esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlation Plenoptic Imaging: An Overview
## Appendix B Theoretical Background of CPI with Entangled Photos  
Let us consider the quantum state of radiation described by the wave function in Equation (25). Here, for the sake of simplicity, we are assuming that SPDC radiation to be degenerate. However, the result that will be discussed in this section admit an immediate generalization to the non-degenerate case [43; 44]. Without loss of generality, we will also approximate the source as monochromatic, and consequently neglect the time dependence of the correlation function. The commutation relations \([a_{k},a_{k^{\prime}}]=0\) and \([a_{k},a_{k^{\prime}}^{\dagger}]=\delta(\mathbf{k}-\mathbf{k}^{\prime})\) and the inversion symmetry \(h_{\text{tr}}(\mathbf{\kappa})=h_{\text{tr}}(-\mathbf{\kappa})\) are useful to evaluate the spatial part of the two-photon correlation function, yielding  
\[\Gamma(\mathbf{\rho}_{a},\mathbf{\rho}_{b})=\left|\int d\mathbf{\kappa}_{a}\int d\mathbf{ \kappa}_{b}g_{a}(\mathbf{\rho}_{a},\mathbf{\kappa}_{a})g_{b}(\mathbf{\rho}_{b},\mathbf{\kappa }_{b})h_{\text{tr}}(\mathbf{\kappa}_{a}+\mathbf{\kappa}_{b})\right|^{2},\] (A17)  
up to irrelevant constants. This result entails a strong coupling between the two distant sensors D\({}_{a}\) and D\({}_{b}\), deriving from by the entanglement in momentum characterizing SPDC biphotons. Let us now evaluate the propagators in the two arms of the setup depicted in Figure 12; we shall assume for simplicity the lenses to be diffraction-limited and propagation is paraxial. The propagator associated with arm \(a\) of the setup reads  
\[ \begin{array}{rl}g_{a}(\mathbf{\rho}_{a},\mathbf{\kappa}_{a})&=\mathcal{C}_{a}(z_{a },z_{a}^{\prime})\int d\mathbf{\rho}_{s}\int d\mathbf{\rho}_{\ell}e^{i\mathbf{\kappa}_{a} \cdot\mathbf{\rho}_{s}}G(\mathbf{\rho}_{\ell}-\mathbf{\rho}_{s})_{\left[\frac{1}{\alpha_{ \ell}}\right]}G(\mathbf{\rho}_{\ell})_{\left[-\frac{1}{\alpha_{\ell}}\right]}G(\bm {\rho}_{a}-\mathbf{\rho}_{\ell})_{\left[\frac{1}{\alpha_{\ell}}\right]}\\ &=\mathcal{C}^{\prime}{}_{a}(z_{a},z_{a}^{\prime})G(\mathbf{\rho}_{a})_{\left[ \frac{1}{\alpha}\left(\frac{1}{\alpha_{\ell}}-\frac{(z_{a}z_{a}^{\prime})}{z_ {a}^{\prime}}\right)\right]}\int d\mathbf{\rho}_{s}e^{i\mathbf{\kappa}_{a}\cdot\mathbf{ \rho}_{s}}G(\mathbf{\rho}_{s})_{\left[\frac{1}{\alpha_{\ell}}\left(\frac{1}{ \alpha_{\ell}}\left(\frac{1}{\alpha_{\ell}}-\frac{(z_{a}z_{a}^{\prime})}{z_ {a}}\right)\right]}e^{-\frac{\frac{\Delta(z_{a}z_{a}^{\prime})}{z_{a}}}}\mathbf{ \rho}_{s}\cdot\mathbf{\rho}_{s},\end{array} \] (A18)  
where  
\[\zeta(z_{a},z_{a}^{\prime})=\left(\frac{1}{z_{a}}+\frac{1}{z_{a}^{\prime}}- \frac{1}{f}\right)^{-1}.\] (A19)  
\(\mathbf{\rho}_{s}\) and \(\mathbf{\rho}_{\ell}\) are transverse coordinates on the source and the \(L_{a}\) planes, respectively, and \(\mathcal{C}_{a},\mathcal{C}^{\prime}{}_{a}\) contain irrelevant constants.
# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 4 Experimental
### Ablation Study  
To test the effectiveness and necessity of the three designed modules of the Attention Mechanism (AM, including the Channelwise Attention Module of the encoder and the Mixed Attention Module of the decoder), the Bidirectional Point Pyramid Network (BiPPN), and skip connections (Skip-C), we exhaustively compared and tested our method BiPPAN with its four variants on ShapeNet-16. The results of the ablation study are shown in Table 4.  
Model A is a very basic baseline that includes the standard CMLP and FPN as the encoder and decoder architectures, respectively. Models B, C, D had the same structure as that of BiPPAN (our entire network) except for the removed/replaced module. Model B was the variant where AM was removed from the BiPPAN model. Adding AM reduced \(\text{Pred}\rightarrow\text{GT}\) and \(\text{GT}\rightarrow\text{Pred}\) errors. This confirmed the effectiveness of AM. Model C focused on demonstrating the effectiveness of introducing BiPPN into network design. After changing the decoder from FPN into BiPPN, \(\text{Pred}\rightarrow\text{GT}\) and \(\text{GT}\rightarrow\text{Pred}\) errors were smaller, as expected. When removing Skip-C from the BiPPAN model, the \(\text{GT}\rightarrow\text{Pred}\) error of Model D increased markedly. This clearly shows that Skip-C alone can bring significant performance improvement. Notably, our BiPPAN method performed better than its variants. A visual comparison is presented in Figure 8. For the shown examples, Baseline Model A and Models B, C, D suffered from some obvious fairness coarsening and exhibited impotence for most of the details. This further confirmed the effectiveness of the BiPPAN design.
## 4 Results and Discussion
### FCS-MPC: Variable-Switching-Frequency Operation
#### 4.1.3. THD for Capacitor Voltage with Different Sampling Frequencies  
In this paper, real-time results of the FCS-MPC with variable switching frequency were obtained at various sampling rates to identify the optimal outcomes of the predictive controller implemented within the two-level, three-phase voltage source inverter.  
This investigation aimed, among other things, to empirically determine the optimal operational sampling rate for the control scheme, taking into account the equipment available at the facility.  
To determine the total harmonic distortion (THD) pertinent to the primary control objective, measurements of the voltage across the capacitor within the \(LC\) filter were conducted. The findings are detailed in Table 11 in Appendix A.  
As indicated in Table 11 from Appendix A, the Total Harmonic Distortion (THD) values for sampling rates of 10 and 11.5 kHz are excessively high, ranging between 11.37% and 24.73%. In contrast, for sampling rates between 20 and 200 kHz, the THD values are significantly more acceptable. This performance improvement is attributed to the improved capability of the established controller to accurately track both the amplitude and frequency reference values for the capacitor voltage. The THD values corresponding to sampling rates ranging from 20 to 200 kHz are illustrated in the graph shown in Figure 9.  
Figure 8: Real-time results of FCS-MPC variable-frequency operation with steps in frequency reference. (**a**) Linear load: capacitor voltages; (**b**) non-linear load: capacitor voltages; (**c**) linear load: load current; (**d**) non-linear load: load current.  
Figure 9: THD for capacitor voltage for predictive voltage control with different sampling frequencies.  
It is important to clarify that this study did not implement a specific normative restriction, such as the THD limits defined in IEEE Std. 519-2022 [59], directly within the FCS-MPC cost function. The primary objective was not to create a controller that actively constrains the THD to a predefined value but, rather, to characterize the intrinsic relationship between the controller's sampling frequency and the resulting capacitor voltage THD. Standards such as IEEE 519-2022 establish critical performance benchmarks at the point of common coupling (PCC) of the entire system, guiding the overall design and ensuring grid compatibility [59]. The results presented here, which demonstrate how THD improves with higher sampling rates, provide the necessary design insight. An engineer can employ these data to determine a sampling frequency that will ensure that the resultant THD comfortably aligns with the relevant normative requirements for a given application, effectively bridging the gap between the performance of the control strategy and the harmonic standards at the system level.
