esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Scientific revolutions and changes in industry have posed new challenges for countries around the world. However, such changes also provide an opportunity for countries to improve their competitiveness [1]. Over the past two decades, many growth feats in countries have been the result of high-tech-based international trade activities. High-tech products drive the performance of countries and lead to positive externalities and dynamism in their economies [2]. Furthermore, many endogenous economic growth theories emphasize that innovative products representing high-tech play an important role in technological progress, creating comparative advantages in trade [3]. Beyond the theoretical framework, many empirical studies have concluded that technological progress promotes international trade, leading to higher economic growth [4]. Therefore, the development of multinational high-tech companies is the means to achieve the above goals. On the contrary, from the perspective of high-tech manufacturers, a right decision on investment destination has a great impact on a company's sustainable development, as well as limiting risks and saving costs for investors. Therefore, this study aims to prioritize investment destinations in the East Asia and South Asia regions for high-tech production lines. This prioritization process determines both the efficiency and the effectiveness of developing countries based on quantitative indicators and qualitative criteria, respectively.  
To address this prioritization problem, a novel composited regret-theory-based spherical fuzzy prioritization approach is proposed. On the one hand, the super-efficiency slack-based model (super-SBM) of data envelopment analysis (DEA) is applied to evaluate the efficiency of thirteen countries in Southeast Asia, based on indicators such as inflation, gross domestic products (GDPs), costs to export, high-technology exports records, and ease-of-doing-business scores. On the other hand, the novel spherical fuzzy regret-theory-based decision-making approach (SfRDMA) is developed and introduced to determine the effectiveness of the thirteen countries, based on ten evaluation criteria. Then, the efficiency and the effectiveness of the countries are combined by a composite-score function that is based on a geometric mean and an arithmetic mean. The novel composited regret-theory-based spherical fuzzy prioritization approach is the primary theoretical contribution of this research to the field of decision science. Meanwhile, the results of destination preferences for high-tech manufacturing moving to Southeast Asia provide the secondary practical contribution of this study.  
The remainder of this paper is set out as follows. A summary of relevant studies is provided in Section 2; A detailed profile of the paper is presented in Section 3. Section 4 provides a discussion, as well as a description of the results of the study. Section 5 provides the paper's conclusions.
###### Abstract
[14] reviewed the production process of existing medium-resolution landcover maps and improved the medium-resolution landcover map generation process using satellite imagery. [PERSON] et al. [15] developed a method to update medium-resolution landcover maps based on various spatial data and used this to support the management of land-based pollutant sources. The estimation of impervious surface area requires a high-resolution landcover map to reflect current land use. At present, it is difficult to trace land use changes in Korea, given the relatively long and irregular four to five years' map updating intervals [16]. Moreover, urbanization-induced dryland has been left barren or used in ways that deviate from the original land use class (such as artificial greenery); this has been aggravated by illegal land use changes and urban sprawl [17]. As a result, the actual land use status is often different from the cadastral land use or cannot be clearly defined, leading to inaccurate area estimations for impervious surfaces. To reflect landcover changes due to rapid increases in the percent coverage of impervious surface area, it is necessary to update existing high-resolution landcover maps. With regard to spatial resolution, updating landcover at the subpixel level is also required to allow for the inclusion of smaller objects. In consideration of these requirements, in this study we tested a method for the accurate and precise updating of Korean landcover maps with details based on various spatial databases in order to ensure accurate estimation of impervious surface areas; this would facilitate their accurate classification as the basis for nonpoint source management and stormwater utility fee collection in new urban areas. To this end, this study collected various spatial data and updated time-series items, corrected classification errors, and proposed a detailed landcover map update methodology that enables the detailed updating of pervious areas existing within impervious areas on an annual basis.
