esa-sceva/satcom-chunk-collection huggingface dataset:
# Application Study of the High-Strain Direct Dynamic Testing Method
## 3 High-Strain Direct Dynamic Test and Static Load Test
### Experimental Setup  
According to the instrumentation setup methods described in Section 2, each test pile was equipped with two piezoelectric strain sensors and two accelerometers, symmetrically installed at the pile top through boreholes. Additionally, a Fiber Bragg Grating (FBG) with 2 m fixed intervals was symmetrically fixed in a \"U\" shape to the main bars of the rebar cage before concrete pouring. Because of the high-temperature sensitivity of the FBG, temperature compensation was required for the measured strain, and thus fiber optic temperature sensors were simultaneously installed. The sensing fiber was fixed at 50 cm intervals and embedded into the concrete-filled pile, ensuring that the fiber deformation was consistent with the pile concrete deformation. Protective casings were used to safeguard the fiber sensors at the pile top and bottom during installation. A schematic diagram of the fiber optic sensor layout is shown in Figure 5.  
Figure 6 presents the on-site construction drawings. In addition to the aforementioned measurement instruments, the dynamic load test was also equipped with a high-strain dynamic tester and a high-dynamic Fiber Bragg Grating (FBG) demodulator. These devices were connected to the strain sensors and accelerometers at the pile top, as well as the FBG sensors within the pile. Both instruments were configured with a sampling interval of dt = 200 us, recording M = 1024 sampling data points. A 12-ton pile hammer was used to apply instantaneous dynamic loads to the pile top, selected according to local code [38]. Additionally, a 10 mm thick steel plate was employed to cushion the impact between the pile hammer and the pile top, and a precision level was used to monitor the single impact penetration depth of the pile.  
For the static load test, a reaction platform with weights was utilized, and hydraulic jacks were used to apply the load incrementally. Since the test piles needed to be used as engineering piles afterward, loading was stopped once the designed bearing capacity limit was reached to avoid damaging the pile or surrounding soil. The loading amount of each level was one-tenth of the vertical allowable bearing capacity of a single pile, and the first and second levels were loaded at twice the graded load. Displacement sensors  
Figure 5: FBG optic layout diagram. (**a**) Cross-section. (**b**) Longitudinal section.  
were symmetrically installed at the pile top to measure the settlement value after each load increment at specified time intervals.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 6 Analysis
### Efficiency Assessment
#### 6.1.1 Different DeFi Use Cases  
The proposed Cross-DeFi framework is specially designed to facilitate interoperability and seamless asset transfer among different blockchain-enabled DeFi systems while ensuring a finality guarantee. By enabling seamless cross-chain asset transfers, CrossDeFi can support a wide variety of use cases across DeFi. Exploiting the proposed CrossDeFi framework across various DeFi use cases can offer significant benefits, including seamless interoperability, increased asset transfers, improved security, and opportunities for diversified investment. However, it has to face some challenges, such as asset loss, additional transaction fees, adaptability level, finality guarantee, implementation complexity, and performance efficiency. By effectively addressing such challenges, Cross-DeFi can drive innovation and growth in DeFi, providing users with more effective, flexible, and secure financial services. Table 7 evaluates the impact of CrossDeFi on various DeFi use cases, illustrating its influence on critical factors such as asset loss, double-spending, additional fees, adaptability, finality guarantee, complexity, and performance.  
Each row in the table provides insights into the specific impacts of implementing CrossDeFi within different scenarios, from trading on decentralized exchanges (DEXs) and engaging in yield farming activities to participating in decentralized autonomous organizations (DAOs). Notably, CrossDeFi enhances adaptability and guarantees finality across all listed use cases, reducing the potential for asset loss and double-spending while slightly increasing transaction fees in some instances. This aligns with the advancedsecurity measures and infrastructure improvements that typically accompany the deployment of such innovative solutions. The assessment underscores CrossDeFi's capacity to address the unique needs and challenges of various DeFi sectors, making it a versatile and valuable addition to the DeFi landscape. Table 7 assists in analyzing the challenges of implementing CrossDeFi across various DeFi use cases, paving the way to utilize its full potential to improve next-generation DeFi systems. By leveraging asset-specific cross-chain bridge selection and enhanced transfer confirmation within the core blockchain layer, the CrossDeFi framework enhances asset transfer efficiency and DeFi user experience. This wider support motivates the proposed approach to utilize CrossDeFi across diverse use cases and enables the growth of DeFi, making it more versatile and efficient.
