esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems
### Step 2: Processing of DER Information
#### 3.2.3 Using the IIDER Model in Current Limiting Mode  
For IIDER, as for the inverter-based ones, the model will work well if the fault enables the DER to work on the current limiting mode. Thus, this study is focused on the model shown in Figure 6 as it is presented previously [27].  
Figure 5: Estimation of the internal voltage in the model of the synchronous generator.  
Figure 6: Equivalent sequence circuits of the inverter during current limiting operation.  
Making some algebraic manipulations, the circuit can be represented by a voltage source \(\mathbf{E}_{DER}\) and a reactance \(\mathbf{X}_{DER}\), which is a similar model to the synchronous generator model as shown in Figure 7.  
Where, \(E_{DER}\) and \(X_{DER}\) are given by Equations (19) and (20).  
\[E_{DER}=\frac{1}{j\omega C_{f}}\cdot I_{sat} \tag{19}\]  
\[X_{DER}=j\omega L_{c}+\frac{1}{j\omega C_{f}} \tag{20}\]  
The previous analysis allows estimating the fault current contribution from DER to the fault point. The voltage \(E_{DER}\) is known, since the saturation current \(I_{sat}\) is typically twice the nominal current of the inverter. The inverter parameters are generally provided to the utilities when access to the distribution system is requested. However, if these parameters are not available, appropriate values may be obtained from technical catalogues by using the rated power and operating voltage of the inverter.
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 4 Results and Discussion
### Seventy-Two-Bar Space Truss
#### 4.2.2 GNN Model Training  
The graph neural network was evaluated using the five-fold cross-validation technique, as depicted in Figure 11. The calculated mean values of MSE and RMSE for the training and validation datasets across all the five folds are shown in Table 6. The surrogate model used in the GNN-based optimization was trained using all 1000 truss samples, as shown in Figure 12, and the resulting final mean square error was MSE = 0.019 in\({}^{2}\).
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 5 Results and Analysis
### Results
#### 5.1.1 Parameter Selection for the Developed Neural Network Model  
To configure the developed neural network, we carefully determined the parameters for optimal performance. The architecture of the model consists of eighteen hidden layers, as depicted in Figure 8. We divided the dataset into three subsets, allocating 70% for the learning phase, 15% for validation, and the remaining 15% for testing purposes. The following results were obtained using this specific configuration.  
Figure 8: Architecture of the developed neural network model.  
Figure 7: Performance of learning algorithms.
# Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia
## 6 Simulations  
In this section, numerical simulations are presented. The FOCMRAC scheme designed in the previous section was implemented in 30 virtual patients. Simulations tested the robustness of the control schemes against the intra-patient variability, inter-patient variability, disturbance, noise, and time delay. Also, this FOCMRAC design was compared with the scalar FOMRAC presented in [43].  
To perform the simulations, we used the Matlab toolbox for fractional control NINTEGER v.2.3 developed in [81]. In this toolbox, various numerical approximations are implemented for the fractional-order operators that are explained in detail in reference [81]. The quality of the simulations is related to the approximation used and the associated parameters. In this work, we used the Crone approximation with the MacLaurin expansion, \(n=10\) and a bandwidth \([0.001\;1000]\). This approximation was chosen to make a trade-off between the quality of the simulation and the computing time.
# Development of a High-Precision Touch-Trigger Probe Using a Single Sensor +
## 1 Introduction  
With the development of various micro-fabrication technologies, many miniaturized structures and components with nano-scale precision have been produced in recent twenty years. Accordingly, many micro-/nano-coordinate measuring machines (micro-/nano-CMMs) have been proposed to satisfy the urgent demand for the dimensional measurement of micro parts [1; 2; 3; 4]. Many touch probing systems that can be equipped onto micro-/nano-CMMs have also been developed, such as (a) the capacitive probe that uses at least three high-precision capacitive sensors to detect the arm's displacement of the floating plate of the probe [5; 6], (b) the strain gauge probe that adheres strain gauges on the membrane or cantilevers symmetrically to detect the ball tip's motion using the piezo-resistive effect [7; 8; 9; 10], (c) the inductive probe that uses three high-precision inductive sensors and a complicated flexure hinges to construct the probe head [11], (d) the fiber probe that uses the imaging system to detect the ball tip's motion [12; 13; 14] or uses long Bragg gratings to detect the axialmotion of the probe tip [15] or its 3D motion [16; 17], and (e) the optical sensing probe that uses different optical principles to detect the probe motion, such as position detector, focus sensor, interferometer, auto-collimator, _etc._[18; 19; 20; 21; 22; 23; 24; 25; 26]. Although all the above probes have good sensitivity, accuracy, resolution, repeatability, and stiffness, their permissible measurement ranges are limited since their adopted sensors do not tolerate a large tilt or a large translation of the probe tip. In addition, due to the use of multiple sensors for detecting 3D motions of the probe, their costs are still high.  
