esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Coverage Control Using Memetic Algorithm
### The CoCMA Proposed for Cluster-based WSNs
#### 3.2.1 Genetic Representation  
Designing a genetic representation that focuses on a given problem is essential for MAs and GAs. A better genetic representation can improve the performance of evolution. For the energy-efficient coverage optimization, it is necessary to convert the schedule for nodes to genetic representations. In this study, an optimal schedule for nodes is used to activate or inactivate sensor nodes in CWSNs at a specific time, so, the network lifetime can be prolonged. For the schedule, solutions are represented by binary strings of 0s and 1s, and the status of a node is represented by an allele. For example, 0 denotes that the sensor node is inactivated, while 1 denotes that the node is activated.  
Figure 3: Flowchart of the optimization strategy using MA in the CoCMA.  
The genetic representation for the energy-efficient coverage optimization is depicted in Figure 4. In this figure, \(\Lambda\) denotes the number of the individuals (chromosomes), and allele \(\ell_{i,j}\) denotes the status of sensor node \(c_{j}\) in chromosome \(i\). Note that the length of each chromosome is \(N\). Since various population sizes lead to different population diversities, it is critical for the CoCMA to use an appropriate population size. According to the review paper presented by [PERSON] _et al._[33], the optimal population size can be controlled by an innovative way [34]. In their discussion, the population size of GA can be one of the variables. Thus, the resulting system favors the propagation of better genes. In order to simplify this work, the population size of CoCMA is set depending on the general experience before activating the CoCMA. Therefore, the population size is fixed for every population generated in each generation. As depicted in Figure 5, there are sixteen nodes used for environmental sensing, and some redundant nodes (red circle) are found. In order to save energy and achieve the optimal coverage ratio, an optimal schedule for nodes is integrated into the CoCMA to inactive these redundant nodes. From the viewpoint of the genetic representation; we use a string \"11111111100000\" to express a chromosome in the example given in Figure 5.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 3 Experimental Evaluation
### Evaluation Metrics  
A variety of commonly used machine learning evaluation metrics are adopted, including Accuracy, Precision, Recall, F1-score, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE).  
For binary classification problem, the sample can be divided into a true positive example (TP), false positive example (FP), true negative example (TN) and false negative example (FN) according to the combination of real situation and model prediction results. The confusion matrix is shown in Table 1.  
Accuracy is defined as the ratio of correctly predicted sample number to total sample number; precision is defined as the ratio of correctly predicted positive samples to the total predicted positive samples. The recall rate is defined as the ratio of correctly predicted positive samples to the actual total number of positive samples; The F1-score is defined as the harmonic mean of precision and recall rate. The calculation formulas of these three indicators are as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}} \tag{15}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{16}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{17}\]  
\[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}. \tag{18}\]  
The root mean square error (RMSE) is the arithmetic square root of the square of the difference between the predicted value and the real value. When the predicted value is completely consistent with the real value, RMSE is equal to 0. The greater the error, the greater the RMSE value. The calculation formula is as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y_{i}}-y_{i}\right)^{2}} \tag{19}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual Class**} & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{2-3}  & **Abnormal** & **Normal** \\ \hline Abnormal & TP & FN \\ Normal & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion Matrix.  
where \(y_{i}\) is the real value and \(\hat{y}_{i}\) is the predicted value, with \(n\) indicating the number of values.  
The Mean Absolute Percentage Error (MAPE) is a percentage value. The smaller the MAPE value, the better the accuracy of the prediction model. The calculation formula is as follows:  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{20}\]  
where \(y_{i}\) is the real value and \(\hat{y}_{i}\) is the predicted value, with \(n\) indicating the number of values.
## 4 Numerical Results and Discussion  
In the current computation, a finite volume method is utilized to discretize the equations of liquid and solid phases. Moreover, the pressure and velocity terms are stored at node center and node faces, respectively by using the staggered grid arrangement. The coupling between the pressure and velocity terms is achieved by SIMPLE algorithm of Patankar [35]. For using SIMPLE algorithm, the mass conservation and momentum equations for fluid are combined to drive the pressure-correction equation. The convection, diffusion, and time terms are discretized by applying a first order upwind scheme, a central difference algorithm, and a first order implicit method, respectively. Finally, the convergence is passed when the values of residuals reduced to \(\leq\)10\({}^{-4}\) for all equations except the energy equation. This considered value is set at 10\({}^{-6}\) for energy equation. All simulations are performed by the commercial software Ansys-Fluent. Figure 2 shows the procedure of the numerical solution in Fluent for this problem.  
