esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Shot Coherent X-Ray Imaging Instrument at PAL-XFEL
## 2 Materials and Methods
### Timing Synchronization  
It is essential to define the time zero between two light sources, X-rays and optical lasers, with femtosecond precision to observe ultrafast phenomena. It realizes high precision time synchronization by detecting the transmitted intensity of the optical laser, which passes through a Ce:YAG crystal. When the Ce:YAG crystal is illuminated by X-rays first, the transmission rate of the optical laser is lower than its native state. By capturing the change in the transmission rate as a time series function, timing synchronization with femtosecond precision can be defined [29; 30].  
We performed timing synchronization using the Ce:YAG crystal. First, coarse temporal overlap with a resolution of a few tens of picoseconds was set using an ultrafast photodetector. As a probe source, the incident X-ray energy was set at 5 keV (0.248 nmwavelength) with a \(\sim\)20-eV bandwidth. The transmission of the femtosecond IR laser was measured while changing the time delay. The change in transmitted intensity was clearly observed without jitter correction, as shown in Figure 5. At a positive delay, the intensity of the transmitted IR laser remains constant. The transmission intensity declines at a negative delay since X-ray pulses arrive first and cause the metallization process of the Ce:YAG crystal. We observed the transient transmission changes of the IR laser pulses and set the time zero with sub-picosecond temporal resolution.  
Figure 4: A schematic view of the detector part. (1) Beam position monitor, photodiode, and YAG crystal. (2) Linear manipulator for beam position monitor. (3) Ta metal beam stopper with 2 mm thickness. (4) XY manipulator for the beam stopper. (5) Beam position monitor, photodiode, and YAG crystal. (6) MPCCD detector.
## 3 Case Study
### Collapse Risk Assessment Based on Advance Geological Forecast Data
It can be seen that the overlap degree is greater than 0, namely \(S_{A}(I,II)=0.38>0\), \(S_{B}(III,IV)=0.3>0\), \(S_{C}(I,II)=0.25>0\). Therefore, there will be some error in predicting the overall collapse level by using the advanced geological forecast data. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Indicators**} & \multicolumn{2}{c}{**I**} & \multicolumn{2}{c}{**II**} & \multicolumn{2}{c}{**III**} & \multicolumn{2}{c}{**IV**} & \multicolumn{2}{c}{**IV**} \\ \cline{2-13}  & **Ex** & **En** & **He** & **Ex** & **En** & **He** & **Ex** & **En** & **He** & **Ex** & **En** & **He** \\ \hline Uniaxial compressive strength of rock/MPa & 80 & 6.67 & 0.004 & 60 & 8.33 & 0.004 & 20 & 3.33 & 0.004 & 7.5 & 0.83 & 0.004 \\ Surrounding rock integrity factor & 0.875 & 0.042 & 0.004 & 0.55 & 0.05 & 0.004 & 0.25 & 0.042 & 0.004 & 0.1 & 0.033 & 0.004 \\ Angle between the main structural surface and the cave axis/\({}^{\circ}\) & 85 & 1.67 & 0.004 & 65 & 5 & 0.004 & 30 & 6.67 & 0.004 & 5 & 1.67 & 0.004 \\ Discontinuous structural surface state & 0.1 & 0.033 & 0.004 & 0.25 & 0.05 & 0.004 & 0.6 & 0.05 & 0.004 & 0.9 & 0.033 & 0.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cloud Model parameter values of four detection indicators. Figure 3: Cloud Model with four types of risk levels under four attributes. The collapse risk assessment of the test samples is predicted using the forward cloud generator obtained above, whose results are shown in Table 3. The classification accuracy is 62%, which still has a big error. This may be due to the fact that expert field research methods are used to determine the actual collapse risk level in the training set, which may be subject to human judgment errors and lead to slight discrepancies between the assessment results and the actual situation.
