esa-sceva/satcom-chunk-collection huggingface dataset:
# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 3 The Proposed Mechanism
### Network Deployment and Cluster Division  
Initially, when the nodes are deployed in the network all the nodes broadcast some control packets to share their parameters, such as energies and locations. The control messages are ultimately received at the base station, where major preliminary decisions are taken. The most important task for the base station is to divide the network nodes into clusters. The clusters are made according to the placement pattern of the nodes and consideration of some other important aspects. There are two tradeoffs while dividing the network into clusters for the sake of equalization of node energy consumption. (i) The nodes laying directly with the BS will have a higher load as compared to higher hop level nodes. The intermediate nodes will likely give the highest relaying service to all their backward nodes. (ii) Shorter routes can be obtained by dividing clusters of large sizes. In this case, the cost of intra-cluster communication will be very high, while making numerous small clusters produces reasonable costs of intra-cluster communication. However, in this case, the total relayed traffic is highly increased due to lengthy multi-hopped routes. So it is obvious that in smaller sized clusters the inter-cluster  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**S\#** & **Symbol** & **Description** \\ \hline
36. & \(\overline{U}_{\alpha}\) & Average profit earning of a node \\
37. & \(\overline{U}_{\beta}\) & The average penalty for a node \\
38. & \(x^{*}\) & Stable strategy set \\
39. & \(EVAL\) & Parameters set for evaluation \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._communication cost will be higher. Therefore, dividing the network into clusters cannot be considered as a simple procedure. Theoretically, we used a network consisting of 16 clusters, as given in Figure 1.  
In this work, the cluster formation is based on the hop level and the number of nodes in each dividable region. The cluster sizes were kept moderate and the main objective was to get the optimal efficiency in intra-cluster and inter-cluster communication overhead. Moreover, the concept of logical clustering was used, in which the sizes of clusters can be unequal. There is a possibility of a dissimilar number of nodes at different segments in the network. At some places, the nodes may be densely deployed, while at another place nodes may be scattered when deployed. Therefore, by keeping these points the number of nodes is kept variable for each cluster in the network. Moreover, as the nodes operate in the network they consume their energies and the cluster sizes are also changed with the passage of time.
# Parameter Extraction of Solar Photovoltaic Model Based on Nutracker Optimization Algorithm
## 2 Solar Cell Model
### Optimization of Photovoltaic (PV) Cell Model Parameters  
The optimization of photovoltaic (PV) cell model parameters using metaheuristic algorithms can be understood as a process of optimizing a single objective function. The objective function is typically defined as an error function that measures the discrepancy between the model's predictions and experimental data. The optimization algorithm iteratively searches for the optimal set of parameters that minimize the error function. The resulting solution is a set of optimized parameter values, denoted as \(X_{S}\), \(X_{D}\), and \(X_{T}\)  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Circuit Equations** \\ \hline \(SDM\) & \(I_{L}=I_{ph}-I_{SD1}\Big{[}\exp\Big{(}\frac{q(V_{L}+I_{L}R_{s})}{n_{1}kT}\Big{)} -1\Big{]}-\Big{[}\frac{V_{L}+I_{L}R_{s}}{R_{sh}}\Big{]}\) \\ \(DDM\) & \(I_{L}=I_{ph}-I_{SD1}\Big{[}\exp\Big{(}\frac{q(V_{L}+I_{L}R_{s})}{n_{1}kT}\Big{)} -1\Big{]}-I_{SD2}\Big{[}\exp\Big{(}\frac{q(V_{L}+I_{L}R_{s})}{n_{2}kT}\Big{)} -1\Big{]}-\Big{[}\frac{V_{L}+I_{L}R_{s}}{R_{sh}}\Big{]}\) \\ TDM & \(I_{L}=I_{ph}-I_{SD1}\Big{[}\exp\Big{(}\frac{q(V_{L}+I_{L}R_{s})}{n_{1}kT}\Big{)} -1\Big{]}-I_{SD2}\Big{[}\exp\Big{(}\frac{q(V_{L}+I_{L}R_{s})}{n_{2}kT}\Big{)} -1\Big{]}-I_{SD3}\Big{[}\exp\Big{(}\frac{q(V_{L}+I_{L}R_{s})}{n_{3}kT}\Big{)} -1\Big{]}-\Big{[}\frac{V_{L}+I_{L}R_{s}}{R_{sh}}\Big{]}\) \\ \(PV\) & \(I_{L}=I_{ph}N_{p}-I_{SD}N_{p}\Bigg{[}\exp\Bigg{(}\frac{q\Big{(}V_{L}+\frac{N_{L} I_{L}R_{s}}{N_{p}}\Big{)}}{nkN_{5}T}\Bigg{)}-1\Bigg{]}-\Big{[}\big{(}V_{L}+ \frac{N_{L}I_{L}R_{s}}{N_{p}}\Big{)}/\frac{R_{sh}N_{p}}{N_{p}}\Big{]}\) \\ \hline \end{tabular}
\end{table}
Table 1: Circuit equations of SDM, DDM, TDM.  
