esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characteristics of Study Participants in Normal and Incident Metabolic Syndrome According to Gender
The BMI and body weight levels were significantly higher in iMetS than in the normal group (25.4 \(\pm\) 2.5 for iMetS vs. 23.9 \(\pm\) 2.6% for normal group, and 71.6 \(\pm\) 9.1 for iMetS vs. 67.4 \(\pm\) 8.4 for the normal group, respectively, \(p<0.001\)). For five metabolic component factors, WC, TG, SBP, and DBP levels were significantly higher in iMetS than in the normal group (\(p<0.001\)). In contrast, HDL-cholesterol was significantly higher in the normal group than in iMetS (\(p<0.001\)). BMI-, weight-, and WC-relative muscle strength mean and maximum levels were higher in iMetS than in the normal group (\(p<0.001\)). In contrast, mean and maximum HGS and BMS were not significantly different between iMetS and normal group. Age (47.5 \(\pm\) 6.5 vs. 48.2 \(\pm\) 7.3, \(p=0.062\)); job (70.1% for normal and 69.2% for iMetS in blue collar workers, 23.9% for normal and 23.4% for iMetS in white collar workers, 6.1% for normal and 7.5% for iMetS in homemakers, \(p=0.594\)); income (27.5% for normal and 28.1% for iMetS in KRW 1-2 million, 50.1% for normal and 46.5% for iMetS in KRW 2-4 million, 15.2% for normal and 17.9% for iMetS in more than KRW 4 million, \(p=0.515\)); more than 12 years of education (33.3% for normal and 30.3% for iMetS, \(p=0.222\)); married status (97.3% for normal and 97.1% for iMetS, \(p=0.820\)); height (167.8 \(\pm\) 5.5 for normal and 167.9 \(\pm\) 5.8 for iMetS, \(p=0.635\)); and exercise (124.0 \(\pm\) 187.9 for normal and 127.1 \(\pm\) 213.1 for iMetS, \(p=0.781\)) were not different between the iMetS and normal groups. Table S2 shows the sex-adjusted correlation coefficient of relative HGS and metabolic components. BMI, Weight, and WC-relative HGS and BMS showed a higher correlation with metabolic components than means HGS. In particular, relative HGS showed higher correlation than relative BMS. Cox Proportional Hazard Ratios for Incident Metabolic Syndrome According to Inverse One Standard Deviation Increase in Mean Hand Grip Strength, Back Muscle Strength, and Relative Muscle Strength in Gender  
The MetS risk of inverse one SD increase in muscle strength and relative muscle strength was shown in Table 2, adjusted for the confounding factors. In univariate analysis, the HR per inverse one SD increase in BMI-, weight-, and WC-relative HGS and BMS was associated with a lower risk of iMetS in women and men (HR = 1.187-0.168, \(p<0.001\)). However, the HGS and BMS in both women and men were not associated with iMetS. After adjusting for confounders, the HR per inverse one SD increase in BMI-, weight-, and WC-relative HGS was associated with a lower risk of iMetS in women and men (HR = 0.135-0.494, \(p<0.005\)). However, HGS and BMS were not
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 3 Methodology
### Vitis AI Deploys CNN  
This paper adopts Xilinx Zynq (AMD Xilinx, San Jose, CA, USA) a software and hardware collaborative acceleration platform combining FPGA and ARM UltraScale + MPSoC realizes the edge deployment of YOLOv4-MobileNetv3. UltraScale + MPSoC is composed of two core devices: programmable logic (PL) and a processing system (PS). It overcomes the lack of real-time computing capability of the ARM processor, and improves the limited algorithm realization capability of the FPGA. UltraScale + MPSoC adopts TSMC's 16 nm FinFET process node, which not only has high performance, but also greatly reduces power consumption.
