esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Vegetative Properties in
## 3 Results and Analysis
### Effect of Different Substrate Ratios on Seed Germination
#### 3.1.3 Relationship between Seed Germination and Substrate pH  
In the study of C.d. seed germination, it was observed that varying ratios of ZL-BC amendments had notable impacts on substrate pH levels, showing a correlation with germination characteristics. Figure 8 illustrates that with the addition of ZL, the pH of the substrate showed a downward trend, and with the addition of BC, the pH of the substrate showed an upward trend. Furthermore, the increase in BC had a more significant effect on the pH of the substrate. Among the substrates labeled 1# to 26#, the pH ranged from 8.13 for 1# (plain soil) to 11.05 for 26# (12% ZL + 7% BC). Specifically, a 3% cement addition increased the substrate pH by 1.44 units. The average pH values for different ZL contents were 9.57, 9.56, 9.62, 9.42, and 9.22, respectively, indicating a gradual increase in pH with ZL content. Similarly, different BC contents showed average pH values of 9.57, 9.57, 9.43, 9.89, and 11, with 1% and 3% BC having minimal impact on pH, while 5% and 7% BC led to a significant pH increase. A 3% increase in ZL content raised the pH by 0.08 on average, whereas a 2% increase in BC content raised the pH by 0.36 on average. Under the same content, the effect of BC on pH value exceeds that of ZL, and the influence of BC is 6.7 times that of ZL.  
Figure 7: Characteristics of the root germination of Cynodon dactylon with different ratios of vegetative substrate: (**a**) germination energy; (**b**) germination index.  
The analysis presented in Figure 9 demonstrates varying degrees of linear correlation between pH and different seed germination indices. It is evident that the GP, GE, and GI all exhibit a decreasing trend with increasing pH levels. The coefficient of determination (R2) serves as an indicator of the strength of the relationship between the variables, with \(p\)-values greater than 0.05 signifying statistical significance. Specifically, both GP and GI display a significant negative correlation with pH, as evidenced by R2 values of 0.532 and 0.639, respectively, with both \(p\)-values below 0.05. The GP reflects the ratio of seeds capable of germination, while the GI encompasses both the speed and uniformity of germination. The decline in these two indices clearly highlights the substantial inhibitory impact of unfavorable pH conditions on seed germination. Conversely, the linear fit between GE and pH reveals a small R2 value of 0.114 and \(p>0.05\), indicating the absence of a significant linear relationship. This suggests that GE, which primarily reflects seed germination vigor, is less influenced by pH levels.
## 7 MIMO Characteristics for 6G Technology
generation cellular technology, 6G communication systems are designed to accommodate growing data-hungry applications with boosting connectivity and enhanced network capabilities [96]. The bandwidth and latency of 6G networks will be significantly higher than those of 5G networks due to their ability to operate at higher frequencies. One of the objectives of the 6G internet is to provide communications with a latency of even less than microseconds. It is predicted that 6G would provide extremely dependable low-latency communication with a strong emphasis on internet devices, the application of artificial intelligence in wireless communication and the improvement of mobile broadband [97]. Massive MIMO technology is currently used in 5G communication networks; on the other hand, there will be a need for dozens or even hundreds of antennas and radio links at the base station when 6G technology will be deployed [98]. At the same time, due to the large scale of deployment, there will be high hardware costs, power usage and complicated designs [98]. To fulfill the requirement, upcoming communication systems (6G) are moving towards the higher frequency bands, such as the terahertz (THz) and millimeter-wave bands [99,100]. The internet of nano-things, health monitoring systems, entertainment services, military and ultra-high-speed on-chip communications are some of the significant uses of THz band wireless communication [97,100]. This has encouraged researchers to continuously develop current wireless networks in order to switch over to 6G cellular systems.
## 4 Study Results
### Confirmatory Factor Analysis Results  
A confirmatory factor analysis was performed to evaluate the construct validity, model fit, and nomological validity of the measurement proposed in this study. Convergent validity and discriminant validity were assessed to evaluate construct validity.
