esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Equilibrium Moisture Content Model and the Acoustic Emission Signals  
EMC was evaluated along the longitudinal and the radial directions because moisture behaves differently depending on the axis or section of wood considered. For this purpose, multilinear regression was applied to analytically derive the EMC within the beam.  
The radial (x1) and longitudinal (x2) directions were used together to understand to what extent these two independent variables affect the dependent variable of EMC (y). For this analysis, eight multiple linear regressions (Equation (5) and the equations in Appendix A) were studied:  
\[\text{y}=\text{b}_{0}+\text{b}_{1}\text{x}_{1}+\text{b}_{2}\text{x}_{2} \tag{5}\]  
The adjusted coefficient of determination (\(\overline{R}^{2}\)) and the standard error (ESTD) were used to understand which regression model could better explain the equilibrium moisture content gradient. Equation (5) was selected on the basis of a simple and realistic physical situation, regardless of the best statistical parameters.  
In this way, at each point where a fracture elongation took place according to brittle phenomenon, the equilibrium moisture content was more specifically determined using this model.  
In accordance with the procedure described in [16], the distribution of each acoustic emission parameter occurring during brittle phenomena was analyzed in order to identify the acoustic fingerprint of the species. Moreover, the cumulative sum of AE parameters was used to evaluate the sensitivity of AE with respect to dryness along the longitudinal and radial directions of the beam, and hence investigate the effect/impact of EMC changes on the mechanical properties of wood. This was a preparatory step to pinpoint how wood behaves with respect to environmental conditions and which AE parameters are more sensitive for calibration with EMC.  
Finally, the energy attenuation rate along the samples at the end of the drying process (after 160 days) can be calculated as the sum of the difference between the initial and final state of EMC, from the path to the outmost layers of the sample. Analytically, this can be expressed using Equation (6), where \(\beta\) is the coefficient of the energy attenuation rate.  
\[\Sigma(\Delta\mathrm{E})=\exp(-\beta\cdot\Sigma(\Delta\mathrm{EMC})) \tag{6}\]  
Equation (6) can be simplified by applying the Taylor series for exponential functions if \(\Delta\mathrm{EMC}\) is small, i.e., when the moisture gradient across the sampling is not appreciable. In this way, \(\beta\) can be defined graphically, calculating a linear best fit from the experimental datasets from Equation (7):  
\[\beta=-\frac{\mathrm{m}}{\mathrm{q}} \tag{7}\]  
where m is the angular coefficient and q is the intercept of the linear best fit.
## 2 Airport Operations Reference Scenario
The DMAN tool is in charge of determining the sequence of TSATs for all outbound flights, computing the optimal sequence of TTOT for all runways and all outbound flights, taking into account multiple preferences and constraints, such as the available runways' capacity, wake vortex separations, slot enforcement, arrivals data from AMAN, _etc._ For doing so, it also takes into account the earliest time at which each aircraft can be reasonably requested to be at the runway, ready for line-up and take-off. In order to estimate such a time, it uses the TOBT as the earliest time an aircraft can be expected to be off-block and relates this condition to take-off by means of the Estimated Taxi-Out Time (EXOT), which is the estimated taxi time between off-block and take-off. The reference inter-operational concept assumed in this paper is that DMAN computes the optimal TTOT sequence using its own EXOT estimates. The TTOT sequence is then delivered to XRP, which determines the TSAT sequence that allows outbound aircraft to meet the assigned TTOT.
## 4 Results and Discussion
### Thermal Evolution History
#### 4.6.2 Erosion Thickness  
Erosion thickness is the basis for restoring the burial history. Currently, there are many methods to determine erosion thickness, including the acoustic time difference method, the vitrinite reflectance method, and the stratigraphic trend method. Using the acoustic time difference method and stratigraphic trend method, the erosion thickness at the end of the deposition of the Yimin Formation in the Hongqi Depression was determined to be 0-300 m [59], with minimal erosion at the center of the sag and greater erosion at the edges.  
Figure 6: Relationship diagram of paleogeothermal temperature calculated from vitrinite reflectance (Ro) and depth.
