esa-sceva/satcom-chunk-collection huggingface dataset:
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 5 Evaluation Results  
The autonomous time scale is a time scale established based on atomic clocks carried by multiple satellites and does not rely on the ground. It is established using seven hydrogen clocks, with the clock offset of all satellites obtained relative to this autonomous time scale. The evaluation data are collected from the Beidou satellite navigation system while providing services globally.  
In our simulations, the Beidou-3 satellite link establishment is conducted in 1-min units. This approach allows the connection of the entire constellation based on the relative clock difference data of the inter-satellite links within 1 min, ensuring the feasibility of the proposed clock error parameter estimation method. The ground station uses a clock group to keep time and is traceable to China's local atomic time TA (NTSC). Its stability is \(8.3\times 10^{16}\) (5 days) and \(4.7\times 10^{16}\) (30 days).  
The performance of the generated space-based time scale is evaluated through the comparison of satellite clock offset fitting residuals and prediction errors. The stability of the space-based time system is also assessed. Towards the end of this section, the accuracy of the centralized two-way time comparison method based on the inter-satellite link is evaluated and compared with that of integration- and anchor-based methods.
## 7 Analytical Methods for Detection  
The recent food safety issue regarding the presence of PA and TA-contaminated food products on the market has led to a significant increase in the number of developed analytical procedures to analyze these contaminants in various food and vegetable matrices. Over the years, the analysis of PAs and TAs has been performed with several analytical techniques, such as gas chromatography, thin layer chromatography, nuclear magnetic resonance, and capillary electrophoresis. Nowadays, chromatography coupled to mass spectrometry (LC-MS) is the most suitable analytical techniques for the determination of PAs and TAs. LC-MS has a high sensitivity and selectivity, allowing accurate determination of the analytes at very low concentrations in different food and vegetable matrices [34; 91]. In recent years, high-resolution mass spectrometry (HRMS) is increasingly used in analysis of trace contaminants in food matrices as it enables simultaneous screening of target, suspect, and non-target compounds [92]. The high structural variety of PAs makes the analysis challenging; therefore, HRMS is extremely suitable for the qualitative analysis of PAs and the structural elucidation of unknown PAs [93]. For this purpose, analytical platforms for the identification and quantification of a high number of PAs at trace levels in various food and vegetable matrices have been developed [94; 95]. Moreover, in the case of highly complex matrices, such as dietary supplements and botanicals, the sample preparation plays a critical role. A purification step based on solid-phase extraction (SPE) is often performed to obtain enriched PA and TA fractions. However, the affinity for PAs and TAs significantly varies depending on the type of SPE method used, which is a major limitation as it may lead to underestimation of certain PAs and TAs [34; 96]. In the last decade, QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe), a type of dispersive SPE introduced in 2002 for the analysis of pesticide residues in plant matrices, was shown to be a promising methodology for the selective extraction of PAs and TAs from dietary supplements and botanical products prior to LC-MS analysis [34; 91].
## 3 Results and Discussion  
### Results of RF Model  
In order to predict the PPV by using the RF model, two typical model parameters including \(m_{\text{try}}\) and \(n_{\text{tree}}\) should be determined first. Hence a parameter investigation was carried out by using the grid search method with \(m_{\text{try}}\) of 1 to 7 and \(n_{\text{tree}}\) of 2000, and 7 was selected as the optimal \(m_{\text{try}}\) value (Figure 5a). In addition, the parameter investigation with \(m_{\text{try}}\) of 7 and \(n_{\text{tree}}\) of 1 to 2000 show that the prediction performance will vary within a range and that range will be stable when \(n_{\text{tree}}\) = 2000. So 2000 was selected as the optimal value of \(n_{\text{tree}}\) (Figure 5b). After optimal RF model development, the testing datasets were inputted into the established RF model for prediction, the prediction performance shows the prediction performance with using the training datasets with \(R^{2}\) of 0.92, \(MAE\) of 0.19 and \(RMSE\) of 0.27 and using the testing datasets with \(R^{2}\) of 0.90, \(MAE\) of 0.35 and \(RMSE\) of 0.40 (see Figure 6).
