esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Ship Trajectory Prediction Model  
An automatic identification system (AIS) can provide real-time ship trajectory data for detecting the navigation status of ships. Nowadays, it is widely used in ship collision avoidance, maritime monitoring, ship traffic flow forecasting, and maritime accident investigation mechanisms [29]. When a ship is sailing, it mainly relies on the AIS data from the target ship to obtain its navigation behavior, so as to make timely and accurate collision avoidance decisions in complex encounters. In actual navigation, the navigation behavior of a ship is mainly reflected in the changes of characteristic variables such as ship position, SOG, and COG [30]. It is assumed that the navigation behavior of a ship at time \(t\) can be characterized as:  
\[y(t)=\{lat_{t},lon_{t},sog_{t},cog_{t}\} \tag{1}\]  
where \(lon_{t}\), \(lat_{t}\), \(sog_{t}\), and \(cog_{t}\) are respectively LON, LAT, SOG, and COG of the ship at time \(t\).  
Generally speaking, the navigation behavior of the ship at the next moment is the result of the current behavior and historical behavior. Therefore, in order to improve the accuracy of the model, the navigation behavior of the ship at the past three moments, \(y(t-2)\), \(y(t-1)\), and \(y(t)\), is taken as the input of the model and the LON and LAT of the ship at the next moment as the output of the model, namely:  
\[\left\{\begin{array}{l}I_{input}=\{y(t-2),y(t-1),y(t)\}\\ O_{output}=\{lat_{t+1},lon_{t+1}\}\end{array}\right. \tag{2}\]  
This is the functional relationship between \(I_{input}\) and \(O_{output}\):\(O_{output}=f(I_{input})\) where \(f(\cdot)\) is the nonlinear transformation function.  
Therefore, for ship trajectory prediction, with some samples of AIS data as the training data set \(\{[y(t-2),y(t-1),y(t)],y(t+1),t=1,2,\ldots,l\}\), obtaining the best estimate of the nonlinear transformation between the input sample \(I_{input}\) and the target output sample \(O_{output}\) is a problem. This paper selects the GA-LSTM model to fit the nonlinear transformation function \(f(\cdot)\), the GA-LSTM model is constructed with training data, and then the test data are input into the GA-LSTM model, and finally the real-time prediction of the ship trajectory is carried out.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 2 Sinkhorn Algorithm
### Regularized Optimal Transport  
In [2], it was proposed to regularize this problem by adding a strictly convex entropy regularization:  
\[\min_{\gamma\in\mathcal{C}_{1}\cap\mathcal{C}_{2}\cap\mathbb{R}^{n_{1}n_{2}}_ {+}}K^{\epsilon}(\gamma)\coloneqq\langle c,\gamma\rangle+\epsilon\,\mathrm{ KL}(\gamma,\mathbf{1}), \tag{1}\]  
with \(\epsilon>0\), \(\mathbf{1}\) the matrix of size \(n_{1}\times n_{2}\) full of ones, and the Kullback-Leibler divergence is:  
\[\mathrm{KL}(\gamma,\xi)=\sum_{i,j}\gamma_{i,j}\Bigg{(}\log\Bigg{(}\frac{ \gamma_{i,j}}{\xi_{i,j}}\Bigg{)}-1\Bigg{)}+\sum_{i,j}\xi_{i,j} \tag{2}\]  
with the convention \(0\log 0=0\). It was shown in [29] that the regularized optimal transport matrix \(\gamma^{*}\), which is the unique minimizer of Problem (1), is the Bregman projection of \(\gamma^{0}=e^{-c/\epsilon}\) (here and in the sequel, exponentiation is meant entry-wise) onto \(\mathcal{C}_{1}\cap\mathcal{C}_{2}\):  
\[\gamma^{*}=\operatorname*{argmin}_{\mathcal{C}_{1}\cap\mathcal{C}_{2}}K^{ \epsilon}(\gamma)=P_{\mathcal{C}_{1}\cap\mathcal{C}_{2}}(e^{-c/\epsilon}), \tag{3}\]  
where \(P_{\mathcal{C}}\) is the Bregman projection onto \(\mathcal{C}\) defined as:  
\[P_{\mathcal{C}}(\xi)\coloneqq\operatorname*{argmin}_{\gamma\in\mathcal{C}} \mathrm{KL}(\gamma,\xi).\]
