esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Verification of the Finite Element Simulations  
In this section, the FEA is evaluated by comparing the simulated residual stress values with those obtained from experimental measurements. The 2D finite element simulation involved defining node paths to extract the residual stress values at different depths during the final simulation step. The residual stress at a particular depth was calculated as the  
Figure 8: Debye rings in 2D and camera image of residual stress evaluation at a position on the rail surface that exhibits the highest compressive stress of \(-\)683 MPa (Position P.5).  
Figure 7: Debye rings in 2D and camera image of residual stress evaluation at a position on the rail surface that exhibits the lowest compressive stress of \(-\)291 MPa (Position P.1).  
average value from nodes P1 to P7, as illustrated in Table 7. The results showed that the simulated residual stress profile closely aligned with the experimental results, but discrepancies were observed at specific depths, leading to an error margin that was deemed unacceptable. To improve accuracy, a 3D finite element simulation was also conducted. In this model, deep rolling was applied to two different sections of the material to compare the effect of angular orientation in the circumferential direction. The node paths were defined in the midsection of the cylinder, and the stress values were extracted from the final simulation step.  
Figure 9 shows the FEA results, focusing on the residual stress distribution after a deep rolling process on a surface. On the left side of the image, the overall view illustrates the affected area with a color gradient, indicating different levels of compressive residual stress across the surface. The red zone in the center corresponds to areas with high compressive stress concentrations, while the surrounding blue and green areas represent zones with lower compressive stresses. The bottom scale shows a distance ranging from 0 to 10 mm, giving a sense of the surface's dimensions. On the right side, a magnified section highlights seven specific nodes (P.1 to P.7), with their corresponding residual stress values, measured in megapsacals (MPa). Each node is labeled with a specific value: for instance, P.1 records 283.97 MPa, while P.5 records the highest value of 632.14 MPa. The table below the magnified image lists these values, demonstrating the variation in compressive stress at each point, crucial for validating the FEA model's accuracy. This analysis is essential for understanding how deep rolling affects stress distribution, providing insights into improving material performance.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 3 Proposed Scheme
### Handover Latency in Non-Location-Based RSS Extensions
#### 3.3.4 Buffering Required in Non-Location-Based RSS Extensions  
The FPMIPv6 is an RSS-based scheme and does not use the location for accurate prediction of the handover moment. Therefore, due to RSS error, the scheme incurs additional buffering when the prediction of the scheme is wrong. Buffering costs in this scheme include the packets stored at nMAG from the pMAG using the established tunnel until the MN reaches to nMAG domain and is attached to nMAG. Buffering required in the FPMIPv6 scheme is calculated using Equation (10) where session length and session rate are represented by \(E(s)\) and \(\lambda s\).  
\[\text{B}_{\text{FPMIPv6}}=\lambda s\cdot\text{E}(s)\cdot\{\ \text{t}_{\text{error}}+ \varnothing-\ (\text{t}_{\text{scan}}+\text{t}_{\text{report}}+\text{t}_{\text{mag}}+ \text{t}_{\text{HI}}+\text{t}_{\text{HACk}})\ +\text{t}_{\text{L2}}+\text{t}_{\text{RS}}+\text{t}_{\text{ WRS}}\} \tag{10}\]  
The smart buffering scheme is also affected by the RSS error due to the environment. In such cases, the handover moment is detected as incorrect and, due to early handover initiation, the packets are buffered. pMAG buffers the packet in a smart buffering scheme until the FReq message is received by nMAG. Furthermore, the buffered packets are forwarded to nMAG from the duration when nMAG achieves FRep and PBA messages. In a situation, where the PBA is received by nMAG from LMA earlier than FRep, the packets are buffered from LMA so that it receives all the packets from pMAG. Buffering can be calculated by using Equation (11).  
