esa-sceva/satcom-chunk-collection huggingface dataset:
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 5 Conclusions  
The Stream-DBSCAN model not only meets the real-time data processing requirements in water quality management, improving the response speed of management departments to water quality changes, but also reflects changes in water quality and pollution time nodes in clustering results. Currently, water quality detection methods mainly rely on fixed-point detection by water stations. However, by utilizing the Stream-DBSCAN model, water stations can achieve automatic detection and data analysis, enabling all-weather, fine-grained, and all-indicator monitoring and thereby improving decision-making efficiency. This model is not limited to the three dimensions tested in this experiment, and different parameter groups can be set for clustering processing as needed. By inputting various water quality indicators with reasonable weights, the Stream-DBSCAN model can output corresponding clustering results with the desired focus, providing more scientific and intelligent guidance for water quality management. It is worth mentioning that the model adopts DBSCAN as the core processing algorithm, inheriting the drawbacks of the DBSCAN algorithm. In the face of different combinations of water quality indicators, it is  
Figure 8: Comparison of intra-cluster points.  
Figure 9: Time curve.  
necessary to determine appropriate Eps and Minpts parameters through pre-experiments, which presents some resistance to practical applications. In future work, we will focus on parameter adaptation in the DBSCAN algorithm, updating parameters through feedback from clustering results, and improving the model's usability.  
Conceptualization, J.Z.; methodology, Y.H.; software, Y.W.; validation, J.Z. and Y.H.; formal analysis, C.M.; investigation, C.M.; resources, S.W.; data curation, S.W.; writing--original draft preparation, Y.H.; writing--review and editing, Y.H.; visualization, Y.H.; supervision, J.Z.; project administration, C.M.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shandong Province grant number No. ZR2020 MF148 and No. ZR2020 QF108.  
Some or all data are available from the corresponding author by request.  
The authors declare no conflict of interest.
# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 3 The Proposed Intrusion Detection System
### Intelligent Detection System  
In this section, we detail an intelligent IDS that utilises SVM and FFNN to identify the vehicles performing rushing attacks and grey hole attack in VANETs. Our proposed IDS makes use of a data set of 30,000 records to define the normal and malicious behaviours in VANETs. The data set, obtained from the trace file, was split into three subsets: (1) the validation set; (2) the test set; and (3) the training set. This is in order to manage a common problem with neural networks Artificial Neural Network (ANN), i.e., the over-fitting of training data.  
The FFNN includes one or more hidden layer, the input layer and the output layer. The input layer is made up of 75 neurons, which equals the number of fuzzified features obtained from the trace file after using a fuzzy set. We utilise two hidden layers to raise the accuracy of detection of the system and to reduce the amount of false alarms. There are five neurons in the first hidden layer while the second hidden layer contains 11 neurons. There are two neurons (normal and abnormal) in the output layer. In order to determine the optimal FFNN configuration in terms of hidden neurons and layers, we made use of the trial and error method in order to configure and choose the best ratio of training based on the situation selected that have been created in the proposed system. The parameters in the initial stages have a vital role to play in the performance of the FFNN, which has a direct effect on the performance of detection. We show in Figure 4 below the optimal structure of the FFNN that was thus determined.  
The formal mathematical formula for the parameters for SVM and FFNN are detailed as follows. Equation (2) is the learning function for the SVM with C data training:  
\[C=\left\{(x_{i},y_{i})\Big{|}x_{i}\in R^{P}\ \right\} \tag{2}\]  
where the range value of \(\mathrm{y_{i}}\in\{-1,\ 1\}\), i = 1, 2, 3, 4,..., \(n\). The value of \(x_{i}\) indicates the class. In FFNN, the training phase ends when the least-square-error \(E\) value between the desired \(d_{i}\) and actual output \(y_{i}\) is less than \(E_{max}=1\times 10^{-5}\).  
\[E=\frac{1}{2p}\ \sum_{p=1}^{P}\ \sum_{i=1}^{m}\ (y_{i}-\ d_{i})^{2} \tag{3}\]  
where \(p\) is the total number of training patterns, and  
\[d_{i}=\left[\begin{array}{c}1\ \mathit{If}\ \mathit{the}\ \mathit{training}\ \mathit{pattern}\ \in i^{th}\ \mathit{texture}\\ -1\ \mathit{otherwise}\end{array}\right.\]  
As indicated above, the initial parameters play a vital role in the FFNN performance, which has a direct effect on the performance of detection. These parameters form the basis of our study, and they influence the detection rate in the training phase of the simulation. The parameters of the training phase used in the FFNN are shown in the Table 3 below:  
Figure 4: Structure of the Artificial Neural Network (ANN).  
The simulation is executed on a PC with an Intel 5744 core i3-380M processor and 4 GB RAM memory.
