esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Extraction  
The 1316 radiomic features available for this study quantified lung characteristics from CT images and were extracted using the open source package _Pyradiomics_[22]. Lung voxels were used in the extraction of seven classes of features: shape-based (14 features), first-order (18 features), Gray Level Co-occurrence Matrix (GLCM) (24 features), Gray Level Dependence Matrix (GLDM) (14 features), Gray Level Run Length Matrix (GLRLM) (16 features), Gray Level Size Zone Matrix (GLSZM) (16 features) and Neighboring Gray Tone Difference Matrix (NGTDM) (5 features). Shape features examine the size and shape of the ROI, employing only the lung segmentation masks in the calculations. First-order features describe the distribution of voxel intensities within the ROI using basic metrics such as mean, median, range, and standard deviation. GLCM features describe the second-order joint probability function of the ROI, while GLDM features quantify gray level dependencies in the image. GLRLM features quantify the length of consecutive pixels that have the same gray level intensity and GLSZM features quantify the number of connected voxels that share the same gray level value. Finally, NGTDM features characterize the difference between the gray value of a pixel and the average gray value of the neighboring pixels within a defined distance [26].  
The radiomic features were computed both on the original image (107 features) and on images obtained after application of wavelet (744 features) and Laplacian of Gaussian (LoG) (465 features) filters. The wavelet transform applies a wavelet filter to each CT image, which is then decomposed in low and high frequencies into 8 different images. It applies either a high-pass filter (represented as H) or a low-pass filter (represented as L) in each one of the x, y, and z directions: LLH, LHL, LHH, HLL, HLH, HHL, HHH and LLL [27]. The LoG filter yields a derived image for each applied sigma value in order to emphasize areas of gray level change, where sigma defines how coarse the emphasized texture should be [28]. In these studies, five filters with different sigma values were applied (sigma = 1.0 mm, 2.0 mm, 3.0 mm, 4.0 mm, 5.0 mm).
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 4 Ground Subsidence Risk Prediction Model Based on Machine Learning  
A machine learning-based ground subsidence risk prediction model using underground facility attribute information has been reported [13]. However, ground subsidence depends on a combination of various factors, and hence, the largest possible number of factors must be incorporated into the model. Therefore, a machine learning-based ground subsidence risk prediction model was developed in this study by incorporating data on the height, permeability coefficient, and stratigraphic thickness of the target area into the underground facility attribute information.
## 2 Related Work
### T-L Model
N data points are obtained by calibration flight, and then the T-L linear model can be expressed as matrix follows:  
\[B=\text{AC}+z \tag{4}\]  
\(B\) represents n scalar magnetometer field data after preprocessing. A is \(n\times 18\) platform attitude matrix. \(C\) is \(18\times 1\) coefficients matrix of T-L compensation model. It is important to solve the compensation coefficients by calibration flight. Therefore, calibration flight is needed before aeromagnetic survey. The least square solution of the compensation coefficients \(C\) are as follows [20]:  
\[C=\left(\mathrm{A}^{T}\mathrm{A}\right)^{-1}\mathrm{A}^{T}B. \tag{5}\]  
In order to obtain good calibration flight data, calibration flight needs some certain requirements. Firstly, the region with a stable geomagnetic field is usually selected for calibration flight. A complete compensation flight needs to be performed in four directions, and three sets of actions need to be completed in each direction: pitch, yaw, and roll. Each action needs to be repeated at least three times, where the standard amplitude of pitch flight is \(\pm 5^{\lx@math@degree}\), the standard amplitude of yaw flight is \(\pm 10^{\lx@math@degree}\), and the standard amplitude of roll flight is \(\pm 5^{\lx@math@degree}\). After completing the calibration flight, 18 T-L compensation coefficients can be obtained by solving the T-L linear equation. After the compensation is completed, the compensation results should be evaluated. The compensation results should be evaluated after the compensation is completed. At present, the general evaluation standard of compensation results of the aeromagnetic survey is to use standard deviation (STD) as the measure of data interference, and to use improved ratio (IR) to measure the compensation results of the algorithm on data [21]. The higher the IR, the better the compensation results. STD is defined as (6) and IR as (7). \[\mathrm{STD}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\mu)} \tag{6}\]  
\[IR=\frac{\mathrm{STD}(H_{b})}{\mathrm{STD}(H_{a})} \tag{7}\]  
where \(\mu\) is the arithmetic mean of \(x_{i}\) and \(H_{b}\), \(H_{a}\) represents the OPM data before and after compensation, respectively.
