esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and Experimental Results
### The Experimental Results
The results in Table 5 confirm the advantage of ARFC in both cases before applying the model-based imputation. Figures 6 and 7 illustrates the ARFC performance with utilizing EFTD for imputation. They show the ability of EFTD to recover the original values for missing class labels and increase the accuracy of ARFC from 0.958 to 0.986 with a 3% missing ratio. Figure 4: ADWIN window size of ARFC when a change in data distribution occurs. Figure 5: Tracking the performance of ARFC and the change in ADWIN window size. An evaluation of the computational costs of the proposed model has been performed in terms of training time, testing time, and memory space. The evaluation using the CHB-MIT dataset included a comparison with the performance of Oza bagging, which is a popular bagging classifier, and it also has accuracy results close to the proposed model. The comparison in Figure 8 showed that ARFC has an average (0.8 s) training time and (0.2 s) testing time, while the average of Oza bagging was (4 s) training time and (3.6 s) testing time. In terms of memory space cost, ARFC consumed 1269.084 kB on average \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Without Missing** & **1\% Missing** & **3\% Missing** \\ \hline VFDT & 0.9894 & 0.9893 & 0.957 \\ Random VFDT & 0.9885 & 0.988 & 0.9571 \\ OzaBagg & 0.9917 & 0.9922 & 0.9571 \\ Rule-based & 0.9872 & 0.9871 & 0.9572 \\ ARFC & **0.9959** & **0.9958** & **0.9588** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of ARFC accuracy with four stream classifiers in presence of missing class values without Model-based imputation. Figure 6: Comparison of Model performance with Model-based imputation: (**a**) ARFC accuracy with 3% missing values; (**b**) Accuracy of missing value recovery with 1% and 3% ratios. Figure 7: Comparison of ARFC performance with using EFDT as Model-based imputation technique against the other three techniques. during the training processes compared with 10,966.367 kB memory consumed by Oza bagging, as shown in Figure 9. In Table 6, a significant reduction can be noticed in the response time of ARFC in the edge node without training reach to 90%; OzaBagging also has a reasonable reduction, and that confirms the benefit of applying the proposed method with other ensemble classifiers. The CHB-MIT dataset has been used in many research articles, and the used techniques included deep learning, support vector machines, and linear classifiers. Table 7 shows a comparison between ARFC and eight randomly selected research articles. The comparisons confirm that ARFC overcomes the results of 15 articles.
## 3 Results
### Depolarization Current Properties  
Figure 10 presents the depolarization current of the insulating layer when nanocomposites with different LSC contents were used as semiconductive layers. Figure 10a shows the depolarization current in LDPE at a 10 kV/mm DC field. It can be seen that the depolarization current increases first and then decreases with increasing temperature. The peak value of the current of all samples appeared at 330-340 K under 10 kV/mm DC field, which indicates that the trap levels are basically the same. At high loading levels, the peak value of the depolarization current of the LDPE increases as the LSC content in the semiconductive layer increases. Figure 10b,c shows the depolarization current of LDPE at 30 and 40 kV/mm. The depolarization current increases as the electric field increases, mainly because of the increased charge injection under a strong electric field. At the same time, the position of the peak moves toward the high temperature direction, mainly because the depth of charge injection increases as the electric field strength increases.  
The depolarization current peak that appears between 300 and 320 K in Figure 10c is due to the dipole polarization of small molecular chains and polar groups in LDPE.  
The total trap charge can be calculated according to the TSDC curves. Figure 11 shows the amount of trap charge in LDPE when a composite with different LSC contents is used as a semiconductive layer. It can be concluded from Figure 11 that the effect of suppressing space charge injection when the composite material with an LSC content of 1% is used as the semiconductive layer is the most obvious. When the composites without LSC were used as the semiconductive layer, the charge amount in the insulating sample is \(1.35\times 10^{-9}\), \(3.26\times 10^{-9}\), and \(4.26\times 10^{-9}\), respectively, under 10, 30 and 40 kV/mm DC electric fields. For LSC content with 1 wt%, the charge of the insulating layer decreased to \(0.75\times 10^{-9}\), \(1.34\times 10^{-9}\), and \(2.75\times 10^{-9}\), respectively, decreasing by 44.4%, 58.9%, and 35.7%. When the LSC concentration in the semiconductive composites is high, the trap charge amount in the LDPE increases. The reason might be that the agglomeration of nanoparticles causes the surface roughness of the nanocomposite to increase, resulting in electric field distortion.  
