esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
These approaches commonly adopted machine learning algorithms and can be categorized into three types: generative approaches, discriminative approaches, and heuristic approaches [13]. Generative approaches, such as naive Bayes [14] and hidden Markov model [15], are flexible, capable of dealing with uncertainty in the data. However, they suffer from the requirement of a large amount of data for training. Discriminative approaches, such as decision tree (DT) [16], K-nearest neighbor [17], conditional random fields [18], support vector machine (SVM) [19] and artificial neural network [20], learn the features mappings to activity labels by creating the decision boundaries in the feature space. For example, DT classification models have been successfully adopted for separating static activities from dynamic activities [21]. In terms of dealing with more complex activates, SVM and artificial neural network classification methods have been concerned due to their capability of separating non-linearly separable classes [22]. Recent literatures have witnessed the increasing application of deep learning on activity recognition task [23]. They have advantages of robustness in prediction and computationally efficient; however, they face the problem of overfitting. Heuristic methods use a combination of both, which can generally achieve better performance than any single one [24]. In addition, recent results in addressing multi-class classification problems has indicated that the adoption of ensembles of classifier models leads to increased classification performance over using only single classifier models [25]. However, much of the previous work on ensembles of classifier models considered only the same type of classifier models [26]. The single classifier has its own strengths as well as weakness when dealing with the different classes during the learning process. Moreover, the voting of various algorithms can decrease the bias among the classes occurring in the usage of a single learning algorithm, therefore result in a relatively generalized classification [27]. In this study, we focus on heterogeneous ensemble of classifiers, which consist of classifiers are not of the same type. Our goal is to determine if heterogeneous ensemble can be used to improve the classification performance by combining various classifiers though the analysis of their classification performance on different activity classes. Our approach is based on an ensemble of heterogeneous classifiers. We have created a dataset with measurements related to different activities (of all types, including transitional activities) and have compared the performance of using isolated classifiers with several topologies of ensemble classifiers.
# Hybrid Fuzzy Regression Analysis Using the \(F\)-Transform
## 2 Preliminaries  
Fuzzy set theory was introduced to provide a suitable concept for dealing with inaccurate data [2]. Following [3; 4], we introduce some definitions required to develop the main results such as the fuzzy sets and the fuzzy numbers.  
A fuzzy set \(A\) is a set of ordered pairs  
\[A=\{(x,\mu_{A}(x)):x\in A\},\]  
where \(\mu_{A}:X\rightarrow[0,1]\) is a membership function of \(A\).  
The support of \(A\) defined on \(\mathbb{R}\) is a crisp set defined as  
\[suppA=\{x|x\in\mathbb{R},\mu_{A}(x)>0\}.\]  
A fuzzy number, \(A\), is a normal and convex subset of the real line, \(\mathbb{R}\), with bounded support. As a special case, a fuzzy number \(A\), denoted by \(A=(m_{A},s_{l_{A}},s_{r_{A}})_{LR}\), is said to be a \(LR\)-fuzzy number if its membership function is denoted by  
\[\mu_{A}(x)=\left\{\begin{aligned} L\left(\frac{m_{A}-x}{s_{l_{A}} }\right)&\text{for}\quad 0\leq m_{A}-x\leq s_{l_{A}},\\ R\left(\frac{x-m_{A}}{s_{r_{A}}}\right)&\text{for} \quad 0\leq x-m_{A}\leq s_{r_{A}},\\ 0&\text{for otherwise,}\end{aligned}\right.\]where \(m_{A}\) is the center, \(s_{l_{A}}\) and \(s_{r_{A}}\) are the left spread and the right spread. \(L\) and \(R\) are functions verifying the properties of the class of fuzzy sets such that \(L(0)=R(0)=1\) and \(L(x)=R(x)=0\), \(x\in\mathbb{R}\backslash[0,1)\). In particular, if \(L(x)=R(x)=1-x\) in \(A=(m_{A},s_{l_{A}},s_{r_{A}})_{LR}\), then \(A\) is called a triangular fuzzy number and is denoted by \(A=(m_{A},s_{l_{A}},s_{r_{A}})_{T}\).  
For any \(\alpha\) in \([0,1]\), the \(\alpha\)-level set of a fuzzy set \(A\) is a crisp set \(A_{\alpha}=\{x\in\mathbb{R}:\mu_{A}(x)\geq\alpha\}\) that contains all the elements in \(\mathsf{X}\) that have membership value in A greater than or equal to \(\alpha\).  
