esa-sceva/satcom-chunk-collection huggingface dataset:
# Veins Depth Estimation Using Diffused Reflectance Parameter
## 7 Conclusions  
NIR imaging is considered the most suitable technique to visualize the location of subcutaneous veins during the IV catheterization process. Calculation of vascular depth is equally important in order to deduce their exact position in skin. With the diffused reflectance parameter, the estimation on depth of veins can be made in order to reduce catheterization errors. The paper presented the method to estimate the veins depth using diffused reflectance from the surface of skin. A four-layered skin model is presented with the varying depth of blood vessels. This model serves as reference for the experiment on veins depth estimation. A mathematical model has been proposed for the analytical calculation of veins depth using the Rd parameter measured from the surface of top layer. This model has been derived from the state-of-the-art literature on the light propagation in tissues. In this work, Rd parameter is obtained from the MCML simulations. The results from the simulations are plotted with depth of vessels against the measured Rd parameter. The results showed a marginal error of maximum value 6.23% in vessel depth estimation using the standard optical parameters, 1.6% for \(-5\%\) and 10.74% for \(+5\%\)change in optical parameters. The mathematical model is found sensitive to the optical parameters which leads to the conclusion that efforts should be made in carefully choosing optical parameters during experimentation. The results revealed that this model is applicable in vein depth estimation process due to having relatively less percentage error and its ease of implementation. The future work will focus on the experimentation with customized layered tissue phantom to validate and fine tune the mathematical model.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the simulations; [PERSON] and [PERSON] analyzed the results; [PERSON] and [PERSON] wrote the paper, [PERSON], [PERSON] and [PERSON] technically reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 61631018).  
[PERSON] [PERSON] (Research Fellow, SiST USTC Hefei, China) personally thanks to [PERSON] for supporting this work. We the whole team also thank the reviewers and lab mates for their important critical comments that largely improved our manuscript to the current shape.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Method** & **\% Error in Depth Estimation** \\ \hline
[13] [PERSON] e.t al. & OD measurement & 30 \\
[16] [PERSON]. [PERSON] e.t al & OD measurement & 4.5 \\
**Proposed** & **Rd measurement** & **6.23** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison table.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MPE for Standard Optical Parameters** & **MPE with \(-5\%\) Change** & **MPE with \(+5\%\) Change** \\ \hline
6.23 & 1.65 & 10.749 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Depiction of percentage error.
## 4 Simulation Experiments and Results
### Effect of Side-Rake Angle  
In this section, the simulation considers a back tilt angle of 21\({}^{\circ}\)and side tilt angles of 0\({}^{\circ}\), 5.0\({}^{\circ}\), 10.0\({}^{\circ}\), 15.0\({}^{\circ}\), 20.0\({}^{\circ}\), and 25.0\({}^{\circ}\), respectively. Figure 13 illustrates the variation curve of the simulated cutting force with the side-rake angle.  
Figure 12: Three-dimensional surface plots of MSE and standard deviation of maximum stress and cutting force at different back-rake angles.  
Figure 13: The standard deviation of mean cutting and cutting forces at the side-rake angle.  
In the side-rake range from 0\({}^{\circ}\) to 25\({}^{\circ}\), it is observed that the average cutting force tends to decrease and then increase as the angle increases, with minimal overall variation. This indicates that a smaller lateral back rake is advantageous for rock breaking, but its impact on the cutting force is limited. However, the standard deviation of the mean cutting force shows a linear decrease from 0\({}^{\circ}\) to 10\({}^{\circ}\). This is attributed to the reduced lateral vibration of the cutting teeth during the breaking process, which enhances the stability of the drilling cut. In the range of 10\({}^{\circ}\) to 25\({}^{\circ}\), the standard deviation of the cutting forces increases rapidly, and the rate of change exhibits a fluctuating pattern of increase and then decrease. This phenomenon results from gradually enlarging the contact area between the cutting teeth and the rock, leading to increased wear between them. The wear between the cutter face and the rock intensifies, increasing rock chip production and retention time. Therefore, choosing a side-rake angle of approximately 5-15\({}^{\circ}\) is optimum for the rock-breaking process. Because of the continuity of the simulation results, the standard deviation of cutting forces shifts significantly when the lateral inclination angle is around 10\({}^{\circ}\) and is the smallest standard deviation of cutting forces, so the optimum side-rake angle during rock breaking should be in the range of 5-15\({}^{\circ}\).