## 3 Results and Discussion
### Penetration Process of Cyclic Grouting  
The penetration process of cyclic grouting is shown in Figure 4. In the initial stage, the slurry mainly flows downward (as shown in t1 moment). Due to the high flow resistance of the soil below the grout pipe, slurry begins to flow upward along the annular space (as shown in t2 moment). As shown in the t3 moment, the slurry penetrating along the left annular space is faster than that on the right side. This is because the back pressure generated with the outlet hinders the flow of slurry in the right-side annular channel. As the slurry spreads, both sides of the annular space become filled with slurry (as shown in t4 moment). Subsequently, under the influence of grouting pressure, the slurry penetrates both sides. In the middle stages of the grouting process, the penetration distance at the lower part of the grouting pipe is greater than the upper part (as shown in t5-t6 moment). As the grouting process continues, the shape of slurry penetration exhibits a typical columnar pattern (as shown in t7-t8 moment).  
Figure 4: Penetration shape of cyclic grouting.  
Because the soil in this study is assumed to be a homogeneous porous medium, the penetration shapes of pulsating grouting and steady pressure grouting are similar. However, it was observed that the penetration distance of pulsating grouting is larger than that of steady pressure grouting. In order to analyze the reasons, the streamlines of the flow field for both grouting methods were monitored. For the convenience of observation, the maximum value of streamline velocity was fixed. Therefore, a larger area with streamline velocity indicates higher velocity within the flow field, whereas a smaller area suggests lower velocity within the flow field. The streamlines of the steady pressure grouting are shown in Figure 5. Since the velocity of the streamlines in other areas of the flow field is almost zero, we only selected the streamlines in the effective region. It can be observed that as the grouting progresses, due to the increase in flow resistance, the grouting velocity under steady pressure decreases.  
Figure 6 presents the steam line of the pulsating grouting method, and it can be found that the slurry velocity is periodic due to cyclic variations in pressure. At times t1 to t3, the slurry velocity gradually increases, while at times t4 to t6, the slurry velocity decreases. Subsequently, the velocity of the slurry will increase again and then experience periodic changes. When resistance within the calculation domain is high, the performance of steady pressure grouting may be limited, while this pulsation effect can improve penetration distance [7].
## 3 Results
### Tests in Sea Scenario  
According to the test plan detailed in Section 2.2.4, two sets of tests were carried out. The first measurements were accomplished in the port environment to check the proper functioning of the system installed (the two smart sensors and the concentrator) in the HarshLab platform. As an illustrative example of the correct behavior of both smart sensors (SS1: Smart Sensor 1 and SS2: Smart Sensor 2), Figure 16 shows a period of results obtained in the verifications tasks.  
As mentioned before, the second set of tests was performed by only one smart sensor on a marking buoy in the Bimpe area ([[https://www.bimep.com/](https://www.bimep.com/)]([https://www.bimep.com/](https://www.bimep.com/)), accessed on 14 November 2022), with some optimizations that were introduced in the system, especially in the employed battery, to increase the duration of the measurements. The main drawback with this new configuration was the determination of the solution's accuracy. Using only one smart sensor and the low frequency of the data provided by the oceanographic buoy notably hinder the validation process of the proposed approach. In this sense, the precision of the solution provided by the smart sensor was calculated as the difference between this data and the results achieved by applying the RTK technique with the base station closest to the Bimpe area. Considering the previous results obtained with the smart sensor and RTK post-processing technique, it is shown that the error is always less than 10 cm, and it can be considered that this method can be used as a precise reference solution (as the real position of the buoy). Therefore, and considering all the above-mentioned, an interval of the smart sensor data obtained during the real-time tests is shown in Figure 17. In these measurements, the sensor was able to provide, with some punctual losses of corrections,  
Figure 15: Solution rotating around A and B simultaneously: (**a**) 18 h, (**b**) 1 h.  
the estimated position of the buoy continuously and with the established precision, as will be discussed later. In the moments that the corrections were lost (red rectangles in the figure), the sensor is capable of rapidly converging and recovering normal operation, providing the real positioning measurements.  
