esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Shifts in Vegetation Phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem
## 3 Results
### Relationship between Vegetation Greenness and Ecosystem CO\({}_{2}\) Fluxes  
Figure 7 shows the comparison of MODIS EVI and PhenoCam G\({}_{\rm CC}\) and the distributions of NEE, GPP, and R\({}_{\rm eco}\) with G\({}_{\rm CC}\), respectively. The good fit between MODIS EVI and PhenoCam G\({}_{\rm CC}\) (Figure 7) (R\({}^{2}\) = 0.44; \(p\) \(<\) 0.001) suggests that PhenoCam can be a useful tool to continuously monitor vegetation phenology in dryland ecosystems. Note that the G\({}_{\rm CC}\) data were screened to match the temporal resolution of the MODIS EVI data. In both years, G\({}_{\rm CC}\) ranged from 0.325 to 0.375, with approximately 73% of the data less than 0.335. We fitted a third-degree polynomial function between NEE, GPP, R\({}_{\rm eco}\), and G\({}_{\rm CC}\), respectively, with the confidence level exceeding 99%, suggesting that G\({}_{\rm CC}\) can be used as a metric to constrain NEE, GPP, and R\({}_{\rm eco}\) at the dryland sagebrush ecosystem (Figure 7b-d). These results indicate that changes in vegetation phenology can significantly influence the carbon balance of the ecosystems in drylands. Long-term and accurate monitoring of vegetation phenology cannot only provide a ground-based reference for validating satellite measurements, but also provide a high-temporal resolution parameter for gap-filling the flux data. Therefore, complementing a low-cost PhenoCam at eddy covariance sites would greatly improve the quality of carbon flux data, especially for dryland ecosystems with sparse vegetation. Furthermore, with PhenoCam images, vegetation indices can be extracted and calculated for different vegetation types, which would be very helpful to assess the influence of different vegetation types on ecosystem CO\({}_{2}\) fluxes in drylands.
# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 1 Introduction
### Objectives  
This research, based on a comprehensive review of cloud insecurities [16], aims to develop, test, and validate a prototype framework for Information Flow Tracking (IFT) in cloud environments. As such, this work seeks to enhance transparency and control over data propagation, thereby bolstering client trust in the security and privacy of their data. By implementing an efficient and effective IFT, organizations can monitor and enforce policies to prevent sensitive data leaks and mitigate the risk of unauthorized access.
## 4 Results and Discussions
### Particle Rotation and Transportation
The first segment distance is about 98 \(\upmu\)m and the second one is about 75 \(\upmu\)m. The center positions of the silica rod in each moving step along with the theoretical trajectories of the synthesized pressure node are illustrated in Figure 14. In general, there is a good agreement between the actual positions and the predictions. The deviation may be caused by the same factors as mentioned in rotation process. But, for the transporting process, only the phase of each transducer is modulated, which greatly reduces the influence of transducer inconstancy and may improve the manipulation accuracy. Correspondingly, the maximal deviation of the particle center is within 8 \(\upmu\)m, smaller than that in rotation process. With appropriate phase modulation, the rod-shaped particle can be transported to any location within the controllable sound field, while keeping its orientation. If the combinations of amplitude and phase modulation are further applied to the transducers, more complicated manipulation, such as performing the rotation and the transportation simultaneously, can be achieved. Figure 12: Measured and theoretical angular in the process of alternating the particle rotation direction. Figure 13: Transportation of the silica rod along different trajectories. (**a**) The particle is moved by 125 \(\upmu\)m along the negative direction of \(x\)-axis; (**b**) The particle is first transported along the direction of \(-165^{\circ}\) by 98 \(\upmu\)m, and then changed to the direction of \(-145^{\circ}\) by 75 \(\upmu\)m. The red dots are the center positions of the particle and the green lines indicate the fluctuation range of the particle center.
## 3 Leica Pegasus Backpack Survey
### Accuracy Assessment: Relative Error with Respect to the TLS Model  
After registration of the backpack and TLS 3D models, the average and RMS point-to-point distances are 4.3 cm and 8.2 cm (Table 2). Histogram of point-to-point distances between the two point clouds is shown in Figure 4a.  