## 3 Dry-Low Emission Gas Turbine
### Faults in DLE Gas Turbine
As reported by [PERSON] in [85], a DLE gas turbine experiences auto-ignition at a particular temperature and pressure that may result in a rapid loss of power as a consequence of a malfunction being detected by the engine control system, which then causes the machine to be shut down. A self-ignition occurs after a specific delay time called auto-ignition delay time (ADT) is reached. This fault can affect the repair or replacement of components in the premix module. In order to prevent auto-ignition, the fuel residence time in the premix tube should be less than the ADT. Therefore, the fuel residence time must be correctly calculated, and the fuel composition should be carefully analyzed to estimate the correct ADT. Similarly, the flashback is an issue that presents itself much like the auto-ignition. Flashback is a phenomenon of flame feeding back from the combustor into the premixing tube. It occurs when the speed of the local flame is faster than the velocity of the air and fuel mixture leaving the duct. As reported in [86], flashbacks are generally caused by high burning velocity instead of the short ADT. It usually happens during the transient time, such as compressor surge. Some cooling techniques can be implemented in response to performing protection towards flashback events. Further, a well-designed flame detection and fuel controller system can be provided to minimize the effect of a flashback. Another problem in the lean premixed system is the instability of combustion. Commonly, the LPM technique implements swirling to stabilize the combustion. However, the premixing of the fuel and air increases the temperature's homogeneity, which makes the combustor more responsive to the swirl-induced oscillation at any given equivalence ratio [69]. According to [87], the oscillations might happen under lean conditions because the creation of positive feedback of temperature combined with negative feedback of fuel concentration on the reaction rate had occurred. This undesirable oscillatory burning can reduce the combustor's reliability and durability. Moreover, it can decrease the lifetime and damage of the combustor due to high acoustic noise levels at its natural frequency when the resonance occurs. The modes of oscillation may be axial, radial, circumferential, or all three concurrently. In order to guarantee the combustor stability, a dynamic pressure transducer can be applied, ensuring the combustor burns uniformly. Hence, it helps control the flow to create a proper mix of fuel and air, producing uniform combustion. However, a deeper analysis, i.e., Computational Fluid Dynamic modeling, is sometimes required to establish the mixing process by investigating the interaction of flows.
###### Abstract  
In this paper, antipodal Vivaldi antenna is designed for 5 th generation (5G) mobile communication and Ku-band applications. The proposed designed has three layers. The upper layer consists of eight-element array of split-shaped leaf structures, which is fed by a 1-to-8 power divider network. Middle layer is a substrate made of Rogers 5880. The bottom layer consists of truncated ground and shorter mirror-image split leaf structures. The overall size of the designed antenna is confined significantly to 33.31 \(\times\) 54.96 \(\times\) 0.787 (volume in mm\({}^{3}\)), which is equivalent to 2\(\lambda_{o}\)\(\times\) 33.3\(\lambda_{o}\)\(\times\) 0.05\(\lambda_{o}\) (\(\lambda_{o}\) is free-space wavelength at 18 GHz). Proposed eight elements antenna is multi-band in nature covering Ku-bands (14.44-20.98 GHz), two millimeter wave (mmW) bands i.e., 24.34-29 GHz and 33-40 GHz, which are candidate frequency bands for 5G communications. The Ku-Band is suitable for radar applications. Proposed eight elements antenna is very efficient and has stable gain for 5G mobile communication and Ku-band applications. The simulation results are experimentally validated by testing the fabricated prototypes of the proposed design.  
5G; Ku-band; millimeter wave; radar; satellite; vivaldi antenna 10.3990/electronics1006067
## 2 Temperature Variation in Particle Flow Modeling Based on Energy Analyses
### Thermoelastic Effect  
The thermoelastic effect indicates the physical behavior that a small change in temperature will occur when the material is subjected to an elastic strain change induced by loading or unloading, and the thermoelastic effect is always described with Equation (2) [49]:  
\[\Delta T_{\rm t}=-T_{0}\frac{\alpha}{\rho C_{\rm P}}\Delta\sigma_{kk} \tag{2}\]  
where \(T_{0}\) is the initial temperature, \(\alpha\) is the coefficient of thermal expansion, \(\rho\) is the density, \(C_{\rm P}\) is the specific heat capacity at constant pressure, and \(\Delta\sigma_{kk}\) is the variation of the principal stress sum.  
Although the real rock grains have micro-structures, a simplified and conceptual model is used in the particle flow modeling, where the particle is assumed to be intact and rigid, and the elastic deformation is simulated with the overlap between particles [50]. Therefore, the thermoelastic effect should be specially considered for the particles to calculate the temperature change in the rock grains.  