The Figure 18 shows the error achieved in ENU components, calculated as the difference between the smart sensor solution and the one obtained by applying the RTK algorithm to raw captured data. As can be clearly noticed, the error was always below the pre-established value (+/ -20 cm), except when the corrections were lost eventually, where the system is capable of reconverging rapidly and quickly, providing the correct solution.  
Figure 16: Test results in the HarshLab platform at the port.  
Figure 17: Twelve-hour measurement at the marking buoy.
## 3 Flow Velocity Measurement Method
### The Principle of Main Steps and Analysis
#### 3.2.2 Image Interpolation and Improvement  
The displacement field may change at times. The image intensity \(\widetilde{g}(i,j)\) must translate floating-point types because of interpolating non-integer values, which will increase computational cost. Image interpolation has been widely used in image processing, but an ultrasonic image needs many kinds of interpolation ways for the velocity measurement based on the PIV method. A concise comparison of many advanced image interpolators used for PIV image deformation schemes can be found in [4, 17, 21, 33, 31]. Because the interpolator of the ultrasonic image should reconstruct steep intensity gradients in the particle image in the first place, we used the B-splines method to balance the computational cost and the performance. Results show that the deviation from the actual displacement is only one-fifth per pixel. If higher accuracy and precision are required, the sinc-based interpolation method [34], such as the Whittaker reconstruction and the FFT-based interpolation algorithm, should be utilized by using many points. However, the computational cost may be increased if an unsuitable approach is taken according to [35].  
Figure 4: The basic principle of the image deformation process.
## 5 Discussion
### Influence on Fault Current  
Figure 20a,b presents the influence of \(R_{FCL}\) and \(L_{FCL}\) on the DC line current, respectively. When \(R_{FCL}\) increases but \(L_{FCL}\) remains at 250 mH, the DCCB capability requirement is low, and the fault current extinguishment is accelerated (Figure 20a). When \(L_{FCL}\) increases but \(R_{FCL}\) remains at 20 \(\Omega\), the peak value of the fault current decreases but the time spent in the fault current interruption is unaffected. Therefore, \(L_{FCL}\) in the proposed scheme hardly influences the dynamic response or the fault current extinguishment.
## 3 Results and Discussion
### CFD Analysis of Flow and Thermal Fields in Optimized Geometries
The heat transfer distribution was relatively uneven in the case of the reduced-pumping-power shape (Figure 9b). There were localized regions of high heat transfer at the boundary where the slit begins, but most areas beyond the slit showed low heat transfer. After the first row of tubes, the main flow and the branched flow develop strongly underneath the fin, leading to the creation of localized regions of high heat transfer on both the upper and lower parts of the fin. Additionally, after flow separation near the tube, the recirculation zone forms become widely distributed, creating the largest low heat transfer area. Figure 9: Local heat transfer distributions on the surface of optimized and reference wavy-slit fins for two objective functions. (**a**) Reference. (**b**) Optimized fin with reduced pumping power. (**c**) Optimized fin with volume goodness factor. Compared to the other two shapes, the Gv shape (Figure 9c) provides a more uniform heat transfer distribution. While the reference shape and the reduced-pumping-power shape have locally high heat transfer coefficients at the end of the slits or locally low heat transfer coefficients at the middle of the slits, the Gv shape is generally uniform in this regard. In the area before the first row of tubes, the Gv shape exhibits a wide region of high heat transfer along the tube. Additionally, the flow directed below the slit immediately following the first row of tubes enhances the heat transfer coefficient compared to the other two shapes. Although low heat transfer areas appear after flow separation near the tubes, these areas are relatively narrow compared to the other two shapes. A low heat transfer area can also be observed at the end of the fin in the z-direction.