## 1 Introduction
### Achieving Industry 4.0-Oriented Intercommunication  
In a smart factory for Industry 4.0, smart machines will deal with the production orders, which are composed of the smart workpieces, via the Human-Machine Interface (HMI) or the social sensors [17; 18]. Under these circumstances, smart devices will get the same positions and permissions as ordinary workers, and the users of MES will no longer limited be to real people, but will extend to machines, tools, and workpieces. Intercommunication in this manufacturing scenario would be more like a Multi-Agent System (MAS) [19], where the interactions between any two Agents are Peer-to-Peer, instead of using the traditional industrial Fieldbus.
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 3 AN Maximizing Relay's SER Performance  
The SER analysis in this section consists of three parts. In Section 3.1, for given instantaneous channel realizations \(h\) and \(g\), we derive the SER expression at the relay for the given AN symbol \(z\) of square \(M\)-QAM modulation. Based on this expression, in Section 3.2, we further study the problem of how to select the phase of \(z\) given its amplitude \(|z|\). The problem of assigning power to the AN symbol \(z\) is considered in Section 3.3, where we derive the optimal distribution of the power of \(z\), i.e., \(|z|^{2}\), based on the average power constraint \(E_{z}\).
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 6 Augmented Reeb Graph Computations: Going from the Reeb Graph to the Manifold
### Consistent Parameterization of the Edges of the Reeb Graph
#### 6.2.2 Unit Gradient Scalar Fields  
Now, given a function \(f:M\longrightarrow\mathbb{R}\) on a triangulated mesh \(M\), we are interested in finding a function \(\hat{f}\) with the following two properties:  
1. The levels sets of the function \(\hat{f}\) are parallel to the level sets of \(f\).
2. The function \(\hat{f}\) has uniform gradient everywhere.  
The above two conditions are equivalent to constructing a function \(\hat{f}\) such that \(\text{grad }\hat{f}\approx\frac{\text{grad }f}{||\text{grad }f||}\). Setting \(X:=\frac{\text{grad }f}{||\text{grad }f||}\) finding such \(\hat{f}\) can be obtained by solving the following Poisson equation:  
\[\min_{\hat{f}}\int_{M}|\
abla\hat{f}-X|dM,\]  
which is equivalent to solving \(\Delta\hat{f}=\text{div }X\), where \(\Delta\) is the Laplacian of the mesh \(M\). Hence, finding the function \(\hat{f}\) can be reduced to the following two simple steps:  
1. Compute \(X=\frac{\text{grad }f}{||\text{grad }f||}\).
2. Solve the Poisson equation \(\Delta\hat{f}=\text{div }X\).  
The previous algorithm works on any generic function \(f\), such that the gradient of \(f\) is not zero. Moreover, Step (2) can be easily solved while using the definitions of the gradient and the divergence provided earlier. The above simple procedure is a generalization of the geodesic in heat method [81], where the desired function \(\hat{f}\) represents a distance function.  
Figure 17 illustrates an example of such a procedure. The right model in the figure shows a solution for a scalar function \(\hat{f}\) obtained as a Poisson equation \(\Delta\hat{f}=\text{div }X\) where \(X\) is the normalized gradient of the original function \(f\) shown on the left. Observe that the level sets of both \(f\) and \(\hat{f}\) are parallel, but now the gradient of \(\hat{f}\) is constant.
## 3 Experiments and Results  
### Influence of Structural Parameters on Cavitation Performance  
#### 3.1.1 Number of Teeth Rows  
The shear hydrodynamic cavitation generator with 1-8 rows of teeth rows were selected to study the influence of the number of teeth rows on the cavitation performance of the hydrodynamic cavitation generator. The results are shown in Figure 11.  
The gas phase volume fraction in Figure 11 increases with the increase in the number of tooth rows, reaching a point of no significant increase when it reaches eight rows of teeth. Pressure is the decisive factor in the generation and collapse of cavitation bubbles. The pressure contour plots of different tooth row numbers in Figure 12 indicate that the shear hydrodynamic cavitation generator exhibits a relatively large low-pressure region. The cross-shearing between the moving and stationary teeth leads to a change in the shearing velocity between them, resulting in the occurrence of a low-pressure area between the rotated and fixed teeth and creating a favorable environment for the generation of cavitation bubbles. As the tooth rows increase, the fluid region between the teeth expands, rendering a decrease in static pressure of the fluid and an enlargement of the negative pressure region within the cavitation inducer. Consequently, the cavitation occurrence area expands, thereby enhancing the cavitation performance of the inducer with an increasing number of tooth rows. However, when the number of tooth rows reaches eight as shown in Figure 12, there is a relatively long distance from the inlet to the outlet section, creating a low-pressure field (highlighted in the red box in Figure 12) independent from the inlet velocity and  
Figure 10: Effect pattern of treatment volume on degradation rate of COD in printing and dyeing wastewater.  