## 3 Developed Solution Approach  
Due to their complex combinatorial nature, designing metaheuristics for multi-objective scheduling problems is a big challenge. This task generally involves a proper selection of a solution representation scheme that suits both the structure of the problem and the search mechanism of a metaheuristic. However, working on the solution representation using the plain diversification and intensification strategies of a metaheuristic are usually not sufficient for achieving non-dominated solutions that are close enough to the optimal Pareto front. Therefore, hybridization with a suitable local search heuristic is usually employed. Nonetheless, the design of such local search heuristics requires a proper selection of neighborhood search functions that promote moves towards the optimal Pareto front, which is another challenge.  
Scatter search is a population-based metaheuristic that was introduced by [PERSON] [39]. It utilizes problem-specific neighborhood search and solution-recombination functions, which provide intensification and diversification mechanisms, to iteratively update a set of reference solutions, denoted as \(\mathcal{R}\). Solution quality and diversity are essential characteristics for solutions to be included in \(\mathcal{R}\). In multi-objective optimization problems, the set \(\mathcal{R}\) can partially include the non-dominated solutions that are found and updated throughout iterations. The first application of the scatter search to multi-objective optimization problems was for the proctor assignment problem by [PERSON] et al. [40]. For non-linear optimization problems, early competitive results for multi-objective scatter search implementations include [PERSON] et al. [41] and [PERSON] [42].  
In the scheduling literature, a multi-objective scatter search (MOSS) was applied to the no-wait-flow shop-scheduling problem [43] and to the permutation-flow shop-scheduling problem [44]. In both papers, the MOSS is computationally compared to the well-established multi-objective evolutionary algorithms. Their results emphasize the efficiency of the MOSS approach. A more recent application of MOSS for the bi-objective MOSP was presented by [PERSON] et al. [30], who also reported competitive results compared to NSGA-II. Building on these previous results, this paper develops a novel implementation of MOSS for the studied Bi-DMOSP.
# A Scheduling Algorithm for Multi-Workshop Production Based on BOM and Process Route
## 5 Implementation of Scheduling Algorithm for Multi-Workshop Production
### Algorithm Implementation Steps  
The main steps of the improved PSO algorithm are as follows.  
* Step 1: Parameter setting, including population size, maximum number of iterations \(n\), learning factor, upper and lower limits of particle position and speed, dimension of the algorithm (this paper takes the total number of operation nodes as the dimension).
* Step 2: Initialize the population by using the coding method based on operation and the method shown in Figure 6.
* Step 3: Update the particles, update the speed and position according to Equations (19) and (20), and calculate the individual extreme and global extreme.
* Step 4: If the global extremum remains unchanged, a new generation of particles will be generated according to Equation (21). Otherwise, step 4 will be adjusted to step 5.
* Step 5: Update the inertia weight according to Equation (22).
* Step 6: Depends on whether the termination condition is satisfied. If not, return to step 3. Otherwise, the optimal solution is output and the algorithm ends.
## 2 Related Works
### Integrated Tensor Decomposition and Quantization Methods  
The integration of tensor decomposition with quantization techniques has been a prominent area of research for the compression of deep learning models. Various methods have been proposed to explore this integration, each leveraging different tensor decomposition techniques and quantization strategies. However, the effectiveness of these methods can vary greatly depending on the specific approaches used.  
[PERSON] et al. presented an approach that combines Tensor Train (TT) decomposition with 8-bit quantization to compress deep learning models [72]. Their efficiently method reduces model size; however, they only achieved up to \(57\times\) compression. [PERSON] et al. proposed a method integrating Quantized Low-Rank Tensor Decomposition (QLTD) with self-attention to compress deep learning models. Their approach employs only Tucker decomposition, followed by 8-bit quantization [73], achieving a \(90.61\times\) compression ratio. In contrast, our work employs 1-bit quantization through binary neural networks (BNNs), and we combine and study three different tensor compression methods, namely CP, Tucker, and Tensor Train. By leveraging a binary neural network (BNN), we achieve \(168\times\) compression, which is almost three times and two times that achieved by [PERSON] et al. [72] and [PERSON] et al. [73], respectively. Our method presents the novel idea of layer sensitivity for rank selection for the tensor decomposition algorithm. Overall, our work not only achieves high compression, but the novel idea of layer sensitivity ensures that critical layers in the model are preserved to maintain a high compression ratio and good performance, making our method more plausible for deployment on edge devices that are limited in terms of storage and computational resources.