## 3 Signal Quality Analysis
### SQA1: Signal Strength for Various Times and Satellite Elevation Angles
Although the C/N\({}_{0}\) values are lower than those for GPS, the standard deviation is smaller. This indicates that the signal of BDS GEO satellites is more stable than that of GPS. The purple circles and lines in Figure 8 are the C/N\({}_{0}\) results of BDS IGSO satellites. The highest satellite elevation angle is around 80\({}^{\circ}\). The signal strength of IGSO satellites is weaker than that of GPS satellites at every elevation angle bin. However, the standard deviation shows that the C/N\({}_{0}\) stability of BDS IGSO satellites is better than that of GPS satellites. Finally, the red circles and lines are the BDS MEO C/N\({}_{0}\) values, which change with the satellite elevation angle. The signal strength of BDS MEO satellites is better than that of GEO and IGSO satellites but weaker than that of GPS satellites. However, the standard deviation of the C/N\({}_{0}\) values at each elevation angle bin is the worst of all satellite constellations. The statistical results for the two systems are summarized in Table 6. The table provides the standard C/N\({}_{0}\) for the user to confirm that received satellite signal power is within normal condition. If the signal power is significantly lower than the statistic result and the user ignores this result, ranging errors may increase and present a greater position error for the user.
## 3 Experimental Results
### Comparison of the Results Obtained with Various Methods  
To verify the effectiveness of the FT-CFNN, we compared its accuracy with the accuracy values reported in other studies [22; 23; 24; 25; 26; 27] conducted on the Malimg dataset. In this experiment, [PERSON] and [PERSON] [22], [PERSON] et al. [23], and [PERSON] et al. [24] classified malware images using traditional machine learning methods such as random forest, KNN, and logistic regression. Although these methods require only a small number of learning parameters and obtain good malware image classification results (Table 11), the user is required to define the malware image features in advance. [PERSON] et al. [25], [PERSON] et al. [26], and [PERSON] et al. [27] used deep learning technology to obtain good malware image classification results. However, these methods require a large number of learning parameters (Table 11). The experimental results indicated that the proposed FT-CFNN is more accurate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Fusion Method** & **Accuracy** & **Sensitivity** \\ \hline \hline CFNN & & 98.13\% & 97.51\% \\ \hline FT-CFNN & & 98.61\% & 97.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracy and sensitivity values obtained with the CFNN and FT-CFNN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Degree of Freedom** & **SS** & **F** & **Contribution Rate** \\ \hline Conv1-Filter (F1) & 2 & 0.001479 & 0.43 & 1.5\% \\ Conv1-Kernel size (K1) & 2 & 0.005514 & 1.54 & 5.6\% \\ Conv1-Padding (P1) & 1 & 0.008557 & 4.81 & 8.7\% \\ Conv2-Filter (F2) & 2 & 0.000047 & 0.01 & 0\% \\ Conv2-Kernel size (K2) & 2 & 0.002313 & 0.64 & 2.3\% \\ Conv2-Padding (P2) & 1 & 0.080943 & 45.53 & 81.9\% \\ \hline Error & 25 & 0.044441 & & \\ \hline Total & 35 & 0.143294 & & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of ANOVA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Kernel Size** & **Number of Filters** & **Stride** & **Padding** \\ \hline Convolution\_1 & \(5\times 5\) & 16 & 2 & 0 \\ \hline Max\_pooling\_1 & \(2\times 2\) & - & 2 & \\ \hline Convolution\_2 & \(6\times 6\) & 16 & 2 & 0 \\ \hline Max\_pooling\_2 & \(2\times 2\) & - & 2 & \\ \hline Feature fusion & - & 64 & - & \\ \hline Fuzzy Rule Layer & - & 64 & - & \\ \hline DeFuzzify Layer & - & 25 & - & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimal parameter combination for the FT-CFNN.  
than the methods used in the previous relevant studies [22; 23; 24; 25; 26; 27] (Table 11). Furthermore, the proposed FT-CFNN requires only 13,609 parameters and achieves 98.61% accuracy.
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
## 3 Methodology of the Multi-Spacecraft Tracking
### Orbit Estimation  
In this subsection, the orbit estimation is implemented using the Kalman filter. As the second-order STTs of the dynamic and measurement equations are obtained in Section 3.1, the SEKF is used for convenience. The SEKF is a special case of the high-order numerical extended Kalman filter (HNEKF). The HNEKF uses the high-order STTs to propagate the dynamic and measurement equations and has appropriate accuracy and efficiency. The detailed derivations of are provided by [PERSON] and [PERSON] [34]. In this paper, the process of the SEKF is briefly given as follows.  