Figure 2: Procedure of numerical solution in Fluent for this problem.
## 3 Materials and Methods
### Modeling  
The relationship between cloud adoption and the stated socio-economic and technological aspects is examined in this study using artificial intelligence (AI) techniques. In particular, the dependent variable is modeled using supervised learning methods such as random forests, support vector machines, elastic net regression, and gradient boosting machines.  
By taking into account non-linearities and intricate relationships that conventional linear models could miss, the models assess cloud adoption levels based on independent variables. The relative influence of each predictor is inferred using partial dependence graphs and variable significance metrics.  
Cross-validation techniques are used for confirming the models once they have been trained on the two core periods (2014-2021 and 2014-2024, respectively).  
To ensure model reliability and avoid overfitting, all machine learning models were trained and evaluated using rigorous out-of-sample validation procedures. Data were randomly split into training (80%) and testing (20%) subsets, with stratification by country where appropriate to preserve representativeness. All parameter tuning, including model hyperparameters (such as the number of trees and mtry for Random Forest, learning rate and tree count for XGBoost, and regularization constants for Elastic Net and SVM), was conducted using 10-fold cross-validation on the training set, with performance assessed by RMSE and MAE. The final model evaluation was always performed on the separate, unseen test set to provide an unbiased measure of generalization performance.  
For Random Forest models, we systematically varied the number of predictors sampled at each split (mtry) from 2 to 10, and explored up to 500 trees in the ensemble. XGBoost models were tuned over nrounds (50-300), max_depth (2-6), and eta (0.1-0.4). SVM models were assessed across a range of regularization constants (C). For each algorithm, the optimal configuration was chosen based on the lowest cross-validated RMSE. Dummy variables were generated prior to standardization, and both steps were embedded within the cross-validation folds to prevent data leakage.  
To further evaluate the risk of overfitting, we compared training and test set metrics, reporting out-of-bag (OOB) error for Random Forests and all relevant test set metrics (RMSE, MAE, R\({}^{2}\)) for all models.  
These validation practices, along with transparent reporting of parameter ranges and results, ensure the methodological soundness and reproducibility of our findings.
# Droop Control Strategy of Utility-Scale Photovoltaic Systems Using Adaptive Dead Band
## 3 Simulation Study and Analysis  
### System Description  
To verify the proposed method, a simulation is conducted in the modified IEEE 39 bus system. In the modified system, the utility-scale PV is connected to buses 3, 8, 16, 18, 23, and 27, and the output at the time that generates the maximum output, on average, within 24 h is assumed to be 200 MW per PV. The simulation system is shown in the form of Figure 7. The range of the proposed adaptive dead band is applied equally to all PV systems connected to the grid. Depending on the variation in PV output, the generators at buses 30, 33, and 36 are sequentially turned off, and when the PV is at its maximum, two generators are turned off and one generator is configured to reduce the amount of power generated.  
The daily data of the PV output and load in the modified system are simulated based on the actual data. For PV generation, the actual data obtained from a utility-scale PV connected to the transmission system in the California region published by NREL are used [31]. In case of load, the daily load curve data obtained from the RTE are used [32]. Both data are collected at 15 min intervals, and in this simulation, the proposed method is applied at 1 h intervals. One of the annual datasets is applied per month, and is shown in a boxplot in Figure 8, where the value in the  
Figure 6: Flowchart of the proposed scheme including adaptive dead band.  
averaged data value, while that in the red line inside blue box indicates the average value among those data. The value of the plus mark at the bottom of the graph is the data that differ substantially from the average value. In case of solar generation, the frequency of the plus mark is high because it is affected by the climate. PV generation amount and load data corresponding to Figure 8a,b are applied to the IEEE 39 bus system and the power flow calculation is performed through the simulation tool. The Jacobian matrix is obtained from the power flow results, and voltage sensitivity analysis, Equation (10), is performed using this matrix.  
