esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Sample applications include personal monitoring (e.g., physical exercise tracking), civil defense (e.g., hazards and hotspot reporting to police officers) and collaborative applications (e.g., information sharing for tourism purposes). [PERSON] _et al._[29] proposed a data gathering scheme in urban streets using mobile phones as the relayed nodes. Relay nodes that roam around the area receive data from fixed sensors and transmit them to data centers. [PERSON] _et al._[14] exploited human-carried or vehicle-mounted sensors to ubiquitously collect data and build various sensing maps. Packets are assumed to be spatial-temporal correlated in the forwarding process, and two cooperative forwarding schemes that use data fusion are proposed: Epidemic Routing with Fusion (ERF) and Binary Spray-and-Wait with Fusion (BSWF), where the number of samplings and transmission overhead were demonstrated to be greatly reduced. The scheme proposed at this paper belongs to the type of _opportunistic_ data gathering schemes, where both the ordinary nodes and data collector are mobile and the \"Store-Carry-Forwarding\" strategy is adopted for message transmissions when collecting the data. In our previous work [13], we have proposed a data gathering scheme called PDA in mobile wireless sensor networks where both the collector and the ordinary nodes move and contact opportunistically. PDA collects the network meta-data to generate a node contact graph, base on which it calculates a data gathering location and sojourn time. The data collector is then controlled to move to the location to collect the data, avoiding unnecessary message transmissions. The sojourn time allocation problem is also considered at [8], yet it assumes that ordinary nodes are stationary. [PERSON] _et al._[30] proposed the distance-aware replica adaptive data gathering protocol in delay tolerant mobile sensor networks, which cuts down the number of redundant replicas of messages and leverages the delivery probabilities of nodes as main routing metrics. Differing from the schemes mentioned at [8,13], we assume the data collector could move faster in ADG, yet with much smaller time length within a data gathering round. So the contact opportunities with other ordinary mobile nodes are fewer, shorter, and more opportunistic in nature, which makes the data gathering a challenging problem. ADG would select the proxy nodes, determine their visiting order and locations, and allocate time slots so that the collector could encounter the proxy mobile nodes and maximize the amount of gathered data from them.
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 4 Results and Discussion
### Sea State Comparisons
#### 4.2.1 Swaying Motion  
Figure 11a-e displays the speed polar diagrams of the swaying motion by combining sailing speeds with wave heading angles under different sea states (1-5). The results demonstrated that when the sea state worsened, the swaying motion increased substantially for wave heading angles between 30\({}^{\circ}\) and 90\({}^{\circ}\); moreover, the strongest swaying motion was observed between the wave heading angles of 45\({}^{\circ}\) and 90\({}^{\circ}\). This suggested that for all sailing speeds, when the wave heading angles ranged from quartering to beam waves, the swaying motion increased as the sea state worsened.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sea State** & **1** & **2** & **3** & **4** & **5** \\ \hline Significant wave height (\(H_{1/3}\)) & 0.5 & 0.8 & 1.6 & 2.4 & 3.2 \\ Average period (\(\overline{T}\)) & 4 & 4 & 5 & 6 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Significant wave height and average period for the five sea states.  
Figure 11: _Cont._
## 2 Image Pre-Processing
### Improved Texture Skew Correction
#### 2.1.1 Improved Sobel Operator  
Edge detection techniques have attracted much attention in recent decades. Among these algorithms, the Sobel operator is well known for its high computation efficiency. As a special realization of discrete differentiation, the Sobel operator computes the approximation of the gradient of the image intensity function. The Sobel operator consists of two kernels of size \(3\times 3\). These kernels are convolved with input images to derive approximations of partial derivatives on two orthogonal directions, which are shown as  
\[G_{x}=\left[\begin{array}{ccc}+1&0&-1\\ +2&0&-2\\ +1&0&-1\end{array}\right]*I\;\text{and}\;G_{y}=\left[\begin{array}{ccc}+1&+2& +1\\ 0&0&0\\ -1&-2&-1\end{array}\right]*I, \tag{1}\]  
where the operator \(*\) denotes the 2-dimensional convolution and the variable \(I\) is defined as the source image [28]. Because the orthogonality between the two directions, the above partial gradient approximations can be combined to derive the gradient magnitude at a specified pixel, which is shown as  
\[G=\Big{(}G_{x}^{2}+G_{y}^{2}\Big{)}^{1/2}. \tag{2}\]After selecting the sensitivity threshold for the classical Sobel operator, edge features can be detected automatically. However, as can be inferred from the above manipulations, the classical Sobel operator is only sensitive to the horizontal direction and the vertical direction, leading to an incomprehensive detection effect.  