## 3 Results
### An Accuracy Assessment of CHM-Based Height
#### 3.3.3 Effect of Data Acquisition Timing on Measurement Accuracy  
The time difference between field inventory and LiDAR data acquisition did not significantly affect the overall height measurement error (Figure 14). However, the height measured by LiDAR data was generally overestimated when LiDAR data were collected five or more years after the collection of field inventory data. Height measurement error had a median value of 0.7 m when the time difference was five years and 2.1 m when the time difference was 6-7 years. We observed height error in relation to the combination of LiDAR and inventory year, which did not show a noticeable trend (Figure 15). However, height error calculated from the 2018 LiDAR data again showed a larger variance with significant underestimation compared to other LiDAR data collection years.
## 5 Experimental Tests
### Time Domain Performance Evaluation
#### 5.2.2 Bump Road Profile  
In a similar manner to the simulation analysis, the performance of the controllers was also experimentally examined when subject to a single bump road input at a vehicle speed of 25 km/h, as shown in Figure 21a. The measured time responses of the controlled active suspensions in terms of the seat acceleration and seat suspension stroke are compared with those of the passive system in Figures 21b and 22. It can be seen that all the proposed active controllers effectively reduce the seat acceleration when compared with the passive system. In addition, it was observed that the controllers as well as the passive system change the static position, especially controller A1 as shown in Figure 22b. This is due to the presence of coulomb friction and results in a small random offset following each test. In summary, the controller A1 delivers the best reduction in seat acceleration followed by the controller A2 when compared with the other controllers as presented in Table 6. Although the controllers (A3 to A5) show the best performance in simulation tests, this was not backed up by the experimental results. Again, this is attributed to the effects of unmodelled higher-order system dynamics and nonlinearities.  
Figure 17: Time responses and PSD of the passive system and active seat suspension with controller A4. (**a**) Seat acceleration; (**b**) Seat suspension travel; (**c**) PSD of the seat acceleration; (**d**) Output control force.  
Figure 18: Time responses and PSD of the passive system and active seat suspension with controller A5. (**a**) Seat acceleration; (**b**) Seat suspension travel; (**c**) PSD of the seat acceleration; (**d**) Output control force.  
Figure 19: Performance assessment of the proposed active seat suspensions under different vehicle speeds. (**a,b**) SEAT factor and percentage improvements, respectively ; (**c,d**) Frequency-weighted RMS seat acceleration and percentage improvements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Seat System** & **Seat Acceleration RMS (m/s\({}^{2}\))** & **Maximum Seat Suspension Travel (mm)** \\ \hline Passive & 0.866 & 9.302 \\ A1 & 0.461 & 24.472 \\ A2 & 0.566 & 11.170 \\ A3 & 0.630 & 14.038 \\ A4 & 0.658 & 12.132 \\ A5 & 0.593 & 10.993 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time response characteristics of the proposed controllers under a bump road profile.  
Figure 20: Health risk assessment, according to ISO 2361-1, of the proposed active seat suspensions and passive system under different vehicle speeds (km/h). (**a**) 40; (**b**) 60; (**c**) 80; (**d**) 100.
## 3 Direct Defences
### JA, ET and SA Interactions  
Jasmonic acid, ET and SA are involved in plant defence by regulating plant defence responses against biotic and abiotic stresses [111, 128, 135, 136]. In some studies [129, 137, 138], synergistic interactions between JA, ET and SA have been reported. For instance, [PERSON] et al. [138] indicated that NPR1 plays an important role in interactions between SA and JA in _Arabidopsis_ plants. Transcription factors MYC2 and ERF1 appeared to function as important nodes that integrate signals from JA and ET pathways upon herbivory by _Pieris rapae_ and the bacterium _Pseudomonas syringae_ in _Arabidopsis thaliana_[85; 96]. Furthermore, an interaction between JA and ET-mediated defence signalling was regulated by transcription factor MYC2 in _Arabidopsis thaliana_ in response to biotic stress [139]. [PERSON] et al. [140] reported the MAPK4 transcription factors as a negative regulator of SA signalling and a positive regulator of JA signalling in _Arabidopsis_. Moreover, in a study by [PERSON] et al. [141], the expression of WRKY70 in _Arabidopsis thaliana_ caused enhanced expression of SA-responsive PR genes and concomitantly suppressed methyl-jasmonate-induced expression of JA-responsive marker gene plant defensin 1.2 (PDF1.2). Some of these regulatory proteins are also known to play a role in shaping the outcome of the _Arabidopsis_ defence response.  
