esa-sceva/satcom-chunk-collection huggingface dataset:
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 3 Results
### Bulk Behavior
The shading indicates the estimated 1-sigma standard deviation of the population decorrelation behavior for each individual trace. The lags are computed in seconds, which correspond to single samples at 1 Hz sampling for the CYGNSS observatory. Note that \(DD\) generally follows the \(SD_{obs}\) trace, suggesting that single-differencing for this particular use case may be a reasonable representation of bulk error correlation behavior. The tuning parameter \(\gamma\) is used to vary the relative magnitude of the near-sample correlated error roll-off. In our tuned model, this parameter remains at 1. The tuning parameter \(\delta\) is used to enhance or reduce the steepness of the roll-off in correlation caused by two observations moving farther apart in the nadir and zenith coordinate systems. If \(\delta>1\), it enhances the steepness of decay. The fact that our optimized tuning maintains \(\delta=1\) suggests that the filtering theory discussed in Lemma A1 is a reasonable model of decay.
# PV-Powered Charging Station for Electric Vehicles: Power Management with Integrated V2G
###### Abstract
This paper studies the power management of a PV-powered charging station with integrated V2G from two aspects of energy scheduling and real-time control. The main contributions of this work are:  
1. An EV charging station model combined with V2G based on a DC microgrid is proposed, which consists of four parts: PV sources, a storage system, a public grid connection, and EVs. This model makes use of EV's \"load-source\" character to bring benefits to users while meeting user needs. 2. A suitable power management strategy for the EV charging station with integrated V2G is proposed. In the strategy, the priority order for charging EVs is PV sources, the storage system, the public grid. During the \"peak\" periods of the public grid, the DC microgrid discharges to the public grid, and the discharge priority order is PV sources and then the EVs, considering the discharge cost. 3. The SPVA is proposed to calculate the optimal charging/discharging start time of EVs to achieve the effect of peak shaving and valley filling, while meeting the charging requirements of EVs, taking into account the EV arrival time, departure time, \(SOC_{EV\_in}\), and \(SOC_{EV\_final\_lim}\). Most importantly, it reduces the cost of the public grid, which is beneficial to the public grid and EV users. 4. A reasonable electricity price mechanism is set which provides the possibility for SPVA. In order to protect the stability of the public grid, the charge/discharge power limits are different in accordance with the \"peak\" periods and \"valley\" periods of the public grid. The presented DC microgrid structure with integrated V2G is described in Section 2. Power flow management: grid-to-vehicle (G2V) and V2G algorithm is introduced in Section 3. Simulation results and analyses are presented in Section 4. Conclusions and further works are given in Section 5.
###### Abstract  
It has long been known that the hysteresis loops of sand under cyclic loading gradually become asymmetric with the increase of strain amplitudes, but a symmetrical hysteresis loop is widely assumed in current practice. Despite several methods which have been proposed recently to consider the hysteresis loop irregularities, previous research has lacked a quantitative study on the effects of determination methods on the shear modulus \(G\) and modulus reduction curve \(G/G_{\rm max}\). The primary objective of the current study is to evaluate the uncertainties associated with the shear modulus measurements introduced by four determination methods. Reconstituted sand specimens prepared at three relative densities are tested using strain-controlled cyclic triaxial tests, at various effective confining pressures. The results in terms of \(G\) and \(G/G_{\rm max}\) with increasing shear strain are presented, following by the difference quantification in the calculated \(G/G_{\rm max}\) caused by the determination methods, the \(G_{\rm max}\) definition and the cycle number. The results show that the calculated \(G/G_{\rm max}\) may differ significantly for the same hysteresis loop, with a maximum percentage change of 40-50%. The aggravated influence at low confining pressure highlights that careful consideration of the asymmetrical hysteresis loop at large strains is warranted.  