## 3 Results and Discussion
### Reliability Analysis of the Components That Have the Most Defects
In the next stage, the reliability variation curve was built for the bucket teeth of the Liebherr 944 L excavator (Figure 10), which shows that for a reliability of approximately 80%, a value imposed by the beneficiaries of the quarry equipment, the operating time without failures is only 29.71 h, for example, approximately 2 working days (the excavator works in two shifts of 7 h/day). Figure 10: Reliability value (the dashed line) for bucket teeth of the Liebherr 944 L excavator: t—operating time; \(\beta\)—shape parameter; \(\eta\)—scale parameter; \(\gamma\)—localization parameter (the solid line represents the same distribution fit but starting at time 0). Figure 9: Classification of the distribution law acceptance for changing bucket teeth on the Volvo L220 E front loader. In the case of the bucket teeth of the front loader Volvo L220 E (Figure 11), reliability of 80% is obtained after about 21 h of operation, for example, after completing three working shifts (the loader works in two shifts of 7 h/day). The maintainability data are not satisfactory, because to be able to obtain a bucket teeth change probability of 80%, it takes about 76 min for the Liebherr 944 L excavator (Figure 12) and 310 min for the Volvo L220 E front loader (Figure 13). Figure 11: Reliability value for Volvo L220 E front loader bucket teeth: —operating time; \(\Phi\)—Laplace function; \(\mu\)—average; \(\sigma\)—mean square deviation. Figure 12: Maintainability value for Liebherr 944 L excavator bucket teeth.
# NextGen ADS-B Software-Defined Reception with Enhanced Techniques
## 4 Analog Front-End and AGC Slow Attack Mode  
SDR system has an analog front end to deal with received analog signals [28]. It is crucial to provide a suitable signal prior to digital conversion with the ADC (Analog to Digital Converter). The slow attack mode is designed for signals that change slowly, such as those in several FDD applications, like WCDMA and FDD LTE. The second-order control loop with hysteresis used by the slow attack AGC modifies gain to maintain the average signal strength within a predefined window. Using the AD9361 RF Agile Transceiver within the Blade-RF, the receiver gain control mode function is activated on AGC slow attack mode. The control loop's concept is shown in Figure 7[28] which stores the inner high threshold and inner low threshold settings in negative decibels relative to full scale (dBFS). The outer high threshold and outer low threshold parameters are set as dB deltas to the inner thresholds. Programmable gain step sizes are available for A, B, C, and D. The gain index pointer's modification, once an average signal power threshold is exceeded, depends on these step sizes.  
Table 1 indicates slow attack AGC s parameters and associated registers [29]. They need to be well-tuned and adjusted to fulfill MOPS requirements in SDAM. In the case of ADS-B In, sensitivity needs to be considered as one of the key requirements [1].  
Figure 6: UGC Application ADS-B In’s output.  
Figure 7: Slow attack AGC mode function example and control parameters.
# Single-Shot Compressed Imaging via Random Phase Modulation
## 2 Fundamentals of Compressed Sensing
### Nonlinear Recovery Algorithm  
Once the measurements vector \(\mathbf{y}\) and measurement matrix \(\mathbf{\Phi}\) are given, the way to find a signal \(\mathbf{x}\) in the class of interest so that \(\mathbf{y}=\mathbf{\Phi}\mathbf{x}\) is complete or approximate is a problem that the reconstruction algorithm should solve, termed the sparse recovery algorithms in CS.  
In recent years, there have been many sparse recovery algorithms proposed to recover a sparse signal from fewer incoherent measurements. These algorithms can be categorized into three main types: convex relaxation algorithms, non-convex optimization algorithms, and greedy algorithms.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 3 Results
### South Jakarta  
With a total administrative area of 144.94 km\({}^{2}\) and 60.1 km\({}^{2}\) of urban green space, the South Jakarta region boasts the largest urban green space, covering approximately 54.11% of the area (Figure 12). This green space primarily comprises mixed areas (34.23 km\({}^{2}\)), followed by shrub-covered areas (24.78 km\({}^{2}\)), grassy expanses (16.66 km\({}^{2}\)), and forested areas (2.76 km\({}^{2}\)) (Figure 13).  
