esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Model-Free Adaptive Sliding Mode Control Based on FFDL for Electric Multiple Units
## 4 Simulation Experiment and Analysis
### Simulation 1: No External Disturbance  
The first set of experiments is simulated using the MFASMC algorithm without considering external disturbances to verify the effectiveness of the proposed algorithm. The initial conditions of the system are set as \(\mathbf{v}_{1}(1)=\mathbf{v}_{2}(1)=\mathbf{v}_{3}(1)=\mathbf{v}_{d}\), \(\mathbf{\Phi}_{1}(1)=\mathbf{\Phi}_{2}(1)=diag\{0.5,0.5,0.5\}\). The controller parameters are set to \(\lambda=1\), \(\rho=0.85\), \(\mu=1\), \(\beta=0.2\), \(\varepsilon=10\), \(q=0.5\), \(\delta_{1}(1)=\delta_{2}(1)=-0.6\), \(\mathbf{P}=diag\{2,2,2\}\).  
Figure 5 is the speed-displacement tracking curve of each power unit by MFASMC method, Figure 6 is the speed tracking error curve, and Figure 7 shows the corresponding velocity level error curve. It can be seen that the whole train speed tracking effect using MFASMC method is better, and the tracking error range of each power unit is between [\(-0.245\) km/h, \(0.175\) km/h], which meets the speed tracking accuracy requirement. According to CTCS-3 [30] (China Train Control System Level 3) speed error requirements (that is, speed error range should be \(\leq\)30 km/h, no more than \(\pm\)2 km/h; when the speed is greater than 30 km/h, it should be within 2% of the speed). In addition, the speed change of the EMUs from Tai'an to Xu'zhou East is stable and the error fluctuation is small.  
**Remark 6**.: _The errors of individual sampling points are too large to represent the overall control performance of the system, so the data of points with large errors are excluded in the experiment._  
Figure 8 is the control force change curve of each power unit using the MFASMC algorithm. The control force range of each power unit is between [\(-47\) N/kN, \(41\) N/kN], and the working condition switching process is relatively stable. Figure 9 shows the acceleration curve of each power unit. The acceleration range of each power unit is between [\(-0.923\) m/s\({}^{2}\), \(0.812\) m/s\({}^{2}\)]. The acceleration change is stable, which can meet the comfort standard of the train operation.  
This section verifies the effectiveness of the MFASMC algorithm by speed tracking error, the control force variation case, and the acceleration variation case without considering external disturbances.  
Figure 5: The speed tracking distribution of each power unit of MFASMC.  
Figure 6: The speed error distribution of each power unit of MFASMC.  
Figure 7: The speed level error curve of each power unit of MFASMC.
## 3 Discussion  
Rice lodging is influenced by many factors in addition to plant height [4]. The intrinsic nature of rice lodging is that the basal culm is unable to support the weight of the upper plants. Therefore, the morphology and strength of the basal culm are also key factors in determining the rice lodging resistance. There is a highly significant positive correlation between basal culm thickness, basal culm diameter and culm strength [10; 33; 34; 39; 40]. The increasing of basal internode thickness and culm thickness could be an effective way to improve the rice lodging resistance [32]. Recent progress has shown that increasing basal culm thickness could significantly improve resistance to lodging. Furthermore, it  
Figure 3: Analysis of yield and lodging-related traits in the near-isogenic lines with strong culm resistance QTL-_qSCM4_: (**a**) comparison of plant size of NILs; (**b**) cross section of the basal internode of NIL-_qSCM4_; (**c**) cross section of the basal internode stem of SN265; (**d**) the basal internode wall of NIL-_qSCM4_; (**e**) the basal internode stem wall of SN265; (**f**) comparison of plant height between two NILs; (**g**) comparison of breaking strength between two NILs; (**h**) comparison of grain number per panicle between two NILs; (**i**) comparison of the number of primary branch between two NILs; and (**j**) comparison of the number of secondary branch between two NILs. * \(p<0.05\), * \(p<0.01\), using Student’s \(t\)-test.  
