esa-sceva/satcom-chunk-collection huggingface dataset:
# Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization
## 2 Materials and Methods
### Proposed Approach
#### 2.2.3 Internal Robustness Measure  
Addressing the issue of robustness requires a formal definition. The measure presented in this study encompasses the variations of the objective functions and the decision variables.  
The robustness measure \(R\) is formulated as:  
\[R=\frac{1}{n_{s}}\sum_{i=0}^{n_{s}}\frac{\overline{\rho}_{i}^{f}}{\overline{ \rho}_{i}^{x}}, \tag{10}\]  
with  
\[\overline{\rho}_{i}^{f}=\frac{1}{m}\sum_{j=1}^{m}|f_{ij}-f_{j}(a)|\quad\forall i \in\{1,\ldots,n_{s}\}, \tag{11}\]  
\[\overline{\rho}_{i}^{x}=\frac{1}{n}\sum_{j=1}^{n}|x_{ij}-x_{j}(a)|\quad\forall i \in\{1,\ldots,n_{s}\}, \tag{12}\]  
where \(x_{ij}\) and \(f_{ij}\) are the \(j\)-th variable and objective of the \(i\)-th sample point, respectively, \(n_{s}\) is the number of sample points, \(x_{j}(a)\) and \(f_{j}(a)\) refer the \(j\)-th variable and objective of population member \(a\).  
The robustness measure \(R\) estimates how robust a given portfolio is, based on the ratio between the deviations in its neighborhood in the decision and objective spaces. As the objective space is defined by risk and return, the average between two variations is considered, as shown in Equation (11). Since risk is taken as the standard deviation, both risk and expected return deviations are expressed in the same scale. The average value of deviations is also considered for multiple weights in portfolio, as given in Equation (12). The neighborhood of a given portfolio is explored by a number of sample points. The value of \(R\) is obtained by averaging over the ratios of deviations, as shown in Equation (10). The smaller the value of \(R\), the more robust the solution.  
The estimation of the robustness measure depends largely on the sample of points involved in its calculation. A common approach to sampling consists in generating a number of points in the neighborhood of the given solution. For the portfolio optimization problem, this process is complicated by a set of cardinality constraints that must be satisfied by every sampling point to obtain a feasible portfolio. The outline of the developed sampling procedure is shown in Algorithm 3.  
```
1:Input: \(\mathbf{x}(a)\), \(l_{j}\), \(u_{j}\)\(\forall j\in\{1,\ldots,n\}\), \(C^{\min}\), \(C^{\max}\), \(\delta\)
2:for\(j\in\{1,\ldots,n:\;x_{j}(a)\
eq 0\}\)do
3:if\(x_{j}(a)-l_{j}<\delta\)then
4:\(r\sim\mathbb{U}(0,\delta)\)
5:elseif\(u_{j}-x_{j}(a)<\delta\)then
6:\(r\sim\mathbb{U}(-\delta,0)\)
7:else
8:\(r\sim\mathbb{U}(-\delta,\delta)\)
9:endif
10:\(x_{j}\gets x_{j}+r\)
11:endfor
12:\(\mathbf{x}\leftarrow\mathbf{x}/\|\mathbf{x}\|_{1}\), ensuring \(\forall j\in\{1,\ldots,n:\;x_{j}\
eq 0\}:x_{j}\in[l_{j},u_{j}]\)
13:Output: \(\mathbf{x}\)
```  
**Algorithm 3** Sampling  
For every investment greater than zero, the perturbation is performed in a feasible direction. To ensure that the budget constraint 3, the decision vector \(\mathbf{x}\) is projected onto the unit hyperplane, which, in turn, corresponds to a feasible portfolio investment scenario.