# Buffer-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
## 6 Simulation Results and Discussion
### Qualitative Assessments
#### 6.1.3 Recall Assessment  
Table 4 demonstrates the recall attainment of the tiny YOLOv3, YOLOv8n, YOLOv8m, YOLOv8n-WF, YOLOv8m-WF, and recommended FER-YOLOv8 models for the CWD system. Table 4 depicts the recall evaluation of the above-mentioned models, both overall and per class. The suggested FER-YOLOv8 reaches a recall value of up to 0.993 for the whole set of test images. Comparably, the gun class's recall is 0.981, appreciably greater than that of the other models. On a similar evaluation metric, the YOLOv8m-WF model's performance is defined as follows: for the gun class, the model's recall value is up to 0.972. Similarly, the YOLOv8m model's performance for the gun class is also the same as the performance of YOLOv8m-WF model. The macro-averaging recall values are also stated in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Overall Precision (\%)** & **Macro-Averaging Precision (\%)** & **Precision for Person Class (\%)** & **Precision for Gun Class (\%)** \\ \hline Tiny YOLOv3 & 98.3 & 93.6 & 100.0 & 87.3 \\ YOLOv8n & 98.5 & 97.9 & 100.0 & 95.9 \\ YOLOv8m & 98.9 & 96.2 & 100.0 & 92.5 \\ YOLOv8n-WF & 98.0 & 96.9 & 100.0 & 93.9 \\ YOLOv8m-WF & 99.0 & 96.4 & 100.0 & 92.9 \\ Proposed & 99.5 & 99.0 & 100.0 & 98.1 \\ FER-YOLOv8 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of precision metric for various classes.  
Table 4. The macro-averaging recall values also suggest that the proposed FER-YOLOv8 model outclasses the other models.  
The recall value simply depends on the true positive and false negative detections, as described in Equation (19). The proposed FER-YOLOv8 architecture outperforms the other competitor models, such as tiny YOLOv3 and YOLOv8 architectures, by decreasing the false negative rate and improving the true positive detections. Again, the enhanced feature extraction capability provided by the pointwise convolution in the FER module in the backbone network of the recommended FER-YOLOv8 architecture improves the recall value of the CWD system. Moreover, the extension of the dropout layer in the modified Conv_Reg module combats overfitting, which also improves the generalization capability of the proposed FER-YOLOv8 architecture as compared to the other models. Thus, the recall of the gun class by the suggested FER-YOLOv8 architecture increases to 98.1%, which is extraordinary compared to the tiny YOLOv3 model and YOLOv8 variants, as mentioned in Table 4.
## 3 The Consecutive Iteration Based on Signal Cancellation (SCCI) Algorithm  
In this section, building upon the principles of the SCCCME algorithm and utilizing the Least Mean Square (LMS) principle, we propose an algorithm known as SCCI. This algorithm has an optimized workflow and incorporates an adaptive variable convergence factor in its process for the characteristics of LEO satellite downlink signals. The final simulation compares the convergence factors of the SCCI algorithm and the SCCCME algorithm, validating that the adaptive convergence factor of the SCCI algorithm can betterbalance between a faster convergence speed and smaller steady-state error during the iteration process.
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## 3 Monotonic Shear Tests
### Test Results
#### 3.1.2 Shear Displacement-Normal Displacement Curve  
Figure 5b shows that when the interface is relatively smooth, the loose sand exhibits a reduction in volume. Additionally, the greater the normal stress, the greater the amount of shear shrinkage. This test stipulates that the normal deformation is the positive value of the upward deformation. When the normal stress increases to 400 kPa, the normal displacement stabilizes at approximately \(-0.35\) mm. In this case, the volume of dense sand hardly changes. For the case of \(R_{n3}\) (Figure 5f), loose sand still shows shear shrinkage, but the volume shrinkage of sand is greater. Similarly, when the normal stress is 400 kPa, the normal displacement reaches about \(-0.9\) mm. In this case, dense sand appears as shear shrinkage at the beginning, and then it undergoes dilatancy. Additionally, with the increase in normal stress, the amount of shearing increases continuously, and the maximum normal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of Interface Shear** & **Monotonic Shear** & **Cyclic Shear** \\ \hline Relative density of sand \(D_{r}\) & 0.3 (\(e=0.79\)) & 0.3 (\(e=0.79\)) \\  & 0.9 (\(e=0.56\)) & 0.9 (\(e=0.56\)) \\ Vertical confining pressure \(\sigma\) [kPa] & 200, 300, 400 & 200, 300, 400 \\ Interface roughness \(R_{n}\) & \(R_{n1}\), \(R_{n2}\), \(R_{n3}\) & \(R_{n1}\), \(R_{n3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Schemes of monotonic shear tests and cyclic shear tests.  
