esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 2 Background
### Black Hole Attack  
The black hole is one of the severe security attacks that may occur against the AODV routing protocol [6]. It is an active attack wherein a rogue node waits for neighboring nodes to send a route request (RREQ) message. When the malicious node receives an RREQ message, this latter, instead of forwarding discovery packets to neighboring nodes, interrupts the route discovery process by instantly sending a fake route reply message (RREP) with the maximum sequence number. Consequently, the source node presumes that route discovery is accomplished and immediately starts the forwarding of data packets to the malicious node believing that the optimal path is through this malicious node. The malicious node then captures all routing packets and drops them. An example of this attack is illustrated below (Figure 1), where node (Vs) wants to communicate with the destination (Vd). The shortest path is Vs-V1-V2-Vd, the rogue node (Black hole) claims to have the shortest path and it unicasts a fake RREP in the reverse path. Afterward, the suspected node completely drops all the data packets forwarded to it. As a result, the overall network performance degrades, and the process of information dissemination is interrupted.
## 3 Experiments
### Parameter Experiments  
We adopted a neural network architecture, REU-UNet(2 EEAM), which integrates two EEAM modules, and performed image segmentation on the Massachusetts dataset to determine the optimal weight values for the loss function. To assess the impact of different weight distributions on model performance, we experimented with six parameter combinations (\(\alpha\), \(\beta\)): (0, 1), (0.2, 0.8), (0.4, 0.6), (0.6, 0.4), (0.8, 0.2), and (1, 0), where \(\alpha\) is the weight for the Edge-Consistency Loss (\(\text{Loss}_{\text{EC}}\)) and \(\beta\) is the weight for the BCE Loss (\(\text{Loss}_{\text{BCE}}\)). The segmentation results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline
**Augmentation Method** & **MIoU** & \(\boldsymbol{\Delta}\) \\ \hline Baseline (No Augmentation) & 0.6820 & — \\ +Horizontal Flip & 0.7235 & +0.0415 \\ +Vertical Flip & 0.7157 & +0.0337 \\ +Rotation (\(\pm 15^{\circ}\)) & 0.7314 & +0.0494 \\ +Translation (\(\pm 15\%\)) & 0.7082 & +0.0262 \\ +HSV Saturation Adjustment & 0.7355 & +0.0535 \\ All Combined & 0.7952 & +0.1132 \\ \hline \end{tabular}
\end{table}
Table 6: Impact of individual augmentation techniques on model performance (MIoU).  
When \(\alpha\) is set to 0.6 and \(\beta\) to 0.4, the segmentation results show significant advantages in terms of P, MPA, MIoU, and FWIoU compared to other networks. Therefore, we set \(\alpha\) to 0.4 and \(\beta\) to 0.6 in the total loss function (\(\text{Loss}_{\text{total}}\)).  
We conducted an ablation study to compare the proposed hybrid loss (BCE + EC) with other common losses. As shown in Table 8.  
It can be found that Dice Loss improves over pure BCE (MIoU: 0.7524 vs. 0.7282) but underperforms BCE + EC due to its focus on regional overlap rather than edge precision. Focal Loss struggles with training stability (fluctuating loss curves) and achieves lower Border IoU (0.7583), likely because it prioritizes hard examples but neglects edge consistency. BCE + EC + Dice shows marginal improvement over BCE + EC but introduces higher complexity. The proposed BCE + EC strikes the optimal balance between edge accuracy (Border IoU: 0.9295) and simplicity.