## 4 Conclusions  
The findings of this study can be summarized as follows:  
* The remarkable MBN heterogeneity along with the sheet width is due to the variable decarburizing developed during hot rolling when MBN fluctuates in the range from 540 mV up to 1440 mV in the RD.
* Remnant magnetization makes the MBN heterogeneity more pronounced, and MBN (especially in the regions of lower decarburization) drops down to 150 mV.
* Also, the _PP_ values fluctuate in the range from 0.3 up to 1.2 kA.m\({}^{-1}\), and this fluctuation becomes stronger in the magnetized state, whereas the fluctuation in _FWHM_ is less remarkable.
* The heterogeneity in the surface state and remnant magnetization affect the evolution of MBN under the tensile stress in a synergistic manner when MBN grows in the regions of low decarburization, whereas MBN drops down in the regions with a stronger decarburizing effect (however, these evolutions saturate above 400 MPa).
* The surface heterogeneity as well as the decarburizing process become decelerated when the C content in the matrix is lower.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14041511/s1](https://www.mdpi.com/article/10.3390/app14041511/s1)]([https://www.mdpi.com/article/10.3390/app14041511/s1](https://www.mdpi.com/article/10.3390/app14041511/s1)). Figure S1. STDV of MBN before and after demagnetization; Figure S2. STDV of PP before and after demagnetization; Figure S3. PP versus tensile stress in RD for the regions of high as well as low MBN before and after demagnetization;  
Figure 17: Metallographic images of the surface emitting different MBN in the RD after demagnetization (1100 QL): (**a**) MBN = 300 mV and (**b**) MBN = 700 mV.  
Figure 18: _HV0.1_ depth profiles for 1100 QL.  
Figure S4. FWHM versus tensile stress in RD for the regions of high as well as low MBN before and after demagnetization.  
Conceptualization, M.P., N.G. and M.N.; methodology, K.Z.; software, M.N.; validation, M.P. and N.G.; formal analysis, J.C. and K.Z.; investigation, M.P., M.N., N.G. and K.Z.; resources, N.G.; data curation, N.G. and K.Z.; writing--original draft preparation, M.P., M.N. and N.G.; writing--review and editing, J.C. and K.T.; visualization, J.C. and K.T.; supervision, N.G.; project administration, M.P. and N.G.; funding acquisition, N.G. and M.N. All authors have read and agreed to the published version of the manuscript.  
This work was realized within the frame of the project VEGA project 1/0052/22. This research was also supported by the Center for Advanced Applied Science, grant number CZ.02.1.01/0.0/0.0/16_019/0000778 \"Center for Advanced Applied Science\" within the Operational Program Research, Development and Education supervised by the Ministry of Education, Youth and Sports of the Czech Republic. This paper was also supported by the Slovak Research and Development Agency under contract No. APVV-22-0040.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.  
References
## 3 Methodology
### Lstm  
The sophisticated design and functionality of recurrent neural networks (RNNs) have significantly advanced with the introduction of various modifications, among which the LSTM unit stands out prominently. The internal structure of the LSTM unit consists of a forgetting gate \(\mathbf{f}_{t}\), equipped with weight parameters \(\mathbf{W}_{xf}\), \(\mathbf{W}_{hf}\), \(\mathbf{W}_{cf}\), and \(\mathbf{b}_{f}\). Additionally, it includes an update gate \(\mathbf{i}_{t}\), which possesses weight parameters \(\mathbf{W}_{xi}\), \(\mathbf{W}_{hi}\), \(\mathbf{W}_{ci}\), and \(\mathbf{b}_{i}\). There is also an output gate \(\mathbf{o}_{t}\), characterized by weight parameters \(\mathbf{W}_{xo}\), \(\mathbf{W}_{ho}\), \(\mathbf{W}_{co}\), and \(\mathbf{b}_{o}\). Furthermore, a data combination component \(\mathbf{g}_{t}\) with weight parameters \(\mathbf{W}_{xc}\), \(\mathbf{W}_{hc}\), \(\mathbf{W}_{cc}\), and \(\mathbf{b}_{c}\), is integrated. Thus, the structure of LSTM is shown in Figure 5.  
