esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 1 Introduction
Afterwards, from both a hardware and firmware perspective, the architecture of the system is analyzed, as well as the custom network layer protocol designed for this implementation. Finally, the description of a series of trials, their methodology, a field test for the validation of the system, a discussion of the results, and the final conclusions are explained.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Pro-Agility CODTT**} & \multicolumn{3}{c}{**Pro-Agility CODD**} \\ \cline{3-7}  & & **r** & **R\({}^{2}\)** & \(p\)** & **r** & **R\({}^{2}\)** & \(p\)** \\ \hline \multirow{4}{*}{Left} & 0–5 m & 0.66 & 43 & \textless{}0.01 & 0.23 & 5 & 0.14 \\  & 0–10 m & 0.63 & 40 & \textless{}0.01 & 0.06 & 1 & 0.66 \\  & 0–30 m & 0.49 & 24 & \textless{}0.01 & \textless{}0.23 & 5 & 0.13 \\  & CODTT & 1.00 & / & / & 0.73 & 52 & \textless{}0.01 \\ \hline \multirow{4}{*}{Right} & 0–5 m & 0.65 & 43 & \textless{}0.01 & 0.24 & 6 & 0.11 \\  & 0–10 m & 0.66 & 44 & \textless{}0.01 & 0.10 & 11 & 0.50 \\ \cline{1-1}  & 0–30 m & 0.58 & 33 & \textless{}0.01 & \textless{}0.16 & 3 & 0.29 \\ \cline{1-1}  & CODTT & 1.00 & / & / & 0.70 & 49 & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correlation coefficients between Pro-agility and 30 m sprint performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**YCODTT**} & \multicolumn{3}{c}{**YCODD**} \\ \cline{3-7}  & & **r** & **R\({}^{2}\)** & \(p\)** & **r** & **R\({}^{2}\)** & \(p\)** \\ \hline \multirow{4}{*}{Left} & 0–5 m & 0.65 & 43 & \textless{}0.01 & \(-0.14\) & 2 & 0.37 \\  & 0–10 m & 0.65 & 42 & \textless{}0.01 & \(-0.23\) & 5 & 0.13 \\  & 0–30 m & 0.49 & 24 & \textless{}0.01 & \(-0.32\) & 10 & 0.03 \\  & CODTT & 1.00 & / & / & 0.59 & 35 & \textless{}0.01 \\ \hline \multirow{4}{*}{Right} & 0–5 m & 0.76 & 57 & \textless{}0.01 & \(-0.19\) & 4 & 0.20 \\  & 0–10 m & 0.78 & 61 & \textless{}0.01 & \(-0.28\) & 8 & 0.06 \\ \cline{1-1}  & 0–30 m & 0.64 & 40 & \textless{}0.01 & \(-0.34\) & 12 & 0.02 \\ \cline{1-1}  & CODTT & 1.00 & / & / & 0.38 & 15 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation coefficients between Y-shaped pre-planned agility and 30 m sprint performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Pro-Agility 0-10 m CODTT**} & \multicolumn{3}{c}{**Pro-Agility 0-10 m CODD**} \\ \cline{3-7}  & & **r** & **R\({}^{2}\)** & \(p\)** & **r** & **R\({}^{2}\)** & \(p\)** \\ \hline \multirow{4}{*}{Left} & 0–5 m & 0.61 & 37 & \textless{}0.01 & 0.06 & 0 & 0.69 \\  & 0–10 m & 0.56 & 32 & \textless{}0.01 & \(-0.05\) & 0 & 0.73 \\  & 0–30 m & 0.42 & 17 & 0.01 & \(-0.15\) & 2 & 0.34 \\  & CODTT & 1.00 & / & / & 0.79 & 63 & \textless{}0.01 \\ \hline \multirow{4}{*}{Right} & 0–5 m & 0.65 & 43 & \textless{}0.01 & 0.12 & 2 & 0.42 \\  & 0–10 m & 0.60 & 38 & \textless{}0.01 & 0.01 & 0 & 0.95 \\ \cline{1-1}  & 0–30 m & 0.47 & 22 & \textless{}0.01 & \(-0.08\) & 1 & 0.57 \\ \cline{1-1}  & CODTT & 1.00 & / & / & 0.79 & 63 & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation coefficients between Pro-agility 0–10 m and 30 m sprint performance.
