esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects on the Content of Phytohormones in Dry Seeds  
The content of phytohormones involved in the control of germination was determined, aiming to compare the amount of phytohormones and CP-induced changes in their amount in seeds of a different color. The obtained results of phytohormone analysis in red clover seeds are presented in Table 2.  
The amount of ABA in control yellow seeds of the 'Arimaiciai' cultivar was almost 3 and 1.6 times lower compared to brown and dark purple seeds, respectively (Table 2). In addition, yellow seeds did not contain GA7, in contrast to control seeds of other colors. The level of GA3 was bellow detectable in extracts from all experimental groups. The amount of GA7 varied greatly depending on the seed color: In the control, the amount of GA7  
The amount of germination-promoting GA7 hormone in the control group of yellow seeds was below the detectable level and seed treatments with EMF but not CP increased it. The high content of GA7 was found in brown control seeds, but it was significantly reduced by treatments (EMF15--1.7, CP5 and EMF10--1.9 and CP7--2.7 times). Seed treatments (except CP7) strongly increased GA7 only in dark purple seeds. GA3 level was below detectable in red clover 'Arimaiciai' cultivar seed extracts from all experimental groups.  
Regardless of seed color, EMF treatments did not have an impact on the content of the germination inhibitor phytohormone IAA; but CP5 treatment reduced IAA content by 51% in yellow seeds, whereas in brown seeds, it did not change the IAA content. Both CP5 and CP7 treatments reduced IAA amount in dark purple seeds by 18-24%.  
Zeatin level in yellow seeds was decreased by CP (30%) but was not affected by CP in dark purple seeds, and it was 6.3 times reduced in brown seeds by CP5 only. The content of zeatin dropped below the detectable level in EMF-treated seeds of all three colors. The amount of stress hormone SA was reduced by CP5 and CP7 treatments in dark purple (20 and 29%, respectively) and yellow seeds (18 and 6%, respectively). In contrast, 20% increased SA amount was detected in brown seeds treated with CP5. EMF treatments to a similar extent (36-41%) reduced SA content in yellow and dark purple seeds but did not change it in brown seeds.  
Thus, the amount of phytohormones in the control seeds of the red clover cultivar 'Arimaiciai' as well as their changes induced by seed treatments depended on seed color. However, the treatment effects on germination (Table 1) did not correlate with the observed shift in the balance of phytohormones (Table 2).
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 2 Materials and Methods
### Research Methodology
Incorporating flow parameters involved entering water flow data into the model, calculated based on discharge and level data from previous stages. For each scenario:  
a. discharge rate (Q): S1 = 8 m\({}^{3}\)/s; S2 = 23 m\({}^{3}\)/s; S3 = 41 m\({}^{3}\)/s; S4 = 60 m\({}^{3}\)/s;  
b. flow level (H): S1 = 3 m; S2 = 3.2 m; S3 = 3.35 m; S4 = 3.5 m;  
c. The completion of the modeling plan was carried out based on the geometries and flow parameters in a subcritical regime. This regime is characterized by a water flow velocity that is lower than the gravity wave velocity. The subcritical regime occurs when the Froude number (Fr) is less than 1. The estimation of Fr < 1 was performed using the Froude number, calculated according to the standard equation (Equation (5)):  
\[\mathrm{Fr}=\frac{\mathrm{V}}{\sqrt{\mathrm{g}\times\mathrm{h}}} \tag{5}\]  
where V is the average water velocity, determined from the hydraulic modeling results, g is gravitational acceleration (9.81 m/s\({}^{2}\)), h is the hydraulic depth, calculated based on the cross-sectional geometry and the water levels resulting from the simulation. In our study, all the values obtained in the analyzed scenarios indicated a subcritical regime (Fr < 1), which is typical for rivers with a low slope and predominantly slow flow. 5. Analysis initialization involved running the designed model under the four different scenarios. 6. Flood impact assessment at maximum discharges on agricultural and non-agricultural lands involved identifying flooded areas and overlaying them with the land use map [59]. 7. Result interpretation was carried out in various formats: tabular, graphical, and cartographic (Figure 2).
## 4 Simulation and Analysis of EKF-GAPF
### Optimization Capability of AGA  
To verify the optimization performance of AGA, two test functions with distinct characteristics are selected. Test function 1 is a simple quadratic function, as shown in Formula (20), which aims to find parameters \(a\), \(b\), and \(c\) by fitting a given set of data points, thereby minimizing the error between the predicted values of the fitting function and the actual values. This function has a regular shape with only one extreme point, making it suitable for evaluating the fitting accuracy of algorithms under ideal conditions. As shown in Formula (21), test function 2 is a complex multi-peak function with numerous local extreme points, easily trapping optimization algorithms in local optimal solutions. By optimizing this function, the global optimization capability of the algorithm can be effectively evaluated. A comparison of the performance between AGA and traditional GA is illustrated in Figure 4 Specifically, Figure 4a shows the performance comparison of AGA and traditional GA in handling minimization problems, and Figure 4b shows their performance comparison in handling maximization problems.  