# An Optimal Order Method for Multiple Roots in Case of Unknown Multiplicity
## 2 Scheme and Analysis of the Local Convergence Rate  
In this paper, we deal with the below mentioned conversion ([PERSON], 1999; [PERSON], 2016):  
\[f(x) = \frac{\psi(x)}{\psi^{\prime}(x)},\;if\;\psi(x)\
eq 0 \tag{10}\] \[= 0,\qquad if\;\psi(x)=0\]  
and employ a three-step [PERSON]'s scheme given by:  
\[y_{n} = x_{n}-\frac{f(x_{n})}{f^{\prime}(x_{n})}\] \[u_{n} = y_{n}-\frac{f(y_{n})}{f^{\prime}(y_{n})} \tag{11}\] \[x_{n+1} = u_{n}-\frac{f(u_{n})}{f^{\prime}(u_{n})}\]  
To escape the computations of primary differentiations of \(f(x),f(y(x))\) and \(f(u(x))\), we estimate them as mentioned below:  
\[f^{\prime}(x_{n})\approx\frac{f(z_{n})-f(x_{n})}{f(x_{n})}=g_{1}(x_{n},z_{n}) \tag{12}\]  
where \(z_{n}=x_{n}+f(x_{n})\). \[f^{\prime}(y_{n})\approx\frac{f[x_{n},y_{n}]f[y_{n},z_{n}]}{f[x_{n},z_{n}]}=g _{2}(x_{n},y_{n},z_{n}) \tag{13}\]  
and:  
\[f^{\prime}(u_{n})\approx b_{2}-b_{1}b_{4}=g_{3}(x_{n},y_{n},z_{n},u_{n}) \tag{14}\]  
where \(b_{1}=f(u_{n})\), \(b_{4}=\frac{f[y_{n},u_{n},x_{n}]-f[y_{n},u_{n},z_{n}]}{f[y_{n},z_{n}]-f[y_{n},x_{n}]}\), \(b_{3}=f[y_{n},u_{n},z_{n}]+b_{4}f[y_{n},z_{n}]\), \(b_{2}=f[y_{n},u_{n}]-b_{3}(y_{n}-u_{n})+f(y_{n})b_{4}\) (for more details, one can follow Equations (6) and (19), respectively, of [PERSON] and [PERSON] (1999)). Using these estimates of \(f^{\prime}(x_{n})\), \(f^{\prime}(y_{n})\) and \(f^{\prime}(u_{n})\), given by Equations (12)-(14), respectively, in the scheme Equation (11), we have the scheme given by:\[y_{n} = x_{n}-\frac{f(x_{n})}{g_{1}(x_{n},z_{n})}\] \[u_{n} = y_{n}-\frac{f(y_{n})}{g_{2}(x_{n},z_{n},y_{n})} \tag{15}\] \[= 0,\qquad if\;\psi(x)=0\]0  
In order to discuss the convergence analysis of the scheme defined by Equation (15), we are moving to validate the following result:  
Theorem 1: _Assume that \(f\in C^{8}(D)\)\((D\subseteq R\to R)\), and contains only one root \(\xi\in D\), where \(D\) is an open subset of \(R\) and \(s\) is sufficiently large. If the starting guess \(x_{0}\) is near enough to \(\xi\), the iterative scheme defined by Equation (15) has an optimal convergence rate of eight._  
Proof of Theorem 1: Let us suppose that \(\psi(x)\) can be expressed as:  
\[= 0,\qquad if\;\psi(x)=0\]1  
where \(\xi\) is a multiple zero of \(\psi(x)=0\), with multiplicity \(m\) and \(h(\xi)\
eq 0\). According to Equation (16), we can write:  
\[= 0,\qquad if\;\psi(x)=0\]2  
After taking the ratio of Equation (16) and (17), this can be written as:  
\[= 0,\qquad if\;\psi(x)=0\]3  
It is clear from the above Equation (18) that the task of approximating the multiple zero of \(\psi(x)=0\) is identical to the effort of estimating the simple root \(\xi\) of \(f(x)=0\). By virtue of [PERSON]'s series expansion, we can write:  
\[= 0,\qquad if\;\psi(x)=0\]4  
where \(b_{k}=\frac{h^{(k)}(\xi)}{k!h(\xi)}\), \(k=1,2,...\) and \(e_{n}=x_{n}-\xi\).
