esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Data Handling and Statistical Analysis  
In order to fulfil the exploratory approach of the study, in which a large number of variables were studied, the protocol data handling was conceived in a double-folded inferential and multivariate statistical analysis:  
* **Inferential Statistical Analysis** Mean and standard deviations (SDs) for all recorded variables and for QEESI and DHI subscale scores were calculated in both HC and MCS.  
In order to assess that data were of Gaussian distribution, the D'Agostino K squared normality test was applied (where the null hypothesis is that the data are normally distributed).  
A 'between-groups' analysis of variance (ANOVA) was performed for each VQ and otoneurological variable. Age and gender were handled as continuous and categorical predictors, respectively.  
This study tested the null hypothesis (H0) that the two group measurements would not show any statistically significant difference. The significant cut-off level (\(\alpha\)) was set at a p value of 0.01 and the Bonferroni correction for multiple comparisons was performed with post hoc tests of significant main effects. Thus, p levels associated with the difference between the two groups were considered as statistically significant if lower than 0.01.  
Then, [PERSON]'s rank correlation was implemented between QEESI and DHI sub-items scores and significant variables and a Bonferroni correction for multiple comparisons was applied in order to avoid family-wise error, (STATISTICA 7 package for Windows).  
- Principal Component Analysis  
Regarding the multivariate analysis step of the study, Principal Component Analysis (PCA) with varimax rotation was performed to detect--with statistically significant otoneurological variables in both groups--hidden phenomena mainly through reducing the variables to find the most important ones, finding patterns in the data and classifying any combination of variables. The number of factors in principle was determined by a combination of the Kaiser/Guttman criterion (only factors with eigenvalues larger than 1 are incorporated in the model) and interpretability. Also by using scree plots, the eigenvalue was plotted against the component number, to evaluate if a factor was relevant or not. Finally, a component coefficient analysis was performed between variables and factors.
## 3 Proposed Protocol
### Lightweight Implementation  
To achieve a lightweight implementation, the proposed framework consists of ECC based algorithm for UWSNs. ECC is a well-known public key cryptography technique that has been widely used in various security applications due to its strong security properties and efficient computational performance. Compared to traditional public key cryptography techniques such as RSA (Rivest-Shamir-Adleman), ECC requires smaller key sizes to achieve equivalent security, which makes it a better choice for resource-constrained environments such as UWSNs. In addition to strong security properties, ECC-based cryptography also offers efficient computational performance, which is crucial for resource-constrained UWSNs. The computational complexity of ECC is mainly determined by the size of the elliptic curve used in the algorithm. In our proposed framework, we use a small size elliptic curve to achieve efficient computation and communication setup of Node A and Node B are shown in Figure 4.  
The security strength and computational efficiency of our proposed ECC-based key management framework for UWSNs can be evaluated through various performance metrics, such as processing time, energy consumption, and communication overhead. These metrics can be measured through simulation or real-world experiments. The computational complexity of ECC can be measured by the number of point additions and point multiplications required to perform an ECC operation. The number of point additions and point multiplications can be calculated as follows.  
Figure 4: Proposed lightweight approach.  
