esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Criticality Analysis Method for Assessing Obesity Treatment Efficacy
## 3 Methodology
### Receiver Operating Characteristic (ROC) Curve  
The Receiver Operating Characteristic (ROC) curve is a graphical representation of the performance of a binary classifier system as the discrimination threshold is varied [19]. In the context of SVM, the ROC curve is used to evaluate the performance of the SVM classifier in classifying data samples into two different classes. The ROC curve plots the true positive rate (TPR) (sensitivity) against the false positive rate (FPR) (\(\approx\)1 \(-\) TNR) at various threshold settings. Figures 10-12 illustrate the ROC curves for the best pair of \(\sigma\) and \(C\) values that satisfy the highest accuracy during the entire trial period.  
Figure 11: The relationship between True Positive Rate (Sensitivity) and False Positive Rate (1-Specificity) at various threshold levels, as determined by the kernel function of the SVM, is displayed through the ROC curves of \(w_{3}\) and \(w_{4}\).  
Figure 10: The relationship between True Positive Rate (Sensitivity) and False Positive Rate (1-Specificity) at various threshold levels, as determined by the kernel function of the SVM, is displayed through the ROC curves of \(w_{1}\) and \(w_{2}\).  
The ROC plots (Figures 10-12) show that, in the context of SVM, parameter \(C\) controls the trade-off between maximising the margin and minimising the misclassification error. When the value of \(C\) is smaller, such as \(C=0.1\), the margin becomes wider, but there are more instances of misclassifications. Conversely, a larger value of \(C\), such as \(C=10\), leads to a narrower margin, but with a reduced number of misclassifications. Parameter \(\sigma\) is used to control the width of the Kernel Gaussian function that is used to map the input data into a higher-dimensional space, where a linear boundary can be found. A larger value of \(\sigma\) results in a wider Gaussian function, which leads to a softer decision boundary and a higher bias, while a smaller value of \(\sigma\) results in a narrower Gaussian function, which leads to a harder decision boundary and a higher variance.  
When \(\sigma\) is small, the decision boundary is more sensitive to input data, which can lead to overfitting. On the other hand, when \(\sigma\) is large, the decision boundary is less sensitive to input data, which can lead to underfitting. Therefore, the value of \(\sigma\) has an impact on generalisation performance of the SVM.  
A good value for \(C\) and \(\sigma\) is the one that balances the trade-off of bias and variance, that is, a good balance between overfitting and underfitting.  
The ROC curves shown in Figures 10-12 perform well with \(\sigma=0.1\) and \(1\) for various values of \(C\) of the SVM, which is likely because the classifier is able to find a good balance between overfitting and underfitting by adjusting the value of \(C\) and \(\sigma\) which in turn results in good performance.
## 3 Discussion of Results
### High Damping Case  
In real operating conditions it could happen that the damping is even higher than the previous cases. In this condition the two close peaks due to mistuning appear as completely merged in one single peak. Figure 12 shows the results of the fitting process for such condition. It can be seen that the original response is successfully reconstructed both by the 2 DOF and by the Sine fit method. However, it should be highlighted that in a case like this where the two peaks are completely merged together, the 2 DOF method is comparable to the Sine fitting for the estimation of the response curve but it is not better in the estimation of the vibration parameters, in particular the damping. In this case in fact,  
Figure 11: Comparison of non-dimensional amplitudes obtained by different fitting methods for SD = 0.10, (**a**) EO = 5, \(\zeta_{1,2}=0.002\), (**b**) EO = 8, \(\zeta_{1,2}=0.003\).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & \multicolumn{3}{c}{**2 DOF**} & \multicolumn{3}{c}{**1 DOF**} \\ \cline{2-6}  & & \multicolumn{3}{c}{**EO = 5**} \\ \hline
**Parameters** & **Exact Value** & **Calculated** & **Difference (\%)** & **Calculated** & **Difference (\%)** \\ \hline Frequency 1 & 157.4456 & 157.4470 & 0.00 & 157.6627 & 0.14 \\ Frequency 2 & 158.1039 & 158.1063 & 0.00 & — & — \\ Damping 1 & 0.001995 & 0.001987 & 0.40 & 0.002243 & 12.39 \\ Damping 2 & 0.002004 & 0.002006 & 0.13 & — & — \\ \hline  & & \multicolumn{3}{c}{**EO = 8**} \\ \hline Frequency 1 & 222.3561 & 222.3663 & 0.00 & 222.2287 & 0.35 \\ Frequency 2 & 223.0106 & 222.9823 & 0.01 & — & — \\ Damping 1 & 0.002995 & 0.002988 & 0.24 & 0.004071 & 35.91 \\ Damping 2 & 0.003004 & 0.003018 & 0.46 & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of vibration parameters identified by 2 DOF and 1 DOF models with the exact values for EO = 5, 8 and SD = 0.10.  
