esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Effect of the Range of \(\varepsilon_{k}\)  
It is worth mentioning that, despite being invariant under the change of many parameters, the RBF-TPU results are affected drastically by the chosen interval for \(\varepsilon_{k}\). All the previous results are obtained with the interval of \([0,1]\). It is observed that scaling down the interval will worsen the results. However, extending the interval to \([-1,1]\) improves the results by increasing the high accuracy region, as illustrated in Figure 8. It is shown that, for the studied ranges of \(m\) and \(\varepsilon\), the errors of interpolation, first derivative, and second derivative approximations are roughly less than seven, four, and three significant digits, respectively. By examining the color-bar of Figure 8, the negligible fluctuation of the error becomes evident. For example, the maximum and minimum errors of interpolation are \(-7.1\) and \(-7.3\), which has a negligible fluctuation. Consequently, the accuracy is independent of \(m\) and \(\varepsilon\) when using \([-1,1]\) as a range of \(\varepsilon_{k}\). Additionally, the accuracy remains the same as the maximum accuracy achieved with the regular RBF (Figure 4). Furthermore, it is shown in the previous section (Section 5.1) that the accuracy is independent of \(N_{e}\). Hence, it can be concluded that the present basis (RBF-TPU), which is derived from the partition of unity with respect to shape parameter, yields accurate results that are independent of the shape parameter, \(N_{e}\), and \(m\). Notably, the optimization of the range of \(\varepsilon_{k}\) needs to be done once, after which the resulting range can be used for different problems.  
Figure 7: The log10 of the norm of the error of approximating the given function \(f(x)\) and its first two derivatives using Gaussian RBF-TPU with different truncation orders for a range of \(\varepsilon\) and m values. The rows are for function, first derivative, and second derivative approximation, while the columns are for the zeroth, first, and second orders of truncation. The \(\varepsilon_{k}\) range is \([0,1]\) and \(N_{e}=5\).
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 3 Cimkg
### Knowledge Graph-Based Shared Encoder Module
#### 3.1.4 Mask Transformer Layer  
The mask transformer is primarily responsible for encoding the embedding representations of the sentence tree and the entity visible matrix \(M\). In contrast, the transformer layer in the standard BERT model can only encode the embedding representations and cannot process the visible matrix. Therefore, by modifying the relevance calculation function within the self-attention mechanism of the original transformer layer, it is possible to restrict the visible range of each token based on the entity visible matrix \(M\), thereby preventing semantic information from being altered by the injection of external knowledge. Specifically, the relevance scores are combined with the entity-relationship visible matrix \(M\), and the final relevance degrees are calculated using the softmax function.  
The original self-attention parameters are kept unchanged, as shown in (4).  
\[Q^{i+1},K^{i+1},V^{i+1}=h^{i}W_{q},h^{i}W_{k},h^{i}W_{v} \tag{4}\]  
where \(W_{q},~{}W_{k},~{}W_{v}\) are trainable parameters; \(h^{i}\) represents the hidden layer output of the \(i\)-th layer; \(Q^{i}\) denotes the query vector of the \(i\)-th layer; \(K^{i}\) represents the key vector of the \(i\)-th layer; and \(V^{i}\) is the value vector of the \(i\)-th layer.  
Subsequently, the softmax function was modified by injecting the entity-relationship visible matrix \(M\) as shown in (5):  
\[S^{i+1}=softmax\left(\frac{Q^{i+1}{K^{i+1}}^{T}+M}{\sqrt{d_{k}}}\right) \tag{5}\]  
Figure 6: Entity visible matrix.  
where \(M\) represents the entity visible matrix, \(S^{i}\) denotes the relevance score of the \(i\)-th layer, and \(d_{k}\) denotes a predefined scaling factor in the model. Finally, the hidden layer output for the next layer is updated as shown in (6).  
\[h^{i+1}=S^{i+1}V^{i+1} \tag{6}\]
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 4 Results
### Evaluation for Urban and Non-Urban Areas  
Land cover affects the water-heat exchange in the boundary layer, which may lead to different performances of SPPs in urban and non-urban areas [79]. Therefore, we investigated the ability of IMERG in this regard with the three main land cover types of cultivated land, forest and urban. Figure 6 shows the hourly results, while the similar daily results are presented in Figure S2.  