\[\text{B}_{\text{smart-buff}}=\lambda s\cdot\text{E}(s)\{\ \text{t}_{\text{error}}+\text{t}_{\text{L2}}+\text{t}_{\text{WRS}}+\text{t}_{ \text{RS}}+\text{t}_{\text{FReq}}+\text{abs}\ (\text{t}_{\text{PBU}}+\text{t}_{\text{PBA}}-\text{t}_{\text{FReq}}-\text{t }_{\text{Frep}})\} \tag{11}\]  
Buffering in low latency extension starts when the DeReg-PBU message is received by LMA. Buffering is continued up to the moment when LMA receives the PBU message from nMAG after the MN's attachment is detected. Having the dependency of the procedure of low latency scheme over RSS measure, the scheme is affected by the RSS error, and additional buffering is performed when RSS error occurs. In response to the PBU, LMA replies with PBA, and the stored packets are provided to nMAG which is further delivered to MN. Buffering in a low latency scheme is computed using Equation (12).  
\[\text{B}_{\text{Low-latency}}=\lambda s\cdot\text{E}(s)(\ \text{t}_{\text{error}}+ \varnothing+\text{t}_{\text{L2}}+\text{t}_{\text{WRS}}+\text{t}_{\text{RS}}) \tag{12}\]
# An Experimental Strain-Based Study on the Working State of Husk Mortar Wallboards with Openings
## 2 HMES Wallboards and Testing Preparation
### Experimental Device and Loading Scheme  
This experiment was carried out at the Heilongjiang Institute of Science and Technology, China. The experimental device is shown in Figure 4. The vertical load was applied by mechanical jacks, and the horizontal load was applied by a hydraulic servo actuator, which was installed in the same plane as the specimen. To facilitate lifting, a steel groove was designed and fixed at the bottom of the tested wallboard by pouring concrete. Two jacks were installed for connecting the specimen and the base. In this way, the base of the specimen was fixed during the test. A pair of two tie rods were installed on the loading end of the horizontal actuator; a cyclic load could be realized with such a loading device. The horizontal cyclic load and monotonic loads were supplied by a 500 kN actuator. As shown in Figure 5, the loading was force controlled for the cyclic loading case as well as the monotonic loading case. The loading scheme was composed of applying incremental multiples of 5.0 kN until the test was terminated for the specimen 1-1, specimen 1-2, specimen 2-1, and specimen 2-2. As for specimen 1-3, specimen 1-4, specimen 2-3, and specimen 2-4, the incremental force was 3.0 kN. In addition, the specimen was exposed to an axial load of 30 kN at the top surface that was applied by two hydraulic jacks on the rigid distribution beam, which represented the gravity load and remained constant throughout the test. The force controlled scheme was chosen over displacement controlled scheme for the reason that force and strain energy data were significant in the analysis of strain energy, which is applied in Section 4.3. As the load capacities of the HMES wallboards with different sizes are usually different, the load increments of specimen 1-1 and 1-2 are set 5.0 kN while the load increments of specimen 1-3 and 1-4 are set 3.0 kN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Configuration \& Size**} & \multicolumn{2}{c}{**Group 1**} & \multicolumn{2}{c}{**Group 2**} \\ \cline{2-5}  & **No.** & **Lateral Loading Case** & **No.** & **Lateral Loading Case** \\ \hline \multicolumn{5}{c}{} \\ Intact Wallboard & & & & \\ \(3300\times 2800\times 250\) & 1-1 & cyclic & 2-1 & monotonic \\ \hline \multicolumn{5}{c}{} \\ Window opening: \(1500\times 1600\) & & & \\ \(3300\times 2800\times 250\) & 1-2 & cyclic & 2-2 & monotonic \\ \hline \multicolumn{5}{c}{} \\ Door opening: \(900\times 2100\) & & & \\ \(2400\times 2800\times 250\) & & & \\ \hline \multicolumn{5}{c}{} \\ Window opening:\(1500\times 1600\) & & & \\ Door opening: \(900\times 2100\) & 1-4 & monotonic & 2-4 & monotonic \\ \(3300\times 2800\times 250\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The configurations and sizes of the experimental HMES wallboards.  
Figure 3: Construction process.
