esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Electrically Large Structures  
It is natural to ask whether the MAR is effective for structures that are electrically much larger than those discussed in the preceding sections. In other words, can the cut-off wavenumbers be calculated accurately, and, bearing in mind that the spectrum becomes increasingly dense or crowded, can the separate spectral lines be resolved? With what computational effort?  
The following example provides positive answers to these questions. Figure 13 displays the spectral dependence of the condition number \(cond\ A_{N}(ka)\) for the circular waveguide with an eccentrically embedded circular rod studied with the same parameters (\(b/a=d/a=0.25\)) as in Section 3.2 (see Figure 4). A truncation number of \(N=512\) was employed in this figure. The condition number was computed in the interval \(90.8\leq ka\leq 91.2\) using 500 points with increment \(\Delta ka=0.002\). The computation times were approximately the same as for the computations described in Section 3.2 for the same truncation numbers.  
It is apparent that the spectral lines are very much more closely packed than those in Figure 5. Nonetheless, the spectrum can be well resolved--with the same computational effort--by restricting the interval of interest to one containing about the same number of lines and by suitably choosing the increment \(\Delta ka\). Fifteen spectral lines can be discerned. Focusing on the mode in the vicinity of the non-dimensional wavenumber \(ka=91.015\), Table 10 displays the sequence of the approximations to the non-dimensional wavenumber \(\gamma\) as a function of increasing truncation number. Convergence to the 3 decimal place estimate of \(\gamma=91.018\) is obtained with a truncation number of 2048. Increasing the truncation number improves the number of correct digits.  
We conclude that the MAR is indeed effective for computing cut-off wavenumbers in waveguides that are much larger electrically than those previously discussed. However, it should be noted that, due to the crowded nature of the spectrum, it is no longer possible to make a correspondence between these modes and those of the empty waveguide. Finally, it is worth remarking that the MAR is effective in a range of problems solving in the low-frequency, diffraction and quasi-optics regions. High-frequency scattering by electrically large cavities of electrical size exceeding 100 wavelengths has been investigated in [23].
# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 4 Experiment
### Evaluation Indicators  
Usually, evaluation indicators are used to measure the quality of clustering results. We adopted three common clustering indicators: Accuracy (ACC) [49], Normalized Mutual Information (NMI) [50] and Adjusted Rand Index (ARI) [51]. It is believed that the clustering performance is better when ACC, NMI and ARI are higher. They were calculated as follows.  
(1) Accuracy (ACC)  
Based on the clustering label P and the true label T, accuracy represents how many samples have been correctly clustered compared to the total number of samples. There is a range of 0 to 1 for the ACC.  
\[ACC=\frac{\sum_{i=1}^{k}max\left|P_{i}\cap\text{T}_{j}\right|}{N} \tag{2}\]  
Normalized Mutual Information (NMI)  
It uses information theory to measure the difference between the clustering partitions, and its range is in [0, 1].  
\[NMI(X,Y)=2\frac{I(X,Y)}{H(X)+H(Y)}\]  
where \(I\) (\(X\), \(Y\)) is the mutual information between \(X\) and \(Y\), and \(H(X)\), \(H(Y)\) are the entropy of random variables.  
(3) Adjusted Rand Index (ARI)  
The Rand Index indicates how closely the predicted and actual values match, and its range is [0, 1]. However, as RI does not guarantee that a random result is close to 0, we recommend using the Adjusted Rand Index, which has a higher degree of discrimination. Values are between [\(-1,1\)]. Larger value indicates better consistency with reality.  
\[RI=\frac{TP+TN}{TP+FP+TN+FN}\]  
\[ARI=\frac{RI-E[RI]}{\max(RI)-E[RI]}\]  
Decisions are represented as TP (true positive), FP (false positive), TN (true negative) and FN (false negative), respectively.