Figure 10: Thermal stimulation current of LDPE when different semiconductive layers are used as electrodes, the applied electric field was: (**a**) 10 kV/mm, (**b**) 30 kV/mm, (**c**) 40 kV/mm at room temperature.  
In general, when the composite material with a 1% LSC content is used as a semiconductive layer, the peak value of the depolarization current is the smallest. The depolarization currents have the same tendency at different polarization voltages.
## 3 Results
### Alternatives to the Coastal Band  
There is no coastal band in other Landsat imagery, which may influence the applicability of the proposed method. Here, we compare and discuss the differences that occur if impervious surface distribution is extracted using the blue band instead of the coastal band.  
The differences between the values for typical samples of impervious surfaces and other ground objects of the ratio of the coastal band to the NDVI (coastal band/NDVI) and the ratio of the blue band to the NDVI (blue band/NDVI) were calculated, as shown in Figure 13. We can see that if the blue band/NDVI is used, the difference between impervious surfaces and other ground objects decreases slightly.  
The coastal band was replaced with the blue band, and the other parameters were the same as those used in the previous workflow to extract the impervious surface distribution from the above three case images. We also determined the accuracy and found that the average recall, precision, and F1 score were 93%, 87%, and 90%, respectively, which represented decreases by 2%, 4%, and 3%, respectively.  
Therefore, it is feasible to use the blue band to extract impervious surface distribution from other Landsat remote sensing images without using the coastal band, albeit with a certain decrease in accuracy.  
In contrast to validation using images for a single region and season [7; 8; 9; 10], our study used three images obtained in summer and winter. The RISI exhibited higher accuracy and better applicability than the other methods used for comparison, and it avoids the additional feature selection and classifier selection processes used in classification methods.  
Figure 12: The impervious surface distribution extracted from Case 3 image before and after atmospheric correction: (**a**) (5, 4, 3) false colour composite with a linear stretch of 2%; (**b**) RGB composite of the impervious surface distribution extracted before atmospheric correction, ground truth for the impervious surface distribution and the impervious surface distribution extracted after atmospheric correction.  
Figure 13: Differences in the coastal band/NDVI and blue band/NDVI between impervious surfaces and other ground objects.
## 4 Results
### Quantitative Evaluation
#### 4.2.2 Rendering Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & **Metrics** & **R0** & **R1** & **R2** & **O0** & **O1** & **O2** & **O3** & **O4** & **Avg.** \\ \hline \multirow{4}{*}{NICE-SLAM} & PSNR \(\uparrow\) & 22.12 & 22.47 & 24.52 & 29.07 & 30.34 & 19.66 & 22.23 & 24.94 & 24.42 \\  & SSIM \(\uparrow\) & 0.689 & 0.757 & 0.814 & 0.874 & 0.886 & 0.797 & 0.801 & 0.856 & 0.809 \\  & LPIPS \(\downarrow\) & 0.330 & 0.271 & 0.208 & 0.229 & 0.181 & 0.235 & 0.209 & 0.198 & 0.233 \\ \hline \multirow{4}{*}{Co-SLAM} & PSNR \(\uparrow\) & 27.27 & 28.45 & 29.06 & 34.14 & 34.87 & 28.43 & 28.76 & 30.91 & 30.24 \\  & SSIM \(\uparrow\) & 0.910 & 0.909 & 0.932 & 0.961 & 0.969 & 0.938 & 0.941 & 0.955 & 0.939 \\  & LPIPS \(\downarrow\) & 0.324 & 0.294 & 0.266 & 0.209 & 0.196 & 0.258 & 0.229 & 0.236 & 0.252 \\ \hline \multirow{4}{*}{Point-SLAM} & PSNR \(\uparrow\) & 32.40 & 34.08 & 35.50 & 38.26 & 39.16 & 33.99 & 33.48 & 33.49 & 35.17 \\  & SSIM \(\uparrow\) & 0.974 & 0.972 & 0.982 & 0.983 & 0.986 & 0.960 & 0.960 & 0.979 & 0.975 \\  & LPIPS \(\downarrow\) & 0.113 & 0.116 & 0.111 & 0.100 & 0.118 & 0.156 & 0.132 & 0.142 & 0.124 \\ \hline \multirow{4}{*}{GS-SLAM} & PSNR \(\uparrow\) & 31.56 & 32.86 & 32.59 & 38.70 & 41.17 & 32.36 & 32.03 & 32.92 & 34.27 \\  & SSIM \(\uparrow\) & 0.968 & 0.973 & 0.971 & 0.866 & 0.993 & 0.978 & 0.970 & 0.968 & 0.975 \\  & LPIPS \(\downarrow\) & 0.094 & 0.075 & 0.093 & 0.050 & 0.033 & 0.094 & 0.110 & 0.112 & 0.082 \\ \hline \multirow{4}{*}{SplaTAM} & PSNR \(\uparrow\) & 32.86 & 33.89 & 35.25 & 38.26 & 39.17 & 31.97 & 29.70 & 31.81 & 34.11 \\  & SSIM \(\uparrow\) & 0.980 & 0.970 & 0.980 & 0.980 & 0.980 & 0.970 & 0.950 & 0.950 & 0.970 \\  & LPIPS \(\downarrow\) & 0.070 & 0.100 & 0.080 & 0.090 & 0.090 & 0.100 & 0.120 & 0.150 & 0.100 \\ \hline \multirow{4}{*}{RK-SLAM (Ours)} & PSNR \(\uparrow\) & 32.90 & 34.55 & 39.69 & 39.49 & 39.17 & 33.39 & 34.13 & 34.98 & 36.08 \\  & SSIM \(\uparrow\) & 0.934 & 0.942 & 0.926 & 0.971 & 0.967 & 0.940 & 0.947 & 0.948 & 0.947 \\ \cline{1-1}  & LPIPS \(\downarrow\) & 0.079 & 0.072 & 0.076 & 0.045 & 0.061 & 0.109 & 0.081 & 0.072 & 0.076 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Map quality on Replica dataset. \(\uparrow\) means that the bigger the metric, the better, and \(\downarrow\) means that the smaller the metric, the better. The table marks the first, second, and third best results. contains missing and noisy depth data. This highlights RK-SLAM's excellent map reconstruction capabilities and its robustness to disturbances in depth information.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 4 Experimental Results
### Predictor Settings
#### 4.2.2 Weight Adaption Parameters  
Figure 9b shows the compression ratio as a function of the weight update scaling exponent final parameter, \(\
u_{max}\), for several different combinations of the weight update scaling exponent initial parameter, \(\
u_{min}\), and the weight update scaling exponent change interval, \(t_{inc}\). It can be seen that the values of \(\
u_{min}\) and \(t_{inc}\) have little influence on the overall performance compared with the value of \(\
u_{max}\). In accordance with the regulations stipulated in the standard, the weight update scaling exponent, \(\rho(t)\), increases by increments of one until the final value of \(\
u_{max}+D-\Omega\), where \(D\) is the bit depth of the image acquired by the CCD detector, and \(\Omega\) is the weight resolution parameter. A smaller value of \(\rho(t)\) yields larger weight increments, resulting in faster adaption to data statistics but worse steady-state compression performance. Therefore, we can see that, when the initial value of \(\
u_{max}\) is small, the adaptation speed of the image statistics is fast, but the stability of the compression effect is poor, resulting in a lower compression ratio. Then, the compression ratio tends to improve as \(\
u_{max}\) increases; when the value of \(\
u_{max}\) increases to a certain point, there will be a compromise between the adaptation speed of the image statistics and the stability of the compression effect, resulting in an optimal compression ratio being achievable. As the value of \(\
u_{max}\) continues to increase, the compression ratio even experiences a slight decrease. The results presented below show that the case of \(\
u_{max}=6\), \(\
u_{min}=-6\) and \(t_{inc}=2^{7}\) provides the optimal performance.
