esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Genetic Characterization
#### 3.1.1 Microsatellite Markers Polymorphism  
The eight primers used to analyze the 60 accessions selected, resulted efficient to discriminate the genotypes. Allelic profile is reported in Table S1 of the Supplementary Materials. The heterozygosity analysis and allelic frequencies allow the individuation of 95 alleles (Table 3). The most polymorphic primer result HIGA23 with 31 alleles observed. He ranged between 0.550 for primer HIGT14 and 0.910 for HIGT23 primer. Ho ranged between 0.550 for primer HIGT14 and 0.783 for HIGT17. The Ho values indicate a high level of genetic diversity in the studied wild population. These data are in accord with prior study on Italian wild hop population [4] and also with a recent study on Portuguese wild hop populations [33], where Ho with the same SSR markers, ranged between 0.63 for HIGA6 and 0.73 for HIGA23. Instead, the number of alleles per locus for the primers HIGA31 and HIGA23 are not in accord with [PERSON] et al. [34], showing that the same SSR markers could differentially perform, depending on the population analyzed. The richness of alleles present in locus HIGA23 is instead in accord with the study of [PERSON] et al. [26]. The lowest value for allelic frequencies is 0.008 and corresponds to the frequencies of unique alleles. The allele present with the highest frequency is instead allele 167 in HIGT14 with a frequency value of 0.608. Unique alleles are present in five loci over eight primers, with the exception of HIGA7, HIGT16 and HIGA31. The presence of unique alleles in the wild population and not detected in cultivars already reported in previous studies, is, probably, due to genetic erosion and selection made using restricted genetic resources [35]. PIC value is an important index to evaluate the discriminant power of locus, and in our study reach, values between 0.482 for HIGA31 and 0.897 for HIGA23, thus showing the best discrimination power. These results are in accord with a prior study on an Italian hop population, where HIGA23 showed the best performances [4; 33]. The use of eight SSR primers allow a good discrimination among the samples, but the primers used were not able to discriminate between sites of provenience. Similar results were obtained by [PERSON] et al. [5] in a study on the wild Croatian hop population; the genetic analysis, carried out in the study, was effective for the discrimination among the genotypes but not useful to discriminated between the eight studied Croatian populations.
## 4 Experimental Results
 \\ \hline \multirow{4}{*}{0\({}^{\circ}\)} & \multirow{2}{*}{1} & \(\tau_{z}\) & \(-3\) & \(-6\) & \(-5\) & \(0\) & \(0\) & \(-1\) & \(0\) & \(0\) & \(0\) & \(0\) \\  & & \(\hat{\phi}\) & \(-1.34\) & \(-2.66\) & \(-2.14\) & \(0\) & \(0\) & \(-0.43\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \cline{2-13}  & \multirow{2}{*}{2} & \(\tau_{z}\) & \(-6\) & \(-5\) & \(-1\) & \(0\) & \(0\) & \(-1\) & \(-1\) & \(-1\) & \(0\) & \(0\) \\  & & \(\hat{\phi}\) & \(-2.68\) & \(-2.15\) & \(-0.45\) & \(0\) & \(0\) & \(-0.42\) & \(-0.43\) & \(-0.43\) & \(0\) & \(0\) \\ \hline \multirow{4}{*}{\(-5^{\circ}\)} & \multirow{2}{*}{1} & \(\tau_{z}\) & \(-10\) & \(-7\) & \(-4\) & \(0\) & \(-1\) & \(3\) & \(0\) & \(0\) & \(1\) & \(2\) \\  & & \(\hat{\phi}\) & \(-4.50\) & \(-3.07\) & \(-1.77\) & \(0\) & \(-0.44\) & \(1.31\) & \(0\) & \(0\) & \(0.44\) & \(0.89\) \\ \cline{2-13}  & \multirow{2}{*}{2} & \(\tau_{z}\) & \(-8\) & \(-5\) & \(-4\) & \(2\) & \(2\) & \(2\) & \(0\) & \(-2\) & \(-3\) & \(-3\) \\  & & \(\hat{\phi}\) & \(-3.60\) & \(-2.21\) & \(-1.77\) & \(0.88\) & \(0.89\) & \(0.88\) & \(0\) & \(-0.88\) & \(-1.33\) & \(-1.32\) \\ \hline \multirow{4}{*}{\(-10^{\circ}\)} & \multirow{2}{*}{1} & \(\tau_{z}\) & \(-2\) & \(-2\) & \(-1\) & \(0\) & \(-1\) & \(3\) & \(2\) & \(-2\) & \(-4\) & \(4\) \\  & & \(\hat{\phi}\) & \(-0.89\) & \(-0.90\) & \(-0.45\) & \(0\) & \(-0.44\) & \(1.33\) & \(0.89\) & \(-0.89\) & \(-1.77\) & \(1.80\) \\ \cline{2-13}  & \multirow{2}{*}{2} & \(\tau_{z}\) & \(-4\) & \(-3\) & \(0\) & \(-3\) & \(4\) & \(4\) & \(2\) & \(2\) & \(0\) & \(2\) \\  & & \(\hat{\phi}\) & \(-1.79\) & \(-1.33\) & \(0\) & \(-1.34\) & \(1.76\) & \(1.76\) & \(0.89\) & \(0.90\) & \(0\) &