SEKF prediction equations:  
\[\delta\mathbf{x}^{l}(t_{k+1};t_{k})=\frac{1}{2}\mathbf{\Psi}^{l,m_{1}m_{2}}(t_{k},t_{k+ 1})\mathbf{P}^{m_{1}m_{2}}(t_{k}) \tag{29}\]\[\dot{\mathbf{x}}^{l}(t_{k+1};t_{k})=\mathbf{F}^{l}(\dot{\mathbf{x}}(t_{k}),\Delta t_{k})+\delta \dot{\mathbf{x}}^{l}(t_{k+1};t_{k}) \tag{30}\]  
\[\mathbf{P}^{p_{1}p_{2}}(t_{k+1};t_{k})= \mathbf{\Phi}^{p_{1},m_{1}}(t_{k},t_{k+1})\mathbf{\Phi}^{p_{2},m_{2}}(t_{k},t_{k+1})\mathbf{P}^{m_{1}m_{2}}(t_{k})-\delta\mathbf{x}^{p_{1}}(t_{k+1};t_{k})\delta \mathbf{x}^{p_{2}}(t_{k+1};t_{k}) \tag{31}\] \[+\frac{1}{4}\mathbf{\Psi}^{p_{1},ab}(t_{k},t_{k+1})\mathbf{\Psi}^{p_{2}, \delta p}(t_{k},t_{k+1})\Big{[}\mathbf{P}^{ab}(t_{k})\mathbf{P}^{a\beta}(t_{k})+\mathbf{P} ^{aa}(t_{k})\mathbf{P}^{b\beta}(t_{k})+\mathbf{P}^{a\beta}(t_{k})\mathbf{P}^{ba}(t_{k}) \Big{]}\]  
SEKF update equations:  
\[\delta\dot{\mathbf{z}}^{l}(t_{k+1}) =\frac{1}{2}\mathbf{H}^{l,m_{1}m_{2}}(t_{k},t_{k+1})\mathbf{P}^{m_{1}m_{2 }}(t_{k}) \tag{32}\] \[\mathbf{P}^{p_{1}p_{2}}_{zz} =\mathbf{P}^{p_{1}p_{2}}(\hat{\mathbf{z}}_{Ti}(t_{k+1}))+\mathbf{R}^{p_{1}p_{2}} \tag{33}\]  
\[\mathbf{P}^{p_{1}p_{2}}_{xz} =\frac{1}{4}\mathbf{\Psi}^{p_{1},ab}(t_{k},t_{k+1})\mathbf{H}^{p_{2},a \beta}(t_{k},t_{k+1})\Big{[}\mathbf{P}^{ab}(t_{k})\mathbf{P}^{a\beta}(t_{k})+\mathbf{P}^{ a\alpha}(t_{k})\mathbf{P}^{b\beta}(t_{k})+\mathbf{P}^{a\beta}(t_{k})\mathbf{P}^{ba}(t_{k}) \Big{]} \tag{34}\] \[-\delta\dot{\mathbf{x}}^{p_{1}}(t_{k+1};t_{k})\delta\dot{\mathbf{z}}^{p_{ 2}}(t_{k+1})\] (35) \[\mathbf{K}(t_{k+1})=\mathbf{P}_{xz}(\mathbf{P}_{zz})^{-1}\] (36) \[\mathbf{\hat{x}}(t_{k+1})=\mathbf{\hat{x}}(t_{k+1};t_{k})+\mathbf{K}(t_{k+1})( \mathbf{z}(t_{k+1})-\hat{\mathbf{z}}(t_{k+1}))\] (37) \[\mathbf{P}(t_{k+1})=\mathbf{P}(t_{k+1};t_{k})-\mathbf{K}(t_{k+1})\mathbf{P}_{zz}( \mathbf{K}(t_{k+1}))^{T} \tag{38}\]  
Note that, in Equations (29)-(38), the subscript '\(Ti\)' is excluded because each target is assumed to be well associated with its measurements.
