esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Generic Numerical Transtibial Model for Limb-Prosthesis System Evaluation
## 2 Materials & Methods
### Geometry of the Residual Limb  
The skeleton of the residual limb was subtracted from a subject-specific TLEM 2.1 lower extremity model, which was scaled to a standard size (50 th percentile size for a European male) using the AnyBody Repository (Figure 1a) [22]. In clinical practice, it is vital that the bones are cut following the surgical guidelines; failure to do so may result in additional stress concentrations and increased pressure, leading to discomfort and pain [3]. Considering the previous point, the leg skeleton was sectioned in the virtual environment according to the guidelines for lower limb amputations in conjunction with the Traumatologist to create an average-size transtibial limb model [3]. The distance from the tibial plateau to the tibial end was 150 mm, which corresponds to the average transtibial limb size. According to the physiatrist's recommendations, the fibula was cut and bevelled 10 mm above the tibial end. The proximal part of the femur was also cut because it was not needed in the simulation and would only increase computational time. In addition, the surfaces of the bones were smoothed using the QuadRemesh function in Rhino 7 (Robert McNeel & Associates) to reduce the specific details that would increase numerical complexity but not affect the quality of the biomechanical behaviour. The final transtibial skeleton model includes the distal femur with the femoral shaft, the proximal tibia with cut and bevelled tibial shaft, the patella, and the fibular head with cut and bevelled fibular shaft (Figure 1b).  
The muscles, ligaments, tendons and other biological tissues were combined to form a bulk soft tissue whose shape was governed by the average mid-thigh, mid-patella and calf circumferences (50 th percentile male, 20 years and older) [23]. The transparent view of the limb's soft tissue with marked circumferences can be seen in Figure 2a. The shape was additionally modified according to The Physiology of the Joints--The Lower Limb by [PERSON], taking into account the position of the bones, and therefore a 4-degree flexion angle was added [24]. A 3D numerical model of the generic residual limb is shown in Figure 2b. The numerical values of the main design parameters used for the modelling are listed in Table 1.  
Figure 1: Scaled TLEM 2.1 leg model representing a size of the 50 th percentile European male (**a**); transtibial bones truncated according to the surgical guidelines (**b**).
# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 5 Simulation Analysis  
Based on the PSCAD platform, this section builds an IEEE-34 node active distribution network model, as shown in Figure 7. The system contains thirty-four switching nodes from \(S_{1}\) to \(S_{34}\) and thirty-two feeder sections from \(L_{1}\) to \(L_{32}\). Python software programs the fault region location algorithm, improved matrix algorithm and verification, and GTA. The performance of the proposed algorithms is tested by simulating single faults, multiple faults in non-T-connected sections, multiple faults with T-connected sections, and faults with fault information errors.  
Considering factors such as power supply reliability, investment, management, and maintenance workload, the number of distribution line sections should not exceed three, which means that a maximum of two switches are installed between two pieces of protection equipment. Therefore, the protection installation position in the system is shown in Figure 7. The system comprises 15 pieces of protection equipment, 32 feeder sections, and 34 switching nodes. Table 5 shows the node number corresponding to each protection. Table 6 shows the system node status list.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protection** & \
\begin{tabular}{c} **Corresponding** \\ **Node Number** \\ \end{tabular} & **Protection** &
\begin{tabular}{c} **Corresponding** \\ **Node Number** \\ \end{tabular} \\ \hline \(e_{1}\) & 1 & \(e_{9}\) & 19 \\ \(e_{2}\) & 4 & \(e_{10}\) & 21 \\ \(e_{3}\) & 5 & \(e_{11}\) & 22 \\ \(e_{4}\) & 8 & \(e_{12}\) & 24 \\ \(e_{5}\) & 10 & \(e_{13}\) & 25 \\ \(e_{6}\) & 14 & \(e_{14}\) & 29 \\ \(e_{7}\) & 15 & \(e_{15}\) & 30 \\ \(e_{8}\) & 18 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Node number corresponding to the protection.  
Figure 7: Topology diagram of the IEEE-34 node active distribution network.  
