score for multiple fire severity categories in the validation data, was used as the evaluation standard after completion of the search.)
4. Process Description: The severity levels were assessed in both ascending (1-4) and descending (4-1) orders. The initial thresholds were set to increase (0.00-1.00) for ascending and decrease (1.00-0.00) for descending. First, the ascending order thresholds were determined by a bidirectional search, identifying the optimal segmentation threshold for level 1. The subsequent search direction was adjusted accordingly (increasing or decreasing). Based on threshold a, the optimal segmentation threshold b for level 2 was determined, and further adjustments along the same direction were made to identify threshold c for level 3. The image was then segmented into four categories (1, 2, 3, and 4) using thresholds a, b, and c, respectively. The same process was applied for the descending order. The final classification result was based on the best evaluation from both processes.  
The search process is illustrated in Figure 5, and the evaluation metric calculation formulas are presented in Equations (9)-(11).  
\[F\_Score=\frac{\left(\alpha^{2}+1\right)\times P\times R}{\alpha^{2}\times(P+R)} \tag{9}\]  
\[\mathrm{SF1}_{i}=\frac{2\times P_{i}\times R_{i}}{P_{i}+R_{i}} \tag{10}\]  
\[\mathrm{WF1}=\frac{\sum_{i=1}^{n}N_{i}\times\mathrm{SF1}_{i}}{\sum_{i=1}^{n}N_ {i}} \tag{11}\]  
Equation (9) represents the general formula for \(F\_Score\), where \(P\) and \(R\) denote precision and recall, respectively. In Equations (10) and (11), \(P_{i}\) and \(R_{i}\) represent the precision and recall for each individual category, respectively. \(\mathrm{SF1}_{i}\) is the harmonic mean of both precision and recall for each individual category, used as the evaluation metric during the search process (\(\alpha=1\)). \(N_{i}\) refers to the total number of pixels for each fire severity category. WF1 is the weighted average \(\mathrm{F1}\_Score\) across all categories, used as the comprehensive evaluation metric upon completion of the search.
# Evaluation and Validation of Viscous Oil Cavitation Model Used in Torque Converter
## 2 Materials and Methods
### Mesh Independence Analysis  
The three impellers were discretized using unstructured tetrahedral elements in ICEM-CFDTM of ANSYS 19.2. As the cavitation bubbles were generally small, a refined mesh around the blade is required to restore the real flow status and capture the cavitation flow behaviors.  
The accuracy and efficiency of the solution are directly affected by the mesh density. In theory, a higher grid number would yield better accuracy; however, more mesh requires higher computing resources. Therefore, an appropriate mesh density should be determined by mesh independence analysis to achieve a proper balance between accuracy and computing cost, and the mesh model is considered independent if further refining the mesh produces less than 3% variation in results.  
A mesh independence test was performed with different mesh resolutions, the reference pressure level was 0.4 MPa, and the pump speed was 2000 rpm, \(SR\) was 0.4. Figure 3 shows the torque results of pump, turbine and stator over variant mesh densities through the CFD calculation. Taking into account the computational efficiency and accuracy comprehensively, the element numbers of pump, turbine and stator are 7,538,148; 4,319,448; and 2,442,091 respectively, and the torque variation of pump, turbine and stator is 1.63%, 1.42%, and 1.90% respectively, which satisfies the requirement of mesh independence, as shown in Table 1, and the bold font is the final chose mesh model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multicolumn{4}{c}{**Number of Elements**} & \multicolumn{4}{c}{**Torque Variation**} \\ \cline{2-7}  & **Pump** & **Turbine** & **Stator** & **Total** & \(T_{\text{P}}\%\) & \(T_{\text{T}}\%\) & \(T_{\text{S}}\%\) \\ \hline
01 & 613958 & 663382 & 305766 & 1583110 & 0 & 0 & 0 \\
02 & 1060830 & 1198570 & 527300 & 2786700 & 4.20 & 5.25 & 8.05 \\
03 & 2415170 & 3555930 & 1201990 & 7173100 & 4.51 & 4.22 & 5.84 \\
**04** & **7538148** & **4319448** & **2442091** & **14299687** & **1.63** & **1.42** & **1.90** \\
05 & 12440100 & 5315250 & 3003070 & 20758500 & 0.69 & 1.27 & 1.85 \\
06 & 16541600 & 6631990 & 3748490 & 26922000 & 0.58 & 0.96 & 1.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data of the mesh independence analysis.  
Figure 2: Full 3D flow model of the torque converter.  