outlet resistance. This stable low-pressure field provides favorable conditions for the stable generation of cavitation bubbles. Therefore, the best cavitation effect is achieved when the number of tooth rows reaches eight, without the need for further increasing the number of tooth rows, while satisfying the requirements of cavitation.  
Figure 11: Volume fraction of cavitation bubbles for different number of teeth rows.  
Figure 12: Pressure clouds at 0.8 s for different teeth rows. (**a**) 1 row of teeth (**b**) 2 row of teeth (**c**) 3 row of teeth (**d**) 4 row of teeth (**e**) 5 row of teeth (**f**) 6 row of teeth (**g**) 7 row of teeth (**h**) 8 row of teeth.
## 1 Introduction
The multi-modal sensor fusion network proposed within this paper posits that by independently extracting valuable information from both camera and radar sensors, one can leverage the advantages of each modality without compromising either in situations of degraded performance due to external factors. This innovative multi-modal fusion approach is rooted in the acknowledgment that cameras and radars offer complementary attributes. The detailed texture and semantic-rich information from cameras can be utilized to identify multiple different objects, while radars offer the advantage of detecting objects over long distances, unobstructed by occlusion, and are reliable in adverse weather conditions such as fog or rain. Thus, this potential to individually extract information serves as a method to enhance the overall reliability of the proposed neural network for 3D object detection and tracking names as NeXtFusion, proposed within this paper. NeXtFusion network utilizes point-cloud data representation for object detection, extracting all information obtained from radar sensor(s), whereas the semantic information obtained from camera sensor(s) primarily serves to distinctly identify objects within an input image/video. Drawing inspiration from this paper's authors' most recent work, NeXtDet [9], significant advancements are being proposed in this paper where the literature on camera-based semantic information extraction is relied upon to independently and efficiently extract these semantic-rich features from camera-based RGB images and fuse them with radar's point-cloud data as illustrated in Figure 1. The structure of this paper is organized as follows: Section 2 offers the reader information on related literature and studies. Section 3 provides information about the methodology for the proposed multi-modal sensor-fusion 3D object detection model called NeXtFusion. Section 4 assesses the performance of the proposed network through benchmarking on a multi-modal large-scale autonomous driving dataset and visualizing the results. Finally, Section 5 presents the concluding remarks for this paper.
## 3 Effects of Freeze-Thaw Cycles on Degradation of Performance
### Splitting Tensile Strength  
After 0, 5, 10, 15, and 20 freeze-thaw cycles, splitting tests were carried out. The test results are shown in Figure 10.  
It can be seen from Figure 10 that the splitting tensile strength of asphalt mixture reduced and the loss ratio caused by damage increased continuously under freeze-thaw cycles. In the early stage of freeze-thaw cycle, the splitting tensile strength reduced rapidly. The performance degradation rate was gradually slowed down after 10 freeze-thaw cycles. When the freeze-thaw cycles of 20, the loss ratio caused by damage of NBAM group was significantly smaller than that of AM group. The loss ratio of splitting tensile strength for AM caused by damage under 20 freeze-thaw cycles is 67%, and for NBAM is 56%. The water stability of asphalt mixture matrix was decreased more significantly under freeze-thaw cycles. When nano-TiO\({}_{2}\)/CaCO\({}_{3}\) and basalt fiber were added, the splitting tensile strength increased and the stability loss rate decreased significantly. It indicated that the addition of nano-TiO\({}_{2}\)/CaCO\({}_{3}\) and basalt fiber could jointly exert the improvements of two materials on water stability. The splitting tensile strength of NBAM group was 16% higher than AM group under 0 freeze-thaw cycles. After 20 freeze-thaw cycles, the splitting tensile strength of NBAM group increased 48% compared to AM group. The freeze-thaw resistance of NBAM group was obviously better than that of AM group. The freeze-thaw cycles reduced the splitting tensile strength of asphalt mixture.  