The lightweight implementation of the proposed framework will ensure that the key management process can be efficiently carried out in UWSNs without consuming excessive resources while maintaining a high level of security.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment
### Fault Feature Extraction
#### 3.3.6 Inverse Difference Moment  
It reflects the regularity and local variation of image texture. The more regular the image texture, the higher the inverse difference moment value, and vice versa. The equation is  
\[idm=\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{N-1}{1\over{1+(i-j)^{2}}}g(i,j) \tag{17}\]
## 3 Basic Methodology  
### Dynamical Movement Primitive  
Complex manipulation tasks can be decomposed into sequences of movement primitives. Individual primitive is encoded with DMP [4]. For a one degrees of freedom (DoF) point attractor system, the point attractor system is defined as:  
\[\tau\dot{v} =K(g-x)-Dv-K(g-x_{0})s+Kf(s), \tag{1}\] \[\tau\dot{x} =v.\]  
Equation (1) is an extended PD control signal with spring and damping constants \(K\) and \(D\), respectively, position and velocity \(x\) and \(v\), goal \(g\), scaling \(s\), and temporal scaling factor \(\tau\). The scaling term originates from an additional system, called the canonical dynamical system, which controls the system's phase execution:  
\[\tau\dot{s}=-\alpha s,\]and where \(\alpha\) can be an arbitrary constant. The forcing term \(f(s)\) is used to alter attractor point dynamics and achieve an arbitrary trajectory (often learned from demonstration [4]). The forcing term can be defined as a phase-dependent linear combination of basis functions \(\psi_{i}(s)\):  
\[\tau f(s)=\frac{\sum_{i=1}^{N}w_{i}\psi_{i}(s)s}{\sum_{i}\psi_{i}(s)}. \tag{3}\]  
Gaussian distributions with mean \(c_{i}\) and variance \(h_{i}\) were used as basis functions: \(\psi(s)=exp(-h_{i}(s-c_{i})^{2})\). The forcing function is then the linear combination of basis functions with variable weights \(w_{i}\) and normalization constant \(\sum_{i}\psi_{i}(s)\). Phase \(s\) monotonically varies from 1 to 0 and controls phase progress by activating Gaussian centered at \(c_{i}\). The diminishing phase value guarantees that the forcing term contribution vanishes and returns to the simpler point attractor dynamics to converge to the target. With regards to spatio-temporal scaling, the \((g-x)\) term in Equation (1), performs spatial scaling such that the system can adjust to varying goals. The \(\tau\) variable in Equation (1) allows the system to speed up or slow down it is execution of a skill.  
Forcing term weights are learned from demonstration. Using kinesthetic teaching \(x(t),\dot{x},\ddot{x}\) with duration \(T\) are extracted as in [14]. The target forcing turn is computed by rearranging Equation (1), integrating Equation (2) to convert from time to phase, and substituting appropriate values to yield:  
\[f_{target}(s)=\frac{-K(g-x(x))+D\dot{x}(s)+\tau\ddot{x}(s)}{g-x_{0}}. \tag{4}\]  
Next, the goal is set to \(g=x(T)\) and \(\tau\) is selected such that a DMP reaches 95% convergence at \(t=T\) before using standard linear regression to compute the weights \(w_{i}\). Such procedure yields a baseline controller that can be improved by reinforcement learning [52] though this is not done in this work.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 4 Key Homomorphism of NFHE Scheme  
### Definition of Key Homomorphism  
It is assumed that \(F:K\times X\to Y\) is a pseudo-random function (PRF) [20], and \(K\) is the key space, which has a group structure and satisfies a certain \(\oplus\) operation on the group. Besides, \(X\) is the plain-text space, and \(Y\) is the ciphertext space. If for any \(k_{1}\), \(k_{2}\in\mathbb{K}\) and \(\xi\in X\), an effective algorithm can be found to calculate \(F(k_{1}\oplus k_{2},\xi)\) from \(F(k_{1},\xi)\) and \(F(k_{2},\xi)\).  
Now its definition is extended to multiple keys, assuming that the number of keys is \(N\). For a public key encryption scheme \(E\), if \((pk_{i},sk_{i})\) is the effective public key or private key pair of the scheme, and for \(pk=g(pk_{1},pk_{2},\cdots,pk_{N})\), \(sk=g^{\prime}(sk_{1},sk_{2},\cdots,sk_{N})\) can be found. \((pk,sk)\) can also be the effective public key or private key pair of \(E\) and \(E\) is called the key pair homomorphic nature. Among them, \(g\) and \(g^{\prime}\) are both effective computable functions. In particular, if both \(g\) and \(g^{\prime}\) are sum (product/linear) functions, then \(E\) is said to have the property of key addition (multiplication/linear) homomorphism.
