esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sample** & **Calculated Parameter** & **PSO** & **GWO** & **GA** \\ \hline \multirow{4}{*}{Oliver30 (423)} & Best Result & 500.20 & 449.86 & 423.74 \\  & Worst Result & 592.66 & 601.78 & 424.69 \\  & Average Result & 542.08 & 522.74 & 423.93 \\  & Error Rate (\%) & 0.29 & 0.24 & 0.003 \\  & Runtime (s) & 1289.25 & 108.17 & 38.60 \\ \hline \multirow{4}{*}{Eil51 (429)} & Best Result & 723.41 & 532.20 & 439.31 \\  & Worst Result & 776.43 & 827.32 & 448.14 \\  & Average Result & 745.65 & 666.53 & 442.89 \\  & Error Rate (\%) & 0.75 & 0.58 & 0.03 \\  & Runtime (s) & 2385.41 & 208.86 & 72.88 \\ \hline \multirow{4}{*}{Berlin52 (7542)} & Best Result & 12,489.38 & 10,981.60 & 7762.72 \\  & Worst Result & 13,731.19 & 13,539.54 & 8316.30 \\  & Average Result & 13,103.28 & 12,447.52 & 8021.78 \\  & Error Rate (\%) & 0.74 & 0.63 & 0.07 \\  & Runtime (s) & 2506.23 & 239.16 & 80.05 \\ \hline \multirow{4}{*}{st70 (675)} & Best Result & 1572.10 & 1194.62 & 695.40 \\  & Worst Result & 1883.93 & 1616.17 & 715.87 \\  & Average Result & 1686.88 & 1373.35 & 704.52 \\  & Error Rate (\%) & 1.56 & 1.08 & 0.05 \\  & Runtime (s) & 3896.87 & 274.51 & 121.83 \\ \hline \multirow{4}{*}{Eil76 (538)} & Best Result & 1212.12 & 927.09 & 578.22 \\  & Worst Result & 1416.46 & 1115.37 & 592.97 \\ \cline{1-1}  & Average Result & 1287.18 & 997.37 & 588.04 \\ \cline{1-1}  & Error Rate (\%) & 1.44 & 0.90 & 0.09 \\ \cline{1-1}  & Runtime (s) & 4036.43 & 282.94 & 103.94 \\ \hline \multirow{4}{*}{KroA100 (21282)} & Best Result & 73,889.32 & 46,057.62 & 23,961.88 \\ \cline{1-1}  & Worst Result & 83,672.94 & 123,953.40 & 27,348.95 \\ \cline{1-1}  & Average Result & 78,544.64 & 79,129.14 & 25,371.21 \\ \cline{1-1}  & Error Rate (\%) & 2.70 & 2.99 & 0.21 \\ \cline{1-1}  & Runtime (s) & 4951.84 & 389.70 & 132.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results obtained by running the PSO, GWO, and GA algorithms on six different samples. evaluated in terms of running times, it is seen that the algorithm with the shortest time is again GA. On all routes, GA calculates the shortest route in a shorter time than the other two algorithms. Also, the GWO algorithm manages to compute up to 10 times faster than PSO, but it appears to be 3 times slower than GA. In fact, this simulation provided a very clear view of the features of the GA. As seen in Table 3, the GA succeeded in finding the near-optimal result in a short time. The PSO reached a working time of up to 5000 s, and the performance of its results could not tolerate this slowness. GWO, on the other hand, worked at an acceptable speed for a transportation problem, but its performance lagged behind GA based on its results. For this reason, GA  
Figure 8: Comparison of algorithms according to average result values on the KroA100 dataset. Figure 7: A comparison of the algorithms presented in Table 3.  
Figure 9: Error rate parameters of the GWO, WOA, and GA methods. has been the preferred algorithm in this study, thanks to its fast work and the success of the results obtained.
## 1 Introduction
[6] overcomes the dilemma of the fixed wavelet in ST by introducing four undetermined parameters (amplitude, energy decay rate, energy delay time, and video rate) to construct the basic wavelet adaptive to the non-stationary signal characteristics in practical application. Due to having no restriction of the time window length, GST can obtain real time-frequency spectra with excellent time-frequency resolution, which provides more possibility and higher accuracy for the detailed information extraction of complicated non-stationary signals than ST. Since seismic signals are non-stationary and non-linear and show the characteristics of broadband and multi-frequency, using the GST instead of ST for seismic time-frequency analysis has been proven to be advisable. In this paper, inspired by the theory of SST and the advantages of the GST over ST, we propose a novel time-frequency analysis method that we have named synchrosqueezing generalized S-transform (SSGST). The rest of this paper is organized as follows: Section 2 gives a brief review of GST and SST and then deduces the mathematical formulas of the newly proposed method in detail. In Section 3, three synthetic examples are employed to explore the performance of the SSGST method. Section 4 illustrates the effectiveness of the proposed method in the field seismic data analysis for hydrocarbon detections. Conclusions are drawn in Section 5.