The received input, unit state, and unit output of the current neuron are set as \(\mathbf{x}_{t}\), \(\mathbf{c}_{t}\), and \(\mathbf{h}_{t}\), respectively. The unit output and unit state of the previous neuron are set as \(\mathbf{h}_{t-1}\) and \(\mathbf{c}_{t-1}\), respectively.  
Figure 5: The structure of LSTM.  
The forget gate \(\mathbf{f}_{t}\), update gate \(\mathbf{i}_{t}\), selected signal information \(\mathbf{g}_{t}\), cell state \(\mathbf{c}_{t}\), output gate \(\mathbf{o}_{t}\), and hidden state \(\mathbf{h}_{t}\) at moment \(t\) can be formulated as:  
\[\begin{cases}\mathbf{f}_{t}=\sigma(\mathbf{W}_{xf}\cdot\mathbf{x}_{t}+\mathbf{W}_{hf}\cdot\mathbf{h }_{t-1}+\mathbf{W}_{cf}\cdot\mathbf{c}_{t-1}+\mathbf{b}_{f})\\ \mathbf{i}_{t}=\sigma(\mathbf{W}_{xi}\cdot\mathbf{x}_{t}+\mathbf{W}_{hi}\cdot\mathbf{h}_{t-1}+\mathbf{ W}_{ali}\cdot\mathbf{c}_{t-1}+\mathbf{b}_{i})\\ \mathbf{g}_{t}=\tanh(\mathbf{W}_{xc}\cdot\mathbf{x}_{t}+\mathbf{W}_{hc}\cdot\mathbf{h}_{t-1}+\mathbf{ W}_{cc}\cdot\mathbf{c}_{t-1}+\mathbf{b}_{c})\\ \mathbf{c}_{t}=\mathbf{i}_{t}\mathbf{g}_{t}+\mathbf{f}_{t}\mathbf{c}_{t-1}\\ \mathbf{o}_{t}=\sigma(\mathbf{W}_{xo}\cdot\mathbf{x}_{t}+\mathbf{W}_{ho}\cdot\mathbf{h}_{t-1}+\mathbf{ W}_{co}\cdot\mathbf{c}_{t-1}+\mathbf{b}_{o})\\ \mathbf{h}_{t}=\mathbf{o}_{t}\text{tanh}(\mathbf{c}_{t})\end{cases} \tag{8}\]
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 6 Pros and Cons of Traditional FSSs  
Up to now, various FSS structures have been developed and introduced in the literature for a broad range of applications. Hence, the literature has shed light on the pros and cons of the FSSs, which are captured in Table 3.
## 3 Results and Discussion
### Model Analysis  
The research results indicate that the high temperatures along the tunnel are mainly caused by the following factors:  
1. Heat conduction through deep major faults: The tunnel passes through deep major faults in the region, such as the Qinghai-Nanshan Fault and the Lajishan Fault. These faults create pathways for heat flow upwelling, significantly increasing the temperature of the surrounding rock masses.
2. High radioactive heat generation rate of rock formations: The tunnel crosses areas with granite distribution, where the rocks have a relatively high radioactive heat generation rate, further exacerbating the extent of heat damage.
3. Crustal thermal structure characteristics: The crustal heat flow in the study area is primarily of the \"hot crust, cold mantle\" type. Deep thermal sources are conducted to shallow areas through faults, and combined with the high thermal conductivity of granite, this results in localized high-temperature zones.
