esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Challenges in EEG-Based Seizure Detection  
Despite the diagnostic utility of EEG signals in seizure detection, several challenges hamper their clinical application. Distinguishing pathological seizure activity from normal brain rhythms and artifacts poses a significant challenge, requiring sophisticated signal pro cessing techniques and pattern recognition algorithms. In addition, artifacts such as muscle activity, electrode movement, and environmental interference can obscure genuine seizure events, necessitating robust artifact removal and noise reduction strategies. Additionally, the imbalanced nature of seizure datasets, where the number of seizure and non-seizure instances is disproportionate, presents challenges for training accurate and generalizable classification models. To overcome these challenges, researchers have employed advanced techniques and methodologies in EEG-based seizure detection. Hybrid feature selection methods, such as PCA combined with DWT, offer enhanced discriminative power by capturing both temporal and spectral features from EEG signals. Furthermore, techniques such as SMOTE enable the generation of synthetic samples to balance imbalanced datasets, ensuring robust model training and improved classification performance. Leveraging state-of-the-art classifiers, including SVM, DT, RF, and KNN, facilitates accurate classification of EEG segments into seizure and non-seizure classes, thereby enhancing the diagnostic accuracy of seizure detection systems.  
Figure 1 depicts the classification of epileptic seizures into generalized and focal types. Generalized seizures involve widespread brain activity, while focal seizures originate in a specific brain region. Focal seizures can be simple or complex, with or without loss of consciousness. Tonic-clonic seizures have distinct phases of muscle stiffening and jerking, while atomic seizures cause sudden loss of muscle tone. Myoclonic seizures involve brief muscle contractions, clonic seizures feature rhythmic jerking movements, and tonic seizures involve sustained muscle contraction. Absence seizures (Petit Mal) are characterized by brief lapses in consciousness. This classification aids in diagnosis and treatment planning.
# 3D U-Net for Skull Stripping in Brain MRI
###### Abstract  
Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and [PERSON]'s method; BSE and ROBEX are two conventional methods, and [PERSON]'s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.  
skull stripping; brain segmentation; brain extraction; deep convolutional neural networks; U-Net +
Footnote †: journal: Medical Imaging
## 3 Simulation Implementation
### Implementation of the Communication System
#### 3.2.5 Digital-to-Analog Conversion  
Eventually, the demodulated signal is converted into the corresponding analog signal, to allow the comparison with the original one. To this end, an analog filter with cutoff frequency 100 Hz is used for digital-to-analog conversion, as shown in Figure 7(b).  
Figure 9 shows the even (brown) and odd (black) bits obtained from the message signal and the resulting QPSK modulated signal (blue). As it can clearly be seen, the QPSK modulated signal has four phase shifts according to the input bit combinations, as expected from QPSK modulator.  
Figure 10 illustrates the original message signal (red) and its scaled down version (black) to 3 V. The scaled down signal is transmitted to the PLC system and recovered using the QPSK demodulator.  
Figure 8: SIMULINK implementation of the (**a**) QPSK demodulator and (**b**) the digital-to-analog converter.  
Figure 9: Binary bits and QPSK modulated signal waveforms.
## 3 Results and Discussion
### Optimized Parameters
#### 3.3.2 Optimization of the Solar Cell with ITO as TCO  
Through the modeling previously performed, the optimal parameters, summarized in Table 4, were obtained for each spectrum. The comparison between \(AM1.5/op\) and \(Ata/op\) solar cells shows that the solar cell for the Atacama Desert should have the \(c-Si(n)\) layer \(\approx 6\%\) thinner, the \(a-Si:H(p^{+})\) layer 16% thicker, and the layer of \(c-Si(n)\) with a lower concentration of minority carriers.  
With these parameters, the electrical properties are obtained by calculating the current density curve as a function of voltage under the standard AM1.5 (\(AM1.5/op\)) and under the Atacama Desert (\(Ata/op\)) spectra (Figure 7). Table 5 shows the main characteristics of the solar cell under each spectrum.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & \(V_{oc}\) **[mV]** & \(J_{sc}\) **[mAcm\({}^{-2}\)]** & **FF [\%]** & **_Eff [\%]_** \\ \hline AM1.5/op & 761.9 & 37.19 & 76.88 & 21.78 \\ Ata/op & 763.9 & 40.82 & 76.49 & 23.85 \\ \hline \end{tabular}
\end{table}
Table 4: Electrical characteristics of solar cells.  
