esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
### Evaluation Metrics  
The performance of the clustering methods is evaluated by two metrics: average clustering accuracy (ACC) and F-score [53]. The statistical analysis of data and the evaluation metrics of unsupervised clustering methods are shown in the next section. The ground truth labels saved in the feature extraction are compared with those predicted by the clustering. The ACC is the proportion of correctly classified samples to the total number of samples. The format of ACC (\(A_{c}\)) is defined as follows:  
\[\begin{split} A_{c}=\frac{1}{N}\sum_{k=1}^{N}\mathbb{F}[I(k),H( k)],\\ \mathbb{F}[I(k),H(k)]=\begin{cases}1,&I(k)=H(k),\\ 0,&I(k)\
eq H(k),\end{cases}\end{split} \tag{1}\]  
where \(N\) is the total number of samples. \(k\) is the index of sample labels. \(I(k)\) is the ground truth labels corresponding to different cetaceans saved in the feature extraction. \(H(k)\) is the prediction label corresponding to different cetaceans predicted by the clustering. \(\mathbb{F}[\cdot]\) is the indicator function to compare the labels. \(I(k)=H(k)\) means the ground truth label is correctly predicted. \(\mathbb{F}[I(k),H(k)]=1\) means the classification of this specie's vocal feature is successful, otherwise \(\mathbb{F}[I(k),H(k)]=0\).  
The F-score is another form of accuracy measurement on a classification dataset. It combines the precision and recall of the model to calculate the harmonic mean. Precision (also called positive predictive value) measures the ratio of correctly identified cetacean species to the total predicted species number. Recall (also called true positive rate) measures the ratio of correctly identified cetacean species to the total true species number. The definition of F-score (\(F_{s}\)) is given as follows:  
\[\begin{split} Precision&=\frac{TP}{PP},\ \ Recall=\frac{TP}{P},\\ F_{s}&=2\times\frac{precision\times recall}{precision +recall},\end{split} \tag{2}\]  
where \(TP\) refers to the total number of correctly classified cetacean species. In other words, it is the number of each cetacean species correctly grouped into their same species. \(PP\) is the number of positive predictive, which is predictions for each cetacean species. \(P\) is the number of true cetacean species. The values of ACC and F-score are between 0 and 1. The higher the ACC and F-score rates, the better the classification performance.
# Deep-Learning-Based Approach for IoT Attack and Malware Detection
## 3 The Proposed CNN
A GELU activation function is used to introduce non-linearity, allowing the model to capture complex patterns. Next, a self-attention mechanism is incorporated, utilizing 3 attention heads and 96 channels, enabling the model to focus on the most relevant features in the input data. After this, the data are passed through a fully connected layer with 96 units to further learn higher-level representations. A dropout layer with a rate of 0.5 is then applied to prevent overfitting by randomly deactivating neurons during training. A residual connection, formed by adding two intermediate outputs (I1 and I2), helps to retain information and improve gradient flow through the network. The model further applies batch normalization to standardize the output from the addition layer, ensuring stable learning. Dimensionality reduction is performed using a global  
Figure 2: Graphical representation of the proposed CNN. max pooling layer, which selects the most important features from the data. Finally, a softmax activation function is used to output the classification probabilities, and the result is delivered as the final model output. This design balances computational efficiency and high classification performance. making it suitable for a wide range of applications. The model achieves a total of approximately 9 million learnable parameters, ensuring robust performance even with a reduced number of trainable parameters. For the optimization of the proposed 1D CNN model, various established techniques were used to improve its performance and efficiency. Stochastic Gradient Descent with Momentum (SGDM) was used as the optimizer during the training process. SGDM helps accelerate gradient vectors in the correct directions, leading to faster convergence, and the momentum term helps to smooth out oscillations and stabilize the updates. Learning Rate Scheduling was maintained with periodic updates every 50 iterations to balance convergence speed and stability, ensuring that the learning rate remains optimal throughout the training process. Dropout layers were incorporated to prevent overfitting by randomly setting a fraction of input units to zero during training, improving the model's generalization capability by ensuring that the network does not rely too heavily on any single neuron. Batch Normalization, applied after each convolutional block, stabilizes and accelerates the training process by normalizing the inputs to each layer, helping to maintain a stable distribution of activations throughout the network, which in turn leads to improved training speed and performance. Various hyperparameters, including the number of filters, kernel size, and dropout rate, were fine-tuned through grid search, allowing us to find the optimal set of hyperparameters that yield the best performance for this model. These optimization techniques collectively contribute to the robustness and efficiency of the proposed model, ensuring high classification accuracy and performance while maintaining low computational overhead.
# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 2 Materials and Methods
### Methods
#### 2.3.3 Correlation Coefficient  
The Pearson correlation coefficient (R) measures the overall change trend of two variables, with a value ranging from \(-1\) to \(1\). The closer the absolute value of the value is to \(1\), the stronger the correlation between the two variables, and the closer it is to \(0\), the weaker the correlation between the two variables.  
\[R=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{ \sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{2}}}\sqrt{\sum\limits_{i=1}^{n}{(y _{i}-\overline{y})^{2}}}} \tag{9}\]  
Mean Squared Error (MSE) is a common metric to measure the difference between the model's predicted values and the actual values. The smaller the MSE, the better the model's predictive performance. MSE is calculated by taking the average of the squares of the differences between the predicted values and the actual values.  
\[MSE=\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}}} \tag{10}\]  
Figure 2: Flowchart for data processing and soil moisture downscaling. MODIS, SRTM, and SMAP are the abbreviations of the dataset that provides the data required for the experiment. NDVI, LST, SLOPE, and VV/VH are auxiliary data used to train the downscaling model. RF and XGB are the names of the models used for training.  
Root Mean Square Error (RMSE) is a commonly used statistical measure to evaluate the difference between the predicted value and the actual observed value. RMSE is the square root of MSE and can intuitively reflect the accuracy of the prediction.  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}}}} \tag{11}\]  
R\({}^{2}\) (coefficient of determination) is a metric used to evaluate the performance of a regression model and represents the proportion of variance explained by the model. R\({}^{2}\) values range from 0 to 1, with values closer to 1 indicating that the model fits the data better.  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}}}{\sum\limits _{i=1}^{n}{{{(y_{i}-\overline{y})}^{2}}}} \tag{12}\]  
Among them, \(y_{i}\) is the actual value, \(\hat{y}_{i}\) is the predicted value, and \(\overline{y}\) is the average of the actual values.
# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 4 Discussion  
From the experimental results of the two datasets, the method in this paper is superior to some existing deep learning methods in each index. For areas with large changes in the load curve, the method in this paper is obviously superior to the existing methods. This also shows the effectiveness of this method for load trend feature extraction. In addition, from the results of ablation, the two-branch attention mechanism can well put the attention of feature extraction at the point of a sudden change of load curve, which is conducive to the network to catch the sudden change trend of load. The existing Transformer methods also perform well in load forecasting, because Transformer itself is a network based on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MSE** & **MAE** & **RMSE** & **MAPE** \\ \hline LR & 0.17 & 0.32 & 0.41 & 0.19 \\ ARMA & 0.16 & 0.30 & 0.40 & 0.18 \\ BP & 0.21 & 0.34 & 0.46 & 0.23 \\ SVR & 0.14 & 0.32 & 0.37 & 0.27 \\ DT & 0.16 & 0.32 & 0.40 & 0.28 \\ CNN & 0.18 & 0.34 & 0.42 & 0.19 \\ LSTM & 0.25 & 0.37 & 0.50 & 0.19 \\ STIE [12] & 0.17 & 0.31 & 0.41 & 0.23 \\ ELM [14] & 0.22 & 0.35 & 0.47 & 0.23 \\ Resnet & 0.17 & 0.34 & 0.41 & 0.18 \\ CNN-Fuzzy [22] & 0.16 & 0.30 & 0.40 & 0.18 \\ MB-LSTM [28] & 0.15 & 0.28 & 0.39 & 0.17 \\ MIFnet [30] & 0.14 & 0.26 & 0.37 & 0.15 \\ Transformer [37] & 0.15 & 0.25 & 0.39 & 0.18 \\ \hline Without MCM & 0.14 & 0.26 & 0.37 & 0.17 \\ Without DAM & 0.15 & 0.25 & 0.39 & 0.16 \\ MFAMNet & 0.11 & 0.23 & 0.33 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different methods on the IHEPC dataset.  