displacement is stabilized at about 0.5 mm. For the case of \(R_{n2}\), the curve behavior is consistent with that when the interface is rough, but the phenomenon of shear shrinkage is not so severe. Additionally, it is also found that with the increase in the interface roughness, the shear shrinkage of loose sand and the shear dilatancy of dense sand are more obvious.  
Figure 5: Comparison of morphological characteristics of sand–steel interface in monotonic shear test (**a**,**b**) \(R_{n1}\); (**c**,**d**) \(R_{n2}\); (**e**,**f**) \(R_{n3}\).
###### Abstract  
The aim of this work is to evaluate the difference between the properties of several aluminum alloy joints welded with the traditional air-cooled friction stir welding process and others obtained by the combination of the traditional friction stir welding setup with a water-cooling system. In particular, precipitation-hardening alloys AA2024-T3, AA6082-T6 and AA7075-T6, and a work-hardening alloy, AA5754-H111, were taken into account. From Rockwell and Vickers hardness maps, it was possible to observe a clear dependence of the hardness distribution on the cooling systems; joints obtained using a water-cooling system showed higher values of hardness, reached in the central zone and a narrow area interested by the hardness reduction for all the tested alloys. From tensile tests executed orthogonally to the welding direction, it was possible to observe that the alloys have responded differently in terms of ultimate tensile strength and final elongation when the water-cooling system was used. The microstructural analysis of the three precipitation-hardening alloys showed a larger average grain size in the nugget zones for the water-cooled condition. Moreover, in the thermo-mechanically altered zones of the water-cooled AA6082 and AA7075 joints, the grains were characterized by a smaller average size than the grains of the same air-cooled welds.  
f riction stir welding; aluminum alloys; precipitation-hardening alloy; work-hardening alloy; water-cooling system Article
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
###### Abstract  
The cybersecurity of wind farms is an increasing concern in recent years, and its impacts on the power system reliability have not been fully studied. In this paper, the pressing issues of wind farms, including cybersecurity and wind power ramping events (WPRs) are incorporated into a new reliability evaluation approach. Cyber-physical failures like the instantaneous failure and longtime fatigue of wind turbines are considered in the reliability evaluation. The tripping attack is modeled in a bilevel optimal power flow model which aims to maximize the load shedding on the system's vulnerable moment. The time-varying failure rate of wind turbine is approximated by Weibull distribution which incorporates the service time and remaining life of wind turbine. Various system defense capacities and penetration rates of wind power are simulated on the typical reliability test system. The comparative and sensitive analyses show that power system reliability is challenged by the cybersecurity of wind farms, especially when the installed capacity of wind power continues to rise. The timely patching of network vulnerabilities and the life management of wind turbines are important measures to ensure the cyber-physical security of wind farms.  
Keywords:cybersecurity; power system reliability; wind power ramps +
Footnote †: journal: Applied Sciences
## 4 Test Results and Analysis
### Grey Relational Analysis  
Grey relational analysis is a method and theory that employed the grey relational order to describe the strength, size, and sequence of relationships between factors. This approach was used to derive and predict incomplete information systems.  
The steps of grey correlation analysis:  
1. Let the dependent variable of the system be the reference sequence and the independent variable be the comparison sequence.  
2. The grey correlation analysis uses the 'initial value' method to divide the test data and the initial value at the same level for dimensionless processing:  
\[x_{i}(k)=\frac{x_{i}^{\prime}(k)}{x_{i}^{\prime}(1)} \tag{3}\]  
In the formula: \(i=0,1,\ldots,\) n, n is the number of influencing factors; \(k=1,2,\ldots,\) m, m is the number of levels contained in each influencing factor.  
Figure 6: The relationship between the flow value of different bitumen aggregate ratio slope and the test time.  
Figure 7: Relationship between bitumen aggregate ratio and slope flow value.  