When the IEEE-34 node active distribution network has complex faults, such as multiple faults with fault information errors, the maximum number of suspicious fault sections is about 15, and the fault hypothesis contains about 30,000 cases. According to the parameter selection rules in Section 4.5.3, the specific data of the parameters of the genetic tabu algorithm are set. The population size \(G\) is set to 1% of the total size of the fault hypothesis, \(G=300\). The population dimension \(D\) is the number of feeder sections in the system shown in Figure 7, \(D=32\). The crossover rate \(P_{1}=80\%\), and the mutation rate \(P_{2}=5\%\). \(N\) is selected as 1% of the population size, \(N=3\). The tabu length \(L\) is selected to be updated once in five iterations, \(L=15\).
# Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model
## 3 The Proposed Model
### Process Involved in BCDNN-Based Classification  
The BCDNN model was developed for microarray gene expression classification [19]. The DNN is separated into decoder, encoder, translator, and simulator. Let \(T\) represent the amplitude response and phase inspired from the finite-difference time-domain (FDTD) methodology and \(T^{\prime}\) represent the forecast from the simulator. When the module is trained, the simulator predicts \(T^{\prime}\) as an input image with a rapidly moving meta-atom structure compared to its arithmetical matching part. For backward calculations, \(T\) with dimensions of \(82\times 1\) is converted to an image with dimensions of \(40\times 40\), which indicates a lower input parameter than the output parameter for regression processes. The enormous divergence makes it problematic for a system to generalize and converge well, particularly once the input spectra have stronger variation near the resonant frequency. The authors of the aforementioned study attempted to avoid this problem by including a generative adversarial network or bilinear tensor layer. Initially, it characterizes every meta-atom with a lower dimension eigen vector with dimensions of \(82\times 1\) through a pretrained autoencoder. The size of each tensor all over the network is noticeable below all the blocks. Dissimilar layers of the CNN are interconnected with convolution operations. The kernel multiplies the value of the tensor in the kernel region and later sums it with a novel value in tensor. In CNN, we attached two FC layers (dimensions are given below) to estimate a spectral tensor. A leaky ReLU of \(\alpha=0.2\) was employed for all the convolution layers, and \(\tan\) h was employed for all the FC layers. The convolution layer maps the input tensor \(x_{k}\) with the output tensor \(x_{k+1}\):  
\[x_{k+1}=leaky\ ReLU[CONV_{k_{1}}(x_{k})], \tag{11}\]  
Leaky ReLU \((\cdot)\) represents the rectified linear unit action, and CONV denotes the convolutional operators (include bias terms). The \(k_{1}\) subscript signifies the number of networks. In the simulator, \(k_{1}=32,32,64,64,128,128\). Strides of two are employed in two, four, and six convolutions for replacing the max-pooling layer. A dropout layer by means of 0.1 drops behindhand all the FC layers except the output layer is applied to prevent overfitting networks. Mean absolute error (MAE) was adapted for calculating the weight and gradient. MAE was determined by:  
\[MAE=\frac{\sum_{i}\left|T_{predicted}-T_{simulated}\right|}{N}, \tag{12}\]  
Now, \(N\) indicates the amount of the entrances of \(T_{predicted}\). For cost functions, MAE is insensitive to outliers; however, it is unconducive to the convergence. To guarantee the module stability, the learning rate declines with the number of iterations.
# Nonuniform Background Correction for Wide-Field Surveillance Camera
###### Abstract  
Space environment surveillance is very important for space security, which is easy to be disturbed by stray light and hot pixels, and the image background presents a certain degree of nonuniformity. The existing methods can not achieve the accurate segmentation of weak targets while correcting the nonuniform background. To solve this problem, this paper presents an accurate and robust correction method for the wide-field surveillance camera, called the enhanced new top-hat transform (ENTHT). Firstly, we analyze the formation mechanism and influence of the nonuniformity background from multiple dimensions. Secondly, because of the dependence and limitations of the background suppression effect of the new top-hat transform on the selection of structural elements, we improve the new top-hat transform by designing a noise structure element (NSE). Finally, we analyze the performance and advantages of the ENTHT method. In the field experiment, the method can accurately correct the complex space nonuniform background, eliminate the stray light and hot pixels, and realize the accurate segmentation of weak targets. In the complex space environment, it brings great help to space-moving target recognition and tracking.  
