esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 3 Materials and Methods
### Preliminaries  
DNNs typically consist of two primary components: the feature extractor, denoted as F, which processes input images and extracts their distinctive features, and the classifier, denoted as G, which generates classification probabilities based on the extracted image features provided by F. In the context of a K-class classification task, a dataset with noisy labels can be represented as \(D=\{(x_{i},\widetilde{y}_{i})\}_{i=1}^{N}\), where \(N\) denotes the total number of samples in the dataset, \(x_{i}\) represents the \(i\)-th sample, \(\widetilde{y}_{i}\in[\mathcal{C}]\) signifies the noisy labels, and \(\mathcal{C}\) denotes the total number of classes. The feature extracted from the \(i\)-th sample by F is represented as \(f_{i}=F(x_{i})\), and the predicted labels for the \(i\)-th sample are denoted as \(p_{i}=G(f_{i})\).  
Promoting the model to correct noise labels requires a direct strategy to minimize the cross-entropy loss between ground truth labels and prediction labels during the training phase. Specifically, for a sample, \((x_{i},\widetilde{y}_{i})\) and its corresponding prediction, \(p_{i}\), the classifier, \(f\), is updated throughout training iterations by minimizing the cross-entropy loss between \(\widetilde{y}_{i}\) and \(p_{i}\). This procedure can be described as follows :  
\[L_{CE}=-\frac{1}{n}\sum_{i=1}^{n}\sum_{j=1}^{\mathcal{C}}\widetilde{y}_{ij} \log p_{ij} \tag{1}\]  
In this context, \(n\) denotes the batch size, signifying the number of samples participating in the training process. Additionally, \(C\) symbolizes the total number of classes, with \(\widetilde{y}_{ij}\) representing the individual element of the label \(\widetilde{y}_{i}\) for the \(i\)-th sample within the \(j\)-th class, and \(p_{ij}\) representing the corresponding element of the predicted label \(p_{i}\) for the \(i\)-th sample within the \(j\)-th class.
# N-Trans: Parallel Detection Algorithm for DGA Domain Names
## 4 Experiment and Analysis
### Experimental Results and Analysis
#### 4.3.4 Network Model Comparison Experiment  
The Naive Bayesian algorithm, XGBoost algorithm, RNN model, LSTM model, Bi-LSTM model, and the parallel detection model proposed in this paper were selected for comparison. The experimental results are shown in Table 5.  
Machine learning algorithms, such as the Naive Bayesian algorithm and XGBoost algorithm, require human selection of features and the process takes a lot of time. The features extracted with bigram alone cannot accurately distinguish legitimate domain  
Figure 6: Observe the trends in _Acc_ and _recall_ by changing the number of heads in Transformer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Training Time/s** & _Acc_ & _Recall_ & _Precision_ & _F1-Score_ \\ \hline Naive Bayesian & 23.2281 & 0.8000 & 0.7276 & 0.8976 & 0.8037 \\ XGBoost & 27.3884 & 0.9055 & 0.8666 & 0.9421 & 0.9028 \\ RNN & 327.4625 & 0.9152 & 0.9208 & 0.9105 & 0.9156 \\ LSTM & 353.3754 & 0.9252 & 0.9574 & 0.8996 & 0.9276 \\ Bi-LSTM & 370.1486 & 0.9395 & 0.9074 & 0.9695 & 0.9374 \\
**Parallel detection** & 77.4251 & **0.9697** & **0.9707** & **0.9748** & **0.9727** \\
**model with \(N=2\),3,4** & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of model training algorithms. The parameters in the experiments are set as follows: the Naive Bayes algorithm and XGBoost algorithm both use features extracted by bigram for processing. The output dimension of the RNN hidden layer was 64, and the output dimension of the Relu activation function was 32; the output dimension of the LSTM hidden layer was 64; the loss rate of the dropout layer was 0.1, and the rest of parameters in the network are default values.  
names from malicious ones. After the word embedding process, RNN, LSTM, and Bi-LSTM improve the accuracy and _recall_ compared to machine learning algorithms and focus more on the features of character frequency provided by word embedding. However, they lack the learning of similarity between phrase elements compared to the Transformer model, which introduces a multi-head attention mechanism. As can be seen from the _precision_, the N-Trans model predicts fewer errors in the results for malicious domains. In terms of the speed of feature learning, the Transformer model significantly improves the speed of training compared to the model that relies on the sequential relationship between elements due to its ability to operate in parallel. Combined with the F1 score, the N-Trans model can better distinguish malicious domain names from legitimate domain names compared with other network models.  