## 3 Loss Calculation of IM Driven FESS
### Mechanical Losses
#### 3.3.2 Bearing Losses  
The bearing losses are usually calculated using the empirical methods as recommended by the bearing manufacturers. However, due to the complexity of the empirical method, in this paper the bearing power losses are calculated using the analytical method. Considering the rotating contacts, the bearing losses in FESS can be defined by Equation (21) [69].  
\[t_{b}=t_{l}+t_{v}+t_{f} \tag{21}\]  
where, \(t_{l}\) is the torque due to an applied load and depends on the type of the load and design of bearing, \(t_{f}\) is end flange friction torque which is only applicable in roller type bearings and is negligible in ball bearings, and \(t_{v}\) is the torque due to viscous friction that is dependent on the speed and property of the lubricant used. The values for \(t_{l}\) and \(t_{v}\) can be calculated using Equations (22) and (23), respectively [70].  
\[t_{l}=f_{l}F_{b}D_{p} \tag{22}\]  
\[t_{v}=10^{-7}f_{0}(v_{0}n)^{\frac{3}{2}}D_{p}^{3}\qquad v_{0}n\geq 2000\] \[t_{v}=160\times 10^{-7}f_{0}(v_{0}n)^{\frac{3}{2}}D_{p}^{3} \qquad v_{0}n<2000 \tag{23}\]where \(f_{l}\) is the design dependent factor and is defined by Equation (24).  
\[f_{l}=z\Big{(}\frac{F_{s}}{C_{s}}\Big{)}^{y} \tag{24}\]  
where \(C_{s}\) is static load rating defined by the manufacturer's datasheets and \(F_{s}\) the static equivalent load. The values of z and y depend on the type and contact angle of the bearing as given in Table 4. \(F_{b}\) is load on the bearing, \(D_{p}\) is the pitch circle diameter (m), \(n\) is the bearing speed (rpm), \(v_{0}\) is kinematic oil viscosity (entistokes) and \(f_{0}\) is a lubrication factor which depends on type of bearing and lubrication.  
The power losses of the ball bearing can be simply defined in terms of the bearing torque and speed as presented in Equation (25) [71].  
\[P_{bearing}=(t_{l}+t_{v})\omega \tag{25}\]  
Substituting the values of the parameters from Tables 2 and 3, Equation (25) can be simplified as:  
\[P_{bearing}=0.013\omega+2.67\times 10^{-4}\omega^{1.66} \tag{26}\]
## 3 Experimental Results and Analysis
### Dynamic Brittleness Index  
Brittleness is an important parameter to describe the deformation and failure process of the rock [35]. Various external factors such as water content and temperature inevitably change the brittle state and failure pattern of rock [24]. Therefore, a brittleness index (\(BI\)) is used to characterize the effect of saturation on the pre-peak deformation characteristics of the frozen sample, as follows:  
\[BI=\frac{\varepsilon_{\text{e}}}{\varepsilon_{\text{p}}} \tag{5}\]  
where \(\varepsilon_{\text{e}}\) and \(\varepsilon_{\text{p}}\) refer to the yield point strain and peak point strain in the stress-strain curve, respectively. If \(BI\) has the value of 1, the sample is considered to be completely brittle, i.e., the larger the value of \(BI\), the more brittle the sample is. From Table 2, the minimum value of \(BI\) is 0.602 and the maximum value is 0.862. The surface of \(BI\) for frozen sandstone as a function of initial saturation and impact velocity is shown in Figure 14.  
As shown in Figure 14, the \(BI\) values of the sample decrease with increasing impact velocity, suggesting that impact velocity promotes the transition from very brittle to ductile frozen sandstone. Thus, \(BI\) values decrease by 17.4 %, 11.6 %, 20.9 %, 14.3 %, and 11.3 % at five saturation states, respectively, when the impact velocity is increased from 4 m/s to 7 m/s. In terms of energy, an increase in \(BI\) value means an increase in the elastic energy accumulation stage (II) and a decrease in the energy release stage (III) within the sample. Therefore, microcracks are less likely to propagate in the frozen sample at low impact velocities, consistent with the experimental results of [PERSON] et al. [5]. In contrast, the \(BI\) value of the frozen sample tends to rise with increasing saturation due to pore ice. As a result, the stress-strain curves show a shortening in the stage (III) and an increase in the stage (II). However, when saturation exceeds critical saturation, pore ice plays a role in eliminating internal defects and drives crack propagation by frost heave forces. Therefore, two effects play a critical role in controlling the deformation behavior of the  
Figure 13: Dynamic elastic modulus versus saturation (**a**) and impact velocity (**b**) of the sample.  