By employing additional templates with directional sensitivity at angles of 45\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 255\({}^{\circ}\), 270\({}^{\circ}\), and 315\({}^{\circ}\), an improved Sobel operator was developed by [PERSON] [29]. As shown in Figure 3, the additional templates can guarantee more comprehensive and accurate edge information.  
During the numerical implementation, the matrix \(\{f(m,n)\}\) represents the image to be detected; \(v[f(m,n)]\) represents the mask window of size 3 \(\times\) 3 (as presented in Figure 3) centering at the pixel \((m,n)\); \(\omega(m,n)\) represents the output image after edge detection. The flow chart of the improved Sobel operator is described below.  
Step 1. Conduct the convolution operation between the templates \(u\{t\}\) (\(t\) = 1, 2,..., 8) and \(v[f(m,n)]\). Then store the absolute value of the calculation results in the corresponding array \(v[t]\) (\(t\) = 1, 2,..., 8).  
Step 2. Find the maximal value of all data in the corresponding array \(v[t]\).  
1. Initialization such that Max = \(v[0]\), \(t\) = 1.
2. If \(v[t]>\) Max, set Max = \(v[t]\)
3. If \(t<8\), then \(t\) = \(t+1\) and go to (2). If not, go to Step 3.  
Step 3. Assign Max to \(\omega(m,n)\) as the output image.  
As a result, edge curves of the original image are extracted.
# A New Period-Sequential Index Forecasting Algorithm for Time Series Data
## 4 Results and Discussion
### Periodic Recognition and Prediction on Volkswagen Sales (VS) Dataset  
For the VS dataset, the Volkswagen sales in China between January 2007 and June 2019 is used as another example [24] to show the correlation coefficient detection and prediction results. Figure 5 shows the prediction results in 2018 by using the PSI, MA, ES, and ARIMA methods. As shown in Figure 5, between January 2018 and May 2018, the predicted VS using the PSI method can be almost identical with actual VS, comparing to the MA, ES, and ARIMA methods, and a lower _MAPE_ of 4.48% is also achieved by Equation (18). Meanwhile, it can be seen that, the trend of the predicted values  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Error Indicators**} \\ \cline{2-4}  & **MAPE (\%)** & **RMSE (10\({}^{4}\) Sets)** & **MAE (10\({}^{4}\) Sets)** \\ \hline SPNN-PSI & 5.34 & 1480.06 & 1179.87 \\ MA & 6.79 & 2062.60 & 1393.56 \\ ES & 8.11 & 2406.33 & 1667.28 \\ ARIMA & 6.97 & 1873.41 & 1535.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error indicators of each prediction model on the EME dataset.  
Figure 4: Time series pattern and prediction on the electric motors exports (EME) dataset.  
between June 2018 and December 2018 is similar to that of the actual values when using PSI method, but the difference between them at each time point is larger than that of using the MA, ES, and ARIMA methods, achieving a value of _MAPE_ at 19.36% for the period between June 2018 and December 2018.  
To clearly show the correlation coefficient value and the prediction results, we show correlation coefficient detection results from January 2016 to December 2017 (shown in Table 1), and Table 3 presents the further prediction errors of each model. Compared with the EME dataset, the time series dataset of VS has a lower correlation coefficient value (=0.8392) of reference historical dataset. However, a higher _MAPE_ value of 9.94% for the VS dataset in 2018 can be achieved in Table 3. Therefore, the example shows that the prediction accuracy and quality of the proposed SPNN-PSI algorithm can be decreased due to the decreasing correlation coefficient value of reference historical dataset (shown in Table 1), as compared to that of the EME dataset.  
To sum up, the _MAPE_, _RMSE_, and _MAE_ values by SPNN-PSI method in Table 3 are all lower than that by MA, ES, and ARIMA methods. Thus, the forecasting quality of SPNN-PSI method is better, and the developed SPNN-PSI algorithm is still suitable for the prediction of VS dataset used in this paper.