###### Abstract  
Ecosystem water-use efficiency (WUE) is defined as the ratio of carbon gain (i.e., gross primary productivity; GPP) to water consumption (i.e., evapotranspiration; ET). WUE is markedly influential on carbon and water cycles, both of which are fundamental for ecosystem state, climate and the environment. Drought can affect WUE, subsequently disturbing the composition and functionality of terrestrial ecosystems. In this study, the impacts of drought on WUE and its components (i.e., GPP and ET) are assessed across the Contiguous US (CONUS) at fine spatial and temporal resolutions. Soil moisture simulations from land surface modeling are utilized to detect and characterize agricultural drought episodes and remotely sensed GPP and ET are retrieved from the moderate resolution imaging spectroradiometer (MODIS). GPP, as the biome vitality indicator against drought stress, is employed to investigate drought recovery and the ecosystems' required time to revert to pre-drought condition. Results show that drought recovery duration indicates a positive correlation with drought severity and duration, meaning that a protracted drought recovery is more likely to happen following severe droughts with prolonged duration. WUE is found to almost always increase in response to severe (or worse) drought episodes. Additionally, ET anomalies are negatively correlated with drought severity and ET is expected to decrease during severe (or worse) drought episodes. Lastly, the changes of WUE are decomposed in relation to its components and the cross-relation among the variables is revealed and a consistent changing pattern is detected.  
d 2019; 11, 731; doi:10.3390/rs11060731  
1
## 5 Camera Placement based on HybVOR Approach
### Generating Voronoi Diagram from Buildings
#### 5.1.1 Minimizing the Number of Cameras for a Maximum Possible Coverage  
It is known that the points belonging to a Voronoi edge have the property of being equidistant to generator objects that share this edge. Thereby, if a camera is placed on a Voronoi edge, it will be as close as possible to the two corresponding buildings (which are the generator objects) at the same time. In addition, if a camera is placed much closer to a building, it will be automatically farther from the facing building. This could increase the required number of cameras in order to cover the whole environment. Figure 6 illustrates the advantage of placing cameras on Voronoi edge in order to reduce their number. Note that the total number of cameras required to cover an entire area depends on the parameters of the cameras to be installed. Section 5.2 is dedicated to explain the methodology used for deploying cameras on Voronoi edges depending on the parameters of cameras and the location of buildings.  
Figure 6: Advantage of placing camera on a Voronoi edge. (**A**) Camera is placed on the Voronoi edge, (**B**,**C**) Camera is placed farther from the Voronoi edge.
# Multiport Single Element Mimo Antenna Systems: A Review
## 3 Isolation Techniques  
Mutual coupling between the antenna elements is the energy absorbed by the antenna element in MIMO system when the nearby antenna is transmitting/receiving. As the energy radiated away from one antenna gets absorbed by a closely placed antenna, it becomes adverse. This affects the efficiency and performance of the antennas in both transmitter and receiver side.  
Figure 1: SISO and MIMO Antenna system.  
Good isolation between the two antennas would be achieved by placing the antennas in such a way that they have sufficient space between them [15]. However, this required space between the antenna elements will increase the antenna size. Other than space, there are various isolation techniques which have been reported in literature for increasing the isolation among antenna elements.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Euclidean Search  
To obtain results using the Euclidean distance method, distances were computed based on the query point's latitude and longitude and the POIs stored within the spatial database, thereby measuring the straight-line distance between them. These calculated distances were then organized in a dedicated column within the database. Subsequently, by employing the precomputed Euclidean distances, both the kNN and range searches were conducted on the spatial databases. This involved implementing kNN queries to find the nearest neighbors and range queries to identify POIs falling within specific distance thresholds. The detailed process of obtaining results for both query methods provides an understanding of the research methodology.
## 3 Results and Discussion
### Determining the Influence of Cutting Parameters  
Table 5 summarizes the information for analysis of variance and case statistics for further interpretation. Table 5 clearly indicates that the feed rate (\(\text{F}_{\text{e}}\)) and the cutting depth (\(\text{a}_{\text{ed}}\)) had more influence on grinding time, while the wheel speed had the least influence on grinding time.  
Table 6 shows that the feed, depth, and speed of cutting are ranked first, second, and third, respectively. It was found that the feed rate had the largest effect on the grinding time, while the influence of the wheel speed on the grinding time was the smallest.  