Figure 6: Generation and recombination with AM1.5 and Atacama spectrum in the five layers of the HIT solar cell.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter/Spectrum** & **AM1.5** & **Atacama** & **Unit** \\ \hline Nd\_c – Si(n) & \(9.5\times 10^{16}\) & \(10\times 10^{16}\) & cm\({}^{-3}\) \\ Na\_a – Si(\(p^{+}\)) & \(6.24\times 10^{17}\) & \(6.28\times 10^{17}\) & cm\({}^{-3}\) \\ Nd\_a – Si(\(n^{+}\)) & \(1.2\times 10^{18}\) & \(1.4\times 10^{18}\) & cm\({}^{-3}\) \\ w\_a – Si(\(n^{+}\)) & 7 & 9 & Nm \\ w\_a – Si(\(p^{+}\)) & 6 & 7 & Nm \\ w\_c – Si(n) & 160 & 150 & Nm \\ w\_a – Si(i) – p & 4 & 4 & Nm \\ w\_a – Si(i) – n & 5 & 5 & Nm \\ \hline \end{tabular}
\end{table}
Table 5: Electrical parameter of the HIT solar cell.  
The optimized cell under the standard spectrum has an efficiency of 21.78%, which is 1.58 percentual points higher than the experimental result. The main difference between the experimental cell and the computed cell is in the open circuit voltage, which is 761.9 mV and 713 mV, respectively. Conversely, the short-circuit current density is 0.1 mA/cm\({}^{2}\) larger for the laboratory cell. When the simulation is performed considering the Atacama spectrum and the cell parameters optimized for it, the short-circuit current has an increment of 3.63 mA/cm\({}^{2}\) compared to the AM1.5 simulation. This is the main cause of the larger efficiency under the Atacama spectrum compared to the standard conditions.
## Materials and Methods
### Vegetation Health Index  
VHI characterizes the health of the vegetation by assuming that stressed conditions are linked to lower than normal NDVI and higher than normal temperature [28; 29]; it may be written as:  
\[\mathrm{VHI}=\alpha\mathrm{VCI}+(1-\alpha)\mathrm{TCI}, \tag{1}\]  
where \(\mathrm{VCI}\) is the Vegetation Condition Index, \(\mathrm{TCI}\) is the Thermal Condition Index and \(\alpha\) is a parameter that quantifies the contribution of each component to the overall vegetation health. \(\mathrm{VCI}\) and \(\mathrm{TCI}\) are defined as:  
\[\mathrm{VCI}=\frac{\mathrm{NDVI}-\mathrm{NDVI}_{\mathrm{min}}}{\mathrm{NDVI}_ {\mathrm{max}}-\mathrm{NDVI}_{\mathrm{min}}}, \tag{2}\]  
\[\mathrm{TCI}=\frac{\mathrm{LST}_{\mathrm{max}}-\mathrm{LST}}{\mathrm{LST}_{ \mathrm{max}}-\mathrm{LST}_{\mathrm{min}}}, \tag{3}\]  
where NDVI, \(\mathrm{NDVI}_{\mathrm{min}}\), and \(\mathrm{NDVI}_{\mathrm{max}}\) are the value for a specific month and pixel and the minimum/maximum value for the same month and pixel over the climatological period of study, respectively. The same notation is valid for LST, \(\mathrm{LST}_{\mathrm{min}}\), and \(\mathrm{LST}_{\mathrm{max}}\).  
Here, three datasets of \(\mathrm{TCI}\) are estimated: with PU LST, CM-SAF PMW LST, and CM-SAF SMW LST; while \(\mathrm{VCI}\) is estimated with the GIMMS NDVI. Consequently, three datasets of VHI are also estimated.  