According to the stress analysis in elastic mechanics, the principal stresses (\(\sigma_{1}\), \(\sigma_{2}\)) under plane stress state have the following relation with the stress components (\(\sigma_{x}\), \(\sigma_{y}\)):  
\[\sigma_{1}+\sigma_{2}=\sigma_{x}+\sigma_{y} \tag{3}\]  
Consequently, in PFC2D modeling, the temperature change \(\Delta T_{\rm t}\) of each particle induced by the thermoelastic effect between time steps t and t + 1 can be expressed as:  
\[\Delta T_{\rm t}=T_{0}\frac{\alpha}{\rho C_{\rm p}}\Big{[}\big{(}\sigma_{x}+ \sigma_{y}\big{)}_{\rm t+1}-\big{(}\sigma_{x}+\sigma_{y}\big{)}_{\rm t}\Big{]} \tag{4}\]  
During the calculation, it is assumed that the thermal expansion coefficient \(\alpha\), particle density \(\rho\), and specific heat capacity \(C_{\rm p}\) are constants, as the changes in temperature and pressure cannot have considerable influences on these parameters during the testing process.
# Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
## 2 Materials and Methods
### Estimating SVF with a Simulation Method for 3D Urban Building Models  
Given the low altitude of the studied area, the influence of topographical factors on this method can be disregarded. In ArcGIS Pro 3.0, the \"skyline\" and \"skyline graph\" functions are indispensable tools for visibility analysis, particularly in urban environments  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Height (m)** & **Count** & **Ratio** \\ \hline Low-rise building & 3–9 & 27,130 & 56.31\% \\ Multi-story building & 9–21 & 13,926 & 28.90\% \\ Middle-rise building & 21–30 & 3997 & 8.30\% \\ High-rise building & 30–100 & 3055 & 6.34\% \\ Ultra-high-rise building & \textgreater{}100 & 74 & 0.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The classification of buildings is conducted based on their respective heights.  
Figure 4: Study area: (**a**) positional maps featuring 3D buildings and the network of urban roads; and (**b**) The digital elevation model (DEM) specific to the study area.  
characterized by varying building heights [45]. The \"skyline\" tool identifies the visible horizon from an SVF observation point, taking into account obstructions such as 3D urban building models (see Figure 5a). This visible horizon is then depicted as the \"skyline graph\" (see Figure 5b), illustrating the angle of elevation to the skyline against the azimuth direction, providing a comprehensive view of the visible parts of the sky from the observer's location. Equation (7) was utilized to ascertain \(SVF\) values from a specific \(SVF\) observation point using a generated skyline graph [26]:  
\[SVF_{P_{A}}=\frac{1}{2\pi}sin\frac{\pi}{2n}\sum_{i=1}^{n}sin[\frac{(2i-1)\pi}{ 2n}]\alpha_{i} \tag{7}\]  
where \(n\) is the total number of rings; \(i\) is the index of the ring; and \(\alpha_{i}\) represents the angular width of pixels of the sky in the \(i\)th ring.
# Discrimination of Smoke-Exposed Pinot Noir Wines by Volatile Phenols and Volatile Phenol-Glycosides
## 3 Materials and Methods
### Quantification of Free-Form VPs in Wines  
#### 3.2.1 Internal Standard (IS) Preparation  
Five volatile phenols, including guaiacol, 4-methylguaiacol, \(m\)-cresol, \(o\)-cresol, and \(p\)-cresol, were quantified in wine samples. Stable isotope-labeled compounds were used as internal standards (IS). The IS (guaiacol-\(d_{4}\), 4-methylguaiacol-\(d_{3}\), \(o\)-cresol-\(d_{7}\), \(p\)-cresol-\(d_{7}\), and \(m\)-cresol-\(d_{7}\)) were dissolved in methanol and then mixed to get a final concentration of 10 mg/L (ppm).
# Path Planning for Laser Cladding Robot on Artificial Joint Surface Based on Topology Reconstruction
## 2 The Laser Cladding System  
The laser cladding system mainly consists of a fiber laser source with a maximum output power of 500 W, a coaxial laser cladding head connected with a powder feeder and inert gas delivery, a six-axis robot and independently developed processing software. The design of the test bench is shown in Figure 1a. The laser cladding process is shown in Figure 1b. The coaxial head remains stationary, while the experimental sample carried by the six-axis robot moves along the planned cladding path. The laser beam stays perpendicular to the surface of the experimental sample all the time.  
The method proposed in this paper was implemented using processing software. Its architecture is shown in Figure 2. It mainly consisted of three modules: the CAD module, the path planning module and the simulation module. The CAD module offered two functions: graphic user interface for model handling and basic geometric operations for model transformation. The path planning module offered three functions: topological reconstruction of surface, cladding path planning and robot program generation. The simulation module was provided by the robot manufacturer.
