esa-sceva/satcom-chunk-collection huggingface dataset:
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 4 Experimental Verification
### 2D Migration  
For the 2D migration test, the receiving coil was moved from the initial position (centered over TX(2, 2)) to the target position (centered over TX(1, 3)) along a diagonal trajectory (\(dx=dy\), from 0 to 200 mm). At each offset step (\(\Delta dx=\Delta dy=20\) mm), the system was stabilized for 5 s to ensure steady-state operation. The reflection impedance \(Z_{(x,\,y)}\) of the active coil and its neighbors (TX(1, 2), TX(2, 3), TX(1, 3)) was recorded.  
The experimental results of the receiving end reflection impedance and the system transmission efficiency are shown in Figure 15.  
It can be seen from Figure 15 that the transmitting coil TX(2, 2) is powered at the beginning, and \(Z_{\text{max}}=Z_{(2,\,2)}\) at this time, and the system transmission efficiency is the highest. As the offset distance \(dx\) and \(dy\) increase, \(Z_{(2,\,2)}\) gradually decreases, and the system transmission efficiency decreases. When the offset distance \(dx=100\) mm, \(dy=100\) mm, \(Z_{\text{max}}=Z_{(2,\,2)}\), and the system transmission efficiency is the smallest. As the offset distance \(dx\) and \(dy\) continue to increase, \(Z_{(1,\,2)}\) and \(Z_{(2,\,3)}\) gradually become larger than \(Z_{(2,\,2)}\), \(Z_{\text{max}}=Z_{(2,\,3)}\). According to the control principle of transmitting array switching, the transmitting array is switched to the transmitting coil TX(2, 3) for power supply.  
After the coil switching is completed, as the offset distance \(dx\) and \(dy\) continue to increase, \(Z_{(1,\,3)}>Z_{(2,\,3)}\). At this time, \(Z_{\text{max}}=Z_{(2,\,3)}\), the transmitting array is switched to the transmitting coil TX(1, 3) for power supply, and the secondary switching of the transmitting array is completed.  
After the second switching, as the offset distance \(dx\) and \(dy\) continue to increase, \(Z_{\text{max}}=Z_{(1,\,3)}\), the system transmission efficiency gradually increases, and there is no need to switch the power supply coil. The experimental results show that when the two-dimensional offset of the receiving coil RX occurs, the transmitting array switching control strategy based on the position estimation of the receiving coil proposed in this paper can achieve the optimal transmitting coil switching control, and the system transmission efficiency is maintained above 80%. Notably, at \(dx=dy=100\) mm (midpoint), the system switched from TX(2, 2) to TX(2, 3) due to \(Z(2,\,3)>Z(2,\,2)\), and later to TX(1, 3) at \(dx=dy=150\) mm, confirming the robustness of the switching strategy under combined lateral and longitudinal offsets.  
Figure 15: 2D migration experiment. (**a**) Receiver reflection impedance curve. (**b**) Transmission efficiency curve.  
Experimental validation was also conducted in a seawater tank, confirming that the designed system maintains high efficiency (>80%) even when accounting for seawater-induced losses.
# Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks
## 3 Methodology
### Construction of a Comprehensive Importance Evaluation Model  
The node index in complex networks can provide valuable insights into the relationships among nodes. However, evaluating the importance of node indicators from a single perspective has limitations. Therefore, a comprehensive importance evaluation model of complex networks was constructed to consider the importance of nodes from a global perspective. To achieve this, the four indexes of node degree, clustering coefficient, intermediary centrality, and closeness centrality were selected to construct a comprehensive importance evaluation model. The comprehensive importance is represented by the \(O\) value, which reflect the node's overall influence, indicating its relative importance in the network. In other words, a node with a larger \(O\) value has a greater impact on the network's function. The construction process of the model is shown as follows:  
(1) When there are \(m\) nodes in the complex network and each node has \(n\) feature indexes, the feature index matrix Y is constructed as follows:  
\[\text{Y}=\begin{bmatrix}y_{11}&\dots&y_{1n}\\ \vdots&\vdots&\vdots\\ y_{m1}&\dots&y_{mn}\end{bmatrix} \tag{10}\]  
where \(y_{mn}\) denotes the \(n\)-th index of the \(m\)-th node.  
