esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Structural Modeling and Analysis
#### 2.2.1 Timoshenko Beam Model  
The Timoshenko beam theory is adopted to model the simplified structural system to analyze the actual 3D building. A 3D prismatic homogeneous isotropic beam element is used for the element formulation. The element considers both the shear deformation and rotational inertia (related to bending). Since the shear deformations are taken into account, the planes that were initially perpendicular to the neutral axis are no longer perpendicular to it after deformation.  
A consistent mass formulation is used. Hence, both the stiffness and mass matrix depend on the relative importance of the shear deformations (relative to the contribution from bending). This ratio is given by  
\[\Phi=\frac{12 EI}{G(A/\alpha)L^{2}} \tag{2}\]  
where \(E\) and \(G\) are the moduli of elasticity and shear of the material, respectively, \(I\) is the moment of inertia of the cross-section, \(A/\alpha\) is the effective shear area, and L is the length of the discrete beam element. In 3D, the properties of cross-sections can change in the two principle directions; hence, two relative shear importance factors may be defined. The torsional stiffness is computed from the polar inertia of the equivalent cross-section. A Rayleigh damping model is assumed. For a dynamic problem, the governing equation of motion (EOM) is shown in (3).  
The numerical structural models of the version described are set up such that the eigenfrequencies can be met. Furthermore, in the initial description of CAARC B, linear mode shapes (with linear referring to the distribution along the height and not referring to geometric/material linearity) are mentioned, as these would be typically achieved by the target study at that time involving a HFFB measurement. These distributions along the height are only possible if the model is an infinitely rigid beam along the height, and deformations are only possible at the mount (clamped edge). This could be seen as a rigid body deformation along the height permitted by springs at the fixity. These mode shapes are, however, not representative (nor realistic) of typical aeroelastic models used in wind tunnels, nor of high-rise structures modeled using computational methods. Realistically, these have mode shapes and sway deformations that lie somewhere between the theoretical limits of pure shear buildings and pure bending cantilevers and are definitely not linear. The latter would correspond to the Bernoulli/Timoshenko beam formulation, whereas the former can only be achieved with a dedicated MDoF shear model. Current modeling techniques and implementations permit the modification of the relative importance of shear. Also, the addition of point mass and stiffness entries to model the possible effect of outriggers is enabled.
# Call Graph and Model Checking for Fine-Grained Android Malicious Behaviour Detection
## 3 The Method
### Fine-Grained Malicious Family Detection Step  
While the first step of the methodology aims to detect potentially malicious application by looking for methods that manifest malicious behaviour, the second step takes into account the entire application and looks for malicious sequences of operations to classify the malware into behavioural families.  
Most of the time, the harm of an application comes from sequences of methods and operation, instead of a single method itself. Every operation could be legitimate for some functionalities or performances reason. For instance, reading SMS messages can be considered as a legitimate action when performed during a verification step for login into an application, while the operation should be marked as malicious if performed together with others, like reading the information in the background, or sending data without user interaction and permission.  
Nevertheless, inter-procedural analyses are tasks requiring a lot of computational time which require deeper checks of all the possible sequence of operations. The number of paths to test grows exponentially, suffering from the path explosion problem, which makes the analysis unfeasible with a brute-force approach. In light of the above considerations, the second step of the methodology aims to address this problem by using the information provided by the first step and completing the classification in a feasible way; while the first step is essential to improve efficiency and reduce false positive, the second one concludes the methodology by performing the classification and by analysing only a subset of methods.  
First of all, a static analysis is performed on the potentially malicious application and a CG is generated. The methods are analysed at the code level and all the call/invoke statements are extracted. A node in the graph represented a method and is linked to all the other methods that invokes in the code, and thus to all the other methods to which is passing values and data. The CG represents the entire structure of the application from an execution flow and data point of view. We already had the information on single methods from the first step, and now we extend our knowledge on the application with the interactions between all the methods.  
Then, the generated CG is visited and we look for the interactions of pair of nodes, and the paths between them. By using the list of malicious methods provided by the first step, the graph is sliced and many subgraphs are generated, one for each couple of malicious methods linked. This subset of nodes and edges contains all the possible paths only between the malicious methods selected previously, and this operation greatly cut down the number of paths to analyse.  