Figure 8: (**a**) Boxplot of daily solar generation, and (**b**) boxplot of daily load curve.  
Figure 7: Modified IEEE 39 bus system diagram.
## 4 Triggering Mechanism
### Monitoring of the Events  
External triggering occurs when monitoring mechanisms located outside of the network initiate a response. Various types of monitoring mechanisms can be employed, such as monitoring of the specific nodes within the network, monitoring the capabilities or monitoring of the quality of data generated by the network. For instance, the temperature readings from sensors can be monitored against predefined rules, such as minimum and maximum values. If the temperature falls outside of these boundaries, then it will trigger a failure event.  
External triggering is also associated with feedback provided by the operator of the IoT network, such as the operator of the greenhouse discussed in Section 5. Feedback enables continuous refinement of the solution, ensuring closer alignment with business requirements and goals.  
Internal triggering, on the other hand, relies on events generated by the network itself. Each node may have the capability to detect and propagate notifications. In this case, notifications are disseminated across the network, potentially leading to the generation of additional events by specific nodes within the network.  
In the example described in Section 5, event monitoring was implemented using Azure Event Hub and Azure CosmosDB. Multiple agents within the IoT network produce various types of events, which are sent to the Azure cloud, normalized and stored in the document database. A periodic reporting mechanism compares the reported values with the predefined set of capabilities specific to the particular instance of the network, such as the indoor greenhouse. If any discrepancies are detected, then a triggering mechanism executes corrective actions.
## 4 Discussion
Thus, in the present study, we introduced synchrotron microCT as an effective method to study bone regeneration and its remodeling, focusing the investigation on the bone mineral density distribution in rat calvarial defects grafted with two alternative constructs, namely COL scaffolds seeded with hPDLSCs, hPDLSCs + derived CM, hPDLSCs + derived EVs, or hPDLSCs + derived PEI-EVs, and PLA scaffolds enriched with hGMSCs, hGMSCs + derived CM, hGMSCs + derived EVs, or hGMSCs + derived PEI-EVs. The study of the mineralized bone density distribution was performed with the support of the innovative Roschger approach [52], as previously successfully achieved in different studies by some of the authors [50; 54]. The mineralized bone density distribution analysis with synchrotron-based microCT showed increased mineralization in COL/hPDLSCs/PEI-EVs compared to other groups based on the same biomaterial and stem cells. Thus, not only bone morphometric analysis with microCT evidenced better bone regeneration in COL/hPDLSCs/PEI-EVs from a quantitative point of view [20] but the present study has contributed to assuring that their calcium content and distribution mimic better the control sites than all the other groups that grafted defects with the other COL-based constructs. Conversely, a different scenario was found in the case of PLA/hGMSCs/PEI-EVs, showing not only the presence of lower mineralization levels but also a wider density distribution variance than in the control sites. This result can be justified, considering for these samples different bone remodeling kinetics. Indeed, as shown in the representative microCT sections of Figure 3, the network in the test sites, close to the PLA scaffold, appeared to be much denser of osteocyte lacunae than in the corresponding ctr sites. Furthermore, the lacunae were found to be larger in size in test sites, suggesting a recent change from the osteoblast condition and suggesting longer times to complete the mineralization process than six weeks from grafting, as experienced in the present study. However, PLA/hGMSCs/CM samples were shown to reach levels of mineralization higher than the controls, with higher mean and variability values. This result completes the analysis on the conditioned medium performances recently started [17], where PLA/hGMSCs/CM showed a better osteogenic performance compared to constructs without CM; this was verified not only in vitro, by transcriptomic analysis, observing in the 3D-PLA + CM + hGMSCs group the upregulation of genes involved in ossification and regulation of ossification, but also in-vivo, efficiently repairing calvarial defects as revealed by histomorphometric evaluation.
## 3 Methods
### Diagram Denoising with Diffusion Model  
Raw time-frequency diagrams exhibit noise and clutter that can obscure the useful signal components. Here we leverage diffusion models based on a U-Net architecture to filter out noise and recover cleaner time-frequency representations.  