\[MSE\;f_{1}(a,b,c)=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-(a\cdot x_{i}^{2}+b \cdot x_{i}+c)\right)^{2} \tag{20}\]  
\[MAX\;f_{2}(x,y)=21.5+x\sin(4\pi x)+y\sin(20\pi y)\;\;-3\leq x\leq 12.1,\;4.1 \leq y\leq 5.8 \tag{21}\]  
As can be seen from Figure 4a, the initial convergence speed of AGA is significantly better than that of traditional GA, and it can quickly approach the optimal solution within almost 10 iterations. In contrast, traditional GA requires more iterations to obtain the same result. From Figure 4b, it can be seen that AGA also rapidly improves the objective function value in the early iterations, and approaches the global optimal solution after 50 iterations, while traditional GA easily falls into local optimal solutions at the same number of iterations. In summary, whether for minimization or maximization problems, the improved AGA significantly outperforms traditional GA in terms of convergence speed, global search ability, and resistance to local optima.
# Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
## 2 Materials and Methods
### Ex-Situ Characterizations
#### 2.2.5 Membrane Swelling Property Testing  
The swelling of a membrane in vanadium electrolyte could affect VRFB performance and durability. Membrane swelling was measured through mass change and dimensional change before and after exposing the membrane to vanadium electrolyte solution. The weight and dimension of a piece of membrane (2 \(\times\) 5 cm) was measured, then it was soaked into 18 mL of 1.6 M VOSO\({}_{4}\) solution (with 2 M H\({}_{2}\)SO\({}_{4}\)) at RT for 24 h. The membrane was then removed from the solution and dried by Kimwipe. The weight and dimensions were re-measured. All the measurements were conducted at 21 \({}^{\circ}\)C with an RH of 40%.  
The quantity of water and electrolyte uptake inside the membrane was determined by mass change according to Equation (7):  
\[Mass\ change\ (\%)=\frac{m_{wet}-m_{dry}}{m_{dry}}\cdot 100\% \tag{7}\]where \(m_{tot}\) and \(m_{dry}\) are the mass of the membrane after and before soaking in VOSO\({}_{4}\) solution, respectively.  
The dimensional change of the membrane was calculated by Equation (8):  
\[Dimensional\ change\ (\%)=\frac{V_{net}-V_{dry}}{V_{dry}}\cdot 100\% \tag{8}\]  
where \(V_{net}\) and \(V_{dry}\) are the volumes of the membrane after and before soaking in VOSO\({}_{4}\) solution, respectively.
###### Abstract  
Soil loss due to wind erosion and dust deposition has become a growing concern, particularly in arid regions like Al-Baha, Saudi Arabia. The aim of this study was to quantitatively assess soil loss and dust deposition using three different dust collection methods across 20 sites during the summer of 2022. The methods include Big Spring Number Eight (BSNE), which measures airborne dust particles using passive samplers; Surface Dust Collector (SDC), designed to collect dust settling on the ground surface; and Marble Dust Collector (MDCO), which utilizes marble-coated surfaces to trap and measure dust deposition. These methods collectively provide a comprehensive evaluation of dust dynamics in the study area. The objective was to evaluate the effects of wind erosion and dust deposition on soil properties, offering insights into the mechanisms of soil loss in arid environments. The study revealed significant variations in soil characteristics, including low organic matter content (\(<\)1%), high calcite (up to 19.62%), and increased salinity levels, with notable quantities of CI\({}^{-}\) (211.58 mega kg\({}^{-1}\)) and Na\({}^{+}\) (165.98 mega kg\({}^{-1}\)). July showed the highest dust deposition (0.0133 ton ha\({}^{-1}\)), particularly at site S11, while soil loss was lowest at site S5. This research offers novel insights into the nonlinear relationship between soil loss and time, contributing to sustainable soil management strategies. By aligning with Saudi Arabia's Vision 2030 and the Sustainable Development Goals (SDGs), the findings underscore the need to mitigate soil loss to enhance environmental sustainability, prevent desertification, and promote long-term resilience in arid regions.  