Parameter \(\alpha\) in Equation (1) takes a value between 0 and 1. Since normally there is no a priori knowledge of the actual contribution of the moisture and temperature conditions to the health of the vegetation in a given region, the value of \(\alpha\) is generally taken as 0.5. However, as pointed out in Ref. [13], with the help of a multi-scalar drought estimator, such as SPEI, it is possible to estimate an optimal value of \(\alpha\) for the different biomes and therefore assess the relative contributions of VCI and TCI for different regions. In the next section, the methodology to estimate \(\alpha\) is presented.
## 2 Materials and Methods
### Determination of Effective Moisture Diffusivity Coefficient (\(D_{\text{eff}}\))  
The number of moisture transfer mechanisms is extensive and often complex. Transfer phenomena are usually classified into pressure diffusion, forced diffusion, and ordinary diffusion (net transfer of matter without fluid motion). According to Equation (6), the [PERSON]'s second law for unstable conditions can describe the transfer of moisture in the descending stage of the drying process [29; 30]:  
\[\frac{\partial X}{\partial t}=D_{\text{eff}}\frac{\partial^{2}X}{\partial^{2}x} \tag{6}\]  
where \(X\) is the local \(MC\) on dry basis, \(t\) is the time and \(x\) is the spatial index. The study of the [PERSON]'s second law of diffusion indicates the mass transfer during the period of the declining drying rate of agricultural products [26]. To apply [PERSON]'s law, it is assumed that the food product is one-dimensional, the initial \(MC\) is uniform, and it has an internal movement of moisture such as the major resistance to moisture transfer. The [PERSON]'s equation for a plane sheet is solved as follows [31; 32]:  
\[MR=\frac{8}{\pi^{2}}\sum\limits_{n=1}^{\infty}{\frac{1}{(2n+1)}\exp\left(\frac {-D_{\text{eff}}{(2n+1)}^{2}\pi^{2}t}{4L^{2}}\right)} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Equations** & **Models** \\ \hline Two-term & \(MR=a\exp(-kt0)+b\exp(-k_{1}t)\) & [24] \\ \hline Two-term exponential & \(MR=a\exp(-kt)+(1-a)\exp(-kat)\) & [20] \\ \hline [PERSON] & \(MR=a\exp(-kt)+(1-a)\exp(-gt)\) & [17] \\ \hline [PERSON] et al. & \(MR=a\exp(-kt)^{n}+b\) & [26] \\ \hline [PERSON] et al. & \(MR=a\exp(-kt^{n})+bt\) & [19] \\ \hline [PERSON] ([PERSON]) & \(MR=a\exp(-kt)\) & [27] \\ \hline [PERSON] & \(MR=a\exp(-kt^{n})\) & [28] \\ \hline [PERSON] and [PERSON] & \(MR=1+at+bt^{2}\) & [13] \\ \hline [PERSON] and [PERSON] & \(MR=a\exp(-kt)\) & [16] \\ \hline Logistic & \(MR=a/(1+b\exp(kt))\) & [29] \\ \hline \(a\), \(b\), \(c\), \(k\), \(k_{0}\), \(k_{1}\) and \(n\) are drying constants. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Applied models to fit the experimental data \(MR=1+at+bt^{2}\).  
From 1 to infinity, \(t\) is the drying time (s), and \(D_{\mathit{eff}}\) is the effective diffusivity coefficient (m\({}^{2}\)/s). The effective diffusivity coefficient is obtained by calculating the slope in Equation (8) [11]:  
\[MR=\frac{8}{\pi^{2}}\exp\left(\frac{-D_{\mathit{eff}}\pi^{2}t}{4L^{2}}\right) \tag{8}\]  
The diffusivity coefficient is usually determined by plotting the experimental drying data versus LnMR over time. When plotting the LnMR value over time, place the resulting line slope in Equation (9) to obtain the \(D_{\mathit{eff}}\):  
\[K=\left(\frac{D_{\mathit{eff}}\pi^{2}}{4L^{2}}\right) \tag{9}\]  
where K is the line slope.
## 4 Related Work
### Reactive Actions in Network Traffic or Communication  
Active responses that affect network traffic or communication are commonly used to stop an intrusion or attack from reaching its target by either blocking-, modifying- or dropping network traffic. At this stage, it is unclear whether or not the intrusion has been successfully mitigated before its consequences have taken place in the IAS. The most common approach to execute this action is the integration of an inline component over the communication path. This component can be a commercial or open source product such as a Firewall or Network IDS, or another security component with similar capabilities.  
