esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The increase in Power Electronic (PE) converters due to the increase in offshore wind energy deployment have given rise to technical challenges (e.g., due to unprecedented fast dynamic phenomena) related to voltage and frequency stability in the power system. In the Offshore Wind Farms (OWFs), the currently available current injection-based voltage control for PE converters are not suitable for voltage control in PE dominated systems due to the absence of continuous voltage control and ineffectiveness during islanding. Moreover, in such power systems, the conventional controllers are not suitable for frequency control due to the absence of dynamic frequency control. The paper presents the Direct Voltage Control (DVC) strategy in a real-time environment to mitigate challenges related to voltage and frequency stability during islanding of OWFs. The control strategy is implemented in the average Electro-magnetic Transient (EMT) model of Type-4 Wind Generator (WG) in RSCAD(r) Version 5.011.1. It is compared with the benchmark model of the control strategy in DIgSILENT PowerFactory(tm) 2019 SP2 (\(\times\)64) in EMT platform. The comparison based on short-term voltage stability and reactive current injection reveals that both the models provide similar results, confirming the validation of the RSCAD model. Moreover, the detailed representation of the converters in the RSCAD model provides a better depiction of the real-world operation.  
Direct Voltage Control; EMT; Offshore Wind Farm; digital twin model +
Footnote †: journal: selected
## 8 Suggested Improvements
### Encryption, Authentication, and Access Control
As the link of data aggregation and communication, the TCU's high-performance HSM ensures secure data transmission without compromising the operational efficiency demanded by the ACN system's dynamic environment. Situating itself between these two extremes, the medium-performance HSM finds its role in various intermediate components. This tier balances cryptographic processing power with efficiency, catering to components that require more processing capability than sensors but less than the TCU, like the ECU. By offering a scalable solution, the medium-performance HSM serves as a versatile cryptographic guardian across a spectrum of ACN system elements. On the first analyzed architecture, a major vulnerability is the misuse of the OBD-II system. In particular, the usage of the OBD-II system, a system meant for diagnostic purposes, is used to retrieve data about the car speed. This misuse can considered as a vulnerability, highlighting its potential for misuse in extracting vehicle speed data. This revelation underscores the urgency of instituting stringent access controls and authentication mechanisms for OBD-II interfaces [67]. By erecting these digital ramparts, it is possible to ensure the veracity of data exchanged and defend against potential unauthorized incursions. Authentication and authorization mechanisms stand as crucial gatekeepers within the ACN system. Their absence exposes the system to the risk of unauthorized access, data manipulation, and even malicious attacks. The usage of HSM, as shown in the previous subsection, is not limited to efficient encryption. These modules are able to guarantee key management, which could be a resolution for the problem of authentication in the automotive context. Decentralized key management enhances security by removing the need for a centralized server. Authorization can be achieved through the usage of a distributed ledger, such as blockchain [68], or a more classical system based on certification [69].
## 3 Results and Discussions  
### Validation Study  
In order to validate the CFD models in terms of sensitivity and accuracy, this study includes a validation study comparing the CFD model with industrial data. Averages at different 10 points of temperature values of the fabric surface before and after each pair of six injector channel pairs (one channel in the upper level and one channel in the bottom level) were therefore compared. Figure 5 depicts a comparison of fabric temperature values obtained from both simulations and experiments. It can be seen that the fabric cooling occurs almost at the same value after each pair of injector channels. The effect of the cabin interior temperature of the stenter machine on the cooling section may explain minor differences between industrial data and CFD simulations. Based on the conditions considered in this study, the CFD model appears well-validated.  
A reference study aimed at validating the turbulence model was conducted using five different turbulence models and considering only two injector channels. Table 3 presents the results of simulations using five different turbulence models, comparing them with the industrial data. Although Realizable k-\(\epsilon\) is a relatively new model compared to other k-\(\epsilon\) turbulence models, early studies have shown that this turbulence model gives better results  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fabric**} & **Thickness** & **Porosity** & **Density** & **Specific Heat Capacity** &
\begin{tabular}{c} **Coefficient of Heat** \\ **Conduction** \\ \end{tabular} \\ \cline{2-5}  & **(mm)** & **(-)** & **(kg/m3)** & **(kJ/kg-K)** & **(W/m-K)** \\ \hline Full Lycra & 1 & 0.5 & 266.39 & 1989.37 & 0.0555 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermophysical properties of the fabric.  
