esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis
### Security Analysis
#### 4.1.1 Replication Attack
\(P_{2}\) denotes the probability that there is at least one node in \(S_{2}\). When \(w_{1}\) in \(S_{1}\), meanwhile \(w_{2}\) in \(S_{2}\), the witness node \(w_{1}\) encounters \(w_{2}\) and they exchange their neighbor lists so that they can detect replication attacks and broadcast the warning messages. We set \(|uw_{1}|=R\) to make the value of \(S_{1}\) the largest, and then we can obtain the upper limit of the detection probability as shown in Figure 5.  
\(|uu^{\prime}|\geq 3R\): Since the value of \(\Delta\) is not high enough in the above cases, nodes will update their location-based private keys too frequently, especially when nodes move quickly. On the other hand,  
Figure 4: An example to illustrate that using the LD-2 detection model fights against a replication attack. Figure 5: The result of detection probability for resisting a replication attack by using LD-2. more frequently updating ensures higher security. Hence the smaller value of \(\Delta\) should be set in some areas required higher security or some lower speed mobile models. In most common cases, we would like to set \(\Delta=\ V_{\max}\ \times\) T. T represents a time period and \(V_{\max}\) denotes the maximum speed of nodes. We could get the upper limit of detection probability by employing the analysis method in [6]. The external square of MSA is partitioned into a number of regular grids. The side length of each grid is \(\frac{\sqrt{2}}{2}\)R. Thus any two nodes could communicate with each other within one grid. We assume that there are q grids in the largest MSA and each node has d neighbors. Let W and W\({}^{\prime}\) denote the set of witness nodes of compromised node u and the set of witness node of replica u\({}^{\prime}\), respectively. Event \(W_{i}\) denotes assigning d elements in set W into i different grids. We have that:  
\[P[W_{i}]=\frac{\binom{q}{i}i!^{d-i}}{q^{d}}=\frac{q!^{d-i}}{(q-i)!^{d}q^{d}} \tag{8}\]  
Event 3 denotes that there are elements in set W but there are not elements in set W' in i grids. The probability of 3 is derived:  
\[\text{P}(|n|\geq 1)=1-\text{P}(|n|<1)=1-e^{-\rho S} \tag{6}\]0  
\(P[D_{j}]\), which denotes the probability that attacks are detected in the first j rounds, is derived:  
\[\text{P}(|n|\geq 1)=1-\text{P}(|n|<1)=1-e^{-\rho S} \tag{6}\]1  
\(j=\frac{T}{\Delta t}\), and \(\Delta t\) denotes a short time gap. The processes for deriving these equations are simplified. If readers are interested in the deduction process, please see the references [6]. It is a corollary that contradictions are detected by exchanging neighbor lists within ordinary nodes [3, 6, 15]. If replicas of the compromised nodes are deployed out of MSAs, their location-based private keys are invalid. When the range is limited to a smaller area, the MSA, there is a higher probability that witness nodes encounter replicas of the compromised nodes. What is more, the farther away the replicas of the compromised nodes, the easier replication attacks are detected in OD. The legality of nodes is verified by the sink node when these nodes ask to update their location private keys.
## 3 Improved Motion Model Based on PE-Match
### Epipolar Constraint Principle
Therefore, eight matching point pairs are needed to solve eight linear equations to derive the unique \(\mathbf{F}\). This method is sensitive to incorrect matches and image noise [28], and the problem of solving the fundamental matrix can be seen as a multi-objective optimization problem for the set of matching point pairs.
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 4 Industrial Applications of Cavitating Jets
### Rock-Cutting Ability of Self-Resonating Jets  
A structured jet can be achieved passively for specific flow rate using a proper nozzle geometry and injection pressure. The self-resonating jet, developed by [PERSON] et al. [23; 26], for achieving structured jet flow through a passive resonating principle, was shaped such that the hydrodynamic frequency (of the vortex ring) is close to the natural acoustic frequency of the supply system (organ pipe). A High-Pressure Cell (HPC) was built by [PERSON] et al. [26] to experimentally investigate the behavior of submerged self-resonating cavitating jets with a pressure of up to 20.7 MPa inside the test chamber. The high-pressure fluid for this cell was provided using a plunger pump. Flow visualization, anemometry, and unsteady pressure measurements were conducted for different flow regimes. Different  
Figure 17: Typical cavitating water-jet nozzle configurations [73].  
critical Strouhal numbers of the resonating jet were obtained using different diameter to length ratios (d/L; d is the jet exit diameter and L is the length of the organ pipe). It was found that the maximum modulation amplitude (\(u^{\prime}/V\)) decreases as the nozzle features are altered in order to raise the critical Strouhal number. This result is explained by the reduction in the spacing between the vortices when the Strouhal number increases. Thus, the circulation strength of each vortex is lower, and the maximum value of \(u^{\prime}/V\) in the jet decreases.  