## 2 Heartbeat Mode-Based Stress Detection Algorithm
### Feature Extraction with bmnn  
In this study, the NNI output of the ECG signals was mapped to a binary sequence using statistical tools for linguistics to simplify the dynamics. As shown in Figure 3, an NNI time series with length \(L\) = 200 can be written as \(\left\{NNI_{1},NNI_{2},\ldots\right.NNI_{L}\)}, where \(NNIn\) is the interval after the \(n\)th heartbeat. The NNI of each pair of consecutive heartbeats was assigned a subscript of zero or one according to the decrease or increase in the NNI.  
\[I_{n}=\left\{\begin{array}{l}0,\text{ if }NNI_{n}\leq NNI_{n-1},\\ 1,\text{ if }NNI_{n}>NNI_{n-1}.\end{array}\right. \tag{1}\]  
A total of \(m+1\) consecutive NNIs were mapped to a binary sequence of length \(m\), an \(m\)-bit word. Each \(m\)-bit word represents a unique fluctuation pattern within that time. After moving one data point at a time, the algorithm generated a set of \(m\)-bit words throughout the time series that reflected the underlying dynamics of the original time series. Different dynamics produce different distributions of these \(m\)-bit words. Each \(m\)-bit word represents a heartbeat mode and was marked with the symbol, \(bm\). For an \(m\)-bit word, the range of values that \(bm\) (total \(2^{m}\)\(bm\)s) could take was from 0 to \(2^{m}-1\). As shown in Figure 3, when \(m=4\), the 4-bit word \"0011\" corresponded to \(bm=3\left(2+1\right)\), whereas the 4-bit word \"0110\" corresponded to \(bm=6\left(4+2\right)\). In an NNI time series with length \(L\), after the value of \(m\) was determined, the times of occurrence for the different \(bm\)s in that period were counted to calculate the frequency of \(bm\) (denoted by \(bmNN\)).  
\[bmNN=\left(\text{the times of occurrence for the }bm\right)/\left(L-m+1\right). \tag{2}\]  
In this study, we chose \(L\) to be 200 and \(m\) to be 4; then, each sample with \(L\) = 200 could statistically generate 16 (\(2^{4}\)) \(bm\)s, corresponding to 16 values for \(bmNN\). Additionally, the average value of all NNIs (\(avNN\), unit: s) in each sample was calculated.  
Using the value of \(bmNN\) as the feature, the relationship between different \(bm\)s and mental states was further analyzed using the k-NN classification algorithm.
# Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System
## 3 Validation of SMPI with Simulated Signals
### Test of the Simulated Signal  
The ideal simulated signal close to the DLFO of the power system is constructed for verifying the effectiveness of SMPI, as  
\[\begin{array}{l}\boldsymbol{y}{=}2\boldsymbol{e}^{-\mathbf{0.12\boldsymbol{ t}}}\boldsymbol{\cdot}\cos(2\boldsymbol{\beta}\boldsymbol{\cdot}\mathbf{0.33 \boldsymbol{t}}-\mathbf{1.5}\boldsymbol{\beta})\\ +4\boldsymbol{e}^{-\mathbf{0.13\boldsymbol{t}}}\boldsymbol{\cdot}\cos(2 \boldsymbol{\beta}\boldsymbol{\cdot}\mathbf{0.78\boldsymbol{t}}+\mathbf{0.5 }\boldsymbol{\beta})\\ +6\boldsymbol{e}^{-\mathbf{0.71\boldsymbol{t}}}\boldsymbol{\cdot}\cos(2 \boldsymbol{\beta}\boldsymbol{\cdot}\mathbf{1.00\boldsymbol{t}}+\mathbf{1.5 }\boldsymbol{\beta})\end{array} \tag{25}\]  
which is a synthetic signal consisting of three dominant modes, and the three oscillation frequency ranges are all within the frequency range (0.1-2.5 Hz) of the LFO of the power system introduced previously. And its true modal parameters are shown in Table 1.  
