esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Physical Properties of the Tablets  
Round, flat disc-shaped tablets were obtained in this study. As powders of Chinese ginseng and _P. sibiricum_ are yellow, and other raw materials are white powders, the color of tablets without Chinese ginseng and _P. sibiricum_ (Control) was white, while tablets supplemented with the two herbs showed pale yellow. Besides the color, weight and thickness of tablets prepared by different formulations were different. As shown in Table 2, the mean weight of the control was 0.82 g per tablet, which was increased with an increase in the content of the two herbs. Except for the H-group (100 mg herbs/tablet), the thickness of the other three tablets showed similar results (Table 2). After adding the tablets to distilled water, the effervescence was mediated by the presence of citric acid and sodium bicarbonate. The disintegration time of the control was short, at about 3.19 min. Interestingly, this parameter was influenced by the presence of the herbs, showing prolonged disintegration significantly with increasing content of the two herbs (\(p<0.05\)). It was 4.48, 6.92, and 9.70 min for the L-group (20 mg herbs/tablet), the M-group (60 mg herbs/tablet), and the H-group, respectively (Table 2). The result indicated that the presence of the two herbs has a slow-releasing effect on the disintegration of effervescent tablets. A disintegration time within 5 min for oral effervescent tablets is considered acceptable in the pharmacopoeia of several countries. The disintegration time depends on the disintegrating solvent, solvent temperature and solvent volume, thus suitable disintegrating conditions should be studied for the M-group and H-group tablets.
# A Stable Backup Routing Protocol for Wireless Ad Hoc Networks
## 2 Stable Backup Routing Scheme (SBR)
### Backup Route Maintenance  
In order to realize local link repairing and switch to backup routes automatically when link breaking occurs, two novel one hop control packets are created in SBR: backup route request (BRREQ) and backup route reply (BRREP). BRREQ contains destinations of the paths going through the broken link. Moreover, inefficient node address is appended into BRREQ to indicate which link needs to be repaired at present. As to BRREP, it is composed of path type, destination and successful rate derived from the backup route table.  
Thus, the route maintenance algorithm in SBR is executed as follows:  
Figure 5: Establishment of (a) IBR, (b) EBR and (c) DBR.
## 6 Conclusions  
A wear prediction model of the high-speed turnout curved switch rail is established in this study. The evolution law of the curved switch rail is analyzed using the wear prediction results. The effect of the wheel and rail wear on the dynamic performance of a vehicle passing through the curved switch rail in a high-speed turnout is investigated. The results show that:  
* When the wheel passes through the area of the curved switch rail with a head width of 3-35 mm, the wheels make simultaneous contact with the stock rail and curved switch rail. As the head width increases, the height of the curved switch rail will exceed the height of the stock rail, and the wheels will travel from the stock rail to the curved switch rail. In the area with the head width of 35-50 mm, the wheel completely transitions to the curved switch rail.
* As the head width of the curved switch rail increases, the position of the maximum wear depth is transferred from the stock rail to the shoulder of the curved switch rail. For the same total passing weight, the side wear of the curved switch rail with a head width of 20 mm suffers the most severe wear. The cross-sectional area of the curved switch rail at the head width of 20 mm is small; the wheels transition from the stock  
Figure 22: Vehicle acceleration under different worn wheels: (**a**) vertical acceleration; (**b**) lateral acceleration.  
rail to the curved switch rail, and the wheel flange is close to the curved switch rail. This causes significant wear in this position.
* The wheel-rail vertical force, lateral force, and derailment coefficient all increase significantly when the vehicle wheels transfer from the stock rail to the curved switch rail. The wear of the wheels and rails has a higher impact on vehicle driving safety and a lower impact on vehicle driving stability.
* In all working conditions, when the standard wheel is matched with a curved switch rail with a total passing weight of 30 Mt, the maximum derailment coefficient reaches 0.79, which is close to the safety limit. It is recommended that for a curved switch rail with a total weight of 30 Mt, the frequency of detection and maintenance of the curved switch rail should be increased. The dynamic performance of the vehicle under other working conditions meets the prescribed limits of each dynamic index.  
Conceptualization, S.Y. and T.J.; methodology, J.Z.; software, S.Y. and T.J.; validation, S.Y. and T.J.; formal analysis, S.Y.; investigation, H.M. and Y.Z.; resources, J.Z.; data curation, S.Y. and H.M.; writing--original draft preparation, S.Y.; writing--review and editing, T.J., H.M. and Y.Z.; visualization, S.Y. and Y.Z.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. and H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China (grant number 51775031) and the R&D Program of Beijing Municipal Education Commission (grant number KM202210016003).  
