esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Valve Opening Characteristics
### Results and Discussion
It can be seen that the opening of the pin valve connects the two chambers to achieve the effect of instantaneous pressure relief. At this time, the big valve in the plug starts to open. As the size of the valve opening increases, the extreme low pressure in the valve gap caused by the simulation limitations disappears, and the maximum flow velocity reduces accordingly, as shown in Figure 9d. At t = 2.0 s, when the valve is fully opened (Figure 9e), the pressure distribution in the watershed tends to be uniform. A high pressure can be found at the inlet side, and a high-pressure gradient can be found in the overall watershed. In this state, the velocity distribution corresponds to the pressure distribution. During the coupling process of the NFFCS, the opening of the pin valve connects the high-pressure cavity with the low-pressure cavity to achieve the effect of pressure relief so that the big valve can be easily opened. In order to observe the impact of hydraulic power on the coupling system, the axial resistance caused by the hydraulic oil to the valve opening during the coupling processes of CFFCS and NFFCS were monitored separately. The results of this study are summarized in Figure 10. The solid line represents the opening force results for the CFFCS and the dashed line represents the opening force results for the NFFCS. It can be observed that the maximum resistance for the CFFCS is about 122.3N, which occurs when the valve starts to be pushed (t = 0 s). This force then quickly drops to an approximately stable value (about 5N). When the valve is opened (t = 1.0 s), the thrust has a small increase and then stabilizes again. On the other hand, the NFFCS releases the pressure by opening the pin valve before the big valve is opened to achieve full conduction. When the pin valve is opened, the maximum axial thrust of 80.7N appears at the start of the push (t = 0 s), which then gradually decreases and stabilizes. It can be seen that the maximum axial resistance occurs at the same time (the beginning of the movement) in both the mechanisms. However, the presented NFFCS showed a resistance value that is 34% less than that showed by the CFFCS. Although the grid size at the gap has been divided to be an extremely small value, it still is difficult to obtain the data that is completely consistent with the actual due to the limitations of the dynamic mesh algorithm. However, the overall trends shown in the simulation can be considered as a reasonable depiction of the actual situation. Thus, we can safely infer that the design of the NFFCS can relieve the high pressure instantly with a small opening force so that the big valve can be easily opened to connect the hydraulic circuit. Figure 10: Comparison of the valve opening forces required by the CFFCS and NFFCS.
# Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
###### Abstract  
This study assessed the effects of irrigation with desalinated water on the growth of lettuce (_Lactuca sativa_ L.). Two experiments, the first using saline and the second desalinated irrigation water, respectively, were designed to grow lettuce in plots (22 m \(\times\) 0.4 m \(\times\) 0.4 m) under controlled greenhouse conditions. Three levels of saline irrigation water and tap water (control) were used for the first experiment. In the second experiment, the three saline levels underwent a desalination process. Each experiment was carried out twice, in succession, with two replications. The results from the first experiment showed that the use of saline irrigation water caused an increase in the salinity level of soil and, consequently, adversely affected lettuce growth and yields. The results from the second experiment showed that the use of desalinated irrigation water does not negatively affect soil salinity and lettuce growth and yield. However, the need for additional application of the elements such as Ca\({}^{2+}\) and Mg\({}^{2+}\) was also identified since continued use of desalinated water irrigation correlated with a decrease in the sodium adsorption ratio (SAR), leading to increased risk of soil sodicity. This is due to the elimination of nutrients during the desalination process.  
