esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed IGVDD-1 DCNN Fault Diagnosis Model
### Fault Diagnosis Process of IGVMD-1 DCNN Model  
The flowchart of the IGVMD-1 DCNN model is shown in Figure 6, and the steps of its model processing process are as follows:  
1. Signal acquisition of rolling bearing data as signal input.
2. The input bearing signal is preprocessed by using the optimized variational modal decomposition method of the improved grey wolf algorithm. The optimal combination of parameters [\(K\),\(\alpha\)] is obtained by continuous iterative search, and \(K\) modal components IMF are obtained.
3. Effective modal component IMF is selected for vibration signal reconstruction by correlation coefficients to complete the effective extraction of features.
4. Reconstructed signal X is divided into training data and test data. The training data are divided into training set Xtrain and validation set Xvalidation, which are used as the input signals of 1 DCNN  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Serial Number** & **Batch\_Size** & **Epochs** & **Learning Rate** \\ \hline
0 & 16 & 10 & 0.1 \\
1 & 32 & 30 & 0.01 \\
2 & 64 & 50 & 0.001 \\
3 & 128 & 100 & - \\ The optimal results & 32 & 50 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The experimental setup and results.  
Figure 6: The flow chart of IGVMD-1 DCNN model.  
* [Step 5] Combining the grid search and the 1D convolutional neural network model, the optimal combination of parameters is determined, and the 1 DCNN fault diagnosis model is established.
* [Step 6] Training sample Xtrain is used as input to train the 1 DCNN model. Model parameters are continuously updated by inputting training samples through forward and backward propagation.
* [Step 7] Validation set Xvalidation participates in model training and continually adjusts the model parameters.
* [Step 8] Whether the model meets the iteration termination condition N is determined. If yes, training is stopped for the next operation, otherwise, step 7 is repeated.
* [Step 9] Test sample Xtest is input into the trained 1 DCNN model for classification and identification detection of fault signals.
## 2 Velocity Estimation
### Weighting Strategy  
In order to fully exploit the available measurements, it is necessary to weight them differently in the estimation algorithm. In the GNSS context, satellite elevation (EI) and \(C/N_{0}\) are usually adopted as quality indicators for defining the weighting scheme. In the specialized literature, several schemes have been defined, based on El only [26; 27], on \(C/N_{0}\) only [28], or on both [29; 30; 31]. In this work, the weighting scheme described in [29] is used, and it is scaled to fit Doppler and TDCP measurements. The weights, \(w\), are computed as the inverse of the measurement error variance, \(\sigma^{2}\), \(w=1/\sigma^{2}\), whose expression is  
\[\sigma^{2}=\sigma_{0}^{2}\begin{cases}\frac{10^{-\frac{C/N_{0}-s_{1}}{B}}}{ \sin^{2}(EI)}\bigg{[}\left(\frac{A}{10^{-\frac{9}{B}-1}}-1\right)\frac{C/N_{0 }-s_{1}}{s_{0}-s_{1}}+1\bigg{]},&if\ C/N_{0}<s_{1}\\ 1,&if\ C/N_{0}\geq s_{1}\end{cases} \tag{15}\]  
\(C/N_{0}\) is compared with a threshold \(s_{1}\). If \(C/N_{0}\) exceeds \(s_{1}\), the corresponding \(\sigma^{2}\) is set to \(\sigma_{0}^{2}\); otherwise, it is increased according to formula (15) as a function of \(C/N_{0}\) and El. The parameters in (15) have been empirically set as \(s_{0}=10\), \(A=30\), and \(B=30\). The value \(\sigma_{0}^{2}\) is set to 5 cm/s for Doppler and 5 mm for TDCP. The choice of the \(\sigma_{0}^{2}\) value is fundamental for an effective RAIM-FDE application.
# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 2 Related Work
[19] adopted fairness-aware learning by eliminating statistical biases in graph embeddings toward privacy attributes through orthogonal subspace projection constraints, demonstrating particular efficacy in healthcare data scenarios requiring structured isolation of sensitive information. [PERSON] et al. [20] introduced variational independence criteria as regularization terms to enforce mutual independence between distributions of privacy-sensitive and non-sensitive features in low-dimensional embeddings. Nevertheless, the above-mentioned approaches do not take into account the different importance of each node on the global graph data and fail to achieve dynamic privacy decoupling, which will lead to problems such as improper protection of important nodes and excessive protection of secondary nodes.