# A Feature Extraction Method Using Auditory Nerve Response for Collapsing Coal-Gangue Recognition
## 3 Feature Extraction and Selection
### Feature Selection  
Feature selection can be summarized as a process of selecting several of the most effective feature subsets from the original features set in order to reduce the dimension of dataset. In this section, variance is chosen as the evaluation parameter of features effectiveness because it can reflect the difference among various indicators. The variance of each type of feature within various characteristic frequencies is ranked and the recognition result of SVM model is used as the selection standard.  
The detailed feature selection procedures are described as follows:  
**Step 1.** Calculate the variance vector \(V~{}=~{}[v(1,1),~{}v(1,2),~{}\ldots,~{}v(i,f)]_{1\times 20}\) where \(v(i,f)\), (\(i=1,2\), \(\ldots,6;f=1,2\), \(\ldots,20\)), is the variance between the \(i\)-th feature's values corresponding to signal of the \(f\) characteristic frequency and calculate a variance matrix as:  
\[v(i,f)=\sum_{n=1}^{N}\frac{(F(n,i,f)-\mu)^{2}}{N-1} \tag{12}\]  
where \(F(n,i,f)\) represents the \(i\)-th feature value of \(n\)-th sample corresponding to \(f\) characteristic signal, \(N\) represents the number of samples, \(\mu\) and \(\sigma\) represents mean value and standard deviation of \(F(n,i,f)\).  
\[V=\left[\begin{array}{ccc}v(1,1)&\cdots&v(6,1)\\ \vdots&\ddots&\vdots\\ v(1,20)&\cdots&v(6,20)\\ \end{array}\right] \tag{13}\]  
**Step 2.** Rank the variance of each type of feature at different characteristic frequencies (\(\
u\)(1,1), \(\
u\)(1,2), \(\ldots,\
u\)(1,20).  
Figure 10: Comparison of auditory spectrum’s auditory spectrum energy entropy (ASEE) at different frequencies.  
**Step 3.** The first \(m\) (\(m\) = 1, 2,..., 20) optimal characteristic frequencies of each feature are selected each time and processed by the SVM model to obtain the recognition result as the selection criterion. The result is shown in Figure 11.  
In Figure 11, the AN-based SVM recognition accuracy generally increases with the increasing number of features. The maximum recognition rate reached 99.23% when the first 13 optimal characteristic frequencies of each feature are selected. As the number of optimal characteristic frequencies of each feature exceeds 13, with an increase in dimension, some secondary relations, noise, and redundant information of the original auditory signal are introduced, which decline the identification accuracy. When the optimal characteristic frequency of each feature reaches 19, the recognition accuracy achieves the optimal value again, but the increase in feature dimension leads to a decrease in model recognition efficiency.
# Multiple SSO Space Debris Flyby Trajectory Design Based on Cislunar Orbit
## 6 Numerical Simulations and Analysis
A key point in the HEO transfer orbit design problem is to appropriately utilize the lunar gravity. In the 2 nd revolution, the lunar flyby is behind the Moon; therefore, the height of the perigee of the spacecraft is increased. However, in the 2 nd revolution, the spacecraft is far away from the nearest debris. Thus, the spacecraft cruises around the Earth without taking any maneuvers in the 2 nd and 3 rd revolutions. In the 4 th revolution, the lunar flyby is in front of the Moon and the spacecraft takes an impulse of only 6.61 m/s to decrease the height of the perigee with the help of lunar gravity. In the 30 th revolution, the lunar flyby is behind the Moon, while in the 32 nd revolution, the lunar flyby is in front of the Moon. Therefore, the spacecraft cruises around the Earth without taking any maneuvers in the 30 th and 31 st revolutions. Using the lunar gravity appropriately, the spacecraft only needs a 43.86 m/s impulse to fly by a piece of debris in the 32 nd revolution. In the 42 nd revolution, the spacecraft takes only a 16.52 m/s impulse to fly by a piece of debris with the help of the lunar gravity. In the 17 th revolution, the spacecraft takes a 17.16 m/s impulse to lower the height of the perigee as the lunar flyby is behind the Moon. Otherwise, the spacecraft would not be able to fly by two pieces of debris in the 20 th and 21 st revolutions. The same occurs in the 45 th revolution. The spacecraft takes 51.41 m/s impulse to increase the orbit height as the lunar flyby is in front of the Moon so that the spacecraft can fly by three pieces of debris in the 47 th, 48 th, and 49 th revolutions, respectively. The time that the spacecraft cruises around the Earth without taking any maneuver is about 0.89 years. This is because of the fact that in order to reduce fuel consumption, except for necessary maneuvers such as adjusting the orbital altitude to prevent the spacecraft from entering the Earth's atmosphere, the spacecraft does not apply any of the impulses until the orbital plane is close to a piece of space debris. On a computer with an AMD 3900X 64-core processor and 128 GB RAM, it takes about 3 days to complete the design of Phase 2. The optimized result obtained by the semi-analytical optimization method in the 1 st revolution is as follows:  