## 3 Research Purpose and Questions  
This current study is primarily aimed to explore and examine Saudi students' experiences of using computer-based simulations to support their unsupervised (without teacher's support) learning of science concepts. Moreover, the study seeks to examine the possible influence of students' independent learning abilities and prior experience of using simulations on the levels of their satisfaction and engagement when they experience simulations to support their unsupervised learning of science concepts. This study is guided by the following research questions:  
1. How engaged and satisfied Saudi students feel when they experience computer-based simulations to support their unsupervised learning of science concepts?
2. Do the male and female students have significant differences in their levels of engagement and satisfaction when they experience simulations to support unsupervised learning of science concepts?
3. Does students' prior experience of computer simulations predict the engagement and satisfaction levels when they experience simulations to support unsupervised learning of science concepts?
4. Do students' independent learning abilities significantly predict the engagement and satisfaction levels when they experience computer simulations to support unsupervised learning of science concepts?
## 2 Machine Learning Applications in Big Data
### Agriculture and Environmental Monitoring  
In agriculture, ML is driving the development of precision farming techniques that increase crop yields, reduce waste, and optimize resource use. By analyzing data from satellites, drones, and IoT devices, ML models can monitor crop health, predict yields, and optimize irrigation and fertilization schedules. These techniques enable farmers to make data-driven decisions that improve productivity and sustainability [86; 87; 88; 89].  
ML is also used in environmental monitoring to analyze data from sensors, satellites, and other sources to monitor air and water quality, predict natural disasters, and assess the impact of climate change. For example, ML models can analyze satellite images to detect deforestation, monitor changes in land use, and predict the impact of these changes on biodiversity and ecosystems. These applications are critical for managing natural resources and mitigating the effects of climate change [90; 91; 92; 93].  
In precision agriculture, ML algorithms analyze data from various sources, including soil sensors, weather stations, and drones, to optimize planting, irrigation, and harvesting schedules. This results in more efficient use of water, fertilizers, and pesticides, leading to higher crop yields and reduced environmental impact. Additionally, ML is used to develop disease and pest prediction models, enabling farmers to take preventive measures before outbreaks occur [94; 95; 96; 97].  
In environmental monitoring, ML is applied to analyze data from satellite imagery, weather models, and ground sensors to track changes in ecosystems, predict natural disasters, and monitor climate change. These models can detect patterns and trends that are not apparent to human observers, providing valuable insights for environmental management and policy making [98; 99; 100].
## 4 Testbed Development
### Characterization of Starlink
#### 4.1.2 Service Performance
We observe outstanding performance on UDP with peaks reaching almost 50 Mbps in uplink and 440 Mbps in downlink. Especially in uplink, there is a notable dispersion of the measurements compared to TCP. The 1-stream TCP connection varies in the range of 11-14 Mbps in uplink and 54-58 Mbps in downlink. If we simplify the expectations by assuming simple linear growth, the performance achieved with 10 streams should be 10 times the previous values, but the improvement is 32-35 Mbps in uplink and 180-300 Mbps in downlink. Figure 5 shows the RTT and jitter. Both are higher with 10 streams due to the larger number of samples, as the 43,200 measurements gathered by 1 stream are increased to 432,000 measurements with 10 streams. The same reason may justify the wider dispersion of both metrics with 10 streams. Likewise, the dispersions of 8 ms with 1 stream and 25 ms with 10 streams demonstrate stable performance, emphasizing the proper management of the dynamism inherent to satellite links by Starlink. We conclude that Starlink offers reliable service performance, although it is heavily impacted by the adopted transport protocol. Moreover, we also see performance fluctuations when presenting the metrics above over time. This may be caused by satellite handovers, as explained in the following section. Figure 4: Boxplots of the bitrate achieved with Starlink in (**a**) uplink and (**b**) downlink.
# Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
## 2 Theoretical Studies
### Structure of a Four-Armed Helical Antenna  
The general modeling of a four-armed helical antenna is shown in Figure 3. The antenna consists of four parallel metal conductor strips of equal length, and the helical arms  
Figure 2: Outline and internal structure of the sounder.  
can be in the form of helical microstrip lines or metal spiral rods of a certain diameter [14]. If the microstrip line form is used, a dielectric plate with flexible material is generally required as the carrier plate of the microstrip line. The working wavelength of the four-armed helical antenna is related to the length of the metal conductor bar, which is generally an integer multiple of \(\lambda/4\), and when N is an odd number, the top of the metal conductor bar is open-circuited; when N is an even number, the top is short-circuited.  