# Optimizing Retention Bunkers in Copper Mines with Numerical Methods and Gradient Descent
## 4 Results
### Second Stage of Optimization  
Figure 11 shows linear response surfaces as a function of independent factors for the second stage of the simulation. Table 5 presents the results of ANOVA test between each independent factor and the obtained responses.  
Figure 11: Response surfaces of the experiment for the second stage of the simulation.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Independent Factor** & \(F\) [kN] & \(v\) [m/s] & \(Q\) [Mg/h] \\ \hline \(f\) [Hz] & 0.889 & 0 & 0.046 \\ \(S\) [m\({}^{2}\)] & 0 & 0.892 & 0 \\ \(\beta\) [\({}^{\circ}\)] & 0.085 & 0.604 & 0.709 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA test results (\(p\)-value) for the second stage of optimization.  
The results clearly show that the surface area of the outlet \(S\) and the inclination angle of the bin drawer \(\beta\) have the greatest impact on the vertical pressure force \(F\) on the drawer. However, a statistically significant effect was noted only for the surface area of the outlet \(S\) in relation to the force \(F\). The frequency of the drawer movement \(f\) has virtually no effect on changes in the value of the pressure force \(F\). Meanwhile, the most significant factor that statistically affects the average velocity \(v\) of particles on the surface of the bin drawer is the frequency of drawer movements \(f\). The impact of the other two independent factors is much less, and additionally, their statistical significance is negligible. The capacity of the receiving bin \(Q\) is statistically determined only by the frequency of the drawer movements \(f\) and the surface area of the outlet \(S\).  
Figure 12 shows the results of the optimization algorithm in the form of the optimal operating point on the surfaces representing the relation of each dependent factor to the independent factor in the second stage of the simulation.  
The optimization algorithm results indicate that the most optimal operating point for the bin, considering the assumed constraints, is a configuration in which the bin operates at a frequency \(f\) of 0.63 Hz, with an outlet opening area \(S\) of 1.27 m\({}^{2}\), and a drawer inclination angle \(\beta\) of 4.86\({}^{\circ}\). Under these conditions, the force \(F\) pressing on the drawer's surface is 190 kN, the average particle speed \(v\) is 0.19 m/s, and the capacity \(Q\) is maintained at about 650 Mg/h.  
Figure 12: Optimization results using the gradient descent method with constraints—the optimal operating point determined for each response as a function of each independent factor.
## 2 Materials and Methods
### Dataset
#### 2.2.2 Remote Sensing Image Data  
The Sentinel-2 MSI satellite image data are part of the Copernicus Program, operated by the European Space Agency (ESA). These satellite images offer a wealth of spectral information, as depicted in Table 11. The imaging resolution of Sentinel-2 MSI is exceptionally high, reaching up to 10 m. Its applications span a wide range, including natural disaster monitoring, land cover surveys, and nearshore environmental monitoring. The first Sentinel-2 satellite (S2A) was successfully launched in June 2015, and the second one (S2B) in March 2017, forming a double-star network. The synergistic effect of these two satellites has shortened the revisit period of the satellites to 2-5 days. This high frequency observation capability provides a stable data source for water quality retrieval. Detailed information regarding the matched images and monitoring times for PC concentration can be found in Table 12.
## 3 Experimental Results and Discussion
### Seismic Tests on Real-Scale Three-Storey Precast Concrete Building
#### 3.3.1 Longitudinal Displacement of the Shake Table and the Building  
Figure 19 shows the diagrams of the longitudinal displacement versus time during the \"El Centro\" earthquake at the following locations: shake table, Concrete Slab 1, Concrete Slab 2 and lightweight roof.  
Figure 19: Diagrams of longitudinal displacement vs. time at different locations, from shake table to lightweight roof. (**a**) Displacement versus time in the shake table; (**b**) displacement versus time in concrete slab 1; (**c**) displacement versus time in concrete slab 2; (**d**) displacement versus time in lightweight roof.  
