esa-sceva/satcom-chunk-collection huggingface dataset:
# Common-Ground Photovoltaic Inverters for Leakage Current Mitigation: Comparative Review
## 1 Introduction
DC current injection may result due to several factors, such as asymmetric operation during positive and negative half-cycles, delays in gate drive circuits, and offset drift in the current sensing [14,15];
* Transformers can offer high boosting gain; thus, there is no need for series connection of PV modules for grid integration. This is highly desirable feature as it facilitates full use of PV modules during partial shading conditions [16, 17, 18]. Figure 1: Typical single-phase grid-connected PV converter. However, using of transformers degrades of the power density, increases the cost, and decreases the efficiency of PV systems [3; 8; 13; 16; 17]. To overcome these problems, research efforts have been carried out to develop transformer-less PV converters with minimized leakage current and DC current components. To ensure their fulfillment of these safety requirements, specific standards must be complied with by PV grid-connected transformer-less converters. Among these standards, the Germany standard VDE-0126-1-1 stipulates the disconnection of PV systems from the grid in case of exceeding certain limits of leakage current; Table 1 lists the RMS values of these limits and the corresponding disconnection times [19]. On the other hand, according to IEC 61727 standard, generated DC currents from grid-connected systems should be limited to 1% of the rated current [20].
## 3 System Model
### Model of Charging and Discharging of Electric Vehicles  
For each EV connected to charging terminal \(i\in\mathcal{I}\), we denote a charging and discharging rate at the time \(t\) by \(u_{it}\), \(v_{it}\ \in\mathbb{R}\), respectively. Generally, the following applies for the charging and discharging rate at each time step \(t\)[30]:  
\[u_{it}\in\left[0,u_{i}^{+}\right],\ \ \forall t \tag{2}\]  
\[v_{it}\in\left[-v_{i}^{+},0\right],\ \ \forall t \tag{3}\]  
where \(u_{i}^{+}\), \(v_{i}^{+}\geq 0\) is the maximum charging and discharging rate, respectively. In practice, the maximum charging and discharging rate are usually equal, i.e., \(u_{i}^{+}=v_{i}^{+}\).  
The state of charge (SoC) trajectory for an EV connected to the \(i\)th terminal denoted as \(x_{it}\in\mathbb{R}\) must at each time step \(t\) satisfy [29; 35]:  
\[x_{it}\in\left[x_{i}^{-},x_{i}^{+}\right],\forall t \tag{4}\]  
where \(x_{i}^{-}\) and \(x_{i}^{+}\) represent the minimal and maximal state of charge values, respectively. These values must be between 0 and 1 for each time step \(t\).  
Then, simplified dynamics SoC trajectory can be represented as follows [29]:  
\[x_{i,t+1}=x_{it}+1/C_{i}\cdot(u_{it}+v_{it}) \tag{5}\]  
with \(C_{i}\) representing the battery capacity of a vehicle connected to the \(i\)th terminal.  
**Assumption 1**.: _EV connected to the \(i\)th terminal can be either charged or discharged at time step 1[29]._  
The constraints for SoC in (4) can be then rewritten using (5) as constraints for charging and discharging rate at time t as follows [29]:  
\[1/C_{i}\cdot(u_{it}+v_{it})\geq x_{i}^{-}-x_{it},\ \ \forall t \tag{6}\]  
\[1/C_{i}\cdot(u_{it}+v_{it})\leq x_{i}^{+}-x_{it},\ \ \forall t \tag{7}\]Then, the set of charging feasible station actions for EV connected at the ith terminal (a set of feasible charging and discharging rates) at the time t can be defined as [29]:  
\[\mathcal{U}_{tt}\triangleq\{u_{it}\}, \tag{8}\]  
\[V_{it}\triangleq\{v_{it}\}, \tag{9}\]  
such that (2), (3), (6), (7) apply.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 3 Materials and Methods
### System Model and Problem Formulation
#### 3.1.5 Training Process of Machine Learning Models  
The SecureDrone authentication mechanism employs a hybrid machine learning strategy that combines a Random Forest classifier for categorical features and an LSTM (Long Short-Term Memory) network for time-series features to provide strong security for drone systems. At the user registration stage, behavioral features like command usage and typing rhythm are extracted to train user behavior models. The LSTM model, aimed at identifying sequential patterns, is created with an initial input size equal to the number of features, two hidden layers of 64 neurons each, and trained for 100 iterations using forward propagation, calculation of the loss, and backpropagation. Simultaneously, a Random Forest classifier of 100 trees with a maximum depth of 10 is trained on a balanced dataset for differentiating normal from anomalous behavior. For drone authentication, the same procedure is adopted with telemetry information such as flight paths and sensor measurements. The LSTM drone model is relatively small, consisting of two hidden layers and 32 neurons in each, and pre-trained on manufacturer-supplied data to model normal drone behavior. The included Random Forest classifier for drones employs 80 trees of depth 8 and is trained on drone-specific data. Parameters of the model were tuned via ablation studies and grid search to balance accuracy against computational cost, with the collective model performing 12-18% better than single models. Also, the system utilizes continuous learning during verification, employing a low learning rate (e.g., 0.01) to slow-learning adapt to normal behavior changes without sacrificing stability. The twin-model approach maintains high security without being computationally resource-intense for resource-limited drone environments.