Figure 1: Equivalent circuit of (**a**) SDM, (**b**) DDM, (**c**) TDM.  
Finally, the optimized parameter values are validated by comparing the simulated model output to the result of experimental measurements.  
In this paper, the root mean square error (RMSE) is applied as the fitness function for algorithm optimization. The error function is represented by the RMSE shown in Table 2 which quantifies the accuracy and goodness of fit of the proposed current-voltage characteristic model. By experimentally measuring a set of actual current values (\(I_{max}\)) of solar cell modules under equidistant voltages, feasible solutions are obtained through MA optimization and then incorporated into the optimization functions of SDM, DDM, and TDM in Table 3. Consequently, the \(I_{cal}\) value is calculated, and the fitness function value is derived by incorporating it into the RMSE formula.
## 2 Materials and Methods
### Total RNA Extraction and Reverse Transcription  
L6 skeletal muscle cells were retrieved from rats and stored in 2 mL of ice-cold TRIzol (Applied Biosystems, USA). In accordance with the TRIzol manufacturer's guidelines, total RNA was extracted from the cells. Using RNeasy purification columns, RNA was cleaned up and on-column DNAse digestion was carried out. A spectrophotometer (JENWAY Genova Nano) was used to determine the concentration and purity of RNA. Then, using 500 ng of total RNA and RevertAid First Strand cDNA Synthesis, reverse transcription was performed.
## 4 Multimodal Fusion for Infrastructure Inspection
### Applications in Practice
#### 3.4.4 Autonomous and Robotic Systems  
Autonomous vehicles, such as drones (UAVs) and ground robots (UGVs), are being equipped with GPR for remote and mobile surveying. These platforms often run lightweight DL models or quantized versions of CNNs for real-time analysis. One such example involves an edge-deployed YOLOv5 model on a robotic rover detecting buried pipes in real-time at speeds of 0.5 m/s, enabling the autonomous mapping of subsurface utilities during construction [69,70]. AI-GPR applications in field robotics are summarized in Table 5.
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 2 Methodology
### State Space Representation of the System in the Overlapping Decomposition Framework
#### 2.3.3 Clustering the Network of IBDGs Using the Extended State Space Model  
The relative norms of the off-diagonal blocks \(\bar{A}_{ik}\), as compared to the diagonal blocks \(\bar{A}_{i}\) in Equation (11), determine how strong the interconnections among the IBDGs are. This relative norm is defined as the norm of the off-diagonal block divided by the maximum norm of all the diagonal blocks. In this study, the \(\mathcal{H}_{\infty}\) norm is used, which is defined as the maximum singular value of a matrix. The general rule to cluster the network of IBDGs is as follows (the parameter \(\epsilon\) determines the threshold of this clustering process):  
* If \(\frac{\|\bar{A}_{ik}\|_{\infty}}{\max\limits_{j=1,\cdots,n}\|\bar{A}_{j}\|_{ \infty}}>\epsilon\), then the interconnection is considered to be strong, and inverters #\(i\) and #\(k\) are clustered into the same subnetwork.
* If \(\frac{\|\bar{A}_{ik}\|_{\infty}}{\max\limits_{j=1,\cdots,n}\|\bar{A}_{j}\|_{ \infty}}<\epsilon\), then the interconnection is considered to be weak, and inverters #\(i\) and #\(k\) are clustered into different subnetworks.  
Based on the above rule, the entire network of IBDGs can be clustered into several subnetworks, allowing the decentralized robust controllers to be designed sequentially for each subnetwork. It should be noted that the decentralized control design is performed by using the original state space representation in Equation (4). The overlapping decomposition framework and the extended state space model in Equation (10) is only used to cluster the entire network into several subnetworks. In the next section, the proposed sequential design process of decentralized robust controllers for each subnetwork is presented.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 5 Performance Analysis
### Delay Performance with Different Main Frequencies and Transmission Powers  
We choose different transmission powers from [1000 W, 5000 W, 9000 W] and observe the delay with different main frequencies. Figure 10 reveals that computation resources expansion effectively alleviates inter-satellite communication bottlenecks, showing a 13.2% data transmission latency reduction per 300 MHz main frequency increase.  
