esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance Analysis
#### 3.3.4 Performance Results after PCA  
Ninety principal components were applied, which explained 95% of the data variance. In addition, with this process, it was possible to eliminate redundant variables. This behavior was observed in certain cardiovascular variables, such as myocardial infarction, angina, stroke, and heart rate. These variables were expressed in more than one variable, so with PCA, the most helpful variable for the models' predictions was kept, thanks to which the models slightly improved their performance. Tables 10 and 11 show the performance of the models after PCA for the two feature matrices.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **Random Forest** & **SVM** & **Logistic Regression** \\ \hline Accuracy (mean) & 0.83 (Train); 0.82 (Test) & 0.82 (Train); 0.81 (Test) & 0.80 (Train); 0.80 (Test) \\ F1 Score (mean) & 0.77 (Train); 0.77 (Test) & 0.76 (Train); 0.75 (Test) & 0.75 (Train); 0.74 (Test) \\ Recall (mean) & 0.75 (Train); 0.74 (Test) & 0.74 (Train); 0.73 (Test) & 0.73 (Train); 0.72 (Test) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance for the models after PCA. Values correspond to the model created with the mode matrix by year for each variable.  
Figure 5: ROC curve of logistic regression model receiving the mode matrix every 3 years as input.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Random Forest** & **SVM** & **Logistic Regression** \\ \hline Accuracy (mean) & 0.81 (Train); 0.80 (Test) & 0.80 (Train); 0.80 (Test) & 0.79 (Train); 0.79 (Test) \\ F1 Score (mean) & 0.76 (Train); 0.75 (Test) & 0.75 (Train); 0.75 (Test) & 0.74 (Train); 0.72 (Test) \\ Recall (mean) & 0.72 (Train); 0.71 (Test) & 0.72 (Train); 0.71 (Test) & 0.70 (Train); 0.69 (Test) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Cross-validation results for each model for the matrix created by calculating the mode per year for each variable. K-fold = 10.  
As can be seen in the results shown in Tables 10 and 11, the application of principal components, in addition to slightly reducing the number of variables, also improved the performance of the three models to some extent. For example, the accuracy of the random forest results in Table 11 is much closer to 90%, and the F1 value and recall also increased to 85% and 80%, respectively.
## 3 Results and Discussions
### Effects of DLC Thickness on the Interaction Force and HMS at the HDI  
The magnitude of the DLC thickness is also of importance in regard to the adhesion and _HMS_ of the HDI. To illustrate the influence of the DLC thickness, Figure 12 demonstrates results of the adhesion force, contact force and _HMS_. Three nominal flying heights of 11 nm, 10 nm and 7 nm, corresponding to the flying state, TP-lube contact state and TP-DLC contact state of the slider, are chosen. In Figure 12a-c, it is observed that the adhesion force is larger and the _HMS_ is smaller for smaller DLC thicknesses on the disk in spite of the operation states. In addition, the contact force that presents during the TP-DLC contact state increases with the decreasing DLC thickness on the disk, as depicted in Figure 12d. It is also found, by comparing the results when the DLC thickness of the slider equals to 1 nm and 2 nm, that a smaller DLC thickness of the slider yields a larger adhesion force and an almost unchangeable _HMS_. Hence, a relatively thick layer is more beneficial since it reduces the adhesion of the HDI.
# Modeling and Resolving Uncertainty in DIKWP Model
## 2 Problem Description
### Uncertainty in Market Conditions
#### 2.1.1 Political Factors  
* **Geopolitical tensions (\(e_{1}\)):** Geopolitical tensions, such as sudden outbreaks of conflict, wars, or sanctions, typically lead to fluctuations in global stock markets. These fluctuations not only impact global markets but also have a particular influence on companies or industries with significant interests in regions of geopolitical tension. For instance, during a political crisis in 2014, concerns over escalating tensions between Country A and Country B led to turmoil in the global energy markets. Country A is one of the world's largest natural gas suppliers, and any threat to its supply capacity could result in energy price volatility. During this period, stocks related to the energy sector, especially European energy companies reliant on Country A's energy supplies, may experience price fluctuations. HFT may exploit this volatility by swiftly buying and selling energy stocks to generate profits, while closely monitoring any further political developments that could affect energy supply and prices.
