esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings
## 2 Physical Models and Numerical Approach
### Validation of the Numerical Methods  
The wind turbine that was used in this study is the National Renewable Energy Laboratory's (NREL) 5 MW reference turbine. The standard parameters of NREL-5 MW are clearly introduced by [PERSON] et al. [27], and some characteristic parameters are shown in Table 1.  
Figure 1: Torque-versus-speed response of the variable-speed controller.  
In order to verify the accuracy of the numerical methods, the wake of two turbines with a spacing of 7D under the neutral ABL inflow is simulated, according to [PERSON]'s research [PERSON] (1973). A precursor simulation is carried out to set up the neutral wind field. The surface aerodynamic roughness height, \(z_{0}\) is set at 0.001 m, which is a typical offshore condition. The horizontally averaged wind speed at the hub height of 90 m is driven to 8 m/s.  
In the precursor inflow simulation, the total physical time is 21,000 s, and the flow fields in the last 3000 s are extracted as the inflow boundaries for the wake simulation. The neutral atmospheric wind field, generated by the precursor inflow simulation, is shown in Figure 2a,b represents the velocity field of the vertical plane in the center line of the wind field. It can be seen from Figure 2 that, due to the influence of ground roughness, the wind speed close to the surface is low, and the wind speed increases in the height direction.  
For wake simulation, the NREL-5 MW turbine is used for simulation in this section. The grid size around the turbine is set to be 2.5 m and the calculation time step is set to be 0.025 s. The velocity deficit profiles in the hub height plane and longitudinal plane are shown in Figure 3. The first profile shown is 1D upstream of the first turbine, and each subsequent profile is 1D downstream of the previous one. The velocity deficit scale for each profile ranges from 0 to 0.5. The time-averaged wake velocity deficit profiles are normalized by the hub-height average freestream speed. From Figure 3, the present results are in good agreement with [PERSON]'s research [PERSON] (1973). This shows that the numerical methods of the wind turbine wake are accurate and effective.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **NREL-5 MW** \\ \hline Rating & 5 MW \\ Rotor Orientation & Upwind \\ Number of blades & 3 \\ Rotor Diameter & 126 m \\ Hub Height & 90 m \\ Rating Wind Speed & 11.4 m/s \\ Rating Rotor Speed & 12.1 RPM \\ \hline \hline \end{tabular}
\end{table}
Table 1: NREL-5 MW standard turbine parameters.  
Figure 2: The wind field in the precursor simulation: (a) overview view; (b) vertical plane.
## 4 Results
### Implementation Details
#### 4.1.2 Data Preprocessing and Enhancement  
For both LiTS and SLiver07, the training sets were further randomly partitioned into training and test subsets in an 8:2 ratio. This division was instrumental in evaluating the model's performance and generalization capacity. During the experimental data preparation phase, all original CT images underwent adjustment so that the Hounsfield values (HU values) fell within the range of [\(-\)200, 200]. This ensured that the images retained maximum liver volume while mitigating the noise interference stemming from other organs and background factors. Subsequently, the images were resampled, and their resolution was downsized from \(512\times 512\) to \(256\times 256\) to reduce computational overhead. Finally, normalization, slicing, and histogram equalization operations were performed sequentially.  
Figure 7 shows some comparison images randomly selected from LiTS before and after preprocessing, where (1) to (3) are the original CT images without processing, and (4) to (6) are the images after a series of preprocessing operations. Obviously, the preprocessed images provided clearer boundary contours between abdominal organs, such as the liver. The contrast with the background was significantly enhanced, accompanied by more complete local details, which helped the network to capture more adequate feature information.  
Compared to other semantic segmentation datasets, our dataset was small in size and originated from a small sample pool, so the data were enhanced by panning and rotating before the experiment to improve the diversity of the dataset.
## Inner Layer Modeling and Optimization
### Inner Layer Optimization Model
#### 2.2.1 Power Balance Constraint  
The first constraint that should be fulfilled is the energy balance in each EH. This constraint ensures that the sum of input powers (electricity/heat/gas) at their buses is equal to the sum of output powers (electricity/heat/gas) at the corresponding buses:  
\[\sum_{i=1}E_{input}^{bus}=\sum_{i=1}E_{output}^{bus} \tag{9}\]where \(\sum E_{input}^{bus}\) and \(\sum E_{output}^{bus}\) denote the sum of input and output electricity/heat/gas energies at each of the corresponding buses in both EHs as shown in Figure 2, respectively.