has been demonstrated that only culm-diameter-related traits were positively correlated with lodging resistance, whereas plant height had an effect only within a certain range [15]. The main factor influencing culm wall thickness was the vascular bundle. The greater number of vascular bundles meant the more thicker culm wall and the greater resistance to bending and lodging [11]. _SCM3/OsTB1/FC1_ was a key gene that regulated culm thickness, and overexpression of this gene could increase the culm thickness and improve the lodging resistance ability in rice [6; 32]. The plant cell wall components including cellulose, hemicellulose, and lignin also influenced the lodging resistance ability [11]. In this study, _qSCM4_ improved the lodging resistance through increasing basal culm thickness and culm wall thickness. The biological mechanism of _qSCM4_ in maintaining culm mechanical strength and improving culm physical support requires further investigation.  
The candidate gene in this study was identified as a novel allele of the well-known gene _NAL1_ via the DNA sequence and variation analysis (Figure 2f). It was discovered that this gene could control leaf growth via influencing cell expansion and division [41]. _NAL1  
from the BC\({}_{3}\)F\({}_{2}\) population, and this line formed a pair of NILs with SN265 for studying the function of _qSCM4_.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 2 An Overview on Common Lifecycle Management Models
### Recent Lifecycle Management Researches  
Current research can be divided into three main areas. The first area deals with improving or enhancing existing and widespread lifecycle management models [51; 52; 53]. Out of the presented models, an impressive number of research is available and is still ongoing concerning the SLM and PLM models, as these are the most evident models for supporting Industry 4.0 and SCM (Supply Chain Management) processes. The second area examines the integration and collaboration issues of the existing lifecycle management approaches, as it was exemplarily mentioned in Section 2.5. Furthermore, the third field is about the development of new lifecycle models in accordance with new needs. This often happens if an existing model needs to be changed so much to fulfill the context-specific function that, in the meantime, it loses its original character or suitable modeling techniques cannot be found at all. Such new approaches often address machine learning [54] and AI (Artificial Intelligence) lifecycle modeling issues [55; 56], or other area-specific models [57; 58; 59].
## 3 Uncertainty-Based Optimization Design for the Multilayer TPS of Hypersonic Wing
### Experimental Validation for the Uncertainty-Based Optimization Design Method
#### 3.5.2 Experimental Verification  
In this work, the thermal insulation testes of two thermal protection tiles are conducted to verify the established uncertainty-based optimization procedure.  
Firstly, the test of the initial thermal tile was utilized to demonstrate the uncertainty analysis method. As illustrated in Figure 11a, the uncertain response of \(T_{4,\text{max}}^{\text{UA}}\) can be calculated as \([340.22,361.60]\) K based on the uncertainty analysis method proposed in Section 3.3. Meanwhile, the measured maximum temperature of the inner surface \(T_{4,\text{max}}^{\text{exp}}\) was 347.15 K. Apparently, the experimental results belonged to the uncertain responses obtained through the uncertainty analysis method, which validated the effectiveness of the proposed method.  
Then, the proposed robust optimization model was utilized to optimize the thermal protection tile. Considering that the design spaces for thicknesses of the C/C panel, FIM and SIP were rather small, only the thicknesses of HTTI and MTTI were considered as the design variables. The objective and constraints were the same as the optimization model proposed in Section 3.4. The optimization model was solved by the Multi-Island GA, and the optimized results were \(d_{1}=18.00\) mm, \(d_{2}=28.00\) mm. The thermal protection tile was fabricated based on the optimized geometric parameters, and the mass decreased from 2.691 kg to 2.537 kg by 5.72%. Later, the heat insulation test was conducted for the optimized tile. As illustrated in Figure 11b, the uncertain response of \(T_{4,\text{max}}^{\text{UA}}\) could be calculated as \([332.50,358.88]\) K based on the uncertainty analysis method. Meanwhile, the measured maximum temperature of the inner surface \(T_{4,\text{max}}^{\text{exp}}\) is 341.48 K. Clearly, the experimental results belonged to the uncertain responses obtained through the uncertainty analysis method, which validated the effectiveness of the proposed uncertainty-based optimization design method to a certain extent.  