* **Policy changes (\(e_{2}\)):** Changes in government or international organizations' policies, such as adjustments to trade policies or monetary policies, can significantly impact economic activities and the profitability of multinational corporations. For example, in 2018, Country D's imposition of tariffs on goods from Country C intensified global trade tensions, leading to profound effects on global stock markets, commodity markets, and currency markets. High-frequency traders may analyze the impact of such policy changes on different markets and assets, adjusting stock trading strategies swiftly in the short term to capture price fluctuations and generate profits.
## 2 The Node Deployment Strategy Based on 2-Fold Coverage
### Coverage-Based QoS Metrics  
In the process of defining the coverage-based QoS metrics, this paper is based on the following three assumptions:  
* During each iteration round, the amount of data each sensor node perceives from the environment is the same.
* The sensory model of the sensor node adopts the 0-1 perception model.  
Figure 1: A typical 0-1 perception model diagram.  
Figure 2: Threefold coverage.  
* The area contains a large number of monitoring target points, and the occupied areas of these points are equal in size.  
(1) Information Integrity: Information integrity is the percentage of the active ingredient in the obtained information required for the entire monitoring area. The effective component, _EIG_, of the information refers to information that is not greater than the target's coverage requirement in the obtained information, and the calculation formula is as follows:  
\[EIG=\sum\limits_{s\in S_{area}}\min(M(s),J(s))\Delta s, \tag{2}\]  
In the above formula, \(M\)(s) and \(J\)(s) represent the coverage requirement and the actual coverage number of the monitoring target point in the area, respectively.  
The information required for the entire monitoring area, _RIF_, refers to the information that satisfies all the coverage requirements, and its calculation formula is as follows:  
\[RIF=\sum\limits_{s\in S_{area}}M(s)\Delta s, \tag{3}\]  
The calculation formula for information integrity is as follows:  
\[Integrity=\frac{\sum\limits_{s\in S_{area}}\min(M(s),J(s))\Delta s}{\sum \limits_{s\in S_{area}}M(s)\Delta s}, \tag{4}\]  
If the coverage requirement of all target points is 2, then the above formula can be modified to  
\[Integrity=\frac{\sum\limits_{s\in S_{area}}\min(2,J(s))\Delta s}{\sum \limits_{s\in S_{area}}2\Delta s}, \tag{5}\]  
(2) Information Validity: Information validity is the percentage of the active ingredients in the obtained information. The calculation formula is as follows:  
\[Validity=\frac{\sum\limits_{s\in S_{area}}\min(M(s),J(s))\Delta s}{\sum \limits_{s\in S_{area}}J(s)\Delta s}, \tag{6}\]  
If the coverage requirement of all target points is 2, then the above formula can be modified to  
\[Validity=\frac{\sum\limits_{s\in S_{area}}\min(2,J(s))\Delta s}{\sum \limits_{s\in S_{area}}J(s)\Delta s}, \tag{7}\]  
(3) Information Redundancy: Redundancy is the percentage of the redundant ingredients in the obtained information. The calculation formula is as follows:  
\[Redundancy=\frac{\sum\limits_{s\in S_{area}}\left[J(s)-min(M(s),J(s))\right] \Delta s}{\sum\limits_{s\in S_{area}}J(s)\Delta s}=1-Validity, \tag{8}\]
## 8 Discussion
### Challenges
#### 8.1.10 Big Heterogeneous Data  
There are many various types of big data sources, each with its data rate, mobility, and packet loss. In wireless networks, analysing diverse data is difficult. Spatial and temporal dynamics are brought about by heterogeneous data. As a result, for massive spatiotemporal data processing in mobile networks, unusual methodologies are necessary.
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 3 Quality Estimation of Earth Surface Reconstruction
### Regression Model for Quality Estimation  
The DSM reconstruction method can generate 3D models based on the captured satellite image pairs. The reconstruction precision can then be compared to the 3D model ground truth to quantify the quality, based on which the quality prediction model can be trained. In this study, the DSM error is used as an assessment metric of reconstruction quality. It is determined by calculating the mean square error (MSE) between the ground truth and the DSM values reconstructed using the algorithm in Section 3.1. The smaller MSE value means the reconstruction result is closer to the ground truth. Different from other regression tasks, the features of a pair of imaging time windows are highly correlated and have complex impacts on the reconstruction quality, depending on not only the raw features but also their intersections. In this section, a CatBoost-based learning method is proposed with feature enhancement and then trained with feature selection. CatBoost is an efficient Gradient Boosting Decision Tree (GBDT) framework with the advantage in significantly improving model performance and stability, through a series of optimization techniques such as ordered boosting, category feature coding, and overfitting reduction [24].  