Figure 5: Comparing fabric temperature values obtained by CFD and experiments at measurement points.  
in certain validation studies [19]. Additionally, the 3.95% error between the industrial data and CFD simulation indicates that the Realizable k-\(\epsilon\) turbulence model is more suitable in this study.  
In Figure 6, obtained from CFD simulation, different positions of the fabric are shown at different times (at initial time, 0.3 s, 1 s, and 1.5 s). The fabric, which was modeled in the CFD simulations, completes its movement with a 0.001 s time step size in 1.57 s for this fabric velocity.
## 3 Calibration and Training the eNose for Ethanol Gas Detection
### Training an LDA Classifier for Ethanol Detection  
The data samples highlighted in Figure 3 have been used to train an LDA classifier. In this work, only two cases are trained: Air and Ethanol. As stated before, the first 500 resistance samples of each calibration experiment were labeled as Air, and the final 500 samples of each calibration experiment were labeled as Ethanol. These samples were then used to train the LDA classifier. Figure 4 illustrates the projection of the training data obtained with the transformation provided by the LDA classifier. Figure 4 represents the samples in a two-dimensional space where the LD1 and LD2 are the axes of maximum variance. The blue and orange boundary areas labeled in Figure 4 represent the classification areas identified by the LDA model, see [62] for more information about the representation of these areas. Therefore, after the LDA transformation, all eNose data samples projected inside the orange region will be classified as Ethanol, and the samples projected inside the blue region will be classified as Air.  
As stated above, the supervised LDA training was performed with two classes: Air and Ethanol. However, for demonstrative purposes, Figure 4 differentiates the two Air data samples obtained in the two calibration experiments and the two Ethanol data samples obtained in the two calibration experiments. The data samples with high Ethanol concentration are represented with orange dots and appear further to the right in the projected  
Figure 4: LDA projection of the data points gathered by the eNose during the calibration process. The figure also shows the boundaries of the two regions obtained when using the LDA projection to predict the class of the gas samples.  
space. The data samples with low Ethanol concentration are represented by the yellow dots and are located closer to the Air-Ethanol frontier. This relative placement agrees with the intuitive perception that a low concentration of Ethanol should be located between the data samples of Air and high Ethanol concentration. Similarly, the Air data samples registered during the low concentration experiment, blue dots, and during the high concentration experiment, violet dots, appear within the same region. Again, this makes intuitive sense because the two Air samples are indistinguishable from each other.
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 4 Fg-Ekf
### State Refinement  
Then, the predicted state \(\mathbf{s}_{k|k-1}\) can be further refined by the detected target position of the observer, yielding the FG-EKF results at the current timing \(k\). According to Figure 3, the message flow of the FG-EKF output \(\mu_{e}(\mathbf{s}_{k})\) is given by:  
\[\mu_{e}(\mathbf{s}_{k})=\mu_{c}(\mathbf{s}_{k|k-1})\mu_{d}(\mathbf{o}_{k}), \tag{59}\]  
where \(\mu_{d}(\mathbf{o}_{k})\) represents the message flow of the observed target state, computed by the proposed 3D location detector.