3. Calculation of correlation coefficient  
\[\zeta_{i}(j)=\frac{\begin{bmatrix}minmin_{i}|x_{0}(j)-x_{i}(j)|+\underset{i}{ \rho maxmax}|x_{0}(j)-x_{i}(j)|\\ \hline\left[|x_{0}(j)-x_{i}(j)|+\underset{i}{\rho maxmax}|x_{0}(j)-x_{i}(j)| \right]\end{bmatrix}}{\left[|x_{0}(j)-x_{i}(j)|+\underset{i}{\rho maxmax}|x_{0}( j)-x_{i}(j)|\right]} \tag{4}\]  
In the formula: \(|x_{0}(j)-x_{i}(j)|\) is the absolute value difference between the corresponding elements of the comparison sequence and the reference sequence, and \((\underset{i}{min},\underset{j}{min},\underset{i}{max},\underset{j}{max}\) are the minimum difference and the maximum difference, respectively. \(\rho\) is the resolution coefficient, \(\rho\) (0, 1), generally 0.5.  
4. Calculating correlation  
\[r_{i}=\frac{1}{m_{j}}\frac{1}{1}^{m}=\zeta_{i}(j) \tag{5}\]  
5. Sorting the calculated correlation coefficients  
The slope flow value is used as the reference sequence, and the gradation index, bitumen aggregate ratio and filler content are used as the comparison sequence to calculate the correlation coefficient. The calculation results are shown in Table 5.  
The correlation calculated from Table 5 is shown in Table 6.  
For the three evaluation items, the comprehensive evaluation of test temperature was the highest (asphal-stone ratio: 0.703118), followed by filler content (correlation degree: 0.686898), and the gradation index was the lowest (correlation degree: 0.616012).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Appraisal Items** & **Correlation Degree** & **Ranking** \\ \hline grading index & 0.616012 & 3 \\ filler content & 0.686898 & 2 \\ asphalt-stone ratio & 0.703118 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Correlation ranking results.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Slope Flow** & \multirow{2}{*}{**Grading Index**} & \multirow{2}{*}{**Filler Content (\%)**} & **Bitumen Aggregate** \\
**Value (mm)** & & & **Ratio (\%)** \\ \hline
1.369 & 0.33 & 11 & 7.2 \\
1.217 & 0.36 & 11 & 7.2 \\
0.813 & 0.39 & 11 & 7.2 \\
0.508 & 0.42 & 11 & 7.2 \\
0.401 & 0.39 & 9 & 6.6 \\
0.568 & 0.39 & 9
# High Pressure and Pasteurization Effects on Dairy Cream +
## 3 Results and Discussion
### Viscosity  
Cream's flow behavior was studied only for raw, thermally pasteurized, and 600 MPa-processed samples in the second batch. Generally, samples showed a qualitatively similar non-Newtonian flow behavior, with apparent viscosity decreasing with shear rate (shear thinning). The observed behavior was expected for an emulsion and is in accordance with [PERSON] et al. (2011), who evaluated the rheological behavior of milk cream under pressure (400-500 MPa for 5-10 min at 25 \({}^{\circ}\)C) and reported that HPP milk cream also behaved as a non-Newtonian pseudoplastic fluid [29].  
The apparent viscosity of the studied samples was compared at a constant shear rate of 33 s\({}^{-1}\) (Table 2).  
No major differences in the initial viscosity values were detected between the raw cream and all treated samples. Also, the apparent viscosity of the thermally pasteurized samples remained similar (\(p\) > 0.05) throughout the storage time. After 3 days of refrigerated storage, HPP samples presented viscosity values almost two times higher (\(p\)\(\leq\) 0.05) than the initial ones (immediately after processing). However, from this day forward, viscosity values of HPP samples remained statistically similar (\(p\) > 0.05), only increasing (\(p\)\(\leq\) 0.05) on the 52 nd day of storage for 600/15 samples. [PERSON] et al. (1996) reported that after HPP, the flow behavior of the pasteurized cream samples did not show considerable changes after 7 days of storage (4 \({}^{\circ}\)C) [PERSON] et al. (1996). In general, pressure-treated samples always presented a higher viscosity (\(p\)\(\leq\) 0.05) than the heat treated ones (Table 2), which can be advantageous in the development of products with different texture characteristics and consumer acceptability.