(2) The values of the above characteristic indicators differ significantly; to ensure the reliability of the data and to mitigate the influence of these values on the results, the above characteristic index matrix Y is normalized. The normalized calculation formula is shown as follows:  
\[Z_{mn}=\frac{y_{mn}-y_{min}}{y_{max}-y_{min}} \tag{11}\]  
where \(Z_{mn}\) is the element in the normalized matrix. \(y_{max}\) and \(y_{min}\) denote the largest and smallest element in Y, respectively.  
The normalized matrix is represented by Z as follows:  
\[Z=z_{ij}=\frac{1}{m}\begin{bmatrix}Z_{11}&\dots&Z_{1n}\\ \vdots&\vdots&\vdots\\ Z_{m1}&\dots&Z_{mn}\end{bmatrix} \tag{12}\]  
(3) Calculate the distance \(D_{i}^{+}\) and \(D_{i}^{-}\) of each metric of the complex network from the positive and negative ideal solutions, respectively. The calculation formula is shown as follows:  
\[D_{i}^{+}=\sqrt{\left[\sum_{l=1}^{n}\Bigl{(}z_{ij}-Z_{j}^{+}\Bigr{)}^{2}\right]} \tag{13}\]  
\[D_{i}^{-}=\sqrt{\left[\sum_{l=1}^{n}\Bigl{(}z_{ij}-Z_{j}^{-}\Bigr{)}^{2}\right]} \tag{14}\]  
where, \(Z_{j}^{+}=\max(Z_{1i},Z_{2i},Z_{3i},\dots,Z_{mi})\); \(Z_{j}^{-}=\min(Z_{1i},Z_{2i},Z_{3i},\dots,Z_{mi})\). \(D_{i}^{+}\) denotes the distance between any index and \(Z_{j}^{+}\), and \(D_{i}^{-}\) denotes the distance between any index and \(Z_{j}^{-}\). \(z_{ij}\) denotes the elements in the normalized matrix Z.  
(4) Construct a comprehensive importance evaluation model for complex networks. \(O_{i}\) denotes the comprehensive importance of node \(i\). It can be expressed by the following expression:  
\[O_{i}=\frac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}} \tag{15}\]
## 3 Results
### Binary Logit Model
#### 3.3.2 Interpretation of the Binary Logit Model Results  
For the facilitation of the interpretation of the results, it was considered advisable to exponentiate the model coefficients and to interpret them as odds ratios. Table 2 presents the odds ratios of the variables, which were included in the model, along with their confidence intervals. It should be noted that no safe results can be drawn for the last level of the \"age\" variable (age \(>\) 64), since this level collects only limited responses, as well as for the \"private car owner\" variable, which was not found to be a statistically significant predictor.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Estimate** & **Std. Error** & **z-Value** & **p-Value** \\ \hline Intercept & \(-\)2.7973 & 1.1514 & \(-\)2.429 & 0.01512 \\ user: no & \(-\)1.2866 & 0.4591 & \(-\)2.802 & 0.00508 \\ age: 25–39 & 0.9286 & 1.1104 & 0.836 & 0.40302 \\ age: 40–54 & 1.5090 & 1.1620 & 1.299 & 0.19407 \\ age: 55–64 & 2.9870 & 1.2176 & 2.453 & 0.01416 \\ age: \(>\)64 & 20.1348 & 1160.8338 & 0.017 & 0.98616 \\ private car owner: no & 0.8164 & 0.5405 & 1.511 & 0.13090 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter estimates.  
