esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Vehicular Networks: An Overview
### Vanets: A Conceptual Framework
A VANET infrastructure can be used to host a variety of applications such as traffic congestion detection and mitigation [29; 30; 31], traffic forecasting [32; 33], fuel-saving vehicle routing [34; 35], or secondary efficiency enabled by internet access, for example, by providing internet during traffic jams [36]. All of these applications prove to be a use case for optimization techniques. Safety-oriented services in a VANET are concerned with vehicular security. Given VANET connectivity, these services can be used to prevent collisions [37; 38; 39], facilitate emergency service response [40; 41; 42], or support safe overtaking [43; 44]. Figure 2 depicts the general operation of a VANET, which includes applications, routing protocols, challenges, communication, and wireless access standards. Above, we discussed the routing protocol and different communications in VANET. In terms of applications, VANETs can provide a wide range of services and applications. The applicability of these services and applications allows us to classify them into safety-related, infotainment, traffic improvement, and driving system monitoring. Additionally, each of these applications presents challenges for VANET. Other challenges could include resource management, in which resources are shared among vehicles, presenting numerous difficulties for VANET deployment. Since vehicles in the VANET have mobile communication devices and share data, data networking is another challenge in this area. Finally, the expansion of VANET services and the need to ensure the continuity and scalability of VANET communication have motivated the use of various types of wireless communications such as DSCR, 4G, 5G, WiMAX, etc. Figure 2: General Operation of VANET.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 3 Optimization Model
### Objective Functions
#### 3.1.1 Lpsp  
_LPSP_ is defined as the probability that the system's output is insufficient to meet the load requirements. The value range is a closed interval of around [0, 1]. The values of 0 and 1 indicate that the load can always be satisfied and the load cannot be satisfied, respectively. Satisfying the load as much as possible is a necessary condition for highly reliable systems. The goal of this work is to always satisfy the load, which requires optimization to achieve a certain level. When the total power generation of the photovoltaic power generation and battery packs still cannot meet the load demand during this period, the time step is recorded as the time of insufficient supply.  
\[LPSP=\frac{\sum_{t=1}^{T}t[(P_{new}+P_{bat})<P_{load}]}{T} \tag{6}\]  
\[P_{new}=\sum N_{pv}\times P_{pv} \tag{7}\]  
\[P_{bat}=\sum N_{bat1}\times P_{bat1}+\sum N_{bat2}\times P_{bat2} \tag{8}\]  
where \(T\) is the total duration of the task, given by the model input. \(P_{new}\) is the total output power of photovoltaic generation, and \(P_{bat}\) is the total output power of the battery pack, and \(P_{pv}\) is the output power of a single photovoltaic panel, and \(P_{bat1}\), \(P_{bat2}\) are the output powers of primary and secondary batteries. It can be seen that for a good mobile photovoltaic-diesel-storage microgrid system, while ensuring that other aspects meet the requirements, the value of \(LPSP\) should be minimized as much as possible to improve the reliability of the system and ensure the completion of the tasks.
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 3 Methods
### Sparse Operation
#### 3.4.2 Submanifold Convolution Operation  
The vast majority of the model's calculation quantity comes from convolution operations. The D-dimensional traditional convolution formula is as follows:  
\[x_{\text{u}}^{\text{out}}=\sum_{i\in\text{V}^{\text{D}}(\text{k})}W_{i}x_{i+u }^{\text{in}},\text{ for }\text{u}\in\text{Z}^{\text{D}} \tag{13}\]  
where k denotes the convolution kernel size, x denotes the feature vector, W denotes the convolution kernel weight, and N\({}^{\text{D}}\) (k) denotes a set of offsets that defines the shape of the convolution kernel.  
However, the proportion of nonzero values in the point cloud data is very small. As a result, it is inefficient to apply conventional convolution to sparse data. For this reason, submanifold convolution based on a sparse tensor is used as the implementation for all convolution layers in the model. Submanifold convolution can reduce the calculation effort by avoiding calculations in the blank space of the point cloud. In addition, the number of valid points is the same for each layer of convolution.  
In the submanifold convolution operation, the nonzero values of the input data are first preserved by indexing and associating features. The convolution output is only calculated when the valid input points are covered by the convolution kernel's center, that is, the input data contain five valid rasters and the output data also contain five valid rasters. Taking the 3 \(\times\) 3 \(\times\) 3 convolution operation as an example, the ordinary convolution performs 27 multiplications. However, in submanifold convolution, the actual number of operations is much lower than 27 because the input features contain a large number of zero elements.