frozen rock. For the tested rock, when the saturation is close to 80 %, the crack development within the sample under frost have forces plays an important role. For example, when the saturation increases from 50 % to 80 %, the _BI_ values decrease by 8.54 %, 9.04 %, 7.49 %, and 1.02 % at four impact velocities, respectively. Therefore, it can be inferred that the saturation distribution of the rock masses in the cold regions could affect the deformation characteristics under the impact loading.
## 2 Theory Based on Resonance Method  
From the linearly electro-thermo-elastic model [20], the constitutive equations are represented as  
\[T_{ij}=c_{ijkl}S_{kl}-e_{ijk}E_{k}-\alpha_{ij}\theta \tag{1}\]  
\[D_{i}=e_{ijk}S_{kl}+\varepsilon_{ij}^{S}E_{j}-\lambda_{i}\theta \tag{2}\]  
\[\eta=\alpha_{kl}S_{kl}+\lambda_{k}E_{k}+C_{o}\theta \tag{3}\]  
\[q_{i}=-K_{ij}\theta_{,j} \tag{4}\]  
In these equations, \(T_{ij}\), \(S_{kl}\), \(E_{k}\), \(D_{i}\), \(q_{i}\) and \(\theta\) are the components of stress, strain, electric field, electric displacement, heat flux and temperature difference with initial condition, respectively. The \(c_{ijkl}\), \(e_{ijk}\), \(\alpha_{ij}\), \(\varepsilon_{ij}^{S}\), \(\lambda_{i}\), \(C_{o}\) and \(K_{ij}\) are the components of elastic, piezoelectric, thermal expansion, permittivity, pyroelectric coefficients, and the thermal conductivity tensor, respectively. If neglecting the thermo-elastic and thermo-electrical effects, the piezoelectric \(e\)-form is  
\[\left\{\begin{array}{l}T_{ij}=c_{ijkl}S_{kl}-e_{ijk}E_{k}\\ D_{i}=e_{ijk}S_{kl}+\varepsilon_{ij}^{S}E_{j}\end{array}\right. \tag{5}\]  
and the \(d\)-form constitutive equations are, respectively,  
\[\left\{\begin{array}{l}S_{ij}=s_{ijkl}T_{kl}+d_{kj}E_{k}\\ D_{i}=d_{kl}T_{kl}+\varepsilon_{ik}^{T}E_{k}\end{array}\right. \tag{6}\]  
The linear piezoelectric constitutive equations for a piezoceramic material with crystal symmetry class \(C_{6 mm}\) are presented in the components of elastic (\(c_{pq}^{E}\)), piezoelectric (\(e_{pq}\)), and dielectric coefficients (\(\varepsilon_{pq}^{S}\)) respectively.  
The IEEE Standards [14] indicate that measuring the complete material constants of piezoelectric ceramic material requires piezoelectric specimens with five different mode shape characteristics (TE, LE, TS, LTE, and RAD), each with its own geometric shape, aspect ratio, polarization orientation,and electrode surface orientation. We used an impedance analyzer (model: HP-4194A) to measure their resonant and anti-resonant frequencies and inversely calculate the material constants of the piezoelectric materials. Based on Equations (7), (8), (12), (13), (19), (20), (22), and (23), the characteristics of the impedance measurements for TE, LE, TS, and LTE are as shown in Figure 1. Only one set of resonant and anti-resonant frequency values (\(f_{r},\ f_{a}\)) needs to be substituted into Equations (25) and (26). In Equations (25) and (26) for RAD, the impedance measurement characteristics, as shown in Figure 1, have two sets of resonant and anti-resonant frequency values (\(f_{1}^{r},\ f_{1}^{r},\ f_{2}^{r},\ f_{2}^{a}\)). Below, we obtain the material constants from the specimens of the five vibration modes according to the explanations in Ref [14].
# Accurate Landmark Localization for Medical Images Using Perturbations
## 4 Results
### Perturbation Performance Comparison  
Table 3 compares the results of perturbation methods on the Digital Hand Atlas dataset. All methods were tested based on U-net. In Table 3, all perturbators except whiteout and smoothing increased performance. Among them, the hybrid perturbator shows significant performance improvement. U-net achieved 2 mm range EDR of 4.27%, and mean PE of 0.66. When hybrid perturbator was applied, 2 mm EDR decreased to 3.96%, and mean PE decreased to 0.64.  