Figure 5: Changes in (**a**) \(MBE\), (**b**) \(Corr\), (**c**) \(RMSE\), (**d**) \(POD\), (**e**) \(FAR\), and (**f**) \(ETS\) for different precipitation intensity levels on a daily scale.  
For the whole of 2018, all three IMERG products have better overall estimation abilities in urban areas, as evidenced by the higher _Corrs_ and smaller \(rRMSE\)s. The two near-real-time products generally overestimated urban precipitation, while showing small overall negative \(MBE\)s (which are the results of offset between overestimation and underestimation) on the other two land covers. With ground-based observations, IMERG FR corrected the overestimation in urban areas. Compared to forests, IMERG products provided more accurate estimates for cultivated lands, and had better \(MBE\), \(Corr\) and \(rRMSE\) scores. Similar results were found in the summer, but the estimation ability exhibited opposite characteristics in the winter, being strongest in forests, second in cultivated lands and weakest in urban areas (Figures S3 and S4).  
In terms of detection metrics, the three IMERG products performed better with cultivated lands than forests, and worst in urban areas. The poor detection ability in urban areas came mainly from the high misdetection rate of precipitation occurrences (Figure 6e). As the IMERG satellite radar obtains precipitation information mainly by detecting precipitation particles, a rise in cloud condensation nodules induced by large anthropogenic aerosol emission in urban areas will increase the chance of false detection [80]. In addition, the roughness of artificial surfaces, urban thermal effects, and water vapor variability can also cause greater uncertainty in urban precipitation occurrence and can affect the SPP detection capability [81; 82]. Such an urban effect occurred throughout the year, despite the observed seasonality of the differences in detection abilities between cultivated lands and forests (Figures S3 and S4).  
We also analyzed the performance differences within the urban areas. Figure 7 shows the changes in the metrics of urban grids with artificial surface area proportion on an hourly scale. It can be seen that almost all the metrics, except \(MBE\), become significantly worse (\(p\)-value < 0.001) with increasing artificial surface area proportion, which indicates a negative impact of the urbanization level on IMERG performance to some extent. The  
Figure 6: Differences of (**a**) \(MBE\), (**b**) \(Corr\), (**c**) \(rRMSE\), (**d**) \(POD\), (**e**) \(FAR\), and (**f**) \(ETS\) on cultivated lands, forests, and urban areas on an hourly scale.
## 4 Network-Reconfiguration Optimization Model
### Constraints of Network-Reconfiguration  
(1) Regular constraints  
(a) DG apparent power constraint.  
\[S_{DGj}\leq S_{DGj\_max} \tag{6}\]  
where \(S_{DGj}\) is the apparent power of the \(j\)th DG.  
(b) Branch distribution power constraint.  
\[S_{c}\leq S_{c\_max} \tag{7}\]  
where \(S_{c\_max}\) is the maximum allowable value of \(S_{c}\), which is the distribution power of the \(c\)th branch.  
(c) Node voltage amplitude constraint.  
\[V_{i\_min}\leq V_{i}\leq V_{i\_max} \tag{8}\]  
where \(V_{i\_min}\) and \(V_{i\_max}\) represent the lower and upper limits of \(V_{i}\), which is the voltage amplitude of node \(i\).  
(2) Comprehensive power quality constraints set  
\[\left\{\begin{array}{c}THD-ul_{i}\ \leq THD_{\max}\\ Cune-ul_{j}\leq Cu_{\max}\\ Vone-ul_{k}\leq Vo_{\max}\\ \Delta f\leq\Delta f_{max}\end{array}\right. \tag{9}\]  
(a) Uncertain node voltage total harmonic distortion (THD) constraint.  
\[THD-ul_{i}\ \leq THD_{\max} \tag{10}\]  
where \(THD\)-\(ul_{i}\) is the upper limit of the distribution range of the THD index of node \(i\). \(THD_{\max}\) is the permissible maximum value of THD, which is defined as 4% for the distribution network of 10 kV according to Reference [20].  