## 3 Proposed Techniques
### Machine Learning  
Based on quadratic programming [41; 42], Figure 3 shows how support vector regression (SVR) transforms the low-dimensional input space to the high-dimensional feature space using a linear cylindrical tube:  
\[Minimize\frac{1}{2}\|w\|^{2}+\frac{C}{2}\sum_{i=1}^{n}({\xi_{i}}^{2}+{\xi_{i}} ^{2}) \tag{8}\]  
\[subject\ to\ w^{T}\phi(x_{i})+b-y_{i}\leq\varepsilon+\xi_{i}, \tag{9}\]  
\[y_{i}-w^{T}\phi(x_{i})-b\leq\varepsilon+\zeta_{i}, \tag{10}\]  
where \(n\) denotes the number of samples, \(w\) is the slope and \(b\) means the intercept. Let us take the derivatives with respect to \(w\), \(b\), \(\xi\), and \(\zeta\) to find the KKT conditions:  
\[w=\sum_{i=1}^{n}(\alpha_{i}-\beta_{i})\phi(x_{i}),\xi_{i}=\alpha_{i}/C,and\ \zeta_{i}=\beta_{i}/C, \tag{11}\]  
where \(\alpha_{i}\geq 0\) and \(\beta_{i}\geq 0\) represents Lagrangian multipliers in Constraints (9) and (10). If we plug Equation (11) back into the primal problem, we also have \(\alpha_{i}\beta_{i}=0\) to derive the dual problem. The details are referred to by [43; 44].  
Figure 2: Multivariate adaptive regression splines (MARS). Dot means data samples ans line is its regression fitting.  
Deep neural network (DNN) consists of the input layer, multiple hidden layers, and the output layer. An error signal defined in Equation (12) needs to be back propagated to adjust the weights between the neurons. When the mean squared error converges, the updating process stops, and the model has been well trained [44,45]:  
\[Error=\frac{1}{n}\sum_{s=1}^{n}\bigl{[}Y_{k}-f\bigl{(}w_{ij},\theta_{i},X_{j} \bigr{)}\bigr{]}^{2},\forall j=1\ldots M,\forall k=1\ldots L, \tag{12}\]  
where \(n\) is the number of training samples, \(M\) is the dimensions of input variables, \(L\) is the dimension of output variables, \(w_{ij}\) means the weights from layer \(i\) to layer \(j\), and \(\theta_{i}\) is the intercept. The universal approximation function, \(f\), needs to be learned to conduct nonlinear fitting. As shown in Figure 4, the error signal can calculate the best fitted weights and intercepts that can minimize forecasting error. In contrast, the forward working signals can conduct forecasting. Based on the chain rules, the optimal weights and intercepts are derived to form a universal approximation that best defines the relationships between the predictors and the outcome [45]. Specifically, hyperparameters, such as the number of hidden layers and associated neurons, drop-out rate, activation function (hyperbolic tangent, sigmoid, and ReLu), and optimizer (stochastic decent, gradient decent, AdaDelta, AdaGrad, and etc.), need to be selected in model training. Thereafter, forecasting can be realized in model testing.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 3 Generator Cooling System Fault  
On MW-class WTs, the temperature of the generator windings is monitored by the temperature sensor. However, the sensor cannot monitor all the spots on the windings. In addition, because the structure of the generator is complicated, it is difficult to obtain an accurate temperature distribution. In [25], a simplified thermal model was used to estimate the temperature, as shown in Figure 7. In this model, the power loss of the stator winding, \(P_{\text{loss}}\), is the heat source, \(R_{\text{th}}\) is the thermal resistance, \(C_{\text{th}}\) is the thermal capacity, \(\theta_{\text{amb}}\) is the ambient temperature, and \(\theta\) is the temperature of the stator winding.  
The \(P_{\text{loss}}\) of the stator winding can be expressed as:  
\[P_{\text{loss}}=I_{\text{s}}^{2}R_{\text{s}} \tag{4}\]  
where \(I_{\text{s}}\) is the current of the stator winding and \(R_{\text{s}}\) is the resistance of the stator winding. The power loss curve is shown in Figure 8.  
According to the steady-state analysis of the thermal model, the following equation can be obtained:  
\[\theta=P_{\text{loss}}R_{\text{th}}+\theta_{\text{amb}} \tag{5}\]  
The steady-state winding temperature depends on \(P_{\text{loss}}\), \(R_{\text{th}}\), and \(\theta_{\text{amb}}\). Assuming \(\theta_{\text{amb}}\) is a constant, \(P_{\text{loss}}\) and \(R_{\text{th}}\) will determine the steady-state winding temperature. Therefore, the temperature rise \(\Delta\theta\) can be expressed as (6):  
\[\Delta\theta=P_{\text{loss}}R_{\text{th}} \tag{6}\]  
When the cooling system is faulty, its cooling capacity decreases. This will lead to a high temperature and a low efficiency of the generator. According to [26], if the coolant in the cooling system does not change, faults can be equivalent to an increase in \(R_{\text{th}}\). To protect the WT, the power  
Figure 6: Distribution of the wind speed of the five-WT wind farm with a 6.5-\(D\) spacing.  