Figure 3: Receiver operating characteristic (ROC) curve.  
Two main conclusions derived from the odds ratios. Firstly, the age of the potential and existing users of the bike-sharing is an extremely important factor which affects users' intention to shift to bike-sharing systems due to a sophisticated app. It is observed that as age increases, the likelihood of users being affected by the advanced app is reduced. The second conclusion from the odds ratios results is that those who are not currently users of the bike-sharing system are much more likely to shift due to an advanced app. More specifically, potential users are approximately 3.6 times more likely than existing users to be affected by the mobile app.
# Multi-Objective, Reliability-Based Design Optimization of a Steering Linkage
## 5 Numerical Experiment  
The proposed MORBDO in Section 3 of this research was applied for solving reliability design of a steering linkage. The MORBDO problem of the steering linkage design is formulated in Equations (18) and (19), which is solved with aRPBIL-DE, where the population size is 50, the iteration number is 80, and the Pareto archive size is 30. The optimization problem is to find the optimal dimensions of a rack-and-pinion steering linkage, which can minimize the steering error and worst-case scenario values. Another dimension is \(L_{r}=678\) mm, while the geometry dimensions of the car are \(W_{b}=2175\) mm and \(W_{t}=1215\). The performance indicator, in this study, is a hyper volume indicator (HV). The reference point \(\{f_{1},f_{2}\}=\{1,2.0\}\) is used for computing the hyper volume indicator. The fuzzy set model in this study is a triangular shape in which the fuzzy variables are the tolerance of the steering arm length \(L_{a}\) and the tie rod length \(L_{t}\), denoted as \(\mathrm{e}_{\mathrm{La}}\) and \(\mathrm{e}_{\mathrm{L}r}\), respectively. The membership function values of the tolerance are \(\mathrm{e}_{\mathrm{La}}=\mathrm{e}_{\mathrm{L}r}=(-0.2,\,0,\,0.2)\) mm. The proposed method solves the optimization problem for 30 iterations using different \(\alpha\)-cutsof1.0, 0.5, and 0.001. The experimental results are shown in the following section, while the discussion is presented in Section 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha\)-Cut & Mass(kg) & Worst-Case Scenario & \(l_{t}\)(m) & \(d\)(m) \\ \hline
1.0 & 2.981(2.988) & \(-3.301\times 10^{-6}\) & 1.06(1.0613) & 0.02142(0.021439) \\ \hline
0.001 & 2.899 & \(-3.433\times 10^{-8}\) & 1.06 & 0.02126 \\ \hline \hline \end{tabular}  
Numbers in the brackets are reference values from the literature.  
\end{table}
Table 1: Optimum results of testing problem.  
Figure 6: Pareto frontier of the testing problem in case of \(\alpha\)-cut = 1.0 and 0.001.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 1 Introduction  
The need to address metering issues arises practically at the same time as the development of distribution electricity grids. There is a remarkable evolution from the first known electricity meter, patented by [PERSON] in 1872 [1], which only provided information about the length of the electricity current flow, to the up-to-date systems, which are able to provide a wide range of applications rather than just metering. The first automatic and commercialized remote meter is attributed to [PERSON] in 1977 [2]. However, the remote metering concept was not realized in the expected electricity context for many years. Climate change, awareness of energy efficiency, new trends in electricity markets, and the gradual conversion of consumers towards more active agents are promoting not only the use of _Renewable Energy Resources_ (_RES_), but also the _Distributed Generation_ (_DG_) and _Distributed Storage_ (_DS_), which urge a dramatic evolution of the actual electricity model. Evolution towards an electricity grid model able to manage numerous generation and storage devices in an efficient and decentralized manner determines the core of the _Smart Grid_ (_SG_) concept, making the deployment of advanced metering systems or _Smart Metering_ one of the basic techniques to reach this goal. The European Parliament, in the 2012/27/EC directive, defined a _Smart Metering_ or intelligent metering system as \"an electronic system that can measure energy consumption, providing moreinformation than a conventional meter, and can transmit and receive data using a form of electronic communication\" [3]. Regarding communication, _Machine-to-Machine_ (_M2M_) communications happen in devices with capabilities to communicate among them without the need of human intervention. The captured event-driven data is sent through the communication channel (wired or wireless) to the servers in charge of extracting and processing the data and generating responses [4]. Therefore, _M2M_ capabilities are key for _Smart Metering_ performance, since they allow the required bidirectional communication between consuming points and monitoring and control centers [5].  