The largest errors are actually in the final part of the left wing of the bastion: this area is critical because of its quite complex structure, shown in Figure 4c. Given the complexity of this area, it would be interesting to compare the two models also discarding the final part of the left wing of the bastion. In this case, average and RMS point-to-point distances are 3.6 cm and 4.6 cm (Table 2), and the distance histogram can be seen in Figure 4b. After discarding the final part of the left wing of the bastion, the obtained RMS distance is close to the nominal relative accuracy (3 cm in indoor environments [15]). It is also worth noticing that discarding the final part of the bastion left wing significantly decreases the maximum error from 193.4 cm to 79.3 cm, as can be seen in Table 2 and comparing Figure 4a,b.  
Figure 4: Leica Pegasus 3D model accuracy assessment: comparison with the TLS 3D model. Point clouds registered in local coordinates with the ICP algorithm. (**a**) Histogram of point-to-point distances between point clouds. (**b**) Histogram of point-to-point distances discarding the end part of the left wing of the bastion. (**c**) Final part of left wing of the bastion.
## 3 Results and Discussion
### Parameters Optimization
The S/N value was used to determine the most significant parameters and which level is highly contributing to the output [23]. The S/N output was calculated according to the \"smaller is better\" equation for static design. The Taguchi method provided by commercial software Minitab 16 was used to calculate the signal to noise ratio of each experiment. The computed signal to noise ratio value was presented in Table 3. As shown in Equation (1), from the objective of the-lower-the-better quality characteristic which is to minimize the response, a greater S/N ratio corresponds to a smaller variance of the output characteristic around the desired value. The S/N response table by factor level for the milling time, milling speed, and size of balls was created in an integrated way and the results are tabulated in Table 4. The delta (\(\Delta\)) value was calculated using (highest S/N-lowest S/N) for each parameter and compared. From the point of the _\"smaller is better\"_ quality characteristic, the size of the ball's performance showed the larger delta (\(\Delta\)) value compared to other parameters. This is evidence that the size of balls showed the greatest influential parameter in the ball milling process followed by milling time and milling speed. The greater delta (\(\Delta\)) values for a parameter, the effect of the parameter on the process will correspond to a smaller variance of the output and generate better performance of the experiment [24; 25]. Therefore, the size of the ball's performance was the most significant parameter for producing POA nanoparticles. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Milling Time** & **Milling Speed** & **Size of Balls** & **SN Ratio** \\ \hline
1 & 1 & 1 & 1 & 0.92 \\
2 & 1 & 2 & 2 & \(-\)0.31 \\
3 & 1 & 3 & 3 & 0.43 \\
4 & 2 & 1 & 2 & 0.32 \\
5 & 2 & 2 & 3 & 1.51 \\
6 & 2 & 3 & 1 & 1.20 \\
7 & 3 & 1 & 3 & 3.94 \\
8 & 3 & 2 & 1 & 3.39 \\
9 & 3 & 3 & 2 & \(-\)0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Signal to noise ratio (S/N) for each experiment.
# BUU-LSPINE: A Thai Open Lumbar Spine Dataset for Spondylolisthesis Detection
## 4 Discussion  
We demonstrated the utilization of the BUU-LSPINE dataset in three experiments.  
The first experiment uses PF images and corner points to create the bounding box ground truth to train lumbar vertebra detection models. The results demonstrate a high precision in detecting lumbar vertebrae positions. However, the experiment also reveals the influence of LSTV conditions on vertebrae detection algorithms, underscoring the importance of considering these conditions during the research of such algorithms.  
The second experiment shows the utilization of the BUU-LSPINE's corner point ground truth. The results show the potential of our datasets in researching lumbar vertebral corner point detection algorithms. However, the detected corner points from the best model in our experiment have a relatively high average error distance compared with the Meyerding Classification System. These errors may lead to incorrect classification of the grade of the vertebrae slippage.  
In the third experiment, we introduce algorithms for predicting spondylolisthesis by pairing the adjacent vertebrae and adjusting the reference points of all corner points within the vertebrae pair. Then, we use classifiers to predict the spondylolisthesis based on the relation between all corner points and new reference points in each pair. The results show the performance in predicting the diseases. However, it is crucial to note that the capability of spondylolisthesis prediction depends on the performance of the vertebral corner point detection algorithms.  
These experiments demonstrate the potential of the BUU-LSPINE dataset, but further research is required to explore our dataset capabilities. However, these experiments serve as an initial demonstration. Our dataset will catalyze new ideas and encourage others to use it in improving models, evaluating algorithms, and expanding the boundaries of what is possible.