The mesh around the blade surface is densified with 12 layers of prism mesh whose minimum height is 0.015 mm, the mesh quality is analyzed again, and the Yplus values (which is a non-dimensional variable representing the distance from the wall to the first node away from the wall) of three impellers are all less than 2, which means that the boundary layer and wall function can meet the requirements of high accuracy. The final total element number is 22,431,217 after the boundary mesh generation.
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 1 Introduction
In [32], the Gray code technique is used to reduce PMEPR while selecting the optimal cyclic sequence to reduce the sidelobes of the IRC waveform. However, the data rate is low. Therefore, we urgently need to jointly minimize the PMEPR and sidelobe level and reduce the influence of random communication data on radar detection capability, while considering communication performance such as data rate and BER. For a TR-based IRC waveform structure with a fixed radar bandwidth (meeting radar resolution requirements), a tradeoff exists between the communication rate and PMEPR. For example, when a low PMEPR is required, the TRR can be increased (i.e., the total communication bandwidth is reduced) to reserve more subcarriers for PMEPR reduction. Additionally, when a high communication rate is required, TRR can be reduced (i.e., more bandwidth is allocated for communication). In this paper, in order to reduce PMEPR and achieve a high data rate at the same time, a multi-symbol OFDM-based IRC waveform is studied, in which one cyclic prefix (CP) is used for multiple OFDM symbols [33]. It is found that both the PMEPR and the maximum autocorrelation sidelobe have infinite-norm form, which can be approximated by the associated \(L_{p}\)-norm, where \(p\) is a large positive integer [23; 34]. Therefore, in our joint optimization algorithm of PMEPR and peak-to-sidelobe ratio (PSLR), we first establish the joint optimization objective function based on the \(L_{p}\)-norm. Then, an efficient algorithm based on the classic conjugate gradient algorithm (CGA) [35] is proposed. The performance of the designed IRC waveform, including the PMEPR, PSLR, radar SNR, Doppler, and BER are analyzed by simulation experiments. The rest of this paper is organized as follows. The multi-symbol OFDM-based IRC signal model and the IRC system are illustrated in Section 2. In Section 3, the joint optimization objective function of PMEPR and PSLR is established. In Section 4, an efficient algorithm based on CGA is presented. The PMEPR and PSLR simulation results obtained by the proposed algorithm are analyzed in Section 5. Some discussions on the evaluation of radar detection and communication performance are given in Section 6. Finally, Section 7 summarizes the conclusions.
## 4 Impact of Network Latency on Teleoperation
### Wireless Technologies Used in Teleoperation
The relatively high data rates and improved reliability over 3G allow sufficient quality of video and control transmission, which is essential for this use case. However, as seen in the table, latency of 100 ms can pose challenges for real-time responsiveness, leading to potential difficulty in vehicle control. Moreover, the bandwidth limitations can restrict the parallel processing and amount of sensor data. Despite these constraints, 4G serves as a standard for demonstrating the feasibility of remote driving. Going one step further, 5G networks significantly enhance remote driving capabilities with their ultra-low latency (on the order of 10 ms), high data rates (up to 10 Gbps), and significantly improved reliability [10; 24]. They offer large volumes of data that can be quickly and reliably transmitted. These features are satisfactory for various autonomous applications, including multiple high-resolution sensor data, responsive control, logistics, and emergency services [3]. They can even ensure reliable management of fleets of remotely operated vehicles. The non-cellular classification includes diverse technologies that are more suitable for applications other than CAV teleoperation. Technologies such as WiFi, Bluetooth, and Zigbee are commonly used for short-range communications with overall good network performance. These are well suited for applications in controlled and confined environments, as in robotics. Satellite communication performs excellently in areas where other traditional terrestrial networks are impractical, with high latency given its large coverage area. However, it is expensive and is sensitive to environmental factors. Both cellular and non-cellular technologies have been emulated/simulated for more flexible, scalable, and secure experimentation. On one hand, these experiments attempt to allow the replication of real-world networks in controlled environments without risking physical systems. On the other hand, they offer the opportunity to define parameters and effectively validate and test various teleoperation applications in different scenarios and conditions. The parameters of simulated/emulated networks depend on how the user defines them. For example, in a simulation experiment, driving control had a latency of 170 ms and flight control had 1.5 to 3 s [28]. Another example, an NS3-IP-based emulator, had a latency of 16-19 ms [10]. The simulated ROS-based virtual network adapter had 64-74 ms, Rosbridge had 270 ms and WiFi had 445 ms [8; 27]. However, these networks are primarily implemented in simulations and may not always yield realistic results due to real-world networks' inherent uncertainty and dynamic nature. Studies have also used other communication technologies in teleoperation, including the Internet through a wired Ethernet cable, VLAN, radio link, inter-process communication (IPC) wireless network, umbilical cable, dual-tone multi-frequency (DTMF) and multimodal radio frequency (RF), VideoLAN Client (VLC), and acoustics [8; 30].