b 2020; 10, 2207; doi:10.3390/app10072207
## 3 Materials and Methods
### Analysis Methods
#### 3.2.1 Exploratory Data Analysis (EDA)  
EDA was used to understand the basic characteristics of waiting times and other relevant variables, identifying preliminary patterns and outliers that guided subsequent analyses. Table 2 summarizes the main techniques employed.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variable** & **Type** & **Description** \\ \hline Origin/Destination Commune & Categorical & Communes where each trip begins and ends. \\ \hline Origin/Destination Macrozone & Categorical & Division of the region into broader areas to analyze travel patterns. \\ \hline  & & Type of public transport used (Bus, Metro Valparaiso, Taxi Colectivo (In the Chilean context, “Taxi Colectivo” refers to a shared transportation service with fixed routes and a cost divided among the users. It is analogous in English to “shared taxi”, although this translation does not capture all of its operational and cultural characteristics.), etc.). \\ \hline Sex & Categorical & Gender of the person making the trip. \\ \hline Waiting Time & Numerical (min) & Minutes between the user’s arrival at the stop/station and boarding the vehicle. \\ \hline Travel Time & Numerical (min) & Total duration of the trip using public transport. \\ \hline Minutes Before/After & Numerical (min) & Time spent walking before using public transport. \\ \hline Start Time & Numerical & Numerical format indicating the start time of the trip. \\ \hline Final Income per Person & Numerical & Total monthly individual income declared. \\ \hline \end{tabular}
\end{table}
Table 1: Variables used in the study and their descriptions.  
\begin{table}
\begin{tabular}{c c} \hline
**Technique** & **Description** \\ \hline Descriptive Statistics & Calculation of central tendency and dispersion measures to summarize waiting time distribution. \\ Histograms & Graphical representation of the waiting time distribution, segmented by macrozone and transport mode. \\ Box Plots & Identification of medians, quartiles, and outliers, facilitating comparisons among different communes and transport modes. \\ Scatter Plots & Exploration of the relationship between waiting time and associated variables (start time, travel time, walking minutes, income, etc.). \\ Temporal Analysis & Evaluation of daily variation in the volume of trips, average waiting time, and the incidence of waits longer than 20 min. \\ Pearson Correlation & Determination of significant linear relationships between waiting time and other numerical variables. \\ \hline \end{tabular}
\end{table}
Table 2: Main EDA techniques used.
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 3 Simulation Model and Results  
A VSC technology-based MMC-structured HVDC network was used as shown in Figure 2 is used for analysis. Various symmetrical and asymmetrical faults with different conditions were simulated for the considered power system model.  
Figure 1: Flowchart of proposed technique.
## 5 Research and Development Challenges
### Artificial Intelligence-Based Techniques  
Recently, artificial intelligence (AI) based techniques have shown significant research attention due to their smart data-driven decision-making capability. To handle the uncertain and dynamic environment conditions, utilizing advanced AI techniques can provide the optimal solution and better performance compared to traditional optimization techniques for the NP-hard problems [6; 84; 85].
# One-Dimensional Creep Consolidation Model for Peat Soil
## 4 Conclusions  
In this study, the characteristics of strain at different stages of peat soil consolidation were analyzed, the relationship between the strain and stress was explored, and a one-dimensional creep consolidation model of peat soil was established. The main conclusions are as follows.  
(1) During the consolidation of the peat soil, the primary consolidation stage mainly produces elastic deformation, whereas the secondary consolidation stage mainly produces plastic deformation. After unloading, following the initial two stages, the rebound deformation is 1.003 times that of the deformation observed during the primary consolidation stage. In the tertiary consolidation stage, which lasts longer and has a slower deformation rate, viscous deformation mainly occurs. The rebound deformation decreases to 0.32 to 0.85 times that of the primary consolidation stage.  
(2) Both the elastic and the time-independent plastic strains of the peat soil showed two-stage linear changes with \(\ln\sigma_{z}^{\prime}\). When the load is greater than the pre-consolidation pressure, the deformation modulus increases by approximately 2.10 and 1.56 times, respectively.  
Figure 14: Calculation results of the semi-analytical method.  
(3) A time-dependent viscoelastoplastic constitutive model of peat soil is proposed. The elastic line and the reference time line in the rheological model of peat soil are defined by the staged strain accumulation curve, and the viscous strain rate of peat soil is defined by the slope of the \(\varepsilon_{z}\sim t\) curve.  
(4) Based on the stress-strain relationship, a one-dimensional creep consolidation model of peat soil was established and verified using a semi-analytical solution. The model can accurately predict consolidation deformation and provide a theoretical basis for modeling peat soil foundation.  
The one-dimensional creep consolidation model proposed in this paper clarifies the deformation characteristics of peat soil and uncovers the settlement patterns of the foundation. This model is not only directly applicable to engineering practices, but also offers theoretical approaches for software simulation and fresh insights for the investigation of high-creep soft soil. However, as the tests in this study only focus on four typical peat soils in the Yunnan region, further research is needed to explore the applicability to peat soils with different regions and compositions. Meanwhile, the anisotropy of peat soil can be further studied.  