Table 4 compares the results of perturbation methods on the ISBI 2015 test1 dataset. U-net achieved 2 mm range EDR of 15.47%, and median PE of 12.03. Significant performance improvement has been observed when using whiteout, binarization, and hybrid  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{\(\boldsymbol{PE}_{all}\) **(mm)**} & \multicolumn{2}{c}{**EDR (\%)**} \\ \cline{2-6}  & **Median** & **Mean \(\pm\) SD** & **\textgreater{}2 mm** & **\textgreater{}4 mm** & **\textgreater{}10 mm** \\ \hline U-net without perturbator & 0.46 & 0.66 \(\pm\) 0.84 & 4.27 & 0.47 & 0.05 \\ \hline Blackout & 0.46 & 0.66 \(\pm\) 0.68 & 4.13 & 0.39 & 0.03 \\ \hline Whiteout & 0.47 & 0.69 \(\pm\) 0.90 & 4.33 & 0.52 & 0.10 \\ \hline Smoothing & 0.48 & 0.68 \(\pm\) 0.83 & 4.31 & 0.49 & 0.07 \\ \hline Binarization & **0.45** & 0.66 \(\pm\) 0.87 & 4.16 & 0.51 & 0.10 \\ \hline Edge detection & **0.45** & 0.65 \(\pm\) 0.77 & 4.19 & 0.46 & 0.06 \\ \hline Hybrid & **0.45** & **0.64 \(\pm\) 0.64** & **3.96** & **0.34** & **0.02** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Localization results according to perturbator on the Digital Hand Atlas dataset.  
perturbator. When using the edge detection perturbator, the network achieved 2 mm range EDR of 14.42%, and median PE of 11.72.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 2 Element Test of Viscoelastic and Rotational Friction Damper
### Loading Protocol  
Table 2 presents a loading protocol of the test parameters for VE and RF dampers. Viscoelastic dampers were evaluated under cyclic loading conditions at a consistent frequency of 0.5 Hz and 1.0 Hz, while the rotational friction damper's cyclic loading frequency was set at 0.5 Hz. The RF damper clamping forces varied at 0, 2, 5, 6, 8, and 10 kN. The VE damper's behavior is primarily governed by its intrinsic material properties, while the performance of the friction damper is influenced by the applied clamping forces, which regulate the sliding resistance.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Explanatory Variables
#### 3.2.3 Periodic Data  
The design of the flight schedule is linked to the time factor. Furthermore, holidays may also affect flight delays. Therefore, we incorporated factors including hour of the day, day of the week, month of the year, seasons, and holidays. In previous studies, time-dependent data such as hour of the day were encoded by the one-hot method [25] as discrete features, characterized by numbers [18], or converted to a value between 0 and 1 [26]. However, studies have ignored the periodicity of these features. For example, hour 23 and hour 0 are close to each other [27], and the distance between hour 5 and hour 18 is clearly different from the distance to hour 6. If data are processed as before, considerable information will be lost. A suitable way to handle these features is to convert them to (x, y) coordinates on a circle [27]. It has been demonstrated that this approach can improve the predictive power of the model [28]. We used the sine-cosine functions for conversion.  
\[CAC=(\cos(\frac{2\pi\cdot V}{T}),\sin(\frac{2\pi\cdot V}{T})) \tag{2}\]  
where \(CAC\) is the coordinate after conversion; \(V\), \(T\), \(\cos\), and \(\sin\) denote the variables to be converted, period of variables, cosine function, and sine function, respectively. Figure 1 shows the circle of hours of the day after conversion. Hour 0 is on the right and the hours increase counterclockwise around the circle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Seats** & **Representative Aircraft Type** & **Grade Number** \\ \hline Less than 60 seats & CRJ2, MA60, etc. & 0 \\
61 to 150 seats & A319, B733, B734, B735, B736, B737, etc. & 1 \\
151 to 250 seats & C919, A310, A320, A321, B738, B739, B788, etc. & 2 \\
251 to 500 seats & A332, A333, A343, A346, A350, B742, etc. & 3 \\
500 seats or more & A388, etc. & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Aircraft capacity class table.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reasons for Abnormal Flights** & **Percentage of Total** & **Compared with Previous Year** \\ \hline Weather & 46.49\% & \(-\)0.96\% \\ Airlines & 18.91\% & \(-\)2.22\% \\ Air traffic control & 1.43\% & \(-\)0.88\% \\ Others & 33.17\% & 4.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reasons for abnormal flights in China in 2019.