## 3 Methods
### Feature Extraction  
Data in the time domain are converted into the frequency domain through Fourier transform for signal analysis. However, the Fourier transform (FT) does not represent the transition of the signal over time. In other words, when using FT, it is impossible to know at what point the frequency changes when the frequency changes over time. A Short Time Fourier Transform (STFT) was proposed to overcome this. STFT divides time by a specific window size and performs a Fourier transform. Window length may vary depending on the application. In the spectrogram, a long window size is used to increase the frequency resolution, and a short window size is used to increase the time resolution. When applying STFT to the heart sound, the appropriate window length is 98.91% of the heart sound data length [24]. A spectrogram represents the frequency content of the audio as colors in an image. The horizontal axis is time information. The vertical axis is the frequency and the lowest frequencies at the bottom and the highest frequencies at the top. The colors are the amplitude of a specific frequency at a specific time. In this paper, time-domain audio signals of about 5 s were transformed into frequency-domain spectrogram features as Figure 6. Figure 7 shows augmented abnormal data spectrograms in train dataset.  
CNN is a kind of artificial neural network, and the structure of CNN can be divided into feature extraction and classification. Feature extraction uses convolution operations to maintain spatial and local information of the image. It learns the features of the image by repeating the convolutional layer, the activation function, and the pooling layer. Classification uses fully connected layers reducing the amount of computation, and the last layer outputs the score of each class. Compared with general artificial neural networks, CNNs effectively recognize image features because they have the advantage of having fewer learning parameters and use filters as shared parameters.  
Figure 6: (**a**) Normal heartbeat spectrogram and (**b**) abnormal heartbeat spectrogram.
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 2 Methods
### Three-Dimensional CTPIM Modeling Based on Coordinate-Time Functions
When the mining strike is fully mined and the tendency is not fully mined, \(W_{0}(y,t_{i})\) in Equation (4) and \(U_{0}(y,t_{i})\) in Equation (5) can be expressed as follows: \[W_{0}(y,t_{i})=\frac{w_{0}}{2}\bigg{[}erf\Big{(}\sqrt{\pi}\frac{y}{r1}\Big{)} -erf\bigg{(}\sqrt{\pi}\frac{y-L}{r2}\bigg{)}\bigg{]}\] (9) \[U_{0}(y,ti)=U_{1}(y)-U_{1}(y-L)+W_{0}(y,ti)\cot\theta\] (10) where \(y\) indicates the longitudinal coordinates of any high-coherence point in the mining area, \(r_{1}=\frac{H_{1}}{\tan\beta}\), \(r_{2}=\frac{H_{2}}{\tan\beta 2}\) respectively, for the upper and lower radius of influence; \(H_{1}\), and \(H_{2}\), respectively, for the mining depth of the upper and lower mountain, which can be calculated according to the formula \(H_{1}=H-\frac{D_{1}}{2}\sin\alpha\), and \(H_{2}=H+\frac{D_{1}}{2}\sin\alpha\); \(L=(D_{1}-S_{1}-S_{2})\frac{\sin(\theta-\alpha)}{\sin
## 2 Materials and Methods
### Model-Driven Data Construction
#### 2.4.4 Model Performance Tests  
Based on the experimental results of the detailed survey data, the simulation results were compared with the measured data. The coefficient of determination (R\({}^{2}\)) and Nash-Sutcliffe efficiency coefficient (NSE) were used to evaluate the goodness of fit between the simulation results and the measurement results. The mathematical expressions are as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Institution** & **Country** & **Spatial Resolution** \\ \hline GFDL-ESM2M & NOAA & USA & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) \\ \hline HadGEM2-ES & MOHC & UK & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) \\ \hline IPSL-CM5A-LR & IPSL & France & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) \\ \hline MIROC-ESM-CHEM & MIROC & Japan & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) \\ \hline NorESM1-M & NCC & Norway & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the 5 CMIP5 climate models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RCP** & **Emission Pattern** & \multicolumn{2}{c}{**Radiative Forcing**} & **CO\({}_{2}\) Concentration** \\
**Scenarios** & & **(W m\({}^{-2}\)) until 2100** & **(ppm)** \\ \hline RCP2.6 & Mitigation scenario & \begin{tabular}{c} Reaches a peak of 3.0 W m\({}^{-2}\) \\ before 2100 and then drops to \\ 2.6 W m\({}^{-2}\) \\ \end{tabular} & \begin{tabular}{c} Reaches a peak of 490 \\ ppm before 2100 and \\ then drops to \\ then drops \\ \end{tabular} \\ \hline RCP6.0 & \begin{tabular}{c} Stabilization \\ scenario \\ \end{tabular} & \begin{tabular}{c} Stabilizes after reaching \\ 6.0 W m\({}^{-2}\) \\ \end{tabular} & \begin{tabular}{c} Reaches a peak of 850 \\ ppm before 2100 and \\ then drops \\ \end{tabular} \\ \hline RCP8.5 & \
\begin{tabular}{c} Business-as-usual \\ scenario \\ \end{tabular} & Reaches more than 8.5 W m\({}^{-2}\) &
\begin{tabular}{c} Reaches a peak of 1370 \\ ppm before 2100 and \\ then drops \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radiation forcing reaching the Earth’s surface and equivalent CO\({}_{2}\) concentrations by 2100 under the three different emission scenarios.  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(S_{i}-M_{i})}^{2}}{\sum\limits_{i=1}^{n}{( \overline{S}-M_{i})}^{2}}\]  
\[NSE=1-\frac{\sum\limits_{i=1}^{n}{(M_{i}-S_{i})}^{2}}{\sum\limits_{i=1}^{n}{(M_ {i}-\overline{S})}^{2}}\]  
where \(M_{i}\) is observed value, \(S_{i}\) is simulated value, and \(\overline{S}\) is average observed value.