Conceptualization, B.P.; Methodology, B.P. and R.F.; Investigation, B.P. and R.F.; Resources, R.F., L.W. and P.W.; Data curation, X.S.; Funding acquisition, R.F. and P.W. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by The Fundamental Research Funds for the Central Universities (2022 JBZY006) and The National Natural Science Foundation of China (51778048, 41801055).  
All data and models used in this study are available from the corresponding author upon request.  
The authors declare that there are no competing interests.
## 2 Materials and Methods: The Original Version of the Ex-PdBI W-Band Receiver
Consequently, a feasibility study for some structural and logistical modifications is needed with a focus on adapting the system to the optics and mechanical constraints of the SRT. In particular, a feasibility study based on the optical design of the receiver is necessary to demonstrate that it can be installed on the SRT. The optical design of the SRT is based on a quasi-Gregorian configuration with a shaped primary mirror (i.e., 64 m diameter) and a shaped sub-reflector (i.e., 7.9 m diameter) that make available two focal positions (i.e., the primary focus, with a focal length to diameter ratio equal to 0.33 and frequency range 0.3-20 GHz, and the Gregorian focus, with a focal length-to-diameter ratio equal to 2.34 and frequency range 7.5-116 GHz). In addition, the SRT has a beam wave guide (BWG) room, where four positions are available for 2.9 m mirrors and one mirror with a diameter of 3.9 m, for a total of four additional focal positions (i.e., the F3 and F4 focus, with a focal length-to-diameter ratio equal to 1.38, and the F5 and F6 focus, with a focal length-to-diameter ratio equal to 2.81 and frequency range 1.4-35 GHz). The ex-PdBI W-band receiver can be installed on the Gregorian focus of the telescope. An octagonal support structure, located in the Gregorian room surrounding the SRT Gregorian focus, can host up to eight receivers. This structure, named a Gregorian receiver positioner (GRP), rotates around an axis parallel to the antenna optical axis allowing the placement of the selected receiver on the precise point (the Gregorian focus) for astronomical observation. Since the Dewar of the receiver is equipped with an inner vessel of liquid helium to achieve a temperature of 4 K, a new cooling system was necessary to prevent the leakage of the cryogenic fluid when the telescope moves in elevation. In addition, a new local oscillator, which replaces the traditional system based on Gunn oscillators installed on the PdBI antennas, was adopted. The feasibility study to demonstrate that the ex-PdBI receiver is suitable for the Gregorian focus of the SRT is presented in Section 3.1. In Section 3.2, the features of the new local oscillator for the SRT are reported. The new cooling system, based on a new cold head with high performance to replace the old system, is described in Section 3.3.
## 4 Results and Discussion
### Analysis of Multi-Span Third Rail Model
Therefore, in this study, the analytical model is compared with the FEM analysis to check for accuracy. Figure 5: Analysis of multi-span third rail structure using analytical and numerical approaches; (**a**) mobility analysis of multi-span third rail: excited at mid span; (**b**) pin-to-pin resonance mode of vibrations. Thereafter, the analytical model was extended to analyze the effect of parameter changes due to structural deterioration. Figure 5b presents the mode shapes of the first three pin-to-pin modes obtained from both analytical solution and Ansys simulation. A two-span length of the third rail was shown to illustrate the mode shapes. In ANSYS modal analysis, the displacement of the beam is arbitrarily scaled. Therefore, the results from both the analytical and numerical solution are normalized by the maximum value. The results show how a point will be deflected or displaced relative to another. Table 4 shows the comparison between the frequencies of the first five pin-to-pin modes of vibration from the analytical and numerical solution. A good agreement was obtained for the pin-to-pin the mode of resonance vibration from both the analytical and numerical approaches. The difference between the analytical and numerical solution was found to be less than 6%.