# Satellite SAR Data-based Sea Ice Classification: An Overview
## 5 Discussion
### Ice Classification and SAR Technical Abilities
The grey level co-occurrence probability texture features applicability for sea ice classes discrimination are widely discussed in [76], where [PERSON] and [PERSON] [76] achieved the best discrimination between ice types by simultaneously utilizing three GLCM texture features. The GLCM approach demonstrates better results than those of other texture discrimination methods and is extensively applied in texture description [77]. The investigation of texture statistics calculation parameters was also provided in many researchers (e.g., [78; 79]).
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### Advantages of the phantom cell approach  
* The access level network provides support at the time of handoff [60].
* Due to prevailing standards on MR-DC, it is easy to implement the operations [27].
* Multiple physical layer connections are possible.
* It offers the capability to permit per-user and per-flow granularity of service.
## 2 Literature Review
### Modeling Approaches: A Comparative Analysis of Temporal Models
Advanced models, such as LSTMs, tend to deliver high accuracy but may lack interpretability. In contrast, models such as CRFs and HMMs, while providing more interpretability, may not always reach the same level of predictive performance, especially in dynamic and multimodal scenarios. The generative framework of the MHMM offers a clearer and more interpretable structure that can directly connect to real-world performance concepts, which is essential for applications focused on enhancing human operator understanding and support. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Strengths** & **Weaknesses** & **Temporal Data** & **Multimodal Data** & **Interpretability** \\ \hline \multirow{3}{*}{Unimodal Hidden Markov Model (HMM)} & Captures temporal dependencies; probabilistic; interpretable states & Limited to a single observation stream, providing an incomplete view of proficiency & \multirow{3}{*}{Excellent} & Poor & Moderate to High \\ \cline{1-1} \cline{5-5}  & Excellent at capturing long-range dependencies; highly flexible & Black-box nature makes interpretation difficult; requires large datasets and is computationally expensive & \multirow{3}{*}{Excellent} & \multirow{3}{*}{Excellent} & \multirow{3}{*}{Low} \\ \cline{1-1} \cline{5-5}  & Excellent & & & & \\ \cline{1-1} \cline{5-5}  & Excellent & & & & \\ \cline{1-1} \cline{5-5}  & Long Short-Term Memory (LST) & flexible & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \hline \multirow{6}{*}{Conditional Random Fields (CRF)} & Discriminative model that considers the entire sequence context (global normalization); & Training can be computationally intensive; may not capture long-range dependencies as well as LSTMs & \multirow{3}{*}{Excellent} & Good & Moderate \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \hline \multirow{6}{*}{Markov Chain} & Simple structure; highly interpretable transitions between observable states & \multicolumn{3}{c}{Cannot model latent/hidden states; Usually limited to a single observation stream} & \multirow{3}{*}{Good} & \multirow{3}{*}{Poor} & \multirow{3}{*}{High} \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \cline{1-1} \cline{5-5}  & \multicolumn{1}{c}{} & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of advanced temporal modeling approaches.
## 3 Results and Discussion
### LNC Estimation Based on the Proposed Fluorescence Index  
LNC estimation based on the proposed fluorescence index served as input parameter to train the BPNN model. The performance of fluorescence index for LNC estimation is shown in Figure 5.  
Figure 5: Performance of the proposed fluorescence indices for LNC estimation based on BPNN model with hidden layer sizes for different training function. The black error bar represents one standard deviation of mean values.  
Figure 5 shows the performance of fluorescence index for LNC estimation based on the BPNN model. The black error bar denotes one SD of mean values with a repetition of 60 times. The effect of hidden layer sizes on the accuracy of LNC estimation cannot be ignored when \"traingd,\" \"traingdm,\" \"traingda,\" \"traingdx,\" \"traincgf,\" and \"trainoss\" were selected as training functions, which belonged to gradient descent methods. The possible interpretation is that the fluorescence characteristics may not be exhibited well as the gradient difference changed with the LNC in this investigation, when the gradient descent methods were selected to train BPNN model. In addition, the selection of training function may need to consider the number of input layers in BPNN model [37], and the detailed causes need to be further studied in future work. Although hidden layer size has a small effect on the accuracy of LNC estimation when the remaining training functions were selected in the BPNN model, it exhibited better performance for LNC estimation with high \(R^{2}\) and low SD. The optimal BPNN model parameter is achieved when \"trainlm\" serves as training function and the number of hidden layers is set to five. \(R^{2}\) and SD are 0.819 and 0.023, respectively. The results show the promising potential of proposed fluorescence index for LNC estimation.