The linear model analysis in the Taguchi method of S/N ratio for grinding time was carried out with the criterion LB. The S/N ratio, which was calculated for three factors including \(\text{a}_{\text{ed}}\), \(\text{R}_{\text{PM}}\) and \(\text{F}_{\text{e}}\) in different levels, is listed in Table 4. The plot about the main effects of cutting parameters for means of grinding time is illustrated in Figure 2. From this figure, it can be seen that when the cutting depth (\(\text{a}_{\text{ed}}\)) increased, the grinding time (t) decreased and received the smallest value at the third level (equivalent to the cutting depth value of 0.03 mm). As the grinding wheel speed (\(\text{R}_{\text{pm}}\)) grew, the grinding time (t) declined. It reached the smallest value at a wheel speed of 5000 rpm (equivalent to the wheel speed value at the third level). When the feed rate (\(\text{F}_{\text{e}}\)) rose, the grinding time (t) fell to the lowest value at the third level (equivalent to the feed rate value of 3500 mm/min).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & \(\text{a}_{\text{ed}}\) & \(\text{R}_{\text{PM}}\) & \(\text{F}_{\text{e}}\) \\ \hline
1 & 16.52 & 14.48 & 16.69 \\ \hline
2 & 13.82 & 14.15 & 13.99 \\ \hline
3 & 12.39 & 14.11 & 12.05 \\ \hline Delta & 4.12 & 0.37 & 4.64 \\ \hline Rank & 2 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The impact level of cutting parameters on part’s medium grinding time (\(\bar{t}\)).  
\begin{table}
\begin{tabular}{c c c c c c c|c c c} \hline \hline  & \multicolumn{4}{c|}{**Analysis of Variance for Means**} & \multicolumn{4}{c}{**Model Summary**} \\ \hline
**Source** & **DF** & **Seq SS** & **Adj SS** & **Adj MS** & **F** & **P** & **S** & **R-Sq** & **R-Sq** (adj)** \\ \hline a\({}_{\text{ed}}\) & 2 & 26.3201 & 26.3201 & 13.1600 & 109.15 & 0.009 & 0.3472 & 99.59\% & 98.38\% \\ \hline RPM & 2 & 0.2502 & 0.2502 & 0.1251 & 1.04 & 0.491 & & & \\ \hline F\({}_{\text{e}}\) & 2 & 32.5820 & 32.5820 & 16.2910 & 135.12 & 0.007 & & & \\ \hline Residual Error & 2 & 0.2411 & 0.2411 & 0.1206 & & & & & \\ \hline Total & 8 & 59.3934 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ANOVA values of part’s medium grinding time (\(\bar{t}\)).
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
### Gaussian Curvature
#### 4.3.2 Analysis of Wavefront Gaussian Curvature in Link 2  
From Figure 11a, the Gaussian curvature map under sunny conditions shows high curvature regions at the edges, indicating significant wavefront distortion in these areas. The overall distribution is relatively uniform, but there are some minor fluctuations at the edges. In Figure 11b, the central curvature peak under cloudy conditions is reduced compared to Figure 11a (sunny), with the peak brightness dimmer and at a lower level, indicating limited atmospheric turbulence effects. The edge regions are relatively smoother, showing fewer fluctuations. In Figure 11c, the Gaussian curvature map under light rain conditions shows significant fluctuations in the central region, indicating increased wavefront distortion. There are slight fluctuations at the edges, and overall, the wavefront distortion is more pronounced than under sunny and cloudy conditions. In Figure 11d, the Gaussian curvature map under heavy rain conditions shows strong fluctuations, particularly in the central region and at the edges, with significantly increased wavefront distortion. Overall, compared to Figure 11a (sunny) and Figure 11b (cloudy), the wavefront distortion is more pronounced under light rain (Figure 11c) and heavy rain (Figure 11d), highlighting the significant impact of weather conditions on wavefront quality.