\(F\) (Fuzzy)-transform was introduced by [PERSON] [17] in 2001. Here, some basic concepts from [18] are introduced.  
**Definition 1**.: _Let \(c_{1}<\cdots<c_{k}\) be fixed nodes within \([a,b]\), such that \(c_{1}=a,c_{k}=b\) and \(k\geq\)2. We say that fuzzy sets \(A_{1},\cdots,A_{k}\), identified with their membership functions \(A_{1}(x),\cdots,A_{k}(x)\) defined on \([a,b]\), constitute a fuzzy partition of \([a,b]\) if they fulfill the following conditions for \(j=1,\cdots,k\):_  
1. \(A_{j}:[a,b]\rightarrow[0,1]\)_,_ \(A_{j}(c_{j})=1\)_;_
2. \(A_{j}(x)=0\) _if_ \(x\
ot\in(c_{j-1},c_{j+1})\) _where for the uniformity of denotation, we put_ \(c_{0}=a,c_{k+1}=b\)_;_
3. \(A_{j}(x)\) _is continuous;_
4. \(A_{j}(x)\) _strictly increases on_ \([c_{j-1},c_{j}]\) _(_\(j=2,\cdots,k\)_) and_ \(A_{j}(x)\) _strictly decreases on_ \([c_{j},c_{j+1}]\) _(_\(j=1,\cdots,k-1\)_)_
5. _For all_ \(x\in[a,b]\)_,_ \[\sum_{j=1}^{k}A_{j}(x)=1.\]  
The membership functions \(A_{1}(x),\cdots,A_{k}(x)\) are called symmetric basic functions. An example of fuzzy sets \(A_{1},\cdots,A_{k}\) with symmetric triangular membership functions on the interval \([a,b]\) is given
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 3 RF-GPR Prediction Model of Cable Temperature  
### Selection of Characteristic Variables  
Considering the influence of the cable trench environmental factors, cable geometric structure and electromagnetic parameters on the temperature distribution inside the cable trench, different parameter combinations were selected in the parameters' setting range for finite element calculation, and, after obtaining several groups of calculation results, the data that can reflect the cable temperature change were extracted. Geometric parameters, electromagnetic parameters and environmental factors were taken as the input characteristic quantity, and the temperature distribution inside the cable trench was taken as the output characteristic quantity. The input quantity and output quantity were combined to obtain a data set that can reflect the change in temperature distribution in the cable trench, which was used as the data set of the machine learning model prediction. In this paper, the influence of various variables on the temperature distribution in the cable trench was analyzed through the finite element calculation results, and a 10-dimensional vector was constructed as a set of input characteristic quantities. The 10-dimensional data are shown in Table 1.
## 4 Experimental Results
### Objective Quality Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Packet Loss Rate**} & \multicolumn{2}{c}{**STOI**} & \multicolumn{2}{c}{**ASTOI**} \\ \cline{2-6}  & **Non\(-\)PLC** & **DNN\(-\)PLC** & **DNN+GLA\(-\)PLC** & **WSOLA\(-\)PLC** & **DNN+WSOLA\(-\)PLC** \\ \hline
5\% & 0.9591 & 0.84\% & 1.15\% & 0.83\% & **2.04\%** \\
10\% & 0.9191 & 1.62\% & 2.25\% & 1.58\% & **4.00\%** \\
15\% & 0.8889 & 2.19\% & 3.04\% & 2.15\% & **5.32\%** \\
20\% & 0.8553 & 2.76\% & 3.94\% & 2.74\% & **6.82\%** \\
30\% & 0.7842 & 4.02\% & 5.82\% & 3.73\% & **9.59\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: STOI results. It should be noted that the DNN\(-\)based PLC methods (DNN\(-\)PLC, DNN\(+\)GLA\(-\)PLC, and DNN\(+\)WSOLA\(-\)PLC) use the same amplitude information. From Table 3, it can be seen that, compared with non\(-\)PLC, the phase spectrum closer to the real one will bring smaller LSD. Among them, the proposed PLC method based on time domain phase correction and DNN achieves the best result. In each packet loss rate, the DNN\(+\)GLA\(-\)PLC method with lower LSD can reduce speech distortion since the GLA can correct the phase spectrum. For the WSOLA\(-\)PLC method, LSD is very sensitive to the amplitude distortion caused by speech segment superposition. Although its LSD is lower than non\(-\)PLC, it still causes high speech distortion. Because the DNN\(+\)WSOLA\(-\)PLC method maintains the period of speech signal, it has better phase spectrum, and achieves the lowest LSD score under all packet loss rates. In addition, because speech signal in the PLC experiment is encoded and decoded by Speex codec [2] to simulate the communication process, the LSD results in Table 3 show that the proposed DNN\(+\)WSOLA\(-\)PLC method has high ability for packet loss concealment and can improve the decoding quality of speech in the burst packet loss environment. To sum up, the DNN\(+\)WSOLA\(-\)PLC method utilized the strong nonlinear mapping ability of the DNN to learn the log\(-\)power spectrum for enhancing the amplitude. At the same time, it used the amplitude spectrum to correct the phase spectrum in the frequency domain and used the WSOLA method to expand the signal in the time domain. This means periodic characteristics of speech signal were unchanged and it achieved better PLC performance. The above results show that the proposed methods are effective and better than the reference methods.
