esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Selection Parameters of the MPPT Algorithms
### True MPPT  
A true MPPT algorithm is one that always tracks the actual MPP of the PV module under all conditions, regardless of external conditions. Some algorithms approximate the maximum power but may not always reach it due to their limitations, such as slow tracking or being affected by local maxima. True MPPT techniques adapt more robustly to rapidly changing irradiance or temperature and can avoid power loss due to inefficient tracking.
# 2 \(\times\) 2 Matrices: Manifolds, Realizations, Applications
## 3 Canonical Integral Transforms and the Universal \(2\times 2\) Matrix Covers
### The Canonical Transform Kernel
\right)\) in (11) yields the Fresnel transform [19] which is a convolution of the signal at \(z=0\) with an oscillating Gaussian \(e^{\mathrm{i}\pi/4}\exp\mathrm{i}[(q-q^{\prime})^{2}/2z]/\sqrt{2\pi z}\). The complex normalization constant (31) clearly depends on the phase that we care to assign to the element \(b\) in the matrix: When \(b>0\) we can choose \(\arg b\in\{0,\pm 2\pi,\ldots\}\) or, when \(b<0\), \(\arg b\in\{\pm\pi,\pm 3\pi,\ldots\}\). This is where the double valuation, inevitable in the square root, shows that the group of canonical integral transforms covers twice the group of \(2\times 2\) real matrices. When \(\arg b=0\) this is the (true and only) group unit, while when \(\arg b=2\pi\) we obtain the'second metaplectic unit'. For \(\arg b=\pi\) or \(3\pi\) we have the first and second negative unit of matrices \(-\mathbf{1}\). This double cover of \(\mathsf{Sp}(2,R)\) is called the metaplectic group \(\mathsf{Mp}(2,R)\), using the Greek root \(\mu e\pi a\), signifying beyond symplectic. The metaplectic group does not have a faithful finite-dimensional matrix representation. For each matrix \(\mathbf{M}\in\mathsf{Sp}(2,R)\) there are thus two operators, \(\pm\mathcal{C}_{\mathbf{M}}\). Of course, the sign of \(\mathcal{C}_{\mathbf{M}}\) must be defined consistently, the unit operator and the inverse \(\mathcal{C}_{\mathbf{M}}^{-1}\) of an operator \(\mathcal{C}_{\mathbf{M}}\) are unique, but the labeling of \(\mathcal{C}\)'s by \(2\times 2\) matrices is not. This is referred to as the'metaplectic sign problem', and reflects the fact that the set of \(\mathcal{C}_{\mathbf{M}}\)'s actually realizes the metaplectic group \(\mathsf{Mp}(2,R)\). The sign problem is controlled with the help of (29)-(30). To the point, consider the Fourier transform \(\mathcal{F}\) in (21), and the canonical transform \(\mathcal{C}_{\mathbf{F}}\) parametrized by the matrix \(\mathbf{F}=\left( \begin{smallmatrix}0&1\\ -1&0\end{smallmatrix} \right)\) with \(b=1\) (\(\arg b=0\)), whose kernel coincide but whose constant \(K_{\mathbf{F}}\) in (31) is \(e^{-\mathrm{i}\frac{1}{2}\pi}/\sqrt{2\pi}\). Clearly \(\mathcal{F}\) and \(\mathcal{C}_{\mathbf{F}}\) differ by this phase that distinguishes them as:  
\[\mathcal{C}_{\mathbf{F}}=e^{-\mathrm{i}\frac{1}{2}\pi}\mathcal{F}, \tag{33}\]  
hence \(\mathcal{F}^{4}=\mathcal{I}\) but \(\mathcal{C}_{\mathbf{F}}^{4}=-\mathcal{I}\), while only \(\mathcal{C}_{\mathbf{F}}^{8}=\mathcal{I}\). On the other hand, when we consider \(\mathbf{F}^{\Pi}=\left( \begin{smallmatrix}0&1\\ -1&0\end{smallmatrix} \right)\) also with \(b^{\Pi}=1\) but \(\arg b^{\Pi}=2\pi\); on the second Riemann sheet the corresponding relation is \(\mathcal{C}_{\mathbf{F}^{\Pi}}=e^{-\mathrm{i}\frac{1}{2}\pi}\mathcal{F}\). Thus, \(-\mathcal{C}_{\mathbf{F}^{\Pi}}^{4}\) is the'second metaplectic unit', while \(\mathcal{C}_{\mathbf{F}^{\Pi}}^{8}=\mathcal{I}\) returns the first, that is the true group unit.
