esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
They showed that the TEC anomalies in the two hemispheres were on different sides of the magnetic meridian that passes through the earthquake epicenter (to the east on the north and to the west on the south). They proposed that the extra electric field due to charged aerosols was transferred to the magnetically conjugate hemisphere and induced a plasma drift, which led to the observed plasma redistribution in both hemispheres. Secondly, it is also difficult to determine the magnitude of the impending event just according to an appeared anomaly. So far, there has not been a well-established relationship between an ionospheric variation and a magnitude of an EQ, although statistically an ionospheric anomaly increases as the magnitude of the EQ involved increases [14]. The increased magnitudes of O\({}^{+}\) density and electron density were of 41.6% and 22.2% for the Maduo shock and 47.3% and 38.4% for the Menyuan one. This information displays a contrary relationship between the varied amplitudes of ionospheric parameters and the magnitudes of EQs, if the Maduo and Menyuan events considered. Additionally, we also found that there was some useless or uncertain information occurring far from the impending EQ, for instance, variations in orbit 18111_1. From Figures 7 and 5b, it is clear that there were three peaks that occurred in the same orbit from left to right in Figure 7 and from bottom to top in Figure 5b. We have confirmed the one near the Maduo \(M_{\rm S}\) 7.4 EQ was the precursory anomaly of the Maduo event. In addition, it was also easy to decide that one near the equator belonged to the anomalous belt around the equator. The middle one was about 1000 km from the Maduo epicenter, well within its effective anomalous range attained by the Dobrovolsky formula [35]; however, we cannot determine this intuitively due to its long distance from the epicenter. So far, only two ionospheric parameters of O\({}^{+}\) density and electron density have been used to support daily EQ prediction work on the Chinese mainland. We also found synchronous variations in other parameters, such as the He\({}^{+}\) density in Figures 7, 10 and 11, which means matched variations of more related parameters can achieve more reliability of effective anomalies than one, especially ones from other observation styles, such as ground-based observations. This can also help us to deal with the difficulty of confirming the correct location of the impending event. It is also necessary to develop more data processing methods to distinguish EQ precursors comprehensively.
## 4 Heat Transport Pipeline Damage Probability Model
### Visualization of Heat Transport Pipeline Damage Probability  
A risk map was prepared using the heat transport pipeline damage probability prediction model presented in this study, as shown in Figure 9. The developed model allows calculation of the damage probability of pipelines segmented into basic units. Sections with high damage risk based on the model's prediction were visualized in red, while those with relatively low risk were shown in blue.  
Because the model heavily weighted the operator (regional characteristics), it was expected that similar damage probabilities would appear within the same operator's jurisdiction. However, when visualizing heat transport pipeline damage probability, the model predicted different probabilities even within the same operator, as it properly considered various factors. This visualization contributes to the development of a risk map, which is expected to serve as basic data for prioritizing efficient heat transport pipeline maintenance and proactively managing hazardous areas.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 2 Direct Visual Odometry
### Optimization
#### 2.2.2 Robustness  
GN optimization assumes Gaussian distributed errors. It is often the case in real-world data, however, that non-Gaussian errors arise due to inaccurate pixel correspondences during disparity computation, motion-induced occlusions, illumination changes and auto-exposure adjustments. A cost function for minimizing photometric error that is insensitive to non-Gaussian distributed noise is said to be robust. In the GN framework, this can be achieved with iterative re-weighted least squares (IRLS) using a robust cost function. The decision of the cost function is somewhat arbitrary, typically selected through empirical evaluation or by means of some prior knowledge regarding the structure of outliers in the data. In this work, we choose to use [PERSON]'s biweight cost function [54; 55] since it suppresses large residuals in contrast to [PERSON]'s cost function [56] which simply down weights their influence.  
Figure 1: Steepest descent images; each image corresponds to a column in the Jacobian matrix.  