## 3 Experiments and Discussions
### Calibration and Comparison Experiments of Angular Error Measurement  
Firstly, the basic performances of the miniature autocollimator kit, including stability and measurement accuracy, were tested. A commercial low-cost collimated laser diode with a nominal wavelength of 635 nm and a divergence angle less than 0.3 mrad was adopted as the laser source. A high-precision QPD (SPOT-4 DMI, OSI Optoelectronics, California, USA) with an active area of 0.25 mm per element, an element gap of 13 \(\upmu\)m, and a responsivity of 0.65 A/W was chosen as the angle detector. The sampling frequency was set to be 10 Hz, and a Butterworth filter was used to reduce the noise level. Figure 6 shows the stability of the angular errors over a duration of 1 h. As seen, the stability of the output signals for measuring the pitch angle error and yaw angle error were approximately 0.55 and 0.35 arcsec, respectively. It should be noted that, although the laser beams for measuring the pitch and yaw angle errors were from an identical laser diode, due to the non-uniformity of the output intensity distribution of the laser diode, the stability in pitch and yaw angle error measurements were not similar, but inversely correlated in Figure 6. The reason for the larger pitch variation could be due to the mechanical vibration. It can be seen that the developed miniature autocollimator kit satisfied stability for angular error measurements with sub-arcsecond precision.  
Prior to the testing the nanopositioning stage's angular error, the miniature autocollimator kit was calibrated by a commercial laser interferometer (HP5529A, Keysight Technologies, California, USA), which has an angle measurement accuracy of \(\pm\)0.2% of displayed value and \(\pm\)0.05 arcsec per meter of distance traveled. The experiments were repeated five times, and the calibration range was set to be \(\pm\)40 arcsec. The comparative results are shown in Figure 7a,b. It can be seen that the residuals of pitch angle and yaw angle were all within \(\pm\)0.3 arcsec.  
The 3 DOFLM system was then mounted on the designed nanopositioning stage [6] to measure the angular error of the stage. The HP5529A interferometer was also adopted for comparison. The laser beams of both the 3 DOFLM and HP5529A systems were adjusted in line with the motion axis of the stage. Figure 8a,b show the measurement results. It is seen that the output of the 3 DOFLM system was in a good agreement with that of the HP5529A system. The maximum residual of both angles was within \(\pm\)0.5 arcsec in the measurement distance of 20 mm. It is verified that the performance of the designed 3 DOFLM system was acceptable for sub-arcsecond precision angular error measurement.  
Figure 6: Stability of output signals of the miniature autocollimator kit.
# Adaptive Aggregated Attention Network for Pulmonary
## 4 Results and Discussions
### Comparison with State-of-the-Art Methods  
We tested our proposed architecture on the ten folds and obtained the average results across the ten folds. We compared our experimental results with the existing advanced methods, including the 2D network, 3D network, and hybrid feature network. The comparison results are shown in Table 4.  
