esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Based Materials for the Separator Functionalization of Lithium-Ion/Metal/Sulfur Batteries
## 2 Preparation Methods of Graphene-Based Materials
### The Use of Graphene-Based Materials for the Separator of a Lithium-Ion Battery
(**B**) (**i**) Schematic diagram of lithium-ion passing through PVDF-co-HFP/GO separator; (**ii**) SEM image of PVDF-co-HFP/GO separator; (**iii**) Contact angle of commercial Celgard 2325; (**iv**) Rate performances at various C rates of LFP/Li cells with different separators; (**v**) Impedance of different separators; (**vi**) Cyclic performances of LFP/Li cells with different separators at 0.5 C; (**vii**) Cyclic performances of LFP/Li cells with PVDF-co-HFP/GO separator at various temperatures [104]. Reproduced with permission: Copyright 2020, Elsevier. (**C**) (**i**) Schematic of the structural and working mechanism of graphene/PVDF composite separator; SEM images of (**ii**) cross-section view of G/PVDF-PL composite separator and (**iii**) surface of G/PVDF-PL composite separator; (**iv**) Rate capability of the battery composed by different separator; (**v**) Electrochemical impedance spectra of PP, G/PVDF-PL separator; (**vi**) The 600 cyclic performance and coulombic efficiency of the battery composed by G/PVDF-PL composite separator; (**vii**) Cyclic performance of the battery composed by different separator at 5 C [105]. Reproduced with permission: Copyright 2017, New Carbon Materials. The introduction of a graphene composite interlayer offers more options for separator design. [PERSON] et al. mixed GO onto a poly (vinylidene fluoride-co-hexafluoropropylene) PVDF-co-HFP separator and prepared a PVDF-co-HFP/graphene oxide composite membrane with only 0.1% GO content. The oxygen-containing functional group of the GO particles absorbed the liquid electrolyte into the membrane, creating excellent wettability with the electrolyte. The suitable pore structure and electrolyte wettability provided a large number of Li\({}^{+}\) transfer channels uniformly distributed in the separator. At 0.5 C, the battery assembled with the PVDF-co-HFP/GO separator exhibited a discharge capacity of 160 mAh g\({}^{-1}\), with only a 5.0% decrease in capacity after 1200 cycles (Figure 5B) [104]. [PERSON] et al. obtained a graphene/polyvinylidene fluoride (G/PVDF-PL) functional layer via coating and post-treatment of the commercial polypropylene separator. Graphene was supported in the coating layer as a skeleton, which is conducive to the absorption and retention of the electrolyte. The membrane with the porous structure on the surface had good wettability with the electrolyte. The liquid absorption rate of the designed composite separator with the G/PVDF-PL functional layer was 326.67%, which was three times higher than that of the polypropylene separator in comparison. The initial capacity of the battery with the G/PVDF-PL separator was 111.3 mAh g\({}^{-1}\) at 5 C, without an obvious decrease after 600 cycles (Figure 5C) [105]. The above relevant literature information is summarized in Table 1.
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 4 Performance Analysis
### Simulation Conditions
#### 4.1.3 Latency  
Figure 10 shows the latency, which is defined as the average duration from the moment a packet is generated to the moment the packet is successfully delivered. For higher traffic loads, in general, the latency increases as collisions become more frequent, and the queueing delay until the channel is free also increases. Moreover, the latencies of the schemes with higher network throughputs (proposed scheme and [25]) are obviously lower because a higher network throughput also implies shorter queueing delays and fewer collisions. As expected, the proposed scheme achieves better latency as a consequence of optimizing the NAV.  
Figure 10: Latency.  
Figure 9: Network throughput.