# A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals
## 4 Conclusions  
This paper proposes an identity authentication method based on a two-dimensional PPG signal and lightweight convolutional neural network. Through innovative data processing and model design, the accuracy of identity authentication is greatly improved, which is especially suitable for the application scenario of wearable devices with limited resources and high real-time requirements. Firstly, compared with the traditional deep learning method of one-dimensional time series data, this method converts a one-dimensional PPG signal into a two-dimensional image by using the Markov transfer field method after filtering. This conversion not only retains the critical information of the PPG signal but also contains more spatial transformation information, thus improving the recognition accuracy. Secondly, a lightweight convolutional neural network (LW-CNN) is designed for wearable devices with limited resources and high real-time performance. Compared with traditional deep learning methods, LW-CNN has the advantages of fewer parameters and a faster running time while maintaining a higher accuracy recognition rate. To sum up, the identity authentication method proposed in this paper is based on two-dimensional PPG data and a lightweight convolutional neural network, which improves recognition accuracy and is especially suitable for application scenarios such as wearable devices. In subsequent research, an identity authentication method with less energy consumption, higher robustness, and higher recognition accuracy should be designed for wearable devices with parameters such as small size, low power consumption, and limited computing resources.  
Conceptualization, F.Z., X.Z. and Z.H.; Data curation, X.Z.; Formal analysis, X.Z.; Investigation, X.Z.; Methodology, F.Z., X.Z. and Z.H.; Project administration, F.Z.; Resources, F.Z.; Software, X.Z.; Supervision, F.Z.; Validation, X.Z.; Visualization, X.Z.; Writing--original draft, X.Z.; Writing--review and editing, F.Z. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & \begin{tabular}{c} **Total Number of** \\ **Parameters** \\ \end{tabular} & \
\begin{tabular}{c} **Total Parameter** \\ **Size (MB)** \\ \end{tabular} &
\begin{tabular}{c} **Iteration Time per** \\ **Iteration (S)** \\ \end{tabular} \\ \hline LW-CNN & 44,016 & 0.17 & 7.82 \\ AlexNet & 1,048,784 & 4.09 & 12.08 \\ (trained from initial) & 1,668,608 & 6.36 & 10.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the performance of LW-CNN, AlexNet, and GoogleNet.
## 3 Results and Discussion
### Effect of Background Chemicals on ILs Adsorption
#### 3.5.1 Effect of Silica and Kaolinite  
The silica (SiO\({}_{2}\)) and some clays, such as kaolinite (Al\({}_{4}\)[Si\({}_{4}\)O\({}_{10}\)](OH)\({}_{8}\)), illite (K\({}_{1\text{-}1.5}\)Al\({}_{4}\) [Si\({}_{7\text{-}6.5}\)Al\({}_{1\text{-}1.5}\)O\({}_{20}\)](OH)\({}_{4}\)), montmorillonite ((0.5 Ca Na)\({}_{0.7}\)(Al Mg Fe)\({}_{4}\)[(Si Al)\({}_{8}\)O\({}_{20}\)] In H\({}_{2}\)O), and so forth, are common minerals in the unconventional oil ones [44; 45]. As demonstrated in Figure 7a,b, the addition of SiO\({}_{2}\) and kaolinite exerted slight effect on the adsorption ratio of [Bmim][BF\({}_{4}\)]. It implied that the entrance of a small amount of SiO\({}_{2}\) and kaolinite into the aqueous solution during the washing process would not influence the adsorption. The insignificant adsorption capacity of SiO\({}_{2}\) for ILs is mainly attributed to the relatively hydrophilic surfaces of minerals. The kaolinite is a layered aluminosilicate with 1:1 dioctahedral layered structure. Each layer consists of a sheet of SiO\({}_{4}\) tetrahedra connected via common oxygen atoms to a sheet of AlO\({}_{6}\) octahedra. The 1:1 layer cannot expand due to the hydrogen bonds between the two sheets [46]. As a result, the kaolinite exhibited a low adsorption potential for ILs.  
Figure 6: Pseudo-first- and pseudo-second-order kinetics for adsorption of ionic liquids ([Bmim][BF\({}_{4}\)] and [Emim][BF\({}_{4}\)]) onto ion-exchange resin (Amberlite IR 120 Na).  
Figure 7: Effect of background chemicals (**a**) SiO\({}_{2}\), (**b**) kaolinite on the adsorption of ionic liquid ([Bmim][BF\({}_{4}\)]) onto ion-exchange resin (Amberlite IR 120 Na).