## 3 Material and Methods
### System Architecture  
The system architecture is shown in Figure 1. The system consists of two users, the user's mobile devices and the digital twin architecture. The user's mobile device performs BLE scanning, BLE advertising, and information transmission to the digital twin from the user.  
Interaction between users requires mutual recognition to find and authenticate the matching people. To recognize the user, the information representing the user's identity is exchanged and authenticated by each user. Before the users exchange their identity information, they must make sure that the matching people are within a distance where communication is possible. The system uses proximity detection using a BLE signal to measure the distance between users. The green lines in Figure 1 indicate what kinds of data are sent to each user for proximity detection. The user can distinguish who sends the BLE signal by referring to the user ID in the BLE packet. If the user checks that the sender is the matching person, the RSSI value is passed to the user's digital twin. The user's digital twin measures the proximity level with the RSSI value. Based on measuring the proximity level, it is recognized that the matching person is within the communication distance to perform the mutual authentication. After that, information to perform mutual authentication is transmitted to the digital twin. The red lines in Figure 1 show the data exchanged for the authentication process. The authentication tokens generated using user ID are sent to each user's digital twin through the physical world. The digital twins compare the received token and the token they have. If two tokens are the same, the authentication is successful.  
An example case in which the system can be used is described. This is a case where the user can be identified only with the proximity level information in the interaction between users. In this case, the user knows a location where the other user is located, and the user can be confident that the person in that location is the other user. A parcel delivery service is one example. The delivery person rings the bell in front of the recipient's  
Figure 1: Mobile-based digital twin networks and inter-twin communication.  
house. The recipient moves to the door. The delivery person and the recipient each check whether the other party is at the door with proximity detection. After that, they can perform authentication in a contactless situation.
## 5 Results
### Comparison of Different Numbers of Quantization Bits  
Figure 5 compares the spectral efficiency performance of the estimated channel vectors for different numbers of quantization bits. When the number of quantization bits is less than six, the spectral efficiency is increased by approximately 1-1.7 bit/s/Hz with each additional quantization bit. Compared with full feedback with 32-bit or higher precision coefficients, quantized feedback of channel coefficients loses some performance but greatly reduces the feedback overhead. Even with a 1-bit quantization feedback scheme, the spectral efficiency is close to that of TDD CB scheme, which is in Figure 4. When the number of quantization bits is more than 6 bits, the spectral efficiency improvement is limited. To keep the curves simple, only up to 8 bits are plotted in Figure 5. The performance is close to that of infinite bits when the number of quantization bits is 8 bits. When the number of quantization bits is infinite, the performance is slightly worse than the perfect CSI, and the performance loss is caused by the channel estimation error.  
Figure 4: Cumulative distribution function of the achievable per-user rates for different schemes with max–min fair power allocations for \(M=100\) and \(K=10\). \(\tau_{p}=100\) for the proposed FDD cell-free massive MIMO (cf-mMIMO), and \(\tau_{p}=10\) for the TDD cell-free massive MIMO, small cell and massive MIMO (mMIMO).
