esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Blade Wrap Angle on the Hydrodynamic Radial Force of Single Blade Centrifugal Pump
## 1 Introduction
[PERSON] analyzed the effect of blade inlet angle on a single blade centrifugal pump performance [21]. The wrap angle is one of the main parameters for the design of centrifugal pumps. However, research concerning the effect of the blade wrap angle on the hydrodynamic radial force and the reliability of single blade centrifugal pump is lacking. In the current research, unsteady simulation is conducted for single blade centrifugal pumps with different blade wrap angles, and the effects of these pumps are compared to reveal the relationship between the blade wrap angle and the pump performance.
## 3 Proposed Methods
### Heuristics  
One of the inputs to ADP is \(\mathcal{H}\), a set containing a list of heuristics. These heuristics are defined as follows:  
1. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmin}}\frac{d_{j}}{\sum_{l\in S_{ i}}d_{l}}\), which favors the shortest processing time;2. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmin}}\frac{\sum q_{ij}}{\sum_{l\in S_{ i}}\sum q_{lk}}\), which choses the minimum amount resources required first;
3. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmax}}\frac{\sum q_{jk}}{\sum_{l\in S _{i}}\sum q_{lk}}\), which favors the maximum amount of resources required;
4. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmin}}\frac{\sum v_{jk}}{\sum_{l \in S_{i}}\sum q_{lk}}\), such that \(v=([q_{i}>0])_{i=1}^{|q|}\) where \(q_{i}\in q\), which considers the activity with the minimum number of resources required;
5. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmax}}\frac{\sum v_{jk}}{\sum_{l \in S_{i}}\sum_{v}n}\), where \(v\) is as defined in heuristics number 4 that considers the maximum number of resources required first;
6. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmin}}\frac{|S_{i}|}{\sum_{l\in S _{i}}|S_{l}|}\), which prioritizes an activity with the minimum number of successors;
7. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmax}}\frac{|S_{i}|}{\sum_{l \in S_{i}}|S_{l}|}\), which favors an activity with the maximum number successors;
8. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmin}}\frac{|P_{j}|}{\sum_{l\in S _{i}}|P_{l}|}\), which prioritizes an activity with the minimum number of predecessors;
9. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmax}}\frac{|P_{j}|}{\sum_{l \in S_{i}}|P_{l}|}\), which favors an activity with the maximum number of predecessors;
10. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmax}}C_{i}\), which favors an activity with the maximum cashflow;
11. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmax}}\left[C_{i}e^{-df_{i}}+I_ {i}e^{-df_{i}}\right]\), which favors an activity with the maximum discounted cashflow;
12. \(h_{ij}=\underset{i\in P_{j}}{\operatorname{argmax}}ff_{i}\), which prioritizes the maximum free floats; and
13. \(h_{ij}=\) random combinations of any of the two heuristics (i.e., pick any two from 1 through 12).  
Given an activity \(j\), free float (\(ff_{j}\)) denotes the amount of time an activity can be delayed without hampering the project completion time. The free float is calculated as:  
\[ff_{j}=ES_{i}-EF_{j},\ \ i\in S_{j} \tag{7}\]  
where \(ES_{i}\) and \(EF_{j}\) are called early start of activity \(i\) and early finish of activity \(j\). To compute these values, we set \(ES_{0}=EF_{0}=0\) (i.e., the early start and early finish of no activity), then \(ES_{i}\) and \(EF_{j}\) are solved recursively as:  
\[ES_{i}=\max\{EF_{k}:k\in P_{i}\};EF_{j}=ES_{j}+d_{j} \tag{8}\]
# A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
## 5 Bubble Departure Frequency
### Bubble Growth Time \(t_{g}\)  
The bubble growth time has a direct effect on the detachment frequency of bubbles and therefore has a significant impact on the boiling heat transfer, which is calculated according to the time interval from the start of bubble growth to its departure.  