## 4 Discussion
### Validity Considerations and Future Work  
Although this system presents a significant advancement in human-drone interaction, several limitations remain. The gesture recognition system remains susceptible to improper hand positioning, lighting variations, and occlusion. Additionally, the obstacle avoidance system currently lacks adaptive decision-making capabilities and does not incorporate learning from prior interactions. To better evaluate and interpret the system's performance, we acknowledge the following potential threats to validity:  
* Internal Validity--The gesture recognition and drone control were tested under controlled indoor conditions with stable lighting and minimal distractions. Performance may decline in less controlled, real-world environments where lighting, background activity, or camera positioning vary significantly.
* Construct Validity--The predefined gestures selected for this study may not be universally intuitive for all users, potentially influencing usability outcomes. Furthermore, users' hand size, motion speed, and articulation could impact gesture recognition reliability.
* External Validity--The findings are based on a specific hardware setup (Crazyflie 2.1, MediaPipe Hands, and AI-Deck). Results may not generalize to other drone models or hardware configurations without significant reengineering.
* Reliability Threats--System performance may degrade over prolonged use due to sensor drift, thermal effects, or battery limitations. Additionally, the gesture classification system may face reduced robustness in multi-user settings or when exposed to unintended hand movements.  
The paper aims to mitigate these threats in future work through more diverse testing environments, user studies across broader demographics, and hardware-agnostic system design to ensure greater generalizability and robustness. Specifically, future research could explore deep learning models for more robust gesture recognition, sensor fusion techniques for improved obstacle detection, and adaptive control strategies to enhance user autonomy while maintaining safety [42; 43]. Also, expanding the 3D mapping system with SLAM integration could enable more detailed environmental awareness, improving applications in multi-drone coordination and autonomous exploration [44]. By addressing these challenges,future modifications of this system could improve accuracy, usability, and adaptability, making drone technology more accessible to a broader range of users and applications. Furthermore, another promising research direction is the application of hand gesture control to robotic swarm systems and very large-scale robotic systems [45]. Finally, it is worth noting that to improve the gesture classification rate, we have implemented gesture classification using the support vector machine (SVM) method, rather than the method proposed in Section 2.4. The results and findings will be reported in our future paper.
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 3 Audio Datasets
### Hi-Fi Tts  
The corpus chosen for the English language was the dataset _Hi-Fi TTS_[36]. It is composed of 10 different speakers, 6 female and 4 male, where each speaker has at least 17 h of speech obtained from recordings from audio books from LibriVox. Each audio file in the dataset is categorised as belonging to the subsets _clean_ or _other_ according to the values of the SNR. From this multi-speaker corpus, the female speaker with ID 92 ([[https://librivox.org/reader/92](https://librivox.org/reader/92)]([https://librivox.org/reader/92](https://librivox.org/reader/92)) accessed on 4 July 2023), with a total of 27:18 h in 35,296 audio files of _clean_ speech no longer than 20 s each, was chosen as the English voice for this work. These audio files were originally found in mono MP3 format sampled to 44,100 Hz, although they were converted to 22,050 Hz and 16 bit WAV PCM. More-detailed information about the features of this dataset can be found in Section 3.4.