## 3 Methodology
### Evaluation Metrics  
The metrics considered for quantitative evaluation of the model on test data are precision, recall, and F1-score.  
Precision is the number of true positives divided by the number of total positive predictions.  
Precision measures the model's ability to predict the positives; it is the true positive divided by the true positive and false negative.  
Recall \[=\frac{\text{True\ Positive}(TP)}{\text{True\ Positive}(TP)+\text{ False\ Negative}(FN)}\]  
F1 score is the harmonic mean of precision and recall given by  
\[F1 Score=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\]  
Categorical Accuracy is the percentage of predicted values that match the actual truth values. The calculation for the same is done as follows:  
* First, identify the index at which the maximum value occurs using argmax.
* If it is the same for both predicted and true value, it is considered accurate.  
Here, since the maximum value index is observed, the predicted value can be a logit or probability function.  
Categorical accuracy \[=\frac{\text{accurately\ predicted\ records}}{\text{total\ number\ of\ records}}\]
* Reciever Operating Characteristics Curve  
The receiver operating characteristic (ROC) curve is determined by calculating the average ROC for each of the five folds. Practically, for each fold, different false- and true-positive-rates exist, and the mean cannot be calculated; hence, the first aggregation of all False-Positive-Rates (FPR) into one vector is done, serving as the x axis of the average ROC curve. The True-Positive Rate (TPR) needs to be interpolated. The TPR represents the corresponding y-points to the previously collected x-points.
## 3 Results and Discussion
### Numerical Simulation of the Shock Wave  
Based on the positive corona current calculated in the previous section, combined with Formula (9), the energy obtained by a single pulse plasma is about 0.15 J, and the mass of TNT is 7.6 \(\times\) 10\({}^{-5}\) g. The contours of the shock wave pressure at different moments are shown in Figure 11. It can be seen that there is an obvious wavefront in the outer ring of the shock wave, and with the development of time, the plasma shock wave continuously propagates outward from the explosion point in the form of an arc. The pressure value in the figure includes the atmospheric pressure, and the actual pressure fluctuation needs to subtract the atmospheric pressure.  
Figure 12 shows the pressure waveforms of 12 monitoring points. It can be seen that the peak overpressure gradually decreases as it moves away from the explosion point, and there are obvious positive and negative pressure zones. The action time of positive pressure is shorter than negative. The negative pressure zone is generated by excessive expansion caused by the inertial action of the shock wave, and the action time is longer, which is consistent with the propagation law of the shock wave. The peak overpressure at 1 cm away from the explosion point reaches 381 kPa and then decays rapidly. When the shockwave propagates to 4 cm, the pressure drops to 30 kPa, and the attenuation reaches more than 10 times. Then, the attenuation speed slows down gradually, and the peak value at 12 cm is 0.5 kPa, showing an exponential decay trend.  
Figure 10: Electric field distribution on the axis at different times.  
The variation of peak overpressure and shock wave propagation velocity with distance is shown in Figure 13, and the trends of both are basically consistent. The pressure and velocity at the same location reaches the peak value at the same time. The propagation velocity decays extremely fast near the explosion point, reaching 384 m/s at 4 cm, and then slowly decreases. Combined with Figure 12, as the distance from the source increases, the duration time of the wave increases, too. It can be explained that as the propagation distance increases, the speed of the shock wave decreases, and the time it takes for the wave to move from one monitoring point to another with the same distance is increased, resulting in an increase in pulse duration. At 10 cm, the velocity is 346 m/s, which is close to the speed of sound. Related studies have shown that when the ratio of shock wave propagation velocity to acoustic velocity is close to 1, the shock wave belongs to a weak shock wave, its propagation process is isentropic, and the pressure change of the wavefront  
Figure 11: Contours of plasma explosion pressure at different moments: (**a**) 0.05 ms; (**b**) 0.15 ms; (**c**) 0.25 ms; (**d**) 0.3 ms.  