Figure 10: Setup of the thermal insulation performance test. (**a**) The thermal protection tile; (**b**) The experiment setup.  
Figure 11: Temperature results obtained from uncertainty analysis method and experiment for the thermal protection tiles. (**a**) The initial tile; (**b**) The optimized tile.
## 5 Discussion
### Developmental Models for Source Rocks  
The formation of lacustrine source rocks is influenced by factors such as the redox environment, productivity, preservation conditions, and the palaeoclimate. According to the aforementioned analysis of organic matter sources, sedimentary environments, and palaeoclimate conditions, the source rock development model of the Shahejie Formation in the Huanghekou Depression can be categorised into two models: the preservation model of E\({}_{3}\)s\({}_{1-2}\) and the productivity model of E\({}_{2}\)s\({}_{3}\). During the depositional period of E\({}_{3}\)s\({}_{1-2}\) (Figure 11), the lake basin experienced a stable phase characterised by gradual structural settlement and a low sedimentation rate [6]. The encroachment of the lake covered a large area, resulting in a shallow water body. Within the depression, there was widespread development of shallow shoreline lacustrine facies. In addition, terrigenous higher plants made only a relatively minor contribution to the organic matter content of the source rocks. Conversely, high water salinity promotes the occurrence of algal blooms. High salinity is also conducive to the formation of halocline from salinity difference and the development of a stable strong reduction environment, which is conducive to the preservation of organic matter. During the deposition of E\({}_{2}\)s\({}_{3}\), the lake basin underwent active rifting characterised by a rapid sedimentation rate [6]. The encroachment area of the lake was extensive and characterised by a substantial water body and a warm and humid climate that facilitated the proliferation of aquatic organisms and algae, resulting in a high productivity level. The simultaneous introduction of a substantial volume of freshwater resulted in the influx of nutrients and higher plants, thereby decreasing the salinity of the aquatic environments. This subsequently created an anoxic reduction zone at the bottom of the lake basin, facilitating optimal conditions for organic matter preservation. At the same time, the semi-deep lake and deep lake are widely developed, and the water depth is large, which is conducive to the formation of an anoxic reduction environment at the bottom of the water body through temperature stratification.  
Figure 11: Organic matter enrichment model representing. (**a**) the preservation model of E\({}_{3}\)s\({}_{1-2}\) and (**b**) the productivity model of E\({}_{2}\)s\({}_{3}\).
# A Two-Port Dual-Band Dual-Circularly-Polarized Dielectric Resonator Antenna
###### Abstract  
This paper presents the design of a two-port dual-band dual-circularly-polarized dielectric resonator antenna (DRA). The proposed DRA is formed by stacking two dielectric resonators (DRs) of different shapes, including a hexagonal DR on top and a cross-shaped DR on the bottom. It is designed to resonate at two near-degenerate orthogonal modes of TE\({}_{111}\) and TE\({}_{113}\), and an aperture-coupled feeding through a cross-like slot is used to achieve dual-band impedance matching simultaneously for right- and left-handed circular polarizations. Tests were conducted on a prototype working in C-band to verify the design concept. The experiment results demonstrate that the proposed DRA has exceptional performance with measured \(-10\) dB reflection bandwidths of 24.4% and 17.4%, 3 dB axial ratio bandwidths of 21.2% and 16.3%, and maximum gains of 5.64 and 8.13 dBic for the lower and upper bands, respectively. Moreover, the measured channel isolation is more than 15.8 dB. The results obtained from the experiments show good agreement with the simulation, and hence, it can be concluded that the proposed DRA is a promising solution that can be used for various wireless communication applications.  
c 2024 14 4062. [[https://doi.org/10.3390/app14104062](https://doi.org/10.3390/app14104062)]([https://doi.org/10.3390/app14104062](https://doi.org/10.3390/app14104062))  
c 2024 14 4062
## 2 Health Condition Monitoring
### Data Acquisition
#### 2.1.2 Data Transmission  
According to the data transmission mode and spatial location, the transmission mode could be divided into local data transmission and remote data transmission.  