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 2 Materials and Methods
### Evaluation of the Efficiency of Hovering Relay by Networking Metrics  
Each of the two amphibious drone systems measured water quality data and transmitted the relevant data to the receiver system. In the API-router mode of the XBeeTM, each XBeeTM chip on the drone relays the data received from other XBeeTM chips to the receiver system with a coordinator XBeeTM chip.  
Before testing the system in actual water bodies, the data relaying efficiency of the system was examined by a hovering relay test. The test was conducted on 12 December 2023, in a flat field at Akita Prefectural University (Akita Campus), Japan. No tall grasses were present in the field on the test date. The weather during the test was cloudy, with a wind speed of 3-4 m s\({}^{-1}\) and an air temperature of 5-6 \({}^{\circ}\)C (278-279 K). The distance between the transmitter drone and the receiver was approximately 100 m. The locations of the devices were confirmed with a controller of the SplashDroneTM 4 (\(\pm\)1 m accuracy, at the accuracy level 10 [18]). The relayer drone was positioned at the center of the line between the transmitter drone and the receiver. Networking metrics (throughput, Received Signal Strength Indicator: RSSI; Packet Delivery Ratio: PDR) were measured both with and without the relayer drone hovering at a height of around 3 m. The payload for measuring networking metrics was fixed at 20 bytes and utilized in loopback mode. The test duration for the throughput test was set as 60 s for the rapid environmental monitoring with drones, and the transmit timeout was set as 10 s for a measurement of water quality at 10 s intervals. In the range test (RSSI and PDR test), 60 packets were sent for each test. Each packet was to be sent ideally at 1000 ms intervals, but the receiver timeout was set as 10 s for consistency with the following field tests. The XBeeTM antenna height of the relayer drone in the landing state was 44 cm and that of the transmitter drone was 45 cm. The receiver XBeeTM antenna height was 12 cm. The baud rate was fixed at 9600 to ensure compatibility with the water quality monitoring system operating at low rates.
## 2 Results and Discussions
### Optimization of SSF Conditions
The effects of interaction terms (X\({}_{1}\)X\({}_{2}\), X\({}_{1}\)X\({}_{3}\), X\({}_{2}\)X\({}_{3}\)) on TPC were visualized on response surface plots. Figure 1a-c clearly shows that at the lower levels of incubation temperature and higher levels of incubation time, TPC increased as the initial moisture content % increased from 30 to 70%. Fungal proteases are known to catalyze the hydrolysis reactions to obtain bioactive peptides [68]. There are numerous reports on the production of protease using fungal cultures under the SSF conditions. Most of them observed the optimum temperature range of 28 to 30 \({}^{\circ}\)C, similar to our findings [51]. Contrarily, [PERSON] and [PERSON] (2014) observed an increase in the protease activity secreted by _A. oryzae_ on wheat bran with increasing incubation temperature up to 55 \({}^{\circ}\)C [53]. However, they observed low enzymatic stability above 45 \({}^{\circ}\)C.  