Figure 10: Relationship between splitting tensile strength and freeze-thaw cycles: (**a**) splitting tensile strength trend chart; (**b**) loss ratio of splitting tensile strength caused by damage.  
Figure 9: Relationship between indirect tensile stiffness modulus and freeze-thaw cycles: (**a**) indirect tensile stiffness modulus; (**b**) loss ratio of stiffness modulus caused by damage.  
When nano-TiO\({}_{2}\)/CaCO\({}_{3}\) and basalt fiber were added, the sensitivity of water stability to freeze-thaw cycles was reduced.
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
### Related Works and Contribution
For this reason, we use a parallel (and not hybrid) position/force control approach, which also deploys rotational degrees of freedom to attain a broader feasible range of forces. It is worth noting that the traditional parallel position/force controller applied to generic six-DoF grounded manipulators is not directly applicable to our system, as it is an under-actuated floating robot. All the wrench components of the contact interaction are transmitted to the robot CoM and affect its orientation, by which the position is controlled, which makes the problem more challenging, especially in the transition from free flight to contact interaction and vice versa. Furthermore, in all the aforementioned papers, the use of haptic feedback is not envisioned. Haptic teleoperation of UAVs is mainly used for obstacle avoidance in free flight, aiming to improve the situational awareness of the human operator using haptic feedback, such as the generic hierarchical passive teleoperation control architecture presented in [25]. We use haptic feedback not only to improve the performance of position tracking in free flight, but also to reflect the applied force, in order to let the user feel the force in the contact interaction, which eventually leads to a more accurate tele-manipulation. The stability analysis and experimental results validate the proposed bilateral teleoperation scheme for aerial manipulation using passive tools. To the best of our knowledge, the problem of teleoperating VTOL UAVs with haptic feedback to establish contact and apply forces on objects of interest while ensuring the compliance with friction constraints and avoiding slipping has not yet been deeply investigated by the community researching aerial physical interaction. We introduced the aerial haptic tele-manipulation idea in [26]. The present paper completes, improves, and extends this concept in the following ways:  
1. the force control in [26] is based on a mapping that calculates the appropriate robot's desired orientation to generate the desired force; in this work, we use a more efficient sensor-based closed-loop force control;the force controller considers the limited friction of the end-effector and object surfaces, and utilizes the independently controlled yaw motion, which allows a wider range of feasible force commands by using the passive compliant spherical joint mechanism of the end-effector, enforcing the contact maintenance;
3. the stability analysis of the system is presented;
4. this work presents experimental results of tracking 3D force vectors applied to both stationary and moving objects.
# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
## 3 Results  
### Data Verification  
The analysis of this study was based on the output datasets of the climate model and remote sensing model, including the \(ET\) and \(SM\) datasets from GLDAS, and the \(NPP\) dataset from the Global Change Research Data Publishing & Repository of China. The verification results showed that the monthly \(ET\) of the GLDAS dataset and the observed \(ET\) data in 12 flux towers in China had a good consistence (Figure 2b). The determination coefficient (\(R^{2}\)), root mean square error (RMSE), mean absolute percent error (MAPE), and Nash-Sutcliffe efficiency coefficient (NSE) between the simulated and measured \(ET\) values were 0.79, 14.78 mm, 31.32%, and 0.77, respectively. In addition, the verification results of \(NPP\) data showed that, although the estimated monthly \(NPP\) data used in this study underestimated the actual \(NPP\), the \(R^{2}\), RMSE, MAPE, and NSE between the estimations and observations were 0.97, 69.02 g C\(\cdot\)m\({}^{-2}\cdot\)mo\({}^{-1}\), 35.09%, and 0.42, respectively, which indicated that the \(NPP\) dataset used in this study was also consistent with the observations (Figure 2a). This study compared the \(SM\) data based on microwave remote sensing data assimilation in China (2002-2011) with GLDAS \(SM\) data (Figure 2c), owing to the lack of measured \(SM\) data. The results showed a good consistency, and the \(R^{2}\), RMSE, MAPE, and NSE values were 0.96, 0.05 m\({}^{3}\cdot\)m\({}^{-3}\), 18.25%, and 0.59, respectively. Furthermore, The monthly \(SM\) from 2001 to 2014 was estimated using Equation (4) and then verified using \(SM\) data of GLDAS. The overall trend of change in estimated \(SM\) was close to the GLDAS \(SM\) data, and the determination coefficient (\(R^{2}\)) was 0.97 (Figure 2d). Meanwhile, the RMSE, MAPE, and NSE of this simulation were 0.01 m\({}^{3}\cdot\)m\({}^{-3}\), 18%, and 0.92, respectively. The fitting error was primarily due to the overestimation of \(SM\), especially when the \(SM\) was low. This study may have overestimated \(SM\) in some arid regions or during dry seasons. Although there were still some simulation errors, the datasets used in this study were generally reliable and could ensure the accuracy of the results.