## 1 Introduction
[11] conducted a study on solving internal flow fields and analyzing blade vibration of high-pressure five-stage moving blades in a certain axial-flow compressor under multiple operating conditions. The distribution of blade dynamic stress under hazardous working conditions was determined, and fatigue failure analysis of the blades was carried out. The research revealed that under critical conditions, the distribution of blade vibration stress was closely related to the resonant modal vibration, and it was pointed out that the first-order resonance of blades at critical rotational speeds was the primary cause of blade failure. [PERSON] et al. [12] conducted a study on the modal analysis of compressor blades under prestress based on fluid-structure coupling. The research revealed that the centrifugal inertial force of the compressor blades themselves and the aerodynamic disturbances they experience are the primary mechanical factors causing blade vibration and fatigue failure. When studying the structural characteristics of compressor blades, most scholars only consider aerodynamic loads, and less consider the centrifugal loads. During the structural analysis of an axial compressor, the centrifugal load significantly impacts both the bending stress at the blade root and the radial deformation of the blade. This research method of overlooking the centrifugal load may result in an inability to accurately simulate the stress and structural deformation of the compressor under real-world operating conditions, ultimately impacting its operational efficiency and the safety of the axial compressor. This paper employed a bidirectional CFD-CSD coupling method to investigate the deformation and stress distribution of compressor blades under aerodynamic loads, centrifugal loads, and the coupling of these two loads. Based on these prestresses, a modal analysis of the blades was conducted to obtain the vibration frequencies under different loading conditions, and the Campbell diagram of the blades was plotted to judge whether the blade would resonate or not. The research findings can provide a theoretical basis for the optimized design of blade structures.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 4 Experimental Validation
### Analysis of Prediction Results of Normal Data of Gearbox Operation
#### 4.2.2 Comparative Analysis of Hyperparameter Optimization of Different Models  
In order to evaluate the accuracy of the model in predicting the lubricating oil temperature of the gearbox, four models, PSO-GRU, IALO-LSTM, ALO-GRU, and GRU, were constructed for comparative analysis. All models used the same data samples and optimized parameter settings to predict the oil temperature of the gearbox during normal operation. The experimental results are shown in Figure 7. To evaluate the accuracy and parameter fit of the five models, root mean square error (RMSE), mean absolute percentage error (MAPE), and goodness of fit statistics (r\({}^{2}\)) were used as the evaluation indexes. The specific expressions are as follows:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{\text{N}}(\text{y}_{i}-\hat{\text{y}}_{i} )^{2}}{\text{N}}} \tag{15}\]  
\[\text{MAPE}=\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\frac{|\text{y}_{i}-\hat{ \text{y}}_{i}|}{\text{y}_{i}} \tag{16}\]  
\[\text{r}^{2}=\frac{\sum_{i=1}^{\text{N}}(\hat{\text{y}}_{i}-\hat{\text{y}})^{2 }}{\sum_{i=1}^{\text{N}}(\text{y}_{i}-\hat{\text{y}}_{i})^{2}} \tag{17}\]  
In the above evaluation indexes, lower RMSE and MAPE values and higher r\({}^{2}\) values all indicate better fit and higher prediction accuracy. As can be seen from Figure 8, the IALO-GRU model based on normal operation data performs the best on the root mean  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Scope** & **Take the Value Type** \\ \hline Number of hidden-layer units & \(32-512\) & int \\ Number of GRU cell layers & \(32-512\) & int \\ Training batch & \(32-512\) & int \\ Learning rate & \(2\times 10^{-3}-10\times 10^{-3}\) & float \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimize parameter settings.  
Figure 7: Oil temperature prediction for different models.  
square error (RMSE) and average absolute percentage error (MAPE) and has the best fit, indicating that it can quickly and accurately detect abnormalities when failure occurs.
## 2 Objectives and Hypothesis  
The general objective of this study is to research the use of AR in informal learning processes, and to establish whether it facilitates learning. In this case, the authors test whether an AR application of guitar learning initiation helps apprentices perform hand posture and finger positioning to play chords and establish the level of usability of the proposed AR model [22] using a system usability scale (SUS) instrument. The apprentices were arranged into two groups: those who had experience playing a different instrument from the guitar and those who had no experience playing any musical instrument.  