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 4 Motivation and System Model
### System Model
#### 4.2.3 Location Based Service Provider (LBSP)  
Without loss of generality, the main role of LBSP is to process users' request packets and return a list of results to users. Note that there are two main ways to return the results back to these users. One way is to return the results to the representative user, then he distributes the results to his member users. The other way is to return the results to his member users directly by their pseudo-IDs, which is adopted in this paper to reduce the load of the representative user.  
In our system model, all roles must follow the relevant regulations and agreements in the LBS system, which means they are all \(honest-but-curious\). For LBSP, it is curious and wants to deduce users' locations and preferences (i.e., their kinds of service category). For a user, he also has curiosity and hopes to deduce other users' request contents including their locations and preferences. But the collusion among users and the collusion between users and LBSP are out of the scope of discussion in this paper.
# Model Test on Effect of Ground Fissure on the Behavior of Oblique Two-Section Subway Tunnel
## 2 Experimental Design and Scheme  
### Test Principle  
The similarity relationship between the model and the prototype was determined based on the geological setting and test equipment. The soil layer and the tunnel model were made separately and the ground fissure was set up in advance. The ground fissure activity was modeled by controlling the soil settlement in the hanging wall while the vibration exciter was used to simulate the vibration of the subway, and then the dynamic responses of the subway tunnel under the action of vibration load were investigated when the subway and the ground fissure crossed obliquely. Furthermore, the stress and strain of the tunnel structure were tested using pressure boxes and strain gauge, respectively, while the subway vibration was tested by accelerometer. The schematic diagram of the test model is shown in Figure 1. The intersection angle between the ground fissure and the tunnel axis is 60\({}^{\circ}\), and the dip angle of the ground fissure is 80\({}^{\circ}\). The subway tunnel structure is two-section.  
The model test in this study was performed in a model box, as shown in Figures 2 and 3. The maximum allowable model size is 2.0 m (length) \(\times\) 1.6 m (width) \(\times\) 1.2 m (height), and the modeled tunnel obliquely crossed the ground fissure at an angle of 60\({}^{\circ}\). The bottom plate of the model box was divided into 2 pieces and 5 lifting jacks were installed at the bottom plate of the hanging wall to simulate its settlement. Additionally, in order to reduce the friction between the soil layer and the model wall, two layers of polystyrene plastic film were laid on the inner wall of the model box, and talcum powder was spread between the two layers of plastic film.  
Figure 1: Test model (unit: cm): (**a**) schematic diagram of test model; (**b**) profile of the test model.
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 3 Frequency Control
### Simple Penalization  
A basic strategy to regulate the switching frequency is to penalize the act of commutation, which can be controlled by the \(\lambda_{sw}\) and yield to :  
\[\Delta S_{k}=\lambda_{sw}(S_{k}-S_{k-1})^{2} \tag{21}\]  
When adding Equation (21), which illustrates the simple penalization frequency control technique (SP) to the conventional or improved CFs, the switching frequency is controlled. In case the voltage or its derivative has a large error, its corresponding value in the cost function would be large enough to overcome the penalization during the switching. Thus, strong importance should be given to the penalization term by setting the value of \(\lambda_{sw}\) high, then the commutation of the last step will be maintained to provide switching's reduction. Figure 4 shows the relative importance of the penalization term with the voltage tracking term. This strategy leads to a lower average frequency and higher voltage error, as shown in Figure 5. Considering that the spectrum will still spread in a wide band range and concentrated more to the lower frequencies. Later on, in the results and discussion section, the weighting factor design for both SP-CMPC and SP-IMPC will be discussed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **5.5 kVA System (Experimental)** & **System Model (Simulation)** \\ \hline \(S\) & 5.5 kVA & 5.5 kVA \\ \(V_{o}\) & 230 & 230 \\ \(V_{dc}\) & 600 & 600 \\ \(\Delta\)I & 10\% & 10\% \\ \(Q_{c}\) & 5\%S & 5\%S \\ \(f_{g}\) & 50 Hz & 50 Hz \\ \(Z_{Rd}\) & \(X_{CF}(\omega_{r})\) & \(X_{CF}(\omega_{r})\) \\ \(f_{res}\) & 10 \(f_{s}\)\(<\)\(f_{r}\)\(<\)1/2 \(f_{s}\) & 10 \(f_{s}\)\(<\)\(f_{r}\)\(<\)1/2 \(f_{s}\) \\ \hline \multirow{4}{*}{\(f_{s}\)} & \multirow{4}{*}{40 kHz} & -40 kHz in order to compare with the experimental (\(T_{s}\) = 25 μs) \\  & & -80 kHz in order to show \\ \cline{1-1}  & & the sampling effects (\(T_{s}\) = 12.5 μs) \\ \hline \(L_{f}\) & 5 mH & -For 80 kHz = 2.5 mH \\ \cline{1-1}  & & -For 40 kHz = 5 mH \\ \hline \(C_{f}\) & 60 μF & -For 80 kHz = 20 μF \\ \cline{1-1}  & & -For 40 kHz = 60 μF \\ \hline \hline \end{tabular}
\end{table}
Table 1: Filter design parameters for the simulation and experimental setup.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 5 Component and Architecture of a LiFi System
### Uplink Transmitter ULT  
The efforts to achieve the UL counterpart in LiFi links and Internet connectivity for futuristic, intelligent applications also play a crucial role in designing LiFi communication systems. Despite the great work dedicated in these areas, few studies have introduced UL solutions [39; 102; 111; 124]. Most studies on LiFi used IR and Wi-Fi for ULT [39]. IR sources with a wavelength of more than 1500 nm are safe for the human eye, which is an additional feature that supports future communications [110]. IR can be used in the UL so that the illumination constraints of a room remain unaffected, and interference with the VL in the DL will be avoided [76]. Similar to VL, the IR emission is susceptible to being blocked by room walls, thereby offering security against eavesdropping. The UL will be established with the IR source built into the user's device. The visible spectrum is not used as the UL because of the alignment difficulty of the latter and the glare from the user's device, which causes visual inconvenience. Different wavelengths are used in the DL and UL of LiFi networks, VL, and IR. Thus, the UE PD is tuned to VL and cannot sense the channel when another UE transmits via IR [36].
## 2 Test Method
### Analysis of Displacement and Electric Permeability Coefficient
#### 3.3.1 Displacement  
The amount of water discharged from soft clay during electro-osmosis is an intuitive reflection of the electro-osmosis effect. The variation of electro-osmotic drainage of anode irrigation and FeCl\({}_{3}\) solution under a normal temperature environment and low temperature environment with soil moisture content is shown in Figure 12.  
As can be seen from Figure 12:  
1. Under the same conditions, the anode filled with FeCl\({}_{3}\) solution can effectively increase the drainage of soil samples during electro-osmosis. At normal temperature, the anode filled with FeCl\({}_{3}\) solution can increase the drainage of soil samples by 6.0-15 mL more than the anode filled with water, and the proportion of drainage increase was 35.3-350.0%, which is equivalent to the increase of drainage after [PERSON] et al. [7] added 1.5 mol/L CaCl\({}_{2}\) solution into soft clay. Under the low temperature condition, compared with the anode filled with water, the anode filled with FeCl\({}_{3}\) solution was able to increase the drainage of soil samples by 7.0-13.5 mL, and the proportion of increasing drainage was 46.7-356.5%. When the water content was 35%, the effect of anode filled with FeCl\({}_{3}\) solution was the best, and the proportion of increasing drainage was the largest.
2. Under the same conditions, the low temperature will obviously reduce the drainage of soil samples during electro-osmosis. When FeCl\({}_{3}\) solution was injected into the anode, the drainage of soil samples at low temperature was reduced by 3.0-7.0 mL compared with that at normal temperature, and the reduction ratio of drainage was 19.0-22.8%. Compared with the normal temperature, the soil sample with drainage at low temperature was reduced by 0.7-6.0 mL under the anode filled with water, and the reduction ratio of drainage was 23.3-44.1%.
3. When the moisture content of soft clay was the same, the electro-osmotic discharge after the anode was filled with FeCl\({}_{3}\) solution at low temperature was obviously higher than that after the anode was filled with water at normal temperature, which indicates that the influence of the anode filled with FeCl\({}_{3}\) solution on electro-osmosis was greater than that of the temperature.  