## 3 Results and Discussion
### Bacterial Inactivation
#### 3.3.1 Testing Natural River Water for Bacterial Contamination
The highest number of bacteria was recorded at point 5, located in the city of Ivano-Frankivsk (Figures 2a and 7d). Further downstream, _E. coli_ and coliform levels decline, likely due to dilution. Another increase in the number of bacteria was recorded at point 9 (Figure 7d), which is located in the smaller town of Yezupil, right next to the river (Figure 2a). Recorded spikes in bacterial counts suggest the presence of local sources of fecal contamination (e.g., discharges of domestic or animal sewage). Fecal bacteria pose a particularly high risk to human health, especially in areas used for recreational purposes. Exfiltration of sewage from sewage systems into the ground and its subsequent leaching into surface waters can lead to contamination of river water with fecal bacteria. The presence of both treated and untreated sewage discharge points in the river basin is one of the reasons for the uneven distribution of bacterial concentrations in river water. This is confirmed by extensive studies carried out in the basin of the Danube River, which is the second longest river in Europe [29]. Similar patterns were observed in the small river Swist in Germany, where _E. coli_ counts at sampling points located in a relatively small area varied by a factor of 50 [2]. This is due to differences in land use, i.e., the presence of urban agglomerations, agricultural areas, bathing areas, etc. In this study, the increased number of bacteria at sampling point 9 (Figure 7d) may be due to poor agricultural waste management practices or leaks in the sewage system in the small town of Yezupil (Figure 2a) [30]. The sewage system in a small town is based more on cesspools than on a central sewage system. Therefore, there may be point sources of discharge of untreated sewage. Methods for detecting and distinguishing sources (human and animal) of fecal bacteria in water are constantly being improved [30]. Exfiltration phenomena are modeled to predict water contamination [31], also using in situ measurements [32]. It can be stated that the inhabitants of the Bystrytsia river basin are supplied with good-quality water. The registered local increase in bacterial loads does not cause any significant deterioration of water parameters.
## 2 Materials and Methods
### Methods
#### 2.3.3 Trend Analysis  
To analyze the short-term spatial-temporal changes in grassland vegetation, monthly mean values of GLI, EVI, NDVI, and SAVI vegetation indices were used for May-July (vegetation peak) of 2018-2023, derived from the LANDSAT, Sentinel, and Planet Scope satellites. These indices were chosen as indicators of the best correlation. The Mann-Kendall statistical test and modifications were used for trend analysis [27; 78] on the Jupiter Notebook platform. These tests were chosen because they are non-parametric and do not require a normal distribution of the data. Moreover, the tests have low sensitivity to sharp time discontinuities due to inhomogeneous time series; they also regulate autocorrelation [79; 80; 81]. The original Mann-Kendall Test, Modified Mann-Kendall Test by [PERSON] (MKKH), Modified Mann-Kendall Test by Yue and Wang (MKKY), Pre-whitening Mann-Kendall Test (PW), Trend-Free Pre-whitening (TFPW) [80; 81; 82; 83] trend tests and Sen's Slope were used. According to [84], the trend is considered significant only if at least three of the five tests suggest a significant trend. As descriptive parameters of the Mann-Kendall test, [PERSON]'s Tau and Z score were used. Kendall Tau, or [PERSON]'s rank correlation coefficient, measures the monotonicity of the curve. [PERSON]'s Tau ranges between \(-1\) and \(1\); it is positive when the trend is increasing and negative when it is decreasing [85].  
The presence of statistically significant trends is assessed using the Z value. A statistically significant trend is determined by the absolute value of Z, \(\mid\) Z\(\mid\) > 1.96, which is the critical value of Z when \(\alpha\) = 0.05. The increasing or decreasing trend is indicated by a positive or negative Z score [79].  
In addition to the Mann-Kendall method, [PERSON]'s slope was also used to estimate the slope of the trend of the observed vegetation indices [86; 87]. The [PERSON]'s slope model is suitable for qualitative characterization of trends over time [88; 89]. The slope of [PERSON]'s slope is better suited for determining the magnitude of the trend. If the result of [PERSON]'s slope is positive, the time series has an increasing trend and vice versa [90].