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 4 Proposed Algorithm
### Complexity Analysis
#### 4.3.2 GS iteration  
Equation (19) can be expressed as \((\mathbf{D}+\mathbf{L})\hat{s}^{(i)}=\hat{\mathbf{y}}-\mathbf{L^{H}\hat{s}^{(i-1)}}=\mathbf{c}\). After \(i\) iterations, the calculation of \(\hat{s}^{(i)}\) mainly comes from the following two steps: First, \(\mathbf{c}\) is a \(2\mathbf{K}\times 2\mathbf{K}\) strictly lower triangular element matrix; \(\mathbf{2K}\times\mathbf{2K}\) and the \(\mathbf{2K}\times\mathbf{1}\) vector \(\hat{s}^{(i-1)}\) are multiplied, and \(\mathbf{c}\) must be multiplied \(\left(\mathbf{2K^{2}}-\mathbf{K}\right)\) times. Second, in Equation (19), the \(m\)th element, \(\hat{s}^{(i)}_{m}\), can be expressed as:  
\[\hat{s}^{(i)}_{m}=\left\{\begin{array}{cc}\frac{C_{1}}{L1},\ m=1\\ \frac{c_{m}-\sum_{k=1}^{m-1}\hat{s}^{(i)}_{k}\mathbf{I_{mk}}}{L_{mm}},\ m=2,\ \dots,\ 2\mathbf{K} \end{array}\right. \tag{22}\]  
where \(\mathbf{c}_{m}\) represents the \(m\)th element of \(\mathbf{c}\), and \(\mathbf{L}_{mk}\) represents the \(m\)th row and \(\mathbf{k}\)th column element of the lower triangular matrix \((\mathbf{D}+\mathbf{L})\). When \(m=\mathbf{1}\), it is obvious that \(\hat{s}^{(i)}_{1}\) requires \(2\mathbf{K}\) multiplications, and all \(\hat{s}^{(i)}_{m}(\mathbf{m=2},\ \dots,\ \mathbf{2K})\) require \(\left(\mathbf{2K^{2}}-\mathbf{K}\right)\) multiplications, so a total of \(\mathbf{2K^{2}}\) multiplications are required for each iteration.
## 4 Numerical Simulation
### Damage Identification Based on the Proposed Method
#### 4.6.4 Damage Case 4
When the eleven substructures of the second floor are to be identified using the first three mode shapes and first six natural frequencies, the time of optimization is about 0.43 s using the improved objective function, while it is about 20.96 s using the traditional objective function. The number of the damage factors (optimization parameters) is increased by three, which is from 8 to 11. When using the traditional objective function, the total time of optimization is accordingly increased to a significant degree. However, when using the improved objective function, the column or beam substructure is much simpler than the previous floor substructure, so the number of the selected virtual distortions is reduced by the SVDM from about 50 to about 12, and the time is reduced from 0.83 s to 0.43 s. This illustrates the high efficiency of the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No** & **Theory** & **The Improved Method** & **The Traditional Method** \\ \hline \multirow{5}{*}{Column} & 7 & 1.0000 & 0.7586 & 0.9992 \\  & 8 & 1.0000 & 0.7669 & 0.9988 \\  & 9 & 1.0000 & 0.8834 & 0.9980 \\  & 10 & 0.5000 & 0.9319 & 0.9964 \\  & 11 & 1.0000 & 0.9388 & 0.8254 \\  & 12 & 1.0000 & 0.9564 & 0.5761 \\ \hline \multirow{5}{*}{Beam} & 54 & 1.0000 & 0.9909 & 0.9982 \\  & 55 & 1.0000 & 0.9931 & 0.9999 \\ \cline{1-1}  & 56 & 1.0000 & 0.9960 & 0.9998 \\ \cline{1-1}  & 57 & 1.0000 & 0.9967 & 0.9998 \\ \cline{1-1}  & 58 & 1.0000 & 0.9970 & 0.9999 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Damage identification results with 11 damage factors of 2 nd floor. Figure 7: The location of 10 th column and the 55 th beam.