## 2 Linearization of the Power Deficit Calculation
### Traditional Aggregation Methods to Obtain the Resulting Average Power \(P_{i}\)  
Various approaches exist for aggregating the wake interactions affecting a single turbine. The traditional ones calculate the wind speed deficit for all the possible combinations between upstream turbines and the turbine \(i\); then aggregate these individual speed deficits and determine the effective wind speed of the airflow reaching the turbine \(i\) and finally obtain the power obtained by using the manufacturer power curve. The resulting expression is  
\[P_{i}(u,\theta)=Pc(u-\delta v_{i}(u,\theta,L))\quad\text{and}\,\,i\leq nt \tag{8}\]  
Among these aggregation methods, the Linear aggregation of velocity deficit (LAVD) and mainly the Quadratic aggregation of velocity deficit (QAVD) are preferred [17]. In them, it is assumed that the total wind speed deficit seen by the interaction of several wakes is the sum or, respectively, the mean square sum of each one separately [18].  
**Linear Aggregation of Velocity Deficit (LAVD)**  
\[P_{i}(u,\theta)=Pc(u-\delta v_{i}(u,\theta))\quad\text{and}\quad\delta v_{i}(u,\theta)=\sum_{j\
eq i}\frac{4\,\,A_{j\to i}^{2}}{\pi D^{2}}\,\,\delta v_{j \to i}(u,\theta) \tag{9}\]  
where, for clarity, the variable \(L\) indicating the farm layout has been omitted, \(4\,\,A_{j\to i}/(\pi D^{2})\) is the percentage of the rotor area of the turbine \(i\) (with diameter \(D\)) that is covered by the wake caused by the turbine \(j\), and \(\delta v_{j\to i}\) is the velocity deficit caused by the turbine \(j\) on turbine \(i\) if only these two exist. \(A_{j\to i}\) depends on \(s_{j,i}\) and \(\varphi_{j\to i}\), while \(\delta v_{j\to i}\) depends on \(s_{j,i}\). In this work, the popular Jensen model for the wake parametrization has been used. For the sake of brevity, expressions to calculate all the involved variables are not included in this work but can be followed at [17; 19]. From (4), the basic equation to calculate \(\mathbb{E}[P]\) or AEP is  
\[\mathbb{E}[P]=\frac{\text{AEP}}{T}=\sum_{i=1}^{nt}\sum_{u=u_{cl}}^{u_{cn}}\sum_{ \theta=1}^{360}Pc\left(u-\sum_{j\
eq i}\frac{4\;A_{j\to i}^{2}}{\pi D^{2}}\; \delta v_{j\to i}(u,\theta)\right)\;\text{f}(u,\theta)\Delta u\Delta\theta \tag{10}\]  
**Quadratic Aggregation of Velocity Deficit (QAVD)**  
\[P_{i}(u,\theta)=Pc(u-\delta v_{i}(u,\theta))\quad\text{and}\quad\delta v_{i}^{ 2}(u,\theta)=\sum_{j\
eq i}\frac{4\;A_{j\to i}^{2}}{\pi D^{2}}\;\delta v_{j \to i}^{2}(u,\theta) \tag{11}\]  
and  
\[\mathbb{E}[P]=\frac{\text{AEP}}{T}=\sum_{i=1}^{nt}\sum_{u=u_{cl}}^{u_{cn}} \sum_{\theta=1}^{360}Pc\left(u-\sqrt{\sum_{j\
eq i}\frac{4\;A_{j\to i}^{2}}{\pi D ^{2}}\;\delta v_{j\to i}^{2}(u,\theta)}\right)\;\text{f}(u,\theta)\Delta u \Delta\theta \tag{12}\]  
The LAVD method results in larger speed deficits, which leads to a lower estimation of the AEP. Although the CAVD method is generally favored [18], there are studies where the linear method provides estimates more closely aligned with those collected by SCADA systems [20].