To further simulate the real oscillation signal, Gaussian white noise at 30 dB SNR is added into the simulated signal as Figure 3, and it will be referred to as signal A.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**True Modes** & **Frequency (Hz)** & **Amplitude (\(\mu\)m)** & **Damping Ratio** & **Phase Angle (Rad)** \\ \hline
1 & 0.33 & 2 & 0.0579 & 1.5708 \\
2 & 0.78 & 4 & 0.0265 & 1.5708 \\
3 & 1.00 & 6 & 0.1130 & \(-\)1.5708 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Modal parameters of the ideal simulated signal.  
Figure 2: Flowchart of parameter matching approach in the proposed SMPI method.
## 1 Introduction
Hence, based on the assimilation framework, it is important to obtain the highly accurate, estimated LAI products first, and it is also important to analyze the performance of crop model data assimilation using different assimilation phenological stages, frequencies and spatial scales. To improve the crop yield estimation and clearly illustrate the influences of the factors described above in the crop model data assimilation, the Crop Environment Resource Synthesis-Wheat (CERES-Wheat) model [58] with PF and POD4 DVar-based strategies was used to assimilate LAI from field or multiple sensors' optical data for wheat yield estimation. The main tasks of this study were to: (1) retrieve LAIs using the PROSAIL model with multiple sensors' data and validate the accuracy of the estimates; (2) assimilate observations of LAI into the CERES-Wheat model with PF and POD4 DVar-based strategies to estimate the wheat yields and compare the performances of these two strategies at the field scale; (3) assimilate observations of LAI into the CERES-Wheat model with the better strategy and analyze the uncertainties of the yield estimates with different temporal scales (i.e., phenological stages and frequencies); and (4) discuss the effects of the spatial scales of LAI at the regional scale. Thus, it is highly beneficial to improve the estimations of the regional winter wheat yields by using an operational system of crop model data assimilation.
## 5 Conclusions  
Kerr nonlinear distortion and Raman amplifier noise have been identified as two major limiting factors in improving the transmission performance of unrepeated ultra-long span systems. A configuration of a hybrid fiber effective core area consisting of a three-segment structure was proposed as an alternative to conventional single-segment fiber effective core area structure. The generalized mathematical formulations and the performance analysis of a bidirectional distributed Raman amplified link using a three-segment hybrid fiber effective core area structure were presented. An improvement of the transmission performance and the possibility to extend the transmission distance of ultra-long span system were simulated.  
The introduction of a hybrid fiber effective core area in a three-segment structure indicated an increase in the flexibility of the gain profile of the distributed Raman amplified link, a reduction of negative impact of the nonlinear Kerr effect due to the high signal power, and an improvement of the delivered OSNR by means of the reduction of the Raman optical pump-induced amplified spontaneous emissions. The three-segment span with the hybrid fiber effective core area reduced the accumulated nonlinear phase shift by 1.29 radian, increased the delivered OSNR by 0.31 dB, and improved the transmission performance of the DQPSK-modulated signal compared to the conventional single fiber core structure.  
The simulated BER performance of the 80 Gb/s data signal showed the reduction of the required OSNR by 2.71 dB to achieve the target BER of \(10^{-9}\). BER performance for various data rates (40, 60, 80, and 100 Gb/s) was examined, and the results showed the stability and consistency of the system performance across different data rates. These single-channel analytical and simulation studies on a three-segment hybrid fiber effective core area structure could be used as basic guidelines for further studies on the multi-channel broadband multi-wavelength Raman amplification scheme, which has useful applications in the dense wavelength division multiplexing (DWDM) optical transmission systems.  
**Author Contributions:** [PERSON] developed the model, performed the simulation, and wrote the manuscript. [PERSON] and [PERSON] verified the methodology, discussed the results, and reviewed the manuscript. [PERSON] supervised the project. All authors approved the final manuscript for publication.  