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Dual-Domain Joint Channel Estimation
#### 4.3.1 Path Delay Estimation in DD Domain  
As fractional Doppler shifts do not impact the estimation of delay taps, we can ignore the IDI interference caused by fractional Doppler shifts. The relationship between \(y(q,l)\) and \(x(q,l)\) with integer Doppler shifts was derived in [15], as follows:  
\[y(q,l)=\sum_{q^{\prime}=-q_{\max}}^{q}\sum_{l^{\prime}=0}^{l_{g}}\left[d(q^{ \prime},l^{\prime})\xi_{q^{\prime},l^{\prime}}x((q-q^{\prime})\text{ mod }N,(l-l^{\prime})\text{ mod }M) \right]+w(q,l), \tag{12}\]  
where \(q_{max}\) is the range of fractional Doppler coefficients after Doppler compensation, \(\xi_{q^{\prime},l^{\prime}}=h^{\prime}(q^{\prime},l^{\prime})\eta(q^{\prime}, l^{\prime})\). \(d(q^{\prime},l^{\prime})\in\{0,1\}\) is the complex channel gain with the phase \(\eta(q^{\prime},l^{\prime})\) and the amplitude \(h^{\prime}(q^{\prime},l^{\prime})=h(q^{\prime},l^{\prime})e^{-j2\pi\frac{q^{ \prime}l^{\prime}}{NM}}\), and \(d(q^{\prime},l^{\prime})\in\{0,1\}\) is the path indicator; for example, \(d(q^{\prime},l^{\prime})=1\) indicates that there is a path with Doppler tap \(q^{\prime}\) and delay tap \(l^{\prime}\) with corresponding path magnitude \(\xi_{q^{\prime},l^{\prime}}\). Furthermore, \(w(q,l)\) denotes Gaussian noise and '(.)mod' represents the modulus.  
Figure 3: The dual-domain joint UWA OTFS channel estimation method.  
The relationship in Equation (12) can be transformed into vector form as follows:  
\[\mathbf{Y}_{p} =\mathbf{X}_{p}\mathbf{h}+\mathbf{w},\] (13) in which: \[\mathbf{h}=[\xi_{-q_{\max},0},\xi_{-q_{\max},1},\ldots,\xi_{-q_{\max},l_{g}}, \tag{14}\] \[\xi_{-q_{\max}+1,0},\xi_{-q_{\max}+1,1},\ldots,\xi_{q_{\max},l_{g} }]^{T},\] (15) \[\mathbf{Y}_{p}=[y(0,m_{p}),y(0,m_{p}+1),\ldots,y(0,m_{p}+l_{g}),\] \[y(1,m_{p}),y(1,m_{p}+1),\ldots,y(N-1,m_{p}+l_{g})]^{T},\] (16) \[\mathbf{X}_{p}=[\mathbf{x}_{-q_{\max},0},\mathbf{x}_{-q_{\max},1 },\ldots,\mathbf{x}_{-q_{\max},l_{g}},\] \[\mathbf{x}_{-q_{\max}+1,0},\mathbf{x}_{-q_{\max}+1,1},\ldots, \mathbf{x}_{q_{\max},l_{g}}]^{T},\] (17) and \[\mathbf{x}_{q^{\prime},l^{\prime}}=\left[\begin{array}{c}x((0-q^{\prime}) \bmod N,(m_{p}-l^{\prime})\bmod M)\\ x((0-q^{\prime})\bmod N,(m_{p}+1-l^{\prime})\bmod M)\\ x((0-q^{\prime})\bmod N,(m_{p}+l_{g}-l^{\prime})\bmod M)\\ x((1-q^{\prime})\bmod N,(m_{p}-l^{\prime})\bmod M)\\ \ldots\\ x((N-1-q^{\prime})\bmod N,(m_{p}+l_{g}-l^{\prime})\bmod M)\\ \end{array}\right], \tag{18}\]  
where \(\mathbf{h}\) is the equivalent channel with sparsity, \(\mathbf{Y}_{p}\) is the received pilot signal, and \(\mathbf{X}_{p}\) is the sensing matrix, for \(l^{\prime}\in[0,l_{g}]\) and \(q^{\prime}\in[-q_{\max},q_{\max}]\).  