## 2 Structural Complexity and Composition of Native Lignin
### Addressing Scalability Challenges in Lignin Valorization: Strategies and Future Directions
Furthermore, most existing kinetic models are primarily focused on cellulose fractionation and fail to adequately capture the multiscale chemical transformations associated with lignin, such as simultaneous delignification and de/repolymerization. This modeling gap becomes particularly limiting in scale-up scenarios, where predictive tools are essential for process optimization and control. In this context, multiscale modeling approaches are emerging as critical tools to guide the design and intensification of lignin extraction processes. Recent work has demonstrated the integration of ordinary differential equations (ODEs) with kinetic Monte Carlo (kMC) algorithms to simulate the coupled reaction kinetics across multiple temporal and spatial scales. Such models can predict key process variables, including residual lignin content in biomass chips and the molecular weight distribution of extracted lignin, with validation against experimental data. To facilitate real-time application, a reduced-order model (ROM) has also been developed for soft sensor design and incorporated into a model predictive control (MPC) framework to dynamically optimize operational strategies. These modeling tools not only enhance mechanistic understanding but also provide a robust platform to support process scale-up by improving selectivity, efficiency, and economic feasibility [76]. These tools can guide decision-making by identifying economic thresholds, environmental trade-offs, and optimal deployment scenarios. For example, recent LCA studies have demonstrated that the use of lignin in carbon fiber production can reduce CO\({}_{2}\) emissions by up to 40% compared to conventional polyacrylonitrile-based fibers, provided that energy-efficient processing and high-purity lignin inputs are employed. To unlock the full potential of lignin in a sustainable bioeconomy, it is imperative that future research efforts prioritize the development of modular, scalable, and cost-effective processing frameworks. Public-private partnerships, industry-academia consortia, and supportive policy measures such as subsidies for green materials, incentives for waste valorization, and harmonized quality standards for lignin products are equally essential to facilitate market penetration and investor confidence. By explicitly addressing the scalability dimension, lignin valorization can move from being a promising laboratory concept to a commercially viable pillar of the circular economy.
## 2 Literature Review
### Other Commonly Used Prediction Methods  
The AdaBoost-CNN method combines a number of base estimators through a weighted linear combination [30]. Unlike typical sequential base forecasting methods, such as the Long Short Term Memory model and Recurrent Neural Network, the AdaBoost-CNN method uses the convolutional neural network to extract crash characteristics. In the training process, weights that are assigned to samples will be updated after each round according to prediction accuracy [31]. A less representative sample, if it is related to crash prediction, will be assigned greater weight. Therefore, AdaBoost-CNN can deal with imbalanced data to some extent. However, since the CNN network cannot integrate time information, while crash occurrences are closely related to time stamp, the effectiveness in crash prediction is subject to specific cases.  
To increase representative crash samples, the Synthetic Minority Over-sampling Technique (SMOTE) is also widely used to reduce the impact of data imbalance. However, the generated data are highly correlated to the existing sample and cannot contain time information, as well.  
The newly initiated graph neural network has one generalized version that contains a spatial-temporal block. The spatial-temporal block consists of temporal gate convolution, spatial gate graph convolution, and a temporal attention mechanism. It can learn both the local and global features of a region's data. Unlike CNN, in the graph neural network process graph with directional arrows, the arrows can model the graph of the road network to construct the macro graph of regions. However, for our specific study, we focus on a bridge section of the G15 freeway that cannot include macro features of road maps.
## 3 Methodology
### Spatial Encoder and Multi-Scale Features  
In addition to RGB images, fine-grained details such as facial expressions, lips, and hand shapes are essential for sign language recognition. We leverage the inherent properties  
Figure 2: Overall model architecture. Our proposed MSF-ET model consists of three main components: spatial encoder, feature fusion module, and temporal encoder. The spatial encoder is composed of multiple 2D convolutional layers, followed by max-pooling to downsample the feature maps with different receptive fields. The feature fusion module uses a self-attention mechanism to fuse the multi-scaled features of the frames. The temporal encoder is composed of the encoder based on Euclidean distance self-attention model and local Transformer layer. The encoder learns the contextual information of the video and the local features for glosses alignment. Finally, connectionist temporal classification (CTC) is used to train the model and decode the gloss sequences.  
of convolutional neural networks (CNNs), which are particularly adept at local perception within images. As layers are stacked in a CNN, the receptive field progressively enlarges, which enables the network to transition from capturing minute details to extracting broader semantic features. We harness this capability by extracting visual features at various receptive field sizes, thus obtaining a range of granularities--from fine-grained details such as facial expressions and hand shapes to more coarse-grained features like overall body posture and gesture dynamics. This fusion of multi-scaled features ensures that our model captures both detailed and global visual information, essential for the nuanced interpretation required in cSLR.  
We design stacked convolutional basic modules as a spatial encoder (SE) to extract features from frames of sign language videos. Each basic module comprises two convolutional layers, batch normalization, and rectified linear unit (ReLU) activation function, as shown in Figure 2. The spatial encoder's last two basic modules yield two distinct feature maps with dimensions of \(7\times 7\) and \(3\times 3\), which are considered fine-grained (FG) and coarse-grained (CG) features, respectively. A larger feature map dimension corresponds to a narrower receptive field, incorporating a higher degree of detail-oriented features. Conversely, smaller dimensions encompass more abstract semantic information. Upon computation, the receptive fields for fine-grained and coarse-grained features are determined to be 31 and 67, respectively. We then propose a self-attention-based approach for fusing these features, leveraging the self-attention mechanism's ability to capture dependencies across different scales.