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 4 Results and Discussion
### Computational Cost Evaluation  
Table 2 reports the model parameters, FLOPs, and inference speed corresponding to each model to compare the computation. All the experiments for measuring inference speed were performed on NVIDIA RTX Titan GPU. FLOPs and inference speed were calculated considering the resolution of input images as 680 \(\times\) 680. ENet constitutes 0.4 million parameters and 6.98 G of FLOPs - the lowest numbers of parameters and FLOPs among all models. Usually, the ENet model exhibits efficient inference speed when the BN and dropout layers are merged into the convolution filters [42]. However, to maintain balance in this study, the inference speeds of the models were calculated without merging BN and dropout layers into the convolution filters. Hence, the ENet inference time was very high during prediction, of around 140 ms per image. CGNet is also a lightweight model that contains 0.5 million trainable parameters and 9.8G of FLOPs. The number of parameters and FLOPs of CGNet were slightly higher than those of ENet, although its inference speed was reported as 60 ms. Another lightweight model, ESNet, with 1.6 million parameters and 41.42 G of FLOPs, showed the inference speed as 96 ms per image, which was higher than CGNet but lower than ENet. DDRNet-23-Slim is a heavyweight model comprising 5.7 million parameters. Regardless of the high number of parameters, its architecture has been designed to have efficient inference speed [47]. Hence, it contained 16.27 G FLOPs and the inference speed was measured as 85 ms per image. DeepLabV3+ (ResNet50) is computationally expensive and consists of 17 million parameters and 156.67 G FLOPs. However, its inference speed was reported to be 80 ms per image, which was better than those of ENet, ESNet and DDRNet-23-Slim. However, ECDs have minimal computational resources and memory. Thus, the application of deep learning models on ECDs must have less parameters, FLOPs, and efficient computational time for inference [58]. Models like DeepLabV3+ or DDRNet-23-Slim show relatively efficient inference speed on GPU, but can occupy or require more memory than that available on ECDs, as a high number of FLOPs is required.  
Therefore, this benchmark demonstrated that the CGNet had similar detectability to that of the representative heavyweight segmentation model (i.e., DeepLabV3+), although the inference speed and computational parameters were minimal. Thus, among the five compared models, CGNet was the best model that could be embedded in ECDs for portable outdoor inspection of concrete structures.
# Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests
## 3 Results
### Real Scanning Data  
We calculate the quantitatively evaluating indexes, Acc, mAcc, and mIoU, according to the results segmented by hard segmentation algorithm (Hard Seg.), soft segmentation without region growing as preprocessing (Soft Seg. without RG), and soft segmentation with region growing as preprocessing (Soft Seg. with RG). The values of the three indexes are listed in Table 5. From the table, it can also be found that the soft segmentation with region growing as preprocessing gets the best performance among the three methods. With our method, the rates of Acc, mAcc, and mIoU have increased by at least 10.8%, 9.6%, and 27.4%.  
Different from those dense forests, the distribution of trees in forest RUSH06 is uneven, with some trees farther away from others. Therefore, the segmentation effect (Figure 14c) has been greatly improved compared to the hard segmentation method (Figure 14a) and to the method of soft segmentation without the region growth step (Figure 14b). The proposed method is used to deal with those small groups that have overlap regions between adjacent trees in each group. At last, the segmentation results are shown in Figure 14c,d.  
Figure 14: The segmentation results of RUSH06 using different segmentation methods. (**a**) Hard segmentation; (**b**) soft segmentation without precession; (**c**) soft segmentation with precession; (**d**) another view of (**c**).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Acc** & **mAcc** & **mIoU** \\ \hline Hard Seg. & 0.859 & 0.8791 & 0.7279 \\ Soft Seg. without RG & 0.8587 & 0.8205 & 0.6826 \\ Soft Seg. with RG & 0.9516 & 0.9632 & 0.9272 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The quantitative evaluation of segmentation results of RUSH06.
## 3 Design and Analysis
### Electrostatic Field Optimization of the FGIL
#### 3.2.2 Contour Irregularity Suppression of Stranded Conductor  
Considering the objectionable deviations in the field utilization of the proposed FGIL due to stranded conductors, irregularity suppression essentially needs to be done in order to acquire the required FUF. A probable solution could be the implementation of a silicon carbide (SiC)-impregnated polyester-based SCF of 0.1-0.4 mm thickness on the stranded conductor in order to acquire a relatively smoother conductor profile [39; 66; 67]. The implementation of such film-coated stranded conductors in gas-insulated equipment necessitates detailed electrostatic and dielectric appraisal, as no published research regarding the implementation of field-graded stranded conductors in gas-insulated equipment exists to date.
## 6 Conclusions  
This study proposed a bilateral dissimilarity attention U-Net for change detection on remote sensing imagery, achieving identical performance regardless of the order of image inputs. The proposed architecture is designed to accomplish order-independency and preserve spectral information based on attention U-Net. The proposed design shares weight parameters in the dual-encoding path to facilitate two image inputs. It can directly receive both image inputs without any pre-processing; thus, the spectral information can be preserved.  