Not applicable.  
The authors declare no conflict of interest.
## 4 System Architecture and Design Procedures
### RF Energy Harvesting System
The integration of adaptive impedance matching networks ensures that the impedance of the rectifier and the antenna remains optimally aligned across varying RF input conditions. Moreover, reconfigurable metasurface-based antenna designs enhance wavefront manipulation, optimising RF energy absorption and redistribution within the MIMO systems. By integrating beam-forming techniques, RF energy can be dynamically focused toward the energy harvesting antenna at higher power densities, maximising received power while reducing interference. High-gain configurations, such as MIMO arrays, metasurfaces, and fractal geometries, improve RF energy capture and directivity while using low-loss substrates and metamaterials to improve radiation efficiency. A critical component for optimised RF energy harvesting is a high-PCE, low-power rectifier architecture, especially under low RF input power conditions. This study employs such a rectifier to improve power output and efficiency.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 5 Results
### Hyperparameter Sensitivity Analysis  
To assess the influence of the entropy-regularization weight \(\lambda\) and the local learning rate \(\eta\) on model performance, we conducted a two-dimensional grid sweep on CIFAR-10 using our SimpleCNN. We trained for three epochs at each grid point:  
\[\lambda\in\{0,\ 10^{-4},\ 5\times 10^{-4},\ 10^{-3},\ 5\times 10^{-3}\},\quad \eta\in\{10^{-2},\ 5\times 10^{-3},\ 10^{-3},\ 5\times 10^{-4},\ 10^{-4}\}.\]  
Figure 2: Binned (10-segment) validation accuracy for ERFO and the three strongest baselines on _PneumoniaMNIST_. Each marker represents the mean of a ten-round window, so the smoothed endpoint is slightly lower (\(\approx\)88%) than the exact round-100 accuracy in Table 3 (90.3% \(\pm\) 1.20).  
Figure 3 displays the resulting heatmap of the test-set accuracy (in %) across this grid (both axes logarithmic).  
Key observations  
The highest accuracies (\(\approx\)68-70 %) are attained at a learning rate of \(\eta=10^{-4}\); larger values (\(\eta\geq 10^{-3}\)) markedly degrade performance regardless of \(\lambda\). Within the \(\eta=10^{-4}\) regime, moderate entropy regularisation with \(\lambda\in[5\times 10^{-4},\,10^{-3}]\) gives the best results, slightly outperforming the \(\lambda=0\) baseline, whereas a very large coefficient (e.g., \(\lambda=5\times 10^{-3}\)) begins to reduce accuracy. When \(\eta\geq 10^{-3}\), entropy regularisation yields only marginal gains and cannot fully counteract the instability introduced by the high learning rate. These results identify a _sweet spot_ around  
\[(\eta,\lambda)\approx\big{(}1\times 10^{-4},\,5\times 10^{-4}\ -\,1\times 10^{-3} \big{)},\]  
which we adopt as our default setting in all subsequent experiments.  
A finer sweep within \(\eta\in[5\times 10^{-5},\,2\times 10^{-4}]\) and \(\lambda\in[2\times 10^{-4},\,2\times 10^{-3}]\), together with longer training runs, could further refine this optimum and confirm the stability of the observed gains.
# Polyp Generalization via Diversifying Style at Feature-Level Space
## 3 Methodology
### Evaluation Metrics  
We employed various metrics to evaluate and compare our proposed method with state-of-the-art (SOTA) methods. These metrics include mean Intersection over Union (mIoU), Dice coefficient score (Dice), weighted F-measure (FM), structure measure (SM), mean absolute error (MAE) and max enhanced-alignment measure (EM). Among these metrics, Dice and mIoU are similar, as both assess the degree of similarity at the region level and measure consistency within. To compute the Dice, FM and IoU, we utilized 256 pairs of recall and precision values between the predicted mask and the ground truth. Specifically, we transformed the predicted output into a total of 256 binary masks by varying the threshold from 0 to 255.  