The QPD (quadrant photo detector)-based angle sensor has high accuracy and high sensitivity, which has been verified in some probes [20; 25; 26], especially in Atomic Force Microscope (AFM) [23]. A new touch-trigger probe with a simple structure, small size, and low cost is proposed in this paper. Only one QPD-based two-dimensional angle sensor is used in this probe, which can simultaneously detect the trigger signals of the probe tip in 3D. Targets of the probe's design include: (1) that the permissible range of the probe is more than \(\pm 6~{}\upmu\)m; (2) that the probe has equal sensitivity and equal stiffness in three dimensions; (3) that the stiffness is less than \(1~{}\text{mN/$\upmu$m}\); (4) that the repeatability of trigger measurement is less than \(5~{}\text{nm}\) (\(K=2\)); and (5) that the cross-sectional diameter is less than \(40~{}\text{mm}\). The design principle and optimal parameters of the innovative touch-trigger probe are addressed in this paper. Experimental results also show the characteristics of the probe.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 3 Opportunities Given by System-Level Testing
### Opportunities Given by SEE Tests  
In this section, the opportunities resulting from performing the SEE system-level radiation tests will be presented. These observations come from the heavy ions, laser and mixed-field irradiations performed on the PoL modules (see Table 2 in Section 2).
## 5 Measurement of Mass Characteristics of the Pico-Satellite
### Determination of Centroid of Pico-Satellite  
The measurement system measures the mass of the three points five times when the satellite is not loaded, and the average values are used for the final experimental results, as shown in Table 12. The measurement system also measures the mass of the three points five times when the satellite is loaded. Hence, the three-point mass of the pico-satellite in three states of Figure 10 was obtained, and the average values were used for the final experimental results. As noted in Section 4.2, \(y_{1}=206\) mm and \(y_{2}=y_{3}=96\) mm, according to Equations (5) and (6), and the experimental values of the pico-satellite in different states can be obtained. The comparison of centroid values between the experimental and theoretical results of the pico-satellite is shown in Table 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mass (kg)** & **Centroid Coordinates (mm)** & \(J_{\text{xG}}\) (**kgm2**) & \(J_{\text{yG}}\) (**kgm2**) & \(J_{\text{zG}}\) (**kgm2**) \\ \hline
24.241 & \(x_{G}=-0.173\), \(y_{G}=8.688\), \(z_{G}=581.196\) & \(5.724\times 10^{-1}\) & \(5.692\times 10^{-1}\) & \(2.8\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Pico-satellite parameters in solidworks.  
Figure 10: Different measuring states (**a**) pico-satellite vertical position; (**b**) pico-satellite horizontal position-\(\alpha\); (**c**) pico-satellite horizontal position-\(\beta\).  
Table 13 shows that the actual deviation of the pico-satellite centroid is 0.766 mm, 1.271 mm, and 0.208 mm, respectively. These results satisfy the design requirements of the measurement system.