In the forward process, we gradually add Gaussian noise to the input noisy time-frequency map \(x_{0}\) in \(T\) timesteps as follows:  
\[q(x_{t+1}|x_{t})=\mathcal{N}(x_{t+1};\sqrt{1-\beta_{t}}x_{t},\beta_{1}\mathbf{I}) \tag{15}\]  
where \(\beta_{t}\) is the noise schedule that controls the variance of the added Gaussian noise at timestep \(t\), \(\mathbf{I}\) is the identity matrix.  
By performing \(T\) iterations of adding small Gaussian noise, we obtain a highly noisy time-frequency map \(x_{T}\):  
\[x_{T}=x_{0}+\epsilon,\quad\epsilon\sim\mathcal{N}(0,\Sigma) \tag{16}\]  
where \(\Sigma\) is the accumulated noise covariance. This forward process injects noises at different scales into the input and destroys its original structure.  
The forward process acts as a form of regularization that smooths the noise-conditional distribution \(q(x_{t}|x_{0})\) for better generation. It is a key component that enables the diffusion model to remove noise and recover clean signals in the reverse process.  
In the reverse process, we aim to recover the clean time-frequency map \(x_{0}\) from the noisy inputs \(x_{0},x_{1},\dots,x_{T}\) in a recursive manner. A conditional diffusion model with a U-Net decoder is used to perform iterative denoising.  
The U-Net decoder takes \(x_{t}\) and extracted feature representations \(h_{t}\) as input, and predicts the noise residual \(\epsilon_{\theta}(x_{t},t)\) for denoising:  
\[\epsilon_{\theta}(x_{t},t)=\sqrt{1-\bar{\alpha}_{t}}(x_{t}-\sqrt{\bar{\alpha} }tx_{t-1}) \tag{17}\]  
where \(\bar{\alpha}_{t}\) is the denoising strength and \(\theta\) denotes decoder parameters.  
The denoised signal \(\hat{x}_{t-1}\) can be obtained by removing the predicted noise residual:  
\[\begin{split}\hat{x}_{t-1}&=\frac{1}{\sqrt{1-\bar{ \alpha}t}}(x_{t}-\epsilon_{\theta}(x_{t},t))\\ &=\sqrt{\bar{\alpha}}tx_{t-1}+(1-\sqrt{\bar{\alpha}_{t}})x_{t} \end{split} \tag{18}\]  
By starting from \(x_{T}\) and performing \(T\) denoising iterations, we can recover the clean time-frequency map \(\hat{x}_{0}\). The U-Net leverages multiscale features and temporal conditioning to enable effective signal denoising.
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
###### Abstract  
With the rapid growth of video data traffic on the Internet and the development of new types of video transmission systems, the need for ad hoc video encoders has also increased. One such case involves Unmanned Aerial Vehicles (UAVs), widely known as drones, which are used in drone races, search and rescue efforts, capturing panoramic views, and so on. In this paper, we provide an efficiency comparison of the two most popular video encoders--H.264 and H.265--in a drone piloting system using first-person view (FPV). In this system, a drone is used to capture video, which is then transmitted to FPV goggles in real time. We examine the compression efficiency of 4K drone footage by varying parameters such as Group of Pictures (GOP) size, Quantization Parameter (QP), and target bitrate. The quality of the compressed footage is determined using four objective video quality measures: PSNR, SSIM, VMAF, and BRISQUE. Apart from video quality, encoding time and encoding energy consumption are also compared. The research was performed using numerous nodes on a supercomputer.  