To improve the effectiveness of the regression model, we perform several preprocessing steps to further adapt CatBoost to the problem of stereo pair quality estimation. First, we enrich the dataset with feature enhancement to ensure that the model is capable of dealing with the rich and highly relevant feature inputs. Second, we perform feature selection using LASSO (Least Absolute Shrinkage and Selection Operator) regression to identify the most predictive features from the enhanced feature set [25].
###### Abstract  
Global Navigation Satellite System (GNSS) tomography has developed into an efficient tool for sensing the high spatiotemporal variability of atmospheric water vapor. The integration of GNSS top signals and side rays for tropospheric tomography systems using a novel height factor model (HFM) is proposed and discussed in this paper. Within the HFM, the sectional slant wet delay (SWD) of inside signals (the part of the side signal inside the tomography area), which is considered a key factor for modeling side rays, is separated into isotropic and anisotropic components. Correspondingly, two height factors are defined to calculate the isotropic and anisotropic part of tropospheric delays in the HFM. In addition, the dynamic tomography top boundary is first analyzed and determined based on 30-year radiosonde data to reasonably divide signals into top and side rays. Four special experimental schemes based on different tomography regions of Hong Kong are performed to assess the proposed HFM method, the results of which show increases of 33.42% in the mean utilization of rays, as well as decreases of 0.46 g/m\({}^{3}\) in the average root mean square error (RMSE), compared to the traditional approach, revealing the improvement of tomography solutions when the side signals are included in the modeling. Furthermore, compared with the existing correction model for modeling side rays, the water vapor profiles retrieved from the proposed improved model are closer to the radiosonde data, which highlights the advantages of the proposed HFM for optimizing the GNSS tomography model.  
Global Navigation Satellite System (GNSS); height factor model (HFM); anisotropic; isotropic; radiosonde; tropospheric tomography +
Footnote †: journal: Remote Sensing, 2020, 12, 1848; doi:10.3390/rs12111848
# A 12-20 GHz Wideband High-Power SP2T Switch Based on Gap Waveguide Technology
## 4 Fabrication and Measurement  
The proposed switch was manufactured using aluminum by a standard CNC milling technique in order to verify and test the design performance. Several photographs of the fabricated switch are shown in Figure 8. In order to change the switch state automatically by an electrical signal, a linear actuator was installed on the switch, which consisted of a DC motor and a rack and pinion structure as shown in Figure 8a. The pinion was connected to the motor shaft and engaged to the rack, which was attached to the sliding part of the switch. By clockwise or anticlockwise rotations of the motor shaft, the rack was moved to the right side or left side, and consequently, the switch state could be changed electrically. The traveling range of the sliding part was restricted by the metallic walls as depicted in Figure 8a. Thus, the motor moved the sliding part as far as possible to place the switch into the anticipated state. By current monitoring of the motor, it was detected that the sliding part reached the end of its traveling range, and then the motor turned off. The switching time of the switch is about 150 to 200 milliseconds.  
The measured S-parameters of the switch are shown in Figure 9. The measurement results agreed well with the simulations. However, due to the measurement noises and mechanical tolerances, insignificant differences were detected. The measured reflection coefficient (S\({}_{11}\)) of the switch was less than \(-14\) dB within the operating frequency range (12-20 GHz), representing a 50% frequency bandwidth. The maximum insertion loss was approximately 0.13 dB, while the isolation level was less than \(-70\) dB for both states A and B. The measured isolation levels were limited by the dynamic range of the measurement equipment setup.  
Figure 7: Simulated effects of the sliding part displacement in (**a**) X-axis, (**b**) Y-axis, (**c**) and (**d**) Z-axis on the performance of the switch, and (**e**) bandgap of the pins with \(ag=0.3\) mm.  
Figure 8: (**a**) Schematic of the switch with the linear actuator and photographs of the fabricated waveguide switch: (**b**) non-assembled, (**c**) assembled bottom view, and (**d**) assembled top view.  
Figure 9: The simulated and measured (**a**) reflection coefficient (S\({}_{11}\)) of the pole-port, (**b**) transmission coefficient, (**c**) isolation level, and (**d**) reflection coefficient of the shorted port, for both states A and B.  