## 3 Results and Discussion
### Statistical Analysis  
Principal component analysis (PCA) was used to sum up information on descriptors found in artificial neural networks. The purpose of PCA is to identify principal components, in this case, the data dimension was reduced to two principal components. Own vectors defined each principal component, PC1-PC6. The first component in the data set explained 60.95% of variance. The second component explained a much lower variance, i.e., 21.06%. Two component variables transferred over 80% of variability of the data set. This means that a good analysis in the two primary components of the selected data set is possible. Color and texture descriptors in Figure 4, supported with a correlation analysis (Table 6), indicate that brightness of color has a significant impact on the identification of puffed pork snacks, present in the data set of the obtained MLP 3-12-1 model. This translates to a negative correlation of L* (brightness) between parameters b* (yellow to blue) (R2 = \(-\)0.745) and a* (R2 = \(-\)0.501). It also appears that hardness (R2 = 0.636) and slope (R2 = 0.637) equally strongly correlate with the brightness of color L* for puffed pork snacks. However, a strong correlation of R2 = 0.854 exists between textural parameters (hardness and slope), which translates to adaptation during the learning process of the data set with textural parameters for the MLP 3-10-1 artificial neural net, obtaining a high coefficient of determination in the validation data set (0.999).  
\begin{table}
\begin{tabular}{l c c c c c c}  & **L* & a* & b* & **Hardness [N]** & **Work of Compression [Nxs]** & **Slope [N/s]** \\ \hline L* & 1.00000 & & & & & \\ a* & \(-\)0.50098 & 1.00000 & & & & \\ b* & \(-\)0.74549 & 0.70870 & 1.00000 & & & \\ Hardness & 0.63644 & \(-\)0.30142 & \(-\)0.65972 & 1.00000 & & \\ Work of compression & 0.26711 & \(-\)0.01180 & \(-\)0.18656 & 0.58499 & 1.00000 & & \\ Slope & 0.6372 & \(-\)0.39516 & \(-\)0.60272 & 0.85497 & 0.56571 & 1.000000 \\ \end{tabular}
\end{table}
Table 6: Correlation of color and texture variables in ANN.  
Figure 3: Scheme of artificial neural network structure using Tanh activation function in hidden layer.  
For comparison, an HCA analysis was performed using the Ward methods. Distances between concentrations were estimated, accounting for the variance analysis approach. As a result of the analysis in data sets concerning textural parameters and the Lab color space model, it was observed that significant similarities exist primarily between the brightness descriptor and textural parameters (Figure 5), which correlate with each other. Both HCA and PCA analysis can confirm the similarity between the discovery samples due to the brightness of the produced puffed pork snacks. We can conclude that the choice of product by the consumer will depend on the brightness of the produced puffed pork snacks [35; 59].  
Figure 4: PCA analysis of variables occurring in neural models.  
Figure 5: Hierarchical dendrogram with grouping using the Ward method, illustrating the impact of simultaneously analyzed properties of color and texture present in learning data sets.
###### Abstract
In this paper, we propose a novel framework to identify the most analogous source domain to a given target domain, leveraging deep learning as our foundational approach. Through a meticulous comparative analysis of the outcomes of similarity assessments and intrusion detection based on deep transfer learning, we significantly enhance the overall rigor of our experimental methodology. To explain in detail, similarity assessment consists of a framework that uses deep learning to determine which source domains are most similar to the target domain when there are multiple source domains. Furthermore, in intrusion detection based on deep transfer learning, we conduct an accuracy experiment, which exists to make sure that the results obtained in a similarity assessment are appropriate. Then, by comparing the results of these two steps, the source domain that is most similar to the target domain is finally selected. This study can provide a guide for identifying which data sets are best suited to the source domain in the transfer learning process and, in turn, contribute to improving the detection accuracy of IDSs in IoT environments. By utilizing four public intrusion detection data sets, we demonstrate the effectiveness of our framework in successfully choosing a fitting source domain data set, ultimately achieving the highest performance in transfer learning. The organizational structure of this paper is outlined as follows: Section 2 is dedicated to substantiating the research's necessity through an exhaustive examination of the relevant literature. Section 3 systematically organizes crucial contextual information. Subsequently, in Section 4, we delve into an intricate exposition of the framework conceived by our research team, accompanied by a detailed presentation of the experimental methodology executed within this framework. Section 5 adeptly illustrates the results obtained through the proposed experimental approach. Lastly, Section 6 summarizes our research findings and delineating prospective avenues for future research endeavors.
# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 2 Methodology  
The proposed GW_DC model could convert the original time-series into two-dimensional images and then classify them into some groups. For comparison, time-series were converted into grayscale images, RP images, MTF images, and GADF images. Meanwhile, the deep clustering network was employed for classifying these enhanced two-dimensional images, and the convolution kernel factorization method was introduced into the DC network for reducing the number of parameters and improving the clustering performance. The frame of the proposed GW-DC model, depicted in Figure 1, consisted of three stages: the image conversion stage, image enhancement stage, and image clustering stage.  
In the image conversion stage, the time-series was transformed into four kinds of two-dimensional images for expanding data volume and enhancing generalization ability, including grayscale images, RP images, MTF images, and GADF images. The low-dimensional features were mapped to a high-dimensional space for amplifying the feature attributes and improving the clustering effect. Through comparison, GADF with the best characterization effect was selected in this study.  
In the image enhancement stage, the wavelet transform algorithm was used to enhance the texture features of the transformed two-dimensional images. The resolution of the images was regarded as the measurement standard of image decomposition, and the image signals were decomposed into high-frequency subband and low-frequency subband. The high-frequency subband was expanded, and the low-frequency subband was scaled for strengthening the change details of the time-series and enhancing the contrast ratio. Then, the processed signal components were reconstructed to obtain the enhanced images.  
In the image clustering stage, a new deep clustering network DC was proposed to improve the clustering effect. In this stage, the DC network based on CNN was designed to learn and represent the features of two-dimensional images. Then, the obtained features were clustered by the K-Means algorithm, and the cross-entropy (CE) [27] was introduced as the loss function to optimize the DC network.
# A Multi-Segment Expanded Anchor for Landslide Emergency Management
## 4 Calculation Method of the Ultimate Bearing Capacity of MSEAs
### Ultimate Bearing Capacity at Stage BC of the MSEAs  
The contact pressure between the bearing blades and the hole wall is evenly distributed when the bearing blades press against the hole wall and can be simplified as a plane problem, as shown in Figure 6b. In this figure, \(\sigma_{re}\) is the radial normal stress of the rock masses around the hole, \(\sigma_{be}\) is the hoop normal stress, and \(\tau_{re}\) is the shear stress; \(p_{m}\) is the average contact pressure between the expanded blades and the hole wall; \(r_{0}\) is the radius of the hole wall; \(r\) is the polar radius of any point; \(\theta\) is the polar angle; \(\sigma_{h}\) is the horizontal in-situ stress; \(\lambda\) is the ratio of two horizontal in-situ stresses.  
The analytical solution of the stress field around the hole was achieved based on elastic mechanics:  
\[\left\{\begin{array}{l}\sigma_{re}=\frac{\sigma_{h}}{2}\bigg{[}(1+\lambda) \bigg{(}1-\frac{r_{0}^{2}}{r^{2}}\bigg{)}-(1-\lambda)\bigg{(}1-4\frac{r_{0}^{2 }}{r^{2}}+3\frac{r_{0}^{4}}{r^{2}}\bigg{)}\cos(2\theta)\bigg{]}+p_{m}\frac{r_ {0}^{2}}{r^{2}}\\ \sigma_{be}=\frac{\sigma_{h}}{2}\bigg{[}(1+\lambda)\bigg{(}1+\frac{r_{0}^{2}} {r^{2}}\bigg{)}+(1-\lambda)\bigg{(}1+3\frac{r_{0}^{4}}{r^{2}}\bigg{)}\cos(2 \theta)\bigg{]}-p_{m}\frac{r_{0}^{2}}{r^{2}}\\ \tau_{r\theta e}=\frac{\sigma_{h}}{2}(1-\lambda)\bigg{(}1+2\frac{r_{0}^{2}}{r^ {2}}-3\frac{r_{0}^{4}}{r^{2}}\bigg{)}\sin(2\theta)\end{array}\right. \tag{1}\]  
For any point on the hole wall,  
\[r=r_{0} \tag{2}\]  
For areas with mild tectonic activities, the horizontal initial in-situ stress can be taken as half of the gravity stress:  
\[\sigma_{h}=0.5\sigma_{z},\lambda=1 \tag{3}\]  
where \(\sigma_{z}\) is the gravity stress.  