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 3 Simulation and Analysis  
### Environment  
To verify the effectiveness of the proposed algorithm, the software Optisystem 15 is used to build a coherent optical communication system, and MATLAB components are used to realize DSP, as shown in Figure 4.  
The system adopts a square root raised cosine (SRRC) pulse signal generator at the transmitter and SRRC filter at the receiver for matched filtering. The fiber link is composed of optical fibers, optical amplifiers (OA), an optical filter, and a loop controller, in which the span length is 100 km, the noise figure is 6 dB and the gain compensation is 20 dB. ADC samples the sequence at twice the symbol rate before sending the sequence to the MATLAB component to complete DSP. Table 1 is the system parameter setting.
## 4 Prediction Based on GA-PSO-BP Neural Network
### GA-PSO-BP Model Accuracy Analysis  
Comparison of the error results of the three prediction indexes (minimum wall thickness, maximum wall thickness, and maximum expansion height) based on the GA-PSO-BP neural network and the network performance evaluation results is shown in  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Training samples & 59 & Input nodes & 3 \\ Test samples & 11 & Hidden layer nodes & 7 \\ Learning rate & 0.01 & Output nodes & 3 \\ Training steps & 1000 & Stock size & 5 \\ Training function & trainlm & Stock iterations & 30 \\ Hidden layer excitation function & tan-sigmoid & Learning factor \(c_{1}\) & 4.494 \\ Output layer excitation function & purelin & Learning factor \(c_{2}\) & 4.494 \\ \hline \end{tabular}
\end{table}
Table 6: PSO-BP neural network prediction performance test.  
Figure 8: Flow chart of GA-PSO-BP algorithm.  
Figures 9 and 10. Cross-validation was not utilised in the model construction for this research. The model accuracy was assessed through an evaluation of prediction errors across multiple forming quality metrics.  
Figure 9: Comparison of RMSE results between training set and test set: (**a**) min. wall thickness, (**b**) max. wall thickness, (**c**) max. forming thickness.  
Figure 10: (**a**) Performance of training process network, (**b**) optimal iteration results, (**c**) distribution of forecast results and coefficient of determination \(R^{2}\).  
As shown in Figures 9 and 10, the optimisation error is very small: the BP neural network was stopped for a total of 20 iterations and reached the optimal validation result at the 17 th iteration with an MSE of 0.0010366. These errors indicate the accuracy of the proposed method in determining the optimal solution. An accurate prediction of tube hydroforming parameters is provided by the THF model based on the GA-PSO-BP neural network, which provides an effective tool for optimising tube hydroforming process parameters in the shortest possible time. Figure 10c shows a plot of the predicted values of the neural network versus the actual values of the output patterns of the design matrix through the regression of training, validation, and test data. The coefficients of determination \(R^{2}\) for the training and test sets are 0.99786 and 0.98228. The artificial neural network-based THF model provides accurate predictions of the aluminium alloy variable diameter tubes, forming parameters with 98% confidence level. Therefore, there is a good correlation between measured and predicted values. Based on neural network analysis, axial feeds and forming pressure have a greater effect on tube thinning and the accuracy of dimensions.
## 3 Finite Element Model of Ballastless Track-Bridge Structural System
### Analysed Results of Fatigue Performance  
#### 4.3.1 Fatigue deformation  
The mid-span deflection of the beam under fatigue loading is shown in Figure 11. The figure shows that the deflection at the beam span increases gradually with the number of loading cycles. The increase was significant in the first fatigue stage when the fatigue life ratio is approximately \(n/N=10\%\). Then, the trend shows a steady and slow increase, from 6.129 mm at the initial non-damage stage to 6.383 mm at the failure stage, with a total increase of 4.144%. The stiffness of the structural system evidently degrades gradually with the increase in the fatigue loading cycle, although the degradation is not significant.  
The rail deflection changes slightly with the increase in the fatigue loading cycle: the initial undamaged rail deflection is 8.834 mm. At the structural system failure stage, the rail deflection increases by 2.898% reaching 9.090 mm. The impact on the track irregularity is minimal, as shown in Figure 12. Figure 13 shows that the mid-span load-deflection of the beam under fatigue load in the numerical simulation is substantially linear, and the change rate is similar to the deflection-fatigue ratio law described in Figure 11: At the initial fatigue loading stage, the slope of the response curve decreases rapidly with the increase in the fatigue loading cycle, and starts to decrease at a steady rate after the fatigue life ratio \(n/N=10\%\), until the structural system fails. The stiffness fatigue degradation of the beam is not apparent. The stiffness fatigue degradation of the track structure has a small effect on the overall stiffness of the structure, since the overall stiffness of the structural system is controlled mainly by the stiffness of the beam.