Figure 19 reveals some interesting findings. First, a progressive increase of the longitudinal displacement with the height is observed. The measured maximum longitudinal displacements values are 2.18, 4.99, 7.15, and 8.23 milimeters for shake table, Concrete Slab 1, Concrete Slab 2, and flexible roof, respectively. The displacement shows almost a linear trend from shake table to Concrete Slab 2, while the variation is much smaller from Concrete Slab 2 to flexible roof.  
Moreover, the visual inspections carried out after the seismic tests revealed that no structural damage is observed in the building (i.e., no cracks in the walls or slabs appeared and, of course, no concrete crushing occurred). This means that the seismic energy was completely dissipated by the connections, i.e., by the low-cost energy dissipation systems placed on the connections between the walls and the foolings and the flexible connections between the walls and the slabs. The main aim of the research, which is the design and validation of a low-cost energy dissipation system, as well as the flexible connection between the walls and the slabs, has been reached.  
At the end of the seismic event, the residual longitudinal displacements of the both concrete slabs and the flexible roofs are almost zero, which means that the building recovers its original position, that is, the walls recover their upright position.  
Finally, it is concluded that the building reached the performance level of \"immediate occupancy\", according to ACI374.2R-13 [34].
## 2 Development of a Process Model for Two-Stage Bio-Oil Hydrogenation (HDO)
### Input Parameters for Plant Operation and Simulation of Hydrogenation Upgrading of Bio-Oil  
The annual throughput of the demonstration plant in this case study was set at 107 thousand tons per year of raw bio-oil. Therefore, the input flow rates of feedstock materials were set as follows: 10,000 kg/h for the organic compounds of raw bio-oil (BIO-OIL), 345 kg/h for the hydrogen (H2), and 10 kg/h for the fresh catalyst feed (CATALYST). Catalyst loading in the reactor (RCSTR) was 200 kg. The ratios among the feed streams were selected from reported values [13]. The Ni-based commercial catalysts were used in the mild stage of hydrogenation (HDO). Three sets of operating conditions for mild HDO step were assessed which were, respectively, (1) 250 \({}^{\circ}\)C, 5.6 MPa; (2) 280 \({}^{\circ}\)C, 8 MPa; and (3) 300 \({}^{\circ}\)C, 10 MPa. The optimum reaction conditions were eventually selected according to the desired product yield.  
According to the theory of mild hydrogenation kinetics, the chemical conversion reactions among the components was established [10]. The model for the conversion of the catalytic bio-oil hydrogenation process was obtained by fitting the experimental data of product distribution and catalyst deactivation in the small industrial reaction [14]. The pre-exponential factor A and the activation energy E in the chemical reactions were obtained from the previous study on kinetic modeling of the bio-oil hydrogenation from which the parameters of the mild-stage reactions were fitted. Their values are given in Table 2, with the parameter setting of reaction conditions as described above.  
The deactivation rate of the catalyst is defined as follows:  
\[\mathrm{Xc}=1-\frac{\mathrm{m_{cat,f}}}{\mathrm{m_{cat}}} \tag{1}\]  
where \(\mathrm{m_{cat}}\) is the total mass of the catalyst at a given time and \(\mathrm{m_{cat,f}}\) is the mass of fresh catalyst. The relationship between the deactivation rate of the catalyst and catalyst coking has been derived from coking theory [14] and experimental results of the batch hydrogenation reactor [15] and is expressed as follows:  
\[\frac{\mathrm{d}X_{\mathrm{C}}}{\mathrm{dt}}=\mathrm{K_{d}}\bigg{[}(1-X_{ \mathrm{C}})\cdot\bigg{(}\alpha\frac{\mathrm{dm_{Cs}}}{\mathrm{dt}}+(1-\alpha) \frac{\mathrm{dm_{Cs}}}{\mathrm{dt}}\bigg{)}\bigg{]}^{\beta} \tag{2}\]  
where \(\mathrm{K_{d}}\) is the deactivation constant of coke deposition, \(\alpha\) is the weighing factor of coke type, and \(\beta\) is the deactivation exponent. The mathematical expression of impacts of catalyst coking deactivation (Equation (2)) on the reaction system in the mild HDO stage is embedded into the Aspen model using a FORTRAN subroutine.  