[PERSON] [75] presented a correlation for the bubble growth time by considering the detachment diameter, the liquid-thermal-diffusion coefficient and the dimensionless Jacob number. It is considered that the bubble growth time is proportional to the square of the bubble detachment diameter, as shown in Equation (39), where \(b\) is an empirical constant, whose size varies from one to \(\sqrt{3}\).  
\[t_{g}=\frac{D_{d}^{2}}{16b^{2}(Ja)^{2}\alpha_{l}} \tag{39}\]  
[PERSON] and [PERSON] [91] analyzed the Plesset and Zwick [69] expression for the bubble growth rate and considered [PERSON]'s [75] parameters. A new bubble growth period relation Equation (40) is proposed by eliminating the Jacob number and considering the cavity radius, which suggests that the bubble growth time is positively proportional to the square of the bubble detachment diameter and the nucleation-cavity radius:  
\[t_{g}=\frac{\pi a_{l}}{3}\Bigg{\{}\frac{(\rho_{v}h_{lv})^{2}D_{d}R_{c}}{8k_{l} \sigma T_{sat}}\Bigg{\}}^{2} \tag{40}\]  
[PERSON] et al. [92] conducted an experimental study of saturated pool boiling for R11 and R113, and the bubble-asymptotic-growth behavior was described with the help of two dimensionless parameters related to time and bubble radius. By utilizing the values of the dimensionless bubble detachment radius and time, the bubble growth period can be obtained as follows:  
\[t_{g}=67.5|a\alpha_{l}\rho_{l}D_{d}\sigma \tag{41}\]
# Automata and Arithmetics in Canonical Number Systems
## 4 Estimation of the Number of States of an Addition Automaton
### Analysis of Binary Polynomial CNS's  
As we have observed before, the states of the addition automaton may represent the possible carries. According to construction, these are polynomials in \(\mathcal{N}[x]\). Since the degrees of the state polynomials are bounded by the constant \(c=\mathbb{I}(2)+c_{2}\) of Theorem 4, we may state the following simple result.  
Theorem 4.1: _Let \((P,\mathcal{N})\) be a CNS, and \(\mathcal{A}\) be the minimal automaton implementing the addition. Then, the number of states of \(\mathcal{A}\) is bounded by \(|\mathcal{N}|^{c}\)._  
We have made an experiment on the construction of addition automata for different CNS polynomials. We determined and counted the number of states for binary Kovacs-type CNS polynomials of different degrees. (i.e., the coefficients of \(P\) have the property \(1=p_{d}\leq p_{d-1}\leq\cdots\leq p_{0}=2\).) [PERSON] in [10] listed the canonical polynomials of binary number systems until degree 8. According to this result, we know, for instance, that the only binary CNS polynomials of degree 3 satisfying the conditions of Theorem 1, are \(x^{3}+2,x^{3}+x^{2}+x+2\), and \(x^{3}+2x^{2}+2x+2\). Using the construction of the proof of Theorem 4.1, we implemented a computer program to calculate the addition automaton for a given CNS polynomial. We determined all different CNS polynomials of degrees from 3 to 10 in the form given in Theorem 1, and constructed the corresponding addition automaton. We collected the size of these automata, which are expressed by the number of states. The results are summarized in Table 4. The table contains the size (number of states) of the smallest and largest automata for different degree CNS polynomials.  
As we advanced to higher degree CNS polynomials, the experiment ran into memory limitations. When we computed the transition function of the automaton in question, we had to store the states and the effect of the input digits. To save some space, we did not care about the output, and we just represented the state transitions. Since the number of states was increasing exponentially with the degree of the CNS polynomials, even for degree 10, we ran out of memory during the tests. The experiment was executed on a computer with an Intel Core i9-9900K CPU on 3.60 GHz equipped with 64 GB of RAM. The running time did not limit the computation since it took only some hours.
## 3 Proposed SPMIPv6 Protocol
### Gateway Router Solicitation and Advertisement Message Format of SPMIPv6  
Figure 6 depicts router solicitation (RS) and router advertisement (RA) message format of SPMIPv6. Figure 6 (a) shows the RS message which is sent by IP-WSN sensor node. The IEEE 802.15.4 MAC header's source and destinations address set to the source and destination sensor node's MAC address. Dispatch of IP-WSN addressing header indicates the compressed IPv6 header. Header compression section compresses different fields of IP-WSN addressing header. Router solicitation message's RS options enable IP-WSN gateway to obtain the sensor node's MAC address, link-local address and sensor node's ID. The RA message format has two options such as home network prefix (HNP) and address options. The HNP options are used to emulate IP-WSN sensor node's home network.  