## 3 Performance-Based Optimization Model for the Life-Cycle Cost of SRHPC Frame Structures
### The Calculation Method of SRHPC Frame Structure's Optimization in Stages
#### 3.2.2 The Second Optimization Stage under a Medium or Major Earthquake  
1. Design Variables Second stage optimization is the detailed optimization of the section size of steel after obtaining the section size of the beam column and the section area of the steel; the optimization design variable vector in this stage is shown in Equation (13): \[X=\Big{[}b_{ij,c}^{af}\ b_{ij,b}^{af}\ t_{ij,c}^{af}\ t_{ij,b}^{sy}\ h_{ij,c}^{sy}\ h_{ij,b}^{sy}\ t_{ij,b}^{sy}\Big{]}.\] (13)
2. Objective Function The sum of failure loss expectation of a structure corresponding to the failure of \"moderate damage\" and \"severe damage\" performance level was the optimization objective in the second stage. The objective function is shown in Equation (14): \[MinW(X)=C_{fm}\widetilde{P}_{fm}+C_{fl}\widetilde{P}_{fl}.\] (14)
3. Constraints (1) Construction requirements; (2) Ductility constraint of SRHPC frame column. The frame columns should have good ductility as it gives the structure plastic internal force redistribution, which gives full play to the ability of each component and prevents brittle failure. In order to ensure the sufficient ductility of the component, the ductility coefficient of frame column should meet Equation (15): \[\mu_{ij,c}=65.45(f_{c})^{-0.551}\Big{(}\rho_{ij,c}^{v}\Big{)}^{036}\times\Bigg{(} \frac{N_{ij,c}}{f_{c}A_{ij,c}^{c}+f_{a}A_{ij,c}^{a}}\Bigg{)}^{-0.665}\geq 3.\] (15)  
(3) Performance requirements under a medium or major earthquake are shown in Equation (16):  
\[\Delta mi\leq[\Delta m],\ \Delta li\leq[\Delta l]. \tag{16}\]  
Comparing the number of optimization variables in Equations (6), (7), and (13), it can be seen that for each stage of optimization, the number of optimization variables was reduced. At the same time, the column ductility constraints and the performance requirements under a medium or major earthquake do not need to be considered during the first stage of the optimization process, and the bearing capacity constraint does not need to be considered during the second stage of the optimization process. Therefore, the number of constraints in each stage is also reduced, which effectively simplifies the optimization process.
## 2 Materials and Methods
### Analysis Procedure  
To demonstrate the applicability of the modeling algorithm under different land cover conditions, four separate locations with different climate and land cover composition were chosen in the United States. The selection was conducted deliberately through a trial-and-error method until NDVI profiles representing grassland, crop, and forest areas were found (Figure 2). The locations of the four study sites are shown in Figures 4a and 4b. The location identifiers are labeled based on the bare percent from the MODIS VCF data set. Therefore, identification marks (IDs) with B0, B1, B21, and B42 labels represent bare percentage of 0.0% in a single pixel in Virginia, 1% in Illinois, 21% in North Dakota, and 42% in Wyoming, respectively. The respective geographic coordinates (lon/lat) for the study sites are: -79.85, 36.92; -89.05, 41.93; -103.35, 48.82; -108.55, 44.82. In addition to the four sites where NDVI profiles have been converted to Kcp parameter functions (Figure 3), two additional sites in Brookings, South Dakota (lon/lat: -96.84, 44.35), and Audubon, Arizona (lon/lat: -110.51, 31.59), were used to validate daily ETa modeling results using AmeriFlux latent heat flux data (Figures 4 and 5). These sites were chosen because of the availability of near-complete data sets (less than 12 days missing) for the entire year in 2005 and their contrast in vegetation dynamics. The contrast between the two sites can be characterized by the differences in maximum and minimum NDVI climatology. The Arizona site has an NDVI climatology range between 0.20 and 0.44, while the corresponding values for the South Dakota site are 0.18 and 0.69.  
The VegET model was run using two years of daily meteorological data from 1 January to 31 December for 2005 and 2006. In order to reduce computational burden, the spatial resolution of the rainfall (5 km) was chosen instead of the LSP (1-km) for the conterminous US. Daily ETa estimates were extracted to compare with two AmeriFlux latent heat flux data sets for 2005. Seasonal ETa totals were made by summing up daily grids from 1 May to 30 September for both years to show the spatial distribution of seasonal ETa in a given year and change on a pixel-by-pixel basis between 2005 and 2006.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 4 Results and Discussion
### Transient Response  
Transient response analysis shows the correlation between the sampling period \(h\) and the evolution of the process state variables \(x_{1}\) (soil moisture) and \(x_{2}\) (soil moisture variations) during irrigation events. Figures 7-9 depict four-day transient responses for the sampling strategies \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\) respectively.