When designing the overall dimensions of a four-armed helical antenna, the following formulas are commonly used for calculations:  
\[L_{z}=m\sqrt{\left(\frac{1}{m^{2}}\right)\left(L_{l}-Ar\right)^{2}-4\pi^{2}r^ {2}} \tag{1}\]  
where \(L_{z}\) is the height of the spiral arm in the vertical direction, \(m\) is the number of helical turns of the spiral arm, \(L_{l}\) is the length of the spiral arm, and \(r\) is the helical radius. When the top is open (\(N\) is odd), \(A\) is 1, and when the top is short-circuited (\(N\) is even), \(A\) is 2. The actual design process of the four-armed helical antenna generally abides by the following law [15]: When the height of the helical arm in the vertical direction (\(L_{z}\)) is fixed and unchanged and when the number of spiral turns (\(m\)) of the spiral arm is fixed, the center directivity becomes stronger, and the antenna half-power beam width becomes narrower. Similarly, when the number of spiral turns (\(m\)) of the spiral arm of the antenna is fixed, the height (\(L_{z}\)) of the spiral arm of the antenna is increased in the vertical direction, and a lower center directivity results in a wider beam width. Therefore, according to the application scenario of the four-arm helical antenna, high center gain can be obtained by reducing the height of the antenna and decreasing the number of helical turns (\(m\)), while uniform radiation in the upper half of the space can be achieved by increasing the height of the antenna and the number of helical turns (\(m\)). Usually, \(m\) is a half turn. At the same time, the height of the vertical direction of the spiral arm (\(L_{z}\)) and the number of spiral turns (\(m\)) also affect the axis ratio of the antenna; the more turns, the higher the antenna height and polarization purity.
## 3 Results  
### Comparison of Achieved Accuracies under Operating Conditions and on a Calibration Base  
Table 1 shows the identifying name of the line section, supplemented by the names of the municipalities from which the railway line starts, the length of the section in kilometers, the number of determined points of the primary GNSS system in the section and the achieved sampling standard deviations in the horizontal plane in two perpendicular \(X\), \(Y\) directions, calculated from the variance of a series of repeated measurements according to the relations (1). The value of the standard deviation represents the uncertainty of a single measurement. If the primary system points are determined from three repeated measurements, a coordinate standard deviation of the mean value of better than 5 mm can be expected.  
When determining the primary system points from three repeated measurements, a coordinate standard deviation of better than 5 mm can be expected.  
Table 2 shows the achieved accuracy of the used geodetic Trimble R2 and Trimble R780 GNSS instruments in the form of the sample standard deviation of one measurement in the direction of the \(Y\) and \(X\) coordinate axes of the planar coordinate system JTSK calculated according to the procedure described in Chapter 2 under practically ideal observation conditions at the calibration base Brno-South. The signals used in the Trimble R2 and Trimble R780 instruments were GPS, GLONASS, Galileo and BeiDou, with tilt compensation disabled in the Trimble R780.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Railway Line**} & **Line Length** & **No. of GNSS Point to** & \(\mathbf{s_{Y}}\) & \(\mathbf{s_{X}}\) \\  & **[km]** & **Be Determined (\(n\))** & **[mm]** & **[mm]** \\ \hline RL 1371 Velká Kras—Vidnava & 4.5 & 8 & 3.2 & 5.7 \\ RL 2302 Bojkovice—Hostêttin & 9.5 & 12 & 6.8 & 7.1 \\ RL 1363 Jesenik—Mikulovice & 16.5 & 27 & 6.9 & 6.9 \\ RL 2302 Bzenec railway station & 5.5 & 6 & 5.3 & 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Obtained accuracy of GNSS measurements in operational conditions on the measured railway lines.  
The accuracy of measurements obtained under railway operating conditions is two-three times worse than the accuracy obtained under practically ideal observation conditions at the calibration base Brno-South. The deterioration of accuracy is due to horizon obstructions, surrounding vegetation and the influence of multipath.  