Besides the control and management capabilities provided by the implementation of the _Smart Metering_, the obtained metering data together with additional information can be used by automatized systems to lead new applications, such as predictive and load management systems.
## 1 Introduction  
As an important participator of the Global Navigation Satellite System (GNSS), BeiDou Navigation Satellite System (BDS) is designed to provide positioning, navigating and time services in the world. BDS is formed by three types of satellites including five Geostationary Earth Orbit (GEO) satellites, three Inclined Geosynchronous Satellite Orbit (IGSO) satellites, and 27 Medium Earth Orbit (MEO) satellites. By the end of 2012, there were five GEO satellites, five IGSO satellites and four MEO satellites in the integrated constellation of BDS, making it capable to provide services in Asia-Pacific area. Since the end of 2012, an additional one GEO satellite and 14 MEO satellites have been launched in order to fill in the incomplete BDS constellation. A fully integrated BDS constellation, consisting of 35 satellites, is to be fulfilled in 2020 and to make global services come true ([[http://www.beidou.gov.cn](http://www.beidou.gov.cn)]([http://www.beidou.gov.cn](http://www.beidou.gov.cn))). Previous studies have widely studied the capacity, the quality and the accuracy of BDS, with their evaluation of the accuracy of BDS code and phase measurements being 33 cm and 2 mm, respectively, which are at the same level with GPS measurements [1; 2; 3; 4; 5]. BDS satellite has been widely applied in many fields [6]. BDS-Reflectometry was used to estimate sea level changes [7]. GEO satellites have large potential for continuous monitoring of space weather effects in low-latitude and equatorial ionosphere [8]. Till now, the positioning, navigating and time services of BDS have already reached or even exceeded its designed targets [9; 10; 11; 12; 13; 14].  
The Crustal Movement Observation Network of China (CMONOC) consists of 260 continuous stations and 2000 campaign stations, which can provide elaborate three-dimensional velocity field and serve as important tools to study the characteristic of crustal deformation and strain accumulation in China mainland and surrounding areas [15; 16]. Since 2015, some CMONOC continuous stations have started to receive BDS signals in order to implement the application of BDS services. Reference [14] compared the positioning results between GPS and BDS, and made the conclusion that the geodetic reference frame established and maintained by BDS can reach the need of centimeter-level accuracy, and proposed the urgent needs on deploying and updating more GPS/BDS dual module receivers in CMONOC. It is necessary to evaluate the quality of the BDS observations and its positioning performance in CMONOC [17; 18].  
This paper aims to evaluate the positioning performance of BDS with observations collected in CMONOC. In Section 2, we detail BDS data processing for daily solution. In Section 3, we show the quality of BDS measurements seriatim, evaluate the accuracy of BDS position time series, make comparisons with GPS results, depict the spatial distribution of differences between BDS and GPS positioning results, and derive three-dimensional BDS velocity field. In Section 4, we try to find the reason causing the relatively low performance of BDS positioning. In Section 5, we make a conclusion of this paper.
## 5 Ablation Studies and Comparative Analysis
### Initialization  
Model initialization plays a significant role in improving model accuracy, speeding up training, and aiding in model convergence. We study and compare three different initialization methods, namely Xavier, Kaiming, and orthogonal initialization, and we apply them to binary neural network models.  