A CSS process is built for each selected path of the graph, and then the Model Checker evaluates if the process exhibits some data flow properties typical of a particular malware family. If so, the application is marked as a malware of that family, and the methods which constitute the malicious path can be manually analysed.
# Chapter Detection in Robotic Milling Using Entropy Features
## 4 Results and Discussion  
### Features Extraction  
Several parameters should be manually set before performing ICEEMDAN. The parameter \(\epsilon_{0}\) affects the desired SNR. According to [31], \(\epsilon_{0}\) should be set as 0.2. Then, the parameter \(N\) determines the averaging number. If \(N\) is large, too many averaging operations need to be conducted, which is a time cost. If \(N\) is small, reasonable results cannot be obtained. For fast detection of chatter, we used \(N=5\). A set of IMFs can be obtained from the ICEEMDAN. Further, only IMFs which are highly correlated with the original signals contain useful information about chatter. Therefore, correlation analyses between original signals and each IMF needed to be conducted. The IMFs with correlation coefficients greater than 0.1 were taken into consideration and used to reconstruct the original vibration signals.  
Next, the _wRCMDE_ could be extracted from the reconstructed signals. Further, it was critical that some parameters were predetermined. The number of classes, \(c\), was the first parameter of focus. When \(c\) is too small, two data points with large differences in amplitude may be classified into the same class, which leads to underestimating the irregularity of the signal. Conversely, when \(c\) is too large, the estimation results are easily disturbed by noise and the robustness is poor. To obtain an ideal estimation result, \(c\) was set to 5 in this paper based on the existing research [40]. The embedding dimension, \(m\), was the second parameter to be considered. With the decrease in the embedding dimension, \(m\), the embedding vectors become shorter, and more embedding vectors can be obtained, which cannot accurately reflect the overall dynamic changes in the signals. However, fewer embedding vectors would be obtained with an increase in \(m\), which may result in the loss of detailed information for the time series. According to [43], \(m\) was set to 5 in this paper. Commonly, \(\tau\) is set as 1 in practice. The largest scale factor, \(s_{m}\), is usually set at \(s_{m}\geq 10\)[43]. However, unstable estimation results would be observed if \(s\) was too large [44]. Consequently, we chose \(s_{m}=15\).  
The _wRCMDE_ was extracted from the reconstructed signals corresponding to all of the robotic milling tests. The typical _wRCMDE_ values under different robot configurations and cutting parameters are shown in Figure 5. It can be seen from Figure 5 that the _wRCMDE_ values showed a downward trend with the increase in the scale factor. It is worth noting that the _wRCMDE_ values obtained from the chatter-free state when the scale factor was \(s\leq 4\). Therefore, _wRCMDE_ values obtained at \(s\leq 4\) were chosen as the features for constructing the chatter detection model.  
Figure 4: The original vibration signals (left), the FFT results of original vibration signals (right): (**a**) Test No. 7: I\(-3000-5-3\); (**b**) Test No. 10: I\(-6000-5-2\); (**c**) Test No. 12: II\(-3000-1-8\); (**d**) Test No. 14: II\(-3000-5-1\).
# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 2 Proposed Model
### Multi-Source Prediction Consistency
The feature extractor includes three network blocks, which have the same network structure, including a convolutional layer, a ReLU layer, a BatchNorm layer, a convolutional layer, and a ReLU layer. The label classifier and domain classifier have the same network structure, including a fully connected layer, a BatchNorm layer, a ReLU layer, a fully connected layer, and a Softmax layer. The network structure of the three network components in MCDANN is shown in Figure 6.  
The pseudo-code of the proposed MCDANN training process is shown in Algorithm 1. ```
0: Source domain data \(D_{5}^{1},\dots,D_{S}^{N}\), target domain data \(D_{t}\)
0: Parameters \(\theta_{f}\), \(\theta_{y}\), \(\theta_{d}\) in the model
1:for\(i=1\to n\)do
2: Forward:
3: Calculate classification loss
4: Calculate adversarial loss
5: Calculate consistency loss
6: Get pseudo labels by majority voting
7: Calculate distance loss
8: Backward:
9: Calculate gradient
10: Update:
11: Update model parameter \(\theta\)
12:endfor
13:return\(\theta_{f}\), \(\theta_{y}\), \(\theta_{d}\)
```  
**Algorithm 1** Training process of MCDANN. Figure 6: Network structure of the feature extractor, label classifier and domain classifier in MCDANN. (**a**) Feature Extractor; (**b**) Label Classifier; (**c**) Domain Classifier.