(b) Uncertain negative sequence current constraint for synchronous generator-based DG  
\[Cune-ul_{j}\leq Cu_{\max} \tag{11}\]where  
\[Cune-ul_{j}=Ine-ul_{j}/In_{j}\]  
where \(Ine-ul_{j}\) is the upper bound of distribution range of the actual negative sequence current of the \(j\)th synchronous generator-based DG while \(In_{j}\) is the rated positive sequence current. \(Cu_{max}\) is the permissible maximum value, which is set as 10% referring to Reference [21].  
(c) Uncertain negative sequence voltage constraint for IIDG  
\[Vone-ul_{k}\leq Vo_{\max} \tag{12}\]  
where  
\[Vone-ul_{k}=Vne-ul_{k}/V_{k}\]  
where \(Vone-ul_{j}\) is the upper bound of the distribution range of the actual negative sequence voltage of the \(k\)th IIDG while \(V_{k}\) is the actual positive sequence voltage of the \(k\)th IIDG. \(Vo_{max}\) is the permissible maximum value, which is set as 2% referring to Reference [22].  
(d) Constraint of island's change in frequency  
\[\Delta f\leq\Delta f_{max} \tag{13}\]  
where  
\[\Delta f=abs(f_{r}-f)\]  
According to Reference [19], \(\Delta f_{max}\), which is the maximum allowable value of change in frequency \(\Delta f\), is 0.5 Hz.
## 4 VoLTE Scenario and Simulation Report
### Simulation Analysis  
The present subsection engages the simulation analysis derived from both the single and dual eNB scenarios. The simulation process has been repeated several times to confirm the percentage of users showing satisfactory communication based on the considered factors. In VoLTE networks, delay, packet loss rate, and jitter are defined as QoS indicators while the mean opinion score (MOS) is a QoE indicator [22]. Thus, to compare the two simulation scenarios, we have statistically examined the following parameters:  
* Frame delay.
* Frame loss ratio (FLR).
* Jitter delay.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Single eNB** & **Dual eNB** \\ \hline Center Frequency/LTE Band & 2.69 GHz & 2.69 GHz, 2.71 GHz \\ Channel Bandwidth & 20 MHz & 20 MHz \\ Number of Channels & 1 & 1 for each eNB \\ Antennas & Omnidirectional & Omnidirectional \\ UE Transmit Power & +24 dBm & +24 dBm \\ eNB Transmit Power & +16 dBm & +16 dBm \\ Path Loss Model & Log Shadow & Log Shadow \\ Codec & G.711 & G.711 \\ Call Duration/Simulation & 60 s & 60 s \\ Time & 1200–1500 m & 800–1100 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: VoLTE simulation RF parameters.  
Figure 2: Single eNB simulation scenario.  
* MOS.  
Thus, to compare each parameter's simulation results, we undertook a statistical F-test followed by a statistical \(T\)-test [23]. Tables 4 and 5 present the acceptable values in VoIP communications [22, 24, 25].  
The mean and standard deviation values are presented in the following tables, while the vector analysis is observed in the figures. All data have been extracted from the OMNet++ simulation and have also been used to generate vector graphs. The statistical results are compared to the vector analysis, in which the following features apply:  
* The boxes represent the frame's loss interquartile range (IQR) for each UE. The IQR measures the data spread between the two extremes of the distribution.
* The horizontal line inside each box indicates the median value. The median value is the numeric value separating the higher half of a sample from the lower half.
* The whiskers extend to show the range of the data, typically up to 1.5 times the IQR from the box (though this can vary depending on the data).
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 4 Discussion and Main Conclusions  
In this section, we discuss the main findings regarding the geomagnetic storm of 12 May 2021.The main goal is to study the positioning errors of GNSS receivers caused by this storm, which can be classified as a moderate one. In addition, in order to verify our methodology and our findings we used previous equivalent geomagnetic storms as comparison. The comparison storms were the 27 March 2017 [61] and the 5 August 2019 [57].