## 2 Background
### Non-Negative Matrix Factorization  
The NMF method, which was developed by [PERSON] and [PERSON] [21; 35], is employed to decompose a non-negative matrix \(\mathbf{V}\in\mathbb{R}^{+}_{K\times N}\) into two matrices, \(\mathbf{W}\in\mathbb{R}^{+}_{K\times R}\) and \(\mathbf{H}\in\mathbb{R}^{+}_{K\times N}\), that satisfy the following relation [35]:  
\[\mathbf{V}\approx\mathbf{W}\mathbf{H}. \tag{1}\]  
The matrices \(\mathbf{W}\) and \(\mathbf{H}\) can be estimated via alternating optimization of the two equations as [35; 36]  
\[\mathbf{W} =\arg\min_{\mathbf{W}}D(\mathbf{V}|\mathbf{W}\mathbf{H})\quad \text{for fixed }\mathbf{H}, \tag{2}\] \[\mathbf{H} =\arg\min_{\mathbf{H}}D(\mathbf{V}|\mathbf{W}\mathbf{H})\quad \text{for fixed }\mathbf{W}, \tag{3}\]  
where \(D(\mathbf{V}|\mathbf{W}\mathbf{H})\) denotes the distance function between \(\mathbf{V}\) and \(\mathbf{W}\mathbf{H}\). The distance functions can be optimized by the multiplicative update rule as [35]  
\[\mathbf{W} \leftarrow\mathbf{W}\otimes\frac{\left[\mathbf{V}/(\mathbf{W} \mathbf{H})\right]\mathbf{H}^{T}}{1_{K\times N}\mathbf{H}^{T}}, \tag{4}\] \[\mathbf{H} \leftarrow\mathbf{H}\otimes\frac{\mathbf{W}^{T}[\mathbf{V}/( \mathbf{W}\mathbf{H})]}{\mathbf{W}^{T}\mathbf{1}_{K\times N}}, \tag{5}\]  
when the Kullback-Leibler divergence (KLD) is used as the distance function, where \(\mathbf{1}_{K\times N}\) denotes a \(K\times N\) matrix of ones, and  
\[\mathbf{W} \leftarrow\mathbf{W}\otimes\frac{\mathbf{V}\mathbf{H}^{T}}{ \mathbf{W}\mathbf{H}\mathbf{H}^{T}}, \tag{6}\] \[\mathbf{H} \leftarrow\mathbf{H}\otimes\frac{\mathbf{W}^{T}\mathbf{V}}{ \mathbf{W}^{T}\mathbf{W}}, \tag{7}\]  
Figure 1: Problem description.  
when the Euclidean distance is used, where \(\otimes\) and the fraction denote the Hadamard (element-wise) product and division, respectively.  
The NMF method has been widely employed in the recent acoustic signal processing studies to analyze a music signal into several musical notes or to reduce noise signals from the speech signals. Such interest can be attributed to the fact that the frequency basis matrix, **W**, and the temporal basis matrix, **H**, represent the frequency characteristics and temporal envelop of acoustic signal components, respectively. More specifically, speech denoizing methods divide the bases into two classes, speech and noise, and remove the noise bases after calculating the temporal bases of each class, as shown in Figure 2.