Since the parallel detection model can effectively extract the location information and multi-text features of domain characters, and the model can capture more features among word combinations, it achieves better results in the malicious domain names classification problem.
## 2 Materials and Methods
### Theory of the Lorenz Chaotic System  
A Lorenz chaotic system is considered in the proposed system [25], which is presented in Equation (1).  
\[S_{\text{cs}}=\left\{\begin{array}{l}\dot{x}_{1}=\alpha(x_{2}-x_{1})\\ \dot{x}_{2}=\beta x_{1}-x_{1}x_{3}-x_{2}\\ \dot{x}_{n}=x_{1}x_{2}-\gamma x_{3}\end{array}\right\}, \tag{1}\]  
where \(x\) represents the initial value of the system and can be used as the input value to be tested. \(\alpha\), \(\beta\), and \(\gamma\) represent adjustment coefficients.  
The three-phase voltage waveforms shown in Figures 3 and 4 are measured with voltage sensors. At the same time, the sampling time is 5 us and 10,000 data have been obtained within 0.05 s and then input into the Lorenz chaotic system in Equation (1). The adjustment coefficients \(\alpha\), \(\beta\), and \(\gamma\) in Equation (1) are set to 10, 28, and 8/3, respectively. Through the aforementioned Lorenz chaotic system, \(\dot{x}_{1}\), \(\dot{x}_{2}\), and \(\dot{x}_{3}\) can be obtained. In the proposed system, \(\dot{x}_{1}\) and \(\dot{x}_{2}\) are used to generate a chaotic states scatter diagram, as illustrated in Figure 5, with the two centroid points in the scatter diagram serving as chaos eyes. The coordinates of the chaos eyes can serve as feature values for inverter fault diagnosis.  
For the conditions described in Figures 3 and 4, the corresponding waveforms can be converted through the Lorenz chaotic system to yield the chaotic scatter states diagrams displayed in Figures 6 and 7, respectively. The chaos eye coordinates in Figure 6 are \(C_{1}\) (\(-\)0.0462, \(-\)6.0298) and \(C_{2}\) (0.0464, \(-\)7.0853), whereas those in Figure 7 are \(C_{1}\) (\(-\)0.0493, \(-\)39.9884) and \(C_{2}\) (0.0456, \(-\)42.5835). After the occurrence of a fault, the Y-axis coordinates of the chaos eye are considerably different from those (\(v_{ao}\)) observed before the fault; thus, \(S_{a1}\)\({}^{+}\) can be suspected to be faulty. The proposed system can consider values of voltage waveforms of equal frequencies as the input for fault detection. After the use of the Lorenz chaotic system to convert the waveforms, the chaos eye coordinates (\(C_{1}\) and \(C_{2}\)) in the resulting chaotic states scatter diagrams can be considered as the fault characteristic values. However, because of the failure of other transistors, chaos eye coordinates of \(v_{ao}\) observed before the occurrence of a fault might differ from those observed after the occurrence of the fault. The chaos eye coordinates are not fixed values; instead, they are distributed in a range. Therefore, the extension method is used for fault diagnosis to locate a fault in transistor switches.  
Figure 5: Chaotic states scatter diagram and corresponding chaos eyes.
## 3 Identification and Quantification of Volatile and Semi-Volatile Compounds in Sparkling Wines
### Sample Preparation
#### 3.1.3 Solid-Phase Microextraction (SPME)  
Solid-phase microextraction (SPME) has emerged as one of the most used techniques for the extraction and pre-concentration of volatile and semi-volatile compounds in complex matrices such as sparkling wine. Developed in the 1990s by [PERSON], SPME revolutionized sample preparation by offering a rapid, solvent-free, and efficient method that integrates the sampling, isolation, and enrichment of analytes in a single step [76; 77].  