The authors [26] also carried out several tests to compare the rock cutting ability of a conventional and a structured cavitating jet. A typical rock-cutting comparison is shown in Figure 18b between a CAVIJET and a conventional Smith nozzle supplied for use in a roller-cone deep-hole bit. Figure 18a shows the dimensions of an organ pipe configuration used to feed either a standard Smith Tool nozzle shape or a CAVIJET nozzle. A top-view of one face of a 6-in. cube specimen of Indiana limestone is shown in Figure 18b. To allow a half-circle cut to be made by each nozzle, one half of the rock face was protected by a metal plate. It is found that under the same operating conditions, the mean slot depth cut by the organ pipe CAVIJET was 4.3 times deeper than that cut by the Smith nozzle.
## 4 Discussion  
### Accuracy of the Model  
As can be seen, maximum error in chemical conversion was 11.58% and 11.81% for the CO model and the C\({}_{3}\)H\({}_{6}\) model, respectively. Notice also that at relative low temperature modes with very low conversion rates, the model shows a 0% conversion. This is due to exclusion of near-to-zero points while developing the kinetic model, in order to better predict the whole operating range. Similarly, the CO kinetic model tends to show near total conversion for all high conversion modes, since these points were excluded when developing it for the same reason as above.  
As can be seen, the maximum error at calibrating points was 4% in outlet temperature and 4.4% in heat losses. Out of training conditions, the maximum error in outlet was 5.9% in outlet temperature and 5.4% in heat losses.  
It is difficult to find published kinetic parameters that fit the performance of a particular DOC, due mainly to the different washcoat materials and loadings of the monoliths and also to ageing effects of the DOC. A method to develop tailored kinetic parameters in the area of interest using numerical simulations was presented. The results show that although error in chemical conversion predictions reaches almost 12%, the error in the overall performance of the thermal model is lower. This is due to the low concentration of reactant species in the exhaust gas (see Table 4): small errors in conversion do not involve big changes in total generated enthalpy of chemical reactions in the DOC. It can be concluded that the model shows good agreement with empirical data.  
Regarding pressure drop, the linear correlation fits the data appropriately but for high velocities, the deviation can be larger (as seen in Figure 9).  
Complex numerical schemes and boundary layer refinement were proven not necessary to obtain reliable results. Standard wall-functions showed good results in comparison with other, more demanding approaches.
# Lightweight Ensemble Network for Detecting Heart Disease Using ECG Signals
## 3 Methodology
### Wavelet Transform  
Equation (6) represents the wavelet function \(\psi(a,\,d)(x)\) using the scale coefficient a and the transition element d.  
\[\psi(a,\,d)(x)=\frac{1}{\sqrt{a}}\int_{-\infty}^{+\infty}x(t)\psi(\frac{t-d}{a})dt, \tag{6}\]  
The discrete signal \(x(n)\) of the ECG can be converted into a discrete wavelet by discretizing the scaling element (\(a\)) and the transition element (d) in Equation (6). At the level \(j=-1\), it can be expressed by combining a high-frequency signal \(D_{2j}[x(n)]\) and a low-frequency signal \(A_{2j}[x(n)]\). The ECG signal is expressed as \(x(n)=D_{2j}[x(n)]+A_{2j}[x(n)]\) and generally satisfies Equations (7) and (8).  
\[D_{2j}[x(n)] =\sum_{k=1}D_{2j}(k)\psi_{2j}\Big{(}n-2^{j}k\Big{)} \tag{7}\] \[A_{2j}[x(n)] =\sum_{k=1}A_{2j}(k)\psi_{2j}\Big{(}n-2^{j}k\Big{)} \tag{8}\]  
In this case, \(2^{j}\) indicates that the number of samples is divided by two as the level \(j\) decreases. Equation (7) represents a signal with a high-frequency component, which is related to the transition elements of the signal. Equation (8) shows the low-frequency component of the signal, which is related to the scale of the signal. The high- and low-frequency components of the input signal are divided according to the level \(j\).  