Figure 7: Simplified thermal model of the generator.  
should be limited to the power value that occurs when the faulty generator reaches the temperature rise of the healthy generator at full load.  
The parameters in the thermal model can be estimated from the model and the actual measured data. When the fault occurs, a specific power limit value can be calculated based on the model parameters. Assuming the \(R_{\text{th}}\) is 0.003 K/W in normal conditions, the normal temperature rise at the rated power of the generator is 96 K. Then, we can get the curves displayed in Figure 9. In order to ensure that the temperature rise of the stator winding does not exceed the value of the rated power in the normal condition, the power limits corresponding to \(R_{\text{thA}}\), \(R_{\text{thB}}\), and \(R_{\text{thC}}\) are \(P_{IA}\), \(P_{IB}\), and \(P_{IC}\), respectively.
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 4 Experiments and Results
### Results
#### 4.4.1 Evaluating the Res-DA Architecture  
In speaker identification, the proposed Res-DA architecture achieves a best top-1 accuracy of 92.0% on VoxCeleb1. Compared with Res-SA + CRL, when solely replacing the self-attention with CBAM, the top-1 accuracy is 89.5%, which is 0.4% higher than that with self-attention. However, when employing dual-path attention, the top-1 accuracy increases by 0.9%, compared with the self-attention version and increases by 0.5% compared with the CBAM version. When evaluating on CN-Celeb, the use of dual-attention improves the top-1 accuracy by 1.3%, compared with using SA alone. For speaker verification, the use of dual-attention alone reduces the EER by 0.3% on VoxCeleb1. As shown in Figure 7(a), the EER curve of DA lies basically below that of SA. All of the above proves the effectiveness of our proposed dual-attention on training.  
The CBAM delves into the informative features along the channel and spatial axes. The channel attention module aims to learn \"what\" is important, and the spatial attention module aims to find out \"where\" it is informative. Self-attention computes the attention map in a straightforward way and aims at directly learning the dependencies between a pixel itself and other pixels. These three attention mechanisms obtain attention maps from different aspects. During the training, it was found that the addition of the CBAM greatly increased the training efficiency especially in the initial stage. By combining these two attention modules, more informative features are captured, and we achieve  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Model** & **Description** \\ \hline GMM-UBM [16] & In this system, MFCCs of 13 dimensions are employed with the Cepstral Mean and Variance Normalization (CMVN) as the features. A 1024 component UBM is trained for 10 epochs. \\ \hline i-vectors/PLDA [16; 43] & Both the i-vector/PLDA methods using 400 Gaussian components [16] and 2048 Gaussian components [43] for the GMM-UBM are compared. \\ \hline VGG-M [16] & a CNN architecture proposed by VoxCeleb based on VGG-Medium(VGG-M), with fewer parameters. \\ \hline x-vector [12] & a famous DNN-based method for speaker verification; a PLDA backend is required to achieve better performance [43]. \\ \hline ResNet-34/50 [19] & Reported with the release of VoxCeleb2; a contrastive loss [44] is employed to train the model. \\ \hline Res-SA + CRL [9] & See Table 6 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Different baselines for speaker verification. UBM, Universal Background Model; CRL, Cluster-Range Loss.  
Figure 8: Metric curves for speaker verification. EER, Equal Error Rate; DET, Detection Error Tradeoff.  
better performance than solely employing either of them in most experiments. Furthermore, due to the light-weight nature of the CBAM and the dual-path architecture, such a combination did not bring obvious additional burden on time, compared with the architecture with self-attention alone.