\[Dice\quad Coefficient=\frac{2*TP}{2*TP+FP+FN} \tag{16}\]  
\[\text{\emph{Jaccard}}\quad Index=\frac{TP}{TP+FP+FN} \tag{17}\]  
\[\text{\emph{Precision}}=\frac{TP}{TP+FP}Recall=\frac{TP}{TP+FN} \tag{18}\]  
\[\text{\emph{Weighted}}\quad\text{\emph{F-measure}}=\frac{Precision*Recall}{ Precision+Recall} \tag{19}\]\[MAE=\frac{1}{N}\sum_{(x,y)}|P(x,y)-G(x,y)| \tag{20}\]  
The F-measure is employed to evaluate a segmentation model's performance by considering both precision and recall simultaneously, providing a single metric that reflects the model's overall effectiveness. The structure measure (SM) quantifies the structural consistency between the predicted mask and the ground truth [39]. Additionally, the E-measure (maxE) evaluates the output at both the region and pixel level [40]. Similarly, the MAE serves as a pixel-level similarity comparison metric, computing the average absolute per-pixel difference between the predicted mask and the ground truth. In Equation (20), P(x,y) denotes the pixel value of the ground truth, and G(x,y) represents the pixel value location of the predicted polyp mask.
## 3 Results and Discussion
### Influence Factor Analysis for PC Properties
#### 3.2.3 Compressive Strength  
The influence of total porosity on compressive strength is shown in Figure 9. Compressive strength decreases with the increase of total porosity. There is an inverse relationship between compressive strength and total porosity and the linear correlation coefficient between the two is 0.78. The linearity between compressive strength and porosity of PC is consistent with the ordinary concrete reported in reference [36]. The mixture has a maximum compressive strength of 24.4 MPa with 16.4% porosity and a minimum compressive strength of 15.4 MPa with 27.1% porosity. The relationship between compressive strength, designed porosity, and aggregate size is shown in Figure 10. The following conclusions can be obtained from Figure 10: (a) when the aggregate size is the same, compressive strength decreases about 24% as designed porosity changes from 15% to 25%; (b) when the designed porosity is the same, compressive strength decreases about 15% as aggregate size changes from 4.75 mm to 16 mm. There is no obvious relationship between W/B ratio and compressive strength.  
Figure 10: Relationship between compressive strength, designed porosity, and aggregate size.  
Figure 9: Relationship between compressive strength and total porosity.
## 5 Experiment Conclusions
### Experimental Results and Analysis  
#### 5.3.1 Correlation Analysis  
First, we utilized the [PERSON]'s tau (\(\tau\)) correlation coefficient to assess the correlation between DNC and the baseline methods. A high correlation between DNC and the baseline methods indicates that the node importance ranking sequences produced by DNC are very similar to those of the baseline methods. This suggests that DNC may be utilizing similar information as the baseline methods to assess node importance. Figure 2 illustrates the correlation coefficients between the node importance sequences computed by DNC and the baseline methods in the Adjnoun, Celegans, Yeast, and Hamstersterster networks. The correlation results for other networks are presented in Appendix A.  
From the abovementioned correlation matrices of different networks, we can observe that the \(\tau\)-values between the node importance rankings acquired from the CI and DNC methods are consistently high in the majority of networks, generally exceeding 0.7. This indicates a strong similarity between these two sequences. Furthermore, in most networks, the correlation coefficients between KS and LGM with DNC are also relatively high, suggesting a substantial similarity between the node importance rankings produced by DNC and these two methods. A high level of correlation is also observed among the rankings generated by the CI, KS, and LGM methods.  
In summary, the node importance rankings yielded by DNC closely resemble those produced by CI, KS, and LGM. DNC likely utilizes comparable information to these metrics for assessing node importance. The SC method also exhibits a relatively high correlation with DNC in most networks, with a few exceptions. Conversely, the correlation coefficients between DNC, CC, as well as KSGC, are generally low in most networks, indicating significant differences between the node importance rankings obtained by DNC and these two methods; DNC may have incorporated more information. At the same time, the correlations among these metrics are additionally influenced by the network's underlying structure.