## 3 Results
### GA Hormone Metabolism and Signaling Genes  
Based on our RNA-seq dataset, a total of 12 unigenes were identified as encoding GA metabolic pathways. Among the GA metabolic genes, we identified 4 copalyl diphosphate synthase (CPS), 5 _ent_-kaurene synthase (KS), 2 _ent_-kaurene oxidase (KO), and one gibberellin 2-beta dioxygenase (GA2 ox) (Figure 3A). Starting with geranylgeranyl pyrophosphate (GGPP), CPS converts GGPP to _ent_-Copalyl-PP. One _CPS_ ([PERSON]_7 BL_02F3E2C78.1) was differentially down-regulated in KP and up-regulated in WP, whereas other 3 _CPS_ ([PERSON]_2 AS_A4 BAAE05.1, [PERSON]_2 DS_7917F0E44.1, and Traes_2 BL_80 AC86A1A.1) were highly up-regulated under KP and WP. The former is located on chromosome 7 (7 BL), and the latter are located on chromosome 2 (2 AS, 2 DS, and 2 BL). The five _KS_ unigenes proved to be located on chromosome group 2. Four of these genes were highly up-regulated in KP and WP ([PERSON]_2 AS_2610 FD515.1, Traes_2 AS_D3212 BACF.1, Traes_2 BL_212C1 CE5.1, and Traes_2 BL_B06E350C2.1) and identified to be located on 2 AS and 2 BL. The other _KS_ was located on 2 DL (Traes_2 DL_BEDDAC0D4.1). _GA2 ox_, which catalyzes biologically active gibberellin \(G_{1}\) to in-active GA\({}_{8}\), was differentially up-regulated in KP, WP, and WA, and not differentially down-regulated in KA.  
Figure 2: Heat map analysis of DEGs from preharvest sprouting (PHS) and Abscisic Acid (ABA) treatment for ABA hormone metabolism and signaling: (**A**) hormone metabolism pathway of ABA and comparative expression of genes related to ABA hormone metabolism and signaling pathway of ABA; (**B**) comparative expression of genes related to ABA signaling. The heatmap shows an expression change compared to the controls for each cultivar, with log2 fold-change values.  
In the GA signaling pathway, 3 unigenes were identified as encoding GA receptors, _GID1_, 7 unigenes as encoding SCF complex (2 _CUL1_, 4 _SKP1_, and 1 _RBX1_), and one DELLA protein (RGA1) (Figure 3B). Although most of the GA receptors and SCF complex genes were up-regulated, gene encoding RGA1, which repress the GA response, is also up-regulated in all treated samples and showed higher expression in 'Woori'. The detailed information and comparative expression value of GA metabolic and signaling genes are shown in Table S10.
# PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model
## 3 Methods
### De-Stationary Attention  
The loss of non-stationarity information in the data typically occurs within the depths of the model, especially during attention computation. To address this issue, this section introduces a non-stationarity attention mechanism [16]. The design objective of this mechanism is to capture the attention information of the data when they are non-stationary and to discover specific temporal dependencies in the original non-stationary data.  
The calculation of the self-attention mechanism is performed as shown in Equation (12):  
\[Attn(Q,K,V)=\mathit{Softmax}\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V \tag{12}\]  
where \(Q,K,V\) are obtained by the product of normalized data \(\widetilde{\widetilde{x}}\) and different learnable weights, leading to a lack of non-stationary information in the process of attention information extraction. In the non-stationarity attention mechanism, they are computed as shown in Equation (13):  
\[DeAttn\left(Q^{\prime},K^{\prime},V^{\prime},\tau,\Delta\right)=\mathit{Softmax }\left(\frac{\tau Q^{\prime}K^{\prime}}{\sqrt{d_{k}}}\right)V^{\prime} \tag{13}\]  
In this equation, \(Q^{\prime},K^{\prime},V^{\prime}\) are also obtained by the product of normalized data and different learnable weights. To incorporate non-stationary information into attention computation, \(\tau\) and \(\Delta\) are introduced in this equation to compensate for the data's non-stationarity. These two non-stationarity factors are obtained from Equation (14):  
\[\left\{\begin{aligned} log\tau=MLP(\sigma_{x},x)\\ \Delta=MLP(u_{x},x)\end{aligned}\right. \tag{14}\]  
In this equation, \(u_{x}\) and \(\sigma_{x}\) are statistical quantities extracted from the original unnormalized time series data. Specifically, \(u_{x}\) represents the mean of the input data x, while \(\sigma_{x}\) denotes its variance. These statistical measures are used to capture the underlying characteristics of the original data, which are essential for compensating for non-stationarity in the attention mechanism. The non-stationarity factors \(\tau\) and \(\Delta\) are shared by various neural network layers in the deep model that utilize the non-stationarity attention mechanism.  