The following research hypotheses (HR) and their corresponding null hypotheses (Ho) have been defined for this study:  
* HR1: Apprentices perform correctly the chords by imitating the movement of the hand and fingers positioning from a three-dimensional animation superimposed on the neck of the physical guitar. Null Hypothesis: \(H_{o}\) = _Apprentices do not perform correctly the chords by imitating the movement of the hand and fingers positioning from a three-dimensional animation superimposed on the neck of the physical guitar._
* HR2: Learners with experience playing musical instruments different to the guitar perform chords more quickly than learners without experience in playing musical instruments. Null Hypothesis: \(H_{o}\) = _Learners with experience playing musical instruments different to the guitar do not perform chords more quickly than learners without experience to play musical instruments._
* HR3: Learners with experience in playing a musical instrument different to the guitar perform chord transitions more quickly than learners without experience in playing musical instruments. Null Hypothesis: \(H_{o}\) = _Learners with experience playing musical instruments different to the guitar do not perform chord transitions more quickly than learners without experience in playing musical instruments._
* HR4: Age influences training when AR technologies are used to teach guitar. Null Hypothesis: \(H_{o}\) = _Age does not influence training when AR technologies are used to teach guitar._
* HR5: Gender influences training when AR technologies are used to teach guitar. Null Hypothesis: \(Ho\) = _Gender does not influence training when AR technologies are used to teach guitar._  
Although many studies have demonstrated the benefits of using AR in the educational area, only few have measured their usability and efficiency [23]. In the case of this study, the authors have taken steps to measure the training benefits and the degree of usability presented by the proposed concept. In addition, in this sense, the authors have tried to avoid the traditional side of pure education [24]. The authors of this paper have opted to use a model that focuses on the user and the training process [25], rather than using models based on deep learning that require complicated cognitive processes.
# Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar
## 2 Methodology
### Vibrational Model of Infrastructures  
In this subsection, we introduce the model to describe the vibrations generated by a distributed body and show how it relates to the measurable quantities through radar data.  
The model of motion for the linear damped forced vibration of a structural dynamic system with a total number of \(M\) degrees of freedom can be expressed as [1]  
\[\mathbf{M}\ddot{\mathbf{u}}(t)+\mathbf{C}\ddot{\mathbf{u}}(t)+\mathbf{K}\mathbf{u }(t)=\mathbf{f}(t). \tag{11}\]  
where:  
* \(\mathbf{f}(t)\) is the external force vector applied to the system over the time \(t\);
* \(\mathbf{u}(t)\), \(\dot{\mathbf{u}}(t)\) and \(\ddot{\mathbf{u}}(t)\) are the nodal displacement, the velocity, and the acceleration vectors, respectively;
* \(\mathbf{M}\), \(\mathbf{C}\) and \(\mathbf{K}\) are the \(M\times M\) global mass, damping and stiffness matrices of the dynamic system, respectively.  
In forced harmonic vibration, if \(\mathbf{f}(t)=\mathbf{f}e^{i\omega t}\) with a driving frequency \(\omega\), Equation (11) becomes  
\[\left(-\omega^{2}\mathbf{M}+j\omega\mathbf{C}+\mathbf{K}\right) \mathbf{y}=\mathbf{f}, \tag{12}\]  
where \(\mathbf{y}\) is the harmonic displacement vector  
\[\mathbf{y}=\mathbf{H}(\omega)\mathbf{f} \tag{13}\]  
with \(\mathbf{H}(\omega)=\left(-\omega^{2}\mathbf{M}+j\omega\mathbf{C}+\mathbf{K} \right)^{-1}\), the frequency response, representing the dynamic flexibility of the structural system. The dynamic stiffness \(\mathbf{Z}(\omega)\) can be defined as the inverse of the frequency response [1]  
\[\mathbf{Z}(\omega)=\mathbf{H}(\omega)^{-1}=\left(-\omega^{2} \mathbf{M}+j\omega\mathbf{C}+\mathbf{K}\right). \tag{14}\]  
In a structural dynamic testing, the frequency response function is obtained by measuring the system responses at different locations, and, the displacement vector \(\mathbf{y}\) in (12) is given by an appropriate measurement tool. In this paper, we will use the radar sensor to estimate \(\mathbf{y}\) and the details of the proposed estimation procedure are given in the next subsection.