## 2 Methods and Models
### Pore Fractal Model  
#### 2.3.1 Sponge Model Based on the Mercury Intrusion Method  
The sponge model [23] assumes that the initial element is a cube with a length of R on one side, and it evenly distributes m\({}^{3}\)-generating elements. Discarding n, the remaining number of generating elements is as follows:  
\[\mathrm{N_{b1}}=\mathrm{m^{3}-n} \tag{2}\]  
After k repeated calculations, the remaining number of generated elements is as follows:  
\[\mathrm{N_{bk}}=\left(\mathrm{m^{3}-n}\right)^{\mathrm{k}} \tag{3}\]  
The side length is as follows:  
\[\mathrm{r_{k}}=\mathrm{R/m^{k}} \tag{4}\]  
The number of generated elements is represented as follows:  
\[\mathrm{N_{bk}}=\left(\frac{\mathrm{r_{k}}}{\mathrm{R}}\right)^{-\mathrm{D}} \tag{5}\]  
where D is the fractal dimension of the porous medium, which ranges from 2 to 3. The formula is expressed as follows:  
\[\mathrm{D}=\mathrm{lgN_{b1}/\mathrm{lgm}} \tag{6}\]  
The larger the value, the rougher the surface of the porous medium, and vice versa.  
The total volume of the cube is as follows:  
\[\mathrm{V_{k}}=\mathrm{r_{k}}^{3}\mathrm{N_{bk}}=\frac{\mathrm{r_{k}}^{3- \mathrm{D}}}{\mathrm{R^{-D}}} \tag{7}\]  
If \(\mathrm{k}\rightarrow\infty\) is satisfied, then \(\mathrm{V_{k}}\rightarrow\mathrm{V_{r}}\) is satisfied, where \(\mathrm{V_{r}}\) is the skeleton volume of the porous medium.  
Then, taking the derivative of Equation (7) yields the following:  
\[\mathrm{d}V/\mathrm{d}r\propto r^{2-D} \tag{8}\]  
According to Equation (1), as follows:  
\[\mathrm{P}=0.7354/\tau \tag{9}\]  
After differentiation, as follows:  
\[\mathrm{P}\mathrm{d}r+\mathrm{d}\mathrm{P}=0 \tag{10}\]  
Substituting into Equation (4) yields the following:  
\[\mathrm{d}V/\mathrm{d}\mathrm{P}\propto r^{2}\times r^{2-D}\propto r^{4-D} \tag{11}\]  
Taking the logarithm on both sides yields the following:  
\[\mathrm{log}(\mathrm{d}V/\mathrm{d}\mathrm{P})\propto(4-D)\mathrm{log} \mathrm{P}\propto(D-4)\mathrm{log}\mathrm{P} \tag{12}\]  
From this, scatter plots can be drawn using \(\mathrm{log}(\mathrm{d}V/\mathrm{d}\mathrm{P})\) and \(\mathrm{log}\mathrm{P}\), and the fractal dimension \(D\) can be calculated using the slope.
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 4 Architecture Overview
### Interest Profiles  
Message interest is represented by a finite set M of distinct parts (A.K.A. \"topics\"). At the start, each node has a subset M of messages of interest. Two nodes (i, j) do not have common interests if \(\text{Mi}\cap\text{Mj}=\emptyset\). So, we assumed that nodes interests are static and do not change during the simulation run. Each message assigned to node \"i\" is initialized with one interest, randomly chosen from Mi.
###### Abstract  
The construction of underwater geodetic networks (UGN) is crucial in marine geodesy. To provide high-precision kinematic positioning for underwater submersibles, an underwater acoustic geodetic network configuration of three seafloor base stations, one subsurface buoy, and one sea surface buoy is proposed. The simulation results show that, for a 3 km-deep sea, based on the proposed UGN, the submersible positioning range and positioning accuracy are primarily affected by the size of the seafloor base station array, while the height of the subsurface buoy has a greater impact on the submersible positioning accuracy than the positioning range. Considering current acoustic ranging technology, the kinematic positioning performance of the UGN is optimal when the seafloor base stations are 9-13 km apart and the subsurface buoy is less than 2.5 km above the seafloor, which can achieve a submersible positioning accuracy of less than 30 m within an underwater space of 25 km \(\times\) 25 km \(\times\) 3 km. The proposed cost-effective UGN configuration can provide high-precision submersible kinematic positioning performance for seafloor surveying and ocean precision engineering. The impact of the underwater environment on the acoustic transmission characteristics should be further investigated.  
GNSS-acoustic; underwater geodetic network; mixed network configuration; position dilution of precision; acoustic transmission characteristics +
Footnote †: journal: remote sensing  
Article