## 3 Results and Discussion
The reduction in \(R_{\mathrm{s}}\) was saturated at eight layers of coating in normal films due to the competition between the reduction in electron surface scattering in thicker films and the increased barrier that inhibits the transport of electrons, which was caused by the residual TBAOH on the NS surfaces. In contrast, in the UVO-exposed films, the saturation of \(R_{\mathrm{s}}\) was not observed until after 10 layers of coating. As a result, the gap of \(R_{\mathrm{s}}\) values between UVO-exposed films and normal films grows with the number of NS layers. The optical transmittance significantly decreased with the increasing number of LB layers in both UVO-exposed as well as untreated films, as depicted in Figure 3b. In the five-layered films, the difference in optical transmittance was \(\sim\)1.5% between the UVO-exposed films and untreated films. The difference was smaller in the case of more layers of NS coating. Moreover, the optical transmittance was almost constant after eight layers of NS coating. Considering that the optical transmittance of an individual monolayer RuO\({}_{2}\) NS was measured to be approximately 98% in our previous work [11], it appears that the original optical transmittance of the RuO\({}_{2}\) NSs is a dominant factor in obtaining the total optical transmittance in the transparent conducting films. Additionally, the sheet resistance changes with time were investigated to reveal the aging effect of RuO\({}_{2}\) NSs films, as shown in Table 1. In this experiment, five-layered RuO\({}_{2}\) NS films with or without 30-min UVO treatment were employed. Each RuO\({}_{2}\) NS film was stored in the dark in an air environment. As shown in Table 1, \(R_{\mathrm{s}}\) was slightly degraded by about 5% after 24 h. In the case of 30-min UVO-treated NS films, the degradation of \(R_{\mathrm{s}}\) was saturated at 48 h. On the other hand, the \(R_{\mathrm{s}}\) of RuO\({}_{2}\) NS films without UVO treatment increased monotonically with time, resulting in an increase in \(R_{\mathrm{s}}\) by more than 10% after 120 h. This suggests that the chemical stability of NS films was more enhanced through the removal of chemical residues by UVO treatment.
## 2 Methodology
### Error analysis between HbA1c estimation models  
To analyze the error level of the models, a reference model should be set. As the first model (7) consists of most of the blood parameters (i.e., oxy-, deoxy-, and glycated hemoglobin), it is considered to be the reference model for estimating the error of the second model due to change in SpO2 levels.  
Now, to estimate the error level of the second model, the models are analyzed within a range of HbA1c and SpO2 levels. The HbA1c range is set to 4-14%, and the SpO2 is set to 70-100%. For each value of HbA1c and SpO2, the \(c_{\text{HDA1c}}\), \(c_{\text{HDO}}\), and \(c_{\text{HHb}}\) parameters are calculated using the following equations and placed in (7) to calculate absorbance values for a certain wavelength.  
\[c_{\text{HbA1c}}=\frac{\%\text{HbA1c}}{100}\times c_{\text{WB}} \tag{9}\]  
\[c_{\text{HbO}}=\biggl{(}1-\frac{\%\text{HbA1c}}{100}\biggr{)}\times\frac{\% \text{SpO2}}{100}\times c_{\text{WB}} \tag{10}\]  
\[c_{\text{HHb}}=\biggl{(}1-\frac{\%\text{HbA1c}}{100}\biggr{)}\times\biggl{(}1- \frac{\%\text{SpO2}}{100}\biggr{)}\times c_{\text{WB}} \tag{11}\]  
\[c_{\text{WB}}=2.2\text{ mol L}^{-1}=2.2\times 10^{-3}\text{M} \tag{12}\]Now from (7), we can define the terms %HbA1c and %SpO2 as  
\[\%\text{HbA1c}=\frac{c_{\text{HbA1c}}}{c_{\text{HbA1c}}+c_{\text{HbO}}+c_{\text{ HHB}}}\times 100\% \tag{13}\]  
\[\%\text{SpO}_{2}=\frac{c_{\text{HbO}}}{c_{\text{HbO}}+c_{\text{HHB}}}\times 100\% \tag{14}\]  
In (9)-(12), the term \(c_{\text{WJ}}\) indicates the molar concentration of whole blood.  