We apply Xavier initialization to the binary neural network models, which ensures that the variance of the activations remains consistent across all layers, helping to prevent vanishing gradient problems [67]. Additionally, we examine Kaiming initialization, which is also used to mitigate the vanishing gradient problem and is particularly effective when combined with the ReLU activation function [66].  
Orthogonal initialization not only improves gradient flow but also achieves dynamical isometry, where all singular values of the input-output Jacobian concentrate near one, facilitating faster convergence. This property is particularly beneficial for training deeper networks, as it prevents gradients from vanishing or exploding during backpropagation [85; 86; 87; 88; 89; 90].  
**Analysis**: Tables 8 and 9 demonstrate the results of applying these initialization techniques to AlexNet and ResNet-20. While all methods offer comparable performance in terms of accuracy, orthogonal initialization slightly improves accuracy for both models. Specifically, orthogonal initialization improves AlexNet's top-1 accuracy to 82.01% and ResNet-20's top-1 accuracy to 82.55% compared to the other initialization methods, indicating its effectiveness in stabilizing deeper networks. These improvements highlight the importance of careful initialization when training compressed models.
## 3 Results and Discussion
### Effect of Operating Conditions
#### 3.2.2 Effect of Pyrolysis Oil Moisture Content  
Figures 4 and 5 present the effects of pyrolysis oil moisture content on syngas composition and SN between 10 wt.% and 50 wt.%. According to water gas shift reaction (R-7) and water gas reaction (R-6), moisture content in pyrolysis oil increases the mole fraction of H\({}_{2}\) and CO\({}_{2}\), while the amount of CO decreased. As a result, the SN value constantly increased from 0.805 to 0.865 with the pyrolysis oil moisture content from 10 wt.% to 30 wt.%. When the moisture content is more than 30 wt.%, the H\({}_{2}\) and CO\({}_{2}\) concentrations continually increase while the CO decreases and the SN value is maintained at 0.865. Therefore, the moisture content in pyrolysis oil seems to be the most beneficial around 30% because excess moisture content has no positive effect on syngas composition and SN value. In contrast, it will consume more heat during gasification process. The effect of pyrolysis oil moisture content on syngas SN value is also similar to [PERSON] experimental research [21]. In this study, beech oil is used to produce syngas through gasification, and the steam/carbon ratio is increased from 2.1 to 3.2, resulting in an increase in the syngas SN value from 0.566 to 0.692.  
Figure 3: Effect of gasifier temperature on stoichiometric number (SN)
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 2 Basic Theory of Transient Stability Analysis
### Dynamic Model of Conventional Generators  
A four-order differential model is introduced to describe the dynamics of conventional generators for transient stability analysis in this paper, and each generator is equipped with one IEEE type I voltage regulator. The differential equations for the four-order dynamic model are as follows.  
\[\frac{d\delta_{i}}{dt}=\omega_{i} \tag{2}\]  
\[M_{i}\frac{d\omega_{i}}{dt}=P_{mi}-P_{ei}-D\omega_{i} \tag{3}\]  
\[T^{\prime}_{di}\frac{dE^{\prime}_{qi}}{dt}=-E^{\prime}_{qi}+(X^{\prime}_{di}-X _{di})I_{di}+E_{fdi} \tag{4}\]  
\[T^{\prime}_{qi}\frac{dE^{\prime}_{di}}{dt}=-E^{\prime}_{di}+(X_{qi}-X^{\prime} _{qi})I_{qi} \tag{5}\]  
where \(M\) is the generator time constant; \(\delta_{i}\) is the \(i\)th generator angles while \(\omega_{i}\) its speed per unit (p.u.); \(P_{mi}\) and \(P_{ei}\) are the mechanical and electromagnetic power of the \(i\)th generator; \(D\) is the \(i\)th generator damping ratio; \(I_{di}\) and \(I_{qi}\) are stator currents on the \(d\) and \(q\) axis; \(T^{\prime}_{di}\) and \(T^{\prime}_{qi}\) are the \(d\) and \(q\) transient time constants;\(X^{\prime}_{di}\), \(X^{\prime}_{qi}\), \(X_{di}\) and \(X_{qi}\) are transient and synchronous reactances on the \(d\) and \(q\) axis; \(E^{\prime}_{di}\) and \(E^{\prime}_{qi}\) are transient potential voltages on the \(d\) and \(q\) axis.  