## Appendix A Variable Retrieval
### Soil Moisture Retrieval
#### a.1.1 Zenith Total Delay Retrieval from GNSS  
The meteorological applications of GNSS are well known [39; 41]. A GNSS receiver is able to determine its coordinates in a global reference frame by exploiting the simultaneous observation of its distance from a number of satellites of known position. The distances are derived from the time it takes to the GNSS signals to cover the satellite-receiver paths, by assuming that they travel with the constant velocity of light in vacuum. This assumption results in an observed distance different from the geometrical one by an amount called atmospheric delay. The delay contains an ionospheric component, which can be removed by a proper (iono-free) combination of the dual frequency GNSS signals and a tropospheric component, which is partly due to tropospheric water vapor partly to the dry gases, called wet and dry tropospheric delays respectively. The total tropospheric delay (wet+dry) has to be accounted for in the GNSS data processing to get an accurate positioning. To this aim, the tropospheric delays affecting the signals received by a GNSS station from all the satellites simultaneously in view are expressed as a common delay in the zenith direction above the receiver. That is, each slant delay is projected onto the zenith direction by a known mapping function. The common zenith projection is then estimated in the general adjustment of GNSS observations, for instance by modelling it as a random walk process, resulting in a high temporal resolution time series of zenith delays for each considered station. In the STEAM experiments the free and open source software goGPS [59; 60] was used to estimate ZTD time series from the GNSS raw observations. goGPS is developed and maintained by GReD srl, it employs a Precise Point Positioning (PPP) strategy to deal with the systematic errors that affect GNSS observations [61], applied trough a multi-epoch joint least squares adjustment.
## 3 Results and Discussion
Fractions with no compound detected or only one from the list were omitted. in both quantity and quality compared to the other samples (Table 1), as it contained significant amounts of fatty acids also present in the AMF-3 sample, suggesting that it may be a valuable source of these compounds. Figure 1 confirms the above statement that the richest (i.e., the most qualitatively diverse) and closest in composition cream fraction was the \(\beta\)-serum light fraction-3. Subsequently, these were \(\alpha\)-serum light fraction-3 and \(\alpha\)-serum light fraction-1, in relation to the appropriate AMF samples. Also, the most common VOC in all samples was hexadecanoic acid (palmitic acid), followed by (\(E\))-9-octadecenoic acid (elaidic acid; example of omega-9 fatty acid), tetradecanoic acid (myristic acid), and octadecanoic acid (stearic acid). The results of [PERSON] et al. [26] showed that the storage method of dairy products has a significant impact on the antioxidant properties, fatty acid profile, and lipid oxidation. Temperature, light, and storage time have a significant effect on maintaining the quality of fats in dairy products, as their inadequate storage can lead to fatty acid oxidation, which was noted in the case of our study. These factors lead to significant changes in the protein and fat composition of milk. Understanding the complexity of these changes is key to understanding their impact on the functionality, sensory characteristics, and nutritional value of milk, which are directly relevant to the quality of this essential product [20,27]. Comparison of the content of individual fatty acids in different samples revealed that cream samples 1 and 2 had similar profiles but with differences in the proportions of individual acids (Tables S3 and S4). On the other hand, cream sample 3 was significantly different from the others, which may be the result of a specific production or storage method (Table S5). In addition, microbiological analysis confirmed our assumptions about the stability of the fat (Table 2). Physicochemical properties (Table S1) may induce changes occurring at late  
Figure 1: Graph showing the contributions of individual VOCs depending on the selected cream fraction. The graph includes all tested fractions. The smaller embedded graph shows an enlargement from the main one for the sums of the intensities of six less abundant compounds. X-axis values are lipid numbers of fatty acids, as explained in Table 1.  
stages of acidification, including fatty acid profiling, pH parameter, color, or production of bioactive compounds [28].