# Imaging Liquid Water in a Polymer Electrolyte Fuel Cell with High-Energy X-ray Compton Scattering
The advantages of the CSI technique are (1) non-destructive observation because of using high energy X-rays such as 100 keV or higher, (2) direct observation of cross-sectional images without sample rotation, (3) sensitivity to light elements such as water, and (4) capability of simultaneous measurements with fluorescent X-rays of heavy elements. The CSI technique has advanced using intense high-energy synchrotron X-rays. For the last decade, the synchrotron-based CSI has been applied to lithium-ion behaviors in rechargeable batteries [21; 22; 23]. Recently, its application was extended to fuel cells, and the liquid water distribution in the porous carbon-based materials of GDLs was successfully visualized [24]. In this work, we have performed a feasibility test of the CSI technique for liquid water observation in an operating PEFC. The incident X-ray energy is as high as 115 keV, which can reduce the X-ray absorption of platinum and thus increase Compton scattered X-rays. Measuring Compton-scattered X-ray distributions at current densities from 0 to 900 mA/cm\({}^{2}\), the current density dependency of generated water distributions in the CL, PEM layer, GDL, and channel have been investigated. In addition, simultaneous measurements of fluorescent Pt-K\(\alpha\) X-rays have been carried out. With the Pearson correlation coefficient, the quantitative correlation between generated water and Pt-based catalyst in the cathode CL has been examined. This result indicates that, in a local area, the amount of generated liquid water is proportional to that of Pt-based catalysts at current densities above 500 mA/cm\({}^{2}\).
## 2 Materials and Methods  
### Image Dataset  
We used the training dataset of the Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS2018) [20; 21; 22]. This dataset included MR images for 285 cases of pathologically proven WHO grade II/III or grade IV glioma patients (grades II and III: 75 cases, grade IV: 210 cases). This dataset did not contain detailed information regarding the cancer type of glioma, and only information about the grade was provided. Each case included four sequences of a T1-weighted image, post-contrast T1-weighted images, T2-weighted image, a FLAIR image, and a ground truth. The ground truth comprised three labels (manually revised by neuroradiologists) of the contrast-enhanced region, necrotic and non-enhanced region, and edema region. The region comprising the contrast-enhanced region and necrotic and non-enhanced region was used as the ground truth of the brain tumor region to target only the substantial tumor region. Additionally, the image size was adjusted to 240 \(\times\) 240 \(\times\) 155 voxels with 1 mm\({}^{3}\) per voxel, and parts other than the brain parenchyma, such as the skull, were removed from the images. The details of the dataset are provided in [20; 21; 22].
## 3 Materials and Methods
### Convolutional Neural Network Model Training  
In our research, we used the multi-layer convolutional neural network (CNN) model proposed in this study. The resulting model produces output to predict 7 class categories in skin cancer. This model was built using TensorFlow and Keras libraries, where each layer is designed for optimal results in the feature learning process.  
The proposed CNN architecture consists of multiple stages and layers, as shown in Figure 5. The architecture is divided into several stages and layers. The model is designed to accept 28 \(\times\) 28 \(\times\) 3 RGB input images. A 2D separable convolution layer is used for preprocessing, which reduces the number of parameters compared to standard convolution. This technique separates the process into depthwise and pointwise convolution. Depthwise convolution extracts spatial features independently in each channel, while pointwise convolution combines information between channels to understand the relationship between features. The he_normal initialization is used to speed up the convergence of the model and ensure better weight distribution at the start of training.  
In the initial Conv-1 stage, the model uses 2D Separable Convolution and Rectified Linear Unit with 32 filters and kernel size 3 \(\times\) 3, followed by 2D MaxPooling and Batch Normalization to normalize the data distribution. The Rectified Linear Unit can help the layer learn more complex data. The combination of MaxPooling and Batch Normalization ensures stable gradient updates, preventing internal covariates.  
Figure 4: Sample images of HAM10000 dataset.  
At the Conv-2 stage, the model has an output size of 14 \(\times\) 14 \(\times\) 64. The model uses two 2D separable convolutional layers, each with 64 filters and kernel size 3 \(\times\) 3, followed by 2D MaxPooling and Batch Normalization to improve stability during training.  
At the Conv-3 stage, the model has an output size of 7 \(\times\) 7 \(\times\) 128, with two 2D separable convolutional layers with 128 filters and a 3 \(\times\) 3 kernel, followed by 2D MaxPooling and Batch Normalization. At the Conv-4 stage, the model has an output of 3 \(\times\) 3 \(\times\) 256, with two 2D separable convolutional layers using 256 filters and a 3 \(\times\) 3 kernel. This layer is followed by 2D MaxPooling and Batch Normalization to normalize the data distribution and speed up the convergence process.  