## 3 Materials and Methods
### Features Selection Using PSO Algorithm with Cross-Entropy (CE) and Kullback-Leibler (KL) Divergence  
As previously indicated, the dataset contains 9504 features. In this phase, we used the choose features technique to select the most essential features that have a high correlation to the type of app that we want to forecast. Choosing a set of important features from the dataset helps greatly in reducing the time to test algorithms. Some features may be unnecessary or redundant, so they are excluded to help the accuracy of the prediction and the speed of analysis of the results. Therefore, for feature selection in our proposed method, we used a PSO algorithm with cross-entropy (CE) and Kullback-Leibler (KL) divergence.  
The KL divergence involved measuring the CE between two sampling distributions and solves an optimization problem by minimizing them and obtaining the optimal probability distribution parameters. CE along with KL divergence has the few important advantages of strong global optimization ability, good adaptability, and strong robustness. Our proposed meta-heuristic PSO algorithm with CE with KL divergence method for feature selection is embedded in the PSO algorithm to provide a good balance between exploitation and exploration. Based on this algorithm, we gain the optimal subset from a given set of features in the dataset, as shown in Figure 2.
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 3 Methods
### Scalable Update Strategy
#### 3.2.3 Heuristic for Activation of Basis Functions  
The proposed method selects a relatively small set of basis functions from a potentially extensive set of possibilities. The computational costs are mitigated by recognizing that inactive basis functions do not interact with those that are active (with non-negligible weights). The inactive components are removed in the final model and excluded from the active subspace optimization. However, our method essentially optimizes over the full space, and thus we have the algorithm allowing for the reactivation of inactive basis functions to ensure optimization across the entire subspace. Due to computational constraints, we consider functions for activation sequentially rather than all at once. Thus, we propose Algorithm 1 to introduce unseen basis functions into the active set using a selective strategy akin to the heuristic approach described in [39].  
The algorithm selects the top function, \(\phi_{Bk}\), from the inactive basis functions \(\{\phi_{Bk}\}_{k}\) by gauging their correlations with residuals and applying an angle-based threshold \(\tau_{\mathrm{ang}}\) relative to the subspace of \(\phi_{A}\). The correlation with residuals for \(\phi_{Bk}\) is measured by \(\sum_{i}\left(\Phi_{Bk}(Y_{i}-\mathbb{E}_{\mathcal{Q}^{(\mathrm{a})}}[Z_{iA} W_{A}+Z_{A}]\Phi_{iA})\right)^{2}\). The angle-based threshold ensures a meaningful distinction from active functions. Next, the current active surrogate posterior is expanded by a dimension for \(\phi_{Bk}^{*}\), initiating optimization from the numerical maximum \(\tau_{\mathrm{max}}\). Postoptimization, the function is retained if it falls below \(\tau_{\mathrm{max}}\). Otherwise, the algorithm terminates. Efficiently, in trial optimization, the approach replaces one function with precision \(\tau_{\mathrm{max}}\), if present.  
```
Sort inactive basis functions \(\{\phi_{Bk}\}_{k}\) by correlation with residuals.  Filter through \(\{\phi_{Bk}\}_{k}\), selecting the most correlated one as \(\phi_{Bk}\).  Copy current active surrogate \(\mathcal{Q}^{(\mathrm{a})}(\Theta)\) posterior to \(\mathcal{Q}_{k}^{(a)^{\prime}}(\Theta)\).  Expand dimension in \(\mathcal{Q}_{k}^{(a)^{\prime}}(\Theta)\) for \(\phi_{Bk}\).  Optimize \(\mathcal{Q}_{k}^{(a)^{\prime}}(\Theta)\) for three iterations using mean field approximation. if expected precision is within threshold then \(\mathcal{Q}^{(\mathrm{a})}(\Theta)\leftarrow\mathcal{Q}_{k}^{(a)^{\prime}}(\Theta)\). endif
```  
**Algorithm 1** Search for new basis functions to activate
## 3 Results and Discussion
### Geographical Patterns of Trend Types
#### 3.2.2 Interrupted Gradual Change  
Interrupted greening was the most common class in our results and interrupted browning appeared also more common than monotonic browning (Figure 3(b), inset). The types of shifts showed different temporal distributions (Figure 5), with bursts concentrated in the 1990s and setbacks or browning reversals just after the millennium transition. However, regional variations in this temporal pattern exist. A setback around 1990 was for instance found in eastern Brazil after which the region showed a continuous increase again. This increase was not picked up by the previous monotonic analysis (Figure 4). Discontinuities around 2000 were found in southeastern United States and in the countries in southern Africa. Noticeably, Botswana appeared as greening hotspot in the monotonic analysis while this pattern seemed more complicated with trend shifts included. The trends changed in the late 1980s, when the central part of Botswana experienced a burst. The gradual change afterwards, however, was found to be negative rather than positive.  