The non-stationarity attention mechanism is depicted in Figure 5. Through this mechanism, it is possible to learn temporal and cross-dimensional dependencies from the normalized stable data \(Q^{\prime}\) and \(K^{\prime}\), and to extract two non-stationarity factors \(\tau\) and \(\Delta\) from the original non-stationary data before normalization. These factors are then multiplied with the normalized values \(V^{\prime}\), allowing them to benefit from the predictability of the stabilized sequence while retaining the inherent non-stationarity of the original time series.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 2 Comprehensive Survey
### Cross-Layer Routing Parameters  
To reach the desired performance advantages, cross-layer routing takes advantage of the reliance among protocol layers. To put it another way, it enables data interchange between layers to increase connection speeds. When routing decisions are taken using parameters accessible at the PHY, MAC, and NET levels, the routing will be more resilient to challenges involving traffic and interruption. The signal-to-interference plus-noise ratio (SINR) of a wireless medium, which is generally possible at the Protocol stack, has difficulty in determining disturbance. Only at the MAC layer, metrics relevant to node features, including as buffer space and retransmission rate, are available, whereas including these metrics results in the decision to reduce congestion and packet drops. These metrics, combined with the typical ETE path characteristics accessible at the NET layer, including hop count and round-trip duration, could be used to make routing information that results in good network quality. As a result, the chosen route or next hop at the NET level will also have the least amount of impact on the aforementioned difficulties.
# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 3 Risk-Based VPP Implementation Strategy  
### Baseline Problem  
In this subsection, a baseline problem in VPP implementation is described. In the baseline problem, it is assumed that the power consumption of an SEC and the power generation of resources are known at the VPP implementation stage. The solution of this problem can be used to verify the feasibility of the proposed VPP implementation strategy.  
Let \(c_{i}\) and \(p_{i}\) be the generation capacity and the unit implementation cost of resource \(i\), respectively. The VPP implementation cost is measured as  
\[O(\mathbf{c})=\sum\limits_{i\in\mathcal{I}}p_{i}\,c_{i}, \tag{1}\]  
where \(\mathcal{I}\) is a set of resources \(\mathcal{I}=\{1,\cdots,i,\cdots,I\}\), and \(\mathbf{c}=\{c_{1},\cdots,c_{i},\cdots,c_{I}\}\).  
When the VPP service participant of the SEC requires power consumption \(d_{t}\), the VPP should support demand using the power generation of resources \(i\), \(g_{t}^{i}\), as follows:  
\[d_{t}=\sum\limits_{i\in\mathcal{I}}g_{t}^{i},\quad\forall t\in\mathcal{T}. \tag{2}\]  
where \(\mathcal{T}\) is the VPP service time as \(\mathcal{T}=\{1,\cdots,t,\cdots,T\}\).  
The generated power from resource \(i\) is limited within the generation capacity purchased by the VPP:  
\[g_{t}^{i}\leq c_{i}\quad\forall\,t\in\mathcal{T},i\in\mathcal{I}. \tag{3}\]  
Therefore, the VPP implementation problem regarding the capacity of resources to purchase to serve the requirements of demand is theoretically formulated as follows:  
\[\begin{array}{lcl}\mathbf{P0}:&\min\limits_{\mathbf{c}}&O(\mathbf{c})=\sum \limits_{i\in\mathcal{I}}p_{i}\,c_{i},\\ \text{subject to}&d_{t}=\sum\limits_{i\in\mathcal{I}}g_{t}^{i},\quad\forall t \in\mathcal{T},\\ &g_{t}^{i}\leq c_{i}\,\forall t\in\mathcal{T},i\in\mathcal{I}.\end{array} \tag{4}\]  
Problem **P0** in (4) appears to be a simple linear problem. This is because it is assumed that the power consumption and generation are known at the VPP implementation stage. However, the generated power \(g_{t}^{i}\) and the demand \(d_{t}\) are not deterministic values. To optimally solve this problem, information including future-generated and demand values during service time \(\mathcal{T}\) is required at the VPP implementation stage, which is not possible. This is the difficulty in applying the solution of the VPP implementation problem to a practical system. In this paper, to overcome this difficulty, problem **P0** is relaxed based on the risk factor, and a risk-based VPP implementation strategy is proposed.