## 4 Energy Efficient Protocols, and Scheduling in WBSNs
### Minimizing Scheduling Conflicts  
The reduction of energy consumed for communications between sensors is treated in [48]. A scheduling technique for sensor nodes is introduced in order to allocate each node in a specific timeslot. Authors suggest spreading each round (period of time) on some slots. Knowing that a slot is the amount of time needed by a sensor to receive data from another sensor, they propose to wake up a sensor at specific times. Pairs of sensors will become active in order to perform at a specific time slot the communication of data. At the end of such time slot, a new pair of sensors will communicate while the previous one will go into sleeping mode until all the sensors have used a time slot which is called a round. At the end of each round, a relay sensor will be able to aggregate the data collected for its transmission to the base station. Since at every time slot only two sensors could communicate, collisions are avoided. The scheduling is adapted in order to cope with network changes due to sensors leaving or joining, so the authors concluded that the adaptive scheduling will allow decreasing energy consumption.  
Inefficient use of battery because of retransmissions, data loss, high latency and excessive traffic data are some of the issues that we could face if scheduling conflicts for sensor transmissions are not resolved. An efficient scheduling design brings a very important reduction in power consumption of each node. An intelligent scheduling algorithm must be applied to allow the maximization of efficiency in performance, acceptable packet delay levels, and minimum power consumption. An example would be to form a characteristic vector for each sensor [49], as:  
\[\{\text{I, RB(t), RE(t), RRi}\} \tag{1}\]  
where I is the Indicator function (Abnormal Data); RB(t) is the Buffer space remaining; RE(t) represents the Energy remaining and RRi is the Importance of the physiological parameter. With the values of each of these variables, the vector is compared among sensors that are struggling to transmit at the same time. Comparison starts by the RRi variable, the second variable would be \"I\", as it may indicate an abnormal vital sign measurement on patients and is considered of high importance, followed by RB(t) to avoid loss of information due to lack of space in the buffer, followed by RE(t). The above terms are compared until a sensor with higher priority is identified and its transmission is allowed over the next period. Another way to find a solution to the problem is to implement a scheduling algorithm, which integrates a fuzzy logic system in each sensor to deal with multiple input variables that allows each node to decide to transmit or not in the next time slot [20].
## 5 Experiments and Results obtained
### Analysis of Results  
RQ1: For our first RQ, we can answer in this way: Deep Learning methods allow us to have a greater number of features, filters, and parameters that can yield the best and most accurate results. By using Convolutional Neural Networks, we can have a variety of parameters that helps us to improve the accuracy, increasing the number of hidden layers, using different types of optimizers, and varying the learning rate all the factors contributes to the accuracy.  
RQ2: The parameters have a great impact on the accuracy. We conducted eight experiments. We have run the two algorithms discussed above four times each.
## 3 Experimental Setup
### Finite Element Modeling (FEM)
\[\eta=E_{max}/E_{max} \tag{6}\]  
while \(E_{max}\) represents the maximum electric field and \(E_{mean}\) can be calculated for each electrode configuration as Equation (7) below:  
Figure 3:0  
Here \(v\) represents the applied voltage, and \(d\) represents the air gap length between the electrodes. To calculate the utilization factor \(\eta\), a voltage of 10 v is applied to the high-voltage electrode and the ground electrode is assigned 0 volts. The gap distance is maintained at 6 mm for both configurations. The non-uniform electric field contributes to a higher \(E_{max}\) value, and hence lower breakdown voltages. Theoretically, a higher field utilization factor increases the breakdown voltage value. The simulation results presented in this work have field utilization factors for slightly uniform (sphere-plane) and uniform (plane-plane) configurations, which are 0.74 and 0.95, respectively, proving the electric field uniformity of plane electrodes, as shown in Figure 3:1  
Figure 3:2 Electric field intensity of plane–plane electrode configuration (uniform field). The electrode configuration proposed has a uniform and quasi-uniform electric field; this transition can be analyzed through field inhomogeneity factor \(\alpha\) calculation using Equation (8). Figure 3:3  
The electric field inhomogeneity factor provides a quantitative measure of how uniform the electric field is within a specified region or across specific boundaries. This factor quantifies how much the electric field deviates from uniformity. If \(\alpha\) is close to zero, it indicates that the electric field is nearly uniform. Conversely, as \(\alpha\) increases, it signifies a more significant deviation from uniformity, indicating a more inhomogeneous electric field.