Figure 12: Variation of electro-osmotic discharge with water content under different working conditions.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Graph Separation  
Graph separation is a fundamental problem in graph theory and has significant applications in areas like communication networks, parallel processing, data mining and community discovery in social networks. Partitioning the graph for distributed systems and finding dense clusters are two key directions in this field.
# A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review
## 3 Errors and Defects Associated with Additive Manufacturing
### Defect Associated  
In the past few decades, with the growth of the fourth industrial revolution, the popularity of 3D-printed goods has grown, and they are also demonstrating financial and time savings. From the CAD model, the additive manufacturing product is constructed layer-by-layer. Nevertheless, the printed product from the AM technology is associated with various defects due to the variation in properties and structure, which further lead to the quality of the printed product [5]. The various defects associated with the AM are summarized in Table 3, along with its cause and effect on the printed component. Recoater streaking can further classify as micro-macro, depending on the grain type [6].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Anomaly** & **Cause** & **Affect** & **References** \\ \hline \multirow{3}{*}{Cracking} & Small cavities, stress buildup, and uneven heating or cooling & \multirow{3}{*}{Failure of a printed part.} & \multirow{3}{*}{[53]} \\ \cline{2-2} \cline{4-4}  & Inadequate printing procedure or material & & \\ \hline \multirow{3}{*}{Material Shrinkage} & Property of the material used. & \multirow{3}{*}{Lead to the generation of residual stress, which can induce cracks in the material.} & \multirow{3}{*}{[54]} \\ \cline{2-2} \cline{4-4}  & Technique and materials used in printing & & \\ \hline \multirow{3}{*}{Stringing} & Printing technique and material used & \multirow{3}{*}{Extra material attached to the part needs post-processing} & \multirow{3}{*}{[56]} \\ \cline{2-2} \cline{4-4}  & Printing technique and material used & & \\ \hline \multirow{3}{*}{Residual Stress} & The print is rapidly heated or cooled. & \multirow{3}{*}{The excessive tensile strength can result in the creation of cracks or flaws such as warpage.} & \multirow{3}{*}{[57]} \\ \cline{2-2} \cline{4-4}  & Incorrect cooling of the printed component or because of the materials’ processing & & \\ \hline \multirow{3}{*}{Wrapping} & Incorrect cooling of the printed component or because of the materials’ processing & \multirow{3}{*}{The component swells upward, causing a change in form.} & \multirow{3}{*}{[58]} \\ \cline{2-2} \cline{4-4}  & Lower layers need to be adequately cooled. & \multirow{3}{*}{Because of the weights of the top layers, the lowest layer swells outward.} & \multirow{3}{*}{[59]} \\ \cline{2-2} \cline{4-4}  & As a result of the recoater blade impacting a component & & \\ \hline \multirow{3}{*}{Recoater Streaking} & It happened because the recoater blade damaged itself or because it dragged a contaminant across the powder bed. & \multirow{3}{*}{[60]} \\ \cline{2-2} \cline{4-4}  & When a section bends or coils upward through the powder layer, this occurs. & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Various defects associated with AM.
## 3 Numerical Verification
### Case 1: A Cylindrical Shell
#### 3.1.2 Velocity Analyses  
Figure 3 shows the relationship of the velocity vs. radius when t = 1.0 s. The continuous red line and green dot line show the value of the numerical result and the analytical solution in the case of the incompressible fluid and the compressible fluid, respectively. In the model described above, it is known that \(s_{r}=0.294\), \(J_{1}(s_{r})=0.146\), \(Y_{1}(s_{r})=-2.334\), and \(D=1\), therefore, the parameters can be derived as \(C=0.425\), \(\varphi=-0.063\). For the case of the compressible fluid, the analytical solution is \(u=-1.98\cos(10-r/34+2.3)/\sqrt{r}\), while for the case of the incompressible fluid, the analytical solution is \(u=-5.44/r\). The correlation coefficients of the two figures are 0.982 and 0.937, respectively, which show that the analytical solutions and the numerical results are in good agreement. It can be concluded from the analytical solutions that the boundary motion is transmitted to the whole fluid instantly. Additionally, among the field of \(r>ct\), all the fluid field remains in the initial situation. To show the whole shape of the analytical solution, the theoretical result was not cut off beyond the range of \(r>ct\). That is why in Figure 2(b), the data ranges from \(r<350\) fits very well and some discrepancy exists between the numerical value and analytical solution in the range from \(r>350\).