## 4 Experimental Results and Analysis
### Performance Analysis with Different Feature Dimensionalities  
Another key parameter is the feature dimensionality for feature selection via the LightGBM algorithm. To find out the optimal value of the feature dimensionality, we explored the AUC values with different dimensionalities through 10-fold cross-validation. Firstly, we set the feature dimensionalities as 50, 500, 1000, 2000, 3000 and 5000, respectively, and found that the optimal value should be between 1000 and 2000. Then, we varied the values from 1000 to 2000 with a step size of 100 and recorded AUC values in all cases. At last, the AUC values versus the feature dimensionalities are plot in Figure 4 through 10-fold cross-validation on the training datasets of three species.  
As shown in Figure 4, the AUC values fluctuate similarly with different feature dimensionalities on the three training datasets. Specifically, the optimal value is 1500 for feature dimensionalities on the datasets of the three species. When the feature dimensionality is less than 1500, the AUC values mainly increase with increases in the values of feature dimensionality. On the contrary, the AUC values gradually decrease with feature dimensionality over 1500.  
To further explore the correlation between different species, we examined the selected features. We found that the overlapping rate of selected features between human and _E. coli_ species was 28.9%. The overlapping rate of selected features between the human and Salmonella species was also only 30.7%. In contrast, the overlapping rate of selected features between Salmonella and _E. coli_ species reached 45.1%. Table 3 summarizes the top 10 and last 10 selected features for different species. It can be found that human and _E. coli_ species shared 2 features in the top 10 features. Human and Salmonella species also only shared 3 features in the top 10 features, while _E. coli_ and Salmonella species shared 5 features. However, no two species shared features in the last 10 features. The above results indicate that RBPs of different species have different characteristics. Meanwhile, the RBPs of closer species have more similar characteristics. This also explains the difference in cross-species prediction performance in Section 4.4.  
Figure 4: AUC values of different species datasets under different feature dimensions.
## 3 Interference Situational Aware Beam Pointing Optimization
### Interference Situational Awareness
The elevation and azimuth angles of AoD and AoA of the communication link can be estimated by the following:  
\[\theta_{t}^{S} = \arccos(\frac{\overline{\widehat{S^{\text{LCS-S}}}U^{\text{LCS-S}} \widehat{S}}.\widehat{S^{\text{LCS-S}}}X_{S}^{\prime}}{\sqrt{|\widehat{S^{\text{ LCS-S}}}U^{\text{LCS-S}}\widehat{S}|\cdot|\widehat{S^{\text{LCS-S}}}X_{S}^{ \prime}|}}), \tag{9}\] \[\varphi_{t}^{S} = \arccos(\frac{\overline{\widehat{S^{\text{LCS-S}}}U^{\text{LCS-S}} \widehat{S}}.\widehat{(-\widehat{S^{\text{LCS-S}}}Z_{S}^{\prime})}}{\sqrt{| \widehat{S^{\text{LCS-S}}}U^{\text{LCS-S}}_{Y_{S}OZ_{S}}|\cdot|\widehat{(-S^{ \text{LCS-S}}}Z_{S})|}}),\] (10) \[\theta_{r}^{S} = \arccos(\frac{\overline{U^{\text{LCS-U}}U^{\text{LCS-U}} \widehat{U}}.\widehat{U^{\text{LCS-U}}Y_{U}^{\prime}}}{\sqrt{|U^{\text{LCS-U}} U^{\text{LCS-U}}U|\cdot|U^{\text{LCS-U}}Y_{U}^{\prime}|}}),\] (11) \[\mathcal{V}=\{\mathcal{S}|el^{\mathcal{S}}\geq el_{th}\}. \tag{8}\]0  