Figure 12: Pressure waveforms of different monitoring points: (**a**) points \(1-6\); (**b**) points \(7-12\).  
can be approximately equivalent to the sound pressure waveform [29]. Therefore, the propagation characteristics of corona-generated audible noise can be analyzed through linear acoustic theory.
# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
###### Abstract  
A UAV-assisted caching strategy considering content cache pricing in vehicular networks is proposed to address the problem of high communication load and high backhaul link overhead in vehicular networks. Consider a traffic scenario consisting of a content provider (CP), a network operator (NO), and multiple mobile users, where the NO has a set of cache-enabled roadside units (RSUs) and an unmanned aerial vehicle (UAV). The CP leases some popular contents to the NO for its benefit and the NO places this leased content in its RSU's local cache to save expensive backhaul transmission overhead and latency. However, both NO and CP are selfish and their interests conflict with each other because they have opposing expectations for content pricing. In order to take into account the interests of both, this paper defines the utilities of CP and MNO and uses the Stackelberg game framework to model the competition between the two entities, where CP acts as a leader and sets the rental price of the content and NO acts as a follower responding to CP's actions. An iteration-based dynamic programming algorithm is also designed to find the Stackelberg equilibrium. Meanwhile, a caching-capable UAV is introduced into the vehicular network and, based on this, a Dijkstra-based path planning algorithm is designed to further increase the total utility of NO by optimizing the trajectory of the UAV. The simulation results show that the strategy in this paper can reasonably allocate the benefits of CP and NO, reduce the average request delay, and increase the utility of NO; for example, we reduced the request latency for vehicle users by 27% and increased the total utility of NO by 13%.  
vehicular network; caching technology; Stackelberg game; UAV 2023  
[PERSON]; [PERSON] UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks. _Appl. Sci._**2023**, _13_, 9246. [[https://doi.org/10.3390/app13169246](https://doi.org/10.3390/app13169246)]([https://doi.org/10.3390/app13169246](https://doi.org/10.3390/app13169246))
## 5 Simulation Results and Discussion
### Simulations and Analysis Under Different Circumstances
#### 5.3.1 Compare APM and RPM  
Figure 6 shows a dynamic response diagram of the transmission power of the Line 7-9 under APM and RPM. As shown in Figure 6, without an additional damping controller, the amplitude of power oscillation of the transmission line is larger than that with an additional controller. Figure 7 shows a dynamic response diagram of the rotor speed of Generator 3 under APM and RPM. Through simulation comparison, it is found that the damping capacity provided by APM is stronger than that of RPM. Oscillation attenuation speed is faster and the transient oscillation amplitude is smaller under the active modulation. The active power modulation has good control performance when the power system appears inter-area oscillations, thereby verifying the control effect.  
Figure 6: (**a**) Transmission power of Line 7-9 at Case A; (**b**) Transmission power of Line 7-9 at Case B.
# Prediction of Cotton Yarn Quality Based on Attention-GRU
## 2 Cotton Yarn Quality Prediction Model
### The Attention Mechanism  
In practical scenarios, cotton yarn quality undergoes continuous variations due to various factors. The impact of raw cotton performance indicators and distinct processing stages on yarn quality often exhibits variations. Conventional GRU neural networks, however, do not differentiate among these feature inputs, making it challenging to discern critical information from the inputs. The attention mechanism emulates the resource allocation mechanism of human attention, concentrating focus on pivotal elements while diminishing attention on non-critical elements [24]. The attention mechanism is commonly employed to enhance Seq-to-Seq models. The Seq-to-Seq model, originally introduced in the field of machine translation, concatenates two RNNs (LSTM, GRU) where the input RNN functions as an encoder convert input sequences into hidden states before transmitting them to another RNN, referred to as the decoder. This process facilitates the mapping of variable-length output sequences. Employing the encoder-decoder architecture for training not only resolves the challenge of fixed input and output lengths in traditional tasks but also enhances training efficiency. As illustrated in Figure 4, the Seq-to-Seq model with the incorporation of the attention mechanism operates by performing correlation calculations between the hidden states of the encoder and a specific unit within the decoder [25]. After obtaining the weighting values, the encoder's hidden states are weighted and summed. The summation result is then concatenated with the hidden state of the respective unit, yielding the model's prediction output with the attention mechanism integrated.  