the 2 DOF least square curve fitting optimizer might not always return the vibrations parameters with the high accuracy as in Table 3.
###### Abstract  
As climate change intensifies the frequency and magnitude of extreme weather events, such as storm surges, understanding how extreme weather events alter mangrove carbon dynamics is critical for predicting the resilience of blue carbon ecosystems under climate change. Mangrove forests are generally recognized for their resilience to natural disturbances, a characteristic largely attributed to the evolutionary development of species-specific functional traits. However, limited research has explored the impacts of storm surges on carbon flux dynamics in both natural and restored mangrove ecosystems. In this study, we analyzed short-term responses of storm surges on carbon dioxide flux and methane flux in natural and restored mangroves. The results revealed that following the storm surge, CO\({}_{2}\) uptake decreased by 51% in natural mangrove forests and increased by 20% in restored mangroves, while CH\({}_{4}\) emissions increased by 14% in natural mangroves and decreased by 22% in restored mangroves. GPP is mainly driven by PPFD and negatively affected by VPD and RH, while Reco and CH\({}_{4}\) flux respond to a combination of temperature, humidity, and hydrological factors. NEE is primarily controlled by GPP and Reco, with environmental variables acting indirectly. These findings highlight the complex, site-specific pathways through which extreme events regulate carbon fluxes, underscoring the importance of incorporating ecological feedbacks into coastal carbon assessments under climate change.  
carbon fluxes; storm surges; eddy covariance; natural mangrove; restored mangrove
# Mathematical Study for Chikungunya Virus with Nonlinear General Incidence Rate
## 2 Mathematical Model and Results
### Basic Properties  
First note that \(\dot{s}\leq\Gamma-\delta s\); then, one can easily deduce that  
**Lemma 1**.: _if \(s(0)\leq s_{0}\), we have \(s(t)\leq s_{0}+\big{(}s(0)-s_{0}\big{)}e^{-\delta t}\leq s_{0},\quad\forall t\geq 0\)._  
The system (1) admits non-negative bounded solutions. In particular,  
**Lemma 2**.: _there exist \(M_{1},M_{2},M_{3}>0\) such that_  
\[\Omega=\{(s,y,p,x)\in\mathbb{R}_{\geq 0}^{4}:0\leq s,y\leq M_{1},0\leq p\leq M_{2},0\leq x\leq M_{3}\}\]  
_is a positively invariant bounded set for system (1)._  
Proof.: Note that  
\[\dot{s}\mid_{s=0} =\Gamma>0,\] \[\dot{y}\mid_{y=0} =\eta_{1}pf(s)\geq 0,\ \forall\ s,p\geq 0,\] \[\dot{p}\mid_{p=0} =\pi y\geq 0,\ \forall\ y\geq 0,\] \[\dot{x}\mid_{x=0} =\lambda>0.\]  
Consider \(S_{1}(t)=s(t)+y(t)\) and \(S_{2}(t)=p(t)+\dfrac{r}{\rho}x(t)\). Then, one has  
\[\dot{S}_{1}(t)=\Gamma-\delta s(t)-\epsilon y(t)\leq\Gamma-\gamma_{1}(s(t)+y(t) )=\Gamma-\gamma_{1}S_{1}(t),\]  
where \(\gamma_{1}=\min\{\delta,\epsilon\}\). Hence \(S_{1}(t)\leq M_{1}\), if \(S_{1}(0)\leq M_{1}\), with \(M_{1}=\dfrac{\Gamma}{\gamma_{1}}\). Therefore, \(0\leq s(t),y(t)\leq M_{1}\) if \(0\leq s(0)+y(0)\leq M_{1}\). Furthermore, one has  