In [13], an industrial Firewall system with intrusion detection capabilities is presented. This system is comprised of four different blocks: packet collection and control block, network layer access control block, application layer access control block and policy and alert management block. Both network- and application layer access control blocks constitute an access control mechanism comprised of four different filters, each of which analyzes different information regarding the network traffic. Based on this analysis and the policies located in the policy and alert management block, it is decided whether or not a network packet is allowed through the system. The packet collection and control block is in charge of executing this action by either delivering or blocking the network packet. Another approach that has benefited from the prevention capabilities of Firewalls is presented in [34]. In this contribution network attacks against Cyber-Physical Systems (CPS) are detected and prevented by implementing a hybrid approach using statistical analysis with fuzzy logic. This approach is capable of detecting anomalies that have been discovered in network packets that have passed through a firewall. Any packet with anomalies is discarded. The commercial IDS Silent Defense by Security Matters [35] also provides active responses in the presence of intrusions by dynamically adding new firewalls rules to an industrial Firewall (i.e., mGuard by Phoenix Contact) in order to block incoming network traffic.  
Other approaches have also benefited from prevention capabilities supported by IDS. In [36], an anomaly-based Multi-Agent IDS is presented. This IDS implements an enhanced ant-based clustering approach for identification of intrusions and is comprised of six types of agents (i.e., monitoring agents, decision agents, action agents, coordination agents, user interface agents and registration agents). The preventive capabilities of the presented IDS are carried out by the action agents. These agents are capable of performing passive responses (i.e., log and notify of security-related events such as intrusions), as well as active responses (i.e., packet filtering and attack redirection towards a Honeynet). In [37], the open source Network IDS Snort [38] is used to detect and prevent intrusions in MODBUS RTU/ASCII traffic. In order to provide preventive capabilities, Snort is implemented in its inline mode which allows it to drop traffic according to special drop rules.
## 2 Literature Review  
### Background and Motivation for Predictive Maintenance  
Predictive maintenance (PdM) [3,4] has emerged as a critical strategy in modern industries for reducing unplanned equipment downtime, extending asset lifespans, and optimizing operational costs [5]. Unlike reactive or scheduled maintenance, PdM aims to anticipate failures by analyzing real-time operational data [6] to determine the health status of equipment and forecast Remaining Useful Life (RUL) [7,8]. This approach is particularly vital in high-stakes sectors such as aviation, in which component failure can have severe safety and financial implications.  
In the aviation industry, jet engines are subject to varying environmental and operational conditions that lead to complex degradation behaviors. Traditional maintenance strategies often fall short due to their reliance on rigid schedules or post-failure interventions. Consequently, there is a growing demand for intelligent systems capable of monitoring engine health and predicting failures in advance.
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 4 Proposed Ensemble of Deep Learning Models for IoT Intrusion Detection
### Step 2: Data Balancing  
Imbalanced datasets are a common challenge in intrusion detection. To mitigate this issue, we employ the synthetic minority over-sampling technique (SMOTE), which generates synthetic samples for the minority class. This balancing method helps prevent the model from being biased towards the majority class, thus improving its ability to detect security threats effectively.  
* Compute the imbalance ratio (I.R.): \[IR=\frac{Number\ of\ Sample\ of\ majority\ class\ D_{1}}{Number\ of\ Sample\ of\ minority\ class\ D_{2}}\]
* Generate synthetic samples for \(D_{2}\) Using SMOTE (Synthetic Minority Over-sampling Technique). \[x_{s}=x+\lambda\times(x_{nn}-x)\] \[x_{s}\] is the synthetic sample. \[x\] is the original sample from \[D_{2}\] \[x_{nn}\] is a randomly selected nearest neighbor of from \[D_{2}\] \[\lambda\] is a random value between 0 and 1 that controls the interpolation.
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 3 Satellite Visibility and Observed Signals
### Signal Quality
In all cases, SNR values for smartphones are lower than for the geodetic receivers.
