esa-sceva/satcom-chunk-collection huggingface dataset:
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 3 Results
### Temporal and Spatial Distribution of Phenology
#### 3.2.2 Trends in Temporal and Spatial Patterns of Phenology
Figure 9: Trends in deciduous forests’ phenology extracted from 5-km SIF data across the Northern Hemisphere’s middle and high latitudes (\(2008\)–\(2017\)).
# Strain Rate of Metal Deformation in the Machining Process from a Fluid Flow Perspective
## 2 Materials and Methods
### Strain Rate Calculations from a Flow Viewpoint  
Parameters considered in metal cutting include strain, strain rate and temperature. Scholars have investigated such parameters to understand their changing laws and applied them to engineering practice. The increase in cutting speed complicates the on-line measurement of physical parameters. An appropriate method must be developed for studying the cutting process parameters. Moreover, studying the strain rate and strain from the point of view of fluid flow is convenient and simple. The entire calculation process is shown in Figure 4.  
First, tiny meshes are created on the surface of the workpiece, which has many small holes on the edge (Figure 4a). The chip root can be easily acquired because of the structure of the hole. As the milling feed increases, the material near the cutting tool side decreases until the chip root is separated from the workpiece due to the tool feed (Figure 4b). In the cutting process, the mesh will be distorted after flowing through the shear plane (Figure 4c), where \(V\) is the cutting velocity, \(V_{c}\) is the chip velocity along the direction of the rake face, \(V_{s}\) is the chip slip velocity along the direction of the shear plane, \(\alpha\) is the rake angle and \(\phi\) is the shear angle. \(X\) is set as the direction along the cutting tool rake face direction, and \(Y\) is the direction along the vertical tool rake face direction. Assuming that the grid with a side length of \(x_{n}\) moves \(L_{x}\) distance after \(t_{x}\) time, becoming \(x_{n+1}\) with a deflected \(\beta\) angle (Figure 4d), the strain along the X direction can be obtained:  
Figure 3: Strain rate distribution in the cutting process: 7050 aluminum alloy, 20 m/s cutting speed.  
\[\varepsilon_{x}=\frac{\left|x_{n+1}\cos\beta-x_{n}\right|}{L_{x}} \tag{10}\]  
Strain rate along the cutting tool rake face direction can be expressed as  
\[\dot{\varepsilon}_{x}=\frac{\left|x_{n+1}\cos\beta-x_{n}\right|}{t_{x}L_{x}} \tag{11}\]  
According to the characteristics of fluid flow, after the first grid passes through the shear plane, the second grids will flow to the original location of the first grid. If adjacent meshes are measured (ignoring the width of the laser beam machining line), then the distance \(L_{x}\) moving along direction X is approximately equal to the measurement length of the first mesh. Therefore, the time can be expressed as  
\[t_{x}=\frac{L_{x}}{V_{c}}=\frac{L_{x}\cos(\phi-\alpha)}{V\sin\phi}=\frac{x_{n} \cos(\phi-\alpha)}{V\sin\phi} \tag{12}\]  
Strain rate along the X direction can be expressed as  
\[\dot{\varepsilon}_{x}=\frac{\left|x_{n+1}\cos\beta-x_{n}\right|V\sin\phi}{x_{ n}^{2}\cos(\phi-\alpha)} \tag{13}\]  
Strain rate along the Y direction can be obtained by the same principle  
\[\dot{\varepsilon}_{y}=\frac{\left|y_{n+1}\cos\beta-y_{n}\right|V\cos\alpha}{ y_{n}^{2}} \tag{14}\]
## 3 Comparative Experiment
### Comparative Experiment
#### 3.3.4 Experimental Environment  
A comparative experiment was conducted using a local computer, with a running memory of 16 GB. The computer used the Win10 Professional operating system equipped with an 8 GB NVIDIA Quadro RTX 4000 GPU. In the patch-level experiment, the four CNN network models were imported from Keras version 2.3.1, and used TensorFlow 2.0.0 as the background. The experimental frameworks for ViT and pixel level were Pytorch 1.7.1 and Torchvision 8.0.2. Table 8 presents the model training and prediction time, and the size of the model during the experiment. From the perspective of model training time, the ViT model was much lower than CNN models, where the ViT training time was 13,992 s, and the Xception training time was the longest, 46,383 s. From the perspective of the model size, the minimum size of the ViT model was 31.2 M, and the maximum size of the ResNet50 model was 114 M. We calculated the times required for the five prediction models. The fastest prediction time for Inception-V3 was 583 s, and the prediction time for a single picture was 0.0027 s. The slowest time for ViT was 1308 s, and the prediction time of a single image was 0.0061 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Train** & **Test** & **Avg.p** & **Size (MB)** \\ \hline ResNet50 & 36,754 & 878 & 0.0041 & 114 \\ Inception-V3 & 24,064 & 583 & 0.0027 & 107 \\ VGG-16 & 34,736 & 781 & 0.0036 & 62.2 \\ Xception & 46,383 & 1014 & 0.0047 & 103 \\ ViT & 13,992 & 1308 & 0.0061 & 31.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: A comparison of the classification results of five-fold cross-validation experiment on train and test sets of \(8\times 8\) pixels patches. Train (Average training time), Test (Average test times) and Avg.p (Single picture prediction time) (In [s]).  