###### Abstract  
Induced seismicity is one of the negative phenomena caused by anthropogenic activities that include mining of minerals. This phenomenon manifests itself as sudden and unpredictable shocks of rock mass, which can cause surface deformation and damage to ground infrastructure. Until the advent of satellite radar interferometry that enables analysis of historical events, the characteristics of these unexpected surface deformations were difficult to assess. The main aim of the research was the spatial analysis of the geometry of surface displacements caused by eight induced tremors in the Rudna copper mine (SW Poland) and the dependence of deformation characteristics (vertical displacements, extent) on the induced shock energy. For this purpose, Sentinel-1 satellite imagery, the differential radar satellite interferometry (DInSAR) method and geographic information systems (GIS) based spatial statistics were used. Vertical displacements were mapped on the basis of 37 calculated interferograms. Spatial statistics on the pixel-to-pixel level were performed in the GIS Map Algebra environment. In the result, descriptive and spatial statistics characterizing deformations caused by individual shocks were calculated. The average values of vertical displacements ranged from \(-44\) to \(-119\) mm. Strong, statistical correlation between the extent, maximum vertical displacement, and energy values was determined. In addition, geometries of the formed deformation areas were analyzed and presented graphically. The results obtained in this research constitute development of a knowledge base on surface displacements caused by induced tremors in underground copper mining.  
induced shocks; underground mining; deformation; DInSAR; spatial statistics +
Footnote †: journal: Applied Physics Letters
## 2 Dynamic Modeling of Space Robot and Analysis of Manipulator End Impedance
### The Impedance Model of Manipulator End  
In essence, the problem of on-orbit insertion and extraction of space robots is a kind of impact dynamic between the end of robot arm and unknown environment entity. In order to avoid equipment damage and mission failure caused by violent impact between the end of manipulators and the targets, it is necessary to make compliant control for the whole process. Impedance control has a good performance in dealing with this kind of problems. The control strategy is to maintain the ideal dynamic relationship between the end-effector and the environment by adjusting the impedance parameters of the manipulator.  
Figure 1: Three-link single-arm rigid space robot structure diagram.  
In general, we use the following equation to represent the terminal impedance model and the environment model:  
\[\begin{split}&\mathbf{M}_{t}\left(\ddot{\mathbf{X}}_{d}-\ddot{\mathbf{X}}\right) +\mathbf{B}_{t}\left(\dot{\mathbf{X}}_{d}-\dot{\mathbf{X}}\right)+\mathbf{K}_{t}(\mathbf{X}_{d}- \mathbf{X})=\mathbf{F}_{t}\\ &\mathbf{B}_{e}\left(\dot{\mathbf{X}}_{d}-\dot{\mathbf{X}}\right)+\mathbf{K}_{e}( \mathbf{X}_{d}-\mathbf{X})=\mathbf{F}_{e}\end{split} \tag{4}\]  
where \(\mathbf{X}_{d}=[q_{0d},{x_{t}}^{\prime},{y_{t}}^{\prime},q_{td}]^{\mathrm{T}}\) are the augmented desired trajectory vector of the carrier attitude, position and the attitude of the end of \(\mathbf{B}_{t}\), and \(\mathbf{X}=[q_{t},{x_{t}}^{\prime},{y_{t}}^{\prime},q_{t}]^{\mathrm{T}}\) are the augmented vector of actual trajectory, \(\mathbf{M}_{t},\mathbf{B}_{t},\mathbf{K}_{t}\in\Re^{4\times 4}\), respectively, are the inertia matrix, damping matrix and stiffness matrix of manipulator, \(\mathbf{F}_{t},\mathbf{F}_{e}\in\Re^{4\times 1}\) are the output force and torque, contact force and torque of end replacement instruments, \(\mathbf{B}_{e},\mathbf{K}_{e}\in\Re^{4\times 4}\) are the damping matrix and stiffness matrix of environment.  
According to Equation (A8), the error between the manipulator end output force and torque and the manipulator end contact force and torque can be calculated, and which can be converted to joint torques from Cartesian moments by following:  
\[\tau_{te}=\mathbf{J}_{ta}(\mathbf{F}_{t}-\mathbf{F}_{e}), \tag{5}\]  
Then, if the impedance control is turned on during the on-orbit operation of the space robot, Equation (1) can be expressed as:  
\[\mathbf{D}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{H}(\mathbf{q},\dot{\mathbf{q}})\dot{\mathbf{q}}=\tau+\tau_ {d}+\tau_{te}, \tag{6}\]