Figure 7: Prediction performance of various time series prediction algorithms and our proposed model on the IHEPC dataset. (**a**) The first comparative test sample on the IHEPC dataset; (**b**) The second comparative test sample on the IHEPC dataset.  
attention mechanisms, so it performs well in load curve forecasting. Although the method based on LSTM can extract temporal correlation features, the prediction effect is not as good as the attention mechanism correlation network. In addition, multi-scale convolution enriches the characteristics of the load series through the convolution operations of different convolution cores, and encodes and decodes the characteristic information of interest in the load series from a global perspective, thus improving the performance of load forecasting.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 4 Sparse Planar Array Optimization Design Method
### Intelligent Optimization Methods
#### 4.2.1 Sparse Area Array Optimization Based on Traditional Intelligent Methods
In addition to the above methods, many emerging intelligent algorithms have been applied to the optimization design of sparse planar arrays, such as invasive weed optimization (IWO) [56], atomic search optimization (ASO) [57], brainstorming optimization (BSO) [11], and firefly algorithm (FA) [58]. More and more new methods have been introduced in this field, and it is still an important research direction to deal with sparse planar array optimization problems based on traditional intelligent algorithms.
# A Stable Backup Routing Protocol for Wireless Ad Hoc Networks
## 2 Stable Backup Routing Scheme (SBR)
### Backup Route Construction
#### 2.2.1 Ibr  
In Figure 5a, node G traverses its MAC cache, if it preserves the information of RTS and responding CTS transmitted by B and C, respectively, and \(p^{\text{(B,\,G)}}.p^{\text{(C,\,G)}}>p^{\text{(B,\,C)}}\) is satisfied simultaneously, the \"IBR\" route entry will be appended into the backup route table of node G with \"inefficient node\" and \"next hop\" set as node C, \"successful rate\" as \(p^{\text{(B,\,G)}}\cdot p^{\text{(C,\,G)}}\).
## 4 Discussion
### Influence of Vertical Resistance in Passive Segment  
In order to analyze the influence of the vertical resistance in the passive segment of the pile shaft, the vertical subgrade coefficients \(k_{vp}\) for the pile passive segment is treated as the main reflex index. The corresponding results are demonstrated in Figure 11, in which \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Type of soil stress distribution & Rectangle \\ Diameter of pile shaft (m) & 2.0 \\ Length of free segment for pile shaft \(L_{p}\) (m) & 5.0 \\ Length of passive segment for pile shaft \(L_{p}\) (m) & 10.0 \\ Length of active segment for pile shaft \(L_{p}\) (m) & 20.0 \\ Elastic modulus (Cpa) & 18.0 \\ Concrete density for the pile shaft (\(\mathrm{kN/m^{3}}\)) & 25.0 \\ Subjected axial face at top of pile shaft (\(\mathrm{kN}\times\mathrm{m}\)) & 6000.0 \\ Subjected bending moment at top of pile shaft (\(\mathrm{kN}\times\mathrm{m}\)) & 400.0 \\ Subjected horizontal force at top of pile shaft (\(\mathrm{kN}\)) & 400.0 \\ Resultant earth pressure (\(\mathrm{kN}\)) & 600.0 \\ Foundation vertical resistance coefficient of passive segment \(k_{vp}\) (\(\mathrm{kN/m^{3}}\)) & \(1\times 10^{4}\) \\ Foundation vertical resistance coefficient of active segment \(k_{vp}\) (\(\mathrm{kN/m^{3}}\)) & \(1\times 10^{5}\) \\ Foundation horizontal resistance coefficient of passive segment \(n_{vp}\) (\(\mathrm{kN/m^{3}}\)) & \(7.5\times 10^{1}\) \\ Foundation horizontal resistance coefficient of active segment \(n_{vp}\) (\(\mathrm{kN/m^{3}}\)) & \(6.0\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detailed parameters. Figure 10: Schematic diagram of engineering case analysis of influence factors. the values of \(k_{pp}\) varied from 0, \(1\times 10^{4}\) kN \(\times\) m\({}^{3}\), \(2\times 10^{4}\) kN \(\times\) m\({}^{3}\), \(4\times 10^{4}\) kN \(\times\) m\({}^{3}\) and \(6\times 10^{4}\) kN \(\times\) m\({}^{3}\) and other parameters are remaining constant. It can be seen from Figure 11 that, during the increasing of the vertical subgrade coefficients \(k_{pp}\) of the foundation, the horizontal deformation and the bending moment at the pile-top showed a unceasingly decreasing tendency. In detail, the horizontal deformations and the maximum bending moment are diminishing by 14.29%, 24.14%, 36.88%, 50.42% and 9.42%, 14.47%, 19.27%, 22.77%, respectively, when the \(k_{pp}\) is assigned the values of \(0,1\times 10^{4}\) kN \(\times\) m\({}^{3}\), \(2\times 10^{4}\) kN \(\times\) m\({}^{3}\), \(4\times 10^{4}\) kN \(\times\) m\({}^{3}\) and \(6\times 10^{4}\) kN \(\times\) m\({}^{3}\), respectively. Meanwhile, it is worth stressing that the distribution of the pile shaft bending moment along the pile length changes from a single peak mode to a double peak mode gradually; the positions of the maximum bending moment are slowly moving to the surface, which reveals that the load bearing ratio of the passive segment for the pile shaft is enlarged, and its horizontal bearing ability is distinctly improved.