Figure 13: Reconstruction of pixel-level segmentation results on transparent images the EMD5-6 test set. (The figure contains the original image, ground truth image and U-net, Trans-Unet, Swin-Unet, U-Net++, SegNet network model predicted segmentation results).
## 2 Materials and Methods  
Five models of the BKS were machined in sizes of 4 mm in diameter by 10 mm in length, which is the standard measure for bone screws in dental implants, and were previously validated in vitro for compacting and collecting material in osteoporortic synthetic bone, with non-cutting threads. The BKS was inserted with a technical drill motor into synthetic bone, like osteoporortic bone (Nacional Osmos PCF 10). The synthetic bone model PCF10,  
Figure 2: Bioactive Kinetic Screw conception: (**a**) cortical bone collected during standard dental implant drilling and bone, blood, and cells collected during drilling of trabecular bone, and (**b**) synthetic osteoporortic bone compacted inside Bioactive Kinetic Screw [30].  
with a density of approximately 0.16 g/cm\({}^{3}\) (10 lb/ft\({}^{3}\)), exhibits a highly porous open-cell structure designed to mimic cancellous bone. It has a compressive strength ranging from 2 to 5 Mpa, a tensile strength between 1 and 2 Mpa, and a Young's modulus of approximately 50 to 150 Mpa. Fabricated from rigid polyurethane foam, PCF10 conforms to ASTM F1839 standards and is widely used for mechanical testing of orthopedic and dental implants in low-density bone simulations.  
The MIT and MRT measurements were taken with a digital torque wrench (TQ-8801 Lutron, Coopersburg, PA, USA). The Lutron TQ-8801 torque meter is a handheld digital device designed for precise measurement of torque in a range of 0.15 to 15.00 Nm, featuring high-resolution readings, data hold and peak hold functions, a backlit LCD display, and a compact, durable design suitable for laboratory and industrial applications, as seen in Figure 3.  
The simultaneous drilling and screwing operations were executed manually, with Tech surgical motor TD Max Surg 1 (Techniful Equipamentos Medicos Ltd., London, UK) with a pre-established torque of 35 Newton-centimeters and a maximum rotation of 300 revolutions per minute.  
The results obtained were described through a direct comparison of the MIT and MRT values, with each value analyzed individually. In the context of BKS, this methodological approach is imperative for the purpose of acquiring precise torque measurements. The crux of this methodology lies in the capacity to ascertain a torque level that exceeds the established MIT benchmark. The utilization of mean values in this context would serve to obscure the true outcomes, thereby compromising the integrity of the experimental results.
## Appendix B Spike-Independent Features
### Cumulative Bispectrum (Cb)  
Cumulative bispectrum is one of the robust methods applied for the non-stationary signals. By using the statistic moments: Equations (14)-(16), the higher-component statistics are computed as defined in Equations (17)-(19). In this work, only the third-order cumulants were considered.  
\[\text{m}_{1}^{\text{s}}=E[S(k)] \tag{17}\]  
\[\text{m}_{2}^{\text{s}}=E[S(k)S(k+T)] \tag{18}\]  
\[\text{m}_{3}^{\text{s}}=E[S(k)S(k+T_{1})S(k+T_{2})] \tag{19}\]where \(T\), \(T_{1}\), and \(T_{2}\) are the leg.  