The proposed work was compared with previously reported microwave switches as mentioned in Table 1. The proposed switch exhibits a wide frequency bandwidth in which both return and insertion losses are low with a high isolation level compared to similar structures. The power handling capacity of the proposed switch is high compared to the others. Only Ref. [10] has a higher power handling capacity than the proposed switch, due to its lower working frequency and larger dimensions.
## 6 Conclusions  
This paper proposes a packet loss differentiation method based on heterogeneous ensemble learning, aiming to address the high bit error characteristics of satellite networks and the inability of traditional TCP protocols to differentiate between bit error loss and congestion loss. Firstly, we summarize the packet features used in existing packet loss differentiation algorithms, including ROTT, IAT, and the number of consecutive packet losses. Relief and mutual information algorithms are employed for feature selection. Then, a heterogeneous ensemble consisting of LSSVM, DT, LR, and kNN is utilized. In order to enhance the generalization ability of the heterogeneous ensemble learning algorithm, an adaptive dynamic weighted ensemble method is introduced to adjust the voting weight of each individual classifier. Simulation results demonstrate that LDM-HEL achieves higher accuracy and significantly improves throughput performance when applied to TCP.  
Figure 11: Comparison of Goodput with different protocols for LEO.  
Figure 10: Comparison of Goodput rates between LDM-HEL, LDM-satellite, TCP-RLLD, Westwood, and New Reno.  
In the future, we will apply LDM-HEL to real network traffic data for further evaluation. LDM-HEL can also be applied to other wireless networks or wired/wireless hybrid networks to distinguish packet loss, but it needs to select suitable packet features and effective individual classifiers according to network characteristics. In addition to link errors and congestion, packet loss caused by routing is also a problem worth studying. Finally, LDM-HEL is a packet loss discrimination method, and the next step is to improve the accuracy of packet loss discrimination by combining cross-layer information.  
Conceptualization, D.W.; data curation, C.G.; formal analysis, L.Y.; investigation, Y.X.; methodology, D.W.; project administration, L.Y.; resources, C.G.; software, C.G.; supervision, L.Y.; validation, D.W.; visualization, Y.X.; writing--original draft, D.W.; writing--review and editing, D.W. All authors have read and agreed to the published version of the manuscript.  
This research was partly financially supported through grants from the National Natural Science Foundation of China (U21B2003).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 2 Communication and Sensing Convergence Using OFDM Waveforms
The IFFT operation, along with subcarriers, identifies the delay associated with the range, as shown in Figure 1b. Afterwards, the FFT operation provides the sensing information in the delay-Doppler profile, as shown in Figure 1c,d. It is clear that the sensing performance depends on the OFDM waveform parameters because bandwidth defines the range resolution, and the duration of the OFDM waveform determines speed resolution. For the speed, the upper limit is selected as \(\Delta f>20f_{c}v/c_{0}\) to maintain the acceptable level of orthogonality among the subcarriers [35].
## 6 Fault Decision
### Fault Classification  
Once the residual signal of healthy and faulty resampled RMS signals is obtained by the AMRXU-PMI observer, the AMRXU-VSPMI observer, and the proposed AMRXU-AVSPMI observer, the SVM is used for classification. The principal concepts of the SVM are explained in [45; 46]. In this work, four conditions for bearing fault diagnosis are ROLF, INRF, and OURF conditions. For each condition, the torque load changes from 0 hp to 3 hp. The crack sizes for ROLF, INRF, and OURF are 0.007, 0.014, and 0.021 inches. For each condition, the RMS resampled residual signal is segmented into 100 samples. The training set included 75% of the sample data, and the testing set included the other 25%. The details on the training and testing datasets are depicted in Table 2. Table 3 shows a summary of the proposed scheme for the rolling element bearing fault diagnosis.
## 3 Materials and Methods
### Performance Evaluation Metrics and Statistical Tests  
In this study, we assessed the performance of the models using the RMSE and MAPE metrics. RMSE represents the standard deviation of prediction errors, measuring the magnitude of differences between predicted and actual values. MAPE provides the average magnitude of prediction errors in percentage terms. Low RMSE and MAPE values indicate better model performance. The formulas for these metrics are given in Equations (10) and (11).  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-y_{i})^{2}} \tag{10}\]  
Figure 5: Flowchart of the hybrid ARIMA-SVR model.  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{x_{i}-y_{i}}{x_{i}}\right|\times 100 \tag{11}\]  
Here, \(n\) represents the total number of observations, \(x_{i}\) denotes the actual value, and \(y_{i}\) represents the predicted value of the model.  