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
###### Abstract  
Soil temperature is a critical environmental factor that affects plant development, physiological processes, and overall productivity. This study compares two modeling approaches for predicting soil temperature at various depths: (i) fuzzy logic-based systems, including the Mamdani fuzzy inference system (MFIS) and the adaptive neuro-fuzzy inference system (ANFIS); (ii) supervised machine learning algorithms, such as multilayer perceptron (MLP), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), along with multiple Linear regression (MLR) as a statistical benchmark. Soil temperature data were collected from Tokat, Turkiye, between 2016 and 2024 at depths of 5, 10, 20, 50, and 100 cm. The dataset was split into training (2016-2021) and testing (2022-2024) periods. Performance was evaluated using the root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R\({}^{2}\)). The ANFIS achieved the best prediction accuracy (MAE = 1.46 \({}^{\circ}\)C, RMSE = 1.89 \({}^{\circ}\)C, R\({}^{2}\) = 0.95), followed by RF, XGB, MLP, KNN, SVR, MLR, and MFIS. This study underscores the potential of integrating machine learning and fuzzy logic techniques for more accurate soil temperature modeling, contributing to precision agriculture and better resource management.  
fuzzy logic; Mamdani; Sugeno; machine learning; soil temperature Article
## 4 Results and Comparison
### Thermal Prestressing Effect
#### 4.3.3 Number of Earth-Anchored Cables  
The influence of the number of earth-anchored cables on the effects of the new closing method is examined. Three cases are compared: the case with six pairs of earth-anchored cables at the each abutment (L-EA-6, Case-L1); the case with two pairs of earth-anchored cables (L-EA-2, Case-L6); and the case with seven pairs of earth-anchored cables (L-EA-7, Case-L7). The heating length, heating region, and temperature rise are all the same of Case-L1.  
The girder moments and axial forces generated by the heating and heat removal processes are represented in Figure 16. As the number of earth-anchored cables is increased, the generated girder moments in the side span increase while those in the main span decrease. In addition, the generated tensile forces in the girders decrease with the increment of the number of earth-anchored cables. The deflections of the girders increase in the side span, but decrease in the main span with the increment of the number of earth-anchored cables (Figure 17).  
Figure 16: Member forces generated by heating and heat removal with various numbers of earth-anchored cables. (**a**) Girder bending moments; (**b**) Girder axial forces.  
With regard to axial girder forces in the state of completed construction, the three cases show decreased axial compressive forces compared with those in the case without using the new prestressing method (Figure 18). The magnitude of the decreased axial forces is reduced with the increment of the number of earth-anchored cables, as is predicted in Figure 16b. The ratio of the decrease is small since the magnitudes of the peak axial forces are relatively large.  
With regard to cable forces in the state of completed construction, the earth-anchored cables show increased cable forces when using the applied closing method (Figure 19). The other cables anchored to the side span girders located near the earth-anchored cables show decreased cable forces. The increase of cable forces resulting from the effects of the new method is more notable in the case with a small number of earth-anchored cables.  
Figure 17: Displacements generated by heating and heat removal with various numbers of earth-anchored cables.  
Figure 18: Axial girder forces with various numbers of earth-anchored cables.  
From the results, it is clear that the number of earth-anchored cables affects the applied key segment closing method for a partially earth-anchored cable-stayed bridge with a long span. However, the variation of the major member forces is relatively small.
# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
## 2 Related Work
The training dataset consisted of six letters performed 16 times by eight users. A system was able to translate fingerspelling into a written word with a recognition accuracy between 85%-92%. The challenging problem of fistlike signs recognition, occurring in ASL, was described in [38]. In the proposed method, the area of several polygons defined by fingertip and palm positions, and estimated using the Shoelace formula, was used. The letters from the ASL alphabet were classified using the decision trees (DT). Seven letters performed 30 times by four persons were used as a training set. For 100 repetitions of each gesture by another user, the method achieved the recognition accuracy of 96.1%. In [39], static hand postures recognition for a humanlike robot hand was described. Ten digits from ASL were recognized using the multiclass-SVM classifier. The feature vector consisted of the distances between the palm position and each fingertip and the distance between fingertips. The method was validated using a test dataset composed of 2000 static posture samples with an accuracy of 98.25%. In [40], five one-handed and five two-handed static gestures of Turkish Sign Language, performed three times by two users, were recognized using artificial neural networks, deep learning, and decision trees. The 3D positions of all bones in the skeletal hand model, measured by the LM controller, were used. The recognition accuracies between 93% and 100% were achieved, depending on the classifier and the number of features used. In [41], a deep-learning-based method for skeleton-based hand gesture recognition was described. The network architecture consists of a convolutional layer for extracting features and a long, short-term memory layer for modeling the temporal dimension. Ten static and ten dynamic hand gestures performed 30 times were recognized with an accuracy of 99%. Based on the literature review, the following conclusions can be drawn:  
* Despite that the currently available devices for obtaining skeletal data are imperfect, we have recently observed a significant increase in interest of using this modality for gesture recognition. In the last two years, several new publications were recorded. * Most authors do not provide data used in experiments, which makes verification and comparative analysis difficult. Only three datasets available in works [15; 18; 24; 34] are known to the authors. * Many cited works omit tests using the leave-one-subject-out protocol. These tests are more reliable because they show the method's dependence on the person performing gestures. * Some of the proposed feature vectors use features directly measured by the sensor. They are not independent of the size of the hand. * Some of the works relate to the recognition of dynamic gestures, in which the hand movement trajectory is a great help. The problem of recognizing hand postures based on skeletal data has not been fully solved, and further work in this area is advisable.