Similarly, a WRKY70 transcription factor known to be a positive regulator of SA-mediated defences while repressing JA signalling was downregulated in a susceptible T200 cassava genotype in response to SACMV [16]. The authors suggested that the repression of this TF may contribute to suppression of the SA pathway, to subvert an induced resistance response in T200. In addition, the expression of JA/ET markers was constantly expressed in a resistant genotype compared to the susceptible genotype after cassava anthracose disease (CAD) infection in cassava [99], suggesting the hormone signal interactions' role in resistance to CAD. Likewise, the plant hormone signal transduction network between JA, ET and SA transduction pathways was reported in cassava in response to the mite _Tetranychus urticae_ infestation [6]. The role of plant hormones in mitigating stress is studied using, among others, hyphenated techniques.
## 1 Introduction
These and other reports have indicated that the protein-to-carbohydrate mass ratio, the molecular weight, functionality, and the physico-chemical properties of the proteins and carbohydrate constituents significantly influence the particles size distribution (PSD) properties of CIWEs, core retention during microencapsulation, core extractability, porosity of the wall matrices, microencapsulation efficiency (MEE), physico-chemical and microstructural features of microcapsules, as well as the rehydration and reconstitution properties of microcapsules. The microencapsulation properties of wall systems consisting of wheat proteins isolate (WHPI) and different maltodext and corn syrup solids have been recently reported by us [30] and the results of the study indicate that the molecular weight profile of the carbohydrate constituents affects the formation and properties of CIWEs, core retention during microencapsulation by spray drying and the MEE. In agreement with earlier reports, results of the latter study suggested the advantage of utilizing low-molecular-weight carbohydrates, especially when core extractability, MEE, and core retention are considered [30; 31; 43; 44]. The utilization of blends consisting of different proteins and lactose as wall constituents for microencapsulation of lipids by spray drying has been reported [34; 37; 41; 42; 45; 46; 47; 48; 49]. These studies have indicated that the incorporation of lactose in the protein-based wall system allowed enhancing core retention and microencapsulation efficiency. It also allowed improving, in a protein type-dependent manner, the oxidative stability of encapsulated oil as well as modulating the microstructural, porosity and moisture sorption properties of the microcapsules. The objectives of the presented research were to: investigate the formation and properties of CIWEs consisting of model oil and blends of WHPI and lactose and then, investigate the formation and properties of spray-dried microcapsules that were prepared with these CIWEs.
## 2 Data and Methods
### Satellite Data
#### 2.2.2 Gaofen (GF) Series Satellite  
GF-1 satellite, launched in 2013, is China's first high-resolution Earth observation satellite. The panchromatic resolution of the satellite is 2 m, and the multispectral resolution is 8 m [21,22]. GF-1 is equipped with a high-resolution multispectral camera, which has the strongest performance among China's Earth observation satellites in operation. The wide-width multispectral camera of the GF-1 satellite has a width of 800 km. The repetition period of similar satellites in the world is mostly ten days, while GF-1 only takes four days. Therefore, it can be said that GF-1 achieves the perfect combination of high temporal and spatial resolution [7].  
The GF-6 satellite was successfully launched by the Jiququan Satellite Launch Center in China in 2018. Notably, GF-6 incorporates a \"red edge\" band, a spectral band highly sensitive to the developmental status of green vegetation [23]. This satellite is primarily dedicated to catering to agricultural and rural domains, as well as addressing the needs of natural resource management, emergency response, and ecological assessment [24]. An exceptional attribute of GF-6 is its expansive coverage capacity. Following its deployment into orbit, GF-6 is poised to establish a synergistic satellite network alongside GF-1, enhancing the collective observational capabilities.  