In addition, this study introduced a DAG to generate order-independent difference features by exploiting all combination orders of joint and difference features. Moreover, the DAG boosts the feature representation response for the most informative channels by channel attention. Difference features are fed into the attention gate at the decoding path to generate a change map.  
Several experiments were conducted to evaluate the effectiveness of the proposed method on the KOMPSAT-3 and CDD datasets. The proposed network achieved average \(F_{1}\)-score and KC values of 0.68 and 66.93, respectively, for KOMPSAT-3. Based on KC interpretation, this implies that the proposed architecture can detect changes at a reasonably good detection rate and yields better performance than the conventional methods in terms of \(F_{1}\)-score and KC. In addition, the proposed network achieved better performance than the conventional methods for the CDD dataset, with \(F_{1}\)-score and KC values of 0.70 and 68.74, respectively, regardless of the image order.  
Methodology, J.L., W.W. and D.S.; investigation, W.W. and D.S.; formal analysis J.L., W.L. and I.M.; supervision, D.S.; Writing--original draft preparation, W.W.; Writing--review and editing, J.L., W.L. and D.S.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and Technology Information and Communication (NRF-2021R1A2C2092848) and the Research Grant of Kwangwoon University in 2021 and the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (IITP-2022-RS-2022-00156215) supervised by the IITP(Institute of Information & Communications Technology Planning & Evaluation).  
Not applicable.  
The authors sincerely appreciate that academic editors and reviewers give their helpful comments and constructive suggestions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Optimization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dynamic Response
## 2 Redundant Degrees of Freedom Optimization in Robotic Milling
### Redundant Degree of Freedom Optimization Model Considering Tool Vibration  
When employing a six-axis industrial robot for a five-axis task, the introduction of a redundant degree of freedom \(\gamma\) occurs, resulting in an infinite number of potential cutting postures for each cutter location point P. In robotic milling, the inherent low rigidity of the robotic milling system increases its susceptibility to chatter phenomena, posing a potential detriment to surface quality. Selecting an appropriate posture becomes crucial to maintaining a steady-state operation and achieving the best surface finish.  
Beyond addressing chatter stability, other constraints must be considered, including avoiding joint angles nearing limit positions, maintaining non-singular postures, and preventing collisions. These constraints are essential for ensuring the safety and optimal functioning of the robotic system, considering practical physical limitations. Additionally, to enhance workpiece surface quality, this article introduces a dynamic response index for determining the optimal redundant degree of freedom \(\gamma\). Taking into consideration the mentioned constraints, the optimization model is formulated as follows:  
\[\begin{array}{l}\underset{\gamma}{\text{minDRI}}(\gamma)=\frac{1}{L}\sum \limits_{l=1}^{L-1}\left|(P_{l+1}-P_{l})(y_{l+1}^{P}-y_{l}^{P})\right|\\ \text{s.t.}\left\{\begin{array}{l}\gamma_{\text{min}}\leq\gamma\leq\gamma_{ \text{max}}\\ \left[\theta_{1},\ldots,\theta_{6}\right]=f^{-1}(\gamma)\\ \theta_{\text{qmin}}\leq\theta_{q}\leq\theta_{q\text{max}}\\ \left|\text{eigen}(\Phi)\right|<1\end{array}\right.\end{array} \tag{3}\]  
where \(P_{l}\) represents the \(l\)-th element in the peak vector of the vibration signal, and \(L\) is the total number of elements in the peak vector. \(y_{l}^{P}\) signifies the vibration displacement in the specified direction, with superscript \(p\) denoting the point P and subscript \(l\) indicating the \(l\)-th element. In the milling experiment, the redundant angle \(\gamma\) is within the range of \([-45^{\circ},\,45^{\circ}]\). The inverse kinematics transformation of the robot is denoted as \(f^{-1}(\cdot)\), and the joint angle is represented by \(\theta_{q}\) (\(q=1,2,\,\ldots,6\)), with the subscript \(q\) representing the number of joint angles. \(\Phi\) denotes the state transfer matrix, and the term eigen\((\cdot)\) refers to the eigenvalues of the matrix \(\Phi\), with specific details provided in Section 2.3.  
Figure 1: Schematic diagram of the redundant angle \(\gamma\).  
Additionally, as part of the effort to enhance the robot performance and surface quality, this article introduces a dynamic response index (DRI) for identifying the optimal redundant degree of freedom \(\gamma\). For more detailed information on the dynamic response index, please refer to Section 2.3.4.