# Dynamics Analysis of Space Netted Pocket System Capturing Non-Cooperative Target
## 4 Dynamic Simulation and Results
### Simulation Results and Discussion
Figure 4:0 Collision forces on the captured target: (**a**) case 2; (**b**) case 3.  
directions appear after the closing phase, and their amplitude is not significantly affected by the closing time. 3. Different cases show similar phenomena, which indicates that the target is captured successfully for different capture strategies. The positions of the center of mass of the target and the service spacecraft still stay before the contact and collision between target and the rope net of the capturing phase of each case. After the closing phase is completed, the service spacecraft flows the target and the service spacecraft and target move together in the Y direction. Furthermore, in the towing phase, the positions of the center of mass in the X and Z directions are not changed obviously, which is represented by the small magnitude for the spacecraft and target. This is due to the slight contact and collision between the spacecraft and the captured target. Dynamics modeling and simulation for the space netted pocket mechanism capturing the target play a crucial role in predicting the characteristics and success rate of the capturing task. This work proposed a computational method for the space netted pocket mechanism capturing the target, which is the basis of the design and performance evaluation of the space netted pocket mechanism in real engineering applications. In this work, we studied different capturing strategies with different closing times of the netted pocket system. More capturing cases related to real engineering requirements will be researched in the future, such as the different inertia of targets, different initial state of the target and so on, which will improve the engineering application. Conceptualization, C.W. and Y.Z. ; methodology, C.T., Z.B. and C.W. ; software, C.T. and C.W. ; validation, Y.D. and C.W. ; formal analysis, C.T., Y.D. and Z.B. ; investigation, C.T. and Y.D. ; data curation, C.T., Y.D. and C.W. ; writing--original draft preparation, C.T. and Y.D. ; writing--review and editing, Z.B. and Y.Z. ; supervision, C.W. and Y.Z. ; project administration, C.W. and Y.Z. ; funding acquisition, C.W. and Y.Z. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (U21B2075), Natural Science Foundation of Shandong Province (ZR2023 ME171) and Stable Supporting Fund of National Key Laboratory of Underwater Acoustic Technology (JCKYS2023604 SSJS012). Not applicable. All the data used in this investigation are presented in the paper. The authors declare no conflict of interest.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 3 Description and Design of the Photovoltaic System
### Wireless Smart Photovoltaic System (WSPS)
#### 3.1.3 Adaptive Lyapunov Control for the DC/DC Converter
The adaptive Lyapunov function is \(\text{V}=V_{1}+V_{2}+V_{3}+V_{4}\). \[\rho^{-1}V_{2}=\frac{1}{2}\rho^{-1}\theta_{1}^{2}=>\rho^{-1}\dot{V}_{3}=\rho^ {-1}\widetilde{\theta}\dot{\widetilde{\theta}} \tag{16}\]  
\[\sigma^{-1}V_{3}=\frac{1}{2}\sigma^{-1}\varnothing_{1}^{2}=>\sigma^{-1}\dot{V }_{3}=\sigma^{-1}\widetilde{\varnothing}\dot{\phi} \tag{17}\]  