## 3 Nano-Elicitation of Flavonoids
### Carbon-Based Nanomaterials as an Elicitor of Flavonoids  
The most commonly employed carbon nanomaterials include single-walled carbon nanotubes (SWCNTs), multi-walled carbon nanotubes (MWCNTs), fullerenes and chitosan-NPs (Table 5). The discovery of MWCNTs, with their nearly perfect cylindrical structure of seamless graphite, together with the equally remarkable high aspect ratio SWCNTs, has led to an intense interest in these remarkable structures [88]. The in vitro callus culture of _Satureja kluzestanica_ was elicited with different concentrations of MWCNTs (0-500 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro m}\)). At lower concentrations (25 and 50 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro m}\)/mL) of MWCNTs, maximal callus growth and biomass accumulated, while at higher concentrations (100 and 250 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro m}\)/mL), the maximum level of secondary metabolites accumulated. Maximum flavonoids (21.48 mg CE/g DW) accumulated at 100 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro m}\)/mL MWCNT concentration [89]. MWCNTs enhanced the TPC, TFC, rosmarinic acid, antioxidants and PAL activity in seedlings of _S. verticillata_ and _Thymus daenensis_[90, 91]. A group of researchers reported that SWCNTs resulted in a higher production of flavonoids (20.66 mg QE/g DW) in plantlets of _Simmondsia chinensis_ than the control [92].  
Chitosan is a modified biopolymer, derived by the partial deacetylation of chitin, which acts as a defense elicitor [93]. Chitosan has been reported to activate pathogenesis genes and plant metabolism-related genes [94]. Chitosan-NPs increased the accumulation of phenolics and flavonoids by 20% and 24%, respectively, in shoot culture of _Camelia sinensis_[95].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Plant Species**} & \multirow{2}{*}{**Type of In Vitro Culture**} & \multirow{2}{*}{**Growth Media**} & \multirow{2}{*}{**NP Type**} & \multicolumn{2}{c}{**NP Size (nm)**} & \multicolumn{2}{c}{**Effective Concentration of NPs**} & \multirow{2}{*}{**Effect on Flavonoid Production**} & \multirow{2}{*}{**References**} \\ \cline{1-4} \cline{6
# The Noise-Reduction Characteristics of Microstructure of Dragonfly Wing Leading Vein
###### Abstract  
Dragonfly wings have many excellent functions, such as superhydrophobic, fatigue resistance, anti-reflection, etc. However, there are few reports on the low noise flight of dragonfly wings. For this reason, the microgeometry of dragonfly wings was studied in this paper to reveal the mechanism of low-noise flight of dragonfly leading veins. The micromorphology of dragonfly wings was observed by scanning electron microscopy. It was found that the leading-edge veins of dragonfly wings have a triangular prism-like serrated structure, which has been proven to have the effect of improving aeroacoustics. According to the principle of scale law of flying organisms, a bionic model with the leading-edge microstructure of dragonfly's front wing was established, and computational fluid dynamics (CFD) analysis of serration bionic microstructure was carried out. The effects of geometric parameters, such as height, width and overall amplification factor of microstructure on aeroacoustics were obtained. The distribution of pressure fluctuation on the surface of the bionic wing was also analyzed in this paper. It was found that the serrated microstructure can significantly suppress the noise generation in the mid-frequency band. Finally, wind tunnel tests were simulated using a designed low-noise rotating test platform. The test results confirmed that the serration microstructure has certain noise-reduction characteristics.  
noise reduction; microstructure; dragonfly wing; leading edge +
Footnote †: journal: Medical Engineering  
1
## 2 Materials and Methods
### Study Design  
As mentioned before, the study was conducted under nearly identical conditions, utilizing a 3D model and phosphate artificial stones of comparable size deposited in the renal pelvis of the model. The 3D-printed pelvicalyceal system, along with the ureterpolvic junction, was sealed with hot-melt adhesive after placing the stone in the renal pelvis. The volume of the system was 20.5 mL. The average stone size was 471.6 mm\({}^{3}\) (the volume was calculated according to the formula of [PERSON] et al., A \(\times\) B \(\times\) C \(\times\) 0.524 [8]). The average dimensions of the stones were the following: length 15 mm, width 12 mm, and height 5 mm. The mean stone density in non-contrast computed tomography (NCCT) was 1021 HU (620-1383, SD \(\pm\) 150). The size of the equipment used in the study was determined in the French system (the French size = diameters in millimeters multiplied by 3). A flexible ureteroscope (Pusen; PU3022A, 9.2 Fr diameter with maximum insertion portion width 3.2 mm) was inserted through the 10/12 Fr (Flexor; Cook Medical; Bloomington, IN, USA) or 12/14 Fr (ReTrace; Coloplast, France) ureteral access sheath, positioned approximately 1 cm below the model ureteropelvic junction. The ureteral access sheath was connected to the model with a rubber connector. Stone fragmentation was achieved using a holmium: YAG laser (Quanta System Cyber Ho 60W; Samarate, Italy) with a 272 mm laser fiber (Quanta System; Samarate, Italy). Constant gravity-based irrigation was maintained at a height of 60 cm above the model. The fluid flow rate for the 10/12 Fr and 12/14 Fr ureteral access sheath was 9.8 mL/min and 2.7 mL/min, respectively. The operator, with 15 years of experience in endourology, including 8 years in RIRS, controlled the laser power (ranging from 12 to 25 watts), pulse frequency (10 to 15 Hz), and energy (0.8 to 2.5 joules). All procedures were conducted using the virtual basket option. The virtual basket is a specific modulation of laser pulses that produces elongated bubbles that can extend further, thereby forming a connection channel with the stone target. This enhances energy delivery, allowing for the effective and rapid ablation of the stone, while also reducing retropulsion. The median pulse duration was 324.8 \(\upmu\)s \(\pm\) 15.3 \(\upmu\)s. Endourological materials, including the flexible ureteroscope, holmium laser fiber, and ureteral access sheath, were recycled and disinfected for study use. Intrapelvic-temperature measurements were recorded after filling the model with fluid shortly before commencing lithotripsy. The baseline intrapelvic temperature in the model after fluid filling ranged from 23.2 \({}^{\circ}\)C to 23.4 \({}^{\circ}\)C (the temperature of the irrigation fluid was comparable to the ambient temperature).