Initial moisture content below the optimum level may lead to low nutrient diffusion, microbial growth, enzyme stability, substrate swelling, and sporulation, whereas above this level the problems may be particle agglomeration, gas transfer limitation, and competition with bacteria. The moisture content required for the fungal growth or secretion of metabolites generally varies between 40-80%, yet it heavily depends on the nature of the substrate. The optimum moisture content levels reported for various strains and substrate pairs in SSF processes of protease production were 50% for wheat bran (_A. oryzae_) [69], 40% for Jatropha seed cake (_A. versicolor_) [70], 50% for wheat bran-Aspergillus isolate [71], and 70.5 for sugar cane bagasse (_A. niger_) [72]. In our study, it was observed that in order to have a high total peptide content, approximately 70% initial moisture content was required by using the hazelnut oil cake-_A. oryzae_ combination with the SSF method (Figure 1a,b). Composition of SSF media may change over time during fermentation. For this reason, the period for maximum production of target component should be determined and fermentation should be terminated quickly at that moment [73]. Otherwise, there may be risks such as contamination, increase in cost, and accumulation of inhibitory metabolites. [PERSON] and [PERSON] (2014) observed that protease production by _A. oryzae_ LBA 01 under SSF of cotton seed meal reached to its maximum level at the 72 nd hour of the process [53]. [PERSON] et al. (2019) reported a maximum degree of hydrolysis level at the 96 th hour of SSF by _Rhizopus_ sp. incubated on brewer's spent grain and they related this result to the maximum peptide yield [64]. As can be seen in Figure 1b,c, at 30% of initial moisture content and 40 \({}^{\circ}\)C of incubation temperature, TPC continuously increased until 3.8 days, then it decreased. According to [PERSON] et al. (2022), the production of protease in the early stage of microbial growth may increase the peptide content [74]. However, at the later
# Discharge Coefficients of a Heavy Suspension Nozzle
## 1 Introduction
The turbulent model they used was RNG \(k-\epsilon\) and the maximum pressure differential was about 0.1 MPa. The results showed that the obstacles located at the converging nozzle of the venturi causes negligible pressure losses, while other obstacles that generate wakes in the flow are responsible for the largest pressure drop. Discharge coefficients of critical nozzles used for flow measurement under compressible flow conditions were evaluated by [PERSON] et al. [22]. Fluid was considered as real, the standard \(k-\epsilon\) turbulent model with a wall function was employed in all simulations. Nozzle roughness was considered, being the maximum pressure differential between nozzle inlet-outlet of 120 MPa. They observed that, when the nozzle roughness was very small, and for pressure differentials until 1 MPa, the effect on the discharge coefficient was negligible. Sonic and supersonic flow inside micro/nanoscale nozzles was studied by [PERSON] and [PERSON] [23]. They used a density-based solver (rhoCentralFoam) employed in OpenFOAM. Second order spatial discretization scheme along with a first order Euler-scheme for time integration were implemented. They observed supersonic flow was impossible to set in nanoscales once Knudsen number exceeded a given value. [PERSON] et al. [24] numerically studied the fuel flow in a nozzle considering the fuel compressibility. They used the RANS method with a Realizable \(k-\epsilon\) turbulent model and they investigated the effect of injection pressure on the fuel flow under fuel compressibility conditions. They concluded that the nozzle discharge coefficient for compressible flow was larger than when fluid was considered as incompressible. According to the authors knowledge, the nozzle configuration studied in the present paper, which has a T shape, has not been previously studied under real gas compressible flow conditions, and just the work done by [PERSON] et al. [15] presents some similarities. In fact, under incompressible flow conditions, a similar shape was studied by Reference [26; 27], where it was stated the discharge coefficient was highly dependent on the flow direction. The present study consists of the following parts: initially, the test rig employed to do all experimental tests is introduced, and then the mathematical equations used to analytically determine the flow parameters are presented. In a third stage, the Computational Fluid Dynamics (CFD) methodology employed to numerically evaluate the compressible flow between the two tanks is introduced. Next, the experimentally-based and numerical results are presented and compared. Finally, the discharge coefficients as a function of the Reynolds number, and for both flow directions, are presented and discussed, and the paper ends with the conclusions.
## 6 Numerical Experiments
### Parameters Optimization  
In this section, we discuss the effect of four key parameters in the ACO of the PSACO algorithm. Our goal is to identify the optimal setting of parameters to produce the best results during the operation of the algorithm. The parameters include ant number \(M\), coefficients that reflect the relative importance of pheromones \(a\) and heuristic information \(b\), pheromone evaporation coefficient \(\rho\). The results of optimal values for parameters are shown in the figures below.  