# Implementation of Pavement Defect Detection System on Edge Computing Platform
## 3 Pavement Defect Detection Scheme
Subsequently, the covariance matrix of the estimated value is to be updated, which can be expressed as  
\[P_{k|k-1}=AP_{k-1|k-1}A^{T}+Q \tag{2}\]  
where \(P_{k|k-1}\) is the covariance matrix at time step \(k\) predicted by the covariance matrix at time step \(k-1\); \(Q\) is the system noise. The optimum state approximation outcome can be reached, and it can be represented in conjunction with the system state and observed values in [11] (i.e., the prediction frame details of the current pothole recognition result). \[\hat{x}_{k|k}=\hat{x}_{k|k-1}+K_{k}(z_{k}-H\hat{x}_{k|k-1}) \tag{3}\]  
where \(K_{k}\) is the Kalman gain, which represents the weighting between the measured value and the estimated value. It also indicates that when the value of \(K_{k}\) is large, it means the credit of observed value is greater than the estimated value currently; otherwise, it means that the credit of estimated value is greater than the observed value. Its Kalman gain can be estimated by the following equation:  
\[K_{k}=P_{k|k-1}H^{T}(HP_{k|k-1}H^{T}+R)^{-1} \tag{4}\]  
where \(R\) is the observation error matrix, and the covariance matrix at the last time step \(k\) can be updated to  
\[P_{k|k}=(I-K_{k}H)P_{k|k-1} \tag{5}\]  
We can estimate the current state (also referred to as the coordinate, length, and width details of the pavement defect prediction frame) based on the state of the prior time step and modify the predictive information with the observation information to obtain the best estimate. Therefore, this paper seizes on the Kalman filter, which may not only prevent the sudden emergence of no prediction frame due to misjudgment but also be less vulnerable to error information due to sudden misjudgment in the prediction frame of each identification outcome. The detection block in Figure 2 is the identification of defects in road pavement based on the YOLOv3 model used in this study. In order to increase the computational efficiency, the image of 30\({}^{\circ}\) FOV lens and the image of 70\({}^{\circ}\) FOV lens were merged into one image via coordinate transformation, and this merged image was used as the input to the YOLOv3 model to identify pavement defects. At this point, it is the identification result when \(t=1\). Then, we used the Kalman filter to track the identified road pavement defect prediction frame for \(t=2\sim j\). However, we must simultaneously mark the object at time step \(t=j\), to prevent any tracking loss that may happen as a consequence of the object's absence or masking, and compare the identification outcome to the tracking consequence by IOU. When IOU > 0.5, the tracking results can continue to be trustworthy; otherwise, the new identification results can be used as the basis of subsequent tracking. This process will increase the recognition rate efficiently and reduce the miss rate.
## 2 System Modelling
### Source Power Sharing  
Since the selected MLI requires four isolated DC inputs with asymmetrical magnitudes, the power required to be delivered by each of the inputs would also be different. The difference was also contributed to by the switching scheme of the topology, where the average conduction period by the sources was non-similar. In simple terms, the power provided by a DC source was the result of multiplying the source DC voltage by the average current flowing through it. By adding the product of current magnitudes and their corresponding conduction times over a half-period of output, including the zero levels, the average current could be obtained. As a result, by examining the width and magnitude of each current level, the average current flowing through each DC source can be given as:  
\[\mathrm{I_{V1}}=\mathrm{I_{C1}}\cdot\mathrm{100}\cdot\frac{\mathrm{V_{DC}}}{ \mathrm{Z}}\cdot\mathrm{pf} \tag{6}\]  
\[\mathrm{I_{V2}}=\mathrm{I_{C2}}\cdot\mathrm{100}\cdot\frac{\mathrm{V_{DC}}}{ \mathrm{Z}}\cdot\mathrm{pf} \tag{7}\]  
\[\mathrm{I_{V3}}=\mathrm{I_{C3}}\cdot\mathrm{100}\cdot\frac{\mathrm{V_{DC}}}{ \mathrm{Z}}\cdot\mathrm{pf} \tag{8}\]  
\[\mathrm{I_{V4}}=\mathrm{I_{C4}}\cdot\mathrm{100}\cdot\frac{\mathrm{V_{DC}}}{ \mathrm{Z}}\cdot\mathrm{pf} \tag{9}\]  
where \(\mathrm{I_{V1}}\), \(\mathrm{I_{V2}}\), \(\mathrm{I_{V3}}\), and \(\mathrm{I_{V4}}\) are the average source currents, \(\mathrm{I_{C1}}\), \(\mathrm{I_{C2}}\), \(\mathrm{I_{C3}}\), and \(\mathrm{I_{C3}}\) are the summation of the product between the current magnitude and conduction period at every output level, \(\mathrm{Z}\) is the load impedance, and \(\mathrm{pf}\) is the power factor. Thus, each source's power delivered to the load can be calculated by multiplying the average current value by the magnitude of their respective DC source as follows:  
\[\mathrm{P_{V1}}=\mathrm{I_{V1}}\mathrm{V_{DC}} \tag{10}\]  
\[\mathrm{P_{V2}}=\mathrm{I_{V2}}\mathrm{V_{DC}} \tag{11}\]\[\mathrm{P_{V3}} =3\mathrm{I_{V3}V_{DC}} \tag{12}\] \[\mathrm{P_{V4}} =3\mathrm{I_{V4}V_{DC}} \tag{13}\]  
where \(\mathrm{P_{V1}}\), \(\mathrm{P_{V2}}\), \(\mathrm{P_{V3}}\), and \(\mathrm{P_{V4}}\) are the power delivered by the sources. Finally, with the knowledge of the source power-sharing, the total input power, \(\mathrm{P_{in}}\) can be estimated using the following expression:  
\[\mathrm{P_{in}}=\mathrm{P_{V1}}+\mathrm{P_{V2}}+\mathrm{P_{V3}}+\mathrm{P_{V4}} \tag{14}\]
## 2 Comprehensive Hydrogen-Integrated Energy System Model
### P2g-CCS Coupling Model  
#### 2.1.1 Ccs Model  
CCS essentially comprises two distinct stages: carbon capture and carbon sequestration.  