# Improvement of Radiant Heat Efficiency of the
## 4 Results and Discussion
### Temperature and Fuel Consumption Measurement in the Pilot Furnace  
Figure 12 shows the measured temperature and cumulative gas (fuel) consumed during the entire heat cycle as shown in Figure 10. The hollow circles indicate the temperatures at position #2 in Figure 9b recorded to control the fuel consumption, and the solid lines denote the amount of cumulative gas consumed. The symbols and lines marked in red are for the plain RT (RT without honeycomb pattern) and those marked in blue are for the honeycomb pattern-printed RT. The temperature changes for both plain RT and honeycomb pattern printed RT follow almost the same evolution because the temperature control system worked correctly. The amount of cumulative gas consumed to maintain a given temperature, however, was different. The amount of cumulative gas consumed for the honeycomb pattern-printed RT is clearly smaller than that of the plain RT. The difference in the amount of cumulative gas consumed between plain and honeycomb pattern printed RT increased with time and soaking temperature.  
Table 6 summarizes the values of the set-up temperatures, measured temperatures, gas consumed for the soaking time, and gas consumed for the entire heat cycle. When the plain surface RT was run, the average amount of gas consumed to maintain the set-up temperature (780 \({}^{\circ}\)C and 880 \({}^{\circ}\)C) for the soaking time (14,400 min) was 48 m\({}^{3}\) and 65 m\({}^{3}\), respectively. The gas consumed for the entire heat cycle (3000 min) was 118 m\({}^{3}\).  
Figure 12: Measured temperature and cumulative gas consumed for plain (no honeycomb pattern printed) and honeycomb pattern printed RT.  
By contrast, when the RT with honeycomb pattern printed was run, the amount of gas consumed to maintain the set-up temperature (780 \({}^{\circ}\)C and 880 \({}^{\circ}\)C) for 14,400 min was 43 m\({}^{3}\) and 57 m\({}^{3}\), respectively. It was observed that the gas usage for each soaking time was reduced. The gas consumed for the entire heat cycle was 105 m\({}^{3}\). When the gas consumption for each soaking time of the two types of RTs was compared, the RT with honeycomb pattern printed consumed 10.42% at 780 \({}^{\circ}\)C and 12.31% at 880 \({}^{\circ}\)C less gas than the plain RT, respectively. The averaged gas usage was reduced by 11.02% when comparing the gas consumption of the two RTs on the entire heat cycle basis. Both results show that the radiant heat efficiency of the RT is increased by the addition of the honeycomb patterns.
# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 4 Simulation and Analysis  
In this section, the results of two simulations, fast planning and path optimization, are presented, along with an analysis. As shown in Figure 6, four types of environments, denoted as Map1, Map2, Map3, and Map4, are utilized to simulate the path planning tasks. The resolutions of the four environments are all 500 \(\times\) 500. In this planning tasks, area \(T\) is set to two-fifths of the entire area, while area \(M\) is set to seven-tenths of the entire area. The parameters for the experiments are shown in Table 1. If no path is found within the maximum number of iterations, the experiment is considered to be a failure. Each environment is run 50 times; it should be noted that only the data from the successful planning tests are used for the statistical analysis.  
The evaluation metrics are the computing cost, which consists of the running time and the average number of nodes, and the quality of the path. The corresponding standard deviation and planning success rate are also recorded. The running time reflects the time cost of different planners. The number of nodes represents the memory usage of different planners. The quality of the path is mainly reflected by the path length. The standard deviation and planning success rate indicate the stability of the planner.  
All experiments were run on a computer based on an Intel i5-11300H 3.1 GHz CPU and 16 GB RAM, using MATLAB R2021a as the software platform.  