Figure 6: **(a)** SPMIPv6 Router Solicitation (RS) Message Format. **(b)** SPMIPv6 Router Advertisement (RA) Message Format.
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 4 Experiments
### Discussion of Experiments  
From the experiments, it can be seen that:  
1. The mathematical model and intelligent algorithm built can solve the TTSA of radio and television transmission.
2. Through the enumeration algorithm verification, when the number of tasks is small, the intelligent algorithm can calculate the global optimal solution, indicating that the intelligent approximation algorithm can achieve the same calculation results as the accurate algorithm. However, the limitation is that not all intelligent algorithms can achieve the same results as accurate algorithms. Comparing different intelligent algorithms is a contribution of this paper.
3. A large number of attempts of the algorithms can find the optimal approximate calculation scheme for specific problems. The parameter selection of the algorithm is the key to achieving optimal results. This paper directly uses the results of parameter testing in previous studies. It is recommended that when using this algorithm to solve other similar problems, the algorithm parameter selection test ought to be conducted again.
4. In order to obtain better results, the algorithm should be adapted to specific problems, for example, the improved crossover operator and probability selection model in this paper.
5. The hybrid algorithm can achieve good results in a certain range, but it may not completely inherit the advantages of the original algorithm. Although the computational results of the PSO-GA and GA-PSO in this paper are better than GA, there is no advantage in comparing the results of IGA and DPSO.  
The advantages and disadvantages of the various algorithms and comparison algorithms proposed in this paper are shown in Table 9.  
Figure 7: Comparison chart of multi-algorithm number of iterations.
## 3 Results
### Evaluation of Soil Sensors  
Soil moisture sensors use capacitance to measure the dielectric permittivity of the surrounding medium. The dielectric permittivity is related to the water content of the soil. A soil sensor creates a voltage proportional to the dielectric permittivity. The sensor averages the water content over the entire length of the sensor that is under the surface of the soil.  
There were a number of issues with commonly available soil probes, which needed to be addressed. First, soil sensing probes are usually coated with nickel plating, which rusts after 3-4 days when they are left in the soil continuously. Frequently replacing the sensing probe is inconvenient for data collection but also increases the error bias in the detected moisture values because replacing the probe may change the soil density and the position of the monitoring depth layer of soil. Second, the whole surface of the soil sensing probes detects soil moisture in the soil so it is hard to accurately estimate the moisture at a specific depth. Third, the rusted material may introduce heavy metal pollution to the soil, which is not acceptable in organic agriculture. Therefore, we redesigned the probe based on our earlier experiences regarding the material and the exposed parts.  
Table 2 shows four kinds of soil sensing probes. The material used in the soil moisture sensors of the first two soil probes is nickel-plated fiberglass. These probes not only rusted quickly but also could not sample soil moisture accurately at a specific depth. Therefore, we initially developed the third probe. The material of the third probe was nickel-plated iron wire, which has nickel-plated fiberglass on the surface with iron inside. It used ethylene vinyl acetate (EVA) to shield the surface and only revealed the tips of the probe. The results showed that the rust-free duration of nickel-plated iron wire was almost double that of the nickel-plated fiberglass. However, when the nickel on the surface of probe rusted, the sampled moisture value changed because the dielectric permittivity was distorted by  
the iron material. Thus, the alteration to the material produced a sampling error in the moisture data. Therefore, we developed the fourth probe, with a 2 mm diameter copper wire.  
The rust-free duration of the copper wire was about two weeks. To accurately measure soil moisture, only 1 cm of the copper probe needed to be revealed. Figure 3 shows how the probe is used to measure the soil moisture at a depth of 3 cm. This probe only exposed the last 1 cm segment to capture the moisture data while the other 2 cm were covered with plastic. The cap was designed using a 3D printer so the rusted probe can be replaced quickly. Note that the probe in Figure 3 is designed for the general requirements of planting leafy vegetables. Different types of crops will need different probe lengths. This redesigned probe can easily and cheaply prolong the active life of the probe.