space surveillance; background correction; noise; stray light +
Footnote †: journal: Applied  
doi:10.3390/app13042594
## 5 Results and Discussion
### Meshed Configuration Results  
As with the radial topology, a population of 20 individuals (salps) was used to test the developed algorithm, as well as 20 consecutive evaluations of the whole methodology, considering 1000 iterations per evaluation. Table 5 summarizes the best five solutions obtained for the meshed IEEE 33-node system.  
In the Table above, it is highlighted that the best result among the iteration entails operating costs of 77,870.17 dollars per year with D-STATCOMs located at nodes 32, 30, and 14, with powers of 202.30, 394.40, and 146.20 kvar, respectively. Note that the optimal solution for this algorithm was found 25% of the times that the entire methodology was evaluated. Moreover, the worst result entails 77,896.67 dollars per year and positions 30, 14, and 32, with sizes of 505.60, 176.20, and 98.20 kvar, respectively (see solution no. 5 in Table 5). The difference between the best and worst result is 26.50 dollars. Note that this difference is very small in comparison with the objective function values, which demonstrates that the proposed methodology can find very high-quality solutions in each evaluation.  
\begin{table}
\begin{tabular}{c c c c|c c c} \hline \hline \multicolumn{1}{c}{**Sol.**} & \multicolumn{2}{c}{**Nodes**} & \multicolumn{2}{c}{**Sizes (Mvar)**} & \multicolumn{2}{c}{**Annual Costs**} & \multicolumn{1}{c}{**Time (min)**} \\ \hline
1 & [30 25 13] & [0.52801 0.10547 0.25851 108,249.36 2.3753 2.177 2.  
Note that the processing times for each algorithm evaluation regarding the best and worst result are 1.9360 and 1.9392 min, respectively, i.e., the algorithm has a tendency towards finding the space and solution of the problem in similar periods of time.  
The next section only compares the proposed algorithm within the meshed configurations, given that, in the specialized literature, the use of industrial, commercial, and residential curves in the studied problem has not yet been presented for radial configurations.
## 4 Effectiveness of Data Imputation
### Experiment Design  
These imputation methods will be evaluated under varying data-missing ratios. There are various types of imputation methods, as reviewed in the literature. However, evaluating the effectiveness of these algorithms is non-trivial, and their accuracies are not adjustable parameters. For this study, where the focus is on the trade-off between missing data and noise, it is crucial to control the accuracy of imputation. To achieve this, an artificial imputation method--\"inserting-noise\"--is proposed. For this method, accurate words (for NLP-SL) or state elements (for Signal-RL) are randomly (with probability \(q\)) replaced with random values, allowing us to control the imputation method noise level \(q\). The example code for NLP-SL is provided at List A2. The function perturb_sentence is responsible for adding missing-type corruption to clean data and carrying out the artificial imputation. Later in this section, traditional imputation methods are also evaluated.  
Let \(S(p)\) represent the model score when the data-missing ratio is \(p\) and no imputation is applied. Let \(\tilde{S}(p,q)\) represent the model score when the data-missing ratio is \(p\) and an imputation method with noise level \(q\) is used to preprocess the data before training. The _imputation advantage_, \(A(p,q)\), is defined as:  
\[A(p,q)=\tilde{S}(p,q)-S(p) \tag{5}\]  
\(A(p,q)\) quantifies the improvement (or harm if negative) caused by imputation relative to no imputation. Figure 7 shows the heatmap of imputation advantage for both experiments.