\(D_{k}\) (detail) is the finite impulse response (FIR) high-band filter coefficient associated with the wavelet coefficient, and \(A_{k}\) (approximation) is the FIR low-band filter coefficient associated with the scale function coefficient. The signal whose length is reduced by half through each filter is repeatedly converted to the next scale level. The wavelet coefficient indicates the similarity to the wavelet-generating function. This represents the frequency signal. Figure 5 shows the results of applying the wavelet transform to the ECG signal.  
Figure 4: ECG signals generated by the matching pursuit algorithm. The matching pursuit algorithm generates a correlation matrix obtained through the inner product and selects values in the order such that the correlation size is the largest. The matching pursuit algorithm uses this value to output similar signals.
# Improving RNN Performance by Modelling Informative Missingness with Combined Indicators
## 1 Introduction

Table 1: Description of types of missingness: missing completely at random (MCAR); missing at random (MAR); missing not at random (MNAR) [14]. Figure 1: Survey completion by all athletes. Absence of linepieces indicate ‘missing’ surveys; color indicates the amount of missingness within surveys. There are filled in surveys throughout the entire sequence, although usage in 2011–2012 is so sparse that it appears as though there are none. between variables, rather than requiring careful consideration of each variable's imputation method. Moreover, in their experiments, the autoencoder outperformed FCS both in terms of predictive accuracy and computational cost. However, their method relies on two strong assumptions: Firstly, there must be a large enough complete subset of the data to train such an autoencoder and secondly, this complete subset must accurately represent the incomplete data (i.e., missing data are MCAR). [PERSON] et al., (2016)[PERSON] et al. (2016) proposed simply including indicators for missingness of the original variables (Equation (1)), followed by 0-1 normalization (Equation (4)) and zero imputation. This method has the strong advantage that the model can learn direct relationships between missingness and the response, meaning that it should be able to model even MNAR. However, the number of variables can be increased by up to a two-fold, depending on how many columns contain missing values. For survey data largely consisting of partial entries, the individual missingness vectors may not bear enough information to justify the resulting increase in parameters. That is to say, the response variable might simply correlate with the number of questions an individual is inclined to answer on a given day. Hence we propose a simple alternative for when the amount of missingness sufficiently correlates with the outcome by summing the missingness indicators (Equation (2)), followed by Z-scoring (Equation (3)) of the non-binary features, including the last recorded outcome in the input, and simplifying the padding of entirely missing surveys (Figure 2).
## 2 Materials and Methods
### Methods  
This study's primary objective is to assess the performance of SIF data within the context of remote sensing phenological monitoring, aligning it with ground-based crop growth observations. The methodology encompasses three core components: time-series phenological pre-processing (Section 2.5.1), time-series phenological modeling (Section 2.5.2), and physiological transaction characterization (Section 2.5.3) [66; 67]. These latter two sections leverage the R module Phenopix to enhance efficiency [68].  
The phenological framework employed here features a meticulously designed set of methodologies tailored for agricultural phenological monitoring. These methodologies include seasonality filtering to mitigate the impact of weeds and cover crops, double logistic-based phenological models to capture rapid changes in crop growth and development, and a range of phenological characterization methods to ascertain a comprehensive spectrum of crop growth stages.
## 4 Results and Discussion
### Model Validation  
Since this cycle was not investigated yet. So, each system was validated individually. For HSR, Reference [47] was considered. Then based on the specific design parameters presented in that reference, the annual thermal energy obtained by HSR was calculated and compared with this value presented in Reference [47]. The annual thermal energy via HSR was calculated around 99.27 GWh that this value was equal to 102.3 GWh. The error was around 3%. For the gas and combined cycles approval, Reference [67] was thought of. There, a basic gas cycle with an HRSG was thought of, and the pinch temperature was 30 K. The most boiler and the base condenser pressures individually were 16 and 0.03 bar. The most extreme superheated steam temperature was 923 K, as in the current framework. The compressor, gas turbine, and steam turbine isentropic efficiencies were 86%, 86%, and 85% separately. The ambient pressure and temperature were 101 kPa and 288 K. The energy efficiency for a combustion temperature of 1375 K was 25%. This parameter was determined by the current model as 23.4%. The error was 6.4%, which is satisfactory.