Figure 3: GRU unit structure.  
Therefore, this study aims to enhance the predictive accuracy of the model by employing the attention mechanism to allocate weights to the hidden states of the GRU neural network. This adaptive allocation enables the model to focus on the most influential input features affecting yarn quality, thereby improving its predictive precision.
## 3 Enhancement of MEMS INS for UAV Navigation  
### Overall Scheme  
According to the above discussion, the drift-free position and heading of the UAV can be calculated from the cross-modal image matching, in which the information will be used to enhance the MEMS INS to improve the autonomous navigation performance of UAV.  
INS obtains the position, velocity, and attitude of the UAV by integrating the acceleration and angular rate from the accelerometers and gyros, respectively. However, the navigation errors of the INS will accumulate due to the drifts of the accelerometers and the gyros used [30,31], which is especially challenging for the MEMS INS due to the unstable characteristics of the MEMS inertial sensors [1,2]. The cross-modal image matching is used to enhance the performance of the MEMS INS herein. The overall scheme is shown in Figure 7, and the main steps are as follows:  
1. The real-time images from the camera mounted in the UAV are transformed and corrected by the reference attitude from the MEMS INS initially, and then from the extended Kalman filter which will be discussed below.
2. The transformed real-time images are used to match with the geo-referenced images, which is pre-prepared and stored in the UAV navigation computer before the task, and obtain the position and heading of the UAV by solving the PNP pose as discussed in Section 2.
3. The MEMS INS position, velocity, attitude, and heading are calculated by the inertial navigation algorithm.
4. An extended Kalman filter is designed to fuse the navigation data from image matching and from MEMS INS.  
Figure 6: Results of coarse matching and fine alignment. Image (**a,b**) shows the results of coarse matching. Image (**c,d**) shows the results of fine alignment. The areas in the windows in these results are enlarged and given in the top right.  
The system model and measurement model for data fusion are derived, respectively. The extended Kalman filter compares the navigation results from the image matching and from MEMS INS, and estimates and corrects the position, velocity, attitude errors, and compensates the drifts of the inertial sensors.
## 4 The Proposed G-TIAG
### Design of Feature Representation Layers
#### 4.3.1 Design of GCN  
The GCN learns the topological relations between nodes and nodes, edges and edges, and between nodes and edges for the TIAGs, and extracts two matrices from each TIAG as the input to the GCN, and we first define the input and output of the GCN.  
* Feature matrix \(H_{p\times x}\), which is formed from the feature representations of each node, where \(v\) denotes the number of vertices in the TIAG, and \(x\) denotes the number of attributive features of each node.
* Adjacency matrix \(A_{v\times v}\), which is obtained from each TIAG.
* The output vector, which is denoted as \(X_{t-L+1}^{t}=\{X_{t-L+1},X_{t-L+2},\dots,X_{t}\}\).  
We design a two-layer convolutional structure to implement low-order graph embedding. Each convolutional layer can be written as a nonlinear function with the following rules.  
\[H^{l+1}=f\Big{(}H^{l},A\Big{)} \tag{4}\]  
\(H^{0}=H\), \(H^{l}=Z\) (\(H^{l}\) is the output of layer \(l\)), \(l\) presents the number of layers, and the models differ in the choice and parameterization settings of the \(f(\cdot,\cdot)\) function. Specifically, the propagation rules for each layer are as follows.  
\[f\Big{(}H^{l},A\Big{)}=\sigma\Big{(}AH^{l}W^{l}\Big{)} \tag{5}\]  
where \(W^{l}\) is the weight matrix of the \(l\)-layer neural network, and \(\sigma(\cdot)\) is the ReLU nonlinear activation function. In addition, we add a unit matrix \(I\) to the matrix \(A\) to achieve self-loop and normalize \(A\). To make each row of A add up to 1, we multiply by the inverse of a degree matrix \(D\). \(D^{-1}\) multiplied with \(A\) indicates averaging over adjacent node features. We adopt the new propagation rule proposed by [PERSON] et al. [24], as shown in the following equation.  