\[ \begin{array}{l}||\Delta V||=8.60\ \mathrm{m/s},\ \alpha=6.24\ \mathrm{rad},\ \beta=0.62\ \mathrm{rad}\\ dt_{1}=3.99\ \mathrm{days},\ dt_{2}=6.40\ \mathrm{days}\end{array}  \tag{44}\]  
Next, Equation (44) is used as an example to analyze the optimality of the maneuver solved by the semi-analytical optimization method. The two-body analytical optimal single-impulse transfer theory is [51]: as shown in Figure 16a, the spacecraft applies an  
Figure 15: Analysis of HEO orbits: (**a**) histories of the distance with regard to the relative velocity; (**b**) impulses; (**c**) flight times. impulse at P\({}_{1}\) and then flies to P\({}_{2}\).
# A Comparison of Electrical Breakdown Models for Polyethylene Nanocomposites
## 5 Conclusions  
The electrical breakdown electric fields simulated by three breakdown models at various nanofiller contents were compared, and the breakdown mechanism of LDPE nanocomposites was illustrated. Doping of Al\({}_{2}\)O\({}_{3}\) nanoparticles into LDPE can change the trap, conductivity, and electrical breakdown properties. The results indicated that the charge trapping, molecular motion, and electron energy gain in free volumes are important factors for the electrical breakdown of polyethylene nanocomposites. Then, space charge transport, electron energy gain, and molecular chain long-distance movement were comprehensively considered to investigate the electrical breakdown mechanism of nanocomposites. EBEF, EBEG, and EBMD models were established via focusing on space charge accumulation due to deep trappings and associated electric field distortion, mobile electrons gaining energy in free volumes, long-distance movement of molecular chains under the Coulomb force expanding free volumes, and accelerating electrons in the enlarged free volumes. Simulation results showed that the EBMD model fits the experimental results much better than the EBEF and EBEG models. The correlation between the long-distance molecular chain movement under the Coulomb force and the electric breakdown characteristics of LDPE was established. The comparisons between simulation results of different models and experiments showed that the electric breakdown electric field of LDPE nanodielectrics is synergistically enhanced by both the strong trapping effect of traps in interfacial regions and the strong binding effect of molecular chains.  
Conceptualization, D.M.; Formal analysis, Z.X. and C.Z.; Funding acquisition, D.M.; Resources, Z.X. and Q.W.; Supervision, Q.W. and D.M.; Writing-original draft, Z.X., C.Z., M.H. and Z.G.; Writing-review and editing, M.H., Z.G. and D.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by State Key Laboratory of Advanced Power Transmission Technology (Grant No. GEIRI-SKL-2018-010).  
Not applicable.  
The authors declare no conflict of interest.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 2 Method  
### Proposed Ensemble Model  
Electricity load data often display complexity and randomness, requiring effective utilization for accurate forecasting. It is necessary to leverage electricity load data for accurate electricity load forecasting. Therefore, this paper proposes the Prophet-CEEMDAN-ARBiLSTM model for electricity load forecasting, as illustrated in Figure 1. The model first employs the Prophet approach to decompose the electricity load sequence, yielding the trend components, periodic components, and residual terms, effectively addressing the impact of excessive data fluctuations. Subsequently, the CEEMDAN algorithm is employed to further decompose the residual data, generating multiple Intrinsic Mode Functions(IMFs) and a final residue. This method enhances the performance to recognize the detailed information, thereby improving the sensitivity to intricate information. Simultaneously, this paper proposes a novel ARBiLSTM block designed with Batch Normalization (BN), BiLSTM, PReLU activation layer, and residual structure. The ARBiLSTM block takes the feature information from the previous 24 h as input data and predicts the electricity load of the following day.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Motion Correction Strategies  
This section details the different motion correction strategies, following the classification established in this survey. It starts with the prospective and retrospective approaches, further dividing the latter depending on the input data and the planes handled by the registration.