# Quantum Key Distribution in Kubernetes Clusters
## 2 Background
### Quantum Software Stack and the ETSI Standards
mSAE at this point could share these IDs with the sSAE, allowing the latter to retrieve the duplicate keys  
Figure 1: Quantum Software Stack (QSS) presented in [3]. Dashed lines represent TCP/IP secure connections. Thick, double lines indicate the combination of classical and quantum channel for the QKD exchange. The numbers enumerate the main interfaces: (1) External Interface; (2) Sync Interface; (3) Southbound Interface; and (4) Northbound Interface. calling the getKeyWithId through the destination QKS. The KRP leverages the External Interface to accomplish this task. When a QKDM registers to a QKS, a QKDM Registration Process (QRP) takes place with Keycloak (QSS uses Keycloak [14] as its Identity and Access Management service). The QRP can start only if the QKDM is available as a Keycloak user. Therefore, for authentication purposes, a registration of the QKDM to Keycloak is mandatory; only afterwards the QKDM can be configured by a system administrator and the QRP can start through the Southbound Interface. First, the QKDM sends a registration request to the QKS, providing information regarding reachable destinations and the underlying QKD device. Then, the QKS grants the QKDM limited access to the Vault instance and the DB. This process enables the QKDM to store keys in an isolated portion of the QKS resources. Another critical aspect of the QSS is the link with the ETSI QKD specifications. The ETSI ISG (Industry Specification Group) on QKD covers different aspects related to the standardization of QKD, starting from the physical devices to the software interfaces that allow the communication of these devices with high-level security applications. The following specifications are relevant to the present paper:  
* _ETSI GS QKD 004 V2.1.1_[15]: the specification \"QKD; Application Interfaces\" describes the interface to the physical device; in our case, it could be associated with the Southbound Interface;
* _ETSI GS QKD 014 V1.1.1_[16]: the specification \"QKD; Protocol and data format of REST-based key delivery API\" describes a high-level interface from the high-level application to a key manager. In our case, this corresponds to the Northbound Interface;
* _ETSI GS QKD 015 V1.1.1_[17]: the specification \"QKD; Control Interface for Software-Defined Networks\" is recent, and it is a first attempt to standardize a relatively recent trend in QKD device management, namely _Software-Defined Quantum Key Distribution_ (SD-QKD). The concept is to leverage an SDN controller who knows all the topology of the QKD network and manages all the QKD nodes accordingly in a centralized way. This approach could be beneficial in scenarios where the QKD network is switch-based, meaning the quantum channels could be adjusted using optical switches, which the SDN controller itself would control.
# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 5 Discussion and Future Perspectives
### Plant Varieties Challenge  
Varieties of plant species have always been a challenge when using ML for plant water stress detection. A specific model developed for a particular plant species usually does not hold the same accuracy when trained with other varieties, as the physiological responses of plants to water stress and their relative importance for crop productivity vary with species, soil type, nutrients and climate [115].  
There is also the need to study the effect of variations that occur within the same species but cultivated in different locations. Although plants may look similar, they vary widely in both shape, chemical composition and the concentration of water in the leaf intercellular spaces due to environmental influences. Thus far no study has been found to have investigated this case, although the result can be significant for improving the scalability of the technique. Theoretically, DL's huge learning capabilities can be used to cater for plant varieties provided sufficient data is available. [PERSON] et al. [126] have used CNN to identify corn hybrids based on tolerance to environmental stress from the inputs of yield, soil and weather data.  