After calculating the \(c_{\text{HbA1c}}\), \(c_{\text{HbO}}\), and \(c_{\text{HHB}}\) parameters for a set of HbA1c and SpO2 values, these are placed in (7) to calculate the absorbance values \(A(\lambda_{1})\), \(A(\lambda_{2})\), and \(A(\lambda_{3})\) for three wavelengths--\(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\)--respectively, considering the value of \(d\) as 1 cm. These three absorbance values are then used to reversely calculate the \(c_{\text{HbA1c}}\), \(c_{\text{HbO}}\), and \(c_{\text{HHB}}\) parameters from (7) and \(c_{\text{HbA1c}}\) and \(c_{\text{NonHbA1c}}\) parameters from (8), using the least square curve fitting algorithm. Due to the differences between the model approximations, the reversely calculated parameters will be different and will result in errors.
## 4 EBCRP: Energy-Balanced Cluster-Routing Protocol
### Clustering Sensor Nodes Based on Particle Swarm Optimization with Five Mutation Operators  
#### 4.1.1 Particle Swarm Optimization  
PSO is a population-based global optimization technology; the individuals in the population are called particles. In PSO, a swarm uses \(M\) particles to search for the best solution, with each particle representing a potential solution. Particles have two attributes: velocity \(v\) and position \(x\). The solution (position) of the \(i\)-th particle can be expressed as \(x_{i}=\{x_{i1},x_{i2},\ldots,x_{iD}\}\), where \(D\) is the dimension of particles. The velocity of the \(i\)-th particle, \(v_{i}=\{v_{i1},v_{i2},\ldots,v_{iD}\}\), represents the speed of particle movement. In PSO, particles can cooperate and share information to find the best solution. All particles iteratively adjust their velocity and position according to their best experience \(Pbest\) and the best experience \(Gbest\) of the entire swarm. The scholars added the inertia weight \(w\) to the traditional PSO to change the convergence speed of the optimization algorithm, which is called standard PSO [43]. The velocity and position of the \(i\)-th particle are updated by Equations (3) and (4), respectively.  
\[v_{i} = wv_{i}+rc_{1}(Pbest_{i}-x_{i})+rc_{2}(Gbest-x_{i}), \tag{3}\] \[x_{i} = x_{i}+v_{i}, \tag{4}\]  
where \(w\) represents the inertia weight, \(c_{1}\) and \(c_{2}\) are acceleration constants, \(r\) is a random value in \([0,1]\), and \(Pbest_{i}\) is the best experience of the \(i\)-th particle.
## 2 Materials and Methods
### Stakeholder Engagement  
An initial list of 46 villages was drafted in order to undertake a process of stakeholder engagement. These were then clustered into 6 groups and half-day stakeholder workshops were conducted. Participants for the workshops were selected amongst community members to represent both the political sphere and traditional authorities, and individuals that will be directly involved in the operation and maintenance of the systems: water and farmers' committee members in villages, pump operators, municipal water managers, ward councilors, prominent citizens and volunteers. During the workshops, the research team firstly presented the project to raise public awareness and promote the interventions amongst key community members. The initial design and installation of the equipment and infrastructure was then explained to the community along with the modes of investment and financing, the requirements for long-term sustainability, operation and maintenance, including safety and security issues, with the main intent to secure a sense of ownership by the community. This was followed by exchange of information and discussion on the way forward followed by physical visits to sites where the community proposed interventions to take place.  
A number of criteria were considered to determine potential case study sites for the feasibility investigation. These criteria were (i) availability and reliability of a water source (groundwater borehole or shallow wells in sand river banks); (ii) community needs (water demand); (iii) crop water requirements for small farming; (iv) water use diversification opportunity; (v) current infrastructure gaps; (vi) system set-up and logistical complexity; (vii) economic activity potential (e.g., agriculture and possibility of value-added products); (viii) access to markets and geographic access; (ix) tribal and traditional support; (x) health and hygiene improvements; (xi) cultural activity and economic potential. A list of nine case study sites was therefore compiled for the feasibility study on solar-powered groundwater pumping systems and MUS, namely four villages in dire need of water supply for domestic use and five small-scale farms: (i) Mbhedle, (ii) Mayephu, (iii) Mazilela and (iv) Matsotsosela villages, (v) Nhlambeto Primary Agricultural Cooperative in Dzumeri village (mixed domestic and small-holder irrigation water use), (vi) Matsumoto Ngamba Projects, (vii) A hi triheni Mqekwa Primary Agricultural Cooperative, (viii) Duvadzi Youth Organic Agricultural Cooperative and (ix) Macena Primary Agricultural Cooperative. The locations are indicated in Figure 1 and their characteristics are summarized in Table 1.