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
###### Abstract
In [39], these parameters have been estimated with an interval branch and bound global optimization algorithm. In [40], simplified TBLO has been applied to estimate the parameters in a TDM. In addition, parameter extraction has been prepared by an improved version of the whale optimization algorithm [41] and chaotic improved artificial bee colony (CIABC) [42]. There is no doubt that the accuracy of the behavior of PVs is based on the estimated parameters, so the optimization techniques need further development to achieve high accuracy of these parameters. Additionally, in [43], another optimization method called forensic optimizer was developed for finding the optimal parameters of various solar cells. In [44], the gradient based optimizer was developed for three diode models. As seen in the literature, incredible work has been performed in the extraction of the optimal PV model parameters. A global solution has not been accomplished as the randomization process is a property of all optimization search algorithms. Among of the previous optimization methods, a new optimization method called turbulent flow of water-based optimization (TFWO) [45] is developed for finding the parameters of three models of PV cells. Several new optimization techniques, such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO), are used to compare the results of the proposed algorithm with the same dataset. Statistical analysis is used to analyze the performance of the proposed optimization algorithm. The P-V and I-V curves are simulated for the value of the estimated parameter that makes the simulated data very close to the experimental data. The organization of this paper is as follows: Section 2 explains the analysis of the objective function to be handled in the problem formulation. Section 3 contains the details of the proposed TFWO algorithm. Section 4 analyzes the results of the studied cases, while the conclusion is drawn in Section 5.
## 3 Results and Discussion
### Mask Layer Deposition and SS Removal  
In the earlier sections, we demonstrated that CHF\({}_{3}\)/Ar RIE could provide very effective selective etching of SS to the Si substrate and well controlled SS size reduction. CHF\({}_{3}\)/Ar RIE process, however, also convey an inevitable issue for SS layer elimination after metal mask layer deposition. The challenge of this SS layer removal has been originated from the thin SiC\({}_{\mathrm{x}}\)F\({}_{\mathrm{y}}\) layer formation on the all exposed surfaces during RIE [33]. This carbon included SiC\({}_{\mathrm{x}}\)F\({}_{\mathrm{y}}\) compound layer is very stable, especially in a hydrofluoric acid (HF) based solution like BOE. As a consequence, removal of SiC\({}_{\mathrm{x}}\)F\({}_{\mathrm{y}}\) covered SS in BOE solution cannot be achieved. The SiC\({}_{\mathrm{x}}\)F\({}_{\mathrm{y}}\) layer effect for SS removal from Si surface is schematically shown in Figure 5c,e. As illustrated in Figure 5e with an actual SEM image, the presence of SiC\({}_{\mathrm{x}}\)F\({}_{\mathrm{y}}\) layer on the surface of SS resulted in a collapsed Cr metal mask layer during the BOE process and no surface patterning for surface structure fabrication. This is because the SiC\({}_{\mathrm{x}}\)F\({}_{\mathrm{y}}\) is stable in BOE, but SS itself was etched away by infiltrated BOE. To solve the issue of SiC\({}_{\mathrm{x}}\)F\({}_{\mathrm{y}}\) layer, therefore, we inserted an ultra-violet ozone (UVO) surface cleaning treatment, which is simple and required a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PressureProperty** & **50 mTorr** & **75 mTorr** & **100 mTorr** \\ \hline Cross-sectional & & & & \\ SEM Image & & & & \\ (Scale-bar:500 nm) & & & & \\ \hline Vertical & & & \\ Etching Rate & 90.3 & 80.3 & 70.3 \\ (nm/min) & & & & \\ \hline Horizontal & & & \\ Etching Rate & 34.8 & 30.3 & 18.1 \\ (nm/min) & & & & \\ \hline Vertical/Horizontal & & & & \\ Etching Ratio & 2.6 & 2.6 & 3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SS shape profile change with CHF\({}_{3}\)/Ar gas RIE after 10-min etching under three different chamber pressures.  