In addition to these measures, the study employed the Wilcoxon Signed-Rank Test [30] and the Friedman Hypothesis Test [22] to compare the performance of prediction models. The Friedman Test was used to identify significant differences among multiple models, and the Wilcoxon Signed-Rank Test was employed to evaluate accuracy differences through pairwise comparisons of prediction models.
# A New Constrained State Estimation Method Based on Unscented Hoo Filtering
## Preliminaries
### The Principle of the Unscented H\(\infty\) Filter
\(\omega_{i}^{(m)}\), \(\omega_{i}^{(c)}\) are the weights of the state and covariance, respectively. 2. Time updating: \[\chi_{kk-1}=f\big{(}\chi_{k-1}^{i},u_{k-1}\big{)}\] (22) \[\hat{x}_{k}^{-}=\sum\
olimits_{i=0}^{2L}\omega_{i}^{(m)}\chi_{kk-1}\] (23) \[P_{x_{k}}^{-}=\sum\
olimits_{i=0}^{2L}\omega_{i}^{(c)}\Big{(}\chi_{kk-1}-\hat {x}_{k}^{-}\Big{)}\Big{(}\chi_{kk-1}-\hat{x}_{k}^{-}\Big{)}^{T}+Q_{k}\] (24) 3. Measurement updating: \[y_{kk-1}=h\big{(}\chi_{kk-1},u_{k}\big{)}\] (25) \[\hat{y}_{k}^{-}=\sum\
olimits_{i=0}^{2L}\omega_{i}^{(m)}y_{kk-1} \tag{26}\]  
The UHF can be regarded as applying the unscented transform to the EHF structure [12]. The measurement covariance and cross-correlation covariance can be approximated as:  
\[P_{\overline{y}_{k}}=\sum\
olimits_{i=0}^{2L}\omega_{i}^{(c)}\Big{(}h\big{(}x _{kk-1}\big{)}-\hat{y}_{k}^{-}\Big{)}\Big{(}h\big{(}x_{kk-1}\big{)}-\hat{y}_{k }^{-}\Big{)}^{T} \tag{27}\]\[P_{x_{k}y_{k}}=\sum\
olimits_{i=0}^{2L}\omega_{i}^{(c)}\left(x_{kk-1}-\hat{x}_{k} ^{-}\right)\left(h\left(x_{kk-1}\right)-\hat{y}_{k}^{-}\right)^{T} \tag{28}\]  
We then have  
\[\hat{y}_{kk-1}=\sum\
olimits_{i=0}^{2L}\omega_{i}^{(m)}h\left(x_{kk-1}\right) \tag{29}\]  
\[\hat{x}_{k}=\hat{x}_{k}^{-}+P_{x_{k}y_{k}}(R+P_{\widetilde{y}_{k}})^{-1} \left(y_{k}-\hat{y}_{k}^{-}\right) \tag{30}\]  
\[P_{x_{k}}=P_{x_{k}}^{-}-\left[ \begin{array}{cc}P_{x_{k}y_{k}}&P_{x_{k}}^{-} \end{array} \right]R_{e\xi}^{-1}\left[ \begin{array}{cc}P_{x_{k}y_{k}}\\ (P_{x_{k}}^{-})^{T}\end{array} \right] \tag{31}\]  
where  
\[R_{e\xi}=\left[ \begin{array}{cc}R+P_{\widetilde{y}_{k}}&\left(P_{x_{k}y_{k} }\right)^{T}\\ P_{x_{k}y_{k}}&-\gamma^{2}I+P_{x_{k}}^{-}\end{array} \right] \tag{32}\]  
and  
\[\gamma_{k}^{2}=\rho\text{max}\{\text{eig}((P_{x_{k}}^{-})^{-1}+(P_{x_{k}}^{-}) ^{-1}P_{x_{k}y_{k}}R^{-1}[(P_{x_{k}}^{-})^{-1}P_{x_{k}y_{k}}]^{T})^{-1}\} \tag{33}\]  
where \(\rho\) is a scalar larger than one, and it is a tuning parameter to balance the tradeoff between the H-infinity performance and the minimum variance performance. If \(\rho\) tends to infinity, the UHF reduces to the standard UKF. On the contrary, as long as \(\rho\) is not very large, the UHF always achieves an excellent performance.