## 2 Materials and Methods
### Methods
#### 2.2.2 GNSS Displacements  
In a previous study [44], it was shown that the accuracy achievable with GNSS is suitable to monitor the displacements of a dam. The same methodological scheme implemented in [44] was applied to obtain the GNSS solutions. Positions were referred to the European Terrestrial Reference System 2000 (ETRF2000) at epoch 2008. The Network Deformation Analysis (NDA) Professional software allowed applying the Saastamoinen tropospheric correction [67; 68], the Klobuchar ionospheric correction [69], and the Schwiderski ocean loading correction [70]. A single baseline was used for connecting GNSS receivers with the permanent station. Double-differenced observations coming from the L5 (wide lane) and L3 (ionospheric-free) frequencies combination were used. To fix the phase ambiguity, the Least-Squares Ambiguity De-correlation Adjustment (LAMBDA) method was used [71]. In the final solution, the wide-lane observable was used to estimate the wide-lane ambiguity and then, the ionospheric-free observables to estimate the remaining narrow-lane ambiguity. All computational strategies are extensively discussed in [PERSON] et al. 2018 [72]. Dam crest displacements were converted into a local reference system spatially averaged to focus the analysis to the whole dam displacements only (no to local deformations), and then, to show the tendency of the long-term displacements, a moving average was applied over a time window according to the findings of a previous study [17].
## 7 Conclusions  
In order to solve the problems of MOC component poor smoothness and distortion caused by the piecewise linear transformation in the LOD method, the rational spline interpolation is used to replace the piecewise linear transformation and the RS-LOD method is proposed in this paper. Then, the RS-LOD method is used for rotating machinery fault feature extraction. The following conclusions are obtained:  
1. The analysis of the simulation signal shows that although the consuming time of the RS-LOD method is a little longer than the original LOD method, it solves the problems of MOC component poor smoothness and distortion in the original LOD method, and overall analysis effects are also better than that of EMD and LOD method.
2. The analyses of fault vibration signals of rolling bearing and fan gearbox show that the envelope spectrum based on RS-LOD can effectively extract the characteristic fault information of the corresponding vibration signal, which provides a new effective way to extract the vibration signal feature.  
Conceptualization, X.N. and K.Z.; methodology, K.Z.; software, L.L.; validation, K.Z., X.C. and L.L.; formal analysis, C.W. and Z.T.; writing--original draft preparation, X.N.; writing--review and editing, K.Z.; project administration, K.Z. and X.C.; funding acquisition, K.Z. and X.C. 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 number 51305046 and 51405033, Hunan Provincial Natural Science Foundation of China, grant number 2018 JJ3541.  
Thanks to the Hunan Province 2011 Collaborative Innovation Centre of Clean Energy and Smart Grid for providing analysis platform and Baling Petrochemical Company for providing test environment, and the authors are grateful to all reviewers and editors for their valuable comments.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Lactate Dehydrogenase (LDH) Release-Based Cytotoxicity Test  
The cytotoxicities of effector cells, such as NK-92 cells and EBV peptide-stimulated PBMCs (EBNA1 PBMCs and LMP2A PBMCs), against target cells, such as SNU719 and YECCL1 cells, were analyzed using the CyQUANT(tm) LDH Cytotoxicity Assay Kit (#C20300, Invitrogen), according to the manufacturer's protocol. The target cells were plated at \(5\times 10^{3}\) cells/well in a 96-well plate and incubated overnight at 37 \({}^{\circ}\)C under humidity. The next day, effector cells were added at various ratios to the tumor cells (1:0, 1:1, 1:5, and 1:15 target cell/effector cell ratio [T:E]). After 4 or 24 h of coculture, the mixture was centrifuged at 250\(\times\)\(g\) for 5 min at room temperature. Then, a 50 \(\upmu\)L aliquot of the medium was used for the LDH released-based cytotoxicity assay using the LDH Cytotoxicity Assay Kit (Invitrogen). Experimental LDH release was corrected by subtracting the LDH amount spontaneously released in the cells at the corresponding dilutions. The percentage of cytotoxicity was calculated as follows: % Cytotoxicity = (Experimental value \(-\) Effector cell spontaneous control \(-\) Target cell spontaneous control)/(Target cell maximum control \(-\) Target cell spontaneous control) \(\times\) 100. The coculture of SNU719 target cells and NK-92 effector cells for 24 h induced up to 15% cytotoxicity depending on T:E. The cytotoxicity induced at each T:E could be partially observed using an optical microscope.