Substituting (1) and (2) into (3) yields:  
\[\left\{\begin{array}{l}\sigma_{re}=p_{m}\\ \sigma_{be}=2\sigma_{h}-p_{m}\\ \tau_{r\theta e}=0\end{array}\right. \tag{4}\]  
When the rock masses of the hole wall are in the ultimate state:  
\[|\sigma_{be}|=|2\sigma_{h}-p_{mb}|=R_{t} \tag{5}\]  
where \(R_{t}\) is the tensile strength of rocks, and \(p_{mb}\) is the contact pressure between the bearing blades and the hole wall when the rock masses are in their ultimate stress state.  
Then, the frictional drag between the hole wall and the bearing blades is:  
\[F_{1}=\mu\pi Dlp_{mb} \tag{6}\]where \(F_{1}\) is the frictional drag between the hole wall and the bearing blades; \(\mu\) is the friction coefficient between the hole wall and the bearing blades; \(D\) is the hole diameter of the underream; \(l\) is the length of the part with applied load.
## 3 Results and Discussion  
### Rhodamine 6G  
For the aqueous solution sample, absorption was increased by \(\Delta A\sim 10^{-5}\) on the shorter-wavelength side of the absorption resonance by irradiation of the cw laser at 633 nm, where the light-induced force should act on the molecule as an attractive force if it is dominated by the gradient force (Figure 1a). On the other hand, for the PVA film sample (Figure 1b), no absorbance change was observed. We reserve discussion about another absorbance increase seen from 540 to 570 nm in the longer-wavelength side of the monomer absorption, because the reproducibility of this absorbance increase is not fully confirmed. In the PVA film, molecules are fixed in polymers and can cause a difference in the absorbance change spectrum between the aqueous and PVA samples, as discussed in Ref. [38]. According to Ref. [47], R6G forms H-aggregates in an aqueous sample, and its absorption wavelength with respect to the aggregation number is analyzed with a singular value decomposition of the absorption spectrum of R6G, as shown in Table 1.  
This table indicates that the R6G sample forms H dimers whose absorption spectrum is shown in Figure 1a. The laser-induced absorbance increase occurs on the shorter-wavelength side than the monomer and H-dimer absorption band. Table 1 shows that the peak wavelength of the absorbance increase is close to the tetramer's absorption wavelength, although it is broad. The ratio of the light gradient force potential (\(\alpha E^{2}/2\)) to thermal motion energy (\(k_{B}T\)), which is estimated from the polarizability volume (876 A\({}^{3}\)), is \(1.0\times 10^{-7}\), and \(\Delta A/A\) (\(\approx 10^{-5}\)) is \(\sim\)100 times larger, i.e., the aggregation efficiency is about 100.  
\begin{table}
\begin{tabular}{l c c c c} \hline Aggregation number & 1 & 2 & 3 & 4 \\ \hline Absorption wavelength & 526 & 498 & 485 & 475 \\ \hline \end{tabular}
\end{table}
Table 1: The absorption wavelength and aggregation number for R6G in aqueous solution.  
Figure 1: The absorbance change (solid line) and absorbance (dashed line) spectra of (**a**) rhodamine 6G (R6G) aqueous solution in 0.3 mm-thick cell, where the irradiation intensity is 640 W/cm\({}^{2}\), and of (**b**) R6G in PVA, where the irradiation intensity is 360 W/cm\({}^{2}\). Both the absorbance and its change were measured with the multi lock-in amp. system.
# A High-Utility Differentially Private Mechanism for Space Information Networks
## 4 Experiments
### Results and Discussion  
The first experiment focuses on the influence of the number of records in each subset. The chosen values of the number of records in each subset are 50, 100, and 150. The experiment was repeated 500 times, and the average outcome was determined. The experiment results are shown in Figure 5.  
The results of Figure 5 show that the proposed method has greater data usefulness than the other two techniques. No matter how many data records are in each subset, when the privacy budget is 0.1, the RMSE of the mechanism proposed in this paper is half of the Laplace mechanism and much lower than that of the sampling and aggregation mechanisms. As would be predicted, the inaccuracy diminishes as the privacy budget increases. The comparison between Figure 5a-c shows that the resilience of the three techniques, and that the amount of records in each subgroup practically has no affects on the inaccuracy.  