## 4 Packet Loss Differentiation Model Based on Heterogeneous Ensemble Learning
where \(w_{i}(i=1,\cdots,M)\) is the output weight of each individual classifier, so for any \(\mathbf{x}^{\prime}\in D\), we define:  
\[H(\mathbf{x}^{\prime})=\left\{ \begin{array}{ll}1,&\quad p(w_{1},w_{2},\cdots,w_{M} )\geq 0.5\\ -1,&\quad p(w_{1},w_{2},\cdots,w_{M})\ <0.5\end{array} \right. \tag{12}\]  
To obtain the optimal weights \((w_{1},w_{2},\cdots,w_{M})\), we employ the cross-entropy loss function as the ensemble learning algorithm's loss function, which is denoted as:  
\[\text{Loss}=-\sum_{i=1}^{n^{\prime}}I(H(\mathbf{x}^{\prime}{}_{i})=y_{i})\log_{2}p (w_{1},\cdots,w_{M})+I(H(\mathbf{x}^{\prime}{}_{i})\
eq y_{i})\log_{2}(1-p(w_{1}, \cdots,w_{M})) \tag{13}\]  
where \(I(\cdot)\) is the indicator function, which is 1 if the condition is true and 0 if the condition is false. The stochastic gradient descent method is employed to dynamically adapt the voting weight vector [27] and obtain the optimal weight. The resulting output is presented as follows:  
\[\hat{H}(\mathbf{x}^{\prime})=\left\{ \begin{array}{ll}1,&\quad p(w_{1}^{*},w_{2} ^{*},\cdots,w_{M}^{*})\geq 0.5\\ -1,&\quad p(w_{1}^{*},w_{2}^{*},\cdots,w_{M}^{*})\ <0.5\end{array} \right. \tag{14}\]  
The network packet loss discrimination algorithm proposed in this paper is referred to as the LDM-HEL algorithm. In the context of packet loss discrimination addressed in this study, channel error-induced packet loss is denoted as \(W\), with samples belonging to this category represented by \(+1\), while congestion-induced packet loss is denoted as \(C\), and samples belonging to this category are represented by \(-1\).
## 2 Methods
### SK Attention Module  
The background of dropper images collected at night is mostly black, and the quality of the images varies according to the illumination intensity of the lighting equipment. Images that are too bright or too dark make the recognition and identification of features more challenging. When the detection target has small defects, the network model will suffer serious information loss in the process of acquiring a low-resolution feature layer  
Figure 2: The structure of YOLOv5 network model.  
by subsampling. To solve this problem, an attention mechanism can be introduced in the shallow feature extraction stage, as this enables the model to effectively select target information, assign more weight to small targets, improve the feature attention of small targets, and improve the detection accuracy of the network model. To focus on the characteristics of fine defects in the suspension strings, the modified SK attention module is added to the YOLOv5 model. In the fuse phase of SK network, the two fully connected layers, that first reduce dimensionality and then increase dimensionality, are not conducive to the recalibration of feature channel information, which weakens the correlation between feature map channels. Therefore, a one-dimensional convolution is used to avoid the accuracy loss caused by dimensionality reduction. The structure of this mechanism is shown in Figure 3.  
The modified SK attention module is divided into three parts: split, fuse, and select. Firstly, after the input feature vectors have been convolved by \(3\times 3\) and \(5\times 5\) convolution kernels, the respective output vectors, \(\widetilde{U}\) and \(\hat{U}\), are added to obtain \(U\in R^{H\times W\times C}\), where \(H\) and \(W\) are the height and width of the input feature, and \(C\) is the number of input feature channels. The global average pooling operation (\(F_{gp}\)) is then used to compress the matrix, \(U\), to \(1\times 1\times C\), applying two separate one-dimensional convolutional \((C_{k})\) to the feature vector, \(s\), and resulting in two separate vectors. After the softmax operation, the cross-channel soft attention module is used to adaptively select information at different spatial scales. The calculation process is shown as Formulas (1)-(5).  
\[s_{c}=F_{gp}(U_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}U_{c}(i,j), \tag{1}\]  
\[p=C_{k}(s),q=C_{k}(s) \tag{2}\]  
\[a_{c}=\frac{e^{A_{c}p}}{e^{A_{c}p}+e^{B_{c}q}} \tag{3}\]  
\[b_{c}=\frac{e^{B_{c}q}}{e^{A_{c}p}+e^{B_{c}q}} \tag{4}\]  
Finally, the output feature, \(V\), obtained according to the attention weights of different cores, is shown in Formula (5).  
\[V_{c}=a_{c}\cdot\widetilde{U}_{c}+b_{c}\cdot\widetilde{U}_{c},a_{c}+b_{c}=1 \tag{5}\]  
In Formula (5), \(V=[V_{1},V_{2},\dots,V_{C}]\), \(V_{c}\in R^{H\times W}\).