## 7 Performance Evaluation  
Since sensor nodes have very limited resources, various overhead must be minimized to reduce energy consumption. In this section, we evaluate our proposed three source authentication schemes in terms of computation overhead and communication overhead through an analytical approach. We then compare the results with Secure Deluge, which provides source authentication for fixed-size packets and has lower overhead than existing well-known secure code dissemination protocols as presented in Section 2.
# Early Detection and Stability Assessment of Hazardous Rock Masses in Steep Slopes
## 3 Method
### Early Identification Model of Dangerous Rock Mass  
Dangerous rock disasters have the characteristics of multiple points and wide areas, strong concealment, high suddenness, and difficult prediction. The traditional survey method requires a lot of manpower and material resources, but for the linear engineering of railways, the difficulty of engineering geological surveys is increased due to the complexity of terrain relief and geological conditions. If the engineering geological conditions are insufficient, it is difficult to identify dangerous rocks quickly and accurately. To solve the above problems, this study proposed an early identification model of dangerous rock masses based on four topographic conditions, including slope direction, slope, topographic elevation difference, and vegetation coverage rate, as key indicators, which could be used to identify potentially dangerous rock mass areas by using the digital elevation model for analysis, as well as independent of rock mass quality and structure information [38]. Four terrain conditions, including slope direction, slope, topographic height difference,  
Figure 5: DTM of study area. (**a**) DSM; (**b**) DEM.  
Figure 6: Pick up feature points to fit the structural plane.  
and vegetation coverage rate, were selected as key indicators for superposition calculation and analysis:  
\[S=D(W_{A}A+W_{C}C+W_{V}V) \tag{1}\]  
In the formula, _S_--dangerous rock identification zone value; _D_--slope zone value; A is the slope zone value; _C_--terrain contrast partition value; _V_--zone value of vegetation coverage; _W_A, _Wc, Wv_--weight of slope, terrain contrast, and vegetation coverage.  
According to the importance of each index to the identification of dangerous rock [38; 39], and referring to previous research and actual engineering situations, the weight of each index is determined, as shown in Table 2.  
Utilizing the ridgeline of the railway slope as a reference, we identify the rockfall track area adjacent to the railway as sensitive (marked as 1), while designating all other areas as non-sensitive (marked as 0) in order to generate a map delineating different slope zones. The study area's slopes are then categorized based on their gradient to produce a zoning map. Subsequently, we compute and allocate regions of abrupt topographic change by comparing DEMs with varying sampling intervals, resulting in a partitioned map displaying differences in topographic height. Vegetation coverage can be assessed through identification of RGB values corresponding to vegetation in point-cloud data obtained from drone photogrammetry or by comparing DSM with DEM. Finally, weighted superposition analysis is performed to derive a hazard zone map for potential rock disasters.
# A Low Cost Vision-Based Road-Following System for Mobile Robots
###### Abstract  
Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.  
color-based detector; texture line detector; UKF; road following +
Footnote †: journal: Applied Sciences
# Retrofit Fatigue Cracked Diaphrag Cutouts Using Improved Geometry in Orthotropic Steel Decks
## 5 Results and Discussion
### Stress Spectra due to a Transversally Moved Vehicle  
For the lateral loading, the measured stress and FEA results of zone A and zone B are shown in Figure 18. The origin of the transverse coordinate is located at T5, and the transverse distance in Figure 18 indicates the distance of the left wheel from T5.  
Figure 17: Stress spectra of diaphragm cutouts for a longitudinally moved vehicle: (**a**) stress spectra of zone A, (**b**) stress spectra of zone B.  
Figure 18: Stress spectra of diaphragm cutouts under lateral moved vehicle: (**a**) stress spectra of zone A, (**b**) stress spectra of zone B.  
(1) When the lateral position of the wheel load is directly above the test diaphragm cutout, the stress at zone A is relatively large. The stress is greatest when the wheel load is at T3 and T5. After the retrofit, the stress amplitude in zone A decreases by 58.7%.  