## 4 Biaxial Failure Prediction for the PRC with Interfacial Debonding
### Effect of Particle Shape  
Particle shape plays an important role in mechanical behaviors for the PRC. However, few research studies refer to the particle shape influences on the biaxial failure envelopes of the PRC with interfacial debonding. Here, three different particle shapes, as shown in Section 3.3, are considered in the cases listed in this study, and the prediction results can be observed in Figure 7. It can be seen from the figure that the biaxial failure envelopes of the PRC are closely related to the particle shape, while the particle shapes' influence on failure strength is only evident to a limited extent in the failure surface. The particle shapes' influences on uniaxial failure strength in tensile and compressive loading show different variations along the semi-major direction. The higher failure strength can be acquired with increasing semi-major axis ratios in uniaxial compressive loading. Compared with the spherical particle-reinforced composites, the failure strength of ellipsoid particle-reinforced composites with semi-major axis ratio 1.5 provide a nearly 8% increase in uniaxial compressive loading. However, it is difficult for researchers to discern the failure strength difference among the three different particle shapes in uniaxial tensile loading along with the semi-major axis, and only a little difference can be found in uniaxial tensile and compressive loadings.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 5 The Influence of Constellation on Space Environment
### Impact of Single Satellite Collision  
A large number of debris will be produced after the collision and break up of the satellite in the course of operation. There are two kinds of collision situations: one is the collision between the satellite and the small debris, and the other is the collision between the satellite and the satellite. The parameters such as mass, area-mass ratio and velocity increment produced by the disintegration of space objects can be simulated by using NASA's EVOLVE 4.0 break up model [21], and the initial motion state of the breakup debris cloud can be obtained. This analysis only aims at the collision between the Starlink satellite and other mission satellites in the 550 km orbit, and the two satellites completely break up  
Figure 12: The mean number of avoidance manoeuvres for each orbital satellite.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(ACP_{d}\) & \(ACP_{w}\) & \(Flux_{d}\) (/m\({}^{2}\)/a) & \(Flux_{w}\) (/m\({}^{2}\)/a) \\ \hline
540 km/53.2\({}^{\circ}\) & \(1.57\  
after the collision. The distribution of fragments larger than 10 cm is simulated according to the breakup model, as shown in Figures 13 and 14.  
The area-mass ratio can reflect the relationship between the size and quality of debris produced by satellite break up. Through simulation, it is calculated that the number of debris larger than 10 cm after the collision between Starlink satellites and other mission satellites is about 540, and the velocity increment of debris is concentrated in 10-150 m/s. Figure 15 shows the orbital distribution larger than that of 10 cm debris after the collision. Because the orbit of the satellite is a 550 km circle orbit, the orbit height of the breakup debris is mainly concentrated in this area, which has a great impact on the parent satellite and there is a collision threat.  
Figure 14: Breakup fragments’ velocity increment distribution.  
Figure 13: Breakup fragments’ area-mass ratio distribution.
## 3 Simulation Results and Discussion
### Performance Analysis
#### 3.2.3 Link-Budget Test  
Next, the link-budget analysis over the Korean Peninsula was conducted. GNSS main lobe signals were observed starting from a section, which was \(8.47^{\circ}\) away from the nadir due to the blockage of the earth, and were observed up to \(14.54^{\circ}\) due to the limitation of the beam pattern of non-MEO GNSS signals, whereas, in case of MEO GNSS, signals in the \(14^{\circ}\) to \(23.5^{\circ}\) could be observed, as shown in Figure 3.  
The signal strength became weaker moving farther away from the nadir because of the free space loss and the beam pattern of GNSS transmission antenna. Figure 10 shows \(C/N_{0}\) for 24 h in the geostationary orbit. The corresponding minimum, maximum, and average values are summarized in Table 4. The reception strength at \(128^{\circ}\) E longitude in the geostationary orbit is in the range of \(-179.76\) dB to \(-163.74\) dB. For \(C/N_{0}\), it is in the range of \(23.24\) dB-Hz to \(39.26\) dB-Hz. In the opposite of the Korea at \(308^{\circ}\) E, the results of received power are similar. The benefits of received signal power from using multi-GNSS and non-MEO satellites are not particularly apparent. If side lobe signals are used, the area of observed nadir angle is broader, but the corresponding range of \(C/N_{0}\) is lower. This means that when using the main lobe signal only, overall \(C/N_{0}\) is large, but many signals cannot be received from each system. On the other hand, if additional side lobe signals are used, the number of weak signals around 20 dB-Hz increases. In this case, the average \(C/N_{0}\) value is reduced, but the benefit from the GDOP obtained due to the increase in the number of received signals is large, which effects navigation performance. In addition, in areas where a non-MEO satellite signal of BDS can be received, the specific signals of GEO and IGSO are always received with high intensity even if the main lobe is used only. As a result, a certain level of visibility could be successfully satisfied in this area for 24 h.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 6 Discussions
### Influence of Activation Function Selection on Predicted Results  
Common activation functions are Sigmaid (sig), Sin (sin) and hardlim, in the forms of (1) sig function  
\[f(a,b,x)=\frac{1}{1+e^{(-(ax+b))}} \tag{14}\]  
\(\sin\) function  
\[f(a,b,x)=\sin(ax+b) \tag{15}\]  
\(\mathrm{hardlim}\) function  
\[f(a,b,x)=\left\{\begin{array}{cc}1&if\ ax-b\geq 0\\ 0&otherwise\end{array}\right. \tag{16}\]  
Three kinds of functions were used to construct the prediction model, and other parameters were constant. The M-CM-GA-ELM prediction model was used to predict subsidence coefficient. Figure 15 and Table 3 show the prediction results.  