GF-1 and GF-6 are equipped with two types of cameras, namely the high-resolution camera PMS and the wide-format camera WFV (Table 2). The PMS camera can obtain 2 m of panchromatic black and white image, 8 m of multispectral color image, and 2 m of true color image after the fusion of multispectral and panchromatic image [23,25].
# Effects of Propanol on the Performance and Emissions of a Dual-Fuel Industrial Diesel Engine
## 2 Experimental Setup
### Methodology  
The experiments conducted on the engine consisted mainly of its indication, during which the pressure in the cylinder was measured and recorded as a function of the crank angle. The pressure courses made it possible to determine the most important parameters of the combustion process, i.e., peak pressure (p\({}_{\text{max}}\)) and pressure increase rate (PPR), normalized heat released (Q\({}_{\text{norm}}\)) and heat release rate (HRR), ignition delay (ID) and combustion time (CD), as well as thermal efficiency of the engine (ITE). Based on the uniqueness of pressure changes in several hundred successive cycles, the stability of the test engine operation was analyzed by determining the COV\({}_{\text{IMEP}}\) coefficient. The engine tests also included measurements of the concentrations of undesirable engine exhaust components such as nitrogen oxides (NO\({}_{\text{x}}\)), hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO\({}_{2}\)), and soot. As part of the research, the engine operation was analyzed depending on the proportion of propanol co-combusted with diesel fuel. The share of energy input in the alcohol fuel was 15% (21% by volume) (DP15), 30% (39% by volume) (DP30), 50% (58% by volume) (DP50), and 65% (72% by volume) (DP65) of the total energy delivered in the fuel to the engine. The tests were carried out at constant and maximum engine load, after achieving its thermal stabilization. The test results were compared with the tests of the engine powered by diesel (D100) as the reference fuel.  
The injection system on this test engine did not allow the injection start angle to be changed. This work concerns the use of alternative fuels in stationary industrial engines. Many such engines are used to drive machines and devices in our environment. One of the main ideas was to show how the engine behaves and its properties when powered by dual fuel.
## 5 Initial Alignment Error Compensation for Shearer's SINS
### Simulation Comparison of Filtering Effects for Optimized UKF  
Simulation analysis is conducted below for different initial alignment error compensation algorithms to compare the filtering effects among extended Kalman filter (EKF), UKF, and optimized UKF algorithms. It is known that the experimental site has an altitude of 36 m, and latitude and longitude of 34.27\({}^{\circ}\) and 117.18\({}^{\circ}\), respectively. The gravitational acceleration, the average earth radius, and the average earth rotation rate are taken as 9.821 m/s\({}^{2}\), 6371.393 km, and 15.041\({}^{\circ}\)/h, respectively. Both initial pitch angle and roll angle are 0\({}^{\circ}\), and the heading angle is 270\({}^{\circ}\). We assume that three axial error angles \(\varphi_{E}\), \(\varphi_{N}\), and \(\varphi_{U}\) in initial alignment are 3\({}^{\circ}\), 3\({}^{\circ}\), and 5\({}^{\circ}\), respectively. Fine alignment filtering is performed using three algorithms, i.e., EKF, UKF, and optimized UKF, separately. The sampling frequency is 200 Hz. The results are shown in Figures 4-6.  
As can be discerned from Figures 4-6, the optimized UKF algorithm has higher accuracy than the EKF and UKF algorithms. The filtering errors of these three algorithms are shown in Table 1.  
It can be discerned from Table 1 that, although the optimized UKF algorithm has a larger filtering range in the estimation of error angles in the east and north directions, it has higher accuracy than the EKF and UKF algorithms. In the estimation of heading error angles, the optimized UKF algorithm displays evidently better accuracy and filtering effect than the EKF and UKF algorithms. Therefore, the optimized UKF algorithm can be used in initial alignment to improve the initial alignment accuracy.