\[\dot{S}_{2}(t) =\pi y(t)-cp(t)+\dfrac{r}{\rho}\lambda-\dfrac{mr}{\rho}x(t)\] \[\leq\pi M_{1}+\dfrac{r}{\rho}\lambda-\gamma_{2}\bigg{(}p(t)+ \dfrac{r}{\rho}x(t)\bigg{)}\] \[=\pi M_{1}+\dfrac{r}{\rho}\lambda-\gamma_{2}S_{2}(t),\]  
where, \(\gamma_{2}=\min\{c,m\}\). Then \(S_{2}(t)\leq M_{2}\), if \(S_{2}(0)\leq M_{2}\), with \(M_{2}=\dfrac{\pi M_{1}+\dfrac{r}{\rho}\lambda}{\gamma_{2}}\). As all variables are nonnegative, therefore, \(0\leq p(t)\leq M_{2}\) and \(0\leq x(t)\leq M_{3}\) if \(0\leq p(0)+\dfrac{r}{\rho}x(0)\leq M_{2}\), with \(M_{3}=\dfrac{\rho M_{2}}{r}\).
## 6 Discussion
Thus, researchers are encouraged to consider latency alongside model development efforts. A review of the results obtained in this study shows that although the RF and DT algorithms performed similarly well, the integration of LightGBM with MLOps stood out by achieving the lowest error rate and latency, with an RMSE of 190.34 kW and a latency of 9 ms. These findings suggest that the effective application of MLOps and integration with LightGBM positively impacts success rates and response times.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design
### Improved Sand Cat Swarm Optimization Algorithm  
The Sand Cat Swarm Optimization (SCSO) algorithm mimics two primary survival behaviors observed in natural sand cat populations: foraging and hunting prey [30; 31]. Compared to other population-based intelligence algorithms, the Sand Cat Swarm Optimization (SCSO) algorithm demonstrates robust optimization capabilities, making it well-suited for addressing complex multi-objective optimization problems. However, its convergence speed and search accuracy are moderate, which increases its susceptibility to becoming trapped in local optima. Thus, this paper introduces three enhancement techniques to strengthen the global search capabilities of the Sand Cat Swarm Optimization algorithm. The Improved Sand Cat Swarm Optimization (ISCSO) algorithm is subsequently applied to solve the edge node deployment problem in mobile device scenarios. The detailed steps of the ISCSO algorithm are presented in Algorithm 2. Figure 6 shows the flowchart of the ISCSO.  
```
0:\(MD\),ME,\(MN\),MF
0: Optimal edge node deployment solution.
1: Initialization: \(MAXGEN\),ME,\(R\),\(r\),\(r_{G}\)
2: The chaotic sequence is generated in accordance with the Equation (19).
3:for all\(me=1\) to \(ME\)do
4: Set an initial value for each sand cat.
5:endfor
6:for all\(t=1\) to \(MAXGEN\)do
7: Update \(r\),\(r_{G}\),\(R\)
8: Check if any sandcasts exceed the search space and modify.
9: Calculate the fitness value of each sand cat according to Equation (15) and find the best sand cat.
10:for all\(me=1\) to \(ME\)do
11: Obtain random angles using roulette algorithm.
12:if\(R\leq 1\)then
13: Update the position of the sand cat according to Equation (26).
14: Execute the strategy to obtain a new position according to Equation (27).
15:else
16: Update the position of the sand cat according to Equation (22).
17: Execute the search strategy according to Equation (23) to obtain the new position.