\[\tau^{-1}V_{4}=\frac{1}{2}\tau^{-1}\omega_{1}^{2}=>\tau^{-1}\dot{V}_{3}=\tau^ {-1}\widetilde{\omega}\dot{\widetilde{\omega}} \tag{18}\]  
\[\dot{V}=-K_{1}e_{1}^{2}-K_{2}e_{2}^{2}+\underbrace{\Big{(}\tau^{-1}\dot{ \omega}-e_{1}\Big{)}\widetilde{\omega}+\underbrace{\Big{(}\sigma^{-1}\dot{ \widetilde{\omega}}-V_{in}\Big{)}}_{=0}\widetilde{\varnothing}+\underbrace{ \Big{(}\rho^{-1}\dot{\widetilde{\theta}}-\breve{V}_{pv,\ ref}-K_{1}^{2}e_{1} \Big{)}}_{=0}\widetilde{\theta} \tag{19}\]  
The obtained equations for the parameter adaptations are (20)-(22). \[\tau^{-1}\dot{\omega}-e_{1}=0\rightarrow\dot{\tilde{\omega}}=\tau e_{1} \tag{20}\]  
\[\sigma^{-1}\dot{\tilde{\varnothing}}-V_{in}=0\rightarrow\dot{\tilde{\phi}}= \sigma V_{in} \tag{21}\]  
\[\rho^{-1}\dot{\tilde{\theta}}-\breve{V}_{pv,\ ref}-K_{1}^{2}e_{1}=0\to \dot{\tilde{\theta}}=\rho\big{(}\breve{V}_{pv,\ ref}+K_{1}^{2}e_{1}\big{)} \tag{22}\]  
The evolution of the parameters follows the dynamic obtained for Equations (20)-(22). Using these equations in the control law, the duty cycle of the DC/DC converter is achieved to reach the MPP taking into consideration the adaptation of the parameters such as the capacitor and the inductor. Figure 4 summarizes the adaptive Lyapunov controller. It takes as input the reference voltage, \(V_{PV,ref}\), which must be tracked and is given by the MPPT algorithm. The controller has two control loops. A faster inner control loop for the current and an outer control loop for the DC/DC converter input voltage. The error voltage, \(e_{1}\), is calculated as the difference between the reference voltage \(V_{PV,ref}\) and the measured voltage at the boost converter input, \(V_{in}\). Then, using the voltage error and Equation (8), the reference current,  
\(i_{L,ref}\) (called stabilization function) is achieved. This reference current is compared with the measured inductor current, \(i_{L^{\prime}}\), obtaining the current error \(e_{2}\). Thus, applying Equation (11) and integrating the parameters Equations (20)-(22), with respect to time, the duty cycle of the DC/DC converter, \(\gamma\), is achieved. Applying this controller, the duty cycle change rate is 7 ms, the sampling time of the adaptive Lyapunov control is also 7 ms, the PWM frequency is 25 kHz, and the resolution of the PWM is 10 bits.
## 3 Discriminative Regional Weight
### Region Based Weight  
Based on the observation from biological vision that the vision system is sensitive to the region of the greater curvature, we propose a region-based weight method which exploits the number of different quantized orientations in a neighborhood. Let \(w_{i}\) be the weight value of the location \(i\), and it can be defined as follows:  
\[w_{i}=1+\frac{\text{POPCNT}(\text{ori}_{i}^{w}(\mathcal{T},r^{w}))}{m}, \tag{10}\]  
where \(\text{ori}_{i}^{w}(\mathcal{T},r^{w})\) is binarized orientations at location \(i\) in the weighted image:  
\[\text{ori}_{i}^{w}(\mathcal{T},r^{w})=\text{ori}_{i}^{w}(\mathcal{T},r^{w}) \big{|}\text{ori}_{i^{\prime}}(\mathcal{T})\forall i^{\prime}\in B(i,r^{w}), \tag{11}\]  
\(B(i,r^{w})\) indicates the window centered at \(i\) with \((2r^{w}+1)\times(2r^{w}+1)\) pixels.  