Next, we can estimate the equivalent channel \(\mathbf{h}\) using a compressive sensing algorithm, such as the orthogonal matching pursuit (OMP) method [29; 32]. Then, the set of path delays \(\{\hat{l}\}\) is obtained according to the index of \(\mathbf{h}\).
## 3 Results and Discussion
### Weighing  
The original disc cutter had a mass of 67 kg. The mass of the worn disc cutter, measured using a hanging scale, is 55.2 kg. The mass loss determined by weighing is therefore 11.8 kg. Given the density of steel \(\mathrm{q_{S}}\) = 7.85 kg/\(\mathrm{dm^{3}}\), the volume loss of the worn disc cutter, determined by weight measurement, is 1.503 \(\mathrm{dm^{3}}\).  
Weighing as a measurement method for assessing the wear of a disc cutter is recommended in combination with other methods as it alone does not allow satisfactory conclusions to be drawn about the type of wear except in the case of normal/uniform wear. However, this type of measurement saves a significant amount of time.  
If the change in a disc cutter's profile during normal/abrasive wear is known or predicted from previous TBM projects or studies, weighing the worn disc cutter can provide results for the worn diameter or profile without the need for measurements obtained by other contact or non-contact techniques.
## 5 Discussion
### Comparison of Compaction and Cementation Effects in the Es4 Member  
The COPL is generally high in the progradation sequences and low in the retrogradation sequences in the two sections. However, the COPL increase in the HST from Lijin to Yanjia indicates that the porosity loss due to compaction is higher in the Yanjia area (Figure 14A). In both areas, the Icp is generally greater than 5, suggesting that compaction remains the leading factor degrading the reservoir quality (Figure 14B).  
Figure 14: Cross plot of the COPL (**A**), compaction index (**B**), cement (**C**), and CEPL (**D**) split by sequence stratigraphic of the different formations of the Es4 member.  
Figure 13: Cross plot of the porosity and permeability split by Formation and different study areas. (**A**) Porosity split by Formation Member and area, (**B**) Permeability split by Formation Member and study area.  
Moreover, the low value of Icp encountered in the Es4x (mainly in the TST) indicates that cementation is the major factor in porosity reduction (Figure 14B-D). The range of the clay matrix is more significant in the Es4s in the Lijin area and Es4x (Figure 14). The lower clay matrix is observed in the Es4s in the Yanjia area (Figure 15A). This lower clay matrix is due to the withdrawal of a part of the clay in the upper Es4s member. However, the high clay matrix content in the Es4x reduces porosity (Figure 15B). Moreover, thin-section observation has shown that authigenic kaolinite is more abundant in the Es4s and few or inexistent in the Es4x. The clay content in the lower part is dominantly illite and chlorite clays.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 6 Performance Analysis
### V2I Average Authentication Delay  
The V2I average authentication delay is defined as the average of the time taken by the RSU and all vehicles covered by the RSU to complete the authentication protocol. The equation of average authentication delay (\(AD\)) is depicted as follows:  
\[AD=\frac{1}{N}\sum_{i=1}^{N}(T_{end}^{i}-T_{start}^{i}) \tag{1}\]  
where \(N\) is the number of vehicles within the communication range of RSU, \(T_{end}^{i}\) represents the end time of V2I authentication protocol, and \(T_{start}^{i}\) refers to the start time of V2I authentication protocol.  
Figure 9 displays the simulation results of PAS, EAAP, TAAP, and LIAP in terms of V2I average authentication delay, with the number of vehicles ranging from 20 to 200. We can see that the average authentication delay tends to increase steadily with the increasing number of vehicles. Due to limited channel bandwidth, the high computational cost and communication cost, EAAP, TAAP, and LIAP lead to longer average authentication delay. Moreover, with the increase of the number of vehicles, the efficiency of RSU continues to decrease, while the average authentication delay of PAS holds steady.