Local data transmission is to transmit the collected data to the monitoring system and send it to the local storage unit through various transmission modes, such as CAN bus [14], ZIGBEE wireless transmission [15], Ethernet [16], and so forth, to facilitate the completion of control, monitoring and data management. These three ways can be single and also be combined to enhance the flexibility and scalability of the data transmission. CAN bus has the characteristics of high transmission rate, high reliability, simple line, and so forth, which can be easily implemented in the engine room. Ethernet communication is easier to build a network on ships with Ethernet wiring than CAN bus. The ZIGBEE wireless transmission can solve the problem of ship wiring difficulties to a certain extent;  
Remote data transmission uses maritime satellites, offshore base stations, and other communication facilities to achieve data transmission from ship to shore. Data stored on the ship are transmitted to the land data center by transmission technologies (as shown in Table 2) that will be selected by the company [17]. Usually, the company completes the selection according to the communication cost and the urgency of data.  
In recent years, the cost of satellite communication has gradually decreased. With the application of maritime broadband, remote data transmission is no longer the main influencing factor of ship-to-shore communication. Some ships have been able to realize the transmission of video data.
## Results
### Differences in Body Electrolyte Composition According to the Type of Fluid and Time of Intake  
Figure 3 shows the results of the body electrolyte composition analysis according to the type of fluids and intake period. First, there was an interaction effect with the hematocrit (F = 3.712, \(p\) = 0.034). Therefore, as a result of a time-dependent analysis, CON (R-P0: t = \(-\)5.032, \(p\) = 0.001; P0-P1: t = 10.039, \(p\) = 0.001) and 3 RINK (R-P0: t = \(-\)15.127, \(p\) = 0.001; P0-P1: t = 11.433, \(p\) = 0.001) showed a statistically significant difference. In contrast, no interaction effect was observed for sodium, potassium, and hemoglobin; however, as a result of a time-dependent analysis, sodium (P0-P1: t = 3.674, \(p\) = 0.005), potassium (P0-P1: t = \(-\)3.000, \(p\) = 0.015), and hemoglobin (R-P0: t = \(-\)5.361, \(p\) = 0.001; P0-P1: t = 10.091, \(p\) = 0.001) showed significant differences with CON. In addition, sodium (R-P0: t = \(-\)4.163, \(p\) = 0.002; P0-P1: t = 3.343, \(p\) = 0.001), potassium (R-P0: t = 4.881, \(p\) = 0.001; P0-P1: t = \(-\)6.011, \(p\) = 0.015), and hemoglobin (R-P0: t = \(-\)9.295, \(p\) = 0.001; P0-P1: t = 7.877, \(p\) = 0.001) showed significant differences after receiving 3 RINK. Next, an analysis of the differences between the groups according to the timing of fluid consumption showed no statistically significant differences between groups for potassium, hemoglobin, and hematocrit, but there were differences between fluids for sodium (P0: t = \(-\)3.008, \(p\) = 0.008; P1: t = \(-\)2.849, \(p\) = 0.011). Furthermore, the analysis of the difference in the amount of change between the time periods showed that the change between R and P0 (P0-R) with 3 RINK compared to that with CON was significant for sodium, hemoglobin, and hematocrit. There were no differences among the other variables.  
Figure 2: Changes in water regulating hormones according to the timing of fluid intake. Data for (**A**) ADH, (**B**) Aldosterone, (**C**) Renin represent the mean and SEM (\(n\) = 10 per group). R = Rest, P0 = Right after exercise, P1 = 1 h after exercise.