# Moving Mesh Strategies of Adaptive Methods for Solving Nonlinear Partial Differential Equations
###### Abstract  
This paper proposes moving mesh strategies for the moving mesh methods when solving the nonlinear time dependent partial differential equations (PDEs). Firstly we analyse [PERSON]'s moving mesh PDEs (MMPDEs) and observe that, after Euler discretion they could be taken as one step of the root searching iteration methods. We improve [PERSON]'s MMPDE by adding one Lagrange speed term. The proposed moving mesh PDE could draw the mesh to equidistribution quickly and stably. The numerical algorithm for the coupled system of the original PDE and the moving mesh equation is proposed and the computational experiments are given to illustrate the validity of the new method.  
numerical solution; moving mesh; adaptive method; nonlinear partial differential equation +
Footnote †: journal:  
0000-0001-1800-0001-1800-0000-0000-0000-0000-000  
In [6; 9], [PERSON] proposed seven kinds of location based MMPDEs based on EP, both the theoretical and numerical problems have been analysed extensively, which offer a outstanding guidance when applying these MMPDEs in the practical problems. We observed that, after Euler discretization on time, the MMPDEs could be taken as deformations of iteration methods on EP. Based on this new explanation, the convergence conditions and orders on fixed time could be obtained. Moreover, the parameter in the MMPDEs which determines the speed of the equidistribution could be estimated.  
The new explanation leaves space to improve [PERSON]'s MMPDEs by adding a velocity-based term. In 2005, [PERSON] et al. [30; 31] constructed moving mesh PDEs which move the mesh along the characteristic line of the original PDE. [PERSON]'s methods are able to maintain the distribution of knots during time iteration.  
Combining the location based method by [PERSON] and velocity based method by [PERSON], we construct the moving mesh PDEs in which the mesh are moved by two steps: firstly the approximation to the EP on fixed time, then the movement along the character line of the original PDE to keep the distribution. The moving mesh PDE accelerated the convergence speed and stabilized the moving process of the mesh to the EP. Compared with the existing two methods, the proposed method is more stable and could simulate sharper and faster propagating shock waves.  
The moving mesh PDE and the original PDE form a coupled PDE system [9]. In this manuscript, the PDE system is simulated simultaneously by methods of line. The system was first discretized in the spatial domain, and then the resulting system of ordinary differential equations (ODEs) is integrated using an variable step size ODE solver to observe the performance of the moving mesh equations.  
The rest of the paper is organized as follows: We propose the improved moving mesh PDE in Section 2. The numerical algorithms are presented in Section 3. Section 4 shows the experiments to illustrate the validity of the new method. We end this paper with a brief conclusion in Section 5.
## 3 Results
### Patient Characteristics
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Training Set** & **Validation Set** & \(p\)**-Value** \\ \hline Region(\%) & & & 0.013 \\ Counties in metropolitan areas ge 1 million pop & 454 (54.57) & 182 (51.70) & \\ Counties in metropolitan areas of 250,000 to 1 million pop & 184 (22.12) & 67 (19.03) & \\ Counties in metropolitan areas of It 250 thousand pop & 64 (7.69) & 34 (9.66) & \\ Nonmetropolitan counties adjacent to a metropolitan area & 67 (8.05) & 38 (10.80) & \\ Nonmetropolitan counties not adjacent to a metropolitan area & 63 (7.57) & 9 (2.56) & \\ Tumor metastasis (\%) & & 0.017 & \\ Localized & 472 (56.73) & 190 (53.98) & \\ Regional & 265 (31.85) & 106 (30.11) & \\ Distant & 95 (11.42) & 46 (13.07) & \\ AJCC stage (\%) & & & 0.151 \\ IE & 99 (11.90) & 45 (12.78) & \\ IEA & 321 (38.58) & 127 (36.08) & \\ IEB & 33 (3.97) & 14 (3.98) & \\ II & 1 (0.12) & 2 (0.57) & \\ IIE & 67 (8.05) & 24 (6.82) & \\ IIA & 10 (1.20) & 5 (1.42) & \\ IIEA & 173 (20.79) & 86 (24.43) & \\ IIEB & 33 (3.97) & 3 (0.85) & \\ IIIE & 1 (0.12) & 1 (0.28) & \\ IIIA & 3 (0.36) & 2 (0.57) & \\ IIIEA & 17 (2.04) & 7 (1.99) & \\ IIIEB & 2 (0.24) & 1 (0.28) & \\ IIIES & 0 & 1 (0.28) & \\ IIIESA & 2 (0.24) & 2 (0.57) & \\ IIIESB & 2 (0.24) & 0 & \\ IIISA & 1 (0.12) & 0 & \\ IV & 12 (1.44) & 3 (0.85) & \\ IVA & 34 (4.09) & 21 (5.97) & \\ IVB & 21 (2.52) & 8 (2.27) & \\ Surgery (\%) & & & 0.003 \\ No & 376 (45.19) & 150 (42.61) & \\ Excision & 454 (54.57) & 202 (57.39) & \\ Unknown & 2 (0.24) & 0 & \\ Degree of tumor invasion (\%) & & & 0.077 \\ I & 453 (54.45) & 186 (52.84) & \\ II & 284 (34.13) & 120 (34.09) & \\ III & 28 (3.37) & 14 (3.98) & \\ IV & 67 (8.05) & 32 (9.09) & \\ Number of malignant tumors (\%) & & & 0.125 \\
1 & 706 (84.86) & 301 (85.51) & \\
2 & 107 (12.86) & 47 (13.35) & \\
3 & 16 (1.92) & 4 (1.14) & \\
4 & 3 (0.36) & 0 & \\ Income (\%) & & & 0.692 \\ \(<\)USD 35,000 & 14 (1.68) & 5 (1.42) & \\ USD 35,000-USD 39,999 & 19 (2.28) & 7 (1.99) & \\ USD 40,000-USD 44,999 & 34 (4.09) & 11 (3.13) & \\ USD 40,000-USD 44,999 & 35 (4.21) & 19 (5.40) & \\ USD 50,000-USD 54,999 & 59 (7.09) & 34 (9.66) & \\ USD 50,000-USD 54,999 & 69 (8.29) & 32 (9.09) & \\ USD 60,000-USD 64,999 & 77 (9.25) & 37 (10.51) & \\ USD 65,000-USD 69,999 & 148 (17.79) & 47 (13.35) & \\ USD 70,000-USD 74,999 & 77 (9.25) & 26 (7.39) & \\ USD 75,000+ & 300 (36.06) & 134 (38.07) & \\ Age & 64.819 \(\pm\) 14.779 & 64.94 \(\pm\) 14.564 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)
# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 5 Discussion
For the _River_ dataset, the RSB method achieves the best OA and K values compared to all the other methods considered, see Table 5.  
Finally, the _Bay Area_ dataset's results are reported in Table 6. The competitors include EUC + EM, a method constructed with Euclidean distance CVA and expectation maximization; EUC + Otsu, Euclidean distance CVA-based and Otsu thresholding; SAM + Otsu, a method based on the spectral angle mapper error function plus CVA and Otsu thresholding; EUC + WAT + Otsu, which adds the spatial processing based on watershed segmentation plus region averaging and spatial regularization; SAM + WAT + Otsu, which replaces the Euclidean distance with the SAM error function, and SAM + WAT + EM, which replaces the Otsu thresholding with the expectation maximization algorithm. The performance of RSB is better than that of Euclidean-based methods and plain techniques, such as the ones adopted for AICA. It is evident that in order to improve the accuracy results for this example, more pre-processing steps should be applied. **Remark 2**. : _It should be noticed that having a higher OA does not necessarily imply a higher K as well. We can observe this fact also here, for example, in the River test. For instance, in Table 5, the method GETNET-2 has a higher OA compared to GETNET-1, but the corresponding K is lower than that of GETNET-1. Another case is with the method CNN, which has an OA (0.9440) higher than PCA-CVA (OA = 0.9434) but the corresponding K is much lower. It is obviously difficult to judge which method is in fact the best among all, since both the indicators give reasonable insights into the achieved accuracy, but the computation of the K coefficient is fundamental when the assayed datasets consist of unbalanced data, as the OA can be strongly biased. Therefore, the computation of K could be considered far more reliable to assess the goodness of the proposed