In Figure 15, the box graph with normal distribution is used to describe errors. When the activation function was sig, the error was between \(-0.075\) and \(0.1\). When the activation function was sin, the error was between \(-0.075\) and \(0.125\). When the activation function was hardlim, the error was mainly distributed between \(-0.25\) and \(0.2\). The sig function had the  
Figure 15: Comparison of prediction results of different activation functions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activation Function** & **sig** & **sin** & **Hardlim** \\ \hline \hline \multicolumn{1}{c}{Subsidence} & \multicolumn{1}{c}{MeaRE} & 5.509 & 6.321 & 11.296 \\ \multicolumn{1}{c}{factor} & \multicolumn{1}{c}{RMSE} & 0.050 & 0.059 & 0.110 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy of the prediction model (unit:mm).  
highest accuracy. The same conclusion could be drawn from the probability curve and box graph in Figure 15. At the same time, according to MeaRE and RMSE values corresponding to different activation functions in Table 3, when the activation function was a sig function, the model had the best generalization performance, followed by the sin function, and the hardlim function was the worst. This shows that when the activation function of the M-CM-GA-ELM model is sig, it can achieve better generalization performance during the prediction of subsidence coefficient.
## 2 Materials and Methods
### Subcellular Location Prediction and Validation of DcTPSb1  
Prediction tools such as WoLF PSORT [24] and PLoc-mPlant [25], which exhibit an accuracy exceeding 70%, were employed to predict the subcellular localization of the DcTPSb1 protein. The 1794 bp coding sequence of _DcTPSb1_, as presented in Table S1, did not contain the stop codon, was initially amplified, and then ligated into a modified pc1300 vector, which was regulated by the CaMV 35S promoter. Following this, 4-week-old tobacco plants with good growth status were selected, and the recombinant DcTPSb1-GFP plasmid bacterial solutions were delivered into the lower epidermis of tobacco leaves via injection with the aid of a syringe in 1 mL size without the nozzle. Then, experimental plants were appropriately labeled and incubated for 2 days under low light conditions. Labeled tobacco leaves injected with Agrobacteriumium were then excised, prepared as glass slides, and observed under a laser confocal microscope, with photographic documentation. During this experiment, fluorescence emissions from GFP and Chloroplast were detected using lasers with wavelengths of 488 nm and 640 nm, respectively, within the observation field of the FV1000 microscope (OLYMPUS).
###### Abstract  
The aim of this study was to investigate the possibility of Ag nanoparticle crystallization in B\({}_{2}\)O\({}_{3}\)-Bi\({}_{2}\)O\({}_{3}\) glass using a heat treatment method and to investigate the possible influence of the obtained nanoparticles on the emission intensity of Eu\({}^{3+}\) ions. Borate-bismuth glasses with different B\({}_{2}\)O\({}_{3}\)-Bi\({}_{2}\)O\({}_{3}\) molar ratios of 50:50, 60:40 and 70:50 with Ag and Eu\({}^{3+}\) ions were successfully synthesized. The structure of the glasses was studied using XRD and FTIR methods. The XRD results exhibited a characteristic amorphous halo, confirming the absence of long-range order in the samples. The glass transition temperatures of various compositions, required to select the annealing temperature, were measured using DTA analysis. The strong maximum in the UV-Vis spectrum of the sample with the highest Bi\({}_{2}\)O\({}_{3}\) content clearly indicated the presence of Ag nanoparticles in the glass. Moreover, a color change was observed for this sample, from slightly yellow to red. The presence of Ag nanoparticles was further confirmed via TEM and XPS studies. However, with a high content of Ag nanoparticles in the matrix, their positive effect on luminescence intensity was not observed. The obtained results show that B\({}_{2}\)O\({}_{3}\)-Bi\({}_{2}\)O\({}_{3}\) glass and glass ceramics, with Ag nanoparticles and rare-earth (Re) ions, could be considered as a new phosphor for light-emitting diodes (LEDs).  
borate-bismuth glass; annealing; Ag nanoparticles; plasmon resonance; Eu\({}^{3+}\) ions; luminescence; phosphors +
Footnote †: journal: Applied sciences