shear modulus; cyclic triaxial tests; asymmetrical hysteresis loop; soil dynamics Article
## 6 Conclusions  
Energy efficient data transmission is one of the key factors for energy constraint wireless sensor network. An energy efficient cooperative technique considering low density parity check codes is modeled and simulated using Matlab. The results show that the cooperative communication outperforms SISO transmission at the presence of error correction code. The energy efficiency remains almost unchanged in different encoding rates but it largely varies with the change in constellation size. BER analysis is also taken to show the similar error characteristics in the cooperative MIMO environment. Data with smaller encoding rate shows better BER results than larger encoding rate for a fixed SNR. Simulation is also performed in the situation of a fading environment. It is also found that cooperative communication is more energy efficient than SISO transmission in smaller targeted BER. Therefore it can be concluded that cooperative MIMO with LDPC can be a good choice for high reception quality signals.
# A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid
## 4 Discussion
### Future Research  
In this paper, the research of 2D displacement measurement based on a single elliptical paraboloid was introduced. Compared with displacement measurement based on internal-reflection, the system proposed had the advantages of better measurement range in two-dimensions. In addition, the system developed was easy to set up and adjust within a short time period and at low cost in normal application, compared to interferometers. Although its measurement range was shorter than that of an interferometer, many methods could be used to solve this shortcoming. In order to improve the measurement range and application field of 2D displacement measurement, future research must focus on the calibration and measurement of the elliptical paraboloid array. The elliptical paraboloid array has great potential in measuring geometric errors of CNC machine tools, which contain positioning  
Figure 12: Error caused by displacement off the focal plane.  
errors, straightness errors and perpendicularity errors. It will provide a new tool for understanding and measuring the geometric errors of CNC machine tools.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 4 Simulations
### Test Scenarios  
#### 4.3.1 Custom Trips  
In this scenario, three custom trips were created for each of the various traffic distributions and densities to measure the trip time for a vehicle from a point of interest moving towards the Prophet's Mosque. This is an example case of a taxi vehicle providing a service  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline Network Simulation & OMNeT++ v5.6.2 \\ Traffic Simulation & SUMO v1.11.0 \\ Data Analysis Tool 1 & MATLAB R2021b \\ Data Analysis Tool 2 & Microsoft Excel \\ \hline \hline \end{tabular}
\end{table}
Table 6: Software specification.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline model & IDM (Intelligent Driver Model) \\ minGap & 2.5 m \\ accel, decel, emergencyDecel & 2.6 m/s\({}^{2}\), 4.5 m/s\({}^{2}\), 9.0 m/s\({}^{2}\) \\ tau & 1 s \\ delta & 4 \\ stepping & 0.25 s \\ actionStepLength & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Car-following model parameters (human-driven model).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline CPU & Intel Core i5-8250U CPU @ 1.60 GHz \\ Operating System & Windows 10 Build 18363 (64-bit) \\ Main Memory & 20 GB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Physical host specification.  
to the pilgrims arriving in the city. The three routes use a primary highway and move towards the major Ring Road encircling the Prophet's Mosque and nearby accommodations, then move towards the local roads to drop off the pilgrim at the nearest location to the mosque.  
This test case scenario, executed and averaged over repetitions, provides the researchers with an estimate of the impact of traffic distributions and densities on the single trip journey of pilgrims from various points of interest. Three custom vehicle trips were considered moving towards the Prophet's Mosque:  
* West Highway to Prophet's Mosque (Figure 6);
* South Highway to Prophet's Mosque (Figure 7);
* North Highway to Prophet's Mosque (Figure 8).
# A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies
## 5 Numerical Experiments
### Comparison of Methods  
To evaluate the performance of QL in this particular problem, we compared the performance of the QL and heuristic algorithms, the genetic algorithm (GA) and particle swarm optimization (PSO), in the model studied by [PERSON] et al. (2018). The reason for selecting the model of [PERSON] et al. (2018) is based on the similarity in the parameterization used in our study. Other candidates for comparison could have been the models proposed by [PERSON] et al. (2018) and [PERSON] et al. (2018), however, these lack some features considered in our study (i.e., the role of delivery and pickup, selection of charging stations).  