## 4 A Hybrid-ISL System with Ranging and LoS Support
### PHY Support for Coarse and Fine Ranging
#### 4.3.3 Phase-Difference Measurement  
Let us recall the metrology setup illustrated in Figure 2b,c. To compute or estimate the LoS angles, \(\alpha\) and \(\beta\), two additional phase-difference measurements are required--one along the \(x\)-axis and the other along the \(y\)-axis--resulting in phase differences \(\Delta\varphi_{x}\) and \(\Delta\varphi_{y}\), respectively. The signal transmitted from satellite \(T\) is received by the main antenna \(A_{0}\) on satellite \(C\), as well as by two additional antennas positioned along the x- and y-axes, \(A_{x}\) and \(A_{y}\), with corresponding phase differences of \(\Delta\varphi_{x}\) and \(\Delta\varphi_{y}\). The phase difference measurement is carried out by detecting the zero crossing of a carrier received by antenna \(A_{0}\) and its delayed version on antenna \(A_{x}\) or \(A_{y}\) and measuring the time difference between two consecutive zero crossings. With this procedure the achievable phase resolution \(R_{\varphi}\) is inversely proportional to the frequency of the baseband sampling frequency, in our setup \(F_{s}\) = 15.36 MHz, and proportional to the carrier frequency, \(F_{C}\). Expressed in rad/s, the resolution yields the following:  
\[R_{\varphi}=2\pi\frac{F_{C}}{F_{S}}. \tag{11}\]  
For example, with a carrier frequency \(Fc\) = 25 kHz, approximately a 0.01 rad/s phase difference resolution may be achieved. Note, that fine range measurement may be carried out based on the received OFDM symbol, just as in [29].
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 4 Case Analysis
### Evaluation of Data Augmentation Capability of the DAE-GAN Model
#### 4.2.1 Evaluation of Similarity of Generated Samples by the Model  
As shown in Figure 7, the four types of PD waveforms: tip discharge, floating discharge, bubble discharge, and surface discharge are presented. A real sample was randomly selected, and the correlation coefficient was employed as the evaluation criterion. For comparison, the PD pulse time-domain waveform with the highest similarity to the real sample, generated by each model, was chosen based on this criterion.  
As observed in Figure 7, both BEGAN and VAE generate relatively smooth samples; however, VAE loses some of the original sample's fluctuations. The waveform generated by CWGAN-GP contains some noise-like fluctuations. In contrast, the samples generated by DAE-GAN are also relatively smooth and do not suffer from the issue of losing parts of the original sample's fluctuations, as seen with VAE.  
The aforementioned real sample and the PD samples generated by each model were subjected to FFT transformation [28], with a time window length of 137 sampling points to obtain two-dimensional time-frequency diagrams for each sample. For a quantitative  
Figure 7: Comparison of PD waveforms generated by different models.  
assessment of the similarity between the time-frequency diagrams, the Multi-scale Structural Similarity (MS-SSIM) [29] metric was employed. This method measures the visual similarity between two images at different scales, where a higher MS-SSIM value signifies a greater resemblance between the two samples. The detailed comparison results obtained from this analysis are provided in Table 3.  
As shown in Table 3, the MS-SSIM values for the samples generated by VAE are generally lower due to the loss of some fluctuations. For BEGAN, the MS-SSIM value is the lowest for the floating discharge waveform because of mode collapse. The MS-SSIM values and mean MS-SSIM values for the waveforms generated by DAE-GAN are the highest for all types of discharge, indicating the highest similarity between its generated waveforms and the original samples.
## 2 Methods and Models for Evaluating Battery SOH Literature Review
However, the KF method and its variants, along with data-driven methods or a combination of these two types of methods, appear to be the most promising candidates for achieving the best possible SOH estimation. The KF method and its variants have been proven effective in state of charge (SOC) estimation under complex working conditions, and can at least provide a rough estimate of battery SOH online. However, the accuracy of these methods in SOH estimation requires further testing, and the selection of different KF variants to balance estimation accuracy and computational cost needs additional study. For data-driven methods, some quick and accurate SOH estimations have been achieved, but the challenge remains in extracting features closely related to the battery's SOH from data obtained under irregular charging and discharging conditions [46].