Figure 5: The influences of the number of records in each subset.  
The effects of the subset size are the main topic of the second experiment. Figure 6 displays the test's findings. The suggested mechanism outperforms the other two mechanisms in terms of data usefulness, according to the findings of the experiment. The number of subgroups affects the Laplace mechanism because of the comparisons between Figure 5(a)-c and Figure 5(d)-f. The error decreases as the number of subsets increases. The sample and aggregation mechanism is not robust enough for the standard deviation function. In particular, the error fluctuates as the number of subsets increases.  
The related RMSE deterioration rates for various subsets and their data records for linear and nonlinear query functions are shown in Tables 1 and 2, respectively. Since the proposed mechanism and the other two mechanisms in the comparison have a similar pattern of change, it virtually has the same data used as the other two mechanisms. It demonstrates the resilience of the method this research proposes. The technique suggested in this research is deployable for actual SINs because of the robustness illustrated by the experimental findings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mechanism** & **50 Data Records** & **100 Data Records** & **150 Data Records** \\ \hline \multirow{2}{*}{Sampling and aggregation} & \(-72.46\%\) & \(-75\%\) & \(-71.43\%\) \\  & \(-76.74\%\) & \(-77.53\%\) & \(-77.27\%\) \\ \multirow{2}{*}{Laplace} & \(-33.92\%\) & \(-33.92\%\) & \(-30\%\) \\  & \(-5.26\%\) & \(-5.26\%\) & \(-11.1\%\) \\ \multirow{2}{*}{Proposed mechanism} & \(-66.7\%\) & \(-66.7\%\) & \(-66.7\%\) \\  & \(-3.38\%\) & \(-3.63\%\) & \(-3.38\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMSE degradation rates corresponding to linear and nonlinear query functions for different data records.  
Figure 6: The influences of the number of subsets.
# Fretting Fatigue Behavior under Tension-Bending Mixed-Mode Loading
## 1 Introduction
Utilizing a self-developed multimode fretting wear test rig, they conducted bidirectional combined fretting wear tests at room temperature and 300 \({}^{\circ}\)C. The results revealed that the temperature variation and phase difference changes significantly influenced fretting wear characteristics, with a 90\({}^{\circ}\) phase difference exhibiting the most severe wear, and the 300 \({}^{\circ}\)C tests showing intensified wear and damage compared to room temperature. It is noteworthy that the aforementioned studies mainly focused on fretting damage under normal-radial or tension-torsion combined loading conditions. However, in engineering applications, components often need to withstand the effects of combined tensile-bending mixed-mode loads. For example, in aerospace engines, blade roots experience combined tension and bending stresses, leading to fretting fatigue at the contact interface with the disk, which can cause blade fracture and engine failure [32; 33]. Similarly, railway axles, subjected to tensile and bending stresses from the vehicle weight and track irregularities, can develop fatigue cracks at the axle-bearing contact area, potentially resulting in axle breakage and derailments [11]. Wind turbine main shafts face similar issues, with varying wind loads causing fretting fatigue at the shaft-bearing interface, impacting turbine operation and requiring maintenance [34]. Additionally, in the bridge cables of suspension and cable-stayed bridges, combined tensile and bending stresses from deck loads and wind can induce fretting fatigue at the cable-anchor interface, posing risks to bridge integrity [35]. Despite the importance of these conditions, there are few reports investigating fretting fatigue behavior under tension-bending mixed-mode loading conditions. Under this complex loading, relative slip in the contact regions can trigger fretting fatigue crack initiation, potentially leading to component failure. This not only accelerates fatigue crack nucleation and propagation but also influences contact stress distributions through the evolution of wear scar morphology, thereby altering crack propagation paths. Consequently, investigating tension-bending mixed-mode fretting fatigue is crucial for accurately predicting and assessing the service life of critical components, enhancing their reliability and safety in practical engineering applications. In this article, the finite element method in conjunction with two critical plane parameters is employed to investigate fretting fatigue under tension-bending mixed-mode loading conditions. The organization of this paper is as follows: Section 2 establishes a cylindrical pad-on-flat contact model to study tension-bending mixed-mode fretting fatigue, where dimensional analysis is also performed to reduce the number of independent variables. Section 3 presents the numerical model and its details. The results and discussion regarding the effects of oblique load, stiffness ratio, and mode angle are provided in Section 4. Finally, the main conclusions of the present work are drawn in Section 5.