Based on the comparison results, our model achieves the highest accuracy and F1-score and the lowest FPR. Moreover, the TPR of our model is also very high, second only to attentive and ensemble CNN (AEC) [16]. Compared to AEC, the metric TPR of our model is slightly lower, but the ACC, FPR, and F1-score are better than those of AEC. A large number of deep features are extracted through 3D DPN, which is effective in reusing features, and the original features of the raw images are captured as much as possible because of the residual connection and dense connection. Based on the rich features, the residual attention module and squeeze-and-excitation attention module are utilized to capture the spatial attention features and contextual attention features. Besides, we proposed a multi-scale attention module to capture attention features of different scales, and the different attention features are fused effectively. After the attention operation, the unimportant features were filtered out, and the salient and fine-grained features were captured, which are important  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Year** & **ACC** & **TPR** & **FPR** & **F1-Score** \\ \hline [PERSON] et al. [27] & 2015 & 75.01 & 83.35 & 39.0 & 70.44 \\ Multi-scale CNN [28] & 2015 & 86.84 & - & - & - \\ Slice-level 2D CNN [29] & 2016 & 86.70 & 78.60 & 8.8 & 84.43 \\ Nodule-level 2D CNN [29] & 2016 & 87.30 & 88.50 & 14.0 & 87.23 \\ Vanilla 3D CNN [29] & 2016 & 87.40 & 89.40 & 14.8 & 87.25 \\ Multi-crop CNN [13] & 2017 & 87.14 & - & - & - \\ Deep 3D DPN [15] & 2018 & 88.74 & - & - & - \\ Nodule Size + Pixel + GBM [15] & 2018 & 86.12 & - & - & - \\ DeepLung [15] & 2018 & 90.44 & 81.42 & - & - \\ [PERSON] et al. [18] & 2018 & 89.53 & - & - & - \\ [PERSON] et al. [14] & 2018 & 89.50 & - & - & - \\ [PERSON] et al. [30] & 2019 & 91.60 & 86.52 & - & - \\ [PERSON] et al. [31] & 2019 & 91.81 & 88.53 & - & - \\ [PERSON] [32] & 2020 & 88.41 & 85.38 & - & 78.83 \\ Attentive and ensemble CNN [16] & 2020 & 90.24 & 92.04 & 11.06 & 90.45 \\ [PERSON] et al. [17] & 2020 & 91.67 & - & - & - \\ [PERSON] et al. [33] & 2020 & 88.00 & 82.00 & - & - \\ [PERSON] et al. [34] & 2020 & 90.60 & 83.70 & - & - \\ Our proposed model & 2020 & **91.90** & **91.30** & **8.00** & **91.00** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with existing methods across the ten folds.  
Figure 7: The effects of different thresholds on F1-score.  
to differentiate between malignant and benign nodules. At last, based on the comparison results in Table 3, our model is proven to achieve a state-of-the-art level.
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 1 Introduction
For example, pinhole spark discharge (PHD) plasma treatment could increase the content of NO in porcine aortic endothelial cells [24]. Various RONS such as -OH, NO and H\({}_{2}\)O\({}_{2}\) resulting from DBD plasma treatment were the main cause of endothelial cell proliferation and migration and could accelerate wound healing [25]. [PERSON], et al. [26] used microsecond pulsed DBD plasma to generate RONS on the surface of the treated material to stimulate the production of VEGF-A, MMP-9 and chemokine CXCL-1 in mice, thereby inducing angiogenesis of the mouse aortic ring in vitro without damage to the mouse body surface. [PERSON], et al. [27] found that plasma effect could penetrate the wound surface, induce angiogenesis around the wound surface, and promote wound healing. [PERSON], et al. [28] used non-thermal N\({}_{2}\)/Ar microplasma to increase the total RONS concentration in the treated tissue lysis fluid and shorten the healing time of mouse burn wounds, suggesting that RONS could affect the process of angiogenesis and epithelization. So far, the effects of PAM prepared by APPJ on cell angiogenesis have not been reported, and the specific effects and exact mechanism of PAM on angiogenesis need to be further studied. We selected HUVECs (passage 5) as the research model to explore the effects of PAM on angiogenesis and the related mechanism, hoping to lay a theoretical foundation for the application of PAM in promoting wound healing and inhibiting tumor angiogenesis. First, MTT assay and cell cycle assay were performed to determine the effects of PAM on HUVECs viability. Second, the effects of PAM on angiogenesis and cell migration and adhesion were detected with angiogenesis experiment, cell migration experiment and cell adhesion experiment. Next, the concentrations of VEGF and bFGF secreted by HUVECs in the supernatant were determined with ELISA. The relative expression of VEGFR-2 and phosphorylation of MEK were determined with Western blotting. Finally, the content of RONS in cells incubated with PAM was determined.