# Real-Time Cloth Simulation Using Compute Shader in Unity3D for AR/VR Contents
## 2 Related Work
### Mass-Spring System  
The mass-spring damper model consists of a list of springs that are defined at the initializing stage of the simulation. Each spring is made by 2 nodes and denoted by \(p_{1}\) and \(p_{2}\) as the first and second node positions, respectively. Similarly, the velocities are denoted by \(v_{1}\) and \(v_{2}\), and the initial length or rest length of the spring is \(L_{0}\)[22]. Figure 1 illustrates the structure of the mass-spring model used in the cloth simulation. A node can be linked with other nodes in the grid where \(i\) and \(j\) are the coordinates (row and column indices) of the 2D grid.  
General nodes can have 12 links except corner nodes and side nodes that have a different number of links since their neighbor nodes do not exist. Therefore, three types of spring can be classified as follows:  
Figure 1: Mass–spring model for cloth simulation.  
Structural springs: node [i, j] can be linked with node [i, j] can be linked with node [i, j+1], [i, j-1], [i+1, j], [i-1, j].
* Shear springs: node [i, j] can be linked with node [i+1, j+1], [i+1, j-1], [i-1, j-1], [i-1, j+1].
* Bend springs: node [i, j] can be linked with to node [i, j+2], [i, j-2], [i+2, j], [i-2, j].  
In the edge-centric algorithm, the spring force is computed using [PERSON]'s law. Variable \(K_{\mathrm{s}}\) is added to the force equation to increase the stiffness of the spring, and parameter \(K_{d}\) is the damping coefficient of the spring. Note that the edge-centric algorithm obtains two different directions of forces to each node in the spring. The force equation of the edge-centric algorithm can be written as follows in Equations (1) and (2):  
\[f_{1}=\bigg{[}k_{s}(|p_{2}-p_{1}|-L_{0})+k_{d}\bigg{(}\frac{(v_{2}-v_{1})\cdot (p_{2}-p_{1})}{|p_{2}-p_{1}|}\bigg{)}\bigg{]}\frac{p_{2}-p_{1}}{|p_{2}-p_{1}|} \tag{1}\]  
\[f_{2}=-f_{1} \tag{2}\]  
where,\(f_{1}\) and \(f_{2}\) are the forces of the first node and second node in a spring. After spring force computation, the accumulation of the force on each node is computed separately. Differently, in the node-centric algorithm, each node accumulates the force from all springs directly at the same time.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 7 Conclusions  
This paper proposed a node depth adjustment based scheme for target tracking in UWSNs. A novel node depth adjustment problem is formulated under the framework of an optimization problem to achieve more accuracy tracking performance. To solve this problem, an improved Harmony Search algorithm is designed to efficiently determine the optimal depth of activated nodes. It is the first time  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Schemes** & _dis = 90_ & _dis = 120_ & _dis = 150_ & _dis = 180_ \\ \hline STATIC & 9.079 & 7.807 & 7.287 & 6.955 \\ MOVE & 6.311 & 6.252 & 6.357 & 6.301 \\ Improvement & 30.49\% & 19.92\% & 12.76\% & 9.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average tracking errors for MOVE and STATIC with different adjacent node distance _dis L_.  
Figure 5: Convergence progress of objective function value with different HS algorithms.  
to use node depth adjustment to improve tracking accuracy. To verify the effectiveness of our scheme, simulations' results are presented and show that our scheme can achieve better tracking performance than existing schemes. Further work will consider the balance between tracking accuracy and energy consumption in node depth adjustment and extend the results to multiple targets tracking. In addition, due to complex underwater environments, node location errors and complex measurement models are also important problems to be solved in the future.  
This work was supported by the National Natural Science Foundation of China under Grants 61531015, U1609204, 61673345, and 61374021.  
[PERSON] and [PERSON] conceived the main idea; [PERSON] and [PERSON] designed the simulation; [PERSON] and [PERSON] performed the simulation; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.