## 2 Materials and Methods
### Methods
#### 2.2.6 Oxidation Kinetics  
To assess oxidation kinetic parameters including activation energy (\(E_{a}\)), pre-exponential factor (\(Z\)), and reaction rate constant (\(k\)), the non-isothermal PDSC variant was employed [14]. The samples of oil (3-4 mg) were heated at rates (\(\beta\)) of 2.5 \({}^{\circ}\)C/min, 5.0 \({}^{\circ}\)C/min, 7.5 \({}^{\circ}\)C/min, 10.0 \({}^{\circ}\)C/min, and 12.5 \({}^{\circ}\)C/min between 30 and 300 \({}^{\circ}\)C. The experiments were conducted in an oxygen atmosphere at a gas flow rate of 50 mL/min and a starting pressure of 100 kPa. The onset oxidation temperature (\(t_{on}\)) was determined by finding the intersection of the leading edge (tangent line) of the recorded curve and the extrapolated baseline. The experimental \(t_{on}\) values as a function of heating rates (\(\beta\)) were recalculated on absolute onset temperatures (\(T_{on}\)). The graphs of the logarithm of heating rate versus temperature of oil oxidation were plotted, and based on that, \(T_{on}\) dependence was described by regression Equation (5):  
\[\log\beta=a\bigg{(}\frac{1}{T_{on}}\bigg{)}+b \tag{5}\]  
where \(\beta\)--heating rate; \(T_{on}\)--onset absolute temperature (\(K\)). Activation energy (\(E_{a}\)) values and pre-exponential factor (\(Z\)) were calculated using Equations (6)-(8) and according to the Ozawa-Flynn-Wall methodology.  
\[E_{a}=-2.19R\cdot\frac{d\log\beta}{d\frac{1}{T}} \tag{6}\]  
where \(E_{a}\)--activation energy; \(R\)--gas constant.  
\[Z=\frac{\beta E_{a}e^{\frac{E_{a}}{RT}}}{RT^{2}} \tag{7}\]  
\[k=Zexp\frac{-E_{a}}{RT} \tag{8}\]  
where \(k\)--reaction rate constant; \(Z\)--pre-exponential factor.
# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 3 Performance Evaluation of Different Classifier Scenarios
### The Performance of MC-MLI under Different Leak Rates  
During the experiment, various leak scenarios were considered. The leak scenarios refer to different leak rates and leak locations. There are three adjustable valves to act as leak points. The leak rate is adjusted by controlling the valve openness. The leak rates vary from 0 mL/s to 40 mL/s with an interval of 5 mL/s. The number of leaks in the scenarios can be either a single leak or multiple leaks.  
The result, as shown in Figure 6, is averaged from all leak scenarios (i.e., both single leak and multiple leaks are involved). The detection accuracies of three pipe branches B1, B2, and B3 are represented by three curves, respectively. Note that the curves of sensitivity and specificity are similar to the accuracy curve. The result demonstrates that the identification accuracy of MC-MLI is directly proportional to the leak rate. This is because a higher leak rate will show more significant changes in feature values, and thus a higher probability of correct classification. In the normal cases (0 mL/s leak rate), the average accuracies of all three classifiers are higher than 80%. Furthermore, the average accuracies of all classifiers are \(>\)80% under \(>\)15 mL/s leak rate, and \(>\)90% under \(>\)30 mL/s leak rate. Several factors lead to unsatisfactory accuracies at a low leak rate, including sensor sensitivity and network structure. For instance, if the noise level of a sensor is sufficiently high to interrupt measured data, a large error will occur.
###### Abstract  
The landforms and human activities play important roles in quantifying surface water and groundwater interactions (SGIs) for water resources management. The study uses the groundwater and surface water flow (GSFLOW) model to quantify the dynamics of SGIs in the Pingtung Plain groundwater basin (PPGB) in southern Taiwan. Specifically, the study uses a physical-based numerical model to quantify the spatial and seasonal variations of water cycles influenced by complex fluvial landform conditions and human activities. Results of the model calibrations show good agreement with the data obtained from the available groundwater monitoring network and the selected stream stations. The basin-scale water budgets show highly nonuniform precipitation in the study area, and over 80% annual precipitation is from wet seasons in the PPGB. With high permeable surficial deposits in the PPGB, the year-averaged surface runoff and infiltration are approximately 57% and 40% of the total precipitation. The fluvial landforms with the high slope in the PPGB lead to 70% of annual surface runoff that becomes the streamflow, and the interflow dominates water interactions near streambeds. Results show that the interflow rate in the wet seasons is 200% more than that in the dry seasons. The net groundwater discharge to the streams is relatively small as compared to the interflow. Only 10% of the river flow is from the net groundwater discharge. In the PPGB, The pumping-induced variations of groundwater levels are insignificant as compared with the factor of the natural landforms. Because of the relatively small area of the proposed artificial lake, the contribution of the artificial lake on the local water budgets is insignificant, indicating the low impact of the artificial recharge lake on the surface water environment.  
Keywords:GSFLOW; surface water and groundwater interaction; numerical model; artificial lake; water budgets +
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