# Innovative System for BIM/GIS Integration in the Context of Urban Sustainability
## 3 Materials and Methods
### BIM and GIS Integration through Cesium Platform
* IFC structure navigation: Once an IFC file has been loaded, the algorithm explores its hierarchical structure. Every structural component of the structure, including doors, windows, floors, and walls, is identified, and data on composition, size, and spatial orientation are collected. * Property extraction: Each element's distinct geometric attributes and semantic information are retrieved. This step is relevant to ensure that all relevant data are retained throughout the conversion. The following step was rearranging the data in the CityGML format that had been made after the extraction of the information from the IFC file. It follows the following phases:  
* Entity mapping: Connections between IFC entities and related CityGML ones are made. The IfcWall in IFC, for instance, is changed to a BuildingWallSurface in CityGML. * Coordinate conversion: The CityGML reference system is used to convert the geometric coordinates of the objects from the IFC file's system. Converting 3D coordinates is carried out to ensure accurate spatial representation, fundamental to geospatial analysis. * Creation of CityGML elements: we used the citygml4j library in order to create corresponding CityGML elements with converted attributes and geometries (typical of GIS environments). * Data aggregation: Buildings, clusters of buildings, and other urban entities are created from the converted data. In addition to the libraries already available in Cesium, using the Deck.gl libraries may be a possibility [50]. Deck.gl is a free and open-source platform for dynamic geospatial data representation on three-dimensional maps, enabling developers to construct sophisticated, dynamic geographic data representations. The display layouts may be tweaked and customized to meet the particular requirements of the application. Further research is being conducted on the possibility of incorporating these types of libraries within the Cesium platform.
# Analysis of Water Quality Improvement Effect of Controlled River by Multi-Functional Weir Operation
## 3 Study Methods
### Model Input Data  
The flow simulation results of the hydrodynamic model were required in the water quality simulation using the water quality model, and precise topographic data and boundary conditions for the simulation section were required as input to improve the accuracy of the flow simulation results [18]. In particular, as sudden topographic changes occur in the section between the Gangjeong-Goryeong Weir and the Dalseong Weir due to the large difference in altitude between the main waterway and the floodplain, precise topographic data capable of considering such characteristics were required [19]. Therefore, in this study, the topographical information measured precisely at the intervals of 40-50 m in the longitudinal and transverse directions in the study section of the Nakdong River was used to construct computational grids [20]. By constructing an orthogonal curvilinear grid using orthogonal curvilinear coordinates, the planar shape of the stream was expressed with a high degree of accuracy with the minimum number of grids centered on the main flow. Figure 3 shows the numerical grid generated using topographic data. The grid configuration is shown in Figure 3a, which consists of 12,000 pieces. Figure 3b show the finite difference between lattice and the bed height for each lattice and the water depth for each lattice [21].  
Figure 4 shows the locations of the multifunctional weirs and water quality observatories in the study section. For flow simulation using the hydrodynamics model, the discharge amount of the Gangjeong-Goryeong Weir and the inflow rate of the Geumho River were used as the upstream boundary conditions, and the inflow rate of the Dalseong Weir was used as the downstream boundary condition. The discharge amount data provided by WAMIS [22] were used for the discharge amount of the Gangjeong-Goryeong Weir, and the daily flow rate data provided by Geumho C were used for the flow rate of the Geumho River. For water quality input data of the Nakdong River, the data measured by the Dasa water quality observatory, located in the upstream area of the Gangjeong-Goryeong Weir, from 1 January to 1 December 2014 were used. For the Geumho River, the simulation was performed using the water quality data measured at Geumho River 6 during the same period.  
Figure 2: Gangjeong-Goryeong Weir and Dalseong Weir Section in Study Area.
## 2 Related Work
### Noise Reduction Methods for Anomaly Detection
#### 2.2.5 K-Nearest Neighbors Classifier  
The K-NN model has the advantage that it is less affected by the presence of noise data because it uses only some adjacent data in the process of classification. These characteristics can be utilized to effectively classify noise data in the noise reduction process. In addition, since the K-NN model is a model that is less affected by the type and characteristics of data, it can be flexibly applied regardless of the environment in which the data is collected [26; 27]. However, the network traffic dataset may have many features depending on the data collection range. In this case, the K-NN model can be significantly affected by the dimensional curse. To alleviate this problem, the norm value \(p\) of the distance formula \(d(x_{i},y_{i})=(\sum_{i=1}^{n}|x_{i}-y_{i}|^{p})^{\frac{1}{p}}\) used in the K-NN model should be appropriately selected as \(\text{manhattan}(p=1)\), \(\text{euclidean}(p=2)\), \(\text{Chebyshev}(p=\infty)\), etc [26; 28].