A region with similar patterns is the Horn of Africa. Parts were previously found to become greener, but Figure 3(b) suggests that this signal might be attributable to a short-term burst in the 1990s (Ethiopia early 1990s, Somalia and Kenya late 1990s). The gradual change appeared either negative throughout the full period or reversed into a negative trend. The resulting decline in vegetation activity seems to have amplified recently due to a period of persistently poor rains, causing severe food insecurity in the region [18, 47].  
Figure 5: Temporal distribution of change points per trend type. Monotonic classes—which may or may not contain an abrupt change event—were omitted. The class colors correspond to previous figures.
# Influence of Spectral Filtration on Pulse Dynamics in Ring-Cavity Mamyshev Oscillator
## 4 Transition from Harmonic Mode-Locking to a Single Pulse Generation
Dark blue circles depict the stable pulse generation with relative energy fluctuations less that 2% over the last 10 roundtrips, while medium blue circles correspond to transient regimes with pulse energy oscillating between two values. The pulse energy does not grow with the increase in spectral separation, because pump power did not change during simulations. It is noteworthy that there is a gap for \(\Delta\lambda\) between 2 and 3 nm, where single pulse generation was not observed (light blue circles in Figure 6a, noise-like regimes). Only multi-pulse generation is possible in this area. Figure 6b shows the obtained multipulse regimes with the number of pulses from 2 to 4. Dark-colored circles depict regimes with less than 2% energy fluctuations. The main trend is that smaller spectral separation leads to more pulses at the laser output. However, the same \(\Delta\lambda\) could lead to formation of different number of pulses, depending on the way the filters are tuned. For example, for \(\Delta\lambda=2\) nm, generation of a single pulse, soliton pair, triplet, or even quartet can be obtained.
## 4 Simulation Results
### Nonlinear Compensation Performance of the SSCN  
Two sets of simulation experiments were carried out to verify the compensation capability of SSCN for a flotation motor replacement manipulator. One set involved SSCN compensation, while the other did not. The manipulator's joint position error curve is depicted in Figure 7. Table 3 presents the time required for system convergence when the position tracking error was below 0.01, with and without SSCN compensation. The table demonstrates that OSMC with SSCN compensation achieves faster convergence, effectively mitigating the impact of parameter uncertainty and external disturbances on the system.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 5 Integrating Cognitive Radio in LPWAN for the Internet of Things
### Motivation for CR-LPWAN  
Compared to other facets of LPWAN development, such as designing better protocols, modulation techniques, and improved access technologies, the current literature provides little on the use of CR in LPWAN. Nevertheless, a few proprietary technologies, such as Sigfox, have led the way in integrating CR in their designs. Sigfox deploys CR technology in their base stations and connects them to back end servers via an IP network [12; 19; 28; 34]. For proprietary reasons, little or nothing is known regarding the type of CR technology deployed; nevertheless, Sigfox technologies are known to operate in both the unlicensed ISM bands, including 868 MHz in Europe, 915 MHz in North America, and 433 MHz in Asia, and in the licensed band, which is made possible most likely by the use of CR capabilities. This major step has also been considered by the Weightless Alliance in their Weightless-W technology [5], thereby further enriching the drive for the widespread development and deployment of CR-LPWAN technologies.  
Regarding why CR should be deployed in LPWAN, authors in [5] noted that because of strict regulations pertaining to the use of limited maximum duty cycle rates in the ISM bands, and its confining effects on the capacity of IoT networks, it may be worthwhile to consider the inclusion of CR in LPWAN. Even though some LPWAN standards, such as the LoRaWAN are yet to incorporate CR in their specifications, authors in [5] however argued that incorporating CR in LPWAN will present several advantages including improved spectral utilization, less transmission power constraints, possibility for longer transmission ranges, and lower device cost [5]. [PERSON] in [42] emphasized the need to incorporate CR in LPWAN in order to maximize spectrum capacity available in the licensed bands while minimizing interference to the licensed network. In this regard, a dynamic spectrum access strategy for CR-LPWANs, which operates both in the licensed and unlicensed bands was proposed. [PERSON] [42] demonstrated that spectrum capacity was maximized and strict QoS of licensed users were maintained.  