[PERSON]'s biweight cost function takes the form  
\[\rho(r_{i})=\begin{cases}\left(1-\left(\frac{r_{i}}{c}\right)^{2}\right)^{2}& \text{if }|r_{i}|\leq c\\ 0&\text{otherwise}\end{cases}, \tag{25}\]  
where the constant \(c\) is usually chosen to be 4.6851 to achieve 95% asymptotic efficiency with the normal distribution and \(r_{i}\) is the \(i\)th residual from the error vector given by Equation (17). The cost function assumes that the residuals have unit variance. A common choice to estimate the scale parameter to standardize the residuals is to compute the median absolute deviation (MAD) and multiply by the expected MAD for a standard normal distribution,  
\[\hat{s}=k\cdot\text{median}_{i}\left|r_{i}\right|, \tag{26}\]  
where \(k=1.4826\). To incorporate robustness in the GN minimization routine, we construct a weight vector \(\mathbf{w}\), where each weight \(w_{i}=\rho(r_{i}/s)\) and set the diagonal entries of a weight matrix equal to the weight vector, i.e., \(\mathbf{W}=\text{diag}(\mathbf{w})\). It follows that the solution to the incremental update of the linearized system under the IRLS framework takes the form  
\[\Delta\xi=-\left(\mathbf{J}^{\text{T}}\mathbf{W}\right)^{-1}\mathbf{J}^{\text {T}}\mathbf{W}\mathbf{r}, \tag{27}\]  
which is used in place of the normal equations shown in Equation (16).
# An Improved Analytical Algorithm on Main Cable System of Suspension Bridge
## 2 Segmental Catenary Theory of Main Cable
### General Solution Procedure  
The tangent stiffness matrix and internal force vector of cable element are determined while using an iterative procedure. This procedure requires the initial values of end forces (\(H\), \(V\)). The iterative procedure for obtaining tangent stiffness matrix and internal force vector of cable element is briefly presented, as follows:  
1. input \(q\), \(E\), \(A\), \(S_{0}\), nodes \(I\) (\(x_{i}\), \(y_{i}\)) and \(J\) (\(x_{j}\), \(y_{j}\));
2. calculate \(l_{0}=x_{j}-x_{i}\), \(h_{0}=y_{j}-y_{i}\);
3. initialize end forces (\(H\), \(V\));
4. update (\(l\), \(h\)) using Equations (1) and (2);
5. calculate incompatibility vector of relative distances \(ds=\{dl\ dh\}^{\rm T}\);
6. if \(ds\) is smaller than the permissible tolerances, calculate [\(K\)] using Equation (5) and internal forces using Equation (3), otherwise continue to next step;
7. calculate the correction vector of end forces \(\{dH,\ dV\}\)using Equation (4);
8. update the end forces \(H_{i+1}=H_{i}+dH\), \(V_{i+1}=V_{i}+dV\) and go to Step (4).
## 2 Optimization without Consideration of the Reliability Criteria
### Objective Functions and Constraints for Market Settlement without Reliability Criteria
#### 2.1.2 Mathematical Model of the Revenue Stream from a Resource Portfolio with a PV and BESS  
The profit from resource coordination between the PV generator and the BESS can be mathematically represented as follows:  
\[M_{t}^{S-B}=PCC_{t}^{S}\cdot SMP_{t}+\left(REC_{1}^{S}+\omega^{S}\cdot REC_{5} ^{S}\right)\cdot X^{S}-P_{sb,t}^{loss}\cdot c_{sb},\quad\forall\ sb,t \tag{7}\]  
\[P_{sb,t}^{loss}=\sum_{sb}\left(P_{sb,t}^{B2G}+P_{sb,t}^{S2B}\right),\quad \forall\ t \tag{8}\]  
\(PCC_{t}^{S}\) represents the energy measured at the point of common coupling in the portfolio with the PV generator and the BESS. \(REC_{1}^{S}\), \(REC_{5}^{S}\), \(\omega^{S}\) and \(X^{s}\) represent the energy eligible for the REC settlement without and with the multiplier, the multiplier, and the price of the REC in the bid. \(P_{sb,t}^{loss,}c_{sb}\), \(P_{sb,t}^{B2G}\) and \(P_{sb,t}^{S2B}\) represent the O&M cost due to its operation, the energy discharged from the BESS, and the energy charging the BESS from the PV generator.  