The principle of SPME is based on the partitioning equilibrium between analytes in the sample matrix and a stationary phase coated onto a fused-silica fiber. These coatings are typically composed of polymers or carbon-based adsorbents, such as polydimethylsiloxane (PDMS), divinylbenzene (DVB), or Carboxen (CAR), and can significantly influence extraction efficiency and selectivity [42; 61; 78]. A comparative analysis of commercial Proseco wines showed that PDMS/DVB was used for sulfur-containing compounds, while DVB/CAR/PDMS was more effective for extracting terpenoids and norisoprenoids, underscoring the importance of selecting the appropriate fiber to ensure optimal compound extraction [71].  
SPME can be employed in two main modes, namely, direct immersion (DI-SPME) and headspace (HS-SPME). In DI-SPME, the fiber is directly immersed in the liquid sample, allowing analytes to diffuse into the coating. Conversely, in HS-SPME, the coated fiber is exposed to the headspace above the sample, where analytes partition between the sample, headspace, and fiber coating. Since the fiber does not make contact with the liquid matrix directly in HS-SPME, the risk of matrix interferences is reduced, and the lifetime of the fiber is significantly prolonged [79]. In the context of sparkling wines, HS-SPME has proven to be a powerful tool for profiling aroma-active compounds such as esters, alcohols, ketones, terpen, norisoprenoids, and sulfur-containing volatiles, and its application is well-documented in the literature [2; 6; 15; 29; 41; 42; 44; 47; 49; 61; 65; 80; 81; 82; 83; 84; 85; 86; 87].  
Despite its many advantages, factors such as temperature, extraction time, and matrix composition can affect HS-SPME efficiency, particularly for low-volatile compounds. [PERSON] et al. [88] and [PERSON] et al. [89] demonstrated how matrix effects can impact quantification accuracy and highlighted the importance of calibration strategies and appropriate internal standards [88; 89].  
Nevertheless, SPME still remains one of the most preferred techniques for the extraction of volatile compounds, due to its simplicity and speed and the fact that it does not require extensive sample preparation. This was demonstrated by [PERSON], [PERSON] et al. [90], who showed it to be a practical alternative to more complex techniques like simultaneous distillation or closed-loop stripping, making it ideal for routine analysis of sparkling wine volatile compounds [90].
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 3 Imaging Processing of AA-BiSAR with One-Moving Transmitter
### Signal Model
\begin{array}{ll}\begin{cases}R_{\mathrm{tr}}(t_{a})=&R(\theta_{r})+R(t_{a} )\\ R(\theta_{r})=&\rho-R_{r}\cos\beta_{r}\cos(\theta_{r}-\theta_{n})+\sqrt{{R_{0} }^{2}+2R_{t}R_{n}(1-\cos(\theta_{n}-\theta_{t}))}\\ R(t_{a})=&-\Big{(}\frac{R_{n}\cos\theta_{n}-R_{t}\cos\theta_{t}}{R_{0}}\Big{)} vt_{a}+\Big{[}\frac{1}{2R_{0}}-\frac{(R_{n}\cos\theta_{n}-R_{t}\cos\theta_{t})^{2}}{2R _{0}}\Big{]}(vt_{a})^{2}\\ &+\Big{(}\frac{R_{n}\cos\theta_{n}-R_{t}\cos\theta_{t}}{2R_{0}}\Big{)}(vt_{a} )^{3}-\frac{1}{8R_{0}}^{3}(vt_{a})^{4}\end{cases}\end{array}  \tag{8}\]  
In this article, the transmitted chirp signal can be expressed as follows:  
\[s_{\mathrm{tr}}(t_{r})=\omega_{r}(t_{r})\exp\Big{(}j2\pi f_{c}t_{r}+j\pi K_{r} t_{r}{}^{2}\Big{)} \tag{9}\]  
where \(\omega_{r}(t_{r})\) stands for the rectangular envelope during the time \(T_{r}\), \(K_{r}\) is the frequency modulation rate, and \(f_{c}\) is referred to as the carrier frequency. \(t_{r}\) is the range time variable. After demodulating to baseband, the reflected echo signal from the target is received by an equivalent sampling point, which is given by:  