It was noted in [12] that CR is a key network enabler for 5G mobile networks including the IoT network. This was attributed to the need to maximize spectral resources in order to support increasing and higher demands for new emerging IoT applications. It was further noted that CR can be incorporated by introducing a spectrum coordinator in the non-access stratum, which will enable cellular technologies to dynamically lease spectrum for IoT applications. These observations have strengthened the need to develop CR-LPWAN technologies.
## 5 Experiments and Results
### International DB  
We also compared emotion recognition using emotion speech databases widely used in previous studies. Table 4 compares prediction accuracy using the databases from the respective papers. Although databases and emotions differed between the various studies, accuracies achieved approximately 60-70%. However, the proposed method achieved 85.79% accuracy for EmoDB, and 87.11% for the RAVDESS database.  
In the case of the result using the RAVDESS database, we used two kinds of deep neural networks for classifying emotions from speech and images, respectively. The networks reflected temporal representations from sequential data of images and speech. We fine-tuned the softmax functions of the pre-trained networks, considering the characteristic of each input, to maximize the ability of the networks. Consequently, the proposed method showed more accurate results than other models. Thus, higher accuracy can be acquired by combining the proposed acoustic features with a FER model.
# Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot
## 4 Experiments and Results
### Field Surfaces and Force Curves  
The potential fields' surfaces and forces' curves of both APF and IAPF are shown in Figures 7 and 8 respectively. Table 3 shows the concrete parameters of the potential field and force model of APF and IAPF.  
The gradient of APF's attractive potential field gradually disappears near the target position, while the gradient of IAPF does not disappear but increases. Therefore, IAPF has stronger resistance to TPNR and a faster convergence rate than APF. Due to the phenomenon of gradient explosion in the repulsive potential field of APF, the gradient of the repulsive potential field of APF is much larger than that of the attractive potential field when it is close to an obstacle, resulting in the lack of features of the surface of the attractive potential field of the resultant potential field. However, IAPF can effectively overcome the above problems. Its resultant potential field surface has the common characteristics of both attractive and repulsive potential field surface, which makes IAPF not only have a faster convergence rate but also have the strong obstacle-avoidance ability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Parameter** & **Parameter** & **Parameter** \\ \hline \(x,y\in\left[\begin{array}{cc}0,&10\end{array}\right]\) & \(k_{r}=1\) & \(\mathbf{P}_{c}=\left[\begin{array}{cc}x,&y\end{array}\right]\) & \(k_{a1}=5\) \\ \(\mathbf{P}_{g}=\left[\begin{array}{cc}0,&0\end{array}\right]\) & \(k_{a2}=10\) & \(\mathbf{P}_{i}=\left[\begin{array}{cc}10,&10\end{array}\right]\) & \(U_{r,\max}=50\) \\ \(\mathbf{P}_{o}=\left[\begin{array}{cc}3,&3\end{array}\right]\) & \(k_{a}=1\) & \(d_{0}=5\) & \(k_{r1}=4\) \\ \(k_{a}=1\) & \(k_{r2}=8\) & \(U_{a,\max}=50\) & \(k_{r3}=100\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The concrete parameters used in experiment Section 4.1.  
Figure 7: Potential fields’ surfaces. (**a**) Attractive potential field of APF; (**b**) Repulsive potential field of APF; (**c**) Resultant potential field of APF; (**d**) Attractive potential field of IAPF; (**e**) Repulsive potential field of IAPF; (**f**) Resultant potential field of IAPF.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 4 Biochar as an Efficient Adsorbent for Organic and Inorganic Pollutants
### Parameters Affecting the Adsorption of Pollutants on Biochar
#### 4.4.3 Adsorbent Dosage  
The adsorbent dosage also has a profound influence on the sorbent-sorbate equilibrium of an adsorption system. The application of an increased adsorbent dosage increased the removal efficiency of organic and inorganic pollutants due to the availability of more sorption sites [159; 160]. A reduction of the biochar adsorption capacity, however, could be observed when the dosage rate is in excess, consequently an overlapping of the adsorption layers could be occurred, which shields the available active sites on the absorbent surface [157; 161]. The adsorbent dosage must be optimized to reach the high removal efficiency and to make the process cost-effective.