Figures 9 and 10 demonstrate an inverse correlation between system latency and computing resource allocation under dual constraints of total power and bandwidth. Enhanced computing capability reduces single-node processing latency while intelligent task allocation optimizes workload distribution, collectively lowering system latency when bandwidth remains constant.  
Figure 9: Delay versus main frequency: simulation and ideal delay curves under different bandwidths.
## 4 Discussion  
In this section, we discuss the main findings regarding the 10 June 2021 annular solar eclipse. The main goal is to study the positioning errors of GNSS receivers caused by this solar eclipse. In order to verify our findings, we compare our ionospheric values with results presented for other solar eclipses over the northern polar region (9 March 1997 [4; 22]; 1 August 2008 [10]; and 20 March 2015 [5; 23]).  
There are several free-to-use software available for single-station TEC estimation methods [47; 48]. We selected GPS-TEC software because it is a widely used method by the scientific community to study phenomena such as geomagnetic storms [29] and solar eclipses [49], among others. GPS-TEC software is fundamentally based on the assumption that ionospheric density depends on altitude to determine VTEC from STEC.
# Enhancing the Content of Hesperidin and Nobiletin in Citrus Wines through Multi-Strain Fermentation
## 2 Materials and Methods  
### Yeast Strains and Raw Materials  
_S. cerevisiae_ (Angel yeast) was obtained from Angel Yeast Co., Ltd. (Yichang, Hubei, China) and stored at \(-80\)\({}^{\circ}\)C before usage in our laboratory. Three non-Saccharomyces yeast strains, Ce, Hg, and Ht, were isolated and identified from the natural fermentation of Nanfeng tangerine wine in preliminary laboratory experiments and were stored at \(-80\)\({}^{\circ}\)C.  
Nanfeng (NF) tangerines (Citrus reticulata Blanco) from NF county (N 27\({}^{\circ}\)12\({}^{\prime}\)48\({}^{\prime\prime}\) and E 116\({}^{\circ}\)31\({}^{\prime}\)31\({}^{\prime\prime}\)), Jiangxi Province, China, were harvested at optimal maturity, washed three times with deionized water (Milli-Q Advantage A10 Water System, Millipore, Billerica, MA, USA), blotted dry with sterilized filter paper, and squeezed with a sterilized hand-press juicer (Guangzhou Onoke Food Equipment Co., Ltd., Guangzhou, China) to obtain the juice. A total of 2 L of tangerine juice was poured into a 5 L sterilized mechanical agitation tank fermententer (BIOTECH-5 JG, Shanghai Baoxin Co., Ltd., Shanghai, China) for spontaneous alcoholic fermentation (SF), hermetically closed, and maintained at 26-28 \({}^{\circ}\)C for 5 days. The raw material was obtained from fresh Nanfeng tangerines, which were peeled and juiced. The basic physicochemical parameters of the mandarin juice were as follows: pH of 4.13, soluble solids content of 14.6 Brix, initial sugar content of 130.86 g/L, and total acidity of 8.96 g/L. K\({}_{2}\)S\({}_{2}\)O\({}_{5}\) and sucrose were used to set the levels of SO\({}_{2}\) and total sugar at 50 mg/L and 200 g/L, respectively. The Nanfeng tangerine juice was mixed thoroughly, pasteurized at 95 \({}^{\circ}\)C for 7 min, and then rapidly cooled to room temperature.
###### Abstract  
This study compared the apatite-forming ability (AFA) levels of flowable and putty formulations of Nishika Canal Sealer BG Multi (F-NBG and P-NBG, respectively) and attempted to clarify the cause of differences in the AFA levels of F-NBG and P-NBG. NBG samples were aged in simulated body fluid (SBF) or 1-, 5-, or 10-g/L bovine serum albumin-containing SBF (BSA-SBF) and analyzed in terms of their ultrastructures, elemental compositions, and Raman spectra to identify apatite formation. The phosphate ion consumption rates of NBG samples in the media were evaluated as an indicator of apatite growth. The original elemental composition, calcium ion release, and alkalizing ability levels of F-NBG and P-NBG were also evaluated. Apparent apatite formation was detected on all NBG samples except F-NBG aged in 10-g/L BSA-SBF. P-NBG consumed phosphate ions faster than F-NBG. As-prepared P-NBG showed more silicon elements on its surface than as-prepared F-NBG. P-NBG released more calcium ions than F-NBG, although their alkalizing ability levels did not differ statistically. In conclusion, the AFA of P-NBG was greater than that of F-NBG, probably because of the greater ability of P-NBG to expose silanol groups on the surface and release calcium ions.  