## 4 Results and Discussion
By computing the gradient of a differentiable output, such as the class score, with respect to the convolutional features in a selected layer, Grad-CAM determines the importance of each neuron. These gradients are then aggregated across spatial and temporal dimensions to obtain weights that represent the importance of each neuron. Subsequently, these weights are used to linearly combine the activation maps, allowing for the identification of the most influential features contributing to the network's prediction. To explain the functioning of the network, a trained CNN was used for epileptic seizure detection. Figure 10 presents the results of the Grad-CAM algorithm applied to EEG signals containing epileptic seizures. Higher values, highlighted in magenta on the graphs, indicate a higher utility of the signal shape for epileptic signal detection. By observing the charts, we can notice that the signal fragments displaying sharp changes (spikes) with a large signal amplitude have the most significance in the context of seizure detection. The results obtained for the Grad-CAM algorithm indicate the segments of the signal corresponding to epileptic discharges. The Grad-CAM algorithm utilizes a neural network to generate activation maps that highlight the significant areas of the signal for classification. In the case of EEG signals, various types of changes are present, but for epileptic discharges, rapid and abrupt signal changes with high amplitudes resembling characteristic spikes are observed. Through the analysis of Grad-CAM, the regions in the signal responsible for these rapid high-amplitude changes are identified as significant. Consequently, the Grad-CAM algorithm allows for identifying the signal regions that contribute the most to the detection of epileptic discharges, which can be valuable in the analysis and diagnosis of such cases. The results obtained from the Grad-CAM algorithm can also be valuable in scientific research. They can contribute to a better understanding of the characteristics of epileptic discharges, thereby aiding in the development of new diagnostic and therapeutic methods. In the future, it is worth considering the utilization of larger datasets of iEEG/EEG signals with greater diversity, including signals from a significantly larger number of patients. As a result, the application of the Grad-CAM algorithm in the analysis of EEG signals with epileptic discharges can provide additional information to healthcare professionals, assisting in the diagnosis, treatment, and study of this disease. Figure 10: _Cont._
# Performance and Measurement Devices for Membrane Buildings in Civil Engineering: A Review
## 3 Performance
### Skeleton Membrane Structure  
A skeleton membrane structure is commonly utilized as the roof of a massive stadium. Nevertheless, its properties complicate the wind field surrounding it. Through field measurements of the skeleton membrane roof of a stadium, [PERSON] et al. [58; 72] discovered that the correlation of wind pressure between the measuring spots on the leeward side was worse than on the windward side, and there was a substantial time delay. At the edge of the windward side, a negative correlation was observed between the wind pressure on the upper and lower surfaces. Furthermore, the upper surface of the membrane had more potent non-Gaussian characteristics than the rigid construction, which displayed the reverse features, with non-Gaussian behaviors mainly on the leeward side. [PERSON] et al. [31] analyzed measured data from the long-span roof structure of the World Cup Stadium in Jeju-do, Korea, and found that when the vibration amplitude increased by five times, the natural frequency was decreased by 5%, and the damping ratio was increased more than twofold.
# Full-Scale Measurements of Wind Characteristics on a High- Rise Building during Typhoon Sarika
## 3 Wind Characteristics of Typhoon Sarika
### Mean Wind Speeds and Wind Directions  
In this paper, the wind characteristic data collected by the mechanical anemometer are decomposed by the vector decomposition method, and the horizontal wind speed \(U_{b}\) and direction \(\alpha(t)\) are decomposed to the orthogonal \(N\) and \(E\) coordinate axes directions as shown in Figure 5, and the decomposition expression is as follows:  
\[\left\{\begin{array}{l}U_{N}(t)=U_{H}(t)\cos\alpha(t)\\ U_{E}(t)=U_{H}(t)\sin\alpha(t)\end{array}\right. \tag{1}\]  
The 10-min main wind speed \(\overline{U}\) and main wind direction \(\overline{\alpha}\) can be expressed by:  
\[\left\{\begin{array}{l}\overline{U}=\sqrt{\overline{U_{N}}^{2}+\overline{U_ {E}}^{2}}\\ \cos\overline{\alpha}=\overline{U_{N}}/\overline{U}\end{array}\right. \tag{2}\]  
where \(\overline{U}_{N}=\frac{1}{7}\int_{0}^{T}U_{N}(t)dt\) and \(\overline{U}_{E}=\frac{1}{7}\int_{0}^{T}U_{E}(t)dt\) are the average values of wind speed component in the orthogonal two coordinate direction within 10 min. Therefore, the formula of component calculation of along-wind wind speed and across-wind fluctuating wind is as follows:  
\[\left\{\begin{array}{l}U(t)=U_{N}(t)\cos\overline{\alpha}+U_{E}(t)\sin \overline{\alpha}\\ v(t)=-U_{N}(t)\sin\overline{\alpha}+U_{E}(t)\cos\overline{\alpha}\end{array}\right. \tag{3}\]  
The 36.7 h wind field data collected at 23:00 on 17 October 2016 and is used to record the wind speed and direction during the passage of Typhoon Sarika. The measured maximum instantaneous wind speed is 34.84 m/s. Before the landing of Typhoon Sarika, the wind direction varies greatly and appears to be unstable, while it shifts about 180\({}^{\circ}\) during the passage of Typhoon Sarika, namely the wind direction shifts almost from south to north. During the most muscular landing passage of Typhoon Sarika, the wind direction  
Figure 5: Diagram of horizontal wind speed decomposition.  