(2) The stresses in zone B are tensile stresses, which are related to the shape of the diaphragm cutout. When the wheel load is applied at T5, the value of the stress at the measuring point 5 is the maximum one. The stress decreases significantly when the wheel load is far away from the measuring point 5.  
(3) According to Eurocode 3, when the fatigue loading model III is used for checking the locally stressed components, the lateral probability of the wheels in the lane should be considered. The spacing between the loading areas 1, 2(3) and 4(5) is 100 mm, and the probability of occurrence at each line is respectively 7%, 18% and 50% according to Eurocode 3 (\"Design of steel structures\"). The equivalent constant amplitude stress with transverse probability distribution is reduced to 55.2% and 51.8% respectively.
# An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming
## 5 Conclusions  
During the search process of genetic algorithms, only using the number of passing test cases to validate and select offspring patches. This approach, without employing other constraints to select more optimal offspring, affects the efficiency of patch generation and results in poor search performance. Therefore, this paper proposes a multi-objective genetic programming-based software defect automatic repair method called MGPRepair. Firstly, the method employs a lightweight context analysis strategy for potential defect locations. This method also uses classification rules to decouple the substitution and insertion statements in the repair material statements, which can filter out some low-quality repair materials. Secondly, a novel encoding pattern is used to re-encode the patches in the search space, allowing a more thorough genetic programming search. We describe defect automatic repair as a multi-objective search problem and use the multi-objective optimization algorithm NSGA-II to find repairs that pass more test cases while being simpler. Then, to reduce computational complexity and search space, a test filtering and patch overfitting detection process is introduced, which can accelerate the fitness evaluation process in genetic programming and reduce verification time. Verified on the real defect library Defects4J, MGPRepair successfully generated repair patches for  
Figure 8: Number of Defects Repaired and Time Consumed by MGPRepair and Its Three Variants.  
35 defects and accelerated the defect repair speed, to a certain extent, addressing the issue of offspring selection.  
Conceptualization, T.H.; writing--original draft preparation, T.H.; writing--review and editing, Y.C.; validation, F.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
_applied sciences_  
_Article_  
**Comparison of Resin Cement's Different Thicknesses and Poisson's Ratios on the Stress Distribution of Class II Amalgam Restoration Using Finite Element Analysis**  
[PERSON] 1,*, [PERSON] 2, [PERSON] 3(c), [PERSON] 4 and [PERSON] 1  
Konya 42090, Turkey Department of Pediatric Dentistry, Faculty of Dentistry, Necmettin Erbakan University, Konya 42090, Turkey Department of Restorative Density, Faculty of Dentistry, Usak University, Usak 64200, Turkey Faculty of Dentistry, Necmettin Erbakan University, Konya 42090, Turkey Correspondence: [EMAIL_ADDRESS]; Tel.: +90-530-581-55-89; Fax: +90-332-220-00-45  
**Abstract:** Using a three-dimensional finite element analysis, this study aimed to evaluate the effect of different cements' thicknesses and Poisson's ratios on the stress distribution in enamel, dentin, restoration, and resin cement in a computer-aided design of a class II disto-oclausal cavity. Dental tomography was used to scan the maxillary first molar, creating a three-dimensional tooth model. A cavity was created with a 95 degree cavity edge angle. Resin cement with varying Poisson's ratios (V1: 0.35 and V2: 0.24) was used under the amalgam. The simulated groups' thicknesses ranged from 50 \(\upmu\)m to 150 \(\upmu\)m. A load of 600 N was applied to the chewing area. The finite element method was used to assess the stress distribution in the enamel, dentin, restorations, and resin cement. The stress in the restoration increased with the use of a 100 \(\upmu\)m resin cement thickness and decreased with the use of a 150 \(\upmu\)m resin cement thickness. For the V1 and V2 groups, the cement thickness with the maximum stress value for the enamel and dentin was 150 \(\upmu\)m, while the cement thickness with the lowest stress value was 50 \(\upmu\)m. The greatest stress values for V1 and V2 were obtained at a 150 \(\upmu\)m cement thickness, while the lowest stress values were observed at a 100 \(\upmu\)m cement thickness. Using resin cement with a low Poisson's ratio under amalgam may reduce stress on enamel and restorations.  