bioactive glass; Nishika Canal Sealer BG Multi; apatite forming ability; albumin; simulated body fluid +
Footnote †: journal: Applied Sci. 2021, 11, 8969  
[[http://doi.org/10.3390/app11198969](http://doi.org/10.3390/app11198969)]([http://doi.org/10.3390/app11198969](http://doi.org/10.3390/app11198969))
# Beam Control in an Intracavity Frequency-Doubling Semiconductor Disk Laser
## 3 Experimental Results and Discussion
It can be seen from Figure 10 that the output power decreases with narrowed D, which accords with our expectations as a reason for more light blocked by this aperture. The other parameter for assessing the performance of the green lasers is the brightness B, which is defined as [21]:  
Figure 1:7  
where P is the output power, \(\lambda\) is the center wavelength. As shown in Figure 10, when D \(>\) 1 mm, the brightness decreases along with the output power dropping. Even though the brightness B error is large at higher power, the biggest B is still below 30 MWcm\({}^{-2}\)sr\({}^{-1}\) and the average values are in the range from 12 to 25 MWcm\({}^{-2}\)sr\({}^{-1}\). When D \(<\) 1.0 mm, the brightness increases to above 30 MWcm\({}^{-2}\)sr\({}^{-1}\) and reaches the maximum of 35 MWcm\({}^{-2}\)sr\({}^{-1}\). Finally, at this aperture size, we achieve a near circular profile, quiet good beam quality and high brightness green beam. Figure 1:8 Dependences of output power and beam brightness on the aperture size D at 25 W input power. Figure 1:9 Dependences of the beam quality M\({}^{2}\) factors of the green laser in x and y directions on the aperture diameter size D at 25 W input power. The inset is the green beam profile at D = 1.5 mm and 0.8 mm, and with the aperture used in this experiment.
###### Abstract  
This study aims to evaluate the fitness, surface microhardness, and trueness of crowns fabricated from three types of dental ceramic blocks (HASS Rosetta, IPS e.max CAD, and VITA Suprinity) and analyze the correlations between them. A crown was first designed in computer-aided design (CAD) software. To create a crown designed model (CDM), the design file was extracted from the CAD software, and a lithium disilicate block was processed from the file with a milling machine. To create a crown scanned model (CSM), the inside of the fabricated crown was digitized using a contact scanner. Using three-dimensional (3D) inspection software (Geomagic Control X; 3D Systems), the CDM and CSM were then superimposed, and their 3D trueness was analyzed. To measure the surface microhardness of the blocks, the specimens were polished and subjected to the Vickers hardness test. The fitness of the fabricated crowns was evaluated by applying a modified silicone replica technique. Pearson correlation analysis was performed to assess the correlations between trueness, surface microhardness, and fitness. In addition, the significance of differences between the three types of dental ceramic blocks was analyzed using one-way analysis of variance (ANOVA). Significant differences in the trueness, surface microhardness, and marginal fit were observed between ceramic blocks of different types. There were also positive correlations between trueness, surface microhardness, marginal fit, and internal fit. While the marginal fit of crowns fabricated from each of the three types of ceramic blocks was in the clinically permitted range (\(<\)120 \(\upmu\)m), there were differences in the trueness and surface microhardness, depending on the type of block. However, crowns fabricated from each of the three materials have surface microhardness that is clinically applicable.  
trueness; marginal and internal fit; surface microhardness; ceramic; dentistry +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
In this section, the relevant data from 2013 to 2020 were used as the training set for the training and establishment of forecasting models. The relevant data from 2021 to 2022 were taken as the validation set for the adjustment and optimization of the model weight parameters and the preliminary evaluation of the modeling effect. The relevant data for 2023 were employed as the independent test set to evaluate the forecasting performances of the established models. Furthermore, the forecasting performances of the models established based on single NTL index and multiple NTL indices were discussed and analyzed, respectively.