Figure 4: SEM images of RIE processed SS after different etching time (**a**) 10 min, (**b**) 13 min, (**c**) 16 min, and (**d**) 19 min, (**e**) graph indicating inter-particle spacing with etching time (All samples were etched at the same RIE condition which are 75 mTorr pressure, 200 Watts RF power, and 25/25 sccm gas flow rate except etching time) (scale bar:1 \(\upmu\)m).  
no-vacuum process. This UVO treatment was performed before the Cr layer deposition. The UVO process provides not only effective cleaning on organic contamination, but also a non-aggressive etching environment to prevent possible surface damage inducing degraded solar cell performance. The effect of UVO surface treatment is clearly shown in Figure 5f. For the sample, a 10-min UVO treatment was performed before Cr layer evaporation, and as expected, the surface revealed very clean SS removal after BOE (10:1) etch, resulting in well-defined surface patterns for SWSS fabrication.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 5 Experimental Studies
### Position Error under Different Loads  
Generally, the repeat positioning accuracy or position precision was high for the robotic manipulator. Therefore, for the same trajectory, the initial point and end point can be considered the same without the end load, and the difference between the spatial position of the initial point and that of the final point under load was measured to verify whether it conformed to the validity of the proposed stiffness model.  
The posture of the manipulator at the initial point was set to (253.419 mm, 6.866 mm, 432.48 mm, 90.26\({}^{\circ}\), 84.23\({}^{\circ}\), 93.55\({}^{\circ}\)), in which the first three parameters are the Cartesian coordinates of the end of the manipulator, and the last three parameters are the attitude angles of the end of the manipulator. Loads of 0 N, 6.04 N, 10.63 N, 14.91 N, 18.64 N, and 21.50 N were added to the end of the manipulator (the force was applied in a downward direction). In each load state, the initial position was repeatedly positioned at the initial point several times to ensure the stability of the initial coordinates, the complete track was followed coherently, and the spatial position of each static track point was recorded. According to the previous stress analysis, this experiment put the applied load on the end  
Figure 10: End position measurement system for a 6-DOF robotic manipulator.  
of the manipulator. The force at the end of the arm was distributed differently in the X, Y, and Z directions, resulting in corresponding error changes in different directions, as shown in Figure 11.  
Figure 11: Position error in (**a**) X, (**b**) Y, (**c**) Z direction under different loads.  
The changes in the position errors when the load is 18.64 N and 21.50 N in Figure 11a-c show that when the load force at the end increased to a certain extent, the errors caused by the load changes gradually decreased; that is, the load force is not a linear factor for the increase in errors at this time. The results in the figures show that when the manipulator was at different points in space (at different postures), the position and pose of the manipulator had a more important role in the errors caused by stiffness. The position error caused by a small force was sometimes greater than that caused by a larger force, for example, a mutation of the position error appeared when the position error at 10.63 N exceeded the error caused by 14.91 N. According to the variation characteristics of most position errors in Figure 11, this sudden change may have been caused by additional errors in the measurement process (possibly the drift of the laser sensor).  
The experimental results in this section show that when the load force at the end of the manipulator increased to a certain extent, its influence on the position error was gradually weakened, while the influence of the manipulator attitude always existed. Under the same conditions, the influence of the joint angle had a greater influence on the error than other factors.
# Drone and Controller Detection and Localization: Trends and Challenges
## 3 UAV Detection Methods
### Acoustic
Node placement, the direction-finding probability that depends on precision and range for a given target, and ambient conditions with the tracker association threshold are among the collected data. Two main components affecting the system's overall performance are (1) hardware specifications, including acoustic sensors and data acquisition tools, and (2) software tools and algorithms, including acoustic fingerprints and features extraction, classification, and localization. Table 4 summarizes the recent studies for acoustic-based UAV detection, classification, and localization. Unlike radar and RF approaches, the acoustic solution does not require a line of sight (LOS). However, this solution has challenges of a short range, the need for an extensive large signature database, and vulnerability to ambient environmental noise and clutters, particularly in urban areas [14; 30; 31; 40], and quiet operation of the drone [9; 30; 38]. The detection of the drone pilot could be very difficult, too, using acoustic sensors.