For ACO, the efficiency of search is greatly affected by the number of ants. In the path search phase of the ant colony, when the number of ants is larger, the global searching ability can be greatly improved. However, until a certain point, the convergence rate will be slower. The relationship between the optimal solution and the number of ants is shown in Figure 5(a). We can found that the critical point is 500. When \(M<500\), the performance of the algorithm is improved. But when \(M=500\), the algorithm reaches the optimal solution, proving the existence of the extremum. Therefore, we can conclude that it is sufficient to set the number of ants \(M=500\).  
Figure 6: _Cont._  
Heuristic factors \(a\) and \(b\) are critical factors for ACO algorithm, and they are used to calculate the transition probability. Pheromone volatilization coefficient \(\rho\) is used to update the pheromone concentration after each search. In Figure 6b, when \(a=1\), the performance of the algorithm is the best. For heuristic coefficient \(b\), the larger \(b\) is, the weaker convergence speed will be. We can find out from Figure 6c that the algorithm gets the optimal solution when its value is 4. Pheromone volatilization coefficient \(\rho\) is also an important factor affecting the amount of pheromones in the path. It is a constant in interval (0, 1). The larger \(\rho\) is, the lower the convergence speed, but it is helpful to get the global optimal solution. Instead, when the value of \(\rho\) is lower, it reduces the searching time of ACO, while easily getting into the local optimum trap. The experiment indicates that the best value of \(\rho\) is 0.3.
###### Abstract  
Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algorithms (MOEAs). In this article, we propose a multi-objective method for solving the path planning problem. It is a population evolutionary algorithm and solves three different objectives (path length, safety, and smoothness) to acquire precise and effective solutions. In addition, five scenarios and another existing method are used to test the proposed algorithm. The results show the advantages of the algorithm. In particular, different quality metrics are used to assess the obtained results. In the end, the research indicates that the proposed multi-objective evolutionary algorithm is a good choice for solving the path planning problem.  
Keywords:robotics; path planning; multi-objective evolutionary algorithm; multi-objective optimization; refiner operators +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Extraction of Schottky Parameters  
Important parameters of the Schottky junction, which are Schottky barrier height (\(q_{B}\)), ideality factor (\(n\)), and series resistance (\(R_{s}\)), are extracted from the forward I-V characteristic at various temperatures. The calculation of the parameters is derived from the Schottky diode equation which follows the thermionic emission model shown in Equation (2) [28; 29; 30].  
\[I\ =\ I_{s}\left[\exp(\frac{qV}{nkT})\ -1\right] \tag{2}\]  
where \(I_{s}\) is the saturation current, \(q\) is the electronic charge, \(V\) is the voltage applied on the diode, \(k\) is the Boltzmann constant, and \(T\) is the absolute temperature. \(Is\) can be expressed using Equation (3),  
\[I_{s}\ =\ AA^{\star\star}T^{2}exp\left(-\frac{q\phi_{B}}{kT}\right), \tag{3}\]  
where \(A\) is the area of the diode, \(A^{\star\star}\) is the Richardson constant (112 Acm\({}^{-2}\):K\({}^{-2}\)), and \(\phi_{B}\) is the Schottky barrier height. For reliable calculation of the parameters, two methods were used to determine the values of \(q_{B}\), \(n\) and \(R_{s}\), as explained in the subsequent sub-sections.
## 2 Research Methodology
### CFD Analysis
#### 2.2.3 Boundary Conditions  
(1) Thermal boundary conditions  
In GTAW welding process, the two workpieces to weld are first melted locally under the heat input from the welding arc between the tungsten electrode tip and the workpieces, and then the molten metals solidify to form a weld that joins the workpieces together. In the simulation, the heat input from the welding arc was modeled using a Gaussian body heat source, in which the heat flux took a Gaussian distribution in radial direction, while the peak heat flux diminished linearly in sheet thickness direction. The expression of the heat input is as follows [17]:  
\[q(r,z)=\Big{(}1-\frac{z}{2h}\Big{)}\frac{4Q\eta}{\pi hr_{0}^{2}}\exp\left(-3 \frac{r^{2}}{r_{0}^{2}}\right) \tag{6}\]  
where \(q\) is the heat flux (\(W\)/mm\({}^{3}\)) of heat source, \(r\) is the distance to the center of heat source, \(r_{0}\) is the radius of the heat source, \(z\) is the coordinates in sheet thickness direction, \(h\) is the sheet thickness, \(Q\) is the output power of the power source (i.e., product of electric current \(I\) and electric voltage \(V\)), and \(\eta\) is the thermal efficiency. The values of the parameters used in simulation are listed in Table 2.  