Figure 6: Four types of planning task environments: (**a**) simple, (**b**) complex, (**c**) indoor, (**d**) narrow passages. The initial state and target state are marked by red and green points, respectively.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 3 Calculation Results and Parameter Analysis of Fire Resistance of Prestressed Concrete T-Beam  
### Determination of Finite Element Model and Analysis Parameters  
Based on the validated finite element model, four prestressed concrete T-beams with the cross-sectional dimensions shown in Figure 7 were designed to develop a more extensive parametric analysis of their fire resistance. The calculated length of the prestressed concrete T-beam is 3.6 m, and the cross-section of the prestressed T-beam is designed as follows: beam height 600 mm, web width 250 mm, the width of flange plate 1200 mm, the height of flange plate 90 mm, 120 mm, 150 mm, and 180 mm. Three cases with load ratios of \(n=0.4\), \(n=0.6\), and \(n=0.8\) (\(n\) denotes the load ratio, calculated from the formula \(n=F/F_{u}\), where \(F_{u}\) is the ultimate load of prestressed concrete T-beam at room temperature) were designed. Additionally, the finite element model is divided into five groups in total to research and analyze different fire modes, different concrete strength classes, different flange plate thicknesses, different ratios of the prestressing bar, and different concrete protective layer thicknesses. The D1 represents the research of different fire exposure patterns. Five fire exposure patterns were selected from H1 to H5 to simulate most of the possible scenarios during a fire, as shown in Figure 8. The D2 represents the research of different ratios of the prestressing bar. The prestressing bar material is prestressing strand type 1 \(\times\) 7 \(-\) 12.7 with an ultimate strength standard value of 1860 MPa, and its single cross-sectional area is 98.7 mm\({}^{2}\). Five ratios of the prestressing bar are set at \(p=0\%\), \(p=0.0658\%\), \(p=0.1316\%\), \(p=0.1974\%\), and \(p=0.3290\%\). The D3 represents the research of four different flange plate thicknesses, noted as Y9, Y12, Y15, and Y18. The D4 indicates the research of different concrete strength classes. According to Article 3.1.1 of the Code for the Design of Prestressed Concrete Structures (JGJ369-2016) [26], the concrete strength grade of prestressed concrete structures should not be lower than C40, and especially should not be lower than C30. In order to better obtain the influence law of concrete strength grade on the fire resistance of prestressed concrete T-beam, the concrete strength grades selected for the model are C30, C40, C50, and C60. The D5 represents the research of different concrete protection layer thicknesses, and the model was set up with four groups of concrete protection layer thicknesses, \(a=15\) mm, \(a=25\) mm, \(a=35\) mm, and \(a=45\) mm.  
Figure 6: MID-span deflection time curve.
## 3 Results
### VI Predictability of Photosynthetic Activity and LUE  
When testing the correlation between temporally linked ground observations and MODIS data, it was found that none of the MAIAC VIs strongly correlated with LUE (\(R^{2}<0.17\) in all cases) (Figure 6) and, with sPRI\({}_{4}\) and sPRI\({}_{12}\), no significant relationships were found (Table 2). However, the correlation analysis yielded significant results for GPP and all the MAIAC VIs. NDVI\({}_{\text{maiac}}\) and sPRI\({}_{1}\) plotted in Figure 7 both showed relatively high \(R^{2}\) values (\(R^{2}=0.47\) and \(R^{2}=0.63\)). Out of the four varieties of PRI tested, sPRI\({}_{1}\) had the strongest relationship with both GPP and LUE and thus this version of the index was used in the time series analysis.  
Figure 5: Daily mean air temperature measurements (five-day moving averages) at the Hyytiala Forestry Field Station for 2013–2014. The start of the growing season (SGS) dates predicted from the NEE data occur almost immediately after air temperatures exceed 2–3 \({}^{\circ}\)C.
## 3 Methodology
### Non-Response Bias and Common-Method Variance  
To investigate possible bias in the self-reported survey data, non-response bias and common-method variance tests were carried out. Non-response bias was checked by t-test analysis of the data sets of the early and late respondents [40]. The results of the 26 measurement items showed no significant difference (\(p<0.05\)) between the two groups, indicating that the data were free of non-response bias.  
As the research relied on the single respondent and perceptual scales to evaluate dependent and independent variables, the presence of common-method variance (CMV) was determined. [PERSON]'s single factor test with confirmatory factor analysis (CFA) tested whether CMV existed. As CMV is a serious threat to research outcomes, [PERSON]'s single factor test can account for most of the variance [41]. When [PERSON]'s single factor test results in a poor fit with the data, CMV is not a problem. The CFA results showed that the results of [PERSON]'s single factor test and the 26 measurement items did not fit (\(\chi^{2}=2891.44\), df = 300; CFI = 0.33; GFI = 0.48; AGFI = 0.39; RMR = 0.21; RMSEA = 0.17), so CMV was not a concern in the current research.