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results
### Performance Metrics  
The metrics of Root Mean Square (RMS), Bit Error Rate (BER), coupling efficiency, and Strehl Ratio (SR) are considered for analyzing the BDCOA. RMS is the square root of changes in the input's phase aberration \((\sigma_{A})\), which is denoted in Equation (19). BER is defined as the ratio of the total amount of errored bits and total amount of transmitted bits, as represented in Equation (20). Coupling efficiency is the proportion of the power existing in the transmitter to the power given to the receiver. SR, represented in Equation (21), is the quality computation metric for FSO. This is denoted as the proportion among WA's peak intensity and higher intensity in optical system's SR.  
\[RMS=\sqrt{\sigma_{A}} \tag{19}\]  
\[BER=\frac{Amount\ of\ bits\ received\ in\ error}{Total\ amount\ of\ transmitted\ bits} \tag{20}\]  
\[SR=\exp\Bigl{(}-\sum_{i=1}^{N}a_{i}^{2}\Bigr{)} \tag{21}\]  
where, the coefficient of Zernike polynomial \(i\) is denoted as \(a_{i}\) which utilized for specifying the input WA, while the amount of Zernike modes is denoted as \(N\).
# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 2 Methods
### Improved POCS Method
#### 2.2.3 Gradient Constraint and Projection Iteration  
In order to further improve the edge details of the range profile, gradient constraints are introduced on the basis of the original constraints, and the formula is calculated as follows:  
\[\
abla g_{d}(x,y) =\left[\frac{\partial d}{\partial x}(x,y),\frac{\partial d}{ \partial y}(x,y)\right] \tag{11}\] \[\
abla g_{D}(i,j) =\left[\frac{\partial D}{\partial i}(i,j),\frac{\partial D}{ \partial j}(i,j)\right]\] (12) \[r_{g}(x,y) =\
abla g_{d}(x,y)-\sum_{(i,j)}\
abla g_{D}(i,j)\cdot h(x,y;i,j) \tag{13}\]  
where \(\
abla g_{d}\) is the local gradient of the low-fraction range profile; \(\
abla g_{D}\) is the reference high-resolution range profile local gradient; and \(r_{g}\) is the gradient-constrained residual, and the final residual is combined as:  
\[r(x,y)=r_{g}(x,y)+r_{d}(x,y) \tag{14}\]  
The new constraint can be defined as:  
\[C^{\prime}_{k}(i,j)=\{D(i,j),|r(x,y)|\leq\delta\} \tag{15}\]  
For any point \(D(i,j)\) on the reference high-resolution range profile, the projection under the constraint \(C^{\prime}_{k}(i,j)\) satisfies:  
\[D^{\prime}(i,j)=D(i,j)+\left\{\begin{array}{lcl}\frac{r(x,y)-\delta}{\sum \limits_{i}\frac{\sum\limits_{j}h^{2}(x,y;i,j)}{h(x,y;i,j)}}&,&r(x,y)>\delta\\ \\ 0&,&|r(x,y)|\leq\delta\\ \\ \frac{r(x,y)+\delta}{\sum\limits_{i}\frac{\sum\limits_{j}h^{2}(x,y;i,j)}{h(x,y ;i,j)}}&,&r(x,y)<-\delta\end{array}\right. \tag{16}\]  
Figure 1: Gaussian pyramid optical flow iteration map.  
To sum up, the improved method firstly adopts the nearest neighbor interpolation that preserves the edge of the range profile to upsample the single low-resolution range profile to construct the high-resolution reference frame. Secondly, the LK optical flow method is introduced to calculate the relative motion vector between low-resolution range profile sequences, and the motion vector is modified with a Gaussian pyramid. Considering the distance difference between the edge and the background, the gradient constraint is introduced under the condition of data consistency constraint. Finally, the reference high-resolution range profile is iteratively modified by projection to improve the resolution of the range profile while retaining more detailed edge information of the target. The overall process of the method is shown in Figure 2.