In their model, 3 UAVs transport items from 1 depot to 10 demand points, as shown in Figure 9, and the following two constraints are considered:  
* Maximum payload of UAV;
* Time window for transportation at each point.  
The objective is to minimize the total transportation distance, taking into account the above two constraints. We performed QL on the same problem setting. The set parameters are shown in Table 4. It should be noted that the number of iterations is different between QL and GA and PSO since QL generates one transportation plan in each iteration, while the GA and PSO generate 40 delivery plans in each iteration.  
The minimum transport distances obtained by each method are shown in Table 5, which is the minimum value of all trials. It is noted that this study does not compare computation times due to differences in computer performance. Figure 9 shows the shortest route obtained by PSO and QL. From these results, it can be observed that the QL method is useful for the UAV transportation planning problem since it can find a shorter route than the other methods. The reason why QL obtained a better solution than other methods is considered to be the large number of evaluations in QL. In QL, the state of each environment is evaluated at the end of each action, which may have resulted in earlier convergence than methods that evaluate plans after all actions have been completed, such as PSO and GA. For this reason, it can be said that the QL method designed in this study is useful to improve the efficiency of the item transportation route.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Value**} \\ \cline{2-4}  & **GA** & **PSO** & **QL** \\ \hline Population number & 40 & 40 & - \\ Iteration & 100 & 100 & 4000 \\ Calculation times & 50 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The set parameters of the GA, PSO, and QL.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Algorithm** & **Minimum Distance [km]** \\ \hline [PERSON] et al. (2018) & PSO & 350.30 \\ [PERSON] et al. (2018) & GA & 379.04 \\ our study & QL & 322.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of minimum transportation costs of each method.
## 3 Damage Identification Process  
A general damage identification procedure is proposed in this paper. As shown in Figure 3, the procedure includes four main steps including finite element modeling, production of training  
Figure 2: Structure of the BP (backpropagation) network.  
samples, training of neuron networks, and prediction of structural damage. In the first step, detailed finite element models of offshore structures should be established for individual projects. The sample number and the prediction precision \(\varepsilon\) also need to be specified in this step. In the second step, the training samples are produced using the updated damaged model. As mentioned in Sections 2.1 and 2.2, the difference of the elastic modulus between intact and damaged elements may be used as a damage indicator (\(\Delta\)E), and the relative difference of the square frequency (RSF) and the modal difference (\(\Delta\mathcal{O}\)) can be used as damage indices. To reduce the total number of damage indices, this paper proposes to use the combined damage index ([RSF, \(\Delta\mathcal{O}\)]) and the damage values of elements (\(\Delta\)E) as the training data for the BP networks. The training data are obtained from modal analysis of the finite element model. In the third step, neural networks are established using the training data, and then the networks are trained using the training samples. Note that the trained BP networks must be validated using a test sample. Steps 2 and 3 involve iterations. If the prediction precision is less than the given value \(\varepsilon\), the process will switch to the second step to enlarge the training sample numbers \(\delta\). In the fourth step, prediction of damage is performed using the modal values from sensor measurements.  
Figure 3: Analysis flowchart; (MSE: Mean Squared Error).