In South Jakarta, there are approximately 383 small parks accessible to the public, including interactive parks, environmental parks, and public building parks. Among these, Taman Ayodia serves as a city park, while three recreational parks, namely Taman Kebun Bibit Ciganjur, Bumi Perkemahan Raghun, and Taman Hutan Kota Tebet and Tebet Eco Park, provide additional green spaces for residents and visitors. There are a total of 18 open urban green spaces specifically designated for cemeteries in South Jakarta, while the remaining serve as green lane roads and water banks.  
One of the most renowned urban green spaces open to the public in South Jakarta is Ragunan Zoo, which ranks among the five largest zoos in Indonesia. Interestingly, the zoo still features some pristine tropical rainforest within its confines, while other forests are located within the University of Indonesia's campus area in the southern part, bordering Depok City in West Java Province.
# Effect of the Sulfonation on the Swollen State Morphology of Styrene Cross-Linked Polymers
## 3 Results and Discussion
### ATR-FT-IR Characterization  
To support the relationship between the observed increased crosslinking degree and the presence of sulfone bridges, the sulfonic materials have been characterized with FT-IR spectroscopy to identify the characteristic signals of the expected functional groups. The spectra, collected in attenuated total reflectance (ATR) geometry, are reported in Figures 2-4.  
Figure 1: Distribution of the polymer chain concentration of \(Gel\_H_{2}SO_{4}\), \(Gel\_oleum\) and \(Gel\_HCISO_{3}\), according to ISEC.  
Figure 4: FT-IR spectrum of \(Gel\_\)_oleum.  
Figure 3: FT-IR spectrum of \(Gel\_\)_HCISO\({}_{3}\).  
Figure 2: FT-IR spectrum of \(Gel\_\)_H\({}_{2}SO_{4}\).  
The relevant FT-IR data and the assignments of signals to vibrational modes are summarized in Table 3[25].  
All the spectra contain peaks which can be attributed to the sulfonic groups, such as those at 1348 cm\({}^{-1}\) and 1033 cm\({}^{-1}\). The signals at 836 and 772 cm\({}^{-1}\) suggest that the functionalization of the phenyl rings takes place mainly in the ortho- and para-positions with respect to the alkyl chains of the polymer framework. However, para- position appears the most likely, due to its relatively high distance from the sterically bulky alkyl chains of the polymer framework. Also, the signal at 907 cm\({}^{-1}\) can be reasonably assigned to the stretching vibration of the S-O bond of the -SO\({}_{3}\)H group, in spite of a significant shift from the literature (897 cm\({}^{-1}\)).  
The signal at 1166 cm\({}^{-1}\) (present as a shoulder in the spectra of _Gel_deum_ and _Gel_HCISO\({}_{3}\)_ materials) has been considered by some authors as diagnostic for the sulfone bridges. In fact, the diphenyl sulfones show the symmetric stretching vibration of two S-O bonds between 1160 and 1164 cm\({}^{-1}\)[26; 27]. Conversely, a signal at 1172 cm\({}^{-1}\), due to the symmetric stretching vibration of the S=O bonds of the SO\({}_{3}\)H group, is also reported for sulfonic polystyrene materials [25]. Therefore, the signal at 1166 cm\({}^{-1}\) cannot unambiguously support the presence of sulfone bridge in the sulfonic resins.  
Moreover, the SO\({}_{2}\)-antisymmetric stretching of the -SO\({}_{2}\)Cl group is not recognized in the FT-IR characterization of _Gel_HCISO\({}_{3}\)_. Accordingly, the chlorine content is very low (0.11 mmol g\({}^{-1}\)) and the conversion of the sulfonyl chloride units into sulfonic groups in the second stage of sulfonation process can be considered almost complete.  
To summarize, the FT-IR characterization of the sulfonic materials is compatible with the possible formation of sulfone bridges and suggest the para- position of the styrene rings as the most probable position for the sulfonation.