varies in the range of 70-110\({}^{\circ}\). Figure 6 shows the 10-min mean wind speed and direction variations during the passage of Typhoon Sarika, the peak of 10-min mean wind speed is about 20.02 m/s and the 10-min mean wind direction is from 0\({}^{\circ}\) to 180\({}^{\circ}\).
## 5 Seismic Risk Assessment of Novel SCPC Frame
### Incremental Dynamic Analysis (IDA)
The exceeding probabilities of each performance limit state for the novel frame are also shown in Figure 17:5 At the DBE level, the exceeding probability is 5.5% for IO limit state and 0.58% for RE limit state with \(\theta_{max}\) as evaluating indicator and the exceeding probability is 1.90% for IO performance limit state and 0.19% for RE limit state with \(\theta_{r,max}\) as evaluating indicator. At the MCE level, the exceeding probability is 36.7% for IO limit state and 2.5% for RE limit state with \(\theta_{max}\) as evaluating indicator and the exceeding probability is 6.65% for IO performance limit state and 0.45% for RE limit state with \(\theta_{r,max}\) as evaluating indicator. Hence, the novel frame system has good seismic performance and less damage possibility under the IO and RE limit states. Based on the Chinese Standard Code [29], the DBE and MCE levels have 10% and 2% 50-year exceeding probabilities, respectively. The design objective of the novel SCPC frame can be also summarized as--the 50-year exceeding probability of IO performance limit state is less than 10% and the 50-year exceeding probability of RE performance limit state is less than 2%. Based on the structural fragility analysis and seismic hazard analysis theory, the annual and 50-year exceeding probabilities of different limit states are shown in Figure 17:6 When the EDP \(\theta_{max}\) is taken as the evaluating indicator, the annual exceeding probabilities of the novel frame are 0.0628% and 0.0159% for the IO and RE limit state and the 50-year exceeding probabilities of the novel frame are 3.09% and 0.79% for the IO and RE limit state, respectively. When the EDP \(\theta_{r,max}\) is taken as the evaluating indicator, the annual exceeding probabilities of the novel frame are 0.0263% and 0.0045% for the IO and RE limit state, the 50-year exceeding probabilities are 1.31% and 0.23% for the IO and RE limit state, respectively. Therefore,  
Figure 17:7 Fragility curves of novel SCPC frames (engineering demand parameters (EDP) of \(\theta_{max}\) and \(\theta_{r,max}\)). \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Earthquake Level**} & \multicolumn{2}{c}{**Exceeding Probability (\%,\(\theta_{max}\))**} & \multicolumn{2}{c}{**Exceeding Probability (\%,\(\theta_{r,max}\))**} \\ \cline{2-5}  & **IO** & **RE** & **IO** & **RE** \\ \hline DBE level & 5.50 & 0.58 & 1.90 & 0.19 \\ MCE level & 36.70 & 2.50 & 6.65 & 0.45 \\ \hline \end{tabular}
\end{table} 
Table 3: Exceeding probabilities of the two performance limit states. according to the seismic risk assessment of different performance limit states based on structural fragility and seismic hazard analysis theory, the novel SCPC frame can satisfy the seismic limit state and has great structural reliability under the near-fault ground motions.