## 3 Results
### Mechanical Properties
#### 3.1.2 Component Analysis  
XRD analysis was used to determine the mineralogical changes in the original and irradiated rock specimens, as shown in Figure 5. According to the XRD results presented in Figure 5a, the main mineral of the original granite consists of calcium-sodium feldspar, potassium feldspar, quartz and biotite, with the molten temperatures of 1215 \({}^{\circ}\)C, 1290 \({}^{\circ}\)C, 1713 \({}^{\circ}\)C and 1800 \({}^{\circ}\)C, respectively. As shown in Figure 5b, quartz and pyroxene are the main minerals of the molten graphite. The diffraction intensity of molten graphite is considerably reduced compared with that of the original graphite specimen, which indicates that the crystalline material is transformed into an amorphous state due to the high temperature generated by laser irradiation.  
Figure 4: Images of granite with different irradiation parameters under uniaxial compression.  
Figure 5: XRD curve of (**a**) original specimen and (**b**) molten graphite.  
The XRF results listed in Table 3 demonstrate that the main component of the molten graphite is similar to that of the original rock. It is also found from the table that the content of SiO\({}_{2}\) of the irradiated specimen slightly increases by 1.22% (from 68.55% to 69.77%); meanwhile, the content of other compositions is slightly decreased comparing with that of the original specimen.
## 3 Proposed Methodology
### Keypoint Encoder  
Instead of randomly choosing the local points as the input, we use a detector inspired by the D3 Feat detector [28] to find the keypoints with local-max scores in the neighborhood within a radius. We denote a query point cloud from the training dataset as \(Q\in\mathbb{R}^{3}\) and the dense feature matrix \(F\in\mathbb{R}^{d}\), where \(d\) is the dimension of feature. The condition of a point \(p_{i}^{Q}\) to be a keypoint of point cloud \(Q\) is given by:  
\[\left\{\begin{aligned} k&=\operatorname*{ argmax}_{t}F_{tk}^{t}\\ i&=\operatorname*{argmax}_{j\in\underset{p_{i}^{Q}}{ \operatorname*{max}}}F_{jk}^{k}\end{aligned}\right. \tag{1}\]  
where \(F_{ik}\) is the \(k\)-th channels \((k=1,\ \dots,\ d)\) of the \(i\)-th descriptor corresponding with point \(p_{i}^{Q}\) detected by the D3 Feat detector, and \(N_{p_{i}^{Q}}\) is the set of radius neighborhood points of \(p_{i}^{Q}\). The maximum channel is chosen before comparing with the same channel of local neighborhood points. [PERSON] et al. [28] used two local scores: Channel max score and Density-invariant saliency score. More details about these scores can be found in their work [28]. Both of these are used to build the final score map for each input of the 3D point cloud. We select the desired number of keypoints with the highest scores \(s_{i}^{Q}\). Inspired by the SuperGlue [53], we encode each D3 Feat keypoint position with its detection score into a higher dimension feature by using a multi-layer perceptron (MLP). The keypoint encoder  
Figure 2: DFGAT pipeline. The architecture is made up of three major components: keypoint encoder (Section 3.1), descriptor encoder (Section 3.2), GAT (Section 3.3).  
provides a better signature position and keeps spatial information around a keypoint in a form as follows:  
\[pos^{Q}_{i}=MLP_{pos}\Big{(}D3 Feat\Big{(}p^{Q}_{i},\,s^{Q}_{i}\Big{)}\Big{)} \tag{2}\]  
where \(pos^{Q}_{i}\) is the encoded information about position and score of keypoint \(p^{Q}_{i}\).