18:endif
19:endfor
20:endfor
```  
**Algorithm 2** Improved sand cat group optimization algorithm
## 2 Materials and Methods
### Calculating CYGNSS Reflectivity  
CYGNSS Level 1 data are delay Doppler maps (DDMs), which map the power of the received signal with respect to time delay and Doppler shift [8]. We derived CYGNSS reflectivity values using the peak power value of each DDM, from CYGNSS version 2.1 L1 data [10]. We excluded all CYGNSS data with signal-to-noise ratios lower than 2 dB. This means the peak power values used to calculate reflectivity values were noticeably higher than the noise floors of the DDMs [8].  
The peak value of each CYGNSS DDM is the power received from the specular reflection point and must be corrected for antenna gain, the bistatic range, the GPS transmit power, and incidence angle, as described by [14]. Assuming a coherent reflection, the coherent component of the bistatic radar equation can be used:  
\[P_{r}=\frac{P_{t}G_{t}}{4\pi(R_{ts}+R_{sr})^{2}}\frac{G_{r}\lambda_{t}^{2}}{4\pi} \Gamma_{s}, \tag{1}\]  
where \(P_{r}\) is the uncorrected peak power of the DDM, \(P_{t}\) is the transmitted power, \(G_{t}\) is the transmitting antenna gain, \(G_{r}\) is the receiving antenna gain, \(\lambda_{t}\) is the transmitted GPS wavelength (0.19 m), \(R_{ts}\) is the distance from the transmitting antenna to the specular reflection point, \(R_{sr}\) is the distance from the specular reflection point to the receiving antenna, and \(\Gamma_{s}\) is the effective surface reflectivity.  
We calculated \(\Gamma_{s}\) by rearranging Equation (1) and converting all terms to dB:  
\[\Gamma_{s}\ [dB]=10 logP_{r}+20 log(R_{ts}+R_{sr})+20 log4\pi-10 logP_{t}-10 logG_{t}-10 logG_{r}-20 log\lambda_{t}. \tag{2}\]  
Finally, we corrected for incidence angle using a modeled, theoretical formula of reflectivity with respect to incidence angle [11; 15]. Assuming accurate knowledge of the terms in Equation (1), the final CYGNSS reflectivity (\(\Gamma\)) is only dependent on surface properties, including surface roughness, vegetation, and the dielectric constant, which depends on soil moisture.
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 3 ISAR Imaging and Method for Estimating the Stripmap SAR Observation Area
### Method for Estimating the Stripmap SAR Observation Area
A plane can be constructed using any three scatterers, \(\hat{P}_{j}\), \(\hat{P}_{k}\), and \(\hat{P}_{l}\), on the parabolic antenna edge in (25), and the corresponding normal vector is expressed as  
\[\hat{\mathbf{n}}=\frac{\left(\hat{\mathbf{p}}_{j}-\hat{\mathbf{p}}_{k}\right)\times\left( \hat{\mathbf{p}}_{j}-\hat{\mathbf{p}}_{l}\right)}{\left|\left(\hat{P}_{j}-\hat{\mathbf{p}}_ {k}\right)\times\left(\hat{\mathbf{p}}_{j}-\hat{\mathbf{p}}_{l}\right)\right|} \tag{29}\]  
Therefore, by selecting any three scatterers from the \(n\) scatterers on the parabolic antenna edge solved in (27), a total of \(\mathrm{C}_{N}^{M}\) planes can be constructed, and the corresponding normal vectors, as in (29), can be obtained, which are denoted as \(\hat{\mathbf{n}}_{j}\), where \(j=1,2,\cdots,\mathrm{C}_{n}^{3}\). In (28) and (29), the mean value of the dihedral angles between the \(\mathrm{C}_{n}^{3}\) planes constructed by the \(n\) scatterers on the parabolic antenna edge in (27) and the plane containing the actual parabolic antenna edge of the spaceborne SAR is expressed as  
\[\mathrm{f}_{DA}=\frac{\sum_{j=1}^{\mathrm{C}_{n}^{3}}\mathrm{a}\cos\left(\left| \mathbf{n}_{0}.\mathbf{n}_{j}\right|\right)}{\mathrm{C}_{n}^{3}}. \tag{30}\]  