\[\begin{split} PCC_{t}^{S}=P_{sp,t}^{S2G}+P_{sb,t}^{B2G}-P_{sb,t}^{S 2B},\quad\forall\;sp,sb,t\end{split} \tag{9}\]  
The output eligible for the SMP revenue can be expressed as Equation (9). \(PCC_{t}^{S}\), the output metered at the coupled point, is the sum of the energy injected into the external grid from the renewable, represented by \(P_{sp,t}^{S2G}\), and the BESS, represented by \(P_{sb,t}^{B2G}\), excluding the energy charging the BESS, which is represented by \(P_{sb,t}^{S2B}\).  
\[\begin{split} REC_{1}^{S}=\sum_{t}\left(\sum_{sb}P_{sb,t}^{B2G}+ \sum_{sp}P_{sp,t}^{S2G}\right)-REC_{S}^{S}\end{split} \tag{10}\]  
\[\begin{split} REC_{5}^{S}=\sum_{t\
eq t^{ch}}\left(\sum_{sb} \left(P_{sb,t}^{B2G}\right)\right)\end{split} \tag{11}\]  
Equations (10) and (11) represent the energy eligible for \(REC_{1}^{S}\) and \(REC_{5}^{S}\). In the market settlement for the multiplier for the DER with a BESS and a PV generator, the energy discharged from the BESS is represented by \(P_{sb,t}^{B2G}\), which is charged by the PV generator from 1000 to 1600 and is eligible for the multiplier. Thus, the conditions for the BESS to discharge are separately designed, considering a scheduling window for the time charging, which is represented by \(t^{ch}\). The energy ineligible for the multiplier is represented by \(REC_{1}^{S}\), which excludes the energy eligible for the multiplier, to avoid dual compensation.
## 3 Results and Discussion  
### Water Absorption and Particle Density of RCA  
Compared with that of the original RCA, the percentage of the decrease in water absorption of RCA and the increase in particle density of RCA after using different treatment techniques are shown in Figures 1 and 2, respectively. In the figures, FC, PC, WC, NS and PCNS represent flow-through carbonation, pressurized carbonation, wet carbonation, NS pre-spraying, and the combined pressurized carbonation with NS pre-spraying, respectively. It can be observed that the decrease in water absorption of the smaller size aggregate (RCA5-10) was larger than that the larger aggregate (RCA10-20). Among the three types of carbonation techniques, pressurized carbonation caused the largest reduction (14.4% for RCA5-10 and 11.9% for RCA10-20) in the water absorption value, followed by flow-through carbonation, and the last one was wet carbonation. After using the NS pre-spraying technique, the water absorption of RCA was only slightly decreased (3.6% for RCA5-10 and 2.8% for RCA10-20) which was even less than that of the wet carbonation. When using the combined pressurized carbonation with NS pre-spraying method, the water absorption exhibited the highest decrease (16.8% for RCA5-10 and 14.4% for RCA10-20) suggesting that these two methods can work effectively to enhance RCA. Generally, the increasing trend of the particle density corresponded to the decreasing trend of water absorption after the RCA was subjected to the different treatment methods. However, the magnitude of the increase in the particle density was much lower than that of the decrease in water absorption.  
The images of the un-treated RCA and RCAs treated by three types of carbonation methods after spraying phenolphthalein solution are shown in Figure 3. A pink color  
Figure 1: Decreasing rate of water absorption of RCA.  
Figure 2: Increasing rate of particle density of RCA.  
(within dotted line in Figure 3) represents that the area was not carbonated. Some of untreated RCA was shown to have been carbonated on the surface because it was placed in air for a long time. However, the interior of the RCA was not carbonated. After using the pressurized carbonation technique, most of the interior of the RCA was carbonated. When using the flow-through carbonation, the carbonation degree was lower than using the pressurized carbonation. The carbonation depth of RCA was very small after subjecting to the wet carbonation. The results indicate that the water absorption and particle density of RCA are dependent on the carbonation degree of RCA.