\[\mathrm{C}_{1}^{\mathrm{s}}=m_{1}^{\mathrm{s}} \tag{117}\]  
\[\mathrm{C}_{2}^{\mathrm{s}}(\mathrm{T}_{1})=m_{2}^{\mathrm{s}}(T_{1})-(m_{1}^{ \mathrm{s}})^{2} \tag{118}\]  
\[\mathrm{C}\mathrm{b}=\mathrm{C}_{3}^{\mathrm{s}}(\mathrm{T}_{1},\mathrm{T}_{2}) =m_{3}^{\mathrm{s}}(T_{1},T_{2})-m_{1}^{\mathrm{s}}[m_{2}^{\mathrm{s}}(T_{1}) +m_{2}^{\mathrm{s}}(T_{2})+m_{2}^{\mathrm{s}}(T_{2}-T_{1})]+2(m_{1}^{\mathrm{ s}})^{3} \tag{119}\]  
_Appendix B.8. Average Amplitude Change (AAc)_  
Given in Equation (100).  
\[\mathrm{AAc}=\frac{1}{N}\sum_{i=1}^{N-1}\lvert s_{i+1}-s_{i}\rvert \tag{120}\]  
_Appendix B.9. Short-Term Fourier Transform (STFT)_  
Given in Equation (101), with a windowing function \(g(r)\).  
\[\mathrm{STFT}(f,k)=\sum_{r=1}^{N-1}s(t)g(t-k)e^{-j2\pi ft/N} \tag{121}\]  
where \(STFT(f,k)\) is a complex-valued Fourier coefficient \(K\) is the window index. The frequency \(f\), time \(t\), and shift \(k\) are discrete.  
_Appendix B.10. Teager Energy (Te)_  
Given in Equation (102).  
\[\mathit{Te}=s^{2}[i]-s[i-1]s[i+1] \tag{122}\]  
_Appendix B.11. Average First Amplitude Difference (AFa)_  
Given in Equation (103).  
\[\mathrm{AFa}=\frac{1}{N}\sum_{k=1}^{N-1}\lvert s_{k+1}-s_{k}\rvert \tag{123}\]  
_Appendix B.12. Second Amplitude Difference (SAd)_  
Given in Equation (104).  
\[\mathrm{SAd}=\frac{1}{N}\sum_{k=1}^{N-1}\lvert FA_{k+1}-FA_{k}\rvert \tag{124}\]  
where \(FA\) is the difference between two successive samples (\(s_{n+1},s_{n}\)). Then, \(FA_{i}\in FA\), \(FA=\{FA_{1},FA_{2},\ldots,FA_{N}\}\).  
_Appendix B.13. Standard Deviation (SD)_  
Given in Equation (105).  
\[\mathrm{STD}=\sqrt{\frac{1}{N}\sum_{k=1}^{N}\left(s_{k}-\bar{s}\right)^{2}} \tag{125}\]
## 3 TDoA Method Experimental Validation  
This section describes a possible hardware suitable for the implementation for the methodology introduced above. To prove the method effectiveness, a massive campaign of DoA estimations was carried out by means of a phase-impaired anchor device based on a SBA array. Thus, Cumulative Density Functions (CDFs) of DoAs estimation errors, with and without the use of phase reconstruction, are compared to assess the improvements given by the proposed solution.
# Clinical Benefits of Hepatic Arterial Infusion Chemotherapy for Advanced Hepatocellular Carcinoma
## 3 HAIC Versus SORafenib
### HAIC versus sorafenibib Based on Sarcopenia  
Sarcopenia has been defined as the loss of skeletal muscle mass, physical performance (e.g., walking speed), and strength according to the European Working Group on Sarcopenia in older People and the Asian Working Group for Sarcopenia [45; 46]. In contrast, the JSH proposed a diagnostic criterion for sarcopenia in patients with chronic liver disease of \"loss of muscle mass plus low muscle strength\" [47]. However, in previous reports, skeletal muscle depletion has been commonly used as the definition for sarcopenia in patients with HCC [48; 49]. Previous studies analyzing HCC patients who received sorafenibly demonstrated that skeletal muscle depletion was almost associated with poor prognosis [48; 49; 50; 51; 52; 53]. Similarly, it has been reported that skeletal muscle depletion was a poor prognostic factor in patients with HCC treated with lenvatinib [54]. Furthermore, it is important to investigate skeletal muscle change during MTA use. The annual rates of skeletal muscle volume decline in cirrhotic patients without HCC were reported to be 1.3%, 3.5%, and 6.1% for Child-Pugh class A, B, and C, respectively [55]. Conversely, our previous study showed that skeletal muscle mass decreased by 5.5% at 3 months after starting sorafenibly [53], and another report indicated that treatment with sorafenibly or lenvatinib showed a significant depletion of skeletal muscle volume regardless of disease progression and hepatic reserve function [56]. In the era of MTAs, sequential therapy using MTAs may decrease skeletal muscle volume markedly higher than a first-line MTA therapy. Currently, there have been no reports regarding the relationship between sarcopenia and clinical outcomes in patients treated with atezolizumab plus bevacizumab and other MTAs, except for sorafenibly and lenvatinib.  