\[ \begin{array}{ll}s(t_{r},t_{a})=&A_{0}\omega_{a}\big{(}\frac{\theta_{r}- \theta_{a}}{\theta_{a}}\big{)}\omega_{r}(t_{r}-R_{tr}(t_{a})\big{/}c)\\ &\cdot\exp\Big{[}-j\frac{2\pi f_{c}}{c}R_{\mathrm{tr}}(t_{a})\Big{]}\cdot\exp \bigg{\{}j\pi K_{r}\Big{[}t_{r}-\frac{R_{tr}(t_{a})}{c}\Big{]}^{2}\bigg{\}} \end{array}  \tag{10}\]The above equation is the echo signal of AA-BiSAR with one-moving transmitter, where \(\omega_{a}(\cdot)\) and \(\omega_{r}(\cdot)\) are the azimuth time envelope and range time envelope, respectively. \(A_{0}\) is the scattering coefficient of the point target in the scene, \(\theta_{a}\) is the array beam width, and \(c\) is the speed of light.
## 3 Results
### Multi-Drone Results
In Figure 14, the route with the shortest time value is given in Table 4 and is made using one drone with the algorithm proposed in this study. The value found for this route is 275.86. Figure 11: Locations of cities in the Oliver30 dataset. Figure 10: A graphic representation of the average time value presented in Table 4.  
Berlin52 is a dataset of 52 samples in an area of 1800 \(\times\) 1200 taken from TSPLIB [37]. In Figure 15, the locations of the cities to be included in the Berlin52 sample set are given in the coordinate plane. According to TSP, the length of the optimum route that can be found is 7542. Figure 16 shows the route with the shortest time value in Table 4, which was made using one drone with the algorithm proposed in this study. The value found for this route is 5464.90. St70 is a dataset of 70 samples in  
Figure 16: The route with the shortest time obtained with one drone on the Berlin52 dataset. Figure 17: Locations of cities in the St70 dataset. Figure 15: Locations of cities in the Berlin52 dataset. KroA100 is a dataset of 100 samples in a 4000 \(\times\) 2000 area taken from TSPLIB [37]. In Figure 21, the locations of the cities to be included in the KroA100 sample set are given in the coordinate plane. According to TSP, the length of the optimum route that can be found in its solution is 21,282. Figure 22 shows the route with the shortest time value in Table 4, which was made using a drone with the algorithm proposed in this study. The value found for this route is 19,788.83. Figure 19: Locations of cities in the Eil76 dataset. Figure 20: The route with the shortest time obtained with one drone on the Eil76 dataset. Applications carried out in terms of the effectiveness of the algorithm proposed in this study:  
1. First of all, in simulations, comparisons were made with some optimization algorithms in the solution of the TSP problem with scenarios where drones are not used in order to measure the effectiveness of the proposed algorithm. As a result of these comparisons, the advantages and some disadvantages of GA were seen, and why it was preferred in this study was explained. In terms of the problem presented in the article, the genetic algorithm has advantages such as being able to provide optimum solutions in variable scenarios, not needing additional information, and being able to work with a large number of variables. Selecting parameters such as initial population size, mutation-crossover probability, and selection technique has partial effects and is useful in finding the global optimum. It has some disadvantages, such as sometimes experiencing convergence problems. However, in the hybrid truck and multi-drone distribution problem given in this article, these disadvantages are minimized, and an approach and results are presented. 2. Then, new simulations were carried out on the same datasets using drones, and it was observed that the use of the drone had a positive effect on the result. Figure 21: Locations of cities in the KroA100 dataset. Figure 11:0 The route with the shortest time obtained with one drone on the KroA100 dataset.
## 3 Impedance Iterative Learning Control
### The Iterative Learning Control  
The ILC is proposed to optimize the impedance parameters for various objects and closing speeds. The inertia parameter \(M\) is fixed at an apparent value selected by the experiment based on the mass of the gripper fingers. Because the damping ratio \(\zeta\) is fixed, only the damping parameter \(B\) is updated during the ILC process. The stiffness parameter \(K\) is calculated by Equation (2).  