## 3 Results
### Performance with New Subjects  
To assess how general the proposed solution is, additional samples were collected from two new subjects, who were not involved in the previous study. Each new subject recorded 20 samples per gesture, resulting in 480 new samples to be classified. The new samples were fed to the existing best resulting classification algorithm (i.e., LSTM with 2 hidden layers and 32 neurons each) without any retraining. The confusion matrices are shown in Figure 8. It can be seen that the accuracy is 92.16% and 96.09% for the recognition of 12 and 10 gestures, respectively. This correspond to a respective accuracy drop of 2.24% and 1.21 %, which is considered insignificant. By comparison, the work [10] reported up to a 40% drop when the classification is executed on samples from a subject not included in the training set.
# Virtual Flux Control Methods for Grid-Forming Converters: A Four-Method Comparison
###### Abstract  
The increasing penetration of renewable energy generation in recent years has introduced significant changes and challenges to modern power systems. One of the most critical challenges is the reduction in system inertia, which decreases grid stability and subsequently weakens the electrical network. To address this issue, grid-forming (GFM) converters have emerged as a promising solution to maintain stability in weak grids. This paper proposes three novel control schemes for GFM converters and compares them with the performance of another topology recently published by the same authors. The four evaluated control schemes are based on the virtual flux variable which allows current limiting without using internal current loops, improving the stability of the control system. The assessment includes methods based on PI regulators, using the mathematical flatness property of differential algebra, direct control (DC), and model predictive control (MPC). The results demonstrate the robustness and correct operation of all four control strategies as GFM converters. Furthermore, through tests involving disturbances such as frequency variations, voltage sags, phase jumps, and transitions to islanded mode, their differences in terms of dynamic response, switching frequency, and current quality are clearly evidenced.  
current limiter; direct control; field-oriented control; flatness; grid forming; model predictive control; virtual flux Article
# Effect of High-Temperature Pavement Paving on Fatigue Durability of Bearing-Supported Steel Decks
## 2 Background of Project  
The Nanjing Yangtze River Bridge (NYRB) was selected as the object of this study. The NYRB, opened to traffic in 1968, was the first bridge to cross the Yangtze River in China. Additionally, it was the first self-designed double-deck railway and highway bridge in China. The NYRB was recorded as \"the world's longest bridge with dual functions of railway and highway\" by the Guinness Book of World Records in the 1960s. The NYRB is not only the important pathway crossing rivers in eastern China, but also one of the transportation junctions serving Nanjing City. After nearly 50 years of service, the railway maintains good working performance, however, the highway composed of concrete deck slabs has faced a series of defects (e.g., pothole and rutting of pavement, serious corrosion of expansion joint, cracking of the concrete slab). To improve the service properties of the NYRB, the concrete deck slabs have been replaced by orthotropic steel deck (OSD) (Figure 3).  
The reconstruction project began in October 2016 and was completed in December 2018. The railway kept in service during the whole reconstruction process. The OSD was placed above the main beams using a set of tension-compression bearings. After that, pouring Gaussian asphalt (PGA) concrete, a widely used pavement material for OSD, was adopted in the pavement paving process (see Figure 3). Generally, PGA has the highest temperature (reaching 220-260 \({}^{\circ}\)C) among all paving materials. As introduced in Section 1, it might affect the fatigue performance of OSD, which is mainly focused on in this study.