# A Design for a Wide-Band Antenna Pair Applied for Mobile Terminals at the Sub-6 GHz Band
## 3 Evolution and Analysis of Antenna Pair
### Parameter Analysis  
The proposed antenna pair is composed of L-shaped patches, T-shaped patches, and a notch on the long L-shaped patch. The length and width of structures can affect the equivalent impedance of the antenna, which leads to differences in the antenna radiation performance. Among them, the T-shaped patch and notch contribute greatly to bandwidth expansion. Thus, their parameters are discussed below to further investigate the mechanism of the antenna bandwidth.  
Figure 5 shows the influence of antenna pair size parameters on the reflection coefficients, which includes \(l_{1}\), \(w_{1}\) of the notch and \(l_{2}\), \(w_{2}\) of the T-shaped patch. One can see from Figure 5a,b that when the notch is connected with the T-shaped patch, where \(l_{1}\) is smaller than 0.6 mm and \(w_{1}\) is smaller than 0.4 mm, the resonant points at 5.6 GHz cannot be generated. When \(l_{1}\) is larger than 0.6 mm and \(w_{1}\) is larger than 0.5 mm, it will have less impact on resonant frequency and impedance matching. In conclusion, the notch is an important factor in determining high-frequency resonance. It is only in the presence of the notch that a new resonance, located at 5.5-6.0 GHz, will appear, which is essential to the principle of exciting multiple resonance points in the same plane for expanding the bandwidth. Thus, \(l_{1}=0.6\) mm, and \(w_{1}=0.5\) mm is adopted. From Figure 5c, it can be observed that \(l_{2}\) influences the impedance matching at low frequency and changes the resonant point at high frequency. The larger the value of \(l_{2}\), the better the match at low frequency. However, when \(l_{2}\) is greater than 7.4 mm, the T-shaped patch can connect to the right side of the notch, and the resonant point exciting at more than 5.5 GHz is destroyed. Figure 5d shows that \(w_{2}\) has a significant impact on impedance matching for both low frequency and high frequency, and they exhibit opposite trends. With the value of \(w_{2}\) increasing, the impedance matching at low frequencies becomes better. Conversely, the impedance matching performance at high frequency becomes poor. Therefore, \(l_{2}=7.4\) mm and \(w_{2}=0.6\) mm is adopted.  
Figure 4: Simulated current distribution; (a) current distribution of Model 1; (b) current distribution of Model 2.
## 3 Factors Governing Survival and Occurrence of Opportunistic Pathogens in Premise Plumbing Systems
### Disinfection
#### 4.3.3 Chlorine Dioxide  
Chlorine dioxide is a water-soluble gas having superior penetration in biofilms than elemental chlorine gas. Multiple studies have confirmed that its correct usage renders it effective in incapacitating certain bacterial pathogens, viruses, and protozoan pathogens, including _Legionella_[3]. Chlorine dioxide disinfection systems have been installed in hospitals to control _Legionella_ and biofilm in hot and cold-water systems. Although a vast majority of laboratory and pilot-scale tests have determined chlorine dioxide to be effective against _Legionella_, a few studies argued that it was not suitable at high levels as would be applied in shock treatment or emergency remediation to inactivate _Legionella pneumophila_[3; 28]. Systems that successfully perform their function have reported dosage rates between 0.4 and 0.7 mg/L, while a residual between 0.1 and 0.5 mg/L at the tap is considered as usually sufficient to control _Legionella_-- naturally higher residuals may be required in a system experiencing severe colonisation [163].
## 4 Simulation Conditions
### Single Wind and Snow Condition
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wall Thickness** & \
\begin{tabular}{c} **Maximum Stress** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Deformation (mm)** \\ \end{tabular} & **Maximum Strain** \\ \hline
3.0 & 32.876 & 10.474 & 0.00015834 \\
2.5 & 44.019 & 16.288 & 0.0002098 \\
2.0 & 256.68 & 37.248 & 0.0012524 \\
1.5 & 250.47 & 70.037 & 0.0013156 \\
1.0 & 312.8 & 30.621 & 0.0015379 \\
0.5 & 353.96 & 43.191 & 0.0020875 \\
0.25 & 651.07 & 164.72 & 0.0082567 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The simulation results for different wall thicknesses. Figure 15: Boundary condition setting for snow resistance performance analysis. Under the combined action of the wind and snow loads, the maximum stress in the box-type substation structure occurs at the edge of the side wall panel, with a maximum stress of 34.413 MPa, which is below the allowable stress of the material. The maximum deformation of the box-type substation structure is observed at the midpoint of the side wall panels, with a maximum deformation of 7.7699 mm, meeting strength requirements under 10-level wind force.