For short 5 min GNSS RTK observations, the entire observation interval is usually affected by multipath. By repeating the observation several times with a time interval of 3-4 h, the effect of multipath is reflected by an increase in the scatter of the partial measurement results. The mean value calculated from a series of multiply repeated measurements is closer to the conventional true value.
## 3 Data and Results
### Results
#### 3.2.1 Predicting Smartphone Technology
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**OEM** & **DMU** & **Reference Year** & **Efficiency Release** & **Best,f (UEF Freentis)** & **rec** & **current (Effective Date)** & **save Percent** & **Date Forecast** & **Date Forecast** \\  & & & & & & & & \\
91 & Oppo & Renzo SG & 2019 & & & 2015.2 & 1.070013 & 2018.497 \\
92 & Apple & Funch F1 & 2019 & & & 2015.826 & 1.070013 & 2017.326 \\
94 & Hercul & vvvvvvvvv & 2019 & & & 2015.890 & 1.070013 & 2017.758 \\
95 & Hercul & vvvvvvv & 2019 & & & 2015.594 & 1.070013 & 2017.76 \\
95 & Maroales & Maroales G7 Flower & 2019 & & 2015.582 & 1.070013 & 2017.76 \\
96 & Scanning Galaxy S05.05 & 2019 & & & 2015.582 & 1.070013 & 2018.68 \\
97 & Sony & Experts1 & 2019 & & 2077 & 1.070013 & 2028.84 \\
98 & vvvv & 300 & 2019 & & 2016.911 & 1.070013 & 2017.986 \\
99 & Xionion & 180 H-S05 & 2020 & & 2015.57 & 1.070013 & 2017.27 \\
100 & Oppo & Renzo SG & 2020 & & 2015.57 & 1.070013 & 2018.49 \\
102 & Suny & Penti & 2020 & & 2014.58 & 1.070013 & 2018.346 \\
103 & LG & Vvvvvvv & 2020 & & 2014.58 & 1.070013 & 2018.346 \\
104 & Maroales & Edge & 2020 & & 2015.75 & 1.070013 & 2018.346 \\
105 & Scanning Galaxy S05.05 & 2020 & & 2014.67 & 1.070013 & 2018.647 \\
106 & Apple & Phone SG (2020) & & 2015.45 & 1.070013 & 2017.727 \\
107 & Yvvvv & 300 Dro & 2020 & & 2016.974 & 1.070013 & 2018.421 \\ \hline \end{tabular}
\end{table} 
Table 3: _Cott._The discrepancy between the predicted value and the actual value in Table 3 means that the market launch of a product is not determined solely by technological superiority but is a product of various business environments. Despite these limitations, in the long run, technological forecasting can recognize opportunities and challenges through scientific methods and predict technological limitations by performing technological forecasting. Separately from this study, multiple regression analysis was performed by selecting the dependent variable as the product release year and the independent variable as the variable used in TFDEA. Because the analysis conditions are different, the results of the two methodologies cannot be compared, but the results of the TFDEA for the year of release are not rejected.
## 2 Materials and Methods
### Electrochemical Characterization  
The electrochemical characterization was performed in triplicate, using a VersaStat3 potentiostat (AMETEK Princeton Applied Research, Oak ridge, TN, USA). Linear sweep voltammetry (LSV) was performed in a 100 mL three-electrode cell, using a platinum sheet as the counter electrode, an Ag/AgCl (3 M KCl) as the reference electrode (3.247 V vs Li/Li+), and a copper rod as the working electrode. All the measurements were performed at 25\({}^{\circ}\)C. After the open circuit potential stabilization, the cathodic LSV was carried from OCP to -5 V (vs. Ag/AgCl), with a sweep rate of 10 mV/s. The overpotential range used in this study was selected to approximate localized electrochemical conditions that may occur during coolant leakage or insulation failure in battery electric vehicles (BEVs). The selected range ensures that hydrogen evolution can be distinguished from oxygen reduction, while avoiding the high gas evolution rates that can interfere with accurate current measurements.