where \(\overline{\widehat{S^{\text{LCS-S}}}X_{S}^{\prime}}\) and \(\widehat{S^{\text{LCS-S}}}Z_{S}^{\prime}\) are parallel to \(\overrightarrow{OX_{S}}\) and \(\overrightarrow{OX_{S}}\) in the LCS-S. \(\overline{U^{\text{LCS-U}}Y_{U}^{\prime}}\) and \(\overline{U^{\text{LCS-U}}Z_{U}}\) are parallel to \(\overrightarrow{OY_{U}}\) and \(\overrightarrow{OX_{U}}\) in the LCS-U. x-axis, y-axis, and z-axis can be represented by the three unit vectors \(\mathbf{\widehat{e}}_{x}^{\ast}=(1,0,0)\), \(\mathbf{\widehat{e}}_{y}^{\ast}=(0,1,0)\), and \(\mathbf{\widehat{e}}_{z}^{\ast}=(0,0,1)\). Moreover, \(U^{\text{LCS-S}}_{Y_{S}OZ_{S}}\) is the projection of the UT \(U\) onto the \(Y_{S}OZ_{S}\)-plane in the LCS-S. \(S^{\text{LCS-U}}_{X_{U}OZ_{U}}\) is the projection of satellite \(S\) onto the \(X_{U}OZ_{U}\)-plane in the LCS-U. Moreover, the beam center pointing of the satellite antenna is given by the following:  
\[\mathcal{V}=\{\mathcal{S}|el^{\mathcal{S}}\geq el_{th}\}. \tag{8}\]1 \[\mathcal{V}=\{\mathcal{S}|el^{\mathcal{S}}\geq el_{th}\}. \tag{8}\]2  
Similarly, we can replace the serving satellite in the above derivation with interfering satellites. Thus, the AoD and AoA of the interfering links can also be estimated. Then, the interference situational aware database can be constructed, and the time-varying interference can be obtained as follows:  
\[\mathcal{V}=\{\mathcal{S}|el^{\mathcal{S}}\geq el_{th}\}. \tag{8}\]3 \[\mathcal{V}=\{\mathcal{S}|el^{\mathcal{S}}\geq el_{th}\}. \tag{8}\]4  
where \(\mathcal{Q}=\{1,\cdots,Q\}\mathcal{N}=\{1,\cdots,N_{b}\}\).
## 2 Engineering Background  
The main mining 2# coal seam in the Ruineng Coal Mine is nearly horizontal, and the thickness of the coal seam is between 1.6 and 2.8 m, with an average of 2.2 m. The length of the 203 working face is 150 m, and the 203 transportation roadway is used as the gob-side roadway. Seven borehole peeping points were arranged and named 1 #-7 #. The peep results show that the lithology of the immediate roof of the working face is mainly black mudstone. The lithology of the basic roof is mainly gray-white fine-grained sandstone; the direct bottom is mudstone and sandy mudstone, containing a small amount of aluminum mudstone. The roof strata are relatively complete as a whole, and the rock mass strength in the local area is low and the water stability is poor. The layout of the working face and the peeping results of the surrounding rock structure are shown in Figure 1. Field sampling and determination of the physical and mechanical parameters of coal rock are shown in Table 1.  
The section of the 203 transportation roadway is rectangular, and the length \(\times\) width of the roadway section is 4.5 m \(\times\) 2.6 m. The coupling support roof of 'ordinary anchor cable + constant-resistance and large-deformation anchor cable' is adopted. Compared with the ordinary anchor cable, the constant-resistance anchor cable has the characteristics of high strength, high constant resistance and high pre-tightening force. By improving the bending moment of the roof rock layer, the bearing capacity of the deep rock mass itself can be brought into full play [27]. The pillar diagram of the roadway support section and the roof and floor lithology are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Lithologic** & **Volumetric** & **Bulk** & **Shear** & **Internal Friction** & \\  & **Characters** & **Weight(/kg/m\({}^{3}\))** & **Modulus(/GPa)** & **Modulus(/GPa)** & **Angle(/c)** & **Cohesion/(MPa)** \\ \hline Overlying strata & Silstone & 2460 & 10.83 & 8.13 & 38 & 2.75 \\ Main roof & Fine sandstone & 2873 & 21.01 & 13.52 & 42 & 3.20 \\ Immediate roof & Mudstone & 2463 & 5.74 & 3.44 & 30 & 1.25 \\ Coal seam & 2\# Coal & 1380 & 4.91 & 2.01 & 32 & 1.25 \\ Immediate floor & Mudstone & 2461 & 6.08 & 3.47 & 30 & 1.20 \\ Main floor & Sandy mudstone & 2510 & 2.56 & 2.36 & 36 & 2.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of rock mass.  