## 2 System Model with Collision Design and Analysis of Our Proposed System
### Avoiding Collisions Between Clusters
#### 2.2.2 Problem of Hidden Terminals  
An Rx node that is blind to the existence of another Rx node is referred to as having a hidden terminal. As stated in [46], the problem of hidden terminals may result in collisions when an Rx node is unable to identify the interference created by an additional Rx node during the communication of data packets. Additionally, collisions might occur if two different Rx nodes communicate data packets to an identical target sensor Rx node. The issue with hidden and exposed communicating nodes is demonstrated in Figure 4. Specifically, both Rx node A and Rx node C are not perceptible to Rx node B, while both of them are accessible to Rx node C. Therefore, collisions with Rx node B may happen if communicating data packets are transmitted from both Rx node A and Rx node C. Additionally, the problem of hidden terminals may lead to poor throughputs as well as very high consumption of energy. These problems need to be handled efficiently so that there is no conflict between the transmitting and receiving times.
# Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors
## 3 Methods
### Travel Mode Detection Model
#### 3.4.2 CNN Model  
As illustrated in Figure 5, the CNN is designed for one dimensional sensor data, which combines four types of sensor data (total 10 features of accelerometer, gyroscope, magnetometer, and barometer). The proposed CNN consists of three types of layers: convolutional layer, pooling layer, and fully connected layer, as shown in Figure 5.  
Figure 4: The workflow of the proposed CNN-based method.  
Figure 5: The architecture of CNN.  
For convolutional layer, the input data is a 1000 \(\times\) 1 vector, which contains four types of sensors data. The input vector can be described as:  
\[x=[x_{0},x_{1},\ldots x_{n-1}]\]  
where \(n\) is the number of data in a sliding window (\(n\) = 1000). Then, we perform a convolution operation on the input data. The filter \(w\) and the output of the convolutional layer \(z\) are:  
\[w=[w_{0},w_{1},\ldots w_{m-1}]\]  
\[z=[z_{1},z_{2},\ldots z_{h}]\]  
where \(m\) is the filter size, and the \(h\) is the length of the output data. For the \(j\)th element \(z_{j}\), there is:  
\[z_{j}=\sum_{i-1}^{m-1}w_{i}x_{i+b*j}\]  
where \(b\) refers to be the stride length of convolution. The relation between \(h\), \(m\), \(n\), and \(b\) can be expressed as:  
\[h=\left(\frac{n-m}{b}\right)+1\]  
Afterward, an extra activation layer is added to improve the express capability of the network, the output of activation layer is \(a=f(z)\), where \(f\) is the activation function. We use Relu as the activation function.  
For the pooling layer, we choose the statistic nearby inputs value to extract more robust features. The maximum, minimum or mean value of a given region is computed as the output of pooling layer for reducing the length of data, simplifying computing, and avoiding over fitting. Max pooling is utilized in the proposed network.  
The fully connected layer is followed as the classifier to recognize different travel modes. The softmax layer is applied to map the result of fully connected layer in the range of \([0,\ 1]\), which can be thought of as the probability that the input data belongs to a certain type of travel mode. The output of fully connected layer is \(f=[f_{1},f_{2},\ldots f_{s}]\), and the output of the softmax is \(y^{\prime}=[y^{\prime}_{1},y^{\prime}_{2},\ldots y^{\prime}_{s}]\), which indicates the probability of travel mode:  
\[y^{\prime}_{i}=\frac{e^{f_{i}}}{\sum_{k=1}^{s}e^{f_{k}}}\]  
We adjust the weights and biases in each layer by minimizing the loss between the initial prediction and the label to get the best prediction model, the loss can be described as follows:  
\[h_{y^{\prime}}(y)=-\sum_{i}y^{\prime}_{i}\log(y_{i})\]  
where \(y\) is the label of sample and \(y^{\prime}\) is the output of the network. In this study, we applied the Adam optimization algorithm to solve this least square problem. For training the sensors data, we chose Mini-batch Gradient Descent to optimize the collected data and we set the power of 2 as the batch size.