## 3 Results and Discussion  
In the experiment, a research hypothesis was put forward that it is possible to modify the content of carotenoids in red clover sprouts (_Trifolium pratense_ L) by applying PEF to seeds before sowing and using light of varying wavelengths during cultivation. Until now, these factors have not been combined in such a way. Thus, this is an innovative approach to this issue. The highest sprout mass was obtained after applying red light to the seeds (38.9 g) and in the case of seeds cultivated in white light after PEF (5 kV/cm) pre-treatment (35.5 g). The lowest final mass was obtained in the case of UVA irradiation. The seeds cultivation in white light after PEF pre-treatment with the field strengths of 1, 2.5 and 5 kV/cm increased the yield by 0.38%, 3.37% and 6.12%. Table 1 presents the final mass of the 100 g samples after 7 days of cultivation (on the day of harvest) in the case of different conditions: in UVA, blue, red and white light, without and after PEF pre-treatment with the field strengths of 1, 2.5 and 5 kV/cm. (Table 1). Statistically significant differences between the experimental series are presented in Table 2.  
Other authors also show a differentiated effect on the growth and yield of different plants depending on the wavelength of the used light. The results of the research indicate that the usage of red LED light in the cultivation of seeds and plants can cause an increase in biomass [33; 34]. The red light is the most effective photosynthetically active radiation [35] referring to absorption. Research shows that blue light can also be used as a factor to increase biomass [36]. In the case of crops of different varieties of lettuce sprouts, it was found that the fresh weight increased in the case of using red or blue light in the growing process compared to crops under white light. The sprouts were irradiated for 12 h and left in the dark for 12 h. The best effect of the growth of germ mass was obtained when the sprouts were exposed to 3 h of blue light, and in one case it was observed that it increased by 23% [8]. On the other hand, in other studies [25] where wheat seeds before cultivation were treated with 50 pulses of 6 kV/cm PEF, a significant increase in fresh weight was noted, which is positively correlated with PEF energy. A statistically significant effect of PEF (used at the initial stage of seed preparation for cultivation) on the mass of obtained red clover sprouts was observed. The exposure of seeds to the pulsed electric field can activate processes at an early stage of photomorphogenesis and thus positively affect its development. That line of research requires continuing experiments to confirm this observation. It was not the primary purpose of the presented studies. In the case of wheatgrass (_Triticum aestivum_ L.) the usage of 0.5 kV/cm PEF did not affect the growth of sprouts compared to the control ones. Increasing the PEF strength to 1.4 kV/cm had a positive effect on sprout growth, however, the 2 kV/cm PEF treatment of the tested seeds had an adverse effect and a lower growth was obtained than compared to the control [24].
## 2 Materials and Methods
### Selection of the Related Factors
To investigate the effect of initial velocity on \(v\), four velocities (0 m\(\cdot\)s\({}^{-1}\), 2 m\(\cdot\)s\({}^{-1}\), 4 m\(\cdot\)s\({}^{-1}\), and 6 m\(\cdot\)s\({}^{-1}\)) were selected considering the initiating velocity of perilous rock in mountainous areas [13]. The initial velocity was controlled using a self-designed release apparatus to ensure the accuracy and reliability of the experiment. The field investigation of the high-frequency falling rock hazard area led to the generalization of the slope angle range from 20\({}^{\circ}\) to 80\({}^{\circ}\). Four horizontal slope angles (30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\)) were selected for the test [32], as shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Type** & **\(E\)/MPa** & \(SH\) & \(\upsilon\) & \(A\) \\ \hline \multirow{4}{*}{Slope surface} & Concrete & 50.0 & 17.2 & 0.17 & 0.1295 \\  & Wood & 35.0 & 16.0 & 0.20 & 0.1996 \\  & Sand & 15.0 & 11.3 & 0.25 & 0.3454 \\  & Turf & 7.8 & 6.6 & 0.24 & 0.4340 \\ \hline \multirow{4}{*}{Falling rock} & Granite & 94.6 & 44.7 & 0.15 & 0.7149 \\  & Marble & 62.4 & 38.2 & 0.17 & 0.7979 \\ \cline{1-1}  & Limestone & 37.3 & 36.1 & 0.22 & 0.8695 \\ \cline{1-1}  & Sandstone & 19.6 & 30.7 & 0.25 & 1.0279 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The physical parameters and material factor. Figure 4: The four different slope surface materials used in the tests: (**a**) concrete, (**b**) wood, (**c**) sand, and (**d**) turf. These materials were selected to represent a range of deformation capacities, frictional properties, and energy dissipation behaviors relevant to real slope conditions. (5) Falling rock type  
Four rock types--marble, granite, limestone, and sandstone--were selected based on their geological structure characteristics, with material factor \(A\) used to characterize falling rock type. To differentiate between the slope surface material and the falling rock type, \(A_{\text{slope}}\) represents the former, while \(A_{\text{rock}}\) represents the latter. Table 1 presents the physical parameters and material factors for each falling rock type. (6) Falling height  
Different falling heights (\(h\)) affect falling rock velocity and energy dissipation [21]. Four falling heights of 0.625 m (1/4 height of bench), 1.25 m (1/2 height of bench), 1.875 m (3/4 height of bench), and 2.5 m (The height of bench) were selected, respectively. (7) Falling rock mass  
Falling rock mass (\(m\)) is a main factor in disaster prevention and mitigation design [7]. Considering the scale effect of the medium-scale model test, four samples with different masses (0.44 kg, 0.65 kg, 0.93 kg, and 1.27 kg) were selected for the experiment. Therefore, the seven influencing factors and the actual levels are shown in Table 2, where the slope angles are converted to radians.