## 3 Materials and Methods
### AI-Based Models Employed
In this table, we provide concise information for each FS technique, and readers are encouraged to refer to the respective references for more in-depth details. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Key Tuned Hyperparameters** & **Tuning Range/Selected Values** \\ \hline ANN & Hidden layers, neurons/layer, learning rate & 1–3 layers; 10–100 neurons; 0.001–0.01 \\ \hline DT & Max depth, min leaf size & Max depth: 5–30; Min leaf: 1–10 \\ \hline SVM & Kernel type, C, \(\varepsilon\)-insensitive loss & Kernels: linear, RBF; C: 1–100; \(\varepsilon\): 0.01–0.1 \\ \hline ELM & Number of neurons, activation function & Neurons: 50–200; Functions: sigmoid, ReLU \\ \hline GBoost & Number of trees, learning rate, max depth & Trees: 100–500; LR: 0.01–0.1; Depth: 3–10 \\ \hline xGBoost & Number of estimators, learning rate, max depth & Estimators: 100–300; LR: 0.01–0.1; Depth: 3–10 \\ \hline RF & Number of trees, max depth, min samples split & Trees: 100–500; Depth: 10–30 \\ \hline TreeBag & Number of learners, learning rate & Learners: 100–500 \\ \hline GLR & Distribution type, link function & Normal/Gaussian, Identity/Log \\ \hline GPR & Kernel function, sigma, basis function & Kernels: RBF, RationalQuadratic; Sigma: 0.1–10 \\ \hline LR & Regularization (if applied) & L2 penalty or none \\ \hline GAM & Spline degree, number of knots & Degree: 2–3; Knots: 5–20 \\ \hline KRR & Alpha (regularization), kernel type & Alpha: 0.01–1; Kernels: linear, polynomial \\ \hline KNN & Number of neighbors, distance metric & k: 3–15; Metric: Euclidean, Minkowski \\ \hline LRR & Regularization strength & Ridge alpha: 0.01–1 \\ \hline BayesLR & Prior type, regularization strength & Gaussian prior; Alpha: 0.01–1 \\ \hline CNN & Filters, kernel size, pooling size & Filters: 16–64; Kernel: 3 \(\times\) 3; Pool: 2 \(\times\) 2 \\ \hline FNN & Layers, neurons/layer, dropout rate & Layers: 2–4; Neurons: 64–128; Dropout: 0.2–0.5 \\ \hline DNN & Depth, neurons, activation function & Layers: 3–5; Neurons: 64–256; ReLU \\ \hline ResNet & Number of residual blocks, learning rate & Blocks: 2–4; LR: 0.001–0.01 \\ \hline FCNN & Layers, neurons/layer, optimizer & Layers: 2–4; Neurons: 64–128; Optimizer: Adam \\ \hline LSTM & Sequence length (lag size), hidden units, learning rate & Lags: 3–24; Units: 50–150; LR: 0.001–0.01 \\ \hline LSTM-RNN & Number of LSTM cells, dropout, batch size & LSTM layers: 1–3; Dropout: 0.2–0.5; Batch: 32–128 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of tuned hyperparameters for each ML & DL model.
## 2 Materials and Methods
### Performance Evaluation Metrics  
In the context of multi-label mRNA subcellular localization prediction studies, the diversity of the multi-label space compared to the single-label space leads to increased complexity in evaluating multi-label learning algorithms. Therefore, to comprehensively assess the learning capability of multi-label models, we need to employ multiple evaluation metrics. We have selected six key evaluation metrics to assess the performance of the model: Hamming loss, One-error, Accuracy, Coverage, Average precision, and Ranking loss [30; 31]. Accuracy reflects the proportion of correctly predicted samples by the model and is suitable for measuring overall classification performance; Average precision combines precision and recall, making it particularly suitable for imbalanced datasets; One-error focuses on whether the most relevant label predicted by the model is in the true label set, with lower values indicating better performance; Ranking loss evaluates the accuracy of the model's label ranking, with smaller values indicating more reasonable ranking; Hamming loss measures the proportion of incorrectly predicted labels in multi-label classification tasks, with lower values indicating better model performance. These metrics collectively provide a multi-dimensional evaluation of model performance, catering to different task requirements and data characteristics. For a test set S = {(x\({}_{1}\), Y\({}_{1}\)), (x\({}_{2}\), Y\({}_{2}\)), \(\ldots\), (x\({}_{p}\), Y\({}_{p}\))} containing multiple samples, we will use the following evaluation formulas to comprehensively assess the model's performance.  