After the convolution layer, the extracted features are flattened by the flatten layer and passed to multiple fully connected layers (dense layers). Regularization is applied using the Dropout layer to reduce the risk of overfitting. The weights in the dense layers are initialized using he_normal, and additional regularization is applied to the last layer before output.
## 2 MSFGWO Algorithm Model
### Hybrid Algorithm
#### 2.3.2 Entropy Weight Method  
The EWM is a widely recognized approach for determining objective weights. It calculates the entropy values of indicators based on the impacts of numerical changes in each indicator on the overall system, thereby assigning appropriate weights to each indicator. In this study, we utilize the EWM method, considering two critical indicators: the DGDOP values of grey wolf individuals at adjacent moments and the observable time of each satellite they represent. This approach allows for optimization of the final satellite selection results. The application of the EWM method not only effectively reduces the frequency of satellite switching between adjacent moments, thereby mitigating issues such as signal interruption and data instability caused by frequent switching, but also extends the continuous observation time of the same group of satellites. This provides more stable and continuous data support for positioning solution calculations, significantly enhancing both the stability and accuracy of these calculations.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 2 Modelling of Stochastic Many-Objective Optimal Power Flow Problem
### Objectives
#### 2.2.4 Constraints  
The efficient functioning of the power grid necessitates the fulfillment of various equality constraints \(\mathbf{g}(\mathbf{x},\mathbf{u})\) and inequality constraints \(\mathbf{h}(\mathbf{x},\mathbf{u})\) to maintain power balance. The power balance, which involves the equilibrium between active and reactive power, is an example of an equality constraint. Specifically, the active and reactive power generated by the generator must align with the corresponding load consumption. Consequently, a collection of nonlinear power flow constraints can be employed to represent the equality constraints \(\mathbf{g}(\mathbf{x},\mathbf{u})\) within the following expression: \(\mathbf{g}(\mathbf{x},\mathbf{u})\):  
\[\begin{split} 0=& P_{\text{G}_{i}}-P_{D_{i}}-V_{i} \sum_{j=1}^{N_{i}}V_{j}\left(G_{ij}\cos\theta_{ij}+B_{ij}\sin\theta_{ij} \right)\\ & 1\leq i\leq N_{0}\\ 0=& Q_{\text{G}_{i}}-Q_{D_{i}}-V_{i}\sum_{j=1}^{N_{i}}V _{j}\left(G_{ij}\sin\theta_{ij}+B_{ij}\cos\theta_{ij}\right)\\ & 1\leq i\leq N_{PQ}\end{split} \tag{8}\]where \(G_{ij},B_{ij}\) are the transfer conductance and transfer susceptance between bus \(i\) and \(j\), and \(N_{0},N_{PQ}\) are the number of buses excluding the slack bus and total number of PQ buses, respectively. In addition to the presence of equality constraints, the inclusion of inequality constraints \(\mathbf{h}(\mathbf{x},\mathbf{u})\) primarily represents the physical limitations imposed on power equipment. These limitations primarily pertain to the active and reactive power output of distributed generators, as well as the terminal voltage of the generator. Furthermore, certain constraints related to power transformers, shunt compensator capacity, and transmission lines are also considered. These constraints are outlined as follows:  
\[\begin{split} P_{G_{i}}^{\min}&\leq P_{G_{i}}\leq P _{G_{i}}^{\max}\quad 1\leq i\leq N_{G}\\ Q_{G_{i}}^{\min}&\leq Q_{G_{i}}\leq Q_{G_{i}}^{ \max}\quad 1\leq i\leq N_{G}\\ V_{G_{i}}^{\min}&\leq V_{G_{i}}\leq V_{G_{i}}^{ \max}\quad 1\leq i\leq N_{G}\\ Q_{C_{i}}^{\min}&\leq Q_{C_{i}}\leq Q_{C_{i}}^{ \max}\quad 1\leq i\leq N_{C}\\ T_{k}^{\min}&\leq T_{k}\leq T_{k}^{\max}\quad 1\leq k \leq N_{T}\\ V_{i}^{\min}&\leq V_{i}\leq V_{i}^{\max}\quad 1\leq i \leq N_{B}\\ |S_{k}|&\leq S_{k}^{\max}\quad 1\leq k\leq N_{E}\\ \end{split} \tag{10}\]  
where \(N_{B},N_{E}\) are the total number of buses and the number of network branches. The parameters utilized in this paper can be referred to in the nomenclature section.