## 2 Dispersion-Managed Link and WDM System Modelling
### Dispersion-Managed Link with Random-Based RDPS
However, according to [PERSON], in a pseudo-linear system, the best NRD should have a value near 0 ps/nm rather than 0 ps/nm to increase the compensation effect [16]. Setting NRD to a value near 0 ps/nm means that some fiber span must play a role in adjusting NRD. In this study, the DCF of the first fiber span plays that role, as indicated by 'pre-DC' in Figure 1, where 'DC' stands for dispersion calibrator. Figure 1: The 960 Gbps WDM transmission system through the dispersion-managed link and the midway-OPC.
## 3 Model Results for the Tested Area
### Interpretation of Subsidence Causality Factors
#### 3.3.2 Former Lakes and Lagoon Dewatering  
In this study area, there are three dried and reclaimed areas from former lakes and lagoon. These include the former Alharda Lake, a part of the former Mareotis Lake in the South Western section of the study area, and a part of former Abu-Qir Lagoon in southeast Alexandria. Table 3 shows the minimum, maximum, mean, and standard deviation values of each of the former lakes and lagoons (FLL) in Alexandria, as well as these values for the overall area dried or reclaimed from former lakes and lagoons, alongside the same values for non-FLL areas in the study area. In contrast to other parts of the city, dried and reclaimed areas from FLL have a high average subsidence, about \(-4.69\) mm/yr, as compared to \(-0.53\) mm/yr in non-FLL areas as revealed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rock Type** & **Highest Deformation (mm/yr)** & **Lowest Deformation (mm/yr)** & **Mean Deformation (mm/yr)** & **Standard Deviation** & **Weight** \\ \hline
**Artificial reclaimed surfaces** & 2.13 & \(-1.57\) & 0.89 & 0.58 & 5 \\
**Carbonate ridge** & 3.26 & \(-6.82\) & 0.50 & 0.83 & 5 \\
**Gravel** & 2.16 & \(-2.62\) & 0.02 & 0.74 & 5 \\
**Reclaimed sand dunes** & 2.19 & \(-4.12\) & \(-0.29\) & 1.40 & 5 \\
**Stabilized sand dunes** & 2.01 & \(-6.39\) & \(-0.46\) & 1.13 & 7 \\
**Oolitic beach and beach ridge** & 2.54 & \(-14.52\) & \(-0.69\) & 1.77 & 8 \\
**Nile silt** & 3.77 & \(-31.70\) & \(-3.09\) & 4.45 & 30 \\
**Sabkha deposits** & 3.55 & \(-28.15\) & \(-3.51\) & 4.44 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deformation characteristics of each lithological type in Alexandria.  
The dried and reclaimed areas from Abu-Qir Lagoon in the southeast of the study area have the highest average subsidence when compared to other dried and reclaimed FLL areas, about \(-6.7\) mm/yr. Based on this average; those areas were given the highest weight, \(45\). These were followed by the dried and reclaimed areas of the former Lake Mareotis, with mean subsidence of \(-3.5\) mm/yr, and a weight of \(30\). The remaining dried and reclaimed areas of the former Lake Alhadra showed the lowest annual average subsidence among dried and reclaimed FLL areas, \(-2.45\) mm/yr, with a weight of \(45\). Areas with no dried or reclaimed FLL were given a weight of \(5\).