method, rather than using the OA alone._ \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**USA**} \\  & **OA** & **K** \\ \hline RSB+PP & 0.9579 & 0.8781 \\ GETNET-1 [35] & 0.9289 & 0.7764 \\ GETNET-2 [35] & 0.9332 & 0.7897 \\ CVA [19] & 0.9272 & 0.7670 \\ MAD-SVM [56] & 0.8612 & 0.4760 \\ IR-MAD-SVM [56] & 0.9285 & 0.7700 \\ Method [56] & 0.9490 & 0.8310 \\ BIC2 [17] & **0.9728** & – \\ AICA [51] & 0.9417 & – \\ HybridSN [24] & 0.9553 & 0.8701 \\ SCNN-S [24] & 0.9631 & 0.8848 \\ SSCNN-S [24] & 0.9651 & **0.8918** \\ TRIFCD-MS [7] & 0.8848 & 0.7024 \\ TRIFCD-Fusion [7] & 0.9263 & 0.7813 \\ BCG-Net [58] & 0.9546 & 0.8662 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The proposed approach, RSB, with post-processing, and other competitors on the _USA_ dataset. The best results are displayed in bold.
## 5 Bioactivity of Secondary Metabolites in the _Leontopodium_ Taxa
### Anti-Inflammatory Activity
_leontopodioides_ methanolic extract was studied using HK-2 cells, a proximal tubular cell line derived from the human kidney, and the results showed a significant decrease in the IL-6 and TNF-\(\alpha\) expression in HK-2 cells and in a culture medium [25]. In the animals' model with acute kidney injury, sepsis induced uncontrolled systemic inflammatory response syndrome, but the extract remarkably improved oxidative stress and inhibited apoptosis in the kidney tissue by reducing the protein expression related to the NF-\(\kappa\)B signalling pathway [25]. In chronic inflammatory skin diseases, such as atopic dermatitis or psoriasis, epidermal keratinocytes respond to pro-inflammatory cytokines released by leukocytes, such as TNF-\(\alpha\) and IFN-Y, through a sustained expression of numerous inflammatory mediators, which maintain the cycles of chronic inflammation [102]. This chronic inflammation was induced in primary human keratinocyte cultures by administering combinations of such pro-inflammatory molecules [12] and treatments with extracts derived from callus cultures or leontopodic acid, combined with the administration of TNF-\(\alpha\) and IFN-Y cocktails, showing inhibitory effects on the growth factor GM-CSF and cytokine IL-8. When using only TNF-\(\alpha\) as a stimulus, the chemokines IL-8, MCP-1, IP-10 and the transcription factor GM-CSF were inhibited in a dose-dependent manner, similar to the corticosteroid triamcinolone. This study assumed that other major compounds from the callus extracts, such as chlorogenic and 3,5-dicaffeoylquinic acids, can exhibit anti-inflammatory activity by inhibiting the overexpression of some chemokines, as previously reported in other works on these compounds [103]. A special type of inflammatory process is atherogenic inflammation. Endothelial cells exposed to pro-inflammatory stimuli can accumulate inflammatory mediators (chemokines, early pro-inflammatory cytokines, intercellular adhesion molecules), resulting in damage to the vascular tissue, followed by an accelerated proliferation of vascular cells [104]. To stop this destructive cascade, inhibiting the production of pro-inflammatory mediators at the level of endothelial cells would represent the first line of attack in the treatment of atherosclerosis [105]. In HUVEC cultures treated with LPS or oxLDL, a trigger for atherogenic inflammation, the treatments with _L. alpinum_ callus extract remarkably reduced the expression of vascular cell adhesion molecules (VCAM-1), which are characteristic in atherogenic inflammation [12]. VCAMs play a key role in initiating inflammatory responses in the vascular system. The extract also displayed inhibitory activity on LPS-induced IL-6 transcription. These results could provide a basis for the development of new vascular anti-inflammatory drugs derived from Edelweiss callus culture extract.