**Keywords:** dental amalgam; cement thickness; finite element method; resin cement; stress distribution
## 3 Results and Discussions
### Validation Results of Improved Non-Destructive Detection Method for SSC in Bunch-Harvested Grapes
#### 3.3.1 Grape Segmentation and Removal of Overbright Areas  
The identification and segmentation results of a single grape, obtained using the Mask R-CNN image instance segmentation model, are shown in Figure 12(a),b.  
By comparing the grayscale images at each wavelength, it was found that the overbright area was the most evident at the wavelength of 670 nm. Therefore, the grayscale image at the wavelength of 670 nm was binarized and inverted as a mask to remove the overbright area. It was multiplied with the original image to obtain the corresponding pixels of the grapes without overbright areas (as shown in Figure 12(c)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Characteristic Wavelengths**} & \multicolumn{2}{c}{**Modeling Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{3-6}  & & \(\text{R}_{\text{m}}\text{${}^{2}$}\) & **RMSEM (Brix\%)** & \(\text{R}_{\text{p}}\text{${}^{2}$}\) & **RMSEP (Brix\%)** \\ \hline SPA & 32 & 0.8163 & 0.6786 & 0.7488 & 1.1770 \\ MCUVE & 35 & 0.8705 & 0.5696 & 0.8755 & 0.9177 \\ SPA-MCUVE & 13 & 0.6874 & 0.8852 & 0.6851 & 1.4146 \\ MCUVE-SPA & 15 & 0.8086 & 0.6973 & 0.8092 & 1.0287 \\ No selection & 256 & 1.0000 & 0.0000 & 0.7327 & 1.5862 \\
1-Der without selection & 256 & 1.0000 & 0.0000 & 0.4495 & 1.8312 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different models.  
Through the process outlined above, we could obtain the corresponding pixels of each grape in the bunch.
## 2 Materials and Methods
### Model Architecture
#### 2.3.6 Comparison with SOTA Models and Experimental Settings  
We compared the proposed MSG-GCN with two machine learning methods (RF [88] and SVM [25]) and five SOTA methods (U-Net [41], U-Net++ [49], fully convolutional networks (FCN) [89], vision transformer (ViT) [90], and GCN [78]). The methods were implemented using their original codes and trained on the datasets used herein. The MSGGCN module had two graph convolutional layers. ResNet101 [91] was used to create the initial feature matrices. AdamW [92] served as the optimiser; the learning rate was \(1\times 10^{-4}\), the weight decay was \(1\times 10^{-3}\), and the learning rate was adjusted by cosine annealing warm restarts. All experiments involved 100 training epochs. The batch size was 33. All models were implemented using the PyTorch 1.7.1 framework and the experiments were conducted on the Wisteria/BDEC-01 Supercomputer System (FUJITSU Server PRIMERGY GX2570 M6 (FUJITSU, Tokyo, Japan)) at the Information Technology Center (The University of Tokyo, Tokyo, Japan), which is equipped with NVIDIA A100 Tensor Core (NVIDIA, Santa Clara, CA, USA) graphical processing unit (GPU) (40 GB SXM). For the RF and SVM models, the OpenCV 3.4.2 [93], scikit-learn 0.24.2, and scikit-image 0.18.1 [94] Python 3.7.0 libraries were used to implement machine learning. Using the methods of [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], edge-based features were extracted as grayscale images that identified and highlighted edge information [95]. Gaussian, median, and variance filters were applied to extract noise and to reduce blurring [96].
## 3 The Dataset
### Data Balancing  
As shown in Table A1, the crash database is highly unbalanced due to the limited number of killed drivers compared to other injury levels. This imbalance is not unexpected considering the nature of traffic crashes. The issue of unbalanced data distribution was addressed using the SMOTE-ENN (Synthetic Minority Over-Sampling Technique-Edited Nearest Neighbor) (Kal