During the carbon capture phase, carbon dioxide emitted by industries is caught and separated into two distinct streams. A single stream is conveyed to the P2G equipment, more specifically the methane reactor, where it undergoes a reaction with hydrogen to generate methane, so enabling the cyclic usage of CO\({}_{2}\). The energy usage during this stage  
Figure 1: Architecture of an integrated energy system.  
is categorized into operational and fixed components. Operational energy consumption is calculated by measuring the quantity of carbon dioxide emitted, whereas fixed energy consumption results from alterations in the regular functioning of equipment, leading to extra expenses. The precise formulation is as follows:  
\[\left\{\begin{array}{l}P_{\text{CCS}}(t)=P_{\text{run}}(t)+P_{\text{CCS}}^{b}\\ P_{\text{run}}(t)=e_{\text{ccS}}M_{\text{CCS}}(t)\\ M_{\text{CCS}}(t)=\eta_{\text{ccS}}e_{\text{g2C}}G_{\text{g}}(t)\end{array}\right. \tag{1}\]  
where \(P_{\text{CCS}}(t)\) represents the total energy consumption of the carbon capture system at time \(t\), \(P_{\text{run}}(t)\) denotes the operational energy consumption at time \(t\), \(P_{\text{CCS}}^{b}\) signifies the fixed energy consumption of the carbon capture system, typically a constant; \(e_{\text{CCS}}\) indicates the electricity consumption per unit mass of CO\({}_{2}\) captured, \(M_{\text{CCS}}(t)\) represents the mass of CO\({}_{2}\) captured by the carbon capture system at time \(t\); \(\eta_{\text{ccS}}\) denotes the efficiency of the carbon capture system; \(e_{\text{g2C}}\) represents the mass of CO\({}_{2}\) generated per unit volume of natural gas combustion; and \(G_{\text{g}}(t)\) represents the natural gas consumption at time t by the gas-consuming equipment. This equation signifies that the input for the carbon capture component is the quantity of carbon dioxide emitted through the combustion of natural gas by the system. While its operation results in an increase in a portion of the system's operational costs, it significantly reduces the overall carbon emissions of the system.  
The remaining portion of the captured carbon dioxide Is subjected to sequestration using a carbon dioxide compressor. The CO\({}_{2}\) is pressurized to achieve a high-density state and subsequently sent underground to avert its emission into the atmosphere. The formula representing this procedure is as follows:  
\[\begin{split}& M_{\text{CCS}}(t)=m_{\text{MR}}(t)+m_{\text{f}}(t) \\ & C_{\text{f}}^{\text{CO${}_{2}$}}=c_{\text{f}}\sum\limits_{t=1}^{T }m_{\text{f}}(t)\end{split} \tag{2}\]  
where \(m_{\text{MR}}(t)\) and \(m_{\text{f}}(t)\) represent the input of methane into the reactor, the mass of CO\({}_{2}\) captured and sequestered at time \(t\), respectively; \(C_{\text{f}}^{\text{CO${}_{2}$}}\) denotes the cost of carbon sequestration, and \(c_{\text{f}}\) signifies the cost of sequestering one unit of CO\({}_{2}\) mass.