For a generator equipped with the IEEE type I voltage regulator as shown in Figure 1 for regulating its potential voltage, its dynamics could be described by Equations (6) and (7).  
\[X^{(1)}_{i}=T^{-1}_{mi}A_{mi}\left[\begin{array}{c}I_{qi}\\ I_{di}\\ X_{i}\end{array}\right]+v_{i}(i=1,2,\ldots n) \tag{6}\]where \(X_{i}=(E_{qi}^{\prime},E_{di}^{\prime},R_{fi},E_{fdi},V_{ri})^{T}\), \(T_{mi}=diag(T_{di}^{\prime},T_{qi}^{\prime},T_{Fi},T_{Ei},T_{Ai})\), and  
\[A_{mi}=\left[\begin{array}{cccccc}0&X_{di}^{\prime}-X_{di}&-1&0&0&1&0\\ X_{qi}-X_{qi}^{\prime}&0&0&-1&0&0&0\\ 0&0&0&0&-1&K_{Fi}/T_{Fi}&0\\ 0&0&0&0&0&-K_{Ei}&1\\ 0&0&0&0&K_{Ai}&-\frac{K_{Ai}K_{Fi}}{T_{Fi}}&-1\end{array}\right] \tag{7}\]  
and \(v_{i}=K_{Ai}(V_{refi}-V_{ti})-S_{Ei}E_{fdi}\).
# A Complete EDA and DL Pipeline for Softwarized 5G Network Intrusion Detection
## 3 Proposed Approach
### Computational Complexity  
To determine the computational complexity of the proposed framework, each module's computational complexity is explored. To that end, assume that the dataset consists of \(N\) training samples, \(M\) features, and \(L\) labels.  
Starting off with the data preprocessing module, the complexity of the data cleaning step is \(O(NM)\), since all the samples for all the features need to be inspected to determine and replace missing values. The complexity of the data encoding step is also \(O(NM)\), since all the samples for all features should be inspected and potentially encoded. Similarly, the data normalization step also has a complexity of \(O(NM)\), since all the samples of all the features should be normalized. Thus, the data preprocessing step has an overall computational complexity of \(O(NM)\).  
The computational complexity of the feature selection module is that of the mutual information feature selection process. The complexity of the mutual information technique is estimated to be \(O(MlogM)\), since it involves sorting the features based on the computation of the mutual information score [61].  
Finally, to determine the computational complexity of the DL-based intrusion detection module, the complexity of each of the DL models needs to be explored. The complexity of the dense autoencoder neural network is \(O(M^{2})\), since it is a fully connected neural network architecture [62]. However, this can be reduced to \(O(MlogM)\) using sparse connections and other mathematical operations [62]. In contrast, the computational complexity of a CNN architecture is \(O(d^{2}ML)\), where \(d\) is the kernel size for the convolution layer within the architecture [63]. Since \(d\) and \(L\) are typically much smaller than \(N\), we can see that CNNs can be more computationally efficient compared to DNNs. Lastly, the computational complexity of RNNs can be estimated as \(O(Mh_{l}^{2}r^{4})\), where \(h_{l}\) is the number of hidden layers and \(r\) is the number of recurrent units [64].  
Thus, the overall complexity of the framework ranges between \(O(NM+MlogM)\) (in the case of dense autoencoder neural networks and CNN models) and \(O(NM+MlogM+Mh_{l}^{2}r^{4})\) (in the case of RNN models).