## 4 Conclusions  
We have proposed a possible approach to handle the problem of universal calibration in XRF spectrometry. The samples with significantly different matrix elements cannot be effectively handled in the framework of a single calibration model, thus increasing the cost of XRF analysis due to the necessity of making individual calibrations for each sample type. The proposed approach is based on the application of the recently introduced IC-PLS method. We have demonstrated the potential of the IC-PLS approach for constructing universal calibration models embracing different types of samples in XRF. Using the example of ore and steel samples, we have shown that the quantification accuracy for certain elements increased up to four times compared to the conventional fundamental parameters method. The flexibility of the IC-PLS method in terms of possible inclusion of various number of spectral intensities, their ratios, and products implies a good promise for further development of universal calibration models in XRF.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13095415/s1](https://www.mdpi.com/article/10.3390/app13095415/s1)]([https://www.mdpi.com/article/10.3390/app13095415/s1](https://www.mdpi.com/article/10.3390/app13095415/s1)), Table S1: Steel samples composition; Table S2: Ore samples composition; Table S3: Steel and ore test samples; Table S4: Threshold values for concentrations.  
Conceptualization, D.K. and V.P.; methodology, Z.S., D.K. and V.P.; software, Z.S.; investigation, Z.S.; resources, V.P.; writing--original draft preparation, Z.S., D.K. and V.P.; writing--review and editing, V.P. and D.K.; visualization, Z.S.; supervision, D.K. and V.P. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Russian Science Foundation, grant number RSF 23-23-00108.  
Figure 5: Certified vs predicted Al content plots for (**a**)—FP, (**b**)—KRLS, (**c**)—IC-PLS. Blue dots indicate calibration points, red dots—validation. The green lines represent an ideal function y = x.
## 3 Results and Discussion
### Characteristics of Sludge
Figure 4: DSC thermogram of raw sludge (**a**) and sludge after thermal solidification treatment at 75 \({}^{\circ}\)C for 30 min (**b**); N\({}_{2}\) adsorption–desorption isotherm of raw sludge (**c**) and sludge after thermal solidification treatment at 75 \({}^{\circ}\)C for 30 min (**d**); Pore size distribution of raw sludge (**e**) and sludge after thermal solidification treatment at 75 \({}^{\circ}\)C for 30 min (**f**). Characteristics of sludge cake after thermal solidification treatment are summarized in Table 2. The moisture content of the sludge after thermal solidification treatment could be reduced to 36.6%, indicating an enhanced dewatering of sludge. The remaining free water content accounted for 68.2% of the total moisture in the treated sludge. This part of water was mainly present in the internal pores of treated sludge. Since no mechanical force was applied in the thermal solidification process, the contraction and condensation of the sludge were totally dependent on the shrinkage of the softened polymer chains. Such shrinkage is very limited, and it cannot extrude all the water contained in the internal pores of sludge. Meanwhile, the existence of internal water would hinder further contraction of the sludge. However, the remaining 68.2% of free water in the internal pores of sludge can be easily removed through a certain mechanical process. The dry weight and volume of sludge cake from thermal solidification of 100 mL raw sludge were 5.93 g and 8.1 mL, respectively. The calculated bulk density of this sludge cake was about 0.730 g cm\({}^{-3}\), indicating that the formed structure of the sludge cake was not a completely dense structure but still filled with a certain number of pores, which was in agreement with the SEM observation (Figure 3b). Figure 5: C 1s spectra and curve resolution of raw sludge (**a**) and sludge after thermal solidification treatment at 75 °C for 30 min (**b**). The black curves is the original data; the red, pink and green curves are the fitting peaks on 284.7–284.8 (peak 1), 286.3 (peak 2) and 288.9–289.1 eV (peak 3), respectively. The summary fitting curves (yellow) almost completely matches the experimental curves (black). The bound water content was reduced to merely 0.183 g g\({}^{-1}\) dry sludge due to the changes in the surface characteristics of sludge after thermal solidification. The hydrophilic groups of polymer chains were enclosed within the sludge, and the sludge shrank by this cohesion under the action of heat, which reduced the specific surface area of the sludge and the interaction between water and hydrophilic groups on the surface of the sludge. With the increase in hydrophobicity of sludge particles, the majority of bound water in the raw sludge was turned into free water.
# Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation
## 1 Introduction  
The engine is the core component of a car, and the O-ring plays a very important role in the airtight structure of a car's engine; the quality of the seal directly affects the life and reliability of the engine's operation [1, 2]. There are currently thousands of O-ring manufacturers in China, but the size of an O-ring still requires manual inspection; in the face of large-scale testing needs, this is not only time-consuming and labor-intensive, but also, due to contact measurement, the detection accuracy is not high and is susceptible to the effects of personal subjective factors [3]. Current O-ring dimension measurement algorithms are mainly based on algorithms at the whole pixel level, such as the Sobel operator, Canny operator, morphological algorithms, etc. However, an algorithm at the whole pixel level has the disadvantages of having poor image edge localization and low accuracy of size measurements [4].  
To address these problems with whole-pixel algorithms, the authors of this paper design a sub-pixel O-ring dimension measurement algorithm based on cubic spline interpolation, and achieve sub-pixel edge detection of O-rings through algorithmic processing. Compared with the whole-pixel edge detection algorithm, it has the advantages of a high detection accuracy, good image edge localization and strong detection stability. It can effectively improve the accuracy of current O-ring dimension measurements.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 6 Conclusions  
In this paper, we propose a novel adaptive SPAD-based receiver for indoor dimmable VLC systems which effectively improves the receiver sensitivity while preventing the saturation effects of SPAD detectors, resulting in decent performance over a wide range of received power and dimming levels. The use of a VOA allows the receiver to adaptively alter the optical power level incident on the SPAD array based on the received signal and  
Figure 5: The achievable data rate versus the received signal power with a background power \(P_{\text{b}}=10\) nW for the considered modulation techniques and maximum bandwidth \(B_{\text{max}}=120\) MHz for considered systems with dimming level \(\gamma=20\%\); (**a**) OOK vs. VOOK; (**b**) DCO-OFDM vs. ACO-OFDM.  
background light power to achieve optimum performance. We studied the performance of the systems in the presence of single-carrier modulation schemes, such as OOK and VOOK, and multi-carrier modulation schemes, such as DCO- and ACO-OFDM. Through extensive numerical results, it is demonstrated that our proposed receiver significantly outperforms traditional SPAD and PD receivers in terms of BER and achievable data rate which validates the effectiveness of the proposed receiver in improving the performance of indoor dimmable VLC systems. In particular, depending on its application, OOK analogue dimming allows the proposed receiver to achieve a higher data rate at low dimming levels compared to the VOOK scheme, while VOOK digital dimming improves the power efficiency of the proposed receiver. Multi-carrier modulation schemes such as DCO- and ACO-OFDM support higher data rates due to their high spectral efficiency but require larger SPAD array sizes and shorter dead times. It is presented that ACO-OFDM performs better at low-light regimes and DCO-OFDM is preferable at high-light regimes.  
Conceptualization, M.H., S.H. and M.S.; data curation, M.H.; formal analysis, M.H., S.H. and M.S.; funding acquisition, M.S.; investigation, M.H.; methodology, M.H., S.H. and M.S.; project administration, M.H. and M.S.; resources, M.H.; software, M.H.; supervision, M.S.; validation, S.H. and M.S.; visualization, M.H., S.H. and M.S.; writing--original draft, M.H.; writing--review and editing, M.H., S.H. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant number EP/S016570/1 (TOWS).  
Not applicable.  
The authors declare no conflict of interest.
# Explainable Machine-Learning Predictions for Peak Ground Acceleration
## 3 Establishment of the Machine Learning Model
### Algorithm Principle
#### 3.1.3 XGBoost Regression Model  
XGBoost is an ensemble learning algorithm designed by [PERSON] and [PERSON] [43], it is committed to making the lifting tree break through its own computational limit to achieve the engineering goal of fast operation and superior performance. It uses the gradient descent framework to improve the weak learner, which is an efficient implementation of the GB (Gradient Boosting) algorithm. Compared with other GB algorithms, XGBoost can achieve better prediction performance in a short time.  