Equation (30) is a function of the look angle \(\gamma\) when the spaceborne SAR operating in boresight stripmap mode observes the Earth. When the estimated area observed by the spaceborne SAR is operating in boresight stripmap mode, that is, the estimated look angle \(\gamma\) is ideal, (30) is approximately zero or the global minimum. Equation (30) is a highly nonlinear function of the look angle \(\gamma\). Based on (30), algorithms such as pattern search algorithms, swarm optimization algorithms, and linear search algorithms can be used to estimate the area observed by the spaceborne SAR operating in boresight stripmap mode. Since the area observed by the spaceborne SAR operating in boresight stripmap mode is parameterized into a look angle of the antenna, the linear search algorithm can meet the requirements of parameter estimation. The flow of the proposed method is as follows: (1) Set the range of the look angle, generally \(15^{\circ}\) to \(55^{\circ}\)[28], and set the search step length \(\Delta_{ia}\) to obtain the search set. (2) For each element in the search set, according to (26) and (27), the estimated coordinates in the component body coordinate system of the scatterers on the parabolic antenna edge are obtained. (3) For each element in the search set, from (30), the mean value of dihedral angles between the plane constructed by estimated scatterers and the plane containing actual parabolic antenna edge of the spaceborne SAR is obtained. (4) The look angle in the search set corresponding to the minimum dihedral angle is the estimated look angle.
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 3 Method
### Weakly Supervised Training  
As previously discussed, the HPBM can represent biomechanical features and finger flexion states in an intuitive manner. By fixing static parameters \([\ell,\phi_{\text{ARB}},\phi_{\text{CPS}},d]\) and randomly generating dynamic parameters \([\theta^{f},\theta^{a}]\) within a reasonable range, it is possible to create an infinite number of 2D-3D pose pairs for training. The generated training dataset is denoted as \(\mathcal{D}\).  
We summarize the proposed weakly supervised strategy in the following steps:  
Step 1: Determine potential threshold ranges, denoted as \([\Theta_{\text{min}},\Theta_{\text{max}}]\), for dynamic parameters \(\theta^{f}\) and \(\theta^{a}\). These thresholds, established as prior knowledge, can be set based on experience or calculated from training datasets, specifically \(\Theta_{\text{min}}=\min(\Theta^{Ia})\) and \(\Theta_{\text{max}}=\max(\Theta^{Ia})\), where \(\Theta^{Ia}\) are sets of \([\theta^{f},\theta^{a}]\) derived from the target individual.  
Step 2: Randomly generate HPBM-encoded poses within the established threshold ranges.  
Step 3: Transform the randomly generated sample into a 3D coordinate-encoded pose \(X_{\text{norm}}\).  
By utilizing this method, a 2D-3D lifting dataset can be created without the need for any manually labeled data. Subsequently, weakly supervised training can be performed based on this dataset.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 3 Receivers
### Mf  
The matched filter (MF) [7] improves the SNR level of the signal but ignores the effect of the interference. The ZF receiver performs better when columns in \(H\) have orthogonal elements. The output signal for in case of a MF can be written as,  
\[\tilde{y}_{mf}=H^{T}x \tag{2}\]
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 4 Results
### Full Data Comparisons  
For the entire year of data, the number of individual observations along the ground tracks is 36763 for ATL12, 37861 for the histogram model, and 37861 for the standard deviation model. CryoSat-2 data are available at each corresponding along-track segment of ICESat-2, as well as for several instances in which ICESat-2 data are obstructed due to clouds. SWHs determined from ICESat-2 and CryoSat-2 show a clear correlation, which can be seen in Figure 6 along with a linear least-squares fit. Some outliers can be seen at 2-3 m for the histogram and standard deviation models; this is due to noisy data. The Q-Q plot clearly shows a deviation below the linear fit to the ATL12 and CryoSat-2 data, whereas the other two models seem to be fairly consistent. To see the effects of the differences between the models and CryoSat-2, we break up all the observations into segments to show these differences as a function of the SWH.