## 3 Results and Discussion
### Analysis of Fermented Milk, Milk-Soya, and Soya Yoghurt Products
#### 3.2.5 Selected Carbohydrates Content
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Carbohydrate** & \multicolumn{1}{c}{**Storage Time [days]**} & **0** & **7** & **14** & **21** \\ \cline{2-6}  & \multicolumn{5}{c}{**Cow’s Milk Yoghurt Products**} \\ \hline  & yoghurt and propionic bacteria (1:1) fermented beverages & & & \\ glucose & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a \\ galactose & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a \\ maltose & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a \\ lactose & 0.13 b \(\pm\) 0.02 & 0.21 b \(\pm\) 0.02 & 0.21 b \(\pm\) 0.02 & 0.20 b \(\pm\) 0.02 \\ sucrose & \textmd & \textmd & \textmd \\ raffinose & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a \\ stachyose & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a & \textless{}0.01 a \\ verbasose & \textmd & \textmd & \textmd \\Propionic bacteria are known to ferment numerous carbohydrates, including lactose, glucose, and trehalose [92]. The _P. freudenreichii_ strain CIRM BIA129 studied by [PERSON] et al. [91] uses lactose, glucose, and galactose, in addition to D-fructose, inositol, and gluconate, but not sucrose and raffinose. The _P. freudenreichii_ examined by [PERSON] et al. [26] was unable to metabolize the sucrose and raffinose present in soya milk. From these results, it can be concluded that the applied propionic bacteria showed the ability to ferment lactose and its hydrolysis products, as well as sucrose, raffinose, stachyose, and verbas-cose. The study showed that propionic bacteria found suitable conditions for carbohydrate fermentation in both cow's milk and milk-soya beverages, as well as soya beverages, but to varying degrees, which may explain the different acidity values, live cell populations, and concentrations of selected carbohydrates observed at the time. The presence of soluble oligosaccharides and simpler sugars, including stachyose, raffinose, and sucrose, in the soy beverage--which are also able to be metabolized by many species of lactic acid bacteria--was also confirmed by [PERSON] et al. [93].
## 3 Results and Discussion
### Optimization of Experimental Conditions
It is observed that the presence of specific organics greatly enhances the signal intensity of Cu I (3d\({}^{10}\)4p-3d\({}^{10}\)4s, 324.75 nm), because it changes the chemical characteristics [32]. The result is obtained under the conditions of 5.6 kV applied voltage, 1.68 L/min Ar flow rate, and pH = 3.59. As shown in Figure 10, with the increase in organics concentration, the emission intensity of copper increases initially and then decreases. A small amount of specific organics can reduce the surface activation energy of positive Cu ions and promote the progress of reaction (1) [33], and can accelerate decomposition of the sample solution to produce H radicals and promote the progress of reaction (2) [34], so the emission intensity is increased. When excess organic matter molecules enter the discharge region, the average energy used for molecular decomposition is decreased under the condition of constant applied voltage, resulting in a decrease in the number of dissociated organic molecules and active free radicals, so the emission intensity is reduced. In addition, different organic compounds have different effects, among which 2% (V/V) ethanol promotes copper atomization the most. To summarize, the optimal experimental conditions are 5.6 kV applied voltage, 1.68 L/min Ar flow rate, pH = 3.59, and 2% ethanol, respectively.
## 2 Data and Methods
### Data and Pre-Processing
#### 2.2.3 MODIS ET Product  
Evapotranspirationiration is a key process in hydrologic cycle. The evapotranspiration data were provided by the MODIS product (MOD16A2) based on Penman-Monteith method from LP DAAC ([[https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac](https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac)]([https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac](https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac)) (accessed on 2 June 2021)), which has an 8-day temporal interval and 500 m spatial resolution. ET reflects information on surface water and heat fluxes, which is closely related to the physical properties of the soil, the physiological activity of plants, and the formation of biological production. Due to the ability of vegetation water and carbon sequestration, surface evapotranspiration will alter accordingly when vegetation changes [21].
## 2 System Model
### The Random Access Procedure and Coverage Enhancement
Therefore, we need optimize the number of repetitions and the size of the back-off window to meet the requirements of NB-IoT terminals in the LEO satellite IoT networks.