The deep hydrogenation step used a Ni/Al2O3 catalyst, under the operating conditions of 400 \({}^{\circ}\)C and 15 MPa.  
The stoichiometry for deep hydrogenation reaction which produces the final upgraded bio-fuel (PRODUCT) is obtained from reported experimental results [16] and shown in Equation (3).  
\[\mathrm{ODF}+0.23\mathrm{H_{2}}\rightarrow\mathrm{PRODUCT}+0.11\mathrm{H_{2}O} \tag{3}\]
# Individual Differences in Signal Perception for Takeover Request in Autonomous Driving
## 5 Discussion
By investigating the effects of sub-attributes of TOR signaling modalities and individual background factors on reaction times and subjective evaluations, valuable insights have been gained that can inform the development of effective TOR systems. Firstly, the findings suggest that signal intensity, such as loudness in auditory signals and amplitude/frequency in tactile signals, significantly impact participants' reaction times. This knowledge can guide the selection of appropriate signal intensities that effectively capture the attention of drivers and prompt timely responses. Autonomous vehicles can leverage this information to optimize the design of TOR signals by ensuring an appropriate level of intensity that balances effectiveness without causing discomfort or annoyance to drivers. Moreover, the observed correlations between individual background factors, such as gender and age, and participants' responses to specific modalities highlight the importance of personalization in TOR systems. Autonomous vehicles could benefit from considering individual characteristics and preferences when delivering TOR signals. For instance, gender-specific signal characteristics or customizable settings could be implemented to cater to individual differences and enhance the user experience. Additionally, the subjective evaluations provided by participants shed light on the comfort, noticeability, and suitability perceptions of the TOR signals. This information can guide the design of user-centered TOR systems in autonomous vehicles. By incorporating participants' preferences and comfort levels, autonomous vehicles can create TOR signals that are not only effective in eliciting timely responses but also contribute to a positive driving experience and promote driver trust in autonomous technology. Furthermore, the study emphasizes the importance of understanding the interaction between stimulus characteristics, individual differences, and TOR responses. This knowledge can aid in the development of advanced algorithms and machine learning models that adapt TOR signals based on real-time assessments of drivers' attention and responsiveness. Autonomous vehicles equipped with such adaptive TOR systems can effectively tailor the delivery of signals to individual drivers' needs and optimize their performance in complex driving scenarios. In conclusion, the study offers practical implications for the application of TOR signals in autonomous vehicles. By considering the findings of this research, autonomous vehicle manufacturers and designers can enhance the design, personalization, and adaptability of TOR systems, ultimately improving driver safety, comfort, and trust in autonomous technology. Further research and development in this area can continue to refine and optimize TOR systems, contributing to the successful integration of autonomous vehicles into transportation systems.
## 4 Discussion
### Potential Mechanism for OSE's Superiority to CSE  
In the present meta-analysis, OSE was found to have a greater benefit for cognitive function than CSE. However, the effect differed according to specific cognitive domains. In particular, OSE was more likely to promote inhibition and cognitive flexibility. We hypothesize that this result was due to participants' need to mobilize more cognitive resources in self-control and transformation to adapt to changes in the external information and the multisensory environments of OSE [55]. In contrast, CSE involves a relatively predictable and stable environment. Thus, fewer cognitive resources need to be mobilized during this mode of exercise. Inhibition and cognitive flexibility are important components of executive function. In OSE, this high-level cognitive process could be improved. In terms of processing speed, since the measurements of processing speed are based on relatively simple tasks, the cognitive resources required for mobilization are very limited. Therefore, no significant differences between OSE and CSE were observed when considering tasks requiring a low cognitive level [48; 56].  