## 3 Results and Discussion of Biosensor Formation and Its Validation  
### Formation of Successive Layers of the Biosensor  
The process of formation of successive layers of the biosensor was verified. For this purpose, successive layers were formed on one of the active sites of the biosensor: cysteamine, ligand (receptor), analyte, and secondary antibody for FGF-2. The obtained data were used to model the course of the SPR curves using the WinSpall software. The course of the SPR curves is shown in Figure 2.  
Figure 1: Diagram of the biosensor: (**A**) sensitive to VEGF-A and (**B**) sensitive to FGF-2.  
The shifting of the minimum of the SPR curve towards increasing values of the SPR angle proves the formation of successive component layers of the biosensor. The smallest difference in angle is observed for the formation of the cysteamine monolayer, but this is due to its low molecular weight of 77.15 g/mol. In other cases, the changes range from 0.1 to 0.3 degrees.
# Experimental Study on the Soil-Structure Responses Induced by Tunnelling in Limited Space
## 5 Conclusions  
Based on the elaborate laboratory model tests, this study deeply investigated the soil-structural responses induced by tunnel construction in a limited space. The following main conclusions can be drawn from the test results:  
1. The limited space in this study was defined as an underground space surrounded by an underground structure. The limited space could have a significant shielding effect on the soil movement. During the tunnelling process, the soil near the tunnel could be disturbed drastically. On the contrary, the soil outside the limited space was hardly influenced by the excavation of the tunnel.
2. When the excavation was completed, the measured maximum ground subsidence and the range of ground settlement were about 60% and 40%, respectively, smaller than those induced by the construction of a tunnel at the \"greenfield\" site. The results revealed that an existing underground structure could effectively restrain the ground settlement caused by tunnel construction. The existing underground structure may also experience an overall settlement. Moreover, in the limited space, a soil settlement trough developed with the excavation of the tunnel.
3. The tunnel inside the limited space was vertically compressed during the whole test. However, when the excavation was completed, the final ovality of the deformed tunnel was about 23% smaller than the results caused by tunnelling at the greenfield site. In general, the bending moments of the tunnel induced by tunnelling in the limited space were 35% smaller than those obtained from the excavation of the tunnel at the greenfield site. Therefore, the presence of the underground structure can also effectively reduce the deformation of the new tunnel.  
Conceptualization, [PERSON] and J.G.; methodology, [PERSON] and J.G.; formal analysis, J.G.; investigation, J.G.; resources, J.G.; data curation, J.G.; writing--original draft preparation, J.G.; visualization, J.G.; project administration, [PERSON]; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC Grant No. 41807242) and the scientific research project of Shanghai science and technology commission (No. 21 DZ1204403).  
The study did not require ethical approval.  
The study did not involve humans.  
The data presented in this study are available upon request from the corresponding author.  
The authors wish to express their gratitude for the contributions of [PERSON] and [PERSON].  
The authors declare no conflict of interest.
# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 2 Methods and Procedure
### Image Segmentation with SPM  
The segmentation is a process in which the image is divided into homogeneous and non-overlapping regions, where pixels or voxels have similar attributes such as intensity or color [48]. In MRI brain analysis, brain tissue should be classified into three tissue types, namely GM, WM, and CSF [48]. This distinction can be performed employing several different methods, e.g., manual segmentation, intensity-based methods, atlas-based methods, surface-based methods, and hybrid segmentation methods [48]. However, none of them is universally accepted in literature.  
Therefore, in this study, the SPM automatic intensity-based Gaussian segmentation was employed to recognize brain structures of interest and to use them for further analysis. In this method, each tissue class is modeled by a Gaussian distribution [48] and the histograms of brain MRI images have three main peaks corresponding to the three main tissue classes. These three peaks allow distinguishing the brain tissues and performing the correct operation of segmentation. This approach uses a \"modified Gaussian Mixture Model\" (GMM) [49], which is a standard technique widely used by many tissue classification algorithms [50]. The main advantages of the standard GMM are the easy implementation and the small number of parameters that can be efficiently estimated [49; 50].