The gradient descent ILC algorithm [24] is applied to derive the learning law. The general form of this algorithm is expressed as:  
\[u^{n+1}=u^{n}+\beta.G^{n}.e^{n} \tag{3}\]  
where \(n\) is the iteration number. \(u\) is the input applied to the ILC process. \(\beta\) is the learning gain. \(G\) is the transfer function of the nominal model. \(e\) is the output error. The product \(G.e\) determines the direction of the update vector.  
The convergence of Equation (3) is guaranteed if [24; 25]:  
\[\left|1-\beta.G^{n}\right|<1 \tag{4}\]  
The gripping process tracks the gripping force, so the output error is determined as:  
\[e^{n}=F_{e}^{n}-F_{e}^{n-1}=\Delta F_{e}^{n} \tag{5}\]  
The input \(u\) of the ILC process in Equation (3) is the damping parameter \(B\). The transfer function \(G\) is calculated based on the gradient scheme to ensure a gradual change of the gripping force by updating \(B\). It is derived as:  
\[G^{n}=\frac{\partial F_{e}^{n}}{\partial B^{n}}=\dot{X}_{r}^{n}-\dot{X}_{c}^{ n}=\dot{X}_{e}^{n} \tag{6}\]  
From Equations (3), (5), and (6), the learning law is formed as:  
\[B^{n+1}=B^{n}+\beta.\dot{X}_{e}^{n}.\Delta F_{e}^{n} \tag{7}\]  
From Equations (4) and (6), the convergence condition of the ILC is determined as:  
\[\left|1-\beta.\dot{X}_{e}^{n}\right|<1 \tag{8}\]  
In each iteration of the ILC process, the learning gain \(\beta\) is adjusted to satisfy the condition in Equation (8).  
The ILC process is stopped if the following conditions are satisfied:  
\[\left\{\begin{array}{l}F_{p}^{n}\leq\sigma_{p}\\ F_{fe}^{n}\leq\sigma_{e}\end{array}\right. \tag{9}\]  
where \(n\) is the iteration number. \(F_{p}\) is the maximum overshoot of the gripping force. \(F_{fe}\) is the final force error. \(\sigma_{*}\) is the desired value.
# Research Progress of Zero-Busbar Technology Based on Heterojunction Photovoltaic Modules
## 3 Results and Analysis
### Electrical Performance Analysis
#### 3.1.2 PV Module Power  
By using the same designs as in Section 3.1.1, the electrical parameters were verified at the module level. Three IFC-based 0 BB modules and three soldering-based 0 BB modules were used for power measurement, and the average results are listed in Table 1. The \(P_{max}\) of the IFC-based modules was 3.55 W higher than that of the soldering-based ones. The two groups of modules had the same \(V_{\infty}\). However, the \(I_{sc}\) of the IFC-based 0 BB modules was 0.03 A higher than that of the soldering-based 0 BB modules, due to the differences in optical utilization. The IFC-based design has less metallized pattern shadow than the soldering-based design, and this contributes to the 1.23 W power gap. The \(FF\) of the IFC-based modules was 0.27% (abs) higher than that of the soldering-based modules, leading to a power increase of 2.32 W.  
The theoretical calculation results of \(\Delta P_{loss}\) and \(\Delta F\)F well match the module test results. Therefore, it is clear that IFC-based technology can achieve lower contact resistances, leading to a lower power loss, a higher fill factor and, thus, a higher power output.
# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 4 Discussion
### Ablation Study
Therefore, we compare the training time of directly concatenating the two-part features and using the feature fusion module to combine the two-part features on four datasets, as shown in Figure 6.  