Figure 1: Working face layout and surrounding rock structure peep results. Seven borehole peeping points were arranged and named 1 #-7 #.
## 4 Results and Discussion
### Forecast of Agricultural Product Prices in the Coming Period  
The prediction model that employs the IOWA operator for optimal neural network residual correction was utilized to forecast pork prices for the forthcoming nine-month period, spanning from October 2024 to December 2025 (as detailed in Table 4). The analysis of the projected pork prices for the latter half of 2024 indicates a general upward trend with fluctuations, reaching a peak in December 2024, followed by an overall downward trend, aligning with the pig production cycle. This alignment suggests that the proposed method of optimal neural network residual correction prediction based on the IOWA operator offers practical and valuable guidance for reference.
## 5 Discussion
### Reliability of Forest Productivity Retrieval  
We approximated FP with GPP. Estimates of GPP from optical EO data are typically based on two approaches: using vegetation greenness to indicate potential photosynthetic activity; or measurements directly related to actual photosynthesis such as SIF. The use of SIF would be the preferred option, but the spectral resolution of the available APEX data is not optimized for SIF retrievals. Although SIF retrievals from APEX data have been successfully demonstrated [49], retrievals in topographically complex environments are likely to be uncertain. Hence, we applied a greenness-based approach in this study. Such simple empirical approaches are frequently used to estimate forest GPP with optical remote sensing data. The approach by [PERSON] et al. [79], as applied in this study, relies on the EVI to constrain estimates of GPP. Previous studies have demonstrated that the sole use of the EVI can predict GPP as accurate as the current version of the MOD17 algorithm [97; 98]. Comparisons of modeled GPP and the MOD17 GPP product indicate, on the one hand, that the modeled mean GPP is about 26% lower than the MOD17 GPP product. On the other hand, [PERSON] et al. [99] report that the MOD17 GPP product tends to overestimate flux tower-based GPP by 20-30%. Such uncertainties can be attributed to the fact that all these approaches rely on vegetation indices representing the greenness of vegetation, and therefore the _potential_ rather than _actual_ photosynthetic activity. Further, we took a single observation in time in combination with the empirical model from [PERSON] to obtain annual GPP. Since the forest ecosystem shows a pronounced phenological cycle, estimated GPP are likely to be overestimated. In fact, [PERSON] et al. [100] and [PERSON] et al. [101] report maximum GPP values of 5 g C m\({}^{-2}\) day\({}^{-1}\) for a Norway spruce forest close to Davos, Switzerland, that is comparable to our observation taken at the peak season. Scaling their flux tower-based GPP estimates to annual values results in roughly 1000 g C m\({}^{-2}\) year\({}^{-1}\), while we observed average values of 1255 \(\pm\) 342 g C m\({}^{-2}\) year\({}^{-1}\)confirming the expected overestimation of GPP when using a single snapshot in combination with the EVI approach. However, GPP estimates based on vegetation greenness provide first meaningful insights into the spatio-temporal patterns of GPP in complex landscapes. The use of time series for the new EO approach involving SIF allows the stated limitations to be overcome, since SIF is the most direct measurement of photosynthetic activity. The measurement of SIF can be considered mature, but the use of SIF to constrain estimates of GPP still requires further attention. Nevertheless, first studies demonstrate and suggest improved SIF-based GPP estimates across different scales, i.e., field scale [102], regional scale [49; 103], and global scale [104; 105; 106], as well as theoretically [107].
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 4 Proposed Method
### On the Senders' Side
#### 4.1.3 Masking  
A chaotic masking method is introduced to ensure the processed signal meet mainstream security benchmarks concerning image encryption. As a result, the proposed method could prevent malicious attacks to some extent by breaking the statistical patterns of original data, providing large key space and sensitivity of the initial values, etc.  