## 3 Operation Principles and Reconfigurable Optical Amplifier Scheme  
As shown in Figure 2a, ROA\({}_{1}\) consists of an EDFA (post-amplifier) and one \(2\times 2\) crossbar optical switch (OS). The \(2\times 2\) crossbar OS should be in the cross state most of the time. When the EDFA in ROA\({}_{1}\) fails, the OS turns into the bar state such that the signal bypasses the failed EDFA.  
Figure 2b shows the block diagram of ROA\({}_{2}\), which comprises an EDFA (pre-amplifier) and a \(2\times 2\) crossbar OS. Similar to ROA\({}_{1}\), the OS in ROA\({}_{2}\) should be in the cross state most  
Figure 1: The proposed protection scheme for WDM-PONs based on ROAs.  
times, and when the EDFA in ROA\({}_{2}\) fails, it turns into the bar state for the signal to bypass the failed EDFA.  
As shown in Figure 2c, ROA\({}_{3}\) is composed of a bidirectional EDFA, a four-port reversible OC [19], and a 2 \(\times\) 2 crossbar OS. By using the reversible OC, ROA\({}_{3}\) can easily change the direction of the signal, thereby achieving various pumping configurations but no change in the direction of the pumping source. Remarkably, the four-port reversible circulator in ROA\({}_{3}\) is a non-strict-sense circulator, so the signal from port 1 to port 4 or port 4 to port 1 is unavailable. Furthermore, the 2 \(\times\) 2 crossbar OS and the EDFA in ROA\({}_{3}\) are generally in the bar state and the off state, respectively; hence, ROA\({}_{3}\) cannot be used to amplify the signal.  
Figure 3 shows the protection state for ROA\({}_{1}\), the downstream optical amplifier in the WDM-PON. When ROA\({}_{1}\) fails, the built-in 2 \(\times\) 2 crossbar OS switches to the bar state, consequently allowing the signal to bypass the EDFA in ROA\({}_{1}\). Simultaneously, ROA\({}_{3}\) switches its own function to that of ROA\({}_{1}\); hence, its built-in 2 \(\times\) 2 crossbar OS switches to the cross state and the direction of the four-port reversible OC becomes clockwise.  
Figure 4 shows the protection state for ROA\({}_{2}\), the upstream optical amplifier in the WDM-PON. When ROA\({}_{2}\) fails, the built-in 2 \(\times\) 2 crossbar OS switches to the bar state, consequently allowing the signal to bypass the EDFA in ROA\({}_{2}\). Simultaneously, ROA\({}_{3}\) switches its own function to that of ROA\({}_{2}\); hence, its built-in 2 \(\times\) 2 crossbar OS switches to the cross state and the direction of the four-port reversible OC becomes counterclockwise.  
Figure 4: Protection state for ROA\({}_{2}\).  
Figure 3: Protection state for ROA\({}_{1}\).  
The design of ROA\({}_{3}\) is the key in the proposed protection scheme and is addressed in the next section.
## 2 Project Overview
### Temperature Measurement Point Layout  
The entire pipe curtain freezing project of the Gongbei Tunnel is equipped with a total of 32 temperature measurement sections, with spacing of 8 m between them. The arrangement of the temperature measurement holes in each temperature measurement section is consistent, and the overall layout of the temperature measurement points in a certain temperature measurement section is shown in Figure 2[6].  
Each curtain wall steel pipe is equipped with temperature measurement points, with seven temperature measurement points arranged in odd-numbered pipes and six in even-numbered pipes. The number of temperature measurement points on the outer side of the steel pipe is between three and seven.
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 4 Experimental Verifications of the Methodology  
This section presents experiments to further verify the proposed damage detection method, divided into two parts. The first part assesses the accuracy of vibration signals obtained via phase-based optical flow estimation by comparing the vision-based approach with the laser vibrometer, focusing on displacement, frequency, and mode shapes. The second part validates the effectiveness of the proposed damage detection method through two experiments. The experiments encompass scenarios involving the detection of single damage and double damages.