\[f\Big{(}H^{l},A\Big{)}=\sigma\Big{(}\hat{D}^{-\frac{1}{2}}\hat{A}\hat{D}^{- \frac{1}{2}}H^{l}W^{l}\Big{)} \tag{6}\]  
where \(\hat{A}=A+I\), \(\hat{D}\) is the degree matrix of \(\hat{A}\), \(H^{l}\in\mathbb{R}^{N\times D_{l}}\) is the activation matrix in layer \(I\), \(W^{l}\in\mathbb{R}^{D_{l}\times D_{l+1}}\). Finally, we obtain the output of the GCN by averaging the output vectors of the second convolutional layer, as shown in the following equation.  
\[X=f\Big{(}f\Big{(}H^{0},A\Big{)},A\Big{)}=\sigma\Big{(}A\sigma\Big{(}AH^{0}W^ {0}\Big{)}W^{1}\Big{)} \tag{7}\]
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 3 GIMAD Prototype Description
### Signal Processing Module (SPM)
#### 3.1.3 Localization Modules/Algorithms  
C-SAM will assess the RFI AoA by performing a statistical analysis of the incoming signal at the CRPA antenna. The C-SAM concept is based on antenna array processing; thus, it combines the phase offsets of the received signal for each CRPA radiating element in order to estimate the AoA.  
In order to accurately measure these phase differences and consequently enhance the AoA accuracy, the CRPA needs to be properly calibrated.  
The selected algorithms for performing AoA estimations are (i) Spatial Filtering (Beamformer), (ii) MUltiple Slgnal Classification (MUSIC), and (iii) CAPON beamformer.  
MUSIC is the default algorithm, while Beamformer and CAPON are kept as secondary methods, mainly because of the typically higher angle resolution of MUSIC with respect to the other two algorithms, although MUSIC is more computationally demanding.
## 1 Introduction  
### Additive Manufacturing  
Additive manufacturing (AM), or 3D printing, is a new group of manufacturing technologies whereby physical objects are built by sequential addition of material on basis of a virtual 3D model.  
These additive manufacturing techniques allow for completely different designs than the subtractive manufacturing techniques such as milling, grinding, drilling, _etc._, which require that a tool can reach the spot that has to be shaped (which is not always possible). With 3D printing, the cost is merely related to the amount of printed material, not to the complexity of the design. As the tendency exists to produce components at the lowest possible cost, there exists a stimulus to reduce the weight of 3D printed components, which is a clear benefit in several sectors such as aeronautics. Three-dimensional printed components also possess the property of embedding smart technologies during the printing process, generating overall smart solutions.  
As additive manufactured components are constructed layer by layer, their metallurgic structure is different from conventionally produced materials. Fatigue and crack propagation propertiesof additive manufactured materials are not very well known yet [1], because a lack of process understanding and a lack of _in situ_ process monitoring and control, especially in metal AM systems, currently results in unknown porosity levels and distributions [2]. Multiple sources report a reduced fatigue lifetime. In the work of [PERSON] _et al._[3], fatigue lifetime of titanium Ti6 Al-4V alloys fabricated by means of additive manufacturing were compared to conventionally produced Ti6 Al-4V alloys (rolled or cast). Conventionally rolled Ti6 Al-4V alloys showed approximately twice the fatigue lifetime of the best additive manufactured Ti6 Al-4V alloys. [PERSON] _et al._[4] stated that as-built surfaces of selective laser melted (SLM) Ti-6 Al-4V samples become crack initiation sites, resulting in a reduced fatigue lifetime when the stress level exceeds 500 MPa. Similar results were also recorded in the paper of [PERSON] _et al._[5], where the SLM Ti6 Al-4V samples indicated a stress level at failure under fatigue loading of 590 MPa and 570 MPa.  
Despite the numerous benefits, the reduced fatigue behavior is currently jeopardizing the introduction of additive manufactured primary components in industries where safety and product availability are very important. Inspection intervals must then be shortened, increasing inspection costs and product unavailability, which inevitably induce large economic losses. Including an effective structural health monitoring system that monitors the fatigue behavior of 3D printed components can largely eliminate this major drawback.