## 4 Discussions
### Electrical Analysis  
We fitted the linear relationship between the forward current in the 80-240 mA range and \(V_{F}\) and calculated the intercept at the \(V_{F}\) axis. The fitting and deriving of intercept with \(T_{j}\) = 193 K for blue LED is shown in Figure 7a. We mention the intercept as threshold voltage (\(V_{TH}\)) below. Figure 11a shows the calculated threshold voltages of the blue and green LEDs at different \(T_{j}\), and it is observed that the threshold voltage increased rapidly with decreasing \(T_{j}\). The relationship between the band gap and \(T_{j}\) was fitted by Equation (1), as discussed above. Based on the aforementioned data, the increase in the band gap was much smaller than the increase in the threshold voltage. Dynamic resistance is the reciprocal of the slope of the above-mentioned I-V curve, and the dynamic resistance results are shown in Figure 7b. It was observed that when \(T_{j}\) decreased from 350 to 200 K, the dynamic resistance of the blue LED remained basically unchanged, while that of the green LED increased. When \(T_{j}\) dropped from 200 to 150 K, the dynamic resistance of the blue LED increased, while that of the green LED changed slightly. Overall, the increased dynamic resistance was a minor cause of the increased threshold voltage. The increase in the threshold voltage was much larger than the voltage due to the band gap and dynamic resistance. Due to the fact that the lattice expansion coefficient of GaN was larger than that of InGaN, the piezoelectric polarization caused by lattice mismatch was greater at low temperatures than at higher temperatures [41; 42; 43]. An electron-blocking layer (EBL) can decrease the injection efficiency of holes at low temperatures, requiring high voltage to overcome the barrier [44]. The higher threshold voltage of the LED at a low temperature compared to that at a high temperature may be caused by the combined effects of the band gap, dynamic resistance, piezoelectric polarization, and EBL.  
Figure 10: Transport mechanism of the excitons in quantum wells at low temperatures for (**a**) green LED and (**b**) blue LED.
# A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions
## 2 SRDCNN-Based Intelligent Fault Diagnosis Method
### Residual Dilated Convolutional Layer
#### 2.2.3 Residual Network  
As described above, stacked dilated convolution can exponentially expand the receptive field of the network to avoid local false features. However, the depth of network is considerable when the receptive field is quite large. In order to address the problem of vanishing/exploding gradients for deep neural networks [34], [PERSON] et al. [35] provided comprehensive empirical evidence that the residual networks are easier to optimize and can gain accuracy from a considerably increased depth, and further proposed the residual network to ease the training of the network model, making it possible to train deeper network structures than those used previously.  
As shown in Figure 4, in the process of forward propagation, \(X_{i+1}\) can be obtained with \(X_{i}\), and the function can be formulated as Equation (1). Therefore, in the process of backward propagation, \(\partial E/\partial X_{i}\) can be calculated with \(\partial E/\partial X_{i+1}\), and the function can be formulated as Equation (2). It is obviously that \(\partial E/\partial X_{i}\) is unlikely to approach zero due to the residual. By this way, the residual network perfectly solves the gradient disappearance caused by too many network layers:  
\[X_{i+1}=X_{i}+F(X_{i},W_{i}), \tag{1}\]  
\[\frac{\partial E}{\partial X_{i}}=\frac{\partial E}{\partial X_{i+1}}\frac{ \partial X_{i+1}}{\partial X_{i}}=\frac{\partial E}{\partial X_{i+1}}(1+ \frac{\partial}{\partial X_{i}}F(X_{i},W_{i})), \tag{2}\]  
Figure 4: Diagram of residual network.  
Figure 3: Diagram of the input gate structure of LSTM.  
Finally, the residual dilated convolutional layer can be obtained by subtly integrating the input gate structure of LSTM and the dilated convolution into the residual network, thereby improving the overall prediction accuracy of the model. The \(k\)th residual dilated convolutional layer is shown in Figure 5, and the equation can be defined as (3), where * denotes a convolution operator, \(\odot\) denotes an element-wise multiplication operator, and \(W\) (including \(W_{1}\), \(W_{2}\) and \(W_{3}\)) is a learnable convolution filter:  
\[Y_{k}=\text{Relu}(\tanh(W_{1}*X+b_{1})\odot\sigma(W_{2}*X+b_{2})+W_{3}*X), \tag{3}\]