This is the first study demonstrating that skeletal muscle depletion is not associated with OS in patients with HCC treated with HAIC compared with sorafenibly [57]. As there have been no similar reports, this finding will need to be validated. Thus, the different results related to skeletal muscle mass between HAIC and sorafenibly may be worthy of notice when considering the use of treatment modalities for advanced HCC.
## 3 Results and Discussion
### Study Area and Background
#### 3.1.2 Change Detection Based on the 2015 and 2016 Images
Due to the high quality of the CI map obtained using the fully polarimetric information based on the test statistic, the change detection map of the test statistic and improved K&I shows a better performance. These experiments show that using the multi-temporal full polarimetric data can achieve a better change detection result than using single-channel or multi-channel data. In order to compare the effect of the threshold choice, we used the CI map based on the test statistic and the threshold methods of 5% significance level, 1% significance level, K&I, CFAR (Pfa = 0.005), and improved K&I. Among these methods of choosing the threshold, the 5% and 1% significance levels and K&I result in more false alarms in the city and forest than the proposed method. In addition, CFAR results in more false alarms in the water bodies than the proposed method. The quantitative comparison of these detection schemes shown in Table 4 also indicates that the proposed approach shows a better performance. The proposed approach of the test statistic and improved K&I achieves the best results in all four indicators (FA (%), TE (%), OA (%), Kappa). This confirms that the proposed method is effective and shows a significant improvement over the other methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **FA (\%)** & **TE (\%)** & **OA (\%)** & **KAPPA** \\ \hline HH and improved K\&I & 7.15 & 7.62 & 92.38 & 0.6402 \\ HV and improved K\&I & 4.78 & 6.23 & 93.77 & 0.6686 \\ VV and improved K\&I & 6.53 & 7.15 & 92.85 & 0.6541 \\ CVA and improved K\&I & 0.09 & 9.35 & 90.65 & 0.0017 \\ Test statistic with 5\% significance level & 6.80 & 7.17 & 92.83 & 0.6593 \\ Test statistic with 1\% significance level & 5.30 & 6.09 & 93.91 & 0.6906 \\ Test statistic and K\&I & 5.64 & 5.92 & 94.08 & 0.6755 \\ Test statistic and CFAR & 7.67 & 8.28 & 91.72 & 0.6130 \\ Test statistic and improved K\&I (the proposed method) & 3.26 & 4.81 & 95.19 & 0.7282 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of the change detection on Yandong Lake between 2015 and 2016. Figure 16: Change detection results in Region 3 between 25 June 2015 and 6 July 2016 based on the following: (**a**) log-ratio (HH) and improved K&I; (**b**) log-ratio (HV) and improved K&I; (**c**) log-ratio (VV) and improved K&I; (**d**) CVA and improved K&I; (**e**) test statistic with 5% significance level; (**f**) test statistic with 1% significance level; (**g**) test statistic and K&I; (**h**) test statistic and CFAR; (**i**) test statistic and improved K&I (the proposed method); (**j**) Ground reference (white denotes the change and black denotes the non-change).
# Trajectories and Resource Management of Flying Base Stations for C-V2X
## 5 Numerical Results
A similar trend can be seen for both Figure 3a,b: when more vehicles ask the network for service, it is more probable that TBSs or the UAV have not enough RRs to serve them. If the percentage of satisfied vehicles does not reach 100%, this is due either to SNR or SIR under the respective thresholds. As happened with the gain \(G\), the larger is the number of active vehicles, the larger is the probability that RRs available are not sufficient. Also, in both plots of Figure 3, the varying capacity of the UAV RR pool is affecting the performance as in Figure 2: an increasing number of RRs employed by the TBSs limit the UAV efficacy. The UAV, in average, becomes especially effective when the active vehicles are below 50%. Being interested in supporting the network in the initial phase when few vehicles are able to communicate, these results are promising both in varying the bandwidth size and the application data-rate requirements. In particular, with a bandwidth size as large as 20 MHz, the advantage of including one UAV reusing the same RRs as TBSs proves to be an interesting choice even at a later stage. As expected, the different needs of resources in the two cases make the network serve 68% of vehicles in Figure 3a and 50% of vehicles in Figure 3b in the best case (i.e., 20 MHz band) when 100% of them is active.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 4 SINR Model Approximation and Optimal Parameter of IoT-over-Satellite
### Uplink SINR in Australia  
Using (16), we calculate the average SINR level of each measurement points over a week, and the result is shown in Figure 7. It indicates that the SINR in the remote and rural areas (Classes 2 and 3) is at least 10 dB, and the level in an urban or suburban area is less than 5 dB since the number of interference sources is much higher in the area. If we assume several IoT modulation schemes such as BPSK, QPSK, 16 QAM, and LoRa CSS are used in the IoT-over-Satellite communication, the SINR level needs to be more than 10 dB to achieve a Bit Error Rate (BER) with less than \(10^{-7}\) using (15). Thus, an IoT-over-Satellite system using an LEO satellite with shared bands may not be an option in an urban or suburban area without an advanced modulation scheme or access techniques to reduce the terrestrial interference.  