## 5 Experimental Settings
### Choosing the Window  
Previous studies [13; 16; 17] on CD have proven that the selection of an appropriate window size is a challenging task because, in scenarios, a small window size may accurately reflect the current data distribution and may identify sudden concept changes more accurately, but it worsens the performance of the system in stable periods. In contrast, a large window improves the performance of the system in stable periods, but its reaction is slow in changing concepts. The training window size can be fixed or variable.  
We selected the best fixed, small window based on the ROC-AUC scores and accuracy of prediction for each dataset after repeatedly experimenting with the procedure for different values of the window sizes starting from 100 to 500 samples; refer Table 5. We were successful in detecting the sudden drift in the experimented software defect data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Modules in Dataset** & **Faulty Modules** & **\% of Faulty Modules** \\ \hline NASA jm1 & 9593 & 1760 & 18.3\% \\ \hline NASA KC1 & 2109 & 326 & 15.5\% \\ \hline prop V4 & 3022 & 214 & 07.1\% \\ \hline prop V40 & 4053 & 337 & 08.3\% \\ \hline prop V44 & 4620 & 295 & 06.4\% \\ \hline prop V85 & 3077 & 942 & 30.6\% \\ \hline prop V192 & 3598 & 85 & 02.4\% \\ \hline prop V236 & 2231 & 76 & 03.4\% \\ \hline prop V256 & 1964 & 625 & 31.8\% \\ \hline prop V318 & 2395 & 364 & 15.2\% \\ \hline prop V355 & 2791 & 924 & 33.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sr No** & **Metric** & **Description** & **Attrib** & **Category** \\ \hline
16 & C \& K & inheritance & ic & numeric \\ \hline
17 & C \& K & coupling & \\  & Extn. & between & cbm & numeric \\ \hline
18 & C \& K & average method & amc & numeric \\  & Extn. & complexity & & \\ \hline
19 & McCabe’s & max cyclomatic & max\_cc & numeric \\  & & complexity & & \\ \hline
20 & McCabe’s & avg cyclomatic & avg\_cc & numeric \\ \hline
21 & Class & number of & & \\  & Variable & defects & bug & numeric \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Window Size** & **Basic-NB** & **PL-NB** & **Basic-RF** & **PL-RF** \\ \hline jm1 & **100 * & 0.5099 & 0.5694 & 0.8356 & 0.8716 \\ \hline jm1 & 200 & 0.5110 & 0.53
## 1 Introduction
Microservices can simplify the handover and migration process by decomposing applications into smaller, autonomous, separately scalable components. This work addresses the challenges posed by the ultimate requirements of 6G networks to enable vehicular communications with ultra-high vehicle mobility. The work presents an architecture for vehicular networks using a microservices-based approach. This design addresses latency and reliability concerns, ensuring seamless communication and effective data management. The key contributions of this work include the following:  
1. Developing a reliable vehicular network architecture based on the distributed edge computing paradigm. The proposed structure deploys edge computing in two linked levels: MEC and fog;
2. Proposing a novel microservice approach for vehicular applications that considers task prioritizing. The proposed algorithm allows for greater flexibility, modularity, and scalability in designing and implementing VANET applications;
3. Implementing an efficient offloading approach for the proposed VANET based on the proposed microservice algorithm. It is difficult to control the energy usage and system delay during the vehicle's movement and quick change in the channels and bandwidth in wireless communication systems. With the assistance of RSU, the MEC server helps vehicles offload the computing tasks to MEC linked to the cellular base station. The offloading scheme effectively manages computational tasks between vehicles and edge servers, reducing latency and improving system energy efficiency;
* Implementing a seamless handover approach to maintain seamless communication as vehicles move across different network coverage zones, ensuring uninterrupted service;
* Developing an efficient migration scheme for efficient data transfer between edge servers, ensuring data availability and consistency across the network. The proposed migration approach uses the proposed microservice algorithm to facilitate task migration between edge servers;
* Assessing the performance of the introduced microservice-based VANET regarding latency, availability, and reliability. Heterogeneous real-based simulation scenarios were considered for evaluating the proposed network and approaches.