## 2 Design and Analysis
### The Proposed DGS Unit Cell and Final Structure
The series reactance value shown in Figure 5 can be easily calculated by using the prototype element value of the one-pole Butterworth response which is provided in various references [28; 29]. The parallel capacitance value can be extracted from the attenuation pole location. Finally, the following equations, fulfilling the requirements of the Butterworth low-pass response, have been used to extract the series inductance and shunt capacitance value as shown in Figure 10b:  
\[C=\frac{\omega_{c}}{Z_{0}g_{1}}.\frac{1}{\omega_{0}{}^{2}-\omega_{c}{}^{2}} \tag{1}\]  
\[\mathrm{L}=\frac{1}{4\pi^{2}\mathrm{f_{0}}{}^{2}\mathrm{C}} \tag{2}\]  
where,  
\(\omega_{c}\) is the angular cutoff frequency,  
\(\omega_{0}\) is the angular resonance frequency, and  
\(g_{1}\) is the prototype value of the Butterworth-type low-pass filter  
Finally, a 2-D EBG which is formed by two 1-D proposed EBGs is designed and etched at the ground plane of the patch antenna with the stub-notch configuration to improve the performance of the antenna. The equivalent circuit of the final EBG structure is the same as the equivalent circuit of the 1-D EBG structure, and the simplified circuit is the same as shown in Figure 5. It is observed that the loading of the proposed 2-D multiperiod EBG with the stub-notch configuration results in increased bandwidth and is more matched.
## 3 Case Analysis and Discussion
### Cluster Results of the Driving Behavior Risk Evaluation  
The K-means clustering algorithm was selected to cluster the result of driving behavior risk measurement in this study. First, the risk measurement model was used to measure the driving behavior risk extracted from the trajectory data of 600 vehicles in the NGSIM dataset, and the MOR value of each driving behavior in each frame was obtained. The MOR value at each moment was used to evaluate the driver's driving behavior at that time. The initial cluster centers were sorted based on the magnitude of the assessed risk, and the driving behavior risk measurement results were correspondingly divided into four categories  
driving behavior of each driver at different times during driving can be analyzed, that is, the same driver may have different driving behaviors under various conditions, as shown in Figure 9.  
In Figure 9, it can be seen that the considered driver showed four driving behaviors during driving, the safe driving behavior accounted for the largest proportion, and the dangerous driving behavior only appeared around the 220 th frame. The four driving behavior clusters obtained by the K-means clustering algorithm can thus reflect the security types of driving behavior in real time during driving accurately.
## 4 The Role of UAV-Base Station in Wireless Communications
### Backhauling  
In cellular networks, the backhaul serves as a connection between the BSs and the core network and this connection is usually established using fibre cable, microwave links, etc. However, in emergency scenarios, where existing backhaul infrastructures have been damaged, or when there is a need to enhance the capacity of existing backhaul links, ad-hoc backhaul connections can be established using UAVs [102; 103]. With respect to UAV-enabled backhaul connections, the authors of [102] proposed a UAV-enabled wireless backhaul mechanism for ultra-dense networks. Game theory was employed to model the formation of the multi-hop backhaul network comprising multiple UAVs. Then, a heuristic algorithm was proposed to determine the optimal network formation strategy that maximizes data rate and minimizes network delays. UAVs can also be used for backhauling in high mobility network scenarios such as in high-speed trains where, due to the very high speed of the train, the channel condition is subject to continuous turbulence and instability. In this regard, a backhauling mechanism using the combination of UAVs andfree-space optical (FSO) communications was developed in [104] to enhance the coverage probability of the high-speed train network.