As can be seen from Figure 5, the IMB model directly splices the output features of the GCN model with the pre-trained BERT word vectors for classification, and the IMGCN model extracts hierarchical features based on the combination of the two features. As can be seen from Figure 5, when the model becomes more complex, the training time required for the model will also increase. Compared with the IMB model, the training time required for each epoch of the IMGCN model is 70 s, 18 s, 15 s, and 40 s for the four datasets of CoLA, SST-2, MR, and R8, respectively. The best results are achieved around 15 epochs of training. Therefore, compared with the IMB model, the overall training time of the IMGCN model on different datasets increases by about 4 to 17 min, while the classification accuracy and Weighted avg F1 value of the IMGCN model on the CoLA dataset increase by 1.92% and 2.32%, respectively. Therefore, compared with the improvement of the model classification effect, the increase in the model training time is within an acceptable range. Therefore, it is \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**SST-2**} & \multicolumn{2}{c}{**MR**} & \multicolumn{2}{c}{**CoLA**} \\ \cline{2-7}  & **Weighted Avg F1** & **Macro F1** & **Weighted Avg F1** & **Macro F1** & **Weighted Avg F1** & **Macro F1** \\ \hline MGB & 91.38 & 91.38 & 86.49 & 86.49 & 80.70 & 76.30 \\ IMB & 91.80 & 91.80 & 87.40 & 87.40 & 82.32 & 79.12 \\ IMGCN & **92.48** & **92.48** & **87.81** & **87.81** & **84.02** & **80.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of ablation study. Figure 5: Histogram of experimental results of ablation study. meaningful for us to further fuse and learn the feature information obtained by the GCN model and the pre-trained BERT by using contextual attention and the hierarchical model.
# Chloroplasts at the Crossroad of Photosynthesis, Pathogen Infection and Plant Defense
## 2 The Chloroplast is a Major Synthesis Site for Many Plant Hormones
### Methionine, the Precursor for ET Biosynthesis, is Made in the Chloroplast
In general, ET obstructs symptom development caused by necrotrophic pathogens and promotes cell death caused by biotrophic and hemibiotrophic pathogens [125; 126; 127]. For example, _Arabidopsis_ mutants with decreased ET sensitivity demonstrated enhanced susceptibility to the necrotrophic fungus _Botrytis cinerea_ but enhanced resistance to the hemibiotrophic pathogen _Pst_ and the biotrophic pathogen _Xanthomonas campestris_ pv. _campestris_[125; 126]. Likewise, ET-insensitive soybean mutants showed more severe symptoms when infected with necrotrophic fungi _Septoria glycines_ and _Rhizoctonia solani_, but displayed less severe symptoms when infected with hemibiotrophs _P. syringae_ pv. _glycinea_ and _Phyophthora sojae_[127]. ET influences the production and signaling of other hormones (Figure 1). ET has negative effects on ABA signaling. As described previously, ABA application induced \(\cdot\)NO production and subsequent stomatal closure [76; 77; 78; 79]; however, ET treatment reversed the effects of ABA on \(\cdot\)NO production and stomatal closure [79]. The negative effects of ET on ABA signaling were also observed in root growth and seed germination [128; 129; 130]. ET has positive effects on JA signaling [92; 131]. ET synergizes the ERF branch of the JA-signaling pathway to activate the expression of genes involved in defense against necrotrophic pathogens [93; 132; 133]. Depending on the pathosystem, ET may have positive or negative impacts on SA signaling [92; 134]. For example, EIN3 (ethylene insensitive 3) and EIL1 (ethylene insensitive 3-like 1), two transcription factors involved in ET signaling, were found to repress the expression of ICS1 [135], the isochorismate synthase required for pathogen-induced SA biosynthesis. Loss-of-function _ein3_ and _eil1 Arabidopsis_ mutants displayed enhanced resistance to _P. syringae_; on the contrary, _Arabidopsis_ plants overexpressing EIN3 exhibited enhanced susceptibility to _P. syringae_[135]. Furthermore, the _ein3-1 eil1-1_ double mutant over-accumulated SA in the absence of pathogen infections [135]. This study demonstrated that in the _Arabidopsis-P. syringae_ pathosystem, ET acts negatively on SA-mediated defense [135]. However, in oilseed rape, ET acted positively on SA-mediated resistance against _Leptosphaeria maculans_, a fungal pathogen [136].