Unfortunately, unbalanced data can occur due to the fact that isohydric plants have better control mechanisms for lack of water and can last longer, while anisohydric plants which have a less effective control mechanism will die within days without water [127], although the use of a deep learning model of Generative Adversary Network (GAN) may provide the required dataset through synthetically generated data [128].
## 5 Discussion
Regarding the relationship between acceleration and deceleration and the curve radius and angle, it has been found that the acceleration and deceleration rates decrease with an increase in the curve radius and increase with an increase in the curve angle [50]. However, we found that acceleration and deceleration first decreased and then increased with an increase in the curve radius and angle of rotation on underground road curves. The reason  
Figure 15: Accelerations at different superelevations. for this difference may be that a curve with a larger radius and a smaller curve angle on underground road would provide the driver with a safe signal, as the choice of speed is higher. The amplitude of the vehicle speed reduction is greater and the vehicle turning speed increases. When a curve presents less driving risk, the driver also chooses a greater acceleration to drive out of the curve as quickly as possible. This study proposes radius and angle value ranges for small-radius curves as R > 60 m and \(\alpha\) < 70\({}^{\circ}\) based on the analysis results of the driving risk model and the model correlating acceleration with curve radius and angle.
## 4 Experimental Characterization of Si-SC Structures with SLARC
### Refractive Index Characteristics  
The refractive index of a thin film is influenced by factors such as wavelength, temperature, and material purity. While these parameters can induce variations, standardized refractive index values are commonly employed at a reference wavelength of approximately 550 nm. Representative refractive indices at this wavelength are 3.5 for Si, 1.45 for SiO\({}_{2}\)2.0 to 2.2 for Si\({}_{3}\)N\({}_{4}\) (dependent on composition), 1.39 for LiF, and 1.5 to 2.5 for CdSe/ZnS NQDs (varying with quantum dot characteristics) [17; 18; 19].  
To characterize the wavelength-dependent refractive index behavior, single-layer thin films of SiO\({}_{2}\), Si\({}_{3}\)N\({}_{4}\), CdSe/ZnS NQDs, and LiF were deposited on n-type Si wafers. The refractive index of silicon was referenced from Green and Keevers [15; 16]. For single-layer thin films, the refractive index was measured over a spectral range of 350 to 1000 nm. Figure 2 presents the measured refractive index as a function of wavelength for each single-layer thin film.  
The results indicate relatively stable refractive index values for all materials across the solar spectrum (approximately 350 to 1000 nm). At a wavelength of 550 nm, the refractive indices for SiO\({}_{2}\), Si\({}_{3}\)N\({}_{4}\), CdSe/ZnS NQDs, and LiF thin films were determined to be 1.46, 2.01, 1.67, and 1.32, respectively. Applying the quarter-wave thickness condition for optimal anti-reflection performance yielded estimated thicknesses of 101, 73, 46, and 112 nm for SiO\({}_{2}\), Si\({}_{3}\)N\({}_{4}\), CdSe/ZnS NQDs, and LiF thin films, respectively. These calculated thicknesses served as a foundation for the design of both continuous and patterned MLARC and SLARC structures, specifically SiO\({}_{2}\)/NQD/LiF and Si\({}_{3}\)N\({}_{4}\)/NQD/LiF configurations.
###### Abstract  
This paper presents a novel robust control strategy for path following of an unmanned surface vehicle (USV) suffering from unknown dynamics and rudder saturation. The trajectory linearization control (TLC) method augmented by the neural network, linear extended state observer (LESO), and auxiliary system is used as the main control framework. The salient features of the presented strategy are as follows: in the guidance loop, a fuzzy predictor line-of-sight (FPLOS) guidance law is proposed to ensure that the USV effectively follows the given path, where the fuzzy method is introduced to adjust lookahead distance online, and thereby achieving convergence performance; in the control loop, we develop a practical robust path following controller based on enhanced TLC, in which the neural network and LESO are adopted to handle unmodeled dynamics and external disturbances, respectively. Meanwhile, a nonlinear tracking differentiator (NTD) is constructed to achieve satisfactory differential and filter performance. Then, the auxiliary system is incorporated into the controller design to handle rudder saturation. Using Lyapunov stability theory, the entire system is ensured to be uniformly ultimately bounded (UUB). Simulation comparisons illustrate the effectiveness and superiority of the proposed strategy.  
Keywords:path following; TLC method; LESO; predictor line-of-sight; fuzzy method; neural network; NTD +
Footnote †: journal: Applied Sciences