H.264; H.265; HEVC; UAV; video compression; FPV; remote reality; power consumption +
Footnote †: journal: Applied  
_articles_
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## Appendix A Cove-Pol Model Formulation and Coefficients  
The form of the CoVe-Pol model parameterization:  
\[\sigma^{0}(\theta,U_{10},\varphi)=\] \[=B0(c_{0},U_{10},\theta)[1+B1(c_{1},U_{10},\theta)\cos(\varphi)+B2(c _{2},U_{10},\theta)\cos(2\varphi)]^{1.6} \tag{1}\]  
where \(B0\), \(B1\) and \(B2\) are functions of wind speed \(U_{10}\) and incidence angle \(\theta\), or alternatively,\(\chi=(\theta-40)/25\). The \(B0\) term is defined as:  
\[B0=10^{\theta_{0}+a_{1}U_{10}}f(a_{2}U_{10},s_{0}) \tag{2}\]  
where,  
\[f(s,s_{0})=\left\{\begin{array}{cc}{(s_{0})}^{a}g(s_{0}),&s<s_{0}\\ g(s),&s>s_{0}\end{array}\right. \tag{3}\]  
where,  
\[g(s)=1/(1+\exp(-s)),\text{ and }\alpha=s_{0}(1-g(s_{0})) \tag{4}\]  
The functions \(a_{0}\), \(a_{1}\), \(a_{2}\), \(\gamma\) and \(s_{0}\) depend on incidence angle only:  
\[\begin{array}{c}a_{0}=c_{1}+c_{2}x+c_{3}x^{2}+c_{4}x^{3}\\ a_{1}=c_{5}+c_{6}x\\ a_{2}=c_{7}+c_{8}x\end{array} \tag{5}\]  
\[\begin{array}{c}\gamma=c_{9}+c_{10}x+c_{11}x^{2}\\ s_{0}=c_{12}+c_{13}x\end{array} \tag{6}\]  
The \(B1\) term is modeled as follows:  
\[B1=\frac{c_{14}(1+x)-c_{15}v(0.5+x-\tanh[4(x+c_{16}+c_{17}v)])}{1+\exp(0.34(v- c_{18}))} \tag{7}\]  
The \(B2\) term was chosen as,  
\[B2=(-d_{1}+d_{2}v_{2})\exp(-v_{2}) \tag{8}\]  
Here \(v_{2}\) is given by,  
\[v_{2}=\left\{\begin{array}{cc}a+b(y-1)^{n}&,&y<y_{0}\\ y&,&y\geq y_{0}\end{array}\right. \tag{9}\]  
where,  
\[y_{0}=c_{19}\,,\quad n=c_{20} \tag{10}\]  
\[a=y_{0}-(y_{0}-1)/n\,,\quad b=1/\left[n(y_{0}-1)^{n-1}\right] \tag{11}\]  
The quantities \(v_{0}\), \(d_{1}\) and \(d_{2}\) are functions of incidence angle only,  
\[\begin{array}{c}v_{0}=c_{21}+c_{22}x+c_{23}x^{2}\\ d_{1}=c_{24}+c_{25}x+c_{26}x^{2}\\ d_{2}=c_{27}+c_{28}x\end{array} \tag{12}\]  
The coefficients are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(c_{1}\) & \(-0.9200\) & \(c_{8}\) & \(0.0159\) & \(c_{15}\) & \(0.0064\) & \(c_{22}\) & \(-3.2592\) \\ \(c_{2}\) & \(-1.1935\) & \(c_{9}\) & \(5.4536\) & \(c_{16}\) & \(0.3141\) & \(c_{23}\) & \(1.2905\) \\ \(c_{3}\) & \(0.0321\) & \(c_{10}\) & \(0.2633\) & \(c_{17}\) & \(0.0117\) & \(c_{24}\) & \(6.0876\) \\ \(c_{4}\) & \(0.3421\) & \(c_{11}\) & \(-2.2313\) & \(c_{18}\) & \(45.4000\) & \(c_{25}\) & \(2.3296\) \\ \(c_{5}\) & \(0\) & \(c_{12}\) & \(0.0472\) & \(c_{19}\) & \(2.0293\) & \(c_{26}\) & \(0.3168\) \\ \(c_{6}\) & \(0.0040\) & \(c_{13}\) & \(-0.0689\) & \(c_{20}\) & \(2.9350\) & \(c_{27}\) & \(4.0550\) \\ \(c_{7}\) & \(0.0882\) & \(c_{14}\) & \(0.0043\) & \(c_{21}\) & \(16.7318\) & \(c_{28}\) & \(1.5237\) \\ \hline \end{tabular}
\end{table}
Table 1: CoVe-Pol coefficients.