## 1 Introduction
Previous studies have conducted multi-scale, quantitative modeling of carbon emissions at national, provincial, municipal or county levels. The quantitative methods used have included the logarithmic mean Divisia index method (LMDI), geographically weighted regression (GWR) model, time series prediction and variables regression, system dynamics method, etc. Research conducted from the perspective of urban spatial expansion has mostly considered two aspects: night light data and the dynamic transfer change of land use. In fact, a land use map of a given city can accurately reflect the changes that occurred during that city's spatial expansion. The influencing factors mainly include land, population, economy, energy, urban layout modes, urban form, patterns and neighborhood design (e.g., urban building typology and assemblage). The research also shows that the system dynamics method is a better method for studying urban carbon emissions. Even though it has been applied, to a certain extent, to the prediction of carbon emission trends in cities and new towns, its application at the urban scale is still very rare. Based on the recent 60 years of energy consumption data and on present land use, this paper constructs a dynamic model considering five urban operation subsystems: residential, commercial, industrial, transportation and green space carbon sink. The data on energy consumption were obtained from the energy statistical yearbook from 1949 to 2017, and the land use data directly from the land use map of the current urban master plan. The paper simulates the dynamic change index of the total carbon emissions, the carbon emissions intensity, the carbon emissions structure, the carbon emissions per unit land, and summarizes the time-rules and the contribution features to carbon emissions. This paper intends to provide the necessary guidance and suggestions for future energy planning and decision making. The carbon emissions assessment model constructed in this paper can be used by other municipal cities.
## 4 Architecture of Software Receiver
### Software Architecture
#### 4.1.8 Calculation of Angle-Frequency Response  
After the STAP processing, antenna array response in the direction of \(\phi,\theta\) in Figure 2 and at the frequency \(f\) is given by:  
\[GP(\phi,\theta,f)=\sum_{n=1}^{N}\sum_{m=0}^{M-1}w_{nm}\times\exp\!\left[f\! \left(\frac{2\vec{\varphi}_{n}\cdot\hat{r}(\phi,\theta)}{\lambda}+\Delta \gamma_{n}-2\vec{\varphi}mT_{s}\right)\right] \tag{20}\]  
where \(T_{s}\) is sampling time. There are three dimensions of the response, two for the spatial domain and one for the spectral domain. If one would like to show the spatial response such as the gain pattern, then the frequency is fixed and gain values of two direction angles are shown on the polar plot. The gain pattern is useful to show the spatial performance of beamforming because directional beams and nulls are clearly illustrated. As another representation of array response, assuming that one of the directional angles of interference is known, one fixes one directional angle and shows the gain of the other directional angle _versus_ frequency, which is called angle-frequency response. The angle-frequency response is used to simultaneously show the spatial and spectral performance of STAP where narrowband notch filter and broadband null steering are simultaneously illustrated.
## 3 FDIA Detection Model Based on GAN-GRU and Transformer
### FDIA Detection Model Based on the Transformer Neural Network
#### 3.2.1 Encoder Layer  
In the encoder, we first implement the feature transformation by mapping the discrete input features into a continuous vector space through the embedding layer. Next, these vectors are fed into a multi-layered Transformer encoder layer for processing. Each encoder layer consists of multiple Transformer encoder units, and in this paper's experiments, six encoder layers are stacked according to the setup in reference [26]. In each Transformer encoder unit, the data are processed using a multi-head self-attention mechanism and a forward propagation network to extract the semantic information of the input sequence. The multi-head self-attention mechanism interacts with the input symbols with other symbols to obtain a set of attention weights that are used to indicate the importance of the symbols to other symbols.  
The forward propagation network is processed through multiple fully connected layers to extract higher-level semantic information. By stacking multiple Transformer encoder layers, the global semantic information is integrated and a rich set of feature vectors is obtained. In classification tasks, the use of a decoder is usually not required, so in this experiment, we only need to focus on the encoder part and do not need to use a decoder.
## 4 Numerical Experiments and Results
### Example and Parameter Setting
#### 4.2.2 Effectiveness Analysis of the Neighbor Path Selection Mechanism  
We set the upper limit \(maxp\) of the maximum interval of the path traversal to 1, 2, 3, and \(floor(numel(Rout)/2)\). With the increase in \(maxp\), the optimal value, worst value and average value of the objective function value all decrease, and the quality of all the solutions improves. The downward trend of the objective function value becomes slower, and the optimization degree decreases, indicating that the increase in the path interval  
Figure 3: Comparison of objective function values between the algorithm based on the improved neighborhood structure set and the algorithm based on the traditional neighborhood structure set. (**a**) Comparison of optimal value, worst value and average value respectively. (**b**) Comparison of standard variance, range, and range percentage respectively.  
leads to a decrease in the search value and optimization space. Moreover, with the increase in \(maxp\), the running time increases gradually. This result indicates that it is necessary to properly set the maximum interval of the path traversal to balance the solution quality and solution timeliness. These results reveal the effectiveness of the designed neighbor path selection mechanism (Figure 4).