Regarding visuospatial attention, our meta-analysis did not identify any differences between the two exercise modes. Exercise in a complex environment has been shown to increase the cortical thickness, neurogenesis, and height neurotransmission [57; 58]. Moreover, a prior study found that prefrontal cortex activation is much greater in children than in adults [59]. In addition, the prefrontal cortex plays an important role in resisting interference and maintaining targets. Thus, the effects of different exercise modes on visuospatial attention vary in different age groups. To date, very few studies have included children and the elderly in the evaluations of modes of exercise. Consequently, a lack of comprehensive coverage of the characteristics of these subjects may account for the insignificant results we obtained. Moreover, the latter partly explains current inconsistencies among views of the two exercise modes regarding visuospatial attention.  
From the perspective of educational applications, the current evidence indicates that OSE was more beneficial to the cognitive development of normal people, and these findings will be helpful to the replanning and arrangement of physical education for education decision-makers. In terms of clinical application, physical exercise has also been proved to be an effective means to improve mild cognitive impairment and dementia [60]. Therefore, we speculate that the unique advantage of OSE in cognitive ability may also exist in special groups.  
Our results suggest that, compared with CSE, OSE had more advantages in improving cognitive function at the behavioral level. It provides some implications for people's choice of exercise modes. This existing finding could be further extended to the level of brain mechanisms in the future.
## 5 Discussion  
The experimental results presented in Section 4 demonstrate that the proposed watermarking algorithm, which is based on element perturbation and invisible characters, can provide a feasible watermarking scheme for BIM data. The advantages of this method in this work are: (1) The combination of element perturbation and invisible character embedding methods enhances the robustness and imperceptibility of the watermark. (2) The robustness of the watermark is further enhanced by a mapping mechanism. This effectively solves the challenge of applying existing 3D robust watermarking techniques to BIM data. In order to provide a better understanding of the proposed method, we offer further discussion in the following three areas.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 4 Experiments and Results  
A two-step evaluation procedure was used to account for the specific composition of our pipeline. The first stage of our pipeline was evaluated on the LC classification task, to make sure that the latent features were relevant for the SOC estimation task. Once the performance of the LC classifier was deemed satisfactory, we proceeded with the extraction of latent features for all the relevant samples in the LUCAS and CHLSOC datasets. These then served to evaluate the performance of the whole pipeline.
# Development of Track Support Stiffness Measurement and Evaluation System for Slab Tracks
## 2 Overview of the Track Support Stiffness Evaluation System  
### Operation Program for Measurement System  
The software for operating the measurement system in this study was designed using wireless communication [18]. The software and measurement equipment communicate wirelessly, and the communication method (Modbus/TCP) is a communication protocol that is widely used worldwide for monitoring and controlling automation equipment [18]. The system for communication with the measurement system uses the Ethernet port of the control box installed in the equipment [18].  
Figure 7: Measured track support stiffness by the conventional field measurement.  
The Operation Program for Measurement System is a program that consists of the track support stiffness evaluation and a measurement system. The measurement system can output track performance test reports by inputting the tested track information (which can be entered in advance) of the test position by using the tablet PC, wirelessly and without any equipment, for the measurement and determination of the test load after mounting the measurement equipment on the field rail [2].  
Figure 8 depicts a schematic of the test and evaluation program pertaining to the measurement system (S/W) [2]. Measurement settings that illustrate selection of the test type are depicted in Figure 9. The test types can be divided into static or dynamic tests [2]. If a static loading test is selected, the test load must be set along with the loading rate and the number of cycles. If the dynamic test is selected, the test load must be set along with the excitation frequency and the load repetition frequency [2].  
The load model of the dynamic test can be implemented as a sine wave or triangular load, and the sampling frequency of the load and measured displacement data can be set up to 20 Hz. Furthermore, buttons for operating the instrument are displayed so that the sensors and actuators can be initialized and tested before commencing the test and paused during the test, or the test can be terminated entirely and saved. These operations can be implemented on the following display [2].  
As shown in Figure 10, the aging level and track support stiffness of the elastic material can be evaluated. The displacement is automatically selected according to the loading data set by the user so  
Figure 8: Schematics of test and evaluation program for measurement system (S/W).  
Figure 9: Setting of test conditions.  
that the spring stiffness, track support stiffness, and change rate of the rail supporting point stiffness can be analyzed by comparing its values with the design value, as shown in Figure 11[2].