## 5 Experimental Test  
To experimentally validate the main conclusions of the methodology proposed in Section 3 for the decomposition of the mode shapes of the rotating disk-blades-disk structure, and its relation with the frequencies seen from the stationary frame (Section 4), a rotating disk-blades-disk test rig has been developed. It consists of a disk-blades-disk assembly, which rotates inside a rigid casing. The structure is excited with a piezoelectric patch (PZT) from the rotating frame and the response is measured simultaneously from the rotating frame with another PZT, and from the stationary frame with a Laser Doppler Vibrometer (LDV).
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 5 Results and Discussion
### Flow Field Structure Analysis
#### 5.2.3. Bed Shear Stress Analysis  
The shear stress contours of different spur dike spacings are shown in Figure 9. The maximum shear stress appeared in the main flow zone between spur dikes I and II on the fixed bed. The maximum shear stress reached 10.86, 9.44, and 10.19 dyne/cm\({}^{2}\) for the spacings of 2\(l\), 3\(l\), and 4\(l\), respectively, reaching up to 10 times that of the approach shear stress, as found in [12]. The high shear stress of the main flow area was close to the spur dike head, causing severe erosion at the head. At the final bed, the shear stress of the main flow zone decreased by 1-2 dyne/cm\({}^{2}\). It can be seen that the sheer stress decreased with the increase of scour depth, and there is some correlation between these two factors. The time-variance correlation between scour depth and shear stress was analyzed, and the relationship curves are shown in Figure 10. The correlation values for scour depth and shear stress are listed in Table 4. Using the point of maximum scour depth (x = 0, y = 12.5 cm for 2\(l\) and 3\(l\) spacings, and x = -40, y = 12.5 cm for 4\(l\) spacing) as a reference point, 25 sets of data were selected for analysis. The Pearson correlations were 0.789, 0.863, and 0.968 for the 2\(l\), 3\(l\) and 4\(l\) spacings, respectively. The significance test (2-tailed) results were all zero (0 \(<\) 0.05), meaning there was significant correlation. This indicates that the bed shear stress plays a prominent role in the scouring process.  
Figure 8. The turbulent energy contours of different spur dike spacing. (**a**) fixed bed for 2\(l\) spacing; (**b**) fixed bed for 3\(l\) spacing; (**c**) fixed bed for 4\(l\) spacing; (**d**) movable bed for 2\(l\) spacing; (**e**) movable bed for 3\(l\) spacing; (**f**) movable bed for 4\(l\) spacing.  
Figure 9. _Cont._Figure 10: _Cont_.  
Figure 9: The shear stress contours of different spur disk spacing. (**a**) fixed bed for 2\(l\) spacing; (**b**) fixed bed for 3\(l\) spacing; (**c**) fixed bed for 4\(l\) spacing; (**d**) movable bed for 2\(l\) spacing; (**e**) movable bed for 3\(l\) spacing; (**f**) movable bed for 4\(l\) spacing.