## 2 Materials and Methods
### Image Quality Metrics  
The image quality was assessed by using Radiant DICOM Viewer software (Medixant, Poznan, Poland) to extract CT numbers and noise values from each protocol by placing the circular region of interest (ROI) about 0.8 cm\({}^{2}\) in the selected image (Figure 2). The information regarding CT numbers and noise was used to calculate the signal to noise ratio (SNR) and contrast to noise ratio (CNR). The SNR was calculated by dividing the mean (CT number) by the corresponding standard deviation, SD (noise value) as shown in the equation below:  
\[\text{SNR}=\frac{\text{mean}_{\text{ROI}}}{\text{SD}_{\text{ROI}}} \tag{3}\]  
Furthermore, the CNR were measured by using the equation below:  
\[\text{NR}=\frac{\left|\text{mean}_{\text{ROI}}-\text{mean}_{\text{BG}} \right|}{\frac{\text{SD}_{\text{ROI}}+\text{SD}_{\text{BG}}}{\text{SD}_{\text{ BG}}+\text{SD}_{\text{BG}}}} \tag{4}\]where the \(\rm{mean}_{BG}\) and \(\rm{SD}_{BG}\) is CT number value of background and noise value of the background, respectively. We quantify the dose information to correlate with the CT image quality. The figure-of-merit (FOM) was used to characterize the radiation dose trade-off with image quality by considering all the image parameters. In this study, the FOM values represent the efficiency between SNR and \(\rm{CTDI}_{vol}\) with different CT CAP protocols and they can be calculated with the equation below:  
\[\rm{FOM}=\frac{SNR^{2}}{CTDI_{vol}} \tag{5}\]  
Besides SNR, CNR also can be used to calculate the FOM with the \(\rm{CTDI}_{vol}\) parameter. The FOM allows SNR to be evaluated independently of the current tube and the dose of radiation. However, as it is determined for a specific scan mode (helical or axial) and a specific diagnostic task, it should not be used, for example, when comparing head and abdomen imaging, to compare different scanned areas of diagnostic tasks [22].  
Data were analyzed using IBM SPSS V25.0 (SPSS, version 25.0 for Windows, Chicago, IL, USA). The data of organ-absorbed dose were presented as mean value with standard deviation (SD). One-way ANOVA with post-hoc Dunnett test was used to analyze the significant value of organ-absorbed dose and image quality between default protocol 1 (P1) with other modified protocols CT CAP. A \(p\)-value \(<\) 0.05 was considered statistically different.
## 2 Materials and Methods
### Surface Wettability Analysis  
To confirm the surface wettability, the sessile liquid drop method was taken to determine the contact angle. The surface contact angle measurements were conducted at room temperature, and one microliter distilled water droplet was used. The measurement was repeated three times with the untreated and heat-treated Ti64 specimens.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 4 Experiment Results and Discussion
### Result Discussions
#### 4.5.2 Chinese SMS Dataset Results and Its Non-Language-Sensitivity  
Apparently, based on the experimental results, the proposed HMM works better in classifying Chinese SMS messages. Especially, it performed the best by securing a remarkable accuracy of 98.5% to classify spam and ham SMS. Compared to the experimental results on English SMS messages, the division ratio of training and testing SMS is similar, but the results are obviously much better.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Class** & **Accuracy (A)** & **Precision (P)** & **Recall (R)** & **F-Measure (F1)** \\ \hline The proposed & Spam & 0.985 & 0.977 & 0.993 & 0.985 \\ HMM & Ham & 0.993 & 0.977 & 0.985 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Evaluation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Dataset** & **Testing Dataset** & **Total Number of SMS** \\ \hline Spam & 700 & 300 & 1000 \\ Ham & 700 & 300 & 1000 \\ Percentage & 70\% & 30\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Training and testing dataset statistics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Actual** & **Predicted** & **Prediction \%** & \\ \hline The proposed & Spam & Ham & Spam & Ham & AUC \\ HMM & Ham & 293 & 7 & 0.977 & 0.023 \\ \hline HMM & Ham & 2 & 298 & 0.007 & 0.993 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Confusion matrix of the results.  
The reason lies in English and Chinese languages themselves. In English, many words have different forms. As these synonyms have not merged in this research, the different forms of words are treated as different observation states. On the contrary, Chinese words never change. Compared to English, the words in the Chinese SMS training dataset are more likely to reappear in the testing dataset. Therefore, the HMM introduced in this paper works better in Chinese SMS spam detection.  
Furthermore, the LSTM and CNN models presented in the paper [11] were dependent on SMS written in English only. However, the HMM present in this paper may be implemented widely in the future because it is not language-sensitive.