wind erosion; falling dust; dust deposition; soil losses; climate change; sustainable development goals (SDGs); environmental sustainability; Al-Baha; arid regions +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A pH Monitoring Algorithm for Orifice Plate Culture Medium
###### Abstract  
Recently, there has been renewed interest in cell therapy, which plays a key role in the clinical research of genetic diseases, advanced blood disease, and other diseases. It shows considerable clinical application value and is known as \"the new pillar of future medicine\". Automatic cell culture and operation technology is the key to ensuring scale, standardization, and stability between batches of therapeutic cells. The pH of the cell culture medium is vital for cell growth. Most cells are suitable for growth at pH 7.2-7.4. A pH of cell culture medium lower than 6.8 or higher than 7.6 is harmful to cells, and cells will degenerate or even die. At present, the monitoring method of cell culture medium pH of automatic cell culture equipment is mainly a visual observation method, which can not accurately or quickly reflect changes in the cell culture medium. To address the issue of monitoring of cell culture fluid pH for automated cell culture equipment and the inability to employ invasive sensors to measure pH during well plate culture, a pH monitoring method for orifice plate culture medium algorithm based on HSV (hue, saturation, value) model is proposed by studying the changes of cell culture medium in the process of cell culture. The research presented here reveals the laws of cell culture fluid pH change and its color moment, and the intelligent monitoring of cell culture fluid pH was successfully achieved. The problem of non-destructive monitoring of the pH of cell culture fluids in well plates is also addressed.  
digital image processing; automated cell culture; HSV; machine vision; pH +
Footnote †: journal: Applied Sciences
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 6 Results
Then, to summarize all the relevant results, the authors have evaluated the MMSE and LR aided MMSE receivers, as presented in Tables 1 and 2. Figure 5.1 Comparison of run-times of different algorithms. Numerous research studies that analyze the capacity improvement due to LLR-aided receivers are presented in the literature. However, there is a lack of interest in the analysis of KZ-aided receivers. Therefore, the main contribution of this paper is to present a comprehensive evaluation and analysis of the effectiveness of KZ-aided receiver for massive MIMO systems. As shown in Tables 1 and 2, KZ aided receiver performs much better than other receiver systems under uncorrelated and correlated channel conditions. Consequently, the results prove the fact that KZ-aided receiver can be considered as a reliable solution for the massive MIMO systems. Figure 5.2 Performance analysis under multi user scenario. Figure 5.3 Comparison between different receiver systems.
## 4 Empirical Study
### Data Description and Preprocessing  
The data used in this study originates from Guangzhou Port Group and Guangzhou Port Security Bureau in China. It consists of inspection records of delivery trucks from September 2013 until May 2014. In total, 5320 records are used, of which 4898 are reports of normal deliveries and 422 are records of delivery with theft, which we also refer to as fraud delivery. Table 1 presents two examples of fraud delivery reports from sampling inspection. First, we apply text segmentation to obtain features with structured storage using Rapidminer tool. Instead of using text storage with meaningless redundancy and difficult implementation, we segment these inspection records as structured features including data about the driver, truck, delivery cargo, operational time, fraud cargo and theft mode, and then store it in respective fields of databases. Then we further extract related operational records from the logistics information system in terms of license plate numbers and timestamps. Based on these data, the domain experts of the practitioner and the police help us to identify 38 relevant elements of fraud risk as the initial feature set (Table 2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Event Description** \\ \hline \multirow{4}{*}{1} & On the night of 14 March 2014, driver [PERSON] drove the truck with plate number XiangX. He stole 3.88 tons of brown coal (Indonesia coal, Lower Heating Value: 4400 Kcal/Kg) from terminal A of Guangzhou Port by reforming or retrofitting the water tank of the truck. The truck came into the terminal A with water in the tank and released the water before loading. Then he secretly transported the stolen cargo, identified to have a value of 455 yuan per ton and 1765.4 yuan in total, to the factory B in DongGuan. \\ \hline \multirow{4}{*}{2} & On the night of 11 April 2014, driver [PERSON] drove the truck with plate number YueY, and stole 3.89 tons of coal (Indonesian Steam Coal, Lower Heating Value: 4651 Kcal/Kg) from terminal C of Guangzhou Port by reforming the car hopper of the truck. The truck came into the terminal C with objects in the car hopper and that were released before loading. Then he secretly transported this cargo from the heft, identified to have a value of 518 yuan per ton and 2011.3 yuan in total, to the factory D in DongGuan. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of fraud delivery records from sampling inspection.
# Quality Index of Supervised Data for Convolutional Neural Network-Based Localization
## 1 Introduction  
Automated guided vehicles (AGVs) have important uses in modern factories. The main functions of an AGV are the localization and detection of pallets and cargo (see Figure 1). In large factories, sensors for AGVs must characterize targets over long ranges, as well as under different lighting conditions, because AGVs are often operated throughout the day both indoors and outdoors. To this end, light detection and ranging (LiDAR) in combination with cameras are useful in many scenarios. LiDAR [1, 2] is a necessary technology to support the functionalities of AGVs and autonomous robots. A popular LiDAR sensor for autonomous robots is the one launched by Velodyne [1]. The Velodyne sensor HDL-64E outputs approximately one million points with 360 fields of view using multiple laser transmitters and receivers. However, these multiple transmitters and receivers hinder the incorporation and development of low-cost and small-sized sensors.  