## 3 Development of Analytical Crack Breathing Model
This result is juxtaposed with an unchanging horizontal neutral axis and superimposed with the FEA results, which was obtained by using the idea given in Figure 5. The foremost finding of the data is that the neutral axis is only horizontal when the crack is at the starting position or when fully closed and otherwise will shift from the horizontal. For the first half of the rotation, the inclination increases until \(\theta_{1}\) and then decreases until becoming horizontal at \(\theta_{2}\), in the second half the neutral axis remains horizontal until \(2\pi\)-\(\theta_{2}\) then becomes increasingly negative until \(2\pi\)-\(\theta_{1}\) and increases until the end of the period; a negative neutral axis value occurs when the neutral axis slopes below the positive \(\overline{X}\) axis. There is strong agreement between the proposed analytical derivation and the results obtained via FEA, with both sets of data showing that the peak inclination increases when cracks become deeper. The development in Appendix A leads to the understanding that Equations (13)-(18) can be incorporated with non-symmetric bending principles as long as the proposed crack closing angle \(\theta_{1}\) described in Equation (9) is used in lieu of Equation (10). This idea is evidenced in Figure 11 where the area moment of inertia of the total non-cracked cross-section obtained through Appendix A is compared to the iterative procedure [12]. The figure shows the result of substituting the proposed \(\theta_{1}\) value into the Fourier series approximations of Equations (13)-(18) with \(p=6\) for \(I_{\overline{X}}\) and \(p=10\) for \(I_{\overline{Y}}\) and \(I_{\overline{XY}}\). While not shown, the approximate model was found to be sufficiently accurate for all p values from 4 to 16. Additionally, when comparing the results in Appendix A to those from the iterative procedure [12], it is revealed that there is a negligible difference between \(I_{\overline{X}}\) values, a miniscule difference between the product moment of area \(I_{\overline{XY}}\) and a pronounced difference occurs between the prediction of \(I_{\overline{Y}}\) for all the crack depths examined (\(\mu=0.3\), \(0.5\) and \(0.7\), \(1.0\)). In particular, the proposed model produces smaller \(I_{\overline{Y}}\) values than the HNA model when both models are neither fully open nor fully closed and this difference becomes more significant the deeper the crack. Since the proposed model and the HNA model show a divergence in area moment of inertia at deeper cracks, the predicted stiffness changes in the cracked shaft may be sufficiently different between the two models. Therefore, an investigation into the vibration behaviour of a cracked rotor with mid-to-late-stage cracks should be further investigated. Figure 10: Variations in the inclination angle of the neutral axis as the cracked shaft rotates for chosen crack depths of (**a**) \(\mu=0.5\), (**b**) \(\mu=0.7\) and (**c**) \(\mu=1\). \(R=6.35\) mm.
## 2 Materials and Methods
### Feature Selection  
Feature selection is the process of removing irrelevant features to improve the efficiency of the model. We have used SFFS for feature selection, which is an extension of sequential forward selection (SFS), to reduce the initial \(d\)-dimensional feature space into a \(k\)-dimensional feature subspace (\(k<d\)) [25]. Let \(Y=\{y_{1},y_{2},\ldots,y_{d}\}\) be a set of all features and \(X_{k}=\{x_{j}|j=1,2,\ldots,k;x_{j}\in Y\}\), where \(k\in(0,1,2,\ldots,d)\) and \(X_{k}\) is a subset of \(Y\). We start the algorithm with \(X_{\sigma}=\emptyset,k=0\). The steps of SFFS are described as follows:  
* \(x^{+}=\text{argmax }J(X_{k}+x)\), where \(x\in Y-X_{k},J\) is an evaluation index and \(x^{+}\) is the feature with the highest evaluation when it chooses.
* \(X_{k+1}=X_{k}+x^{+}\). The feature with the highest evaluation by selecting is used.
* \(k=k+1\).
* **Step 1 to Step 3** is repeatedly iterating. Then, \(x^{+}\) when \(k\) reaches the specified number which is the set of the most appropriate features obtained.
* SFFS is performed up to **Step 3** of SFS, and a process for searching for features to be deleted is added. At first, **Step 1** to **Step 4** are performed starting from \(X_{0}=\emptyset,k=0\), as in the SFS.
* \(x^{-}=\text{argmax }J(X_{k}-x)\), where \(x\in X_{k}\) and \(x^{-}\) is the feature with the best performance when the feature is deleted.
* If \(J(X_{k}-x)>J(X_{k})\) : \(X_{k-1}=X_{k}-x^{-}\) \(k=k-1\) Go to **Step 1**.  
In **Step 1**, we capture the features that best improve the performance of the feature subset from the feature space. Then, we proceed to **Step 2**. In **Step 2**, remove features only if they improve the performance of the resulting subset. In this study, the Sequential Feature Selector in mlxtend library was used and implemented [26].