\[\textit{Hamming Loss}=\frac{1}{p}\sum_{i=1}^{p}\frac{1}{q}|R_{i}\bigtriangleup Y _{i}| \tag{3}\]  
\[\textit{One-error}=\frac{1}{p}\sum_{i=1}^{p}\Bigl{\{}\Bigl{[}arg_{y^{\prime} \in y_{i}}\textit{maxf}\bigl{(}x_{i},y^{\prime}\bigr{)}\Bigr{]}\in Y_{i} \Bigr{\}} \tag{4}\]  
\[\textit{Accuracy}=\frac{1}{p}\sum_{i=1}^{p}\frac{R_{i}\cap Y_{i}}{R_{i}\cup Y _{i}} \tag{5}\]  
\[\textit{Coverage}=\frac{1}{p}\sum_{i=1}^{p}\textit{max}_{y^{\prime}\in Y_{i} }\textit{rank}_{f}\bigl{(}x_{i},y^{\prime}\bigr{)}-1 \tag{6}\]  
\[\textit{Average precision}=\frac{1}{p}\sum_{i=1}^{p}\frac{1}{|Y_{i}|}\Bigl{|} \Bigl{\{}\frac{\Bigl{\{}y^{\prime}\Bigr{|}\textit{rank}_{f}(x_{i},y^{\prime} )\leq\textit{rank}_{f}(x_{i},y),y^{\prime}\in Y_{i}\Bigr{\}}\Bigr{|}}{\textit{ rank}_{f}(x_{i},y)} \tag{7}\]  
\[\textit{Ranking loss}=\frac{1}{p}\sum_{i=1}^{p}\frac{1}{|Y_{i}|\big{|} \big{|}\big{\{}(y_{1},y_{2})|f(x_{i},y_{1})\leq f(x_{i},y_{2}),(y_{1},y_{2}) \in Y_{i}\times\overline{Y}_{i}\big{\}}\big{|}} \tag{8}\]  
where \(\Delta\) represents the symmetric difference between two sets, f(\(\cdot\)) is the multi-label classifier, \(\textit{rank}_{f}\) represents the rank of y in Y based on the descending order, q is the cardinality of \(Y_{i}\), \(Y_{i}\) is the complement of \(\overline{Y}_{i}\), \(Y_{i}\) and \(R_{i}\) are, respectively, the true label set and the predicted label set for a sequence.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 5 Discussion  
The PWM method is a mature technology of thruster control and is widely used [6; 7; 8], but its high-frequency on/off thruster valve operation and valve lifetime are the main causes of the failure problems studied in this paper. Motion-based diagnosis has been proven to be effective [19], and Kalman filtering has been applied in engineering for a long time, however, some shortcomings existed in the proposed research of motion-based fault diagnosis. The approximate control signal noise has a serious impact on Kalman filter model-based state estimation. The frequency of the noise is different from that of the control signal. Therefore, a bandpass filter was introduced to suppress the approximate control signal noise. At the same time, Kalman filtering was used to estimate the motion state and to detect the failure of the thruster valves. The simulation shown in Figure 11 showed that the results met the criterion of the false alarm rates of 5% by blending the filter detection. In addition, to support the redundancy management of the thruster, a recovery evaluation method of employing the remaining fault-free thruster to track the command performance under the failure of a thruster valve was employed; Figure 14 demonstrates the effectiveness of this method. The algorithm running time and stability were tested in 1000 cases, as shown in Figure 14. The running time was between 3.2 s and 4.8 s, and the algorithm operated stably without a dead cycle.  
In this study, several thruster missions, including on-orbit, de-orbit, and initial reentry, were assumed to be performed, in which the atmospheric density is close to vacuum. In the future, the extended application of this method, such as the aero-assisted orbital transfer mission, will be focused on, in which the atmospheric density is in the low-density state. In addition, the redundancy management of actuators will also be one of the key research directions in the future.