After the compressed, encrypted, and concealed signal \(y_{e}\) is obtained, we mask it with a chaotic sequence before data transmission. Here the matrix \(A\), which is generated by Equation (7), is suggested to be partially reused for masking with the consideration to save computing power and energy consumption.  
The masking process is shown in Equation (12).  
\[y_{c}=ay_{e}+\beta a \tag{12}\]  
where parameters \(\alpha\), \(\beta\) are added to adjust masking magnitud, \(e\) and \(a\) is a column of matrix \(A\). Apparently, the chaotic matrix \(A\) can be expanded using the chaotic sequence according to the method depicted in Equation (6) if the columns are exhausted when masking.
## 5 Conclusions  
In this study, a feature fusion leak detection model was proposed, combining TL and a bidirectional attention mechanism to identify micro-leaks in aging pipelines. The model utilized TL and feature fusion based on pre-trained information, incorporating an attention mechanism within the bidirectional LSTM to focus on critical independent features. This approach effectively addressed challenges such as data scarcity and ambient machine noise by enabling seamless transfer from the source domain to the target domain across distinct environments. A detection accuracy of 99.99% was achieved under controlled experimental conditions using a dataset comprising leak signals and ambient noise collected from multiple acoustic sensors. The ability of the model to suppress irrelevant noise and enhance key signal features through optimized signal preprocessing and feature fusion significantly contributed to this high level of accuracy. However, this exceptional performance requires further validation in real-world environments, where factors such as pipeline material variability, sensor placement, and diverse noise sources could impact the robustness of the model. Despite these potential limitations, the proposed method demonstrated strong potential for practical applications in industrial pipelines and critical infrastructure, where accurate and timely leak detection is crucial for safety and operational efficiency. Future research directions include deploying the model in embedded systems for real-time monitoring, validating its performance in diverse industrial settings, and exploring its scalability for multi-sensor environments. These efforts aim to develop a robust and adaptable early leak detection technology capable of operating effectively under real-world conditions, thereby enhancing industrial safety and efficiency.  
Figure 8: Performance comparison among various models according to training data size.  
**Author Contributions:** Conceptualization, Y.H. and J.-H.B.; methodology, Y.H. and J.-H.B.; software, Y.H.; validation, Y.H., Y.C., and J.L.; investigation, Y.H.; resources, Y.C.; data curation, Y.C.; draft writing and preparation, Y.H.; manuscript review and editing, J.L. and J.-H.B.; visualization, Y.H.; supervision, J.-H.B.; project administration, Y.H. and J.-H.B.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea through the Government (Ministry of Science and Information and Communications Technology) in 2022 under Grant RS-2022-00165225 and Grant RS-2022-00144000.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets presented in this article are not readily available because the data are part of an ongoing study and security reasons. Requests to access the datasets should be directed to the Korea Atomic Energy Research Institute.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid
## 6 Analysis of Results and Design of a Protected System  
### Analysis of Results of Testing Models  
As the results show the use of inverters significantly reduced the amount of short-circuit current supplied by the source to the grid, resulting in an impact on protection systems. This is only a part of the problem, as shown in Figures 14 and 20, the occurrence of faults for the inverters caused an immediate limitation of the current and a significant deformation of the sinusoidal course. Compared to hydro and thermal power plants, this is not a problem and the short-circuit current supplied by these sources is several times higher than the nominal current. As shown in Figures 15, 18, 21, 24 and 27, the large voltage drop was caused by a near short circuit, and the hardness of the sources can be clearly seen from the simulated values.  
According to the simulated data, we can evaluate the impact of models of the power system, while using many photovoltaic stations or battery systems, there are problems with maintaining the system and there is a risk of the blackout due to a significant drop in voltage and decreasing short-circuit current. When using thermal power plants and hydropower plants supplemented by diesel generators, we had a stable electricity system. For this reason, it is necessary to determine the appropriate mix of energy sources.  
The use of the models of individual renewable sources mentioned in this article can be used to create a model microgrid, where further research will be carried out for fault localization and parameterization of protections, where we will already consider a dynamic load. Figure 29 shown example for microgrid model with RES.