Figure 6: (**a**) Remoteness index and (**b**) IoT population based on human population. The higher value represents a higher level of remoteness. The red dash line is the satellite track over a week [32]. (**a**) Remoteness index based on Reference [32]. (**b**) Number of IoT devices in Australia.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 6 Simulations and Results
### Simulation Scenarios
#### 6.1.2 Simulation Scenario 2  
The second simulation scenario extends the first scenario by adding a DRONET layer to the multilayered model, which performance will be compared to the traditional WSN network depicted in Figure 14. The distribution of WSN nodes with their parameters and technologies is the same as in the case of the first simulation scenario.The number and parameters of MANET nodes remained unchanged, but some positions of MANET nodes were partially modified in order to divide the original MANET network into two subnets. This change was made to simulate the division of the MANET network into individual subnets in order to use UAVs of the DRONET network.  
The UAVs uses IEEE 802.16 WiMAX technology with the AODV protocol to communicate with other UAVs and dock. Therefore, the radio range of UAVs was set to 200 m with a data rate of 250 Mbps. The number of UAVs and their distribution in the area is determined by the number of clusters and their location in the area. The PSO clustering algorithm was used in this task, which divided the individual MANET nodes into 4 clusters or logical subnets based on positions of the MANET nodes. The output of the clustering algorithm can be seen in Figure 16.  
Figure 16: The example of the PSO clustering output.  
Figure 15: (a) Example of simulation scenario 1 with all positions of AP for WSN-MANET network. (b) Example of optimal routing path from WSN-MANET source sensor to AP deployed in Position 2 (in the middle of the simulation area).  
Based on the assumptions mentioned above, the variables of the simulation scenario 2 were set according to Table 2.  
As in the first simulation scenario, three different access point positions were used in the second simulation scenario as well. The specific locations coincide with the first simulation scenario and together with the locations of the WSN, MANET and UAV nodes are depicted in Figure 17. As in the case of the first simulation scenario, the second simulation scenario was run 1.000 times for all AP positions with the same initial positions of WSN, MANET and UAV.
###### Abstract  
In order to solve the problem of an uncertain initial state and big errors for hypersonic glide vehicle (HGV) tracking, a hybrid model algorithm is proposed by combining a single model algorithm with a multiple model algorithm. To develop the tracking algorithm with the Cubature Kalman filter, in every model filter the process equation is established based on the HGV aerodynamic model and the measurement equation is established based on the radar measurement principle. The proposed hybrid model algorithm is developed by using the multiple model algorithm in the initial tracking stage and using the single model algorithm in the stable tracking stage, and they are divided by a proposed parameter. The former can avoid divergence and reduce the errors caused by the uncertain initial state. The latter can track the HGV at higher accuracy. The simulation indicates that the proposed hybrid model has high speed accuracy in the whole tracking stage and high position accuracy in the stable tracking stage. The average position root mean square error (RMSE) using the hybrid model algorithm is almost the same as that using the single model algorithm but the average speed RMSE using the single model algorithm is about 30% greater than that using the hybrid model algorithm. In a system for defending the HGV, the speed accuracy has more effect on the trajectory prediction as time goes on. Thus, the hybrid model algorithm is an engineering algorithm for HGVs with high accuracy. In future research, the hybrid model algorithm will be studied for general maneuvering target tracking.  
hypersonic glide vehicle tracking; hybrid model algorithm; Cubature Kalman filter; aerodynamic model; multiple model; single model +
Footnote †: journal: Applied Mechanics and Physics Letters