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 3 Data and Results
### Unit Commitment Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline
**Approach** & **Best (S)** & **Average (S)** & **Worst (S)** & **Avg. Time (s)** & **Max Iter** & **n** \\ \hline WICPSO [18] & 563,937,69 & - & - & 39.6 & 100 & 10 \\ HGICA [25] & 563,935.31 & - & - & 2.44 & 32 & 55 \\ GA [26] & 563,937.69 & - & - & 300 & 64 \\ QGKOW [27] & 563,936.30 & 563,963.30 & 563,936.30 & 62.3 & 500 & 30 \\ BASA [28] & 563,937 & 563,937 & 563,937 & - & - & - \\ BGWO [29] & 563,937.30 & 563,937.30 & 563,937.30 & 66.15 & 500 & 30 \\ MILP-SMOC [30] & 56,3937 & - & - & - & - & - \\ PSPSO & 563,934.17 & 564,112.10 & 564,357.63 & 3.57 & 1000 & 50 \\ \hline \hline \multicolumn{8}{c}{**(b)With Ramp Constraint**} \\ \hline
**Approach** & **Best (S)** & **Average (S)** & **Worst (S)** & **Avg. Time (s)** & **Max Iter** & **n** \\ \hline HGICA [25] & 564,186.63 & - & - & 1.61 & 32 & 55 \\ MILP-SMOC [30] & 564,326.80 & - & - & 1.08 & - & - \\ PSPSO & 564,111.36 & 564,178.22 & 564,563.00 & 5.9 & 1000 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: System Cost ($) and Computational Time (sec) of the 10-unit UC problem with increased. Dimension compared with other PSO variants.
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
###### Abstract
For UWA OTFS communication, SC-CNN-BiLSTM signal detection outperforms other previous proposed DL-based and conventional linear and nonlinear signal detection methods. The remainder of this paper is organized as follows. Section 2 presents the UWA OTFS system model. Section 3 proposes the SC-CNN-BiLSTM scheme for signal detection. Section 4 evaluates the performance of SC-CNN-BiLSTM-based signal detection with the simulation and experimental data and compares its performance with other signal detection methods. Section 5 provides a discussion about the results. Section 6 concludes our research.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Model Accuracy Evaluation and Validation  
To assess the estimated maize yield, we computed the coefficient of determination (\(\rm R^{2}\)), root mean square error (RMSE), mean absolute percentage error (MAPE) and the revised [PERSON]'s index of agreement (\(\rm d_{r}\)) [52]. The general rule is that the higher the \(\rm R^{2}\), the lower the RMSE, signifying a higher accuracy of the estimated yield, and the lower the MAPE, the smaller the difference between the estimated yield and the statistical yield. The \(\rm d_{r}\) is a good indicator to evaluate the model performance, especially relative to the 1:1 best-fit line, and the \(\rm d_{r}\) provides information about the predicted and observed values on the deviation from the observed mean [53; 54]. The following calculation formula is used:  
\[\rm RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}-X_{i}\right)^{2}} \tag{6}\]  
\[\rm MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{Y_{i}-X_{i}}{X_{i}}\right|\times 100 \tag{7}\]\[\mathrm{d}_{\mathrm{r}}=\left\{\begin{array}{ll}1-\frac{\sum_{i=1}^{n}|Y_{i}-X_{ i}|}{c\sum_{i=1}^{n}|X_{i}-X_{i}|},&\mathrm{when}\Sigma_{i=1}^{n}|Y_{i}-X_{i}|\leq c \Sigma_{i=1}^{n}|X_{i}-\overline{X}|\\ \frac{c\Sigma_{i=1}^{n}|X_{i}-\overline{X}|}{\sum_{i=1}^{n}|Y_{i}-X_{i}|}-1,& \mathrm{when}\Sigma_{i=1}^{n}|Y_{i}-X_{i}|>c\Sigma_{i=1}^{n}|X_{i}-\overline{X}| \end{array}\right. \tag{8}\]  
where n is the number of administrative regions, \(Y_{i}\) denotes the estimated yield, and \(X_{i}\) denotes the statistical yield for the ith region. \(\overline{X}\) represents the statistical yield, on average, across all administrative regions. Moreover, \(\mathrm{d}_{\mathrm{r}}\) has a range from \(-1\) to \(1\), which implies that the model is performing better when its value is nearer \(1\), and c equals \(2\).