**Acknowledgments:** This work was supported in part by Universitas Indonesia through the HIBAH TADOK 2018 funding scheme under grant No. 1346/UN2.R3.1/HKP.05.00/2018 managed by the Directorate for Research and Public Services (DRPM) Universitas Indonesia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 3 Advantages and Challenges
### Challenges
Some of the reasons that OFDM is selected for NR uplink and downlink are [27]
* OFDM is a scalable waveform with low implementation complexity,
* Compatibility with multi-antenna technologies,
* High spectral efficiency. However, OFDM has some drawbacks such as lower frequency localization and high PAPR. Therefore, although an early decision was taken to use the OFDM as 5G waveform, the exact waveform has not yet been decided and other waveforms such as SC-FDMA (also denoted DFT spread OFDM) are also being investigated [24]. * _Hardware impairments_: Since in massive MIMO systems we have large numbers of antennas, we need to use low-cost hardware to have a cost effective system. This means that hardware impairments will appear, which affect the performance of the system. Therefore, analyzing the performance of massive MIMO systems in presence of hardware impairments and proposing low complexity techniques for compensating them is crucial.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Performance Evaluation of the Proposed Model
#### 4.4.1 Evaluation on the Medetec Dataset  
To examine the efficacy of the proposed model for the Medetec dataset, we conducted a 3-class classification experiment with the wound classes Diabetic, Pressure, and Venous. Each class of the dataset is split into training and testing sets. Then, we applied augmentations to the training samples to increase the number of images in the training set. Among the training samples, 80% of the data were used for training while 20% was used for validation. The trained model was then evaluated on the unseen examples in the testing set. The model achieved an accuracy, precision, recall, and F1-score of 88%, 84%, 79%, and 81.42%, respectively, on the Medetec dataset.  
Moreover, we also generated the confusion matrix of our method on the Medetec dataset, and results are presented in Figure 5. From the confusion matrix, it can be observed that our SEEN-B4 model classified the Pressure wounds with the highest accuracy, while the Diabetic wounds were not classified accurately. More specifically, we observed three false positives and six false negative instances for the Diabetic wound. Moreover, half of the Diabetic wounds were misclassified as Pressure wounds. This might be because the Diabetic wound images are significantly fewer in number in the Medetec dataset, which could make it difficult for the model to learn the Diabetic wound features. The model is more biased toward Pressure wounds since the _FN_ is 1 for Pressure wounds, and because the number of images in the Pressure wounds class is the highest. Our model has attained a reasonably good accuracy on the Medetec dataset, considering the vast class imbalance problem and the inconsistent standards of the acquired images.
# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 6 Results and Discussion
### Comparison with Previous Studies  
We assessed our proposed models and compared their results with those of previous studies that used SEED with different extracted features and various classification methods. The comparison in Table 4 shows that our LSTM model provided promising results.  
Figure 11: Performance measurements of different classifiers.  
Figure 10: Accuracy of classifiers used before and after resampling.  
In this paper, we achieved similar results to [18], with the hybrid model and CNN reaching accuracies of 93.46% and 89.53%, respectively. Our work also achieves similar results to [29], which achieved 90.12% accuracy using RNN with LSTM for eight channels. Further, the accuracies reached by our SVM and LR are 83% and 82%, respectively, corresponding to researchers' work [8] for SVM and LR, which were 83.99% and 82.70%, respectively.  
Based the feature extraction techniques, we notice that LSTM performs better with PSD and DE at 96% compared to WT that achieved 90.12% [29]. The reasons behind the improved accuracy, including the ability of spectral features to capture frequency-specific information and the complementary nature of statistical features.  