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 5 Experiments
### Experiment Results and Analysis
We sort the ACC, NMI, F-score, and ARI of the compared methods from high to low and obtain the average ranking of each method in terms of all data sets. When \(\alpha\) = 0.05, the threshold for the Friedman test was 1.8544. According to Equation (14), the \(\Gamma_{F}\) values can be calculated for different clustering evaluation metrics (ACC, NMI, F-score, and ARI), which are 8.4348, 13.0826, 5.9892 and 11.3655, respectively. These \(\Gamma_{F}\) values are all greater than the threshold of the Friedman test, which rejects the hypothesis that all the methods being compared hold the same performance. Then, we perform the Nemenyi post-hoc test to further distinguish multiple methods. After obtaining the critical distance, CD = 6.2982, according to Equation (15), we can draw the [PERSON] test chart as Figure 2. For each method, the blue dot marks its average rank. The horizontal lines with the dot at the center indicate the critical distance, CD. If the lines do not have overlapping areas this indicates a significant difference in the comparison methods. From the figure, we can see that there are significant differences between the method in this paper and Co_reg_c, Co_reg_p, MLRSSC, and PMSC, and that the other methods do not differ from one another significantly. Compared with other methods, our method has the best average ranking regardless of the clustering evaluation index. In summary, our proposed method holds statistical advantages. Figure 2: [PERSON] test charts. (**a**) [PERSON] test on ACC, (**b**) [PERSON] test on NMI, (**c**) [PERSON] test on F-score, (**d**) [PERSON] test on ARI.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 3 Coding Based Batching and Caching in CoFi
### Coding Based MAC Layer Caching  
A major component of CoFi is the coding based MAC layer caching scheme, as illustrated in Figure 3. The intuition behind MAC caching is to allow each client to opportunistically overhear packets intended for a co-located peer, and pass them later to it. This will further reduce the delay due to bursty losses, which is quite typical in the wireless environment.  
With random linear network coding, the \"packet selection\" problem, i.e., choosing which packets to send in the cache, is circumvented. However, two critical issues still need to be addressed in the coding based MAC caching scheme: who should relay and when should the relay help, which correspond to a relay selection problem and relay scheduling problem, respectively.
## 3 Material and Methods
### Fuzzy-Rule-Based System for Supplier Evaluation and Selection  
A fuzzy system is a control system based on fuzzy logic. This mathematical system is based on the logical variables of the simulated inputs of logical variable analysis (these logical variables are consecutive values between 0 and 1 rather than the conventional Boolean logic). A fuzzy system is a good way of processing the uncertainty in decision-making. In a fuzzy system, fuzzy inference is the process of using fuzzy logic to map from a given input to an output [48]. The fuzzy inference process involves five phases: (1) fuzzification, (2) application of logical operators in the antecedent of each rule, (3) implication to the consequent of each rule, (4) aggregation of consequents, and (5) defuzzification. An overview of the fuzzy inference process is shown in Figure 2.  
For convenience, crisp values are obtained using the Fuzzy Logic Toolbox from MATLAB 2017b (The MathWorks, Inc., Natick, USA). In this study, the input is a linguistic variable and the output is a crisp numerical value (in this case, the intervals for the input and output all are between 0 and 10). Figures 3-5 show the input linguistic labels, output crisp numerical values, and fuzzy rules as applied in the Fuzzy Logic Toolbox. Figure 3 shows the membership functions of the five fuzzy sets for the input \"linguistic labels\". Figure 4 shows the seven membership functions of the output \"crisp numerical values\". In total, 72 fuzzy rules were developed for the proposed method as shown in Figure 5.  
Figure 3: Input linguistic labels.  
Figure 2: Fuzzy inference process.  
An example of supplier selection is used to illustrate the fuzzy inference process. First of all, using a five-point Likert scale questionnaire, a score between 1 (Very Poor (VP)/Very High (VH)) and 9 (Very Good (VG)/Very Low (VL)) can be obtained for four evaluation criteria (cost/price, quality, delivery and service). The score for each evaluation criterion was obtained by a decision maker as follows: price (Low = 7), quality (Very Good = 9), delivery (Normal = 5), and service (Very Good = 9). During the fuzzification process, the inputs Low = 7, Very Good = 9, Normal = 5, and Very Good = 9 are fuzzified according to the corresponding membership functions. After the fuzzy rule reasoning, the output crisp numerical value among the four inputs can be computed using the Fuzzy Logic Toolbox from MATLAB 2017b as 86.4. Figure 5 shows the process of max-min fuzzy inferencing for computing the fuzzy output crisp numerical value. From Figure 5, it can be seen that only fuzzy rule 68 was executed, and the last row of the output column shows the results of max-min inferencing for the only fuzzy rule. In this case, the center of gravity method was used in the defuzzification process.  