For a given training set \(N=\{(x_{1},y_{1}),(x_{2},y_{2}),\,\ldots,\,(x_{n},y_{n})\}\), select a sample \((x_{i},y_{i})\), and \(x_{i}\in R^{n},y_{i}\in R^{n},i=1,2,\,\ldots,\,n\). Then, \(K\) additivity functions are used to predict the samples, and the results are as follows:  
\[\hat{y_{i}}=\sum_{i=1}^{n}f_{k}(x_{i}), \tag{1}\]  
where \(x_{i}\) is the n dimensional eigenvector of the \(i\)th sample; \(y_{i}\) is the true value of the \(i\)th sample; \(\hat{y_{i}}\) is the predicted value of the \(i\)th sample; \(f(x)\) is a function that maps the feature to the leaf node weight of the tree structure, which can be expressed as \(f(x)=\omega_{q}(x)\); \(\omega\) is the weight of the leaf node; and \(q\) represents a tree structure.  
The objective function of the XGBoost regression model is composed of the loss function and regularization, which ensures the accuracy and generalization ability of the model. The objective function formula is as follows:  
\[Obj=\sum_{i=1}^{n}l(y_{i},\hat{y_{i}})+\sum_{k=1}^{K}\Omega(f_{k}), \tag{2}\]  
where \(Obj\) is the objective function, \(l(y_{i},\hat{y_{i}})\) is the loss function, and \(\Omega(f)\) is used to control the complexity of the model and prevent overfitting. The model is trained in the following iterative way:  
\[\hat{y_{i}}^{(t)}=\hat{y_{i}}^{(t-1)}+f_{t}(x_{i}), \tag{3}\]  
where \(\hat{y_{i}}(t)\) is the predicted value of the \(i\)th sample after the \(i\)th iteration.  
The iterative form of the objective function is  
\[Obj^{(t)}=\sum_{i=1}^{n}l\Big{(}y_{i},\hat{y_{i}}^{(t-1)}+f_{t}(x_{i})\Big{)} +\Omega(f_{t}). \tag{4}\]  
If the loss function is expanded by second-order Taylor expansion, the objective function can be written in the following form:  
\[Obj^{(t)}=\sum_{i=1}^{n}\bigg{[}l\Big{(}y_{i},\hat{y_{i}}^{(t-1)}+g_{i}f_{t} (x_{i})\Big{)}+\frac{1}{2}h_{i}f_{t}{}^{2}(x_{i})\bigg{]}+\Omega(f_{t}), \tag{5}\]  
where  
\[g_{i}=\partial_{g_{i}^{t-1}}l(y_{i},\hat{y_{i}}^{(t-1)}), \tag{6}\]\[h_{i}=\partial_{g_{i}^{t-1}}^{2}l(y_{i},\hat{y}_{i}^{(t-1)}). \tag{7}\]  
It can be seen that the objective function of XGBoost mainly depends on the first-order partial derivative and second-order partial derivative of the loss function, which is also a great advantage of this method. As long as the first-order partial derivative and second-order partial derivative are provided, XGBoost can support the use of a user-defined loss function.
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 3 Methodology
### Tuna Swarm Algorithm for Optimizing the Network  
The selection of hyperparameters in the network critically affects the prediction accuracy and efficiency of backpropagation. To address issues such as gradient explosion or vanishing gradients, the TSO is employed to determine the optimal hyperparameters. The outcomes of backpropagation updates are considered the best solutions under this schema.  
The TSO algorithm, a novel metaheuristic optimization approach introduced by [PERSON] et al. [43], mimics the predatory behavior of tuna. It utilizes three distinct search strategies: random search, spiral search, and parabolic search, to locate optimal solutions.  
During the model tuning phase, the improved TSO utilizes the forecasting deviation of the network as the evaluation metric. This setup adheres to a triple-random selection method to continuously update the best solution until the termination condition of the iterations is met. Figure 5 illustrates the workflow for optimizing the TCN and DPMI module using the improved TSO.