Forced convection boundary conditions were applied on the regions of top and bottom surfaces in contact with the fixture, while natural convection boundary conditions were applied on the surfaces exposed to the environment. The convection coefficients used in simulation are also given in Table 2.  
(2) Arc pressure  
The arc is not only a heat source to melt metals, but also a pressure source in GTAW, which may affect the movement of molten metals in the weld pool. A Gaussian distribution of the arc pressure was assumed in the study as follows [18; 19]:  
\[P(r)=\frac{3\mu_{0}I^{2}}{4\pi r_{p}^{2}}\exp\left(-3\frac{r^{2}}{r_{p}^{2}}\right) \tag{7}\]  
In Equation (2), \(\mu_{0}\) is space permeability, \(I\) is the welding current, \(r\) is the distance to the arc center, and \(r_{\rm p}\) is the radius of pressure source.  
(3) Surface tension  
The dependence of surface tension of molten metal on temperature for the 2219 aluminum alloy was assumed to obey the expression [20]:  
\[\sigma=0.914-0.35\times 10^{-3}\times(\rm T-930) \tag{8}\]  
where T is the temperature in Kelvin. In the CFD modeling, to account for the effect of Marangoni stress on the fluid flow of molten metal in weld pools, the thermal gradient of the surface tension was set to be \(-0.35\times 10^{-3}\) N/(m\(\cdot\)K).  
(4) Gravity  
With the numerical models established, the welding processes under climbing and flat welding positions were numerically studied. Different welding positions were realized by changing the directions of gravity action relative to the orientation of workpieces. For flat welding, the gravitational acceleration (\(g\)) is totally in the minus \(z\) direction, while, for climbing welding, the gravity has two components, i.e., \(g_{x}\) and \(g_{z}\), in the minus \(x\) and minus \(z\) directions, respectively.
## 3 Samples and Methods  
### Collection and Pretreatment of Soil and Rock Samples  
Soil samples for this research were collected from the Fengxiangshugou-Mahuanggou section of the Mahuagou mining area (as shown in Figure 2b) located in Yiling District, Yichang City, Hubei Province (as shown in Figure 1). In the Fengxiangshu section, following the trend of the IP (Intensification Polarization) section, 11 bags (approximately 700 g/bag) of soil, fault gouge, and fault cataclastic rock samples were collected from the northeast to southwest. In the Mahuagou section, along exploration line No. 7 of the mining area, 6 bags (approximately 700 g/bag) of soil samples were collected from the northeast to southwest, crossing the ore body along the X206 village road. Additionally, 1 bag (approximately 700 g) of loose soil sample was taken from beside the Kaziya trench.  
After returning the collected samples to the laboratory, the sealed bags were opened, and the moisture inside the bags was allowed to air dry for a day using ambient airflow naturally. After the preliminary drying, each sample was individually and evenly spread out on A4 paper. These samples were placed on a shaded, well-ventilated tabletop away from direct sunlight and areas with artificial airflow sources, such as air conditioning and frequent human activity. Subsequently, for a week, daily inspections, turning of samples, removal of small stones and plant roots, and gentle tapping of consolidated soil were conducted using small sticks; this ensured that the surface and interior of the samples dried thoroughly, preventing consolidation at the bottom. Once the drying process was completed, the samples were promptly returned to sealed bags and stored properly.  
In order to accommodate the requirements of specific analytical tests, we subjected a portion of the total sample to grinding and sieving. Prior to grinding, we manually removed plant residues, stones, bricks, and non-soil components. The grinding process utilized an agate mortar and pestle while sieving employed a 200 mesh (75 \(\upmu\)m) nylon sieve. Soil powder obtained after sieving was collected on A4 paper and labeled with corresponding numbers before being placed in paper bags, which were then sealed in plastic bags for preservation. Following the completion of grinding and sieving for each sample, all tools were rinsed with deionized water and then dried in an oven. Tabletops were cleaned, and A4 paper was replaced as needed. Throughout these procedures, stringent measures were taken to ensure the independence of each sample, preventing cross-contamination and mixing of sample components, thus ensuring the accuracy and effectiveness of subsequent experiments.