To summarize, in the SEED dataset, the emotional stimuli were task-relevant, as participants were required to recognize an image, which likely contributed to the manifestation of the emotional response in the EEG signals. Instead of 62 channels, we used 12 channels to extract features that were fed to our models. This reduces computation time and can be more useful for EEG datasets recorded using devices with fewer channels. Unlike other research, we combined multiple extracted features to indicate choice states, such as choice indices and DE. We achieved good results with our proposed LSTM model, with 96% accuracy. Compared with previous studies that used SEED, our work used the least number of electrodes and more different feature sets and classifiers.
## 4 Markov Detection and Prediction (MDP) Model
### MDP Model
#### 4.2.3 Solving the Linear System  
Based on the above equations with the variables \(\pi_{(i,j)}\), we can build our linear system.  
To recall, the system moves from one state to another, when a service is accomplished or a new request arrives (by increasing or decreasing \(i\) or \(j\)) with a steady-state probability \(\pi_{(i,j)}\) that should respect the following two constraints:  
\[\sum_{i=0}^{c}\sum_{j=0}^{c-i}\pi_{(i,j)}=1 \tag{14}\]  
\[0\leq\pi_{(i,j)}\leq 1 \tag{15}\]  
The ten equilibrium equations can be written in a matrix form, \(\Lambda\Pi=0\), where the square matrix A represents the coefficients of a linear system, and \(\Pi\) represents the steady-state probability vector:  
\[\Pi=\left(\pi_{(0,0)}\ \pi_{(1,0)}\ \pi_{(0,1)}\ \pi_{(1,1)}\ \pi_{(0,2)}\ \pi_{(2,0)}\ \pi_{(1,2)}\ \pi_{(2,1)}\ \pi_{(3,0)}\ \pi_{(0,3)}\right)^{T} \tag{16}\]  
\[A\Pi=(0\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0)^{T} \tag{17}\]where A is a (10 \(\times\) 10) rank-deficient matrix. By replacing the first row of the matrix A by the coefficients of (14), we obtain the following modified system:  
\[B\Pi=(1\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0\ \ 0)^{T} \tag{18}\]  
where B becomes a full-rank (10 \(\times\) 10) matrix.
## 3 Background
### Distribution-Based Scientific Data Modeling  
A dataset could be enormous if the spatial resolution of the volume dataset and vector dataset, or the number of members of the ensemble dataset, is large. When the dataset size increases, data I/O and analysis time will increase and obstruct the data analysis pipeline. One of the most popular approaches for handling large scientific datasets is to model datasets using distributions. For example, [PERSON] et al. (2018) and [PERSON] et al. (2018) divided a volume dataset into sub-blocks, where data values of each sub-block are stored by a univariant GMM or histogram when the spatial resolution of the dataset is huge. [PERSON] et al. (2018) used the same idea to model vectors in a sub-block with a multivariant GMM to store the vector dataset compactly. Figure 2a illustrates the above ideas. This distribution-based approach reduces the data size and preserves the statistical characteristics to support the post-hoc analysis task more accurately. Another example is that scientists are usually interested in analyzing the statistical characteristics across all members in the ensemble dataset instead of individual members. [PERSON] et al. (2019) modeled data values at each grid point in an ensemble data using a univariant GMM and store. This compact distribution representation reduces the storage requirement, and the statistical characteristics can still be visualized in the post-hoc analysis stage. Figure 2b illustrates this distribution-based ensemble data representation. More sophisticated distribution-based representations for scientific datasets have been proposed, as discussed in Section 2.1.  
Figure 1: (**a**) is the visualization of a 2D slice of a 3D hurricane pressure dataset; (**b**) is the illustration of the corresponding grid structure of (**a**) and the pressure values are stored at grid points; (**c**) is an illustration of an ensemble dataset. In this example, we have \(n\) ensemble members and each ensemble member is a volume data or vector data from the same simulation.  
Figure 2: (**a**) illustrates the approach that uses a distribution to model data values in each sub-block to compactly represent a volume or vector dataset. If the data is a vector dataset, the distributions should be multivariant distributions; (**b**) shows the use of a distribution to model data values of all ensemble members at the same grid point to compactly store an ensemble dataset.