Recently, some approaches have leveraged deep convolutional neural networks (CNN) to perform localization [3, 4, 5, 6]. However, to perform accurate CNN-based localization, the quality of supervised training data is important. If the supervised training data are noisy, poor localization results are obtained (see Figure 1 left), whereas clean supervised training data yields good localization results (see Figure 1 right).  
To resolve these problems, we propose a quality index for supervised training data based on the correlations between consecutive frames that visualize the important pixels for CNN-based localization. First, we conducted a fundamental study to determine the important pixels for CNN-based localization. Our study showed that the important pixels differ depending on the quality of the supervised training data (see Figure 2). If the supervised training data are clean, the important pixels change gradually with the movement of an AGV. On the other hand, if the supervised training data are noisy, the important pixels change dramatically. We utilized these differences in pixel change rates to determine the quality of supervised data.  
Secondly, we defined the quality index of supervised data based on the difference in important pixels. We evaluated our quality index in indoor environment localization using our small LiDAR sensor and compared the localization performance according to our index. Our results demonstrate that the index correlates to the quality of supervised training data for CNN-based localization.  
Our results are presented as follows. Section 2 presents the related work, Section 3 presents our small LiDAR sensor, named single-photon avalanche diode (SPAD) LiDAR, and the quality index for supervised training data, and Section 4 presents the experimental findings and results through CNN-based localization. Finally, we conclude with suggestions for future research in Section 5.
# Traffic-Aware Optimization of Task Offloading and Content
## 4 Joint Optimization for Offloading and Content Caching with Traffic Stream Prediction
### MOEA/D-Based Joint Optimization  
The IoV environment always consists of multiple regions, so the joint optimization problem of task offloading and content caching with the objective of minimizing the average execution time and total energy consumption in each region under the constraints  
Figure 3: Heat maps of the real dataset and the FOST prediction.  
of maximum latency and computational power is a multi-objective optimization problem. The multi-objective optimization problem is converted to a multi-group single-objective optimization problem using the Tchebycheff approach. A collaborative approach combined with a population evolution strategy is used to optimize these subproblems simultaneously using the neighborhood relationship between the subproblems.
# Delay Bounded Multi-Source Multicast in Software-Defined Networking
## 5 DBMS Routing Algorithm  
The DBMS problem is an NP-hard and cannot be solved in polynomial time. Therefore, in this section, we designed DBMS algorithm to solve the problem. The focus is on designing an efficient method to select a potential source node from the source node set \(S\) for the multicast nodes,then construct minimum delay cost trees within a certain delay bound constraint for the DBMS-Forest. The quality of service for the tree construction is enforced by the delay bound constraint.
## 4 Results and Discussion
### Master Curve Generation  
The master curve of the complex modulus and the rutting factors of the base asphalt and the bio-asphalt with 5% bio-oil were generated in the broadband range using the time-temperature equivalent principle. The reference temperature was 64 \({}^{\circ}\)C.  
As seen in Figure 13a,b, unaged bio-asphalt with 5% bio-oil content, and 50# matrix asphalt, had a similar trend from a low to high-frequency range, and the complex modulus and rutting factors increased with the increase of frequency. In the lower frequency range, the complex modulus and rutting factors of unaged bio-asphalt with 5% bio-oil were consistent with those of the 50# base asphalt, indicating that the bio-asphalt with 5% bio-oil had the same resistance to rutting as the 50# base asphalt. In the higher frequency range, the complex modulus and rutting factors of the unaged bio-asphalt with 5% bio-oil reduced to some extent, compared with 50# base asphalt, which indicated that it was superior to matrix asphalt in low-temperature, anti-cracking performance in the higher frequency range.  
Figure 12: The changes of different parameters of RTFO-aged bio-asphalt with 5% bio-oil content. (**a**) Phase angle; (**b**) Storage modulus; (**c**) Loss modulus; (**d**) Complex modulus.  
As shown in Figure 14a,b, RTFO-aged bio-asphalt with 5% content bio-oil and 50# matrix asphalt had a similar trend from low to high frequency range, and the complex modulus and rutting factors increased as the frequency increased. In the lower frequency range, the complex modulus and rutting factors of RTFO-aged bio-asphalt with 5% bio-oil were larger than those of the 50# base asphalt, which indicated that bio-asphalt had a better ability to resist rutting deformation. In the medium frequency range, the complex modulus and rutting factors of RTFO-aged bio-asphalt with 5% bio-oil were similar to those of the 50# matrix asphalt, and the anti-cracking performance of both was not much different. In the higher frequency range, the complex modulus and rutting factors of the RTFO-aged bio-asphalt with 5% bio-oil content were larger than those of the 50# base asphalt, and its rutting resistance increased. This was because the performances of bio-asphalt after RTFO had been affected by the aging, although the impact was not significant.