As depicted in Figure 5, the weight \(w\) is proportional to the number of the different quantized orientations in a neighborhood. In Equation (10), a small \(m\) will result in large weight in the region of greater curvature but also a loss of contributions from other regions. In practice, we found that \(m\)= 12 is a good trade-off. The methods of descriptor-based usually use feature points as the basis for object recognition. The feature points are generally detected in regions with significant gradient changes and they are more discriminative. In this way, the discriminative regional weight combines the advantages of feature points. It can not only effectively distinguish similar objects, but also enhance the robustness of occlusion.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 5 Partially Demineralized Dentin Matrix (PDDM)
### Radiologic Outcomes  
Radiographic assessment was made by X-ray panoramic and cone beam computed tomography (CBCT). This analysis showed that the radiopacity of the PDDM decreased gradually with the lamina dura around the graft becoming progressively indistinguishable [36]. Comparing the dimensional changes of the alveolar ridge between PDDM and \(\beta\)-TCP, the width and height loss was lower in the PDDM group, with these values being statistically significant [41]. In terms of ridge height, while in the PDDM group there was a reduction of 0.28 \(\pm\) 0.13 mm, in the \(\beta\)-TCP group there was a reduction of 1.72 \(\pm\) 0.56 mm and in the control group it was reported a reduction of 2.60 \(\pm\) 0.88 mm (\(p\) < 0.05) [41]. In terms of width, a similar result was achieved, with the control group showing an increased reduction (2.29 \(\pm\) 0.40 mm), followed by the \(\beta\)-TCP group (1.45 \(\pm\) 0.40 mm) and finally the PDDM group (0.15 \(\pm\) 0.08 mm) (\(p\) < 0.05) [41].
###### Abstract  
The current deployment of 5G NR in the frequency band 4800-4990 MHz in multiple countries near the coastlines indicated that there might be a potential risk of harmful interference impacting aeronautical and maritime systems located in international airspace and international waters. This raised numerous concerns about whether 5G NR system rollouts in that band need to be restricted by the power flux density limits created at the border of the territorial waters, which equals 12 nautical miles from the coastline. This work contains a case study based on the example of the Pacific region, where a simulation of aggregate interference from 5G NR base stations and user equipment deployed in the cities near the coastline using Monte Carlo analysis was conducted. The results of the study show that no harmful interference to the aeronautical and maritime services operating in international airspace and waters is expected even when the aircraft or vessels are located at the border of the territorial airspace and waters from the coastline. Therefore, no restriction to the 5G NR deployment in the coastline regions in the frequency band 4800-4990 MHz is required.  
5G NR; AMS; MMS; Monte Carlo analysis; frequency sharing; spectrum engineering; electromagnetic compatibility; aeronautical services; maritime services; interference analysis Article Evaluation of Interference Analysis from 5G NR Networks to Aeronautical and Maritime Mobile Systems in the Frequency Band Band 4800-4990 MHz  
1  
A: [1]External Links: Link Cited by: SS1.
# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 4 Experimental Results and Discussions
### Comparative Study with the Baseline Model  
To assess how the proposed model performs with the baseline models, the proposed model and baseline models are placed side by side. Table 8 shows the comparison of the proposed model with the baseline model. In [3], CART performs better when compared with other ML models used for the classification of the dataset with 88.0%, and LR and SVM models have the least accuracy, with 61.0% each. CART has the overall best performance across all the performance metrics used to evaluate the datasets. Therefore, it can be said that the proposed model, using feature selection with ensemble classifiers, performs better than the baseline models. In addition, the computational time of the proposed models is very fast, since the number of parameters used is reasonably reduced compared with the baseline model. Table 12 shows the comparison of the proposed model with the baseline model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Models** & **Accuracy** & **Precision** & **Recall** & **F1\_Score** \\ \hline \multirow{8}{*}{Baseline Model [12]} & LR & 0.61 & 0.37 & 0.61 & 0.46 \\  & LDA & 0.68 & 0.74 & 0.68 & 0/62 \\  & KNN & 0.84 & 0.85 & 0.84 & 0.84 \\  & RF & 0.85 & 0.87 & 0.85 & 0.85 \\  & CART & 0.88 & 0.90 & 0.88 & 0.88 \\  & NB & 0.66 & 0.63 & 0.62 & 0.51 \\  & SVM & 0.61 & 0.37 & 0.61 & 0.46 \\  & LSTM & 0.81 & 0.83 & 0.81 & 0.80 \\ \hline \multirow{8}{*}{Proposed Model} & Bagging & 0.99 & 0.99 & 0.99 & 0.99 \\  & XGBoost & 1.0 & 1.0 & 1.0 & 1.0 \\ \cline{1-1}  & RF & 0.99 & 0.99 & 0.99 & 0.99 \\ \cline{1-1}  & ET & 0.99 & 0.99 & 0.99 & 0.98 \\ \cline{1-1}  & Ada & 0.70 & 0.69 & 0.66 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The proposed model is compared with the baseline model.  
Figure 3: (**a–g**) XGBoost ROC curve for all datasets considered for this study.