# Numerical Simulation of Water Migration during Soil Freezing and Its Resulting Characterization
## 5 Conclusions  
In this study, we developed a model to investigate water migration in permafrost, focusing on the resulting mechanical behavior such as deformation and pore water pressure. The study's results on frozen deformation indicate that the model aligned well with experimental data when soil saturation was low, with a discrepancy of about 13%; however, when the soil was fully saturated, the difference increased to 25%. The findings from three experimental sets confirm the trend that lower saturation degrees resulted in smaller discrepancies. This is attributed to higher saturation levels leading to thicker ice lenses in the experimental samples, causing moisture to accumulate and freeze at the front of the ice  
Figure 6: Comparison between simulation results and experimental data: The change mode of pore water pressure during the freezing and thawing of Qinghai–Tibet clay sample SC2.  
lens, thus resulting in greater actual freezing expansion than predicted by the simulation. Consequently, the model proves more accurate for predicting frost heave in unsaturated soils or those with a lower degree of saturation.  
Regarding pore water pressure, we formulated equations to calculate it both with and without ice lenses and simulated changes during soil freezing. The results demonstrate that the presence of an ice lens significantly increased the value of pore water pressure, which remained negative throughout, reaching its lowest at the cold end and gradually increasing to zero at the freezing front. Moreover, the modeling results of pore pressure closely matched the overall trend and peaks observed in the freeze-thaw cycling experimental results, suggesting that the model is effective for predicting pore pressure in frozen soil.
# Experimental Study of Vegetative Properties in
## 3 Results and Analysis  
### Effect of Different Substrate Ratios on Seed Germination  
#### 3.1.1 Effect on Seed Germination of Cynodon Dactylon  
In the study of C.d. seed germination, the addition of ZL and BC had notable impacts on the process. Throughout various stages of the experiment, the effects on seed germination exhibited diverse trends. For detailed information on germination, refer to Figure 5.  
Figure 4: Temperature and rainfall monitoring data.  
Figure 5: Germination rates of different vegetative substrates.  
During the early stage of germination (days 1-4), varying ratios of ZL and BC did not significantly impact the germination process of C.d. seeds. There was no significant difference in GP between groups, and overall seed germination performance was relatively consistent. The middle stage (5-15 days) emerged as a critical period for rapid seed germination, with the impact of different ecological substrate ratios on GP becoming more pronounced. The presence of ZL and BC actually hindered seed germination during this phase. In the later stage (after the 16 th day), seed germination was minimal, and the germination process essentially ceased.  
Among the substrates labeled 1# to 26#, the substrate with the highest germination number is 1# with 1129, while the substrate with the lowest germination number is 26# with 595. The average germination number across all substrates was 839. Substrates 1#-6#, 8#-10#, 12#-14#, and 19# exhibited germination numbers higher than the average, while the rest were below. Notably, substrates 11#, 16#, 21#, 22#, 25#, and 26# had GP below 50%. Furthermore, the GP surpassed 60% for substrates 1#, 2#, 3#, 4#, 8#, 9#, 12#, and 14#.  
Figure 6 illustrates the impact of different ZL and BC contents on average germination numbers. ZL content ranging from 0 to 12% resulted in average germination numbers of 832, 873, 916, 799, and 717, respectively. The highest germination number was observed at 6% ZL content, showing increases of 9.17%, 4.92%, 14.64%, and 27.75% compared to other ratios. The average GP increased to 6% ZL content, enhancing the vegetative effect of the substrate. However, beyond 6% ZL content, the GP declined rapidly, inhibiting the vegetative effect. On the other hand, increasing BC content from 0 to 7% led to average germination numbers of 959, 840, 860, 779, and 700, respectively. The highest germination number was achieved at 0% BC content, exhibiting increases of 14.16%, 11.51%, 23.10%, and 37% compared to other ratios. Generally, the addition of BC hindered the vegetative effect of the substrate, with noticeable inhibition at content levels above 3%.