Figure 4: Output crisp numerical values.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 4 System Architecture
### G-Svm
```
Input: Dataset: \((A_{i},B_{i})(i=1,2,\cdots,k)\), learning algorithm: SVC; the number of training iterations: k. Output: \(avg_{\max}\) (Maximum accuracy average) \(\rightarrow\) optimal parameters \((C,g)\). Initialization: penalty factor \(C\in\)[\(2^{-8}\), 28], kernel parameter \(g\in\)[\(2^{-8}\), 28]. 1. For i = 1, 2... k do
2. Construct Grid\((C,g)\) coordinate system at intervals of 1;
3. For\((C,g)\)= \((C,g)_{1}\)\((C,g)_{2},\ldots(C,g)_{k}\)do
4. K = Test, K - 1 = Train, Test[(C,g)]K-fold cross-validation, replace the training set and the test set
5. while Not is \((C,g)_{k}\)do
6. repeat step 3 and 4, Calculate the average classification accuracy under each parameter combination \(avg\)
7. end
8. endfor
9. For\(avg=avg_{1}\), \(avg_{2},\ldots avg_{k}\)do
10. sort(avg) and select \(avg_{\max}\)
11. endfor
12. endfor
13. Output: Return\(avg_{\max}\)\(\rightarrow\)\((C,g)\)
```  
**Algorithm 1** G-SVM  
Eventually, the results of the returned penalty factor c and kernel parameter g were used as the optimal parameters, which could find the most accurate classification results for human detection, effectively avoiding the problems of overlearning and under learning. This could effectively distinguish between two types of feature data (human and non-human), improve the feasibility and accuracy of data classification, and achieve better human detection results.
# HI-Sky: Hash Index-Based Skyline Query Processing
## 4 Performance Evaluation
### Result of Experiments
#### 4.2.3 Comparison of Skyline Computation Time
This is because a small number of data has a small number of skylines, where the sequential comparison is more efficient than using other structures. On the contrary, in the case of an anti-correlated dataset where more skylines occur, HI-Sky is faster than SFS. Additionally, indexing methods based on tree structures have a greater increase in computation time with increased data. This is because the number of nodes increases with the increase of data, and the number of nodes to be stored in the heap/queue increases for the skyline computation. The number of data stored in the GLAD also increases as the data increases when using HI-Sky, but because the comparison is performed only when dominance can occur using the GLAD, the increase of the skyline computation time is more directly correlated with the increase in the number of data.
## 5 Application of MSWOA in Fault Diagnosis of Analog Circuits
### Fault Diagnosis of Four-Op-Amp Biquad High-Pass Filter Circuit  
#### 5.2.1 Simulation Settings  
The second group of fault diagnosis experiments was conducted on the four-op-amp biquad high-pass filter circuit, as shown in Figure 19. The parameters of which were set as follows: the value of resistor tolerance in the circuit was 5%, while the capacitance tolerance was rated to 10%. In addition, we applied a sinusoidal excitation signal (amplitude 10 Vrms, frequency 2 kHz) to this circuit, and collect the voltage signal outputs from the out node.  
The maximum fault deviation rate was set to 40%, and the parameter settings of different fault types are exhibited in Table 5, where \(\uparrow\) represents the actual value is higher than the normal state, and \(\downarrow\) represents the actual value is lower than the normal state. It can be concluded from Table 5, there  
Figure 19: Four-op-amp biquad high-pass filter circuit.  
Figure 18: Fault diagnosis accuracy curves of the three methods optimized support vector machine (SVM).  
were seven kinds of different running states, including one normal state and six soft fault sates, for the four-op-amp biquad high-pass filter circuit.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### ik-NN Method in Storm Damage Recognition
In cases of equal sums for